Solid-state nanopore-sized counting for lamp false positive differentiation

Abstract: Nucleic acid detection is crucial in disease diagnostics and environmental monitoring. Loop-mediated isothermal amplification (LAMP) is a noteworthy method in this space due to its ability to work under isothermal conditions and its high sensitivity and specificity. LAMP has been applied to various applications, from identifying certain pathogens to detecting specific types of RNA and DNA. The LAMP reaction, triggered by the specific target and facilitated by 4 or 6 specific primers, results in the generation and elongation of characteristic cauliflower-like DNA amplicons. Yet, even with cautious primer design, LAMP may still yield false positives. Predominantly, LAMP assays apply intercalating dyes for a real-time assessment of amplification. This method, however, can't confidentially separate true positives from false positives due to a similar cycle threshold (Ct), which can result in incorrect determination. Several methodologies have been proposed to improve the fluorescence-based LAMP assays, involving fluorescence resonance energy transfer (FRET) and specialized probes. However, these approaches often exacerbate assay complexity and increase the cost of the assay, while not entirely avoiding false positives. To address the challenge of differentiating false positives in LAMP, our study introduces label-free nanopore sized counting technique as a novel approach for LAMP result analysis. This method facilitates the single-molecule level direct detection and sizing of LAMP amplicons. Notably, the variation in amplification efficiency and pathways between true and false positives leads to distinct differences in amplicon size and distribution. Therefore, with the tool of nanopore sized counting, we could effectively differentiate between the true and false positives in samples that remain ambiguous when using fluorescence-based detection. With this development, we aim to improve the accuracy of LAMP-based assays interpretations, advancing the reliability of nucleic acid detection across diverse applications.


Fast and robust 3D MINFLUX excitation with a variable phase plate

Abstract: MINFLUX has achieved record resolution in superresolution imaging and single fluorophore tracking. It is based on localizing single fluorophores by rapid probing with a patterned beam that features a local intensity minimum. Current implementations, however, are complex and expensive and are limited in speed and robustness. Here, we show that a combination of an electro-optical modulator with a segmented birefringent element such as a spatial light modulator produces a variable phase plate for which the phase can be scanned on the MHz timescale. Bisected or top-hat phase patterns generate high-contrast compact excitation point-spread functions for MINFLUX localization in the x, y, and z-direction, respectively, which can be scanned around a fluorophore within a microsecond and alternated among different excitation wavelengths. We discuss how to compensate for non-optimal performance of the components and present a robust 3D and multi-color MINFLUX excitation module with record speed, which we envision as an integral component of a high-performance and cost-effective open-source MINFLUX.


How fast does a random walk fill a finite region?

Abstract: The filling of a finite region of a lattice by a random walk is of interest, fundamentally and for biophysical applications. Filling is described by the cover time, defined as the time required for the walker to visit all conducting sites at least once, and the blanket time, defined as the time required for the walker to visit the sites uniformly for a specified level of uniformity. For the unobstructed case, a theorem states that the blanket time is proportional to the cover time. That is, the cost of uniformity is just a multiplicative constant, not a more stringent time dependence. The cover time is a measure of diffusive reaction times, and of the observation time needed for a single-particle tracking experiment to visualize the entire region. The blanket time is related to the work of Ellery et al. [J Chem Phys 144 (2016) 171104], who proposed a new method of obtaining the crossover time from anomalous to normal diffusion in obstructed diffusion. They claimed that an eigenvalue of the transfer matrix yields the crossover time. I claim on the basis of numerics and the math literature that this eigenvalue instead yields the characteristic time for approach to a uniform distribution. It is useful to compare the approach to uniformity in the Monte Carlo and transfer matrix methods. Monte Carlo requires averaging over many random walks for a particular initial position and many initial positions for a particular obstacle configuration, but the transfer matrix method generates a numerically exact average over both. Either method requires averaging over obstacle configurations at the prescribed obstacle concentration. The two methods have distinct asymptotic time dependencies: the transition matrix method as 1/t, and Monte Carlo as 1/sqrt t.


Edtech and Equity in Panama: Mobile Technology for Levelling the Learning Playing Field

Abstract: Access to quality education in the Republic of Panama is a topic of growing concern. The COVID-19 pandemic served to highlight the importance of this issue and its connection with information and communications technology. As remote learning assumed a critical role during the global pandemic, education technology (edtech) became a familiar concept and a determinant of families’ abilities to access instruction for their school-aged children. The situation prompted complex questions about the political economy of edtech, digital inequality, and the future of learning. This chapter examines edtech and equity in Panama, focusing on mobile technology as a potential means to levelling the learning playing field. It discusses two collaborative low-tech projects to propel basic skills acquisition for children in their early years of schooling that were implemented nationwide in 2020 while schools were closed. It links the knowledge generated from the study of these projects to the broader discussion of global forces that shape education delivery and local responses that work to drive equity and inclusion.


Improved free energy calculations using multi topology ensemble of expanded ensemble

Abstract: Protein binding interactions are difficult to adequately sample using computational methods due primarily to the relatively high degree of conformational flexibility of proteins. Our group has developed a novel method termed multi-topology ensemble of expanded ensemble (MTEEXE) which allows for expanded ensemble calculations to be run in parallel for iterative relative free energy calculations in GROMACS and to allow conformational swapping between the parallel transformations. These iterative transformations can be applied to any systems with a common back bone or central substructure. We have applied this method to calculate the relative binding free energy of acyl-ACP to the ketoacyl ACP synthase FabF for acyl chain lengths varying from 4 to 16 carbons. This system features both a flexible protein-protein binding interface and a highly flexible acyl chain which is covalently attached to ACP. The degree of conformational flexibility in our system poses a significant challenge to traditional computational free energy methods. We have also applied this method to multi-site perturbations of a small molecule core similar to multisite lambda dynamics in the CHARMM infrastructure, but without the need for modification of the MD code. We outline a method of setting up topologies in order to ensure separability of the partition function for each iterative transformation and provide instructions on how to perform inter-replicate coordinate modifications in order to generalize our proposed method. We demonstrate that MTEEXE not only increases efficiency of running iterative transformations, but also increases conformation sampling, especially when transforming bulky groups like the acyl chain, to produce more accurate and reproducible free energy estimates in GROMACS.


Marangoni spreading on liquid substrates in new media art

Abstract: Abstract With the advent of new media art, artists have harnessed fluid dynamics to create captivating visual narratives. A striking technique known as dendritic painting employs mixtures of ink and isopropanol atop paint, yielding intricate tree-like patterns. To unravel the intricacies of that technique, we examine the spread of ink/alcohol droplets over liquid substrates with diverse rheological properties. On Newtonian substrates, the droplet size evolution exhibits two power laws, suggesting an underlying interplay between viscous and Marangoni forces. The leading edge of the droplet spreads as a precursor film with an exponent of 3/8, while its main body spreads with an exponent of 1/4. For a weakly shear-thinning acrylic resin substrate, the same power laws persist, but dendritic structures emerge, and the texture of the precursor film roughens. The observed roughness and growth exponents (3/4 and 3/5) suggest a connection to the quenched Kardar–Parisi–Zhang universality class, hinting at the existence of quenched disorder in the liquid substrate. Mixing the resin with acrylic paint renders it more viscous and shear-thinning, refining the dendrite edges and further roughening the precursor film. At larger paint concentrations, the substrate becomes a power-law fluid. The roughness and growth exponents then approach 1/2 and 3/4, respectively, deviating from known universality classes. The ensuing structures have a fractal dimension of 1.68, characteristic of diffusion-limited aggregation. These findings underscore how the non-linear rheological properties of the liquid substrate, coupled with the Laplacian nature of Marangoni spreading, can overshadow the local kinetic roughening of the droplet interface.


Bayesian filtering for macroscopic hidden Markov models: Potential and limits of minimal informative priors to improve parameter inference

Abstract: Inferring the functional dynamics of ion channels from ensemble currents or combined with fluorescence data is daunting because the chemical reaction network (CRN) is only partially observable. We addressed this problem by applying a parallelized Bayesian filter to specify first-order CRNs for macroscopic current and fluorescence data leading to a more accurate likelihood (Muench 2022 eLife 11:e62714) than previous gold-standard algorithms. We generalized the previous signal model (Moffatt, Biophysical journal 93.1, 2007, 74–91) and derived approximate closed-form filtering equations beyond the Kalman filter solution even if state-dependent noise contributions in the signal are present. Incorporating the details of the single-channel Markov dynamics into the likelihood of a macroscopic signal supersedes classical deterministic approaches with rate equations in two ways. Firstly, the posterior is closer located to the true parameter values. Secondly, the uncertainty quantification is meaningful. In contrast, deterministic rate equations approaches underestimate the true parameter uncertainty even for large ensembles molecules. The likelihood misspecification intrinsic to rate equations approaches causes p values, confidence regions, or posteriors to be too confident for any finite data amount. Using Bayesian statistics requires defining a prior distribution. When little information about the parameter is known, and information in the data is poor, minimally informative priors are crucial to make the posterior as sensitive as possible to the data. Applying such a prior reduces the number of channels required for a reasonable inference by orders of magnitude compared to the standard uniform prior, which is often considered mistakenly uninformative. Nevertheless, the likelihood of HMMs contains usually a degree of practical parameter identifiability which poses challenges for minimally informative priors which we will discuss.


Trained image analysis techniques for characterizing cell phenotype in electron microscopy images of mouse thrombi

Abstract: The thrombus formation following a puncture wound is integral to vascular healing. However, the mechanism(s) underlying blood platelet recruitment and their activation is still poorly understood. Platelet differentiation is fundamental to sealing leaks at sites of vascular injury during wound healing. Investigating platelet activation and distribution within thrombi can elucidate the mechanism behind clot formation. Furthermore, the application of deep learning to characterization of morphology-based cell phenotypes of tens of thousands of cells within a single thrombus, can be generalizable to other tissue types. Montages of 500–1,000 high-resolution TEM images of mouse femoral artery and jugular vein thrombi cross sections were our study models for mapping platelet activation states. After individual platelet auto-segmentation using Amira software, each platelet was assigned to one of five activation states: discoid (least activated), loosely adherent, tightly adherent, degranulated, and procoagulant (most activated). A trained machine-learning model was used to produce a heat-map localizing different platelets types. Remarkably, the thrombi formed in the femoral artery and jugular vein samples showed considerable consistency in organization across samples, which indicated that staged platelet activation resulted in a discoid platelet tethering zone that was pushed progressively outward as platelets converted from one activation state to another. Our analysis supports a model that we term “capture and activate,” in which the initial high platelet activation is directly linked to the exposed adventitia. All subsequent tethering of discoid platelets is to loosely adherent platelets that convert to tightly adherent platelets, and self-limiting, intravascular platelet activation over time is the result of decreased signaling intensity. In the future, we hope that this analysis technique can be expanded to other image processing applications for cellular characterization, as well as 3D imaging modalities.


High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Abstract: Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R² is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.


Does the Manufacturing Industry Also Need Gamification? – A Gamification Design Practice for CNC Machine Tool Skill Training

Abstract: In CNC (Computer Numerical Control) vocational colleges, the current traditional pedagogy has become conflicting with the learning preferences of the “Generation Z”, resulting in their low sense of competence, low learning autonomy, and insufficient recognition of industry identification. Thus, gamification design provides novel ideas for addressing these issues. Based on the theories of self-determination theory and motivational affordances, this study analyzes the sore points and study needs of students who are majoring in CNC technology at vocational colleges. Hence, according to the 12 dimensions of motivational affordances, the “needs-affordances-features” framework and the course content of CNC technology, a set of gamification design strategies for CNC machine tool skill training is proposed. Consequently, a smartphone-based interactive design practice is completed. Following this design practice, a user experience test is executed, and a positive result is obtained. Theoretically, this study explores the feasibility of modified gamification design processes that combine the motivational affordance model and the “needs-affordances-features” framework. Practically, the integration of gamification design into CNC machine tool skill training is not only a bold attempt to boost the learning motivation of students, but also a practical research foundation for the innovative development and transformation of CNC technology vocational education.


Penerapan Metode K-Means Clustering Dalam Pengembangan Strategi Promosi Berbasis Data Penerimaan Mahasiswa Baru (Studi Kasus :Universitas Nurul Huda)

Abstract: The admission of new students is a key element in the success of a university. In its effort to enhance efficiency in admitting new students, Nurul Huda University proposes applying the K-Means Clustering method as a solution to manage candidate student data more effectively and intelligently. The data used in this research is derived from the Admission of New Students process for the academic years 2021/2022 and 2022/2023, totaling 1275 entries, which will then be processed using data mining techniques to generate analysis. The goal is to determine promotional strategies based on the origin of profiles of newly enrolled students. The method applied is clustering with the K-Means algorithm. After data processing, analysis is conducted using the Knowledge Discovery in Databases (KDD) technique, consisting of five stages: selection, preprocessing, transformation, data mining, and evaluation. The implementation in this research utilizes Rapidminer software, resulting in three data clusters: Cluster 1 with 345 entries, covering 27% of the total; Cluster 2 with 86 entries (7%); and Cluster 3 with 835 entries (66%). For promotion, the marketing team is deployed to districts dominant in the East OKU region and potential areas outside the East OKU District. They conduct direct visits to introduce Nurul University to students, distribute brochures, display pamphlets, and adapt strategies using a promotion mix strategy


Method of Named Entity Recognition for Chemical Industry Accidents Based on ALBERT-BiLSTM-CRF

Abstract: In recent years, there have been frequent production safety accidents in the chemical industry, prompting the need for named entity recognition of chemical industry accidents. However, due to the scarcity of annotated corpus in the chemical industry field, this paper obtain 659 accident investigation reports from websites such as the Safety Management Website, and manually constructed a BIO annotated corpus. To process text data using the ALBERT model, semantic representation vector of the text were obtained, followed by BiLSTM network layers and CRF layers. After 30 iterations of training, experimental results showed that the ALBERT-BiLSTM-CRF model outperformed four other groups of models: BiLSTM, CRF, BiLSTM-CRF, and IDCNN-CRF. Recall, precision, and F1 score all increased as a result of using this model.


A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

Abstract: Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.


Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching

Abstract: This research designed an improved collaborative filtering algorithm to be responsible for music recommendation tasks in the online music teaching platform. This algorithm integrates the user's social trust into the similarity calculation formula. Then, the algorithm uses behavioral feature data driven by preferences, music tags, and popularity as the basis for recommendation calculation. It adopts user data testing on an online music teaching platform. The results showed that when the number of recommended music was eight, the recommended recall rates of XCF, CTR, TSR, and UB-CF recommendation models reached their maximum, reaching 97.82%, 95.26%, 93.95%, and 88.72%, respectively. The AUC and average computational time of the ROC curves for XCF, CTR, TSR, and UB-CF recommended models are 0.7, 0.68, 0.64, 0.57, and 160ms, 136ms, 114ms, and 88ms, respectively. The experimental data shows that the recommendation accuracy of the music recommendation model designed in this study is significantly higher than that of traditional recommendation models.


Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs

Abstract: In today’s world, where information keeps growing rapidly and changing constantly, language models play a crucial role in making our lives easier across different fields. However, it is tough to keep these models updated with all the new data while making sure they stay accurate and relevant. To tackle this challenge, our study proposes an innovative approach to facilitate the propagation of complex entity knowledge within language models through extensive triplet representation. Using a specially curated dataset (CTR-KE) derived from reliable sources like Wikipedia and Wikidata, the research assesses the efficacy of editing methods in handling intricate relationships between entities across multiple tiers of information. By employing a comprehensive triplet representation strategy, the study aims to enrich contextual understanding while mitigating the risks associated with distorting or forgetting critical information. The study evaluates its proposed methodology using various evaluation metrics and four distinct editing methods across three diverse language models (GPT2-XL, GPT-J, and Llama-2-7b). The results indicate the superiority of mass-editing memory in a transformer (MEMIT) and in-context learning for knowledge editing (IKE) in efficiently executing multiple updates within the triplet representation framework. This research signifies a promising pathway for deeper exploration of data representation for knowledge editing within large language models, and improved understanding of contexts to facilitate continual learning.


DEEP-IMAGE-MATCHING: A TOOLBOX FOR MULTIVIEW IMAGE MATCHING OF COMPLEX SCENARIOS

Abstract: Abstract. Finding corresponding points between images is a fundamental step in photogrammetry and computer vision tasks. Traditionally, image matching has relied on hand-crafted algorithms such as SIFT or ORB. However, these algorithms face challenges when dealing with multi-temporal images, varying radiometry and contents as well as significant viewpoint differences. Recently, the computer vision community has proposed several deep learning-based approaches that are trained for challenging illumination and wide viewing angle scenarios. However, they suffer from certain limitations, such as rotations, and they are not applicable to high resolution images due to computational constraints. In addition, they are not widely used by the photogrammetric community due to limited integration with standard photogrammetric software packages. To overcome these challenges, this paper introduces Deep-Image-Matching, an open-source toolbox designed to match images using different matching strategies, ranging from traditional hand-crafted to deep-learning methods (https://github.com/3DOM-FBK/deep-image-matching). The toolbox accommodates high-resolution datasets, e.g. data acquired with full-frame or aerial sensors, and addresses known rotation-related problems of the learned features. The toolbox provides image correspondences outcomes that are directly compatible with commercial and open-source software packages, such as COLMAP and openMVG, for a bundle adjustment. The paper includes also a series of cultural heritage case studies that present challenging conditions where traditional hand-crafted approaches typically fail.


HEALTHCARE PRACTICES AND DIGITAL TECHNOLOGIES AMONG PARENTS OF PUPILS ATTENDING FEDERAL COLLEGE OF EDUCATION (TECHNICAL) STAFF SCHOOL AKOKA, LAGOS.

Abstract: The study examined healthcare practices and Digital Technologies among parents of pupils attending Federal College of Education (Technical) Staff School Akoka, Lagos. Four (4) research questions were raised and answered to guide the conduct of the study. The study adopted a descriptive survey research study of which population of the study comprised parents of pupils attending FCE (T) staff school Akoka, Lagos numbering five hundred (500). The sample comprised of two hundred (200) parents constituting forty percent (40%) of the population. Simple random sampling technique was used in the selection of pupils in the different classes to take the questionnaire home to their parents. It was concluded that healthcare practices and Digital Technologies among parents of pupils attending Federal College of Education (Technical) Staff School Akoka, Lagos is significant. It was also concluded that the parents shun all form of untrusted healthcare practices such as traditional medicine among others. Recommendations made among others is that Digital Technologies in health care practices should be made accessible to all and sundry.


Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model

Abstract: This paper addresses the challenges associated with the centralized storage of educational materials in the context of a fragmented and disparate database. In response to the increasing demands of modern education, efficient and accessible retrieval of materials for educators and students is essential. This paper presents a hybrid model based on the transformer framework and utilizing an API for an existing large language model (LLM)/chatbot. This integration ensures precise responses drawn from a comprehensive educational materials database. The model architecture uses mathematically defined algorithms for precise functions that enable deep text processing through advanced word embedding methods. This approach improves accuracy in natural language processing and ensures both high efficiency and adaptability. Therefore, this paper not only provides a technical solution to a prevalent problem but also highlights the potential for the continued development and integration of emerging technologies in education. The aim is to create a more efficient, transparent, and accessible educational environment. The importance of this research lies in its ability to streamline material access, benefiting the global scientific community and contributing to the continuous advancement of educational technology.


Using Social Media as a Platform of Communication Between School Administration and the Local Communities to Promote Inclusive Education for Children with Special Needs

Abstract: This study aimed to explore the level of using social media as a platform of communication between school administration and the local communities in Saudi Arabia schools from the parents' perspectives. The descriptive method was used as a methodology to achieve the study objectives. The study involved 265 parents of students from different academic stages and different cities in Saudi Arabia to take part in an online questionnaire of ten items, which was sent to the participants online. The results showed that the best practices of school administration in electronic communication included creating WhatsApp groups with parents for informal communication and employing Twitter and Instagram accounts as a media interface for the school. These results reveal that male parents think more slightly that school administration uses social media to communicate with the local community. The findings also indicated that the age of the parents did not affect their views because the school uses similar platforms and parents are familiar with using the social media websites regardless of their age.


Experimental and Numerical Study of the Effect of Cloud Cover on the Electrical Performance of Photovoltaic Plants

Abstract: The production of electricity from the sun mainly consists of transforming the light emitted by the sun into electrical energy through photovoltaic cells. This production varies proportionally with the light that illuminates the solar photovoltaic module. According to the literature, we noted that during the period of cloudy passage, the production of the solar modules could see a fall going from 5 to 60% according to the size of the photovoltaic fields. The present work consisted of studying the impact of cloudy passages on the electrical production of PV fields at four sites (Loumbila, Sandogo, Ouaga 2000 and IRSAT). To achieve this, a theoretical and experimental study was conducted. The simulation results were compared to the experimental results of two daily sunshine profiles (sunny and cloudy). The results show significant differences between the experimental and simulated data for the two sunshine profiles considered. The losses of electrical production generated by these cloudy passages vary from 27 to 32% and from 35 to 52%, respectively, for the simulated and experimental results. These differences were explained by the probabilistic character of the cloudy passages and by the fact that other major parameters (aerosols, dust deposits and ageing of the solar modules) were not considered. As perspectives, we recommend a continuation of the work by studying, in addition to the cloudy passages, the influence of aerosols and dust deposits on the PV fields realized by us.


Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Abstract: Abstract In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-intensive simulations. Data-driven methods can mitigate this challenge by harnessing historical data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine-learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering situations. Additionally, there is an inherent trade-off between training speed and accuracy, with faster methods sometimes compromising precision. In our paper, we demonstrate that a recently released general-purpose transformer-based classification model, TabPFN, is both fast and accurate. Notably, it requires no dataset-specific training to assess new tabular data. TabPFN is a Prior-Data Fitted Network, which undergoes a one-time offline training across a broad spectrum of synthetic datasets and performs in-context learning. We evaluated TabPFN's efficacy across eight engineering design classification problems, contrasting it with seven other algorithms, including a state-of-the-art AutoML method. For these classification challenges, TabPFN consistently outperforms in speed and accuracy. It is also the most data-efficient and provides the added advantage of being differentiable and giving uncertainty estimates. Our findings advocate for the potential of pre-trained models that learn from synthetic data and require no domain-specific tuning to make data-driven engineering design accessible to a broader community and open ways to efficient general-purpose models valid across applications.


Message Binary Matrix Reshaping and Rotating Left to Encrypt-Decrypt Secret Message

Abstract: Secret messages are the most circulated via communication environments digital data, these messages may be secret, private, and the communication environment may unsafe, so they need protection from being hacked. In this paper research a simple method of message cryptography will be proposed, the method will use one round and simplified set of instruction to apply message encryption-decryption. The method will use a simple private key to determine the reshaping factor and the number of rotating left digits. The message binary matrix will be reshaped to a number of columns equal the reshaping factor, and each columns will be rotated left for a selected number of digits to apply message encryption-decryption. The reshaping factor will be tested to show the best choice of the reshaping factor, which will provide the best performance, the message length will be tested and some recommendations will be provided. The quality of the proposed method will tested by implementing various messages and examining the quality parameters values to prove that the proposed method will satisfy the quality requirements of good crypto system.


Proposed LSB4 of Data Steganography

Abstract: The process of hiding confidential data is one of the important processes used to protect this data from intruders or protect it from the penetration process. The most popular methods used in data steganography are LSB and LSB2 methods. These methods are efficient methods of data hiding, they provide a good stego media quality and good speed parameters. They are capable of hiding short and long secret messages. To increase the capacity and to decrease both the hiding and extraction times LSB4 method will be introduced and used. The proposed method will be implemented using various covering images and various in length secret messages, the obtained results will be analyzed to prove the quality and the speed of the proposed method. The obtained results of the proposed LSB4 method will be compared with the results of LSB method to show the enhancements provided by the proposed method. The proposed LSB4 method will be supplied by a security issues to protect the hidden message, the secret message will be protected by using a special private key to apply bits’ substitution on the contents of characters’ binary matrix.


An Insight into the Consequences of Digitalization and Digital Technologies for Small and Medium Enterprises (SMEs) in Africa

Abstract: IMF (2020) estimates that 20 million jobs must be created annually in SSA and Africa at large to accommodate the region's expanding labor force. However, digital technologies like email, the internet, and mobile money have a huge potential to generate wealth and jobs that African businesses still need to realize. Even though mobile phone technology has helped spread Internet-based innovations throughout the region, this process is slowed down by a vast Internet divide; businesses and people use these technologies less than they could. In 2015, Internet penetration rates in African nations were below 60% of the total population, with penetration rates as low as 5% in some nations including Niger, Sierra Leone, and Guinea-Bissau. Again in 2015, small African firms employed almost 80% of the labor force on the continent. But surveys by the World Bank between 2013 and 2018 show that less than 60% of SMEs used email for business, and less than 30% used websites for the same thing. In contrast, 90% of major businesses polled within the same time period acknowledged utilizing email and/or a website for conducting business. Since SMEs are currently the largest employers and wealth creators in the region, the poor dissemination and adoption of digital technology severely limit their ability to advance. More specifically, and unlike previous research-based analyses of the digitalization of African firms, the research article combines quantitative analysis and qualitative data to give readers a bird's-eye view of how digital technologies affect the performance of small and medium-sized enterprises (SMEs) in Africa and the opportunities for private sector growth that come with the ongoing digitalization of the economy.


Long‐range quantum energy teleportation and distribution on a hyperbolic quantum network

Abstract: Abstract Teleporting energy to remote locations is new challenge for quantum information science and technology. Developing a method for transferring local energy in laboratory systems to remote locations will enable non‐trivial energy flows in quantum networks. From the perspective of quantum information engineering, we propose a method for distributing local energy to a large number of remote nodes using hyperbolic geometry. Hyperbolic networks are suitable for energy allocation in large quantum networks since the number of nodes grows exponentially. To realise long‐range quantum energy teleportation (QET), we propose a hybrid method of quantum state telepotation and QET. By transmitting local quantum information through quantum teleportation and performing conditional operations on that information, QET can theoretically be realized independent of geographical distance. The method we present will provide new insights into new applications of future large‐scale quantum networks and potential applications of quantum physics to information engineering.


Geo-Navigation in Museums: Augmented Reality Application in the Geological Museum Indonesia

Abstract: Navigational challenges in large buildings with multiple rooms, such as museums, often result in inefficient visitor experiences. Traditional signage and direction plans, while common, do not always effectively convey the necessary information. This paper introduces an innovative solution leveraging Augmented Reality (AR) technology to enhance navigation in such complex environments. We developed a mobile application utilizing the Immersal Software Development Kit (SDK) to facilitate interaction with the surroundings in the Bandung Geological Museum. The application serves as a digital guide, providing clear directions and route information to various rooms within the museum. Our study's findings reveal that the application not only facilitated easier navigation through its accurate room identification and route suggestions but also enhanced the overall visitor experience by making it more interactive and immersive. Furthermore, the user engagement and experience survey, encompassing a broad demographic range, highlighted a significant increase in visitor satisfaction and interaction. The application's intuitive and user-friendly interface played a key role in this enhanced engagement. The survey results reflect the application's success in meeting its main objectives, demonstrating usability, and offering an effective user interface.


Crypto Technology - Impact on Global Economy

Abstract: The last decade has been marked by the evolution of cryptocurrencies, which have captured the interest of the public through the offered opportunities and the feeling of freedom, resulting from decentralization and lack of authority to oversee how cryptocurrency transactions are conducted. The innovation in crypto space is often compared to the impact internet had on human life. There is a new term called Web 3.0 for denoting all new computing innovations arising due to the blockchain technologies. Blockchain has emerged as one of the most important inventions of the last decade with crypto currencies or financial use case as one of the domains which progressed most in the last 10 years. It is very important to research about Web 3 technologies, how it is connected to crypto economy and what to expect in this field for the next several decades.


The Decision-Support Modeling with Fuzzy Analytic Hierarchy Process (AHP) to Determine the Career Path for Bachelor Informatics Students

Abstract: The pursuit of success in one's chosen profession is a universal aspiration that requires individuals to engage in competition, not just domestically but also internationally, particularly in today's era of globalization. The rapid advancement of technology presents both opportunities and challenges, necessitating proactive management to achieve professional success. In the field of Informatics Engineering, there is a wide range of professional pathways, each offering unique opportunities and requiring distinct skill sets. For university graduates, it is crucial to have a comprehensive understanding of the specific requirements and demands associated with various job paths to make informed decisions. This knowledge enables them to select a professional route that aligns with their individual aspirations and goals, thereby avoiding potential discontent in their chosen career. Lack of knowledge or awareness during the decision-making process can negatively impact productivity and overall performance. There is a growing demand among university graduates, especially at the undergraduate level, for career-related information. This has led to heightened competition among graduates in terms of skills, knowledge, and availability. To effectively navigate this competitive landscape and engage in meaningful competition with fellow graduates, individuals must possess a comprehensive understanding of their desired career trajectory. To address these challenges, the Decision Support Model (MPK) can be utilized, employing the Fuzzy Analytical Hierarchy Process methodology. This approach considers four primary criteria—Financial Compensation, Non-Financial Compensation, Soft Skills, and Hard Skills—each with several sub-criteria. These criteria are evaluated based on the perspectives of certified graduates of Informatics Engineering and industry specialists. The study successfully identified the primary criteria influencing career decisions, such as job conditions, incentives, and prospects. It also highlighted the most favorable career path, with the role of a Data Analyst being identified as particularly promising. This career path involves roles within the field of Informatics Engineering that focus on processing data according to specific requirements, highlighting the importance of understanding the evolving landscape of technology and its impact on professional opportunities. Doi: 10.28991/ESJ-2024-08-01-011 Full Text: PDF


An Adaptive Harmony Search Part-of-Speech tagger for Square Hmong Corpus

Abstract: Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.


N-N-E-Trending Tectonic Systems

Abstract: Introduction to Global Tectonic Systems, pp. 117-145 (2024) No AccessChapter 5: N-N-E-Trending Tectonic SystemsYuzhu Kang, Shuwen Xing, Zhihong Kang, Yue Zhao, Zhihu Ling, Zhijiang Kang, and Huijun LiYuzhu KangSinopec Petroleum Exploration and Production Research Institute, China Petrochemical Corporation, Beijing, China, Shuwen XingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihong KangChina University of Geosciences (Beijing), Beijing, China, Yue ZhaoInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihu LingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhijiang KangSinopec Petroleum Exploration and Production Research Institute, Beijing, China, and Huijun LiInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, Chinahttps://doi.org/10.1142/9789811285561_0005Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In this chapter, N-N-E-trending tectonic systems are introduced, including those in China, New Zealand–Tonga, the Eastern United States and the east coast of South America. Keywords: Tectonic systemN-N-E-trendingType FiguresReferencesRelatedDetails Recommended Introduction to Global Tectonic SystemsMetrics History KeywordsTectonic systemN-N-E-trendingTypePDF download


Multi-genre Digital Music Based on Artificial Intelligence Automation Assisted Composition System

Abstract: In the present paper, we conduct an in-depth study on multi-styled digital music and design an artificial intelligence-based multi-styled digital theme automation-assisted composition system. In the process of system development, B/S software design architecture is selected, J2EE ecosystem-related technology is used, and the system framework is built following the principle of sub-module development. A web hosting platform is provided for the whole Internet publishing system to realise the online and real-time functions. In addition, to improve the robustness, scalability, security, and ultimately, the system's quality of service, the functional design analysis, software architecture system, development technology, and optimisation strategy are carefully studied and discussed. After learning and analysing the file format of MIDI digital music, we chose the feature extraction method for each instrument to preserve the instrument's characteristics. We used its parameter model for each device during the composition to ensure the instrument's characteristics. After optimisation of the model, composition experiments were conducted, and the compositional effect was measured; the average ratio of adjacent notes with intervals within one octave was 83.57% for the composed pieces. The composition system eliminates using only a single algorithmic composition technique method in the system platform. Instead, it adopts the direction of a hybrid system that integrates multiple processes, an inevitable trend. The composition system provides flexible human-computer interaction at all music composition levels to improve the system's usefulness and effectiveness.


Enhancing Disease Prediction with a Hybrid CNN-LSTM Framework in EHRs

Abstract: This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.


Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach

Abstract: Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.


Deployment of optoelectronic devices based on improved NSGA-II

Abstract: In order to improve the security of the defended targets in our region and efficiently utilize various types of optoelectronic devices, the research is conducted on optoelectronic devices deployment within the region. With the objectives of maximizing the protection effectiveness and minimizing devices operational cost, a multi-objectives optimization model is established by considering constraints such as devices-target visibility conditions and device types, quantities, etc. Non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) improved by Q-learning is designed to solve the model. To address the difficulty of fixed parameter settings adapting to dynamic changes, Q-learning is adopted to adaptively adjust the crossover probability and variation probability. In order to search for Pareto front solutions that are close to the global optimal more efficiently. The correctness of the model and the effectiveness of the algorithm are verified through simulation examples.


Fuzzy-PID-based trajectory tracking for 3WIS robot

Abstract: This paper presents a trajectory tracking approach for a three-wheel independent steering (3WIS) robot using a fuzzy PID controller. The kinematic model of the 3WIS robot is introduced, highlighting its capability for omnidirectional motion control. A comparison is made between traditional PID controllers and fuzzy PID controllers, emphasizing the advantages of the latter in handling nonlinear systems. The concept of fuzzy PID control is explained, and fuzzy rules for parameter adjustment are proposed. The accuracy of the kinematic model and the effectiveness of the fuzzy PID controller are verified through simulation using the Webots platform and real-world experiment. Results indicate successful trajectory tracking, although slight fluctuations in the heading angle are observed due to frictional torque and gear backlash. The findings validate the accuracy of the kinematic model and the effectiveness of fuzzy PID controller in trajectory tracking.


Supplemental Material: The Zaozigou orogenic gold-antimony deposit, West Qinling Orogen, China: Structural controls on multiple mineralization events

Abstract: Table S1: Detailed operating parameters and conditions for dolomite U-Pb dating. Table S2: LA-ICP-MS U-Pb isotope data for dolomite.


An all-in-one lightweight image restoration network based on polarized attention mechanism and efficient feature extraction

Abstract: Removing noise from images is a challenging task in front-end computer vision applications, especially in snowy, rainy, foggy, and underwater environments, which pose significant difficulties for various visual tasks. Existing image restoration methods often struggle to achieve robust generalization and are burdened with immense computational complexity. This paper introduces a lightweight image restoration network based on polarized attention mechanisms and efficient feature extraction, capable of addressing tasks like rain removal, snow removal, fog removal, and underwater image enhancement. Initially, a polarized self-attention mechanism is proposed to intricately learn weather noise, followed by an efficient noise removal module complementing the initial network. This addition compensates for any noise information overlooked by the polarized self-attention mechanism. Furthermore, residual connections are integrated into each module of the network, along with grouped convolutions, to prevent network degradation and reduce computational load. In comparison to existing models, this network not only learns noise with similar shapes but also adapts to noise with significant shape variations. Additionally, the introduced residual structure ensures the network's stability, avoiding performance degradation before achieving expected results. The collaborative use of the efficient noise removal module and polarized self-attention mechanism sets a high standard for noise learning in this network. Experimental results demonstrate the efficacy of this algorithm on publicly available datasets including Snow100K, CSD, Rain200H, Rain200L, Rain800, RSID, and EUVP. The proposed method efficiently removes various image noises, effectively achieving the desired image restoration objectives.


The Impact and Integration of Artificial Intelligence in Human Resource Management: A Middle Eastern Perspective

Abstract: This big study looks into how Artificial Intelligence (AI) and Human Resource Management (HRM) connect in the Middle East. In the area where technology is changing very quickly, the study wants to find out how AI integration is changing HR practices in this place. The main part of the study is carefully created surveys for HR people and experts in AI/CS. These aim to find out how AI is used now, what more can be done with it, and ethical questions around its use in HRM. The main results show that AI is drastically making human resources jobs like hiring new workers, managing their paychecks, and checking their performance easier. This lets people working in HR avoid boring tasks and concentrate on big roles. The study also looks into the problems and limits that come with using AI. These include thinking about what's right, keeping your data secret, and needing a human link in HR jobs. This study is very important because it looks at how AI makes HR better. It also shows the careful mix needed between using new technology and keeping people in mind. It gives a future outlook on how AI can change HRM in the Middle Eastern business scene. It offers helpful information for both people who work there and those studying it.


Large Language Models and Artificial Intelligence for Police Report Writing

Abstract: Large Language Models (LLMs), such as ChatGPT, are advanced artificial intelligence systems capable of understanding and generating human-like text. They are trained on vast amounts of textual data, enabling them to comprehend context, answer questions, generate summaries, and even engage in meaningful conversations. As these models continue to evolve, their potential applications in various industries, including law enforcement, are becoming more apparent, as are the potential threats <https://www.europol.europa.eu/publications-events/publications/chatgpt-impact-of-large-language-models-law-enforcement> . One particularly promising area of application for LLMs in policing is report writing. As many police executives know, not all officers possess strong writing skills, which can lead to inaccurate or incomplete reports. This can have serious consequences for criminal prosecutions, as well as expose departments to civil liability concerns. Implementing LLMs like ChatGPT for report-writing assistance may help address these issues. Even if not fully implemented at the agency level, officers across the country are already using these tools to help in their report generation. Given the stakes, it is wise for agencies to have a sophisticated view and policy on these tools. This paper introduces practitioners to LLMs for report writing, considers the implications of using such tools, and suggests a template-based approach to deploying the technology to patrol officers.


Simulation of exchange processes in multi-component environments with account of data uncertainty

Abstract: The goal of the work. Proposals for methods of solving systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients that defined in interval form and intended for modeling exchange processes in multicomponent environments. Research subject: systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients defined in interval form. Research method: interval analysis. The obtained results. Systems of linear homogeneous and non-homogeneous differential equations, which are used in modeling exchange processes in multicomponent environments, are considered. Such systems can be considered, for example, in problems of chemical kinetics, materials science, and the theory of Markov processes. To obtain the solution of these equations, specialized calculators of analytical transformations were used and tested. The Matlab system (ode15s solver) was used for numerical analysis of systems of differential equations. It is shown that the application of interval methods of numerical analysis at the initial stage of system modeling has some advantages over probabilistic methods because they do not require knowledge of the laws of distribution of the results of the system state parameter measurements and their errors. It is shown that existing methods of solving systems of linear differential equations can be divided into two groups. Common to these groups is the use of interval expansion of classical methods for solving differential equations given in interval form. The difference between these two groups of methods is as follows. The methods of the first group can be used for all types of differential equations but require the creation of special software. The peculiarity of the methods of the second group is that they can be used to solve equations analytically or using numerical analysis packages. The application of the methods of the second group is shown on the example of solving a system of differential equations, the coefficients of which are determined in interval form. The system of these equations is intended for modeling the processes of exchange with the external environment of the elements of the model of a specific physical system. In the case when the coefficients of these equations are variables, their piecewise-constant approximation is applied and a criterion that determines the possibility of its application is given. The technique proposed in the paper can be applied to solve systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients if they are given by slowly varying functions. In the case when the coefficients of the equations are determined in the interval form, the technique allows obtaining their solution also in the interval form and does not require the creation of special software.


ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION

Abstract: The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder.


Horizontal Learning Approach to Discover Association Rules

Abstract: Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case).


Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments

Abstract: In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method.


Demystifying Recommendations: Transparency and Explainability in Recommendation Systems

Abstract: Abstract: Recommendation algorithms are widely used, however many consumers want more clarity on why specific goods are recommended to them. The absence of explainability jeopardizes user trust, satisfaction, and potentially privacy. Improving transparency is difficult and involves the need for flexible interfaces, privacy protection, scalability, and customisation. Explainable recommendations provide substantial advantages such as enhancing relevance assessment, bolstering user interactions, facilitating system monitoring, and fostering accountability. Typical methods include giving summaries of the fundamental approach, emphasizing significant data points, and utilizing hybrid recommendation models. Case studies demonstrate that openness has assisted platforms such as YouTube and Spotify in achieving more than a 10% increase in key metrics like click-through rate. Additional research should broaden the methods for providing explanations, increase real-world implementation in other industries, guarantee human-centered supervision of suggestions, and promote consumer trust by following ethical standards. Accurate recommendations are essential. The future involves developing technologies that provide users with information while honoring their autonomy.


Current Status of Machine Learning and Artificial Intelligence in Cervical Cancer Screening and Diagnosis: A Systematic Review

Abstract: Background: Cervical cancer poses a substantial global health challenge, predominantly affecting underprivileged countries. The limitations of current screening methods, such as cytology and visual examination, underscore the need for improved techniques. Artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks, offer promising solutions in this regard. Methodology: Fifteen studies meeting the inclusion criteria were examined. The PRISMA criteria guided the exploration of cervical cancer screening studies employing AI, ML, and deep learning on PubMed/MEDLINE and Google Scholar. The search focused on "artificial intelligence" and "Pap smear." The investigation specifically delves into English-language studies post-2019 that pertain to the machine learning and deep learning classification of cervical cancer using mobile devices. Histology, animal research, and pre-2019 investigations are excluded. Titles and abstracts were carefully reviewed for any discrepancies and subsequently discussed. The process of data extraction involved compiling information from the selected articles. Result: The systematic review investigates the impact of AI and ML on cervical cancer detection, screening, and diagnosis. Our review reveals enhanced accuracy and efficiency in innovative technologies such as CytoBrain and computer-aided diagnostic systems employing Compact VGG and ResNet101. ML techniques like logistic regression, MLP, SVM, KNN, and naive Bayes prove beneficial for managing complex datasets, particularly when combined with class-balancing procedures. The promising aspects include the application of deep learning for automation and AI-assisted digital microscopy. These findings signify a transformative shift in cervical cancer screening, underscoring the potential of ML and AI technologies to elevate diagnostic accuracy and accessibility. Conclusion: Our study demonstrates advancements in both accuracy and responsiveness. Despite recognizing scientific and ethical considerations, the study underscores the potential of AI to enhance cervical cancer care. This systematic review advocates for policymakers and healthcare practitioners to use ongoing research for informed decision-making in this rapidly evolving field.


The Positive Identification of Skeletal Remains from the Church of the Assumption of the Virgin Mary in Přeštice: a Case Study

Abstract: The reconstruction and subsequent rescue archaeological excavation in the baroque chapel of St Barbara in the Church of the Assumption of the Virgin Mary in Přeštice has resulted in the discovery of one atypical burial in a small bricked vault. The vault contained skeletal remains in a very poor state of preservation. The discovery of the skeletal remains in the chapel was unexpected given the absence of any signs of a grave being present. In this article we describe the attempt to identify the person to whom the skeletal remains belong. Based on anthropological analysis, these remains were interpreted as probably female over 50 years of age. By combining archaeological context, anthropological analysis and historical sources, the skeleton was interpreted as the remains of Marie Anna Lindauer, a resident of Pilsen and the mother of Josef Ondřej Lindauer, the dean in Přeštice and the bishop in Budweis.


EXPLORING THE DETERMINANTS EFFECTING PROMOTION OF RESEARCH CULTURE AT UNIVERSITY LEVEL

Abstract: The study in hand was undertaken to explore the determinants which are effecting promotion of research culture at university level. Although many factors are effecting but the most relevant determinants affect was checked which are environmental and institutional determinants. The population was comprised of 1477 the faculty members of BZ University, Multan & Islamia University Bahawalpur as well as Gomal University, Dera Ismail Khan and University of Science and technology, Bannu. The selected sample from the population was comprised of 253 faculty members hailing from the above-mentioned higher education institutions out of 1477 was selected for study. The research design was quantitative in which survey questionnaires were distributed for data collection. T-test and ANOVA were used for analysis of data. The results offered significant information for reaching the conclusion, and making the desired decisions. Also, some recommendations have been extracted from results offered to the policy-makers and future researchers about research issues.


What are the challenges in integrating LLMs into organisations' data workflows?

Abstract: Integrating Large Language Models (LLMs) such as GPT into organizations’ data workflows is a complex process with various challenges. These obstacles include but are not limited to technical, operational, ethical, and legal dimensions, each presenting hurdles that organisations must navigate to harness the full potential of LLMs effectively.


Online Trust Determinants, Consumer Perception, and Purchase Intent in Saudi E-Commerce: Exploring Determinants and Evidence

Abstract: This study aimed to explore the growing use of the internet as a commercial sales channel, emphasising the pivotal role of trust in online consumer relationships. This research focused on Saudi e-commerce platforms (e.g. Amazon.sa, Noon, Namshi, and Shein), due to their remarkable growth over previous years, by investigating the effect of consumer trust on online purchase intentions. The study surveyed 449 Saudi online shoppers, using a structural equation model to analyse the factors that determine online trust (perceived information quality, company competency, security, and online consumer reviews) and their influence on consumer online trust and perceived risk. The results revealed significant relationships between consumer online trust and perceived information quality, company competency, and security, challenging expectations regarding the link between online consumer reviews and consumer online trust. Surprisingly, online consumer reviews primarily influenced perceived risk rather than consumer online trust, impacting purchase intention to a limited extent. The research underscores the substantial influence of consumer online trust on perceived risk and purchase intention, offering micro perspectives on online consumer behaviour. By identifying key influences, including information quality and security, this study provides theoretical and managerial insights for e-commerce platforms, guiding the development of responsible online channels. KEYWORDS company competency, consumer reviews, e-commerce antecedents, information quality, perceived security, Saudi consumers


Invisible Publications: A Study of Academic Productivity in the Web of Science Database

Abstract: The present study aims to outline the profile of uncited or rarely cited articles indexed in the Web of Science database between 2017 and 2021. We used stratified random sampling of 14,425 journals to extract our study sample of 373 journals. The dependent variable was defined as the number of citations, with independent variables being the JCR index, maturity, quartile, language of publication, and whether the research was funded. Descriptive statistics were used to characterize the sample according to field of research and variable. A negative binomial regression method was used to estimate which variables influence the number of citations received by articles in the sample. We found that of the 256,524 articles that make up the research corpus, 39,469 (15.39%) received no citations and 91,963 articles (35.85%) received up to four citations. The variables that positively influenced estimates of the number of citations were maturity (0.481), JCR index (0.159), and funding (0.020), while quartile (-0.170) and language (-1.032) had a negative influence on number of citations. We conclude that the pressures of academic productivism create a vicious cycle in which studies are referenced based on their ranking in the struggle for privileged editorial space, with articles in higher quartile journals being more prone to citation.


Risk First Mindset in “Security First vs Compliance First” Debate

Abstract: The idea of Security first vs compliance first approach has been plaguing the Cybersecurity industry and others, for a while now, Subject matter experts on both sides of the aisle have been debating and trying to make their point heard across the other side, explaining why is it that a certain approach is better than the other!. All of these points are valid and backed with facts and use cases.


Logical analysis of data and knowledge without using an axiomatic approach

Abstract: Logical analysis of data and knowledge is required not only in artificial intelligence systems, but also in mathematical proofs. This work shows the difficulties of logical analysis caused by the indisputable application of the formal axiomatic approach adopted at the turn of the XIX and XX centuries as the only theoretical basis of modern logic and the foundations of mathematics. In this approach, the main role is played by the axioms and methods of syntactic analysis, which underlie predicate calculus. Instead of set theory, we propose to use more simple algebra of sets as a theoretical basis of such a logic, for which the substantiation of the basic laws does not require axioms. A new approach to logical analysis based on the laws of set algebra is considered. Some advantages of this approach are shown, and a list of unresolved problems is provided.


Questions of Managing the Trajectory of E-Learning Using Neural Network Technologies

Abstract: This article discusses the application of neural networks in managing the educational trajectory in electronic educational systems. A model of learning management in an electronic information and educational environment is built, and an algorithm for managing adaptive learning is described. A test example of a neural network is constructed, showing the capabilities and problems of applying neural networks to the tasks of constructing trajectories of students’ adaptive personalized training. The following stages of constructing a neural network model are implemented: determining the structure of a neural network and its software implementation using the PyTorch deep machine learning library; preparing a test training sample for training the neural network; importing the initial data for training the model using the modules of the Pandas library; training the model using torch.optim Adam, Adamax, and Rprop optimizers; visualizing the obtained data in the form of graphs based on the matplotlib library; exporting the obtained numerical results and analysing the data. The paper also states the problems of preparing a training data set for teaching a neural network. The paper concludes that using neural networks in the field of building personalized adaptive educational trajectories has great prospects and will be one of the urgent tasks in the coming years.


Ontology-oriented geoanalytics for determining concentration of road accidents on areas of the road network.

Abstract: Geoanalytics is an important tool in analyzing the urban environment. This work focuses on the development of geoanalytics tools by ontology-oriented integration of information from heterogeneous sources to identify potentially accident-prone sections of the road network, which can be used to build routes along minimally dangerous roads. The heterogeneity of a large number of sources is caused by the variety of types of information provided, collected both centrally by road and emergency services, and distributed with the help of volunteers. The novelty of this work is the combination of heterogeneous sources of information in a single analytical system and conducting a geoanalysis of the frequency of accidents on individual road sections, determining the most frequent type and cause of accidents to build a route bypassing sections of the road network with a high concentration of accidents.


Property, Institutions, and Social Stratification in Africa By Franklin Obeng-Odoom. New York: Cambridge University Press. 2020. 364 pp., $27.70 Paperback.

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A Forward Approach for Sufficient Dimension Reduction in Binary Classification for Large-scale Data

Abstract: Sufficient dimension reduction, aimed at finding a lower-dimensional subspace in explanatory variables that contains response variable information, typically relies on inverse-based methodologies. These methods are easy to implement but often require linear or constant variance conditions. To address these limitations, techniques for forwardly estimating the central subspace have been developed. In particular, methods utilizing the Reproducing Kernel Hilbert Space have gained attention, but their use in analyzing large datasets is limited due to the characteristics of the kernel space. In this paper, we study a novel forward approach for sufficient dimension reduction in binomial classification of large-scale data. We propose a method that employs a divide-and-conquer technique to split data into subsets, then independently perform dimension reduction on each subset before synthesizing them into a final model. It was shown that when the number of partitions of data was appropriately selected, the loss in prediction accuracy was not significant compared to the existing method, while being efficient in terms of storage space and calculation cost. In addition, simulations in various models showed superior prediction accuracy than other inverse-based techniques. The utility of the proposed method was confirmed through the real data analysis.


DEMYSTIFYING LEADERSHIP: A HOLISTIC ANALYSIS OF ITS ROLE, INCLUDING DEMOGRAPHIC FACTORS IN E-COMMERCE IN SMES IN AD DAKHILIYAH GOVERNORATE, OMAN

Abstract:
This quantitative study with a sample of 375 participants from small and medium enterprises (SME) in the Ad Dakhiliyah region of Oman examines the impact of transformational and transactional leadership styles on E-commerce adoption (ECA). A multi-group (MGA) analysis was conducted to investigate the influence of demographic factors on ECA through leadership styles. Using statistical analyses via SPSS and SmartPLS 4, the study shows significant gender differences in the influence of leadership styles on ECA. In addition, comparisons were made with two other demographic factors — age and leadership position. However, caution should be exercised when generalising beyond the study's focus on the Ad Dakhiliyah region. Despite a comprehensive investigation, the age factor shows an insignificant difference in the impact of transformational and transactional leadership on ECA. This study makes an important contribution to the understanding of leadership dynamics in the digital age in Omani SMEs, highlighting the gender nuances. The integration of robust research models and statistical tools enhances methodological rigour and provides practitioners and future researchers with nuanced perspectives on leadership and technology adoption in the Ad Dakhiliyah region and in the broader context of Omani SMEs.
 Keywords: Leadership Style, E-commerce Adoption, Technology Adoption, Gender, SME, Oman, SmartPLS.


Analysis of The Construction Management Application to Project Success Using Importance Performance Analysis on Superflat Concrete Floor Work

Abstract: In pursuit of Indonesia's equitable and sustainable development, the surge in industrial city development, notably in Karawang, has led to the construction of various facilities such as factories, warehouses, and workshops. Efficient planning methods, encompassing cost, time, and quality considerations, are imperative for these projects. A critical aspect is the construction of concrete floors, vital for supporting diverse loads like heavy vehicles, static loads from goods, storage racks, and large machines. The ongoing development of the Superflat Concrete Floor Method, with its productivity and precision advantages, underscores the need for effective construction management. This study aims to evaluate the substantial impact of construction management on project success and to map application indicators on the IPA diagram – Importance Performance Analysis. Employing both quantitative and qualitative methods, the research distributed questionnaires with Likert scales to 30 respondents, including contractors, owners, and design planners. Smartpls 3.0 software analysis reveals that Knowledge Management significantly and positively influences the success of the superflat concrete floor project at the Karawang Industrial Factory site. The IPA mapping places Knowledge Management and Total Quality Management in Quadrant I (Keep Up the Good Work), emphasizing their pivotal roles in ensuring project success. This study provides essential insights for stakeholders involved in industrial development, guiding them toward more effective construction management strategies for optimized outcomes.


Redefining university infrastructure for the 21st century: An interplay between physical assets and digital evolution

Abstract: In the evolving landscape of the 21st century, universities are at the forefront of re-imagining their infrastructural identity. This conceptual paper delves into the transformative shifts witnessed within university infrastructure, focusing on the harmonisation of tangible physical assets and the expanding world of digital evolution. As brick-and-mortar structures remain pivotal, integrating digital platforms rapidly redefines the academic landscape, optimising learning and administrative experiences. The modern learning paradigm, enriched by this symbiotic relationship, offers dynamic, flexible, and comprehensive educational encounters, thereby transcending traditional spatial and temporal constraints. Therefore, this paper accentuates the broader implications of this infrastructural metamorphosis, particularly its significant role in driving economic development. The synergistic effects of physical and digital infrastructures enhance academic excellence and position universities as key players in addressing and navigating global challenges, setting forth a resilient and forward-looking educational blueprint for the future. In conclusion, integrating physical and digital infrastructures within universities heralds a transformative era, shaping a holistic, adaptable, and enriched academic environment poised to meet 21st-century challenges. This study illuminates the symbiotic relationship between tangible university assets and digital innovations, offering insights into their collective impact on modern education and broader economic trajectories.


Research on Exploratory English Teaching Methods in Art Colleges

Abstract: English teaching has met troubles in students specialized in arts who can’t understand the teachers’ explanation. Force feeding duck teaching in classroom hasn’t completed the task of teaching. Inquiry English teaching is introduced in this article to improve the status.


A Study on Computing Sample Quantiles of Discrete Probability Distributions

Abstract: In this paper, we studied the methods of computing sample quantiles for the case of discrete probability distributions. There are two main methods which are introduced in statistics textbooks. We compared these two methods by simulation from practical and educational necessity. We considered some cases of Poisson, binomial, geometric, negative binomial, and discrete uniform distributions by setting up the parameters, and for each distribution we performed 10,000 times of simulation of drawing random samples of size 20 and 50. At each time of simulation, we computed the difference between the population quantile and the sample quantile obtained by each method. We compared the two methods by using two criteria: one is the mean square error over 10,000 times of simulation, and the other is the frequency of obtaining closer sample quantile to the population quantile than the competing method. We also obtained the estimated probabilities of exact estimation of population quantiles for the two methods, and performed the tests of homogeneity on the probability distribution of under-estimation, exact estimation, and over-estimation of the population quantiles.


The Provisions of Learning Experiences in the Early Childhood Development Centers against the COVID-19 Pandemic

Abstract: This study aimed to explore the provision of learning experiences for early childhood development at Childhood Development Centers (CDCs) during the Covid-19 pandemic, focusing on the "what" and "how" aspects. Data were collected from 57 participants through in-depth interviews and group interviews involving parents of young children, teachers/caregivers at CDCs, community leaders and committees, as well as relevant government agencies in two areas of Thailand. Content analysis was utilized to analyze the qualitative data gathered between July and November 2020. The findings revealed that CDCs employed various forms of capital, including: human, group and network, local wisdom, organizational, financial, and natural resources, to adhere to the National Childhood Development Center Standards. CDCs provided three distinct patterns of learning experiences for children: 1) Community participation within the local area, involving parents and networks associated with CDCs. 2) Enhancement of systems and mechanisms for collaboration with early childhood development among government, private sectors, and academic sectors at the district level. 3) Promotion of health and wellness among early childhood during the Covid-19 pandemic through encouraging child development, improving nutrition, and preventing the spread of Covid-19.


Multilingual Named Entity Recognition in archaeology: an approach based on deep learning

Abstract: This short paper describes a collection of BERT models for the archaeology domain. We took existing language specific BERT models in English, German, and Dutch, and further pre-trained them with archaeology specific training data. We then took each of these three archaeology specific models and fine-tuned them for Named Entity Recognition.


Involment of additional constraints of subject domain in machine learning problems

Abstract: The study deals with the concideration of a range of issues related to the involving additional knowledge about subject domain when solving machine learning problems. The techniques for accounting such knowlwdge based on the modification of classical methods of classification, clustering, associative rule discovery are described. It is concluded that the analysis of bachgroung knowledge is able to increase reliability and accuracy of classical machine learning methods, although their modification, taking into account additional constraints, sometimes turns out to be a rather time-consuming procedure. In addition, different types of machine learning problems require different types of additional constraints to increase the reliability and accuracy of their results. This provides certaint difficulties in solving complex problems that require gradual involvement of various types of additional constraints.


Prospects for Applying Neural Networks for Procedural Generation of Game Content in Esports

Abstract: This article provides an overview of research and practices on using neural network technologies in the field of procedural content generation for esports games. The authors explore the history, current approaches to using artificial neural networks in creating game elements, including levels, characters and scenarios, with the aim of enhancing the gaming experience and increasing the uniqueness of the virtual game world. The article covers various aspects of using neural network methods, such as generative models, autoencoders, generative adversarial networks, deep learning, and recurrent neural networks, for creating dynamic and unpredictable content. Examples of the successful implementation of these technologies in popular esports games are discussed, and potential challenges and issues related to applying neural networks in this context are explored. The authors discuss the prospects for the further development of neural network technologies in esports and offer recommendations for their optimal implementation. Overall, the article presents an analysis of the current state and future opportunities for using neural network approaches for procedural content generation in esports scenarios.


Accompanying the Sociocultural Adaptation of Older People in the System of Operating the Higher People’s School

Abstract: The article presents a study of psychological and pedagogical support for the sociocultural adaptation of older people in the context of operating the higher people’s school. It is shown that the mental phenomenon of sociocultural adaptation can be represented in three components that make up its structure, namely social, cultural, and personal. The content aspects of each component of the phenomenon structure of sociocultural adaptation are displayed. It is revealed that a specially designed, scientifically based and experimentally tested program for managing the operation of the “Active Longevity” higher people’s school, focused on developing all components of the studied phenomenon structure contributes to increasing the level of older people’s sociocultural adaptation in terms of functioning the higher people’s school.


Generation of PAS-Stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

Abstract: In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.


Method for named entities linking with concepts of the Wikidata knowledge base

Abstract: The paper presents a method for automatically linking named entities in Russian-language texts with concepts from the Wikidata knowledge base. It is based on the use of named entity search tools with subsequent semantic analysis of the correspondence of the found entity to the concept in the knowledge base. The resulting links can later be used to form a linked corpus of texts in any subject area. The difference between the presented method and existing ones is the analysis of both the named entity itself, its attributes, as well as associated words without the use of machine learning methods. This approach allows to increase the accuracy of searching for a corresponding concept in the knowledge base and eliminates the need to constantly retrain the neural network model to recognize new entities added to the knowledge base.


Digitalization of Vocational Education: Neurotechnologies and Robots in Educational Dialogue

Abstract: The aim of the study is to analyse the psychological and pedagogical problems of using neurodigital and smart technologies in modern vocational education as leading trends in its digitalization. Neurodigital technologies and robots should be developed, implemented, and improved into modern and future vocational education, as truly high-tech, complex products that help a person in his development, set and solve “super tasks”, and improve the subjects of education, educational relations.


Ranking assessment of the popularity of publications for online social media monitoring

Abstract: The paper examines the possibility of using various methods for generating ranking estimates of the popularity of messages in online social media. Using experimental data collected from the VKontakte network as an example, it is shown that using quantitative indicators of likes, reposts and comments as the basis for an ranking assessment of popularity gives an equivalent result. The need to use popularity metrics, normalized by the number of views or the size of the community audience, is noted when analyzing several online communities at the same time.


ON THE USE OF THE METHOD OF EXPEDIENT PROBLEMS TO ACTIVATE STUDENTS' COGNITIVE ACTIVITY IN THE PROCESS OF TEACHING MATHEMATICS IN THE NEW UKRAINIAN SCHOOL

Abstract: Formulation of the problem. The motivation of students to study a subject at school largely depends on the methodology used by the teacher in the teaching process, and his or her ability to convey new knowledge to students in an interesting and accessible form. This problem becomes especially relevant in the context of the New Ukrainian School project, which involves shifting the emphasis in the learning process from the teacher to the student and puts in the forefront the needs of the latter. For a child to be interested in learning and sufficiently motivated, a teacher should have an answer to the question "Why do we need to learn this?" The method of expedient problems gives one of the possible answers to this question, helps the teacher to motivate the student to study mathematics, and indicates directions for further use of mathematical knowledge. The importance of developing students' ability to apply mathematical knowledge in practice is further confirmed by the results of the International Comparative Studies PISA 2018 and PISA 2022, as well as statistical reports of the Ukrainian Center for Educational Quality Assessment in Mathematics. Materials and methods. To achieve our goal, we use a theoretical analysis of the methodological literature related to the chosen research topic. We also implement empirical methods, including conducting our own survey via Google Forms, observing the educational process in secondary schools, and analyzing students’ performance. Results. To study the feasibility and possibility of using the method of expedient problems in teaching mathematics in the NUS, we surveyed 290 mathematics teachers with different pedagogical experiences and qualifications from all regions of Ukraine. In particular, we assumed the effectiveness of using expedient problems in the process of teaching mathematics. The survey was conducted anonymously and online via the Google Forms platform. The results of the survey show that a significant number of respondents are familiar with the method of expedient problems and regularly or constantly use such tasks in the learning process. In addition, they are convinced of the need to include such tasks in existing math textbooks. The survey also shows at what stages of the lesson and when studying which content areas of the mathematics course, in the opinion of the respondents, it is most appropriate to use the method of expedient problems. Conclusions. To ensure the proper quality of students' acquisition of the necessary competencies envisaged by the New Ukrainian School project, the use of appropriate tasks at different stages of the lesson and in the study of different content areas of the school mathematics course is quite appropriate and possible. Although only half of the surveyed teachers currently use such tasks in their pedagogical activities regularly, most of the respondents consider them useful. In addition, teachers express a wish to provide methodological support for the effective implementation of appropriate tasks in the educational process, in particular, in the form of ready-made didactic materials and methodological recommendations for their creation.