Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.

Visual exploration of complex data helps interpretation, especially in the case of omics data analysis in life sciences and clinical research. Generating and analysing omics data requires bioinformatic skills, while they are interpreted by domain experts, for whom parsing large and complex data structures may be challenging. However, outcomes of visual analytics are often difficult to quantify, which emphasises precision and reproducibility, especially for research on human diseases. Here, we propose a workflow combining a reproducible computational environment with a dedicated visualisation functionality for systems biology diagrams. By linking the Galaxy with the MINERVA Platform, we visualise and explore COVID-19 transcriptomic data to demonstrate the utility of our workflow. Visualised data recapitulate findings of the original publication and offer new insights about the COVID-19 pathology. Our results offer a blueprint for quick prototyping of computational workflows that facilitate communication and exploration of complex data in biomedical research.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

The study aimed to estimate the prevalence of respiratory symptoms and to assess respiratory health risks associated with Particulate Matter (PM) exposure among the residents of Windhoek, Namibia. Objectives: To measure particulate pollution concentration in Windhoek through monitoring of particulate matter concentration and to identify any associations between particulate pollution, individual location and respiratory health among the Windhoek resident’s. Methods: an adapted standardized self-administered questionnaire was used to collect respiratory health related data as well as previous exposure, while PM monitoring was done using ASTM D1739 reference method. Results: A high prevalence was observed for cough (43%), breathlessness (25%), and Asthma (11.2%). PM was found to be a significant risk factor for episode of cough and phlegm, while high PM exposure category had increased odds ratio for episode of phlegm and cough (OR: 2.5, 95% CI: 0.8-8.0). No association was observed between location and respiratory health outcomes. Conclusion: The study found high levels of PM concentration across all Windhoek suburbs which were above the German, American and EPA. Enactment of legislation relating to the control and monitoring of PM related emissions at point of generation is required at country and city level.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Involving industrial waste into economic circulation, in particular flotation waste from coal enrichment, is an urgent task. The coal preparation waste of ArcelorMittal Temirtau JSC was studied with a view to the possibility of using it as an alternative substitute for coke breeze in the sintering process. During a thermoanalytical study on a synchronous thermal analyzer STA 449 NETZSCH in dynamic mode with a heating rate of 10, 20, 30 °C/min in an air flow of 30 cm³/min in the temperature range 30–1000 °C, we determined the lower heat of combustion of carbon-containing material, change mass during calcination, change and rate of change in the mass of the sample, thermal effect of the process, the temperature of the beginning, end and maximum development of thermal effects. It has been established that the greatest mass loss is observed in the temperature range 580 °C- 610 °C, which is about 65–67% of the total mass of the organic component of coal. The maximum burnout rate corresponds to a temperature of 540–563 °C. The lowest calorific value of the studied waste was 15.9 MJ/kg. The results of thermal analytical research and technical analysis substantiate the possibility of using flotation waste in a mixture of up to 10% with coke breeze for combustion in the sintering layer for the production of sinter. Analysis of the composition of flotation waste showed that they have a high specific surface area (56.8∙10³ m²/kg), are highly dispersible, soggy, and capable of swelling, and the predominant amount of clay substance makes them a plastic material. The possibilities of using coal flotation waste as an alternative substitute for coke breeze have been studied after the regimes of its preparation for sintering have been developed and a rational regime of layer-by-layer combustion in the sintering layer has been established. The optimal ratio of coke and OPC in the fuel mixture of the sintering charge was determined.

It is unavoidable that the characteristics of the robot system alter inexactly or cannot be correctly determined during movement and are impacted by external disturbances. There are many adaptive control methods, such as sliding control or neural control, to improve the quality of trajectory tracking for robot motion systems. Still, those methods require a large amount of computation related to solving the problem of constrained nonlinear optimization problem. The article presents some techniques for determining an intelligent controller's online learning function parameters, including two circuits: the inner circuit is a state feedback linearized controller, and the outer course is an Interactive learning controller and does not use the robot's mathematical mode with function parameters online optimal learning and intelligent online learning closely following the joint model to stabilize the robot system. Finally, the 2-DOF planar robot simulation is performed on Matlab software R2022b. The findings indicate that outstanding tracking performance is achievable.

This article analyzes the perspectives of professional training of the public administration staff, from the viewpoint of the sustainability criteria, starting from the experiences of the online courses run in the last two years. In order to understand the nature, type and magnitude of the changes caused by the Covid-19 pandemic in the field of professional training for public administration, we performed a survey among public institution employees, by applying a questionnaire. The instrument included qualitative elements, to allow the framing of statistical results. The article is grounded on the hypothesis that the professional training activities in the last two years were organized preponderantly online and it aims to analyze the sustainability of the new teaching systems/methods. The limitations of the study are given by the fact that each administrative system has structure particularities and its own legal framework regarding the professional training of public administration staff, what makes that the model proposed cannot be applied in all countries. The study provides a model of online professional training for public administration staff, sustainable, based on the experience accumulated in the last two years.

We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.

Emotions are indicators of affective states and play a significant role in human daily life, behavior, and interactions. Giving emotional intelligence to the machines could, for instance, facilitate early detection and prediction of (mental) diseases and symptoms. Electroencephalography (EEG) -based emotion recognition is being widely applied because it measures electrical correlates directly from the brain rather than the indirect measurement of other physiological responses initiated by the brain. The recent development of non-invasive and portable EEG sensors makes it possible to use them in real-time applications. Therefore, this paper presents a real-time emotion classification pipeline, which trains different binary classifiers for the dimensions of Valence and Arousal from an incoming EEG data stream. After achieving a 23.9% (Arousal) and 25.8% (Valence) higher f1-score on the state-of-art AMIGOS dataset, this pipeline was applied to the dataset achieved by an emotion elicitation experimental framework developed within the scope of this thesis. Following two different protocols, 15 participants were recorded using two different consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. For an immediate label setting, the mean f1-score of 87% and 82% were achieved for Arousal and Valence, respectively. In a live scenario, while continuously being updated on the incoming data stream with delayed labels, the pipeline proved to be fast enough to achieve predictions in real time. However, the significant discrepancy from the readily available labels on the classification scores leads to future work to include more data with frequent delayed labels in the live settings.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

Introduction: Dental students’ use of online material to supplement learning has been studied, but educator awareness and reaction to the findings of this research is unknown. This small local study aimed to investigate dental students’ use of online content as a learning tool from an educator’s perspective.Methods: Educators at an Irish dental school completed a survey based on dental students’ use of online learning. Quantitative descriptive analyses were carried out as appropriate on the data collected, and a subsequent focus group was held to gain deeper insight into survey results. Data were analysed for themes by deductive and inductive coding methods.Results: This study found a sample of educators were aware of some student behaviours around online videos. Most educators correctly thought that students are likely to refer to online videos to prepare for dental procedures. Several educators believed that teachers should incorporate videos into their learning, but do not themselves upload or recommend online videos. Most recalled discussing accuracy and/or relevancy of online content with their students. Fewer thought that students would discuss contradictory videos with them. Focus group participants expressed concern over the accuracy of online content. This along with a lack of time were some reasons deterring them from referring students to online videos. Conclusions: An opportunity exists for dental educators and their students to have local dialogue around (1) student use, critical appraisal, and sharing of dental procedural video material; and (2) perceived benefits and challenges of incorporating both school-produced and online videos into teaching and learning. Although arguably non-generalizable beyond this context, this study may inform future research and serve to spark conversations within schools regarding video use for teaching clinical dental procedures.

In recent years, online learning has become one of the most popular methods of educational delivery due to advances in technology, which has been made even more evident in the COVID-19 lockdown period. Online education has evolved into a distinct field of study within the educational system over the last few years. It is also important to note that parallel with the growth in this field, there has also been an increase in the number of scholarly journals that regularly publish research in this field, reflecting the importance of this field in the modern day. In spite of the fact that online learning offers a wide range of educational options, from short courses to full-time degrees, as well as being accessible, flexible, environmentally friendly, and affordable, there are also certain challenges associated with this educational approach. These challenges include the lack of social interaction, technical errors, a lack of hands-on training, and difficulties in assessing students. It is, therefore, imperative to ask the crucial question of whether online learning can replace traditional classroom learning or whether it can supplement it in hybrid models with it, as well as what factors and conditions are likely to determine this in the short- and long-term, as well as how it will be blended together in the future. The purpose of this commentary is to provide a brief summary of the current status of both learning models, as well as their pros and cons, in order to answer the question that was posed above.

Researchers and organizations are pursing 100 Day Vaccine Development to reduce the impact of any new pandemic-causing virus. They propose monitoring viral emergence and developing vaccines for discovered viruses which would undergo safety testing in humans. If one of these viruses emerges, phase III trials would then take place. There are two problems with this approach - first, phase III trials are too long for the 100-day goal and second, the virus that emerged might not have been discovered in their surveillance. An alternative approach would be to first develop vaccines for viral families similar to the pan coronavirus vaccines being proposed for next generation COVID vaccines. Phase III trials for them would be run even if a virus in the family wasn’t having an outbreak. If a virus in a family for which a vaccine has been developed has an outbreak and the phase III trialed vaccine is not effective against it, develop a booster based on the phase III trialed vaccine. Since booster development and approval takes less than 100 days, the 100 Day Vaccine Development goal would be met.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Abstract: Brassinosteroids (BR) are essential plant hormones that play a crucial role in regulating plant growth and development, as well as their resistance to abiotic and biotic stresses, including pathogen infections. In the context of plant im-munity, BR influence various processes such as the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the re-inforcement of the plant cell structure. The aim of work is understand the role of BR in plant defense against abiotic and biotic stresses. BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, contributing to increased plant resistance to abiotic stresses. The study discusses various mechanisms of BR action on plant immunity, including the regulation of gene expression related to defense, activation of the plant defense system, in-teraction with other plant hormones, regulation of water and salt balance, and influence on photosynthetic processes. In the context of plant immunity, BR influence various processes, including the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the reinforcement of the plant cell structure. Additionally, BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, con-tributing to increased plant resistance to abiotic and biotic stresses. The paper emphasizes the importance of further research on the role and mechanisms of action of BR in plant immunity, which may contribute to the development of more effective strategies for the use of BR in agriculture, horticulture, and plant protection.

The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers' emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves highly applicable to online reviews.

The COVID-19 pandemic is shifting teaching to an online setting all over the world. The Galaxy framework facilitates the online learning process and makes it accessible by providing a library of high-quality community-curated training materials, enabling easy access to data and tools, and facilitates sharing achievements and progress between students and instructors. By combining Galaxy with robust communication channels, effective instruction can be designed inclusively, regardless of the students' environments.

The Universe is treated as an unbound thermodynamic system in which two forces of Nature compete: Attractive gravity loss and repulsive entropic gain. Entropic gain, although not presently considered a force of Nature, is otherwise well known and can be expressed with the gas laws. The Universe at the time of last scattering was all gas. Its instant gain is expressed as gas pressure. When the Gibbs equation is properly applied to this unbound gas, the resulting atomic Hubble parameter is found to be exclusively dependent on baryon density, giving a constant and perpetual two-to-one gain/loss ratio. Instant loss to gravity cannot offset gas pressure in this “thermal equation”. By contrast, the Friedmann equation, a ΛCDM foundation, treats baryon mass as accreted, having little if any entropic gain. These two equations are actually equivalent. Expanding accreted matter at its comoving critical density is herein shown to behave exactly like a freely expanding gas. However, neither equation includes all of today’s Universal kinetic energy, and cannot by themselves fully account for observed stellar movement. The present paper proposes the plausible existence of highly energetic or “suprathermal” free electrons which comprise about half of the intergalactic medium’s kinetic total. It is this suprathermal energy which Λ expresses. The fluid equation, another ΛCDM foundation, eliminates entropic gain from the Gibbs equation. Additionally, rest mass’s Einstein energy E=Mc² is treated as a thermal variable. These two assumptions, isoentropy and energy conflation, led astronomers to populate Λ as they followed the prior art.

Educational institutions worldwide have adopted e-learning due to the COVID-19 pandemic. While this shift to online learning (OL) has presented challenges for students and teachers, it has also sparked innovative educational approaches. This study investigates college students' perceptions regarding OL, specifically focusing on gender differences in the experience with OL A quantitative survey gathered information from distinct college students in Gulf Cooperation Council (GCC) nations who experienced OL during the pandemic. The survey included questions related to satisfaction with OL and specific aspects such as course content delivery, interaction with instructors, behavioral changes, and challenges with OL. Male and female student OL experience was compared statistically. The results conclude that male students are more satisfied than female students. Students struggled with internet connection, OL adaptation, focus, and workload during OL. Understanding these gender differences in students' satisfaction with OL is crucial for educators and institutions as they strive to optimize the effectiveness of e-learning strategies. By recognizing and addressing male and female students' unique needs and challenges, educational institutions can enhance the overall educational experience during times of crisis and beyond.

Most systems that are the subject of control engineering studies have some non-linearity. An example of this is the horizontal cylindrical tank, commonly used in process industries. To deal with cases like this, several control theories have been developed over time, each one presenting better results in certain systems. This work presents an alternative for the control of nonlinear systems, without necessary modeling or previous information about the system, based on a new optimization law for the artificial neural network training in real time.

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student's usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers' perceptions of the usability of the virtual classroom system and an increase in students' academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

Deep-learning-based speech enhancement techniques have been recently grown in interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio processing scenarios (i.e. feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of great interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e. feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. Meaning, this work measures how the output signal-to-interference ratio (as a separation metric), the response time and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability: MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.

The COVID-19 pandemic forced the transition to emergency online learning without prior preparation or guidelines. This transition has been particularly challenging in developing countries and low-resource contexts and hindered student engagement. We aim to identify the engagement strategies which students, engaging in emergency online learning in low-resource contexts, perceive to be effective. We conducted a sequential mixed-methods study based on Moore’s interaction framework for distance education. First, we conducted a literature review and interviewed ten teachers and ten students to identify a list of engagement strategies. Then, we designed a questionnaire that examines student perceptions of these strategies. We administered the questionnaire to 313 students engaging in emergency online learning in low-resource contexts. Our analysis results showed that student-content engagement strategies, e.g. screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student-teacher strategies, e.g. Q&A sessions and reminders. Student-student strategies, e.g. group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies depends on the context and the students’ characteristics, e.g. gender, major, and technology access. To support instructors, instructional designers, and researchers we propose a ten-level guide for engaging students during emergency online classes in low-resource contexts.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for artificial intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) was identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine were conducted to compare the performance of OML with traditional Batch Machine Learning. The evaluations clearly indicate that OML adds significant value to CPS and is strongly recommeded as an extension of related architectures such as the cognitive architecture for AI discussed in this article. Additionally, surrogate model-based optimization is employed to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.

The COVID-19 pandemic in Indonesia makes a significant impact both physically and psychologically. One month after the President of the Republic of Indonesia announced about the COVID-19 patient cases, the Indonesian Child Protection Commission data during April 2020, depicted that 76.7% of children were not happy to participate in distance learning because 81.8% were only given assignments by the teacher and 73.2% felt they had a heavy task and had a short period of time to complete. This reaction is an indicator of the children’s anxiety about distance learning. The anxiety that occurs in these students is assumed to depend on their social support and emotional maturity. When students get optimal family support and are able to control their emotions in the face of a pandemic, they can reduce anxiety in facing online learning. The subjects of this study were 202 junior high and high school students. The results showed that social support and emotional maturity simultaneously affect anxiety in online learning (Freg = 45.066, p = 0.00 <0.01). These results can be used as a basis for providing psycho-education to increase family support and emotional maturity to reduce anxiety in online learning.

Background: Diets based on meat products are not recommended in the case of ulcerative colitis (UC). However, some foods, as those containing high oleic acid and a low omega-6/omega-3 ratio show anti-inflammatory properties. The objective here is to test if some traditional cured meat products, as acorn-fed ham (high levels of oleic acid), may be useful for controlling inflammatory diseases as UC in animal models. Methods: 3 rat cohorts have been used: vegetable rat feed, control ham and acorn-fed ham (a traditional ham where high oleic acid concentration from acorns is storage in the muscle fat). UC was induced with DSS in drinking water ad libitum for one week. Short-chain fatty acids (SCFAs) and 16S rRNA from bacterial populations were analyzed in cecum samples. Colon samples were analyzed for histological parameters (inflammatory cell density, mucosa damages, myeloperoxidase). Results: In the acorn-fed ham cohort, a protective effect was observed with respect to UC disease activity index, inflammatory cells density, colon mucosa alterations, myeloperoxidase levels, blood total antioxidant capacity and lower levels of pro-inflammatory cytokines, in comparison with feed cohort. Both ham diets caused a reduction in Firmicutes and an increase in Actinobacteria, Bacteroidetes and Proteobacteria in comparison with rat feed diet. Also, acorn-fed ham diet induced changes in gut microbiota composition, with pronounced enrichments in anti-inflammatory bacterial genera such as Alistipes, Bacteroides, Blautia, Butyricimonas and Parabacteroides. Conclusions: In the acorn-fed ham cohort, as a result of the dietary intake of oleic acid and low intake of omega-6 fatty acids, a strong preventive effect against UC symptoms was observed, indicating a valuable effect of this traditional Mediterranean cured meat product.

Consuming fish sustainably means consuming less and better, considering environmental, social, and economic impacts. This demands more knowledge to make informed decisions. In this study, the main goal was to understand how sustainability education can be used as a tool to raise awareness and promote new habits regarding the sustainable consumption of fish in 4th-grade students at primary school. During the COVID-19 pandemic, face-to-face and online learning methodologies were used in two different groups of students to understand whether the teaching method impacted learning. A sequential explanatory mixed methodology was applied, with a pre-test/post-test design, combined with focus group sessions. Between the pre and post-tests three learning modules were performed. The results showed that the students were unaware of many aspects related to fish sustainable consumption, but the developed activities significantly increased their knowledge of the subject and impacted students to change their consumption behaviours. It was also found that the construction of knowledge by the students after the activities was achieved either by face-to-face or online learning. Interviews from focus group sessions showed that students got to know new fish species, learned about the concept of minimum landing size and closure season and that fish are labelled like other types of food.

Bio-absorbable stents are a novel tool which relieve stenosis and then resorb as the patient grows, avoiding obligatory re-intervention. This case describes the novel use of an iron bio-absorbable stent in a child with surgically repaired Tetralogy of Fallot who post-operatively developed a severely stenotic right ventricular outflow tract.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel impact-echo analysis system using online machine learning, which aims at achieving near-human performance for assessment of concrete structures. Current computerized impact-echo systems commonly employ lab-scale data to validate the models. In practice, however, the echo patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for echo characterization. More specifically, the proposed system can adaptively update itself to approaching human performance in impact-echo data interpretation. To this end, a two-stage framework has been introduced, including echo feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 echo instances from multiple inspection sites with each sample had been annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable echo pattern classification accuracy with high efficiency and low computation load.

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

In the livestock industry, maintaining the health of animals is very important, and the use of technology has greatly helped this. One of the existing challenges has been that most of the available sensors and electronic processing devices are designed to fit the human body, and the use of these devices for livestock faces limitations due to the difference in the structure of the body and skin of animals and humans. Reliable solutions tailored for animal husbandry can be achieved by modifying the physical and software structure of existing devices. In this article, we have presented the current situation, by reviewing the research done and the existing technology in the field of animal husbandry. The aim is to obtain comprehensive parameters for monitoring livestock health by leveraging available information and exploring new methods based on the Internet of Things (IOT), Artificial Neural Networks (ANN), and Artificial Intelligence (AI). We take a proactive approach by utilizing available technologies based on sound-based diagnostic techniques. Initially, we propose utilizing piezoelectricity and amplifying received sound using a diaphragm attached to a collar. Subsequently, we discuss the results of relevant tests that can be conducted using the phonocardiography system. For the next phase of research and designing a more advanced livestock health monitoring system, we suggest employing an ultrasonic system to measure heart rate and blood pressure for the next phase of research and designing a more advanced livestock health monitoring system. This system, coupled with a piezoelectric system to make electric energy from the pulse of the veins, could be subcutaneously implanted for continuous monitoring

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Majority of published articles have talked about the challenges faced by students in learning online however, little has been talked about what the teachers have gone through especially during these times of the pandemic. This paper discusses the factors university teachers face when teaching online. These factors include accessibility to the internet, level of interaction between teachers and students, costs incurred, availability of training and academic policies kept in place to enhance effectiveness of teaching online. These are further divided into personal, social and economic factors where the teachers’ age, gender, remuneration, availability of resources, location and the economic status of the country is discussed in relation to the effectiveness of online learning. Upon carrying out a literature review on articles written on the effectiveness of online learning with the main focus being teachers, it was noted that the main factors affecting the effectiveness of online learning was the availability of internet connection and training provided to teachers. In Kenya, majority of the rural areas lack access to the internet and devices to learn online which makes it difficult for a teacher to teach effectively given the pandemic constraints. This study sheds light on the need for institutions and governments to take input from their teachers and train them on how to make online learning more effective. It also shows the status of universities in Kenya which had to shift to learning online due to the pandemic. Majority of them took time to adapt to this new change due to the discussed factors. Therefore it is recommended that the Government should train teachers and address the issue of lack of internet and electricity in Kenya.

There is a crucial need for advancement in the online educational system due to the unexpected, forced migration of classroom activities to a fully remote format, due to the coronavirus pandemic. Not only this, but online education is the future, and its infrastructure needs to be improved for an effective teaching-learning process. One of the major concerns with the current video call-based online classroom system is student engagement analysis. Teachers are often concerned about whether the students can perceive the teachings in a novel format. Such analysis was involuntarily done in the offline mode, however, is difficult in an online environment. This research presents an autonomous system for analyzing the students' engagement in the class by detecting the emotions exhibited by the students. This is done by capturing the video feed of the students and passing the detected faces to an emotion detection mode. The emotion detection model in the proposed architecture was designed by finetuning VGG16 pre-trained image classifier model. Lastly, the average student engagement index is calculated. We received considerable performance setting reliability of the use of the proposed system in real-time giving a future scope to this research.

In the last decade, readily available electronic devices have created unprecedented opportunities for teens to access a wide variety of information and media – both positive and negative – on the internet. Despite the increasing number of initiatives taking place worldwide intended to assess and mitigate the online risks encountered by children and adolescents, there is still a need for a better understanding of how adolescents use the internet and what consequences they may face. We conducted a cross sectional online survey of a convenience sample of 733 8th and 9th grade students in Utah. The survey contained eight questions regarding students’ exposure to three types of online risk scenarios: content risk, contact risk, and commercial risk. Independent variables included students’ online behaviors, use of social media and private messaging apps, and adult supervision of online activities. Logistic and negative binomial regression models indicated that female gender, social media use, and chatting with strangers were associated with exposure to multiple unsafe online scenarios. Our results provide critical information to practitioners involved in the development of educational initiatives by building a profile of potentially risky online behaviors and allow them to tailor their initiatives to meet the needs of more vulnerable populations.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Museums are called to respond to contemporary challenges and contribute to the formation of more progressive societies. One of the major challenges of our time is climate change and museums could construct cocreated narratives around climate and implement targeted actions for the audiences through their sensorial and affective experiences. More specifically, Geology and Palaeontology museums are repositories of knowledge on geoheritage and geoconservation, recognized for their scientific, pedagogical, cultural and recreational values. The educational potential of geological and paleontological exhibits could be used in informal education activities regarding the climate emergency. Taking the above into consideration, this study focuses on both the creation and evaluation of an educational program for the Natural History Museum of Arsakeio Campus at Psychicko.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.

Negative mental health in students currently is classified as a global crisis with the highest and lowest student achievers recognized at greatest risk. Public schooling, in reproducing accepted psychosocial beliefs through standardized learning, developed separately from necessitating student mental health, in contrast to self-directed learning. Differing from standardized learning, the objective of self-directed learning in public schools is the creation of relevant support structures for student mental health, promoting positive psychosocial outcomes. The designed separation of public schooling from both mental health and self-directed learning was first acknowledged—and lamented—by John Dewey, over 100 years ago, in anticipating today’s mental health crisis. Yet, in responding effectively to the limitations of COVID-19, self-directed learning became an acknowledged learning method in public schools, potentially able to be accommodated by them regularly in support of mental health through the use of online technology. This study investigates the COVID-19 results of self-directed online learning in public schools through a Google Scholar search of peer reviewed research regarding self-directed learning, online learning, and mental health during COVID-19, recommending support for self-initiated self-directed online learning so that self-directed learning can continue, post COVID-19, improving student mental health in public schools, leading to positive psychosocial outcomes.

This study aims to evaluate the determinants that influence the adoption of online databases in the learning process of students at economics universities in Vietnam. A quantitative study with a meta-analysis was conducted by utilizing structural equation modeling (SEM). The sample consisted of 492 students from economics universities located in Vietnam who were surveyed using stratified random sampling. The results indicate that the adoption of online databases in student learning is influenced by six determinants, namely: (i) perceived effectiveness, (ii) perceived ease of use, (iii) technical barriers, (iv) personal usefulness, (v) usage attitudes, and (vi) convenience. Our study has revealed that students' intention to use the online database system is positively influenced by their perceived ease of use and perceived usefulness. These findings could be valuable in shaping policies for enhancing the online database system at economics universities, taking into account the students' characteristics and the institution's needs.

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. The paper also briefly outlines some potential applications in the fields of Big Data, Data Mining, Natural Language Processing, and their related disciplines, with a specific focus on online learning during this Omicron wave that may be studied, explored, and investigated by using this dataset.

A proof is given showing the Collatz Conjecture is true for all positive integers. The proof was possible once the perspective of the proof was changed from looking at the pattern of the positive integers to looking at the conjecture rules. The conjecture rule for even numbers organizes all positive integers into unique sets and the rule for odd numbers interconnects the unique sets into dendritic pathways to “1.” Infinite loops, other than the minor 4-2-1 loop after reaching “1,” and pathways to infinity are shown to be mathematically impossible. The proof predicted a general equation that shows all positive integers ultimately reach a final value of “1," and calculates the values and locations of the odd positive integers during the iterations for each tested positive integer.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

Water depth estimation holds paramount importance in various domains including navigation, environmental monitoring, and resource management. Traditional depth measurement methods such as bathymetry can often be prohibitively expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, with a specific focus on the Baidu Easy DL model, for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using the ACOLITE software, the research compares the performance of several machine learning algorithms, including the Stumpf Model, Log-Linear Model, and the Baidu Easy DL Model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0-11 meter depth range. This study showcases the substantial potential of machine learning in the realm of remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research not only opens avenues for further exploration of machine learning applications in remote sensing but also highlights the promising prospects of online model APIs in streamlining remote sensing data processing.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through simulation illustrating the effectiveness of the proposed methodology.

A hybrid organic molecules as ligands and inorganic NCs in this research combines the chemical properties of organic semiconductors with the chemical stability of inor- ganic nanoparticles like PbS. It is possible to improve the ligand exchange and PbS-NCs' distribution by adding Amino phynel anchor group to TIPs-pentacene and Carboxylic anchor group to Tetraphenylporphyrin molecules. By adding anchor groups to one or two sides of these molecules, they would convert the molecules to ecient ligands to obtain superlattice coupled organic-inorganic nanostrcuture semicondcutor layers (COINs). Adding on side group would cap the PbS nanoparticles while adding two an- chor group would play a linker role between two PbS NCs. This morophology enhance- ment paves the way to manipulate the physical properties of the COINs and fabricate solar cells. Singlet ssion phenomena demonstrates pentacene molecule's advantage for high eciency in organic solar cells and its ability to overcome the Schockley-Queisser limit. Ultraviolet photo-emission spectroscopy (UPS) determines the highest occupied molecular orbital (HOMO) and the work function of COINs, while the lowest unoccu- pied molecular orbital (LUMO) of COINs were calculated subsequently by adding both the exciton binding energy and the optical band gap to the measured HOMO energy. The current versus voltage characteristics and impedance spectroscopy were studied for these molecules as well.

Carbapenem antibiotics are the drug of choice for treating multidrug-resistant bacterial infections. Metallo-beta-lactamases (MBLs) are carbapenemase capable of hydrolyzing nearly all therapeutically available beta-lactam antibiotics. Consequently, a need to assess the frequency and phenotypic resistance phenomena of two MBL genes in diarrheal and urinary tract infections (UTIs). Samples were collected through a cross-sectional study, with MBLs genes detected via PCR. Two hundred twenty eight diarrheal bacteria were isolated from 240 samples. The most predominant pathogens were Escherichia coli (32%) and Klebsiella spp. (7%). Phenotypic resistance to amoxicillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 50.4%, 65.6%, 66.8%, 80.5%, 54.4%, 41.6%, 25.7%, 41.2%, 37.2%, and 42.9%, respectively.  Total 142 UTI pathogens were obtained from 150 urine samples, with Klebsiella spp. (39%) and Escherichia coli (24%) are the major pathogens. Phenotypic resistance to amoxycillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 93.7%, 75.0%, 91.5%, 93.7%, 88.0%, 72.5%, 13.6%, 44.4%, 71.1%, and 43%, respectively.  Twenty four diarrheal isolates carried either blaNDM-1 or blaVIM genes; the overall MBL gene prevalence was 10.5%. Thirty six UTI pathogens carried either blaNDM-1 or blaVIM genes (25.4%). Seven isolates carried both blaNDM-1 and blaVIM genes.  MBL genes exhibited a strong association with phenotypic carbapenem and other beta-lactam antibiotic resistance. Resistance to carbapenems requires active surveillance and stewardship.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

Africa was the only continent not to achieve the 2015 Millennium Development Goal of 50% poverty reduction. This paper asks whether Africa will fare better in meeting Sustainable Development Goals (SDGs) addressing poverty and hunger by 2030. To answer this question we examine literature, including our field research published over the last thirteen years. We find that ‘sustainable development’ is a failed concept immersed in the contemporary global economic system that favors growth over ecosystem stability and patriarchal systems of governance that undervalue women’s capacity for sustainability in their care-work as food providers. We examine barriers to women’s farming (climate change, gender bias, limited access to land, technology, finance) and provide examples of women’s innovative strategies for overcoming these barriers in their care practices toward family and community well-being and ecosystem health. We conclude that sustainability is only possible through transformation of thinking away from approaches that value profit over people and ecosystems and toward gender-based approaches for achieving the goals laid out in the SDGs through holistic, integrative systems of ecosystem fit.

o estudo visa explicar os efeitos diferentes entre as equações de multiplicação, soma, divisão e subtração. Nesse sentido, as equações de multiplicação e soma desenvolvem um novo elemento ou fato, enquanto isso, as equações de divisão expressar informações dentro do elemento ou fato, a subtração é a seleção de elemento ou fato Alem disso, para uma equação de multiplicação e soma resulta no novo elemento ou fato é necessario dependência de conceitos físicos específicos, sem alteração extrema, pois resultaria na alteração do elemento ou fato. vale ressaltar, a importância de valores fixos na equação para descreve a natureza específica do elemento ou fato. O estudo possibilita o entendimento das equações em aberta(sem solução) na matematica e fisica, além de compreender equações existentes. O estudo faz referência à teoria da necessidade obrigatória e teoria da diferenças entre elementos.

This paper advances privacy theory through examination of online shaming, focusing in particular on persecution by internet mobs. While shaming is nothing new, the technology used for modern shaming is new and evolving, making it a revealing lens through which to analyze points of analytical friction within and between traditional conceptions of privacy. To that end, this paper first explores the narrative and structure of online shaming, identifying broad categories of shaming of vigilantism, bullying, bigotry and gossiping, which are then used throughout the paper to evaluate different angles to the privacy problems raised. Second, this paper examines shaming through three dominant debates concerning privacy - privacy’s link with dignity, the right to privacy in public places and the social dimension of privacy. Certain themes emerged from this analysis. A common feature of online shaming is public humiliation. A challenge is to differentiate between a humbling (rightly knocking someone down a peg for a social transgression) and a humiliation that is an affront to dignity (wrongly knocking someone down a peg). In addition, the privacy concern of shamed individuals is not necessarily about intrusion on seclusion or revelation of embarrassing information, but rather about the disruption in their ability to continue to participate in online spaces free from attack. The privacy interest therefore becomes more about enabling participation in social spaces, enabling connections and relationships to form, and about enabling identity-making. Public humiliation through shaming can disrupt all of these inviting closer scrutiny concerning how law can be used as an enabling rather than secluding tool.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

Preprints is delighted to have passed the milestone of one thousand preprints online. This is a significant step for the platform, which was launched in May last year with the objective of speeding up the process of communicating research across all disciplines. We are proud to be the first preprint server to cover all disciplines.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage and performance optimised deep learning approach to predict the capacity based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a 1-dimensional convolutional and long-short term memory neural network and processes time, voltage and incremental capacity of partial charging curves as time series. The model is cross-validated on different aging scenarios reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real world application.

We discuss three key points: defining mathematically the notions of phase, symmetry breaking, topological defects and their stability in a formal language of homotopy theory and fiber bundle formalism; postulating and proving two theorems for successive symmetry breaking scenarios in condensed matter physics and cosmology, and demonstrating application of theorems to superfluid Helium 3 and Kibble-Lazarides-Shafi (KLS) walls, supporting and mathematically justifying the results of [Volovik, 2020].

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

While traditionally people relinquish their pets to an animal shelter or pound, the internet provides a newer method to re-home. We analysed advertisements (ads) on the largest website in Australia for trading dogs and cats, gumtree.com.au. Data was collected in 2016. Dogs were sampled on February 7, 16 and 24, 2016 and cats on February 9, 19 and 26, 2016 with 2640 ads for relinquished dogs, and 2093 ads for relinquished cats. It was estimated >31,000 puppies/dogs and >24,000 kittens/cats are relinquished on gumtree.com.au per year. The median age of dogs was 1.42 and cats 0.9 years of age. There were 23% of dog ads and 62% of cat ads for free animals. Compared to the human population there were proportionately more ads in Queensland and fewer ads in Victoria. A total of 15 people were surveyed who had relinquished a dog or cat using gumtree.com.au. The dog owners used gumtree.com.au because they believed the shelters were full (n = 4), and; they wanted to see/interview the new owner (n = 2). For cat owners it was because they had originally got the cat on gumtree.com.au (n = 2); they use gumtree.com.au for other things, and it works (n = 2), and; they wanted to see/interview the new owner (n = 2). The data collected will be valuable for implementation of policy and interventions to protect the welfare of un-wanted dogs and cats.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermo-optic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

In this paper, we study the constrained minimization problem for an energy functional which is related to the following Kirchhoff type equation \begin{equation*} -\Big(\eta+b\big(\int_{\R^{3}}|\nabla u|^{2}dx\big)^{s}\Big)\Delta u+V(x)u=\mu u +\lambda|u|^{p}u,\end{equation*} where $b$ is a positive constant, parameters $\eta\geq0, \lambda>0$, exponents $s>0$, $0

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Structures inevitably suffer damages after an earthquake, with their severity ranging from minimal damages to non-structural elements to partial or even total collapse, possibly with loss of human lives. Thus, it is essential for engineers to understand the crucial factors that drive a structure towards suffering higher degrees of damage, so that preventative measures can be taken. In the present study, we focus on three well-known damage thresholds, namely Collapse Limit State, Ultimate Limit State and Serviceability Limit State and analyze which of the features obtained via Rapid Visual Screening, determine whether or not a given structure will cross that threshold. To this end, we use Machine Learning to perform binary classification for each damage threshold, as well as Explainability via SHAP values (acronym for SHapley Additive exPlanations) to quantify the effect of each parameter. The quantitative results obtained demonstrate the potential applicability of ML methods to re-calibrate the computation of structural vulnerability indices using data from recent earthquakes.

Online class now is the demand of the day as little scopes are to find out alternatives to online class in these unprecedented days caused by corona pandemic across the globe. The study was qualitative in approach and data were collected from secondary sources i.e. different newspapers and journals in the recent times along with a mini interview with students of private universities studying in different subjects over mobile phone by the researcher. Findings of the study show that though online education has a number of challenges faced by two main stakeholders; students and teachers, handling all these challenges carefully can have the chance to create a positive atmosphere in the field of education as an alternative teaching learning resulting in positive outcomes in all regards.

Background: Public misconception and demand for the indication of antibiotics could lead to inappropriate prescribing and consumption. Successful treatment can only be achieved when the public and industrial users have knowledge on antibiotic use and resistance. This survey is aimed to assess antibiotic usage and knowledge regarding antibiotics and antimicrobial resistance (AMR) among undergraduate students of Universiti Brunei Darussalam (UBD), public university located in Brunei Darussalam. Methods: A cross-sectional study was performed using a self-administered questionnaire. The questionnaire was adapted from the World Health Organization (WHO) Antibiotic Resistance, Multi-country public awareness survey distributed online. Students at UBD were invited to participate in the online survey through internal email. The questionnaire consisted of 5 sections: demographic information, antibiotic usage, knowledge on antibiotics, antibiotic resistance (AMR) and use of antibiotics in agriculture. Data were analyzed descriptively and appropriate inferential statistics was used accordingly. Cronbach’s alpha was also done to determine the internal consistency. The section on antibiotic use and knowledge showed good internal consistency of Cronbach’s alpha 0.66 and 0.86 respectively. Research ethics approval was obtained from the PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam (UBD). Results: A total of 145 students returned the complete questionnaire. The result of the study found that 50% of the students had good level of knowledge of antibiotic and antimicrobial resistance with a mean total knowledge score of 11.4 out of 14. Respondents reported the use of antibiotic in the past (69%). Many of the students could identify the use of antibiotics for the treatment of bacterial infection. However, there were also students who incorrectly thought that antibiotics can be used for cold and flu (43%) and fever (41%). Moreover, 76% of the respondents mistakenly believed that antibiotic resistance is the result of the body becoming resistant to antibiotics. Only 12% of the respondents were found to have poor knowledge in the study. Conclusions: Misconceptions in regards to the use of antibiotics for conditions related to viral illnesses was noticed among the respondents in our study. Thus, improving knowledge on antibiotics is crucial to address those beliefs.

Self-consciousness can be considered as the internal disposition to direct attention to oneself. This dispositional tendency can be focused on private aspects of the self, but also on public characteristics of the individual. We examine self-consciousness in online consumer behavior. This concept has been poorly investigated in consumer research. The main objective of this paper is to analyze the influence of the dimensions of self-consciousness in consumer adoption of online shopping. This study is based on a sample of 725 Spanish undergraduates. Results indicated that public self-consciousness is a significant predictor of the adoption of online shopping.

This short paper raises and answers a question related to orthographic lexicography in general and its reference to efforts in making contemporary dictionary portals. As orthographic dictionaries have not yet been researched as a specialized lexicographic variety, part of their metalexicographic description in those European languages that have online normative orthographic dictionaries is presented. Metalexicographic elements that are analyzed were chosen from the perspective of casual and professional users and online dictionary visitors. Regardless of the fact that this is a specific kind of dictionary, as well as of the fact that European orthographic tradition and practice is quite heterogeneous, the belief that the European Dictionary Portal should also include available online orthographic dictionaries is defended. An argument in favor of this could contribute to an awareness of the importance of orthography for online dictionary users, even in those languages whose written form greatly corresponds to the spoken form.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.