Machine learning for effective EHR management in blockchain-cloud integration

Abstract: Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.


An Analysis of Students Reading Comprehension on Students Reading Aloud Activities at the XI Grade of MAN 1 Solok Selatan

Abstract: Students' ability in reading, especially reading comprehension is still low. The first problem most of students have limited vocabulary in English, The second problem is that the teacher still uses the conventional strategies in class in reading comprehension. The third problem is students are not able to understand the words conveyed by the teacher orally in English. The purpose of this research was to find out student’s reading comprehension on student’s reading aloud activities at the XI Grade of MAN 1 Solok Selatan. Then, the researcher used descriptive quantitative method. In the collecting the data, test wich consisted of reading comprehension through students reading aloud activities. The data was analyzed by using T- Test Formula. Based on results shows that that 3 students get the score between 95 - 100. That 2 students get the score between 83 - 94. Then 13 students get score between 71 – 82. Then 5 students get the score between 59 – 70. Then 2 students get the score 47 – 58 and 7 students get the score between 35 – 46. In conclusion, the students reading comprehension on reading aloud activities at the XI.IPS.1 students at MAN 1 Solok Selatan was considered medium. Evidenced from when students reading aloud activities the students still confused, and not confidence.


The Effect of Product Knowledge and Market Orientation on Business Performance in MSMEs

Abstract: Globalization has had a significant impact on the business world, especially in terms of expanding market reach and increasing competition. In the growing digital era, information and communication technology has affected many aspects of human life, including the business sector. The development of digital technology has accelerated the pace of business and enabled access to global markets on a larger and more efficient scale. This study aims to analyze the effect of product knowledge and market orientation on firm business performance mediated by product innovation and omnichannel on MSMEs in Boyolali district. There were 100 respondents in this study through an online survey using google form with a Likert scale. The sampling technique uses non probability sampling using the slovin method. Data analysis using SmartPLS. Validity and reliability tests using smart PLS analysis on the outer model. Hypothesis testing using the SmartPLS inner model. The analysis results show that Product Innovation is able to mediate the relationship between Market Oriented and Firm Business Performance and Omnichannel is able to mediate the relationship between Market Oriented and Firm Business Performance.


Target Signal Communication Detection of Black Flying UAVs Based on Deep Learning Algorithm

Abstract: Background: Unmanned aerial vehicles (UAVs) are being widely used in many fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent occurrence of unsafe incidents. Objective: The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life. Methods: In this paper, to assist the public security department in detecting the phenomenon of UAV black flying, our team conducts a series of research based on the deep learning YOLOv5 (You Only Look Once) algorithm. Results: Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are combined to enhance the attention of small target UAVs. Conclusion: Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs in a specific area can be improved, and the loss of life and property of people can be reduced or saved as much as possible.


A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs

Abstract: With the rise of machine translation systems, it has become essential to evaluate the quality of translations produced by these systems. However, the existing evaluation metrics designed for English and other European languages may not always be suitable or apply to other Indic languages due to their complex morphology and syntax. Machine translation evaluation (MTE) is a process of assessing the quality and accuracy of the machine-translated text. MTE involves comparing the machine-translated output with the reference translation to calculate the level of similarity and correctness. Therefore, this study evaluates different metrics, namely, BLEU, METEOR, and TER to identify the most suitable evaluation metric for Indic languages. The study uses datasets for Indic languages and evaluates the metrics on various translation systems. The study contributes to the field of MT by providing insights into suitable evaluation metrics for Indic languages. This research paper aims to study and compare several lexical automatic machine translation evaluation metrics for Indic languages. For this research analysis, we have selected five language pairs of parallel corpora from the low-resource domain, such as English–Hindi, English-Punjabi, English-Gujarati, English-Marathi, and English-Bengali. All these languages belong to the Indo-Aryan language family and are resource-poor. A comparison of the state of art MT is presented and shows which translator works better on these language pairs. For this research work, the natural language toolkit tokenizers are used to assess the analysis of the experimental results. These results have been performed by taking two different datasets for all these language pairs using fully automatic MT evaluation metrics. The research study explores the effectiveness of these metrics in assessing the quality of machine translations between various Indic languages. Additionally, this dataset and analysis will make it easier to do future research in Indian MT evaluation.


PENGGUNAAN MEDIA PAPAN BERHITUNG PADA PEMBELAJARAN MATEMATIKA UNTUK SISWA KELAS II SDN 6 WONOGIRI

Abstract: Mathematics is a natural science that has an important role in the development of science and technology. Many learners in school view maths as the most difficult subject. In fact, until now there are still students who are less interested in mathematics and the results of learning mathematics have not shown optimal results. This type of research is Research and Development (R&D), which is research to produce new products or perfect existing products. that the counting board product is "Very Feasible" to use, so it can be concluded that the counting board media product is effectively used as a mathematics learning medium in grade II SD Negeri 6 Wonogiri


ANALISIS TINGKAT LITERASI MEMBACA CERITA JATAKA DI SMA BHAKTI KARYA KECAMATAN KALORAN KABUPATEN TEMANGGUNG

Abstract: This study aims to determine the level of literacy in reading jataka stories. The type and approach of research used in this study is a quantitative approach. In this study, the target of the study was Buddhist students in grades 10, 11, and 12 at SMA Bhakti Karya Kaloran District, Temanggung Regency with a total of 46. Techniques in data collection using questionnaires. The results showed that literacy activities at SMA Bhakti Karya were classified in the medium category with the largest number, namely 34 students in the medium category with a percentage of 74% while the level of reading jataka stories at SMA Bhakti Karya was also included in the medium category with the largest number of 35 students entering the medium category with a percentage of 76%.


Media Pembelajaran Mata Pelajaran Bahasa Indonesia SD Di Era Digital

Abstract: Education is very important in the development of children`s knowledge and insight. Education is a conscious effort in the process of changing a person`s attitude, the knowledge possessed by each individual, both his knowledge and the things he does in his life to be better than his thoughts and attitudes through teaching and training efforts. Moreover, the education of elementary school-age children in this era of digital development. The developing digital era has indeed utilized in education as a means and infrastructure for pasticipation between teachers and students. Then social science is needed for students in social life and socializing as well as adapting to the surrounding environment. Moral attitudes need to be taught from an early age so that children can face the problems they will face later. The method in this study uses the library study method. Elementary Indonesian language subjects relate to 4 aspects, namely listening, speaking, writing and reading. Elementary school Indonesian language learning requires learning media that supports teaching and learning. Digital-based learning media can help teachers and facilitate student learning.


Nanosensors from Logistics and Supply Chain Perspective: A Literature Review

Abstract: Introduction: Nanosensors are one of the most recent technologies obtained from the nanoworld. They have enhanced properties with potential applications in a wide spectrum of sectors. They exhibit exceptional properties in temperature monitoring, hazardous gas and vapor detection, humidity detection, as well as pathogens, toxins, and chemical contaminant detection. Among various nanosensor applications, one of the most recent usages of nanosensor technology is in logistics and supply chain management (SCM). Aim: This study aims at investigating the potential of nanosensor technology from logistics & SCM perspective, and specifically questions how can this technology contribute to food supply chains. Method: A systematic comprehensive literature review was conducted with the keywords: nanosensors & logistics, nanosensors & supply chain, nanosensors & smart packaging, and nanosensors & food logistics for the date range 2001-2022, covering a broad set of references. Findings revealed that nanosensors have significant potential in providing smart packaging, and improving traceability and visibility across all the logistics and SCM processes including end-to-end transportation and storage activities Result: Hybrids of different types of nanosensors with the existing material identification systems provide new levels of traceability and visibility in SCM, enabling accurate tracking of a variety of parameters. The study has shown that the intersection of nanotechnology and logistics & SCM domains is still immature. Conclusion: This intersection is especially critical for food supply chains for meeting their special traceability requirements, and for serving food safety and organic food traceability across all stages of logistics.


role of digital reading corner in increasing the community's reading interest in Bekasi district

Abstract:
Background: A culture of interest in reading or literacy movement must be fostered in Indonesian society.
 Purpose: The aim is to describe the existence of a Digital Reading Corner (POCADI) managed by the Bekasi Regency Archives and Library Service, which shows an increase in people's interest in reading in the Bekasi Regency.
 Design and methods: This article uses qualitative research and a literature review. Data collection techniques in the field are observation and interviews. The research objects in the field are visitors and officers at the Digital Reading Corner location. Collections of digital media such as computers, Android tablets, e-books, LED TV, and Wi-Fi increase visitors' enthusiasm to increase their interest in reading. The efforts of the Bekasi Regency Archives and Library Service in managing POCADI include outreach to the community, educational visits from schools to POCADI and storytelling activities.
 Results: The results of the research can be concluded that the Digital Reading Corner provides benefits in increasing people's interest in reading in the Bekasi district, namely as a place to read with a collection of reading materials equipped with digital media, a place to read for people who are located far from the Bekasi Regency Public Library


Optimizing University Libraries in Palangkaraya City as a Supporting Facility for Independent Campuses

Abstract: Background: Libraries play an essential role by meeting students' needs, offering a variety of library resources, facilitating access to study spaces, managing lending services, and providing information services. Kampus Merdeka is a support and effort to encourage students to obtain a variety of helpful information for their future job prospects. Purpose: This study aims to know extend on how to optimize libraries to facilitate independent campuses. Design and methods: This article uses a qualitative descriptive methodology, utilizing library reference materials as the primary means of data collection and analysis. According to Sugiyono (2018), qualitative research methods are research methods based on philosophy, which are used to research scientific conditions (experiments) where the researcher is the instrument, data collection techniques, and qualitative analysis emphasize meaning. Results: The role of libraries is in line with the government's program for national education, as stated in Law No. 2 of 2003, which states that education is a conscious and planned effort to realize the teaching and learning process so that students actively develop their potential. Optimizing university libraries in the city of Palangkaraya by utilizing information technology can be used as a means of supporting independent campuses to utilize library resources. Steps that university libraries can take to optimize libraries include: 1) Optimizing the use of information technology and 2) Promotion of libraries by utilizing websites and social media networks to improve service quality. Librarians should strive to facilitate as many opportunities as possible for students to develop into competent individuals.


Website-based Gray Literature Collection Management Darmajaya Repository

Abstract:
Background: College libraries not only provide printed collections such as books for lecture materials, but some of them provide collections not published to the public, usually called Gray Literature. The Gray Literature Collection is one of the main factors to consider when assessing a university library. The Gray Literature Collection contains various research results that the library/user needs. The Repository is an archival storage place designed to collect, preserve, and disseminate digital copies, especially the Darmajaya Repository Gray Literature Collection..
 Purpose: This research aims to describe how the Repository is managed at the IIB Darmajaya Library and the obstacles librarians face during the management stage.
 Design and methods: This research uses a qualitative descriptive method using case study research. Data collection uses observation and interviews. Data analysis in this research includes data reduction, data presentation, and conclusion, while a triangulation approach is used to measure validity.
 Results: Based on the findings of this research, Repository management at the IIB Darmajaya Library is carried out by determining management policies, preparing hardware and software, Human Resources, and procedures for receiving collections. The final stage is uploading files into the Darmajaya Repository system. Problems are faced in managing the Darmajaya Repository because it is still in the development process, so there are still areas for improvement in management, implementation, and presentation.


TN-MR: topic-aware neural network-based mobile application recommendation

Abstract: Purpose With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed. Design/methodology/approach In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked. Findings Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR. Originality/value In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.


AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach

Abstract: Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.


Supply Chain Management in Agriculture using Blockchain

Abstract: Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross- border nature of digital currencies like Bit coin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity


Multi-objective optimization design of scramjet nozzle based on grey wolf optimization algorithm and kernel extreme learning machine surrogate model

Abstract: This study delves into the parametric design of the scramjet nozzles, utilizing the Catmull–Rom curve, to meet the high-performance design requirements. It establishes a high-dimensional, multi-objective optimization design method based on a surrogate model for the nozzle. In addition, this research proposes a surrogate model for nozzle performance to enhance the accuracy of the traditional surrogate model and prevent the multi-objective optimization design method from optimizing in an incorrect direction. This model incorporates the grey wolf optimization (GWO) algorithm and kernel extreme learning machine (KELM). Various machine learning algorithms are compared and analyzed, demonstrating that the performance parameters predicted by the GWO-KELM model are the most accurate, and the generalization of GWO-KELM is verified. Utilizing the particle swarm optimization algorithm assisted by GWO-KELM, the multi-objective optimization of the nozzle is further investigated. This study obtains the optimal Pareto front, analyzes the distribution of design variables in the Pareto solution set, and reveals the impact of geometric parameters on nozzle performance. Comparing the representative nozzle from the Pareto front with the truncated maximum thrust nozzle, it is found that the thrust, lift, and outlet Mach number increase by 3.3%, 12.2%, and 0.5%, respectively, while the outlet height decreases by 5.3%. This research contributes to overcoming the limitations of traditional design methods, which are typically time-consuming. The proposed GWO-KELM surrogate model effectively tackles the issue of low prediction accuracy.


Overcoming State and Local Barriers to Stock Inhaler Policy Implementation: Creation of a National Toolkit

Abstract: Asthma is common, yet many children are undiagnosed or have inadequate access to medications. Seventeen states passed stock inhaler policies which allows schools to stock inhalers to de-escalate asthma emergencies. Since policy implementation is slow, a national stock inhaler toolkit for schools was developed. The School-Based Asthma, Allergy and Anaphylaxis Committee of the American Academy of Allergy, Asthma, and Immunology and Code Ana created a stock inhaler toolkit that pools resources nationwide. The toolkit consolidates evidence-based resources from multiple sources, including model policies from the American Lung Association, Centers for Disease Control and Prevention, and several state-led asthma programs. Components include help with understanding state-specific laws, recognizing respiratory distress, quality evidence-based staff-training, and a comprehensive resource library with documentation examples. A 58-page human-centered design inspired toolkit was produced that involved iterative input from a total of 27 participants. Collaborative agreements were established with the American Academy of Pediatrics, National Association of School Nurses, and the American Lung Association to provide a flexible framework that can be tailored to diverse school environments. Creation of a national toolkit mitigates stock inhaler policy implementation barriers and addresses state-level differences. Guideline concordant school-based asthma care is necessary. Stock inhaler programming provides an infrastructure to accomplish this at schools with varied resources across the US. Collaborative solutions to barriers are necessary to overcome persistent asthma health disparities in children.


Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Abstract: Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.


Graph convolution networks for social media trolls detection use deep feature extraction

Abstract: Abstract This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.


A Survey on Secure Eye: Blink-Based Morse Authentication

Abstract: Real-time password authentication methods are widely used by people, but they can be exploited or defeated with the help of a hot track or high-speed scan. By using blinding hand motions in place of visible fingerprints, pin verification offers a more secure method of password entry. The procedure of blinking the eyes while taking many picture frames and coming up with a PIN is known as blink-based authentication. This paper provides a range of real-time applications that combine instant-based PIN, facial detection, and One-time Passwords to eliminate shoulder-to-shoulder and scratch tracking


On the Presentation of the Physical and Mathematical Solutions Process of Problems in Physics to Engineering Students

Abstract: Community College of


Analysis and prospect of power transformer fault diagnosis technology

Abstract: The transformer stands as a vital component within the power system, serving a crucial role in voltage regulation and energy transmission throughout the entire process of electricity generation, transmission, distribution, and consumption. Furthermore, it holds a pivotal position in facilitating Alternate Current (AC) and Direct Current (DC) conversion within DC transmission. This paper begins by offering an overview of common anomalies and fault categories that arise during transformer operation, focusing on the role of power transformers within system operations. It subsequently delves into the contemporary methods and diagnostic processes used for identifying and diagnosing transformer faults. The paper then proceeds to expound upon cases involving winding deformation and insulating oil failure, illustrating instances of these particular types of faults. Finally, the paper outlines two prospective avenues for transformer fault diagnosis technology. The first entails the incorporation of artificial intelligence theories in the realm of transformer fault diagnosis. The second involves the development of an expert system for comprehensive transformer fault diagnosis, grounded in artificial intelligence technology.


Education and Training Program to Improve Preparedness and Increased Access to Energy Workforce for Engineering Technology Graduates

Abstract: Abstract Elizabeth City State University (ECSU) implemented a comprehensive education and training program to strengthen the renewable energy curriculum and increase the pipeline of qualified workers, especially underrepresented minorities for the growing energy industry. The overarching goal is to meet the current and future energy industry workforce needs, especially in the renewable energy sector. The project activities were designed based on three tenets, which include mentoring, research, and education/training. The project components included, enhancing renewable energy courses, establishing a wind and solar energy systems training laboratory, a year-round hands-on project-based training, a residential summer bridge program, undergraduate research experience, and summer internships. The activities afforded through the project will help ensure that the energy sector has a sustained pipeline of a qualified workforce. The project implementation and challenges, project delivery components, and results from pretests and posttests and participant surveys are presented in the paper.


Analysis of Complete Blood Count Data and Use in Classification Machine Learning Models to Predict Icans Incidence Following CAR-T Cell Therapy

Abstract: CD19 directed CAR-T cell therapies are increasingly used for treatment of hematological malignancies, however immune cell-associated neurotoxicity syndrome (ICANS), remains a challenge to predict and treat. We retrieved demographic and complete blood count (CBC) data from electronic health records (EHR) to evaluate whether this simple, accessible, and routinely collected data could be used to train classification machine learning (ML) models for prediction of ICANS. Demographic and CBC measures from n=96 patients treated for B-cell malignancies at the University of Michigan (UM) were analyzed for univariate significance at baseline (prior to lymphodepletion) and through day (D) 3 post CAR-T infusion. Complete datasets were available for n=76 patients. These were then used for feature selection and ML model training. Features found significant were used as inputs in binary classification models to predict ICANS incidence within 20 days post-infusion. Models were validated using data from a test set of n=29 patients treated at Johns Hopkins Hospital (JHH). Data analysis was completed in RStudio-4.1.3, R Core Team 2022. Overall incidence of ICANS was 45% in the training set (UM) and 34% in the test set (JHH). While baseline data alone was not predictive, between D0 and D3, univariate analysis revealed lymphocyte count, monocyte count, red blood cell count, hemoglobin, hematocrit, C-reactive protein, ferritin, and white blood cell count as differentially associated with patients that developed Grade 3 ICANS versus those that developed Grade 1-2 or No ICANS. On D0 (after lymphodepletion and prior to CAR-T cell infusion), the combination of C-reactive protein, monocyte count, red blood cell count, and age used in ML models were able to predict incidence of any grade ICANS with an AU-ROC of 0.80-0.82, MCC=0.51-0.54, and F1=0.74-0.76. This model was robust to validation in the independent test set cohort (i.e., JHH patients), with an AU-ROC of 0.92-0.95, MCC=0.63-0.85, and F1=0.67-0.90. ML-based classification using CBC data and commonly measured clinical blood analytes holds promise for ICANS prediction. Future work may integrate additional blood- clinical-, and/or digital-based biomarkers from other predictive models of ICANS to develop further improved models. Our findings suggest that ML-based models may be able to risk-stratify patients receiving CAR-T therapy, to identify those who may benefit from close monitoring and/or early therapeutic intervention to prevent or mitigate severity of ICANS.


Logical activation functions for training arbitrary probabilistic boolean operations

Abstract: In this work, we introduce a family of novel activation functions for deep neural networks that approximate n-ary, or n-argument, probabilistic logic. Logic has long been used to encode complex relationships between claims that are either true or false. Thus, these activation functions provide a step towards models that can efficiently encode information. Unfortunately, typical feedforward networks with elementwise activation functions cannot capture certain relationships succinctly, such as the exclusive disjunction (p xor q) and conditioned disjunction (if c then p else q). Our n-ary activation functions address this challenge by approximating belief functions (probabilistic Boolean logic) with logit representations of probability and experiments demonstrate the ability to learn arbitrary logical ground truths in a single layer. Further, by representing belief tables using a basis that associates the number of nonzero parameters with the effective arity of each belief function, we forge a concrete relationship between logical complexity and sparsity, thus opening new optimization approaches to suppress logical complexity during training. We provide a computationally efficient PyTorch implementation and test our activation functions against other logic-approximating activation functions on both traditional machine learning tasks as well as reproducing known logical relationships.


Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics

Abstract: Purpose: The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Findings: (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.


The Influence of Knowledge Sharing, Transformational Leadership on Organizational Citizenship Behaviour (OCB) and Job Satisfaction in the Msmes Sector in Minahasa

Abstract: Purpose: The purpose of this research is to analyze the relationship between transformational leadership on organizational citizenship behavior (OCB), transformational leadership on job satisfaction, job satisfaction on OCB, knowledge sharing on OCB, knowledge sharing on job satisfaction. Theoretical Framework: Several research studies have explored the correlation between job satisfaction and knowledge sharing within the tourism industry, hese findings indicate that the sharing of knowledge can have a beneficial effect on job satisfaction within the MSMEs Sector. Design/Methodology/Approach: This research method is quantitative through surveys, data obtained by distributing online questionnaires via social media to respondents. The respondents of this study were 378 MSMEs Sector in Minahasa who were determined by simple random sampling. Findings: Employee trust in superiors has been proven to mediate the relationship between transformational leadership style and OCB, so management or the authorities are advised to be more selective in giving promotions to get officials who have competencies that are in accordance with their positions, have high integrity and have concern for their subordinates. In addition, officials or leaders whose competence is still lacking need to be improved through training. Job satisfaction has been proven to mediate the relationship between transformational leadership style and OCB, so that leaders or management is advised to pay more attention to aspects of job satisfaction, both intrinsic and extrinsic job satisfaction of employees. Research, Practical & Social Implications: We suggest a future research agenda and highlight the contribution it makes to job satisfaction. Originality/Value: The results indicate to deepen the study on OCB, it is also recommended for further research to examine each dimension of OCB.


Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Abstract: Online payment fraud detection is crucial for safeguarding e-commerce transactions against sophisticated fraudsters who exploit system vulnerabilities. This paper proposes an efficient framework for predicting online payment fraud, employing six diverse machine learning algorithms, namely constant, CN7Rule induction, KNN, Tree, Random Forest, Gradient boosting, SVM, Logistic regression, Naive Bayes, Ada boost, Neural network, and stochastic gradient descent, on three distinct datasets. The gradient-boosting algorithm consistently outperformed others through rigorous testing, achieving an impressive accuracy rate of 99.7%. This algorithm demonstrated resilience across various testing scenarios, establishing itself as the most effective online payment fraud detection solution. With the highest accuracy score of 99.7% in all testing phases, gradient boosting is optimal for preemptive measures against fraudulent activities in electronic transactions, providing a robust defense mechanism for e-commerce platforms.


Signature Scheme Based on Alternant Codes

Abstract: In this paper, we present a version of the signature on subcodes of generalized Reed-Solomon codes defined on a subfield. We show that the use of alternating codes reduces the key size and the use of subcodes has the caracteristic of hiding the code structure. This makes the system more secure.


Generation of Revision Identifier (rsid) Numbers in MS Word

Abstract: The 2007 implementation of the Office Open XML standard for Microsoft Word introduced the assignation of individual revision save identifiers (Rsid) to document editing sessions that end in a save action. The relevant standards ECMA (2016) and ISO/ IEC 29500-1:2016 (2016) stipulate that these Rsid should be allocated randomised but with increasing numerical value, thereby documenting the progress of the editing. As MS Word is the most ubiquitous word processing software, Rsid appear to be a useful tool to examine and provide evidence for a wide range of common document generation editing and modification processes and file management operations, with implications for document analysis including, but not limited to academic integrity issues in student assignment submissions (e.g. contract cheating). This paper presents the results of a series of experiments conducted to assess whether and how well MS Word implements the ECMA and ISO/ IEC standards. The results show that the number of allocated Rsid indeed increases with each edit and save action, with the previous Rsids carried over and retained. The newly allocated Rsid, however, do not conform to the standard as the numerical value of a Rsid associated with a save action may be larger or smaller than any or all of those allocated during that of the previous save actions. The allocation of a new Rsid is not necessarily caused by an edit event but that a new Rsid can also be generated if a file is saved as rtf or if it is sent as an e-mail from within MS Word, although the file was not edited in any way. Rsid numbers are not generated if a person opens a MS Word document, reads it and closes the file without saving, making this action impossible to detect. MS Word template files on a given machine contain document (root) Rsid numbers that are generated when a newly installed application is launched for the first time. As these will be embedded as legacy Rsid into every new file generated from that template file, they act as signatures for all MS Word documents that are created. The experiments have shown that user behaviour has a direct influence on the number of Rsid represented in a given file. Although the implementation of Office Open XML chosen by Microsoft is not compliant with the relevant standards, and thus Rsid cannot be used determine the exact chronological order of all editing sequences within a given document, the Rsid retain their value for document forensics as they are associated with specific edit events, and illuminate the document writing and editing process.


Advancements and Challenges in 360° Augmented Reality Video Streaming: A Comprehensive Review

Abstract: As the demand for immersive multimedia experiences continues to grow, 360° augmented reality (AR) video streaming has emerged as a promising technology to deliver interactive and engaging content to users. This review paper provides a comprehensive overview of the current state of 360° AR video streaming, focusing on recent advancements, challenges, and potential future directions. The paper begins by presenting an in-depth exploration of the fundamental concepts and technologies underpinning 360° AR video streaming, including camera capture techniques, video encoding and compression methods, and the integration of augmented reality overlays. Subsequently, it delves into a critical analysis of the existing literature, highlighting key developments in the field. This encompasses advancements in video quality, real-time rendering, and the integration of interactive AR elements within the 360° video environment. Furthermore, the review addresses the challenges and limitations associated with 360° AR video streaming, ranging from bandwidth constraints and latency issues to hardware requirements and user experience considerations. Solutions and strategies proposed in the literature to mitigate these challenges are discussed, providing insights into the ongoing research efforts aimed at enhancing the overall performance and user satisfaction. In addition to technical aspects, the paper explores diverse applications of 360° AR video streaming across various domains, such as entertainment, education, healthcare, and industry. This section highlights successful implementations, innovative use cases, and potential benefits realized in each domain. The review concludes by outlining potential future trends and research directions in the dynamic field of 360° AR video streaming. Anticipated developments in hardware, software, and content creation are discussed, along with their potential impact on the widespread adoption of this technology. By synthesizing and critically examining the existing body of knowledge, this review paper serves as a valuable resource for researchers, practitioners, and industry professionals interested in gaining a comprehensive understanding of the current landscape, challenges, and opportunities in 360° augmented reality video streaming.


Abstract: Nonprofit Business AdvisorVolume 2024, Issue 414 p. 4-4 Conferences & Events Nonprofit Learning Lab' Essential HR Practices for Mission Driven Leaders workshop First published: 10 February 2024 https://doi.org/10.1002/nba.31655Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2024, Issue414March 2024Pages 4-4 RelatedInformation


Pengembangan Kemampuan Bahasa Ekspresif Anak Melalui Membaca Nyaring Buku Berbasis Nilai Agama dan Moral di TKIT Permata Bunda Merauke

Abstract: The development of expressive language needs to be developed so that children can express their opinions and thoughts to other people. Children's expressive language development can be developed through the reading aloud method. This research aims to examine the development of children's expressive language skills through reading aloud at TKIT Permata Bunda. The research method used is descriptive qualitative. The subjects of this research were group A children aged 4-5 years and two teachers. Data collection techniques include interviews, documentation and direct observation. The results of the research show that developing children's expressive language skills can be done through the activity of reading aloud books based on religious and moral values. The implementation stage includes planning by selecting books according to the child's development level and containing religious and moral values. The next stage is implementation in the form of introducing the book, reading the story book aloud with the right intonation, holding a discussion and asking stimulating questions and motivating the child to retell the story. Next, the teacher carries out an assessment to determine the achievement of expressive language development.


Peningkatan Hasil Belajar Menulis Recoun Text pada Siswa Kelas Viiig Melalui Model Pembelajaran Problem-Based Learning di SMP Negeri 1 Sambungmacan

Abstract: This research aims to enhance the learning outcomes of recount text writing for kelas VIIIG SMP Negeri 1 Sambungmacan in the academic year 2023—2024 through the implementation of the problem-based learning instructional model. The objectives of this research are (a) to ascertain the effectiveness of the problem-based learning model in improving the ability to write recount texts, (b) to understand the process of students' learning outcomes improvement, and (c) to determine the extent of the improvement in recount text writing skills for eighth-grade students at SMP Negeri 1 Sambungmacan in the academic year 2023—2024 before and after the implementation of the problem-based learning model. This study follows a Classroom Action Research (CAR) design, conducted over two cycles, each involving four stages: planning, implementation, observation, and reflection. The research subjects are 30 students kelas VIIIG at SMP Negeri 1 Sambungmacan in the academic year 2023—2024. Both quantitative and qualitative analyses were employed for data analysis. The results of the analysis indicate an improvement in recount text writing learning outcomes in the first cycle with the topic of "unforgettable experiences," where the average score was 73.53, falling into the less satisfactory category. However, in the same material in the second cycle, the average score increased to 84.03. Based on these findings, it is concluded that the problem-based learning model is suitable for enhancing students' learning outcomes in recount text writing with the topic of "unforgettable experiences." Moreover, the problem-based learning model can be applied to other English language topics and across various subjects.


PENERAPAN MODEL PEMBELAJARAN ACTIVE LEARNING DALAM MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN AQIDAH AKHL.pdf

Abstract: This research discusses the implementation of the Active Learning model in improving students' learningoutcomes in the subject of Islamic faith and morals at MTs Alkhairaat Madinatul Ilmi, Dolo District, Sigi Regency. Themain issues addressed in this thesis are: How is the implementation of the Active Learning model in the subject ofIslamic faith and morals? What steps are used in implementing the Active Learning model in the subject of Islamic faithand morals? What are the obstacles and solutions to the Active Learning model in the subject of Islamic faith andmorals?The research approach is qualitative, employing data collection techniques such as observation, interviews,documentation, followed by data reduction, data presentation, and verification through triangulation methods.The results of this thesis research indicate that the implementation of the Active Learning model greatlyassists educators and students in the teaching and learning process. Students are encouraged to create an atmospherewhere they actively ask questions, inquire, and develop ideas, thereby becoming the center of learning activities.Educators act as facilitators, motivators, and inspirators, controlling, supervising, and providing opportunities forstudents to think critically, develop inspiration, and creativity. The implementation involves designing learningactivities, motivating students, organizing them, and presenting the material. Challenges in implementing the ActiveLearning model include students' lack of confidence and limited time allocation, while solutions involve motivatingstudents and training them to articulate thoughts and manage time effectively.


Learning Using TikTok for Reducing Pronunciation Errors

Abstract: Technology has advanced swiftly within the realm of education, and it has had a significant impact on teaching and learning. The researcher uses a social media TikTok duet feature, which is currently very popular among students as a medium for English learning. These studies specifically focus on the potential of TikTok for reducing pronunciation errors. The aim of this research is to reduce students' pronunciation errors by using the TikTok application. The problem that will be examined is whether TikTok can reduce students' pronunciation errors. This research employed a quantitative methodology and adopted an experimental design. The participants were segregated into two categories: an experimental group and a control group. The study sample was derived from two classes at SMP Negeri 1 Pandaan, specifically the 9th grade, comprising a total of 62 students. The instruments used were pretest and posttest administered before and after treatment. The examination test comprised of typically dispersed understudies with comparable attributes, guaranteeing homogeneity. Factual procedures, for example, free examples t-test and estimated time of arrival squared test were utilized for examination. The SPSS test yielded a value of 0 that was statistically significant. 000. As 0.000 under 005. Consequently, H0 is dismissed and H1 is acknowledged. This study saw as a higher η-square worth (0.733) in the exploratory gathering contrasted with the benchmark group (0.582), showing a bigger impact size because of exploratory condition. This demonstrates that roughly 73. The treatment was responsible for 3% of the experimental group's variance in the dependent variable. It showed that students reduce after using TikTok as a learning media because TikTok is popular social media. So that student can be motivated to learning material. This implies that using TikTok application as a learning media can reduce pronunciation errors in students.


Pages to Screen: Digitalized Yearbook with Chatbox and Gallery

Abstract: This study explores the potential of Digitalized Yearbook as a transformative tool. Aimed at enhancing customer engagement, streamlining yearbook production, and expanding service offerings, Digitalized Yearbook promises to position its client as a leader in innovative event photography and printing solutions. With a focus on improving alumni engagement and school administration efficiency, Digitalized Yearbook serves as a dynamic platform for reconnecting graduates, managing data, and preserving school memories. Utilizing the Waterfall Model, this project adhered to clear, defined stages, facilitating effective planning, resource allocation, and quality control. This structured approach minimized risks and enhanced the efficiency of the development process. The evaluation of Digitalized Yearbook revealed a 94.37% effectiveness rate, marking it as 'Excellently Functional.' This score reflects not only the system's technical proficiency but also its successful integration of functionality, reliability, and user-centric design, indicating its substantial potential to revolutionize the landscape of event photography and alumni engagement.


Notes towards infrastructure governance for large language models

Abstract: This paper draws on information infrastructures (IIs) in science and technology studies (STS), as well as on feminist STS scholarship and contemporary critical accounts of digital technologies, to build an initial mapping of the infrastructural mechanisms and implications of large language models (LLMs). Through a comparison with discriminatory machine learning (ML) systems and a case study on gender bias, I present LLMs as contested artefacts with categorising and performative capabilities. This paper suggests that generative systems do not tangibly depart from traditional, discriminative counterparts in terms of their underlying probabilistic mechanisms, and therefore both technologies can be theorised as infrastructures of categorisation. However, LLMs additionally retain performative capabilities through their linguistic outputs. Here, I outline the intuition behind this phenomenon, which I refer to as “language as infrastructure”. While traditional, discriminative systems “disappear” into larger IIs, the hype surrounding generative technologies presents an opportunity to scrutinise these artefacts, to alter their computational mechanisms and introduce governance measures]. I illustrate this thesis through Sharma’s formulation of “broken machine”, and suggest dataset curation and participatory design as governance mechanisms that can partly address downstream harms in LLMs (Barocas, et al., 2023).


Image encryption based on s-box and 3D-chaotic maps and secure image transmission through ofdm in rayleigh fading channel

Abstract: Data security is vital if transferred over a wireless network or stored on a personal computer. The essential properties of chaos, such as initial state sensitivity and unpredictability, make it a crucial candidate for encryption applications. This paper proposes a 3D chaotic map and a cascaded S-Box that can be combined to get a high-efficiency and complex cryptographic algorithm. The first stage is ciphering using a 3D cat map, the second stage is an S-box based on a 3D Henon map, and the third stage is another ciphering stage using a 3D Henon map. This study combines various encryption techniques, including cipher algorithms and substitution boxes, with the OFDM system to establish a secure image transmission over a Rayleigh fading channel. BPSK modulation is used to ensure the simplicity of the proposed system. Three grey images are used, Lena, the camera-man, and Pepper, for testing and comparing with previous works. Security analysis is performed to evaluate the quality and security of the encryption process. For image transmission evaluation, the PSNR and BER are utilized. According to the statistical results, the proposed image encryption scheme is secure and efficient. The comparison with the previous studies shows that this system is competitive with previous works.


Organizational Culture’s Involvement Dimension and Academic Staff Task Performance

Abstract: The influence of empowerment, team orientation, and capability development dimensions of involvement on academic staff’s task performance at Cihan University-Erbil is examined in this paper. By shedding light on the link between these involvement characteristics and academic staff task performance, this study adds to the body of knowledge in the field of organizational behavior and management. This research uses a quantitative methodology. Based on well-known theories and scales for task performance, empowerment, team orientation, and capability development, a questionnaire is created. Academic staff members from Cihan University-Erbil’s various departments make up the sample. To guarantee representation and generalizability of the results, a random sampling approach is used. The study contributed to the body of knowledge on involvement performance in this setting by demonstrating how academic staff task performance was positively impacted by empowerment, team orientation, and capability development. Furthermore, the results offer significant perspectives for academic staff and university administrators on how to raise the degrees of empowerment, team orientation, and capability development to enhance academic staff task performance.


A Scoping Review on the Hidden Curriculum in Education

Abstract: This article provides a systematic overview of constituting elements of the hidden curriculum in education: norms reproduced in the social context, group roles and behaviors, media for norm transmission; their effects on individuals and society, and various coping strategies. A scoping review was conducted, analyzing 23 articles based on defined categories. The literature reveals different reproduced norms (e.g., conformity, temporal rhythmizing), roles with specific attributes (e.g., teacher power, student recognition), and media for norm transmission (e.g., teaching materials, routines and rituals). Effects of the hidden curriculum occur on individual and societal levels, with various coping strategies identified.


Organizational information security threats: Status and challenges

Abstract: Organizational information security is a critical concern in today’s interconnected and data-driven world. With the increasing frequency and sophistication of cyber threats, organizations face significant risks to the confidentiality, integrity, and availability of their sensitive information. This paper provides an overview of the key aspects and challenges related to organizational information security. It highlights the importance of implementing robust security measures, such as firewalls, intrusion detection systems, encryption technologies, and secure coding practices, to protect against external threats. It also demonstrates the need for continuous monitoring, threat intelligence sharing, and incident response capabilities to detect and respond to security incidents effectively. This survey shows importance of user awareness, training, and adherence to security policies and procedures. In addition, the significance of establishing a security-centric culture within organizations to mitigate the risk of insider threats and promote a strong security posture is discussed. The evolving threat landscape, including challenges associated with advanced persistent threats, zero-day vulnerabilities, and the security of emerging technologies such as IoT and AI are highlighted, together with the need for ongoing research and innovation to address these challenges and enhance the effectiveness of preventive measures.


Supplemental Material: Cenozoic Pb–Zn–Ag mineralization in the Western Alps

Abstract: Analytical methods and monazite U–Th–Pb, allanite Th–Pb and phengite Rb–Sr data analysis for the Pb–Zn–Ag deposits in the French Northern Alps; chemical map with summary table of the phengite and albite compositions; Tables S1–S8; and Figures S1 and S2.


Enhancing Data Access Security through the Utilization of [2048,512,4] Linear Code

Abstract: Linear code is a type of error-correcting code that satisfies the property of closure under addition. Linear codes have been a major component in data security and cryptography and has been proven as an important tool in maintaining privacy. Linear code, [n,k,d] is a type of linear code with specific parameters that describe its properties and capabilities with n being the length of codewords, k, the dimension of the code and d denotes the minimum Hamming distance. Code [2048,512,4] is an example of [n,k,d] Linear code with large codeword length, high dimension and a moderate minimum hamming distance. It was generated by a combination of two of [1024,256,4] linear code and its validity has been verified and confirmed using Gilbert Varshamov bound. This paper proposes a technique to enhance data access security by utilizing linear code [2048,512,4]. The proposed algorithm utilizes a sophisticated linear code equivalence test methodology to create a robust and secure system that generates and validates unique [2048, 512, 4] linear codes. These codes serve as highly effective authentication mechanisms, granting individualized access privileges to different users within the system. Also, by employing cutting-edge techniques in linear code equivalence testing, the algorithm ensures the generation of distinct and non-repeating [2048, 512, 4] linear codes, uniquely tailored to be each user's credentials. The analysis of the results reveals that to breach such a security measure, an intruder would need to contend with a vast number of permutations and combinations. In particular, there are 2048 P 2048 patterns and an astounding 131,328 distinct combinations of codewords that remain unknown to any potential breacher. These findings underscore the robustness and invulnerability of the proposed technique for enhancing data access security. The proposed technique also demonstrates a potential for significantly enhancing the protection of sensitive data for organisations in both pre-quantum and post quantum computing within the digital ecosystem.


Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis

Abstract: Objectives: To analyse students’ academic performance based on assessment methods and determine the most prevalent one through which students can be categorised for recommending optimal student-centered pedagogies that enhance students’ performance. Methods: Exploratory Data Analysis identifies the implications of the assessment methods based on the marks obtained by students in Continuous Assessments (CA) and the Cumulative Test (CT). Continuous Assessment (CA) and Cumulative Test (CT) marks of three subjects that come under foundation science, elective, and skill-based course of 100 undergraduate students are collected from a reputed Arts and Science Institution using stratified sampling technique, analyzed, and the recommendations are made based on the statistical observations and cluster analysis. Clustering recognises learning patterns of the students’ on the learners’ data. The Elbow method determines the number of clusters where the Silhouette score identifies the best suitable clustering technique for the dataset. K-Means Clustering categorises students based on their performance, that helps to give recommendations to improve. Findings: Based on Univariate and Bivariate analysis on the dataset, this work identifies Continuous Assessment (CA) as a prevalent evaluation strategy that motivates students to get engaged throughout rather than just before the exam. Based on the Silhouette Score (above .5), K-Means clustering is chosen to discover hidden patterns in the assessment marks depending on the three clusters determined by the Elbow method. It helps to identify the underperformers (46%) and suggest personalised recommendations for improving student’s academic performance as per clusters. Novelty: This work integrates Statistical Analysis and Clustering Analysis as per the optimal clusters determined by the Elbow method for identifying patterns hidden in assessment marks based on the prevalent assessment types. As a result, it enables more personalised recommendations for recognising the predominant assessment method and boosting academic achievement. Keywords: Continuous Assessment, Cumulative Test, Statistical Analysis, Exploratory Data Analysis, Univariate, Bivariate, Cluster Analysis, Elbow Method, KMeans Clustering


FUTURE-PROOFING THE WORKFORCE: TRAINING AND DEVELOPMENT IN THE DIGITAL AGE

Abstract:
In the context of quick technological development, this qualitative research study focuses on training and development strategies in the digital age. Relying on secondary data analysis, it synthesises diverse sources such as literature, articles, websites, reports, and case studies. The research explores workforce readiness for digital transformation, emphasising key themes like adaptive learning, immersive technologies (virtual and augmented reality), and continuous learning. Uncovering challenges in accessibility, engagement, and resource allocation, the study offers valuable insights for organisations navigating the digital landscape. It provides a nuanced understanding of the shifts in workforce development, guiding practitioners with practical, data-driven recommendations. This research is valuable for organisations and practitioners aiming to equip their workforce with the essential skills for success in an ever-changing digital environment.

 Article visualizations:


Digital Literacy for Women's Empowerment: A Solution to Grow Awareness of Countering Hoaxes

Abstract: The purpose of this study is to explore the effectiveness of digital literacy programmes for women to raise awareness and combat hoaxes. The object of the research is the women's recitation community who are members of the Aisyiyah organisation. The importance of digital literacy in today's digital era and the need for continued investment in programmes to help women become more digitally literate. Using a quasi-experimental design, this study used pre-test and post-test data to measure awareness and combat hoaxes. Data was collected from primary and secondary sources, including surveys and literature reviews. The results showed an improvement in literacy and this reflected the participants' understanding and application of digital literacy skills, including social media, blogs, videos, images and editing tools. A comparison between the pre and post-test results and the change in error rates can give an idea of the increase or decrease in digital literacy skills after the programme.


DATA QUALITY ASSESSMENT: A CASE STUDY ON ASSET VALUATION COMPARISON DATA

Abstract: To realize a data-driven organization, good data quality is needed as a foundation for solving various problems related to data management. The case study used in this research is asset valuation comparison data. The purpose of this research is to define dimensions, measure and analyze data quality on asset valuation comparison data. There are three dimensions used in measuring data quality in this study which are adjusted based on existing regulations at Ministry X, namely accuracy, completeness, and validity. This research uses the stages in the Total Data Quality Management (TDQM) framework to measure data quality. The results of measuring all dimensions, 29 out of 58 business rules cannot be fulfilled completely. The business rules that can be fulfilled in each dimension are 47.06% in the completeness dimension, 60% in the validity dimension, and 44.44% in the accuracy dimension. The main factor causing the existence of data attributes that have not met the data quality business rules is because the asset valuation comparison data comes from various data sources. In addition, there are methods or standards for recording data from data source units that are not uniform, so an evaluation of the uniformity of data standardization and the implementation of data governance is needed. The results of this study can be used as material for organizational consideration to be more aware of the current state of data quality. In addition, it can be used by organizations to design strategies and steps to improve data quality so that it can support leaders in making the right decisions.


Does the Effect of Different Governance Channels Depend on the Level of Development of African Countries?

Abstract: The level of development of African States is consistent with the discussions surrounding the need for countries in the region to be fully immersed in practical philosophy. Decision-makers must adopt a coordinated strategy to stimulate the sustainable development of their economy; by incorporating various national plans aimed at encouraging well-being into national governance mechanisms. This paper studied the impact of governance on sustainable development. We use a dynamic panel estimation model, incorporating the PCA technique, to group the governance indicators of Kaufmann et al. [1] into three main composite indicators: economic, institutional and political governance. The Pooled Mean Group (PMG) estimate shows that the development level of African countries is strongly influenced by established institutions. In the short- and long-term, various governance indicators (political, economic, and institutional) have a significant impact on human development and the ecological footprint of countries with high levels of development. On the other hand, they can only significantly stimulate development in countries with a medium level of development in the long term. Much more effort should be made in poorly developed countries to address the shortcomings that limit the implementation of laws and the fulfillment of the obligations of companies, the state, and citizens to ensure sustainable development.


Application UI UX Design Selling Laptops Online Using The Design Thinking Model

Abstract: Nowadays, there are several online payment systems over the internet to serve the needs of online businesses. Buying and selling laptops online has become popular due to its convenience, wider selection, and competitive prices. Customers can browse various brands and models, compare features and prices from various sellers on e-commerce platforms. The purpose of this research is to design a mobile-based online laptop selling application that can make it easier for users to order laptops that are more efficient by using design thinking modeling. After designing the user interface, further testing is carried out using the System Usability Scale (SUS) approach which obtained a score of 65 which is at grade D, so it can be concluded that the design of the user interface of this laptop selling application is okay to be developed into a mobile-based application.


Study of digital approach in education and psychology among selected users in Ukraine

Abstract: The purpose of the article is to study the digital approach in pedagogy and psychology of the future, to characterise the main trends and globalisation challenges that will accompany this process in the future. The scientific research methods used for the study are survey (with a coverage of 300 people), analysis, synthesis. Descriptive statistics, correlation and thematic analysis were used to summarise the quantitative results. The results of the study indicate that significant transformations are observed at the level of interaction between mentors and students, in the process of shaping the content of education and using tools for knowledge transfer. Until now, the focus of digital technologies has been on the interaction between teachers and students, but in the modern context, this approach has evolved into comprehensive horizontal communication within the educational community. The results of the study confirm that the use of digital tools is a productive and useful measure, which is recognised by most respondents. At the same time, a significant number of respondents use digital tools on a regular basis, even daily or several times a week. This indicates a high level of integration of digital technologies into the work of teachers and psychologists in general. It is important to note that, unlike European and American colleagues who actively introduce elements of artificial intelligence into the educational process and activities of psychologists, Ukrainian respondents mainly focus on the use of online platforms such as Zoom, Microsoft Teams, Google Meet, Moodle, Google Classroom, etc. This is in line with current trends in assessing the impact of digitalisation on the professional activities of teachers and psychologists. The conclusions indicate that it is equally important to further develop research in the direction of using foreign experience that actively implements the capabilities of artificial intelligence.


Cryptocurrency in the Light of Sentiments : A Bibliometric Approach

Abstract: Purpose : Cryptocurrency has drawn interest from academia and business, particularly amid notable spikes in the price of Bitcoin. The presence of cryptocurrencies and their technology-focused investments are intimately related to investor behavior. Due to its unpredictability, mood has a greater impact on Bitcoin investing than technical factors. The purpose of this study was to perform a bibliometric review in this field. Methodology : Using VOSviewer software, this article attempted to do a bibliometric study of papers on sentiments and cryptocurrencies included in the Scopus database. The study complied with the PRISMA framework, which is the recommended practice for systematic reviews. Findings : The findings showed that this field is seeing a massive increase in research activity. A total of 483 distinct authors wrote 151 articles. Studies that examined changes in the Bitcoin industry have received the attention they deserve in finance publications. Three connected clusters were discovered by co-citation analysis, indicating that studies are looking at how cryptocurrencies work as financial market investments using social media sentiments. Practical Implications : The study on cryptocurrency sentiments offered valuable insights for investors, policymakers, and the market. It informed decision-making on investments, risk management, and regulations, providing a foundation for practical tools in the dynamic cryptocurrency market. These insights will contribute to a resilient and sustainable ecosystem, guiding public awareness for responsible cryptocurrency use. Originality : To the best of our knowledge, this work is the first in this developing field to do a bibliometric literature evaluation on Scopus articles.


Increasing student engagement through digital branding in higher education marketing

Abstract: As a result of the employment of social media, interpersonal contact and user involvement are made possible, which results in the modification of traditional hierarchical communication techniques and the introduction of an unexpected component into the decision-making process of customers. Even though it is considered to be a component of internet marketing, social media marketing is a crucial component for individuals and businesses that need to retain their competitiveness. On the other hand, the success of social media marketing is dependent on the presence of innovative items, the responsiveness of organizations to the market, and the construction of powerful online platforms. Twitter, Instagram, and Facebook are just some of the social networking sites that have evolved into indispensable tools for marketers. Since they enable interactive contact, these platforms are of tremendous value when it comes to the marketing of companies and services. Websites that facilitate social networking play an important part in the spread of information about educational institutions of higher learning. These websites also serve as effective means for recruiting prospective students. It has not yet been determined whether or not the content that is broadcast on these internet platforms has an effect on the cognitive processes that prospective students use when making choices about their education. The acknowledgment of user interaction on social media platforms is the most important non-financial benefit that organizations may get from their use of these platforms. When we have a thorough awareness of user interaction across a variety of platforms, we may have a better grasp of the influence that it has on how people perceive businesses. Instagram has become a significant medium for educational institutions of higher learning due to its visual-centric qualities, as well as its heightened engagement rates among a varied, youthful, and multinational community. This article places a strong emphasis on the relevance of brand equity within the context of the marketing strategy used by educational institutions of higher learning. When it comes to the idea of brand equity, there are several elements that may have an impact on it. Some of these characteristics include, but are not limited to, the personality of the brand, social credibility, and market position. It is quite necessary for educational institutions to make certain that they provide excellent services and facilities to their students. Nevertheless, the construction of a communication system that is both competent and effective is equally important for the purpose of boosting the positioning of the university's brand and effectively managing the value of its brand. The concept of "brand soul" serves as the basis for this, and it refers to the way the teachers and staff of the educational institution become the embodiment of the brand.


Prospects of re-signifying life through the methods of the realistic theater of Constantin Stanislavski and Ivana Chubbuck

Abstract: Acting is a complex and difficult to define art. It's easy to distinguish between good acting and bad acting. Being able to be captivating in a job while someone else, playing the same role and saying the same words, could be better or worse? If this depended solely on the script, on the beauty of the language, on a skillful construction of the sentence, we would just need to read it. But it's a matter of interpretation.


Detecting Similarity of Program Code Using an Aggregated Approach to the Problem of Plagiarism Detection

Abstract: In this work, the primary strategies employed for code plagiarism were explored, alongside an analysis of prevalent methods for detecting copied content. Based on the results of the analysis of various approaches, as well as the analysis of the subject area itself and on the basis of the formulated requirements, a new System for automatically checking software similarity for plagiarism was successfully designed, implemented and tested. When developing the System, an aggregated approach was used, which made it possible to use several basic similarity detection algorithms. Namely, the Greedy Row Tiling algorithm and the Sifting algorithm. Since the System is designed for programmers, in particular, for teachers, and also with the possibility of local launch, it is proposed to perform user interaction with the System in the form of a command line interface. The System is implemented in Python, which ensures that the suggested System is platform independent. Regular expressions are used to implement preprocessing and exclusion functions, and the libclang library is used for С++ code parsing and tokenization functions. Promising applications for the developed System include education and programming competitions. So universities and colleges can use the System to check code written by students to detect plagiarism. And in competitive environments such as hackathons or programming competitions, the System can be used to ensure fairness and prevent plagiarism among participants.


Estimating total organic carbon of potential source rocks in the Espírito Santo Basin, SE Brazil, using XGBoost

Abstract: Identifying and constraining source rocks is critical for petroleum system modeling and risk assessment. Traditional methods for total organic carbon (TOC) estimation based on well logs have limitations, leading to the emergence of machine learning techniques like XGBoost. This study compiled a comprehensive data set of well log and geochemical data from the Espírito Santo Basin, SE Brazil, and XGBoost, which was integrated with pySpark, was used for running multiple machine learning models to predict TOC. Parameter tuning was performed by randomly combining model configurations over multiple replication data frames. XGBoost effectively predicted TOC, yielding a coefficient of determination R2 of 0.71, RMSE of 0.55 and MAE of 0.30, based on the average of all 10-fold cross-validation test sets. Heteroscedasticity was observed, possibly related to the presence of outliers in the target TOC variable, which may be linked to variable organic-matter deposition and preservation processes through the geological time, such as during Oceanic Anoxic Events (OAEs). The results indicate the potential of machine learning for TOC prediction in large, heterogeneous data sets, outperforming the traditional ΔlogR method and offering a promising tool for the usage of available public data sets in similar applications, such as the oil and gas (O&G) industry's exploration phase or field reassessment.


Implementing Cognitive Semantics of Autoepistemic Membership Statements: The Case of Categories with Prototypes

Abstract: This article presents a model of an architecture of an artificial cognitive agent that performs the function of generating autoepistemic membership statements used to communicate beliefs about the belonging of an observed external object to a category with a prototype. The meaning of statements is described within the model by means of cognitive semantics. The presented proposal builds upon a pre-existing architecture and a semantic model designed for a simpler case of categories without a prototype. The main conclusion is that it is possible to develop an interactive cognitive agent capable of learning about categories with prototypes and producing autoepistemic membership statements fulfilling requirements of Rosch’s standard version of prototype semantics and satisfying pragmatic and logical rules for generating equivalents of these statements in natural languages. Detailed results include the following: an original proposal for an agent’s architecture, a model of an agent’s strategy of learning categories with a prototype, a scheme for determining the computational complexity of particular implementations of the learning strategy, definitions of cognitive semantics for particular cases of autoepistemic membership statements, and an analytical verification of properties of the proposed cognitive semantics. Finally, this article discusses the directions of further development and potential variants of the proposed architecture.


Geogas prospecting for igneous ore deposits covered by regolith: The Zijinshan high-sulfidation epithermal Cu-Au deposit in the Cathaysia Block

Abstract: The mining industry faces a challenge due to the scarcity of outcrop or near-surface mineral deposits, necessitating the development of low-cost and efficient prospecting methods to explore through cover. Inductively coupled plasma mass spectrometry (ICP-MS) has been applied to geological sample analysis, and scientists have gradually improved the method of geogas prospecting. As a result, geogas prospecting has shown promising results in detecting underlying concealed Au, U, and Cu-Ni deposits covered by Gobi sand and Quaternary sediments.To verify the effectiveness of this method for exploring underlying concealed mineral deposits developed in igneous covered areas, the Zijinshan high-sulfidation epithermal (HSE) Cu-Au deposit, a concealed mineral deposit in southeastern China, was selected as the experimental field. Our experiments revealed nanoscale particles composed of Au, Cu-Fe, and Cu-other elements in the geogas captured above the ore bodies of the deposit. Furthermore, Cu-nanoscale particles retain the isotopic composition of copper found in both the oxidation zone and deep copper ores. The geogas samples exhibited similar C1-chondrite normalized rare earth element (REE) distributions, with REE patterns indicating significant enrichment of LREE relative to HREE and similar (La)N/(Sm)N and (Gd)N/(Yb)N ratios. These characteristics are similar to those of the gold ores, copper ores, altered rock, and bedrock near the ore bodies. These findings suggest that deep-penetration geochemical methods using geogas can be a valuable tool for uncovering underlying concealed mineral deposits in igneous covered areas.


Supplemental Material: Lithospheric mantle as a metal storage reservoir for orogenic gold deposits in active continental margins: Evidence from Hg isotopes

Abstract: Geology of the Jiaodong gold province, analytical methods, supplementary notes, Figure S1, and Tables S1–S2.


Strategies for Enhancing the Practical and Innovative Abilities of Environmental Major Students

Abstract: In order to meet the quality requirements of applied talent cultivation in local universities and enhance students' innovation ability, this study which take the environmental major students of Zhaoqing University as an example, explores the role of the “industry-university-research-application” platform, college student innovation and entrepreneurship training programs and various subject competitions in talent cultivation. It is benefit to encourage students to complete self practice ability cultivation through school enterprise platforms, training programs, and competitions. This study provide reference for improving the abilities of environmental majors in similar universities.


Digital Teaching Competence and Educational Inclusion in Higher Education. A Systematic Review

Abstract: This article presents a systematic review of the literature with the aim of providing an updated framework for the scientific production developed in the field of digital competence and inclusive education in Higher Education, as indexed in the Web of Science (WoS) and SCOPUS databases. Twenty-one scientific articles published between 2016-2023 were included and analyzed, with no temporal cohort established. The search matches yielded a first document of the topic of study in 2016 in the Social Sciences Citation Index. The results obtained on the conceptual structure of the analyzed documents were carried out through a multiple correspondence analysis (MCA) observing the existence of two dimensions composed of three clusters. Cluster one was composed of studies on digital competence, virtual learning environments, digital divide, and functional diversity, among others. Cluster two comprised studies carried out on digital tools and competencies, inclusive education, educational processes, and digital strategies; while cluster three was made up of educational inclusion related to interculturality and digital competence. Among its limitations, there were aspects related to the heterogeneity of the studies, which make it difficult to compare the data, and the sample and size of the study, which makes the results obtained and the generated data have a lower percentage of generalization compare to studies that use larger simples. This study has implications for researchers and Higher Education institutions interested in research on digital competence for inclusive education, with the possibilities of digital competence for inclusive education being established from the studied variables, allowing teachers to adapt and personalize learning to meet individual student needs. All of this is in line with the goals of the 2030 Agenda, concerning the empowerment of citizens and the digitization of public services to ensure the population's access to such services via the internet.


An Improved Public Key Cryptographic Algorithm Based on Chebyshev Polynomials and RSA

Abstract: Due to its very desirable properties, Chebyshev polynomials are often used in the design of public key cryptographic systems. This paper discretizes the Chebyshev mapping, generalizes the properties of Chebyshev polynomials, and proposes an improved public key encryption algorithm based on Chebyshev chaotic mapping and RSA, i.e., CRPKC−Ki. This algorithm introduces alternative multiplication coefficients Ki, the selection of which is determined by the size of TrTdxmodN=TdTrxmodN, and the specific value selection rules are shared secrets among participants, overcoming the shortcomings of previous schemes. In the key generation and encryption/decryption stages, more complex intermediate processes are used to achieve higher algorithm complexity, making the algorithm more robust against ordinary attacks. The algorithm is also compared with other RSA-based algorithms to demonstrate its effectiveness in terms of performance and security.


Quadripartite quantum steering generated by single-pass cascaded nonlinear processes

Abstract: Abstract Quantum steering has become an important resource for quantum information due to its unique asymmetry. In this paper, quadripartite quantum steering generated by single-pass non-degenerate optical parametric amplification cascaded with a sum-frequency process is investigated. Firstly, idler beam is produced by different-frequency process between pump and signal. Secondly, sum-frequency beam is generated by cascaded sum-frequency process between idler and pump in the same optical superlattice. Quadripartite quantum steering among pump, signal, idler and sum-frequency beams id demonstrated based on the criterion for multipartite quantum steering. The influences of nonlinear coupling coefficients $\kappa$, dimensionless propagation length $\xi$, and the initial values of pump and signal on the variation characteristic of the quadripartite steering are also investigated. The results indicate that slightly larger nonlinear coupling coefficients of cascaded nonlinear processes and weaker signals can achieve better quadripartite quantum steering. This simple quadripartite quantum steering preparation scheme does not require a resonant cavity and is easier to implement experimentally, with potential applications in quantum communication and computing.


Entanglement and Replica Symmetry Breaking in a Driven-Dissipative Quantum Spin Glass

Abstract: We describe simulations of the quantum dynamics of a confocal cavity QED system that realizes an intrinsically driven-dissipative spin glass. A close connection between open quantum dynamics and replica symmetry breaking is established, in which individual quantum trajectories are the replicas. We observe that entanglement plays an important role in the emergence of replica symmetry breaking in a fully connected, frustrated spin network of up to 15 spin-1/2 particles. Quantum trajectories of entangled spins reach steady-state spin configurations of lower energy than that of semiclassical trajectories. Cavity emission allows monitoring of the continuous stochastic evolution of spin configurations, while backaction from this projects entangled states into states of broken Ising and replica symmetry. The emergence of spin glass order manifests itself through the simultaneous absence of magnetization and the presence of nontrivial spin overlap density distributions among replicas. Moreover, these overlaps reveal incipient ultrametric order, in line with the Parisi replica symmetry breaking solution for the Sherrington-Kirkpatrick model. A nonthermal Parisi order parameter distribution, however, highlights the driven-dissipative nature of this quantum optical spin glass. This practicable system could serve as a test bed for exploring how quantum effects enrich the physics of spin glasses.4 MoreReceived 21 July 2023Revised 19 November 2023Accepted 10 January 2024DOI:https://doi.org/10.1103/PhysRevX.14.011026Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtoms, ions, & molecules in cavitiesCavity quantum electrodynamicsDissipative dynamicsPhysical SystemsSpin glassesTechniquesCavity resonatorsAtomic, Molecular & OpticalQuantum Information, Science & TechnologyStatistical Physics & ThermodynamicsCondensed Matter, Materials & Applied Physics


Philosophy of future: analytical overview of interaction between education, science, and artificial intelligence in the context of contemporary challenges

Abstract: Currently, artificial intelligence is changing the future landscape of technology and digitalization and transforming the way knowledge is generated and shared in education and science. The aim of the article is to interpret the future through interactions between education, science, and AI and to outline their potential challenges when digital transformations occur. While answering the research questions the mixed-methods approach was applied. For the study both quantitative and qualitative data was collected through descriptive and empirical survey. The empirical survey included computer-assisted web interview to assess the most relevant concepts according to the participants responses. The study involved 42 participants that were selected randomly. The selection criteria concerned educational background, previous experience, representation of diverse perspectives, theories, or approaches within the philosophy, active engagement in educational practice, and wiliness to participate. To analyse the data statistical software NVivo was applied. Approximately 70 recent scientific works were studied to present the problem through multifaceted approach. The findings show the participants’ anticipations are related to the cognitive load theory, constructivist theory, and socio-cultural theory. A number of challenges threatening innovative developments arise within the educational and scientific environments. They include ethical concerns, misinformation, digital divide, unequal infrastructure, lack of regulation, lack of digital skills, resistance to change, technology integration, and limited digital pedagogy. Ethics plays a crucial role in shaping digital transformations in education and science. The category of academic virtue is closely connected to ethical and responsible use of Ai. The paradigm of academic virtue includes ethical of knowledge, collaboration, responsibility, honesty, accuracy, adaptability, openness. AI brings automated content creation, language processing opportunities, real-time translation, and enhanced accessibility and, therefore, changes communication in the field of education and science. The research showed that the principles of effective implementation of AI in education and science include philosophical, educational, and ethical dimensions.


Development of Vinzons Pilot High School Online Enrollment with Student Track Identification System

Abstract: The Vinzons Pilot High School is a top secondary school in the Camarines Norte province. About 6,000 students attend VPHS every year with the number seeing a steady increase. which recruited around 200 instructors and staff members. Enrolment allows around 6000 students every year which poses a concern for the student and the employees of the school. It is for that reason that the digitizing enrolment system is among the alternatives that can help to manage the large number of participants and the data to manage. The agenda on the establishment of online enrolment system coupled with student track identification for Vinzons Pilot High School was offered as an alternative for the usual manual process, and to facilitate flow and eliminate disorganization of transactions and work. The research focuses on how hand enrolling works on the side of school transactions and students' opinions about their selective track. The enrolment procedure is time-consuming and is not so efficient as manual enrolment method is used to cover multiple transactions like the updating and adding of student data, the provision of Learner Information System reports, student sectioning, assisting students to choose a specialty among others. The study requires proper data management because the study concentrates on monitoring adherence to the restrictions and assessing the manual system. In this, the proponent obtained the necessary data that would guide the development of redesigning and automating the existing process and serving students to find the right path to their career.