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.

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.

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.

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.

Preschoolers with disabilities and their caregivers have been neglected in health and social service provision in most low-income countries and arguably too in low resourced areas of more affluent nations. Yet as this rapid review of published literature identifies, there are low-cost, evidence-based strategies to address their needs that can be implemented in communities by local people. Five key features of the necessary supports are examined. First the leadership functions required to create and implement the support services. Second, the family-centred, home-based support provided to caregivers and the personnel undertaking this form of support. Third, providing opportunities for peer support to flourish and encouraging the formation of advocacy groups across families. Fourth, mobilizing the support of significant groups within the community; notably traditional healers and leaders; health services and poverty alleviation initiatives. Fifth, devising ways in which preschool educational opportunities can be offered to children as a prelude to their inclusion in primary schools. The review serves a further purpose. It provides an example of how public health researchers and academics could achieve more rapid implementation of evidence-based knowledge into existing and new support services through dissemination to community practitioners.

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.

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.

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 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.

This study examines the effectiveness of a comprehensive psychomotor development program combined with adapted football training for adolescents diagnosed with intellectual disabilities in Romania. Twelve participants, with a mean age of 17.42 years underwent a carefully designed intervention program spanning 36 weeks. The program aimed to enhance fundamental movement skills, integrate these skills into specialized football training, and culminate in a Unified 7-a-side Football Match. Results indicate significant improvements in psychomotor skills, including static and dynamic balance, pulse processing speed, and identified stimuli. Moreover, the intervention positively impacted social interaction among participants. Statistical analysis, employing Pearson Correlation Analysis and Paired Samples t-Test (One-Sample), compared assessments. Results revealed significant differences between pre- and post-intervention measurements, providing robust evidence of the program's efficacy. The positive correlations between physical parameters and social interaction highlight the interconnectedness between physical fitness and social engagement. Our findings suggest that improvements in physical abilities may contribute to increased social interaction among children with intellectual disabilities, underscoring the holistic nature of our intervention, which addresses multiple dimensions of development simultaneously. The findings underscore the holistic benefits of integrating psychomotor development and adapted football programs for adolescents with intellectual disabilities, emphasizing the importance of promoting inclusive physical activities for their overall well-being.

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.

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.

Digital literacy is the ability to use digital technologies to find, evaluate, create, and communicate information. People with disabilities benefit from having these skills because they allow them to access various opportunities in the digital world. To ensure that they can fully participate in the digital realm, digital literacy efforts should prioritize inclusive design principles. People with disabilities often rely on assistive technologies such as screen readers, alternative input devices, voice recognition software, or specialized hardware to navigate digital platforms. Digital literacy programs should not only focus on consuming content but also on creating and sharing it through documents, multimedia, and online platforms. Peer support and collaboration are important for enhancing their learning experience. Additionally, digital literacy programs address cybersecurity or online safety considerations specific to such people and provide them training and support for using assistive technologies effectively while educating them about potential risks, privacy settings, and safe online practices. This narrative review highights the meaning and levels of digital literacy, digital divide, history, before expanding on the quantative metrics in the available research on the theme of people with disabilities. Retrieved data from freely accessible internet search engines revealed 196 publications, including books, chapters in books, original research articles, proceedings of seminars, and reviews, which were classified into harvest plots to be analyzed by their format, timelines, topics, or themes, respectively. While results indicate an upward curve in the quantity of publications, especially in the post-millennium era, the topics addressed are unevenly distributed, leaving scope for the future to focus empirical and evidence based research on themes related to measurement as well as training on digital literacies for both teachers and students with disabilities. By promoting digital literacy in people with disabilities, we can bridge the digital divide and empower them to participate fully in today's increasingly digital society.

The health status of Hungary's population is unfavorable, with significant differences in health indicators not only compared to the EU15 but also to the Visegrad countries. Unfavorable health indicators can be disproportionate and particularly affect vulnerable groups, such as people with disabilities. The aim of the present study is to compare the health behaviour and indicators of adolescents with disabilities in Hungary with those of the HBSC survey in the Visegrad countries, and to compare the results of the Hungarian special needs adolescent population with those of the Visegrad countries. Eating habits are unfavourable for both the Visegrad countries and the young people we studied. Children with disabilities have significantly higher rates of schoolwork stress compared to other countries. Significantly higher prevalence of somatic complaints and worse mental well-being parameters higher in Hungary than in the other Visegrad countries. The results suggest that further interventions are needed in Hungary and that differentiated, professionalised health promotion is needed for young people with disabilities. The researchers propose to extend the research to adolescents with disabilities living in the Visegrad countries, on the basis of which a methodology for injury-specific health promotion could be developed, including through interna-tional interprofessional cooperation.

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.

Abstract: Background: This research aims to investigate the oral health status and needs of individuals with disabilities, a vulnerable population that is facing barriers in accessing and maintaining oral care. Methods: Data for this study were obtained through self-administered questionnaires distributed to individuals with disabilities who were receiving assistance from Helping Restore Ability and Neuro Assistance Foundation. The data were analyzed using statistical methods using SPSS and Microsoft Excel to identify patterns and associations related to oral health maintenance among individuals with disabilities. Results: The findings of this research indicate a high prevalence of poor oral hygiene, periodontal diseases, and untreated dental caries, risk factors of oral cancer among individuals with disabilities. Social determinants were found to have a significant influence on the oral health status of this population. The study also highlights the crucial role of caregivers in ensuring oral health maintenance. Conclusion: The results of this study underscore the urgent need for targeted interventions and improved caregiver awareness to address the oral health needs of individuals with disabilities. By addressing the disparities and barriers faced by people with disabilities in accessing dental care, we can mitigate the health, social, and economic burdens associated with poor oral health in this vulnerable population.

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.

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.

Background: The high prevalence of learning disabilities among children confirm that learning disabilities are surprisingly common. In absence of routine screening, many children still go undetected with a huge individual and family burden, while at the same time existing interventions are conflicting. The purpose of the study is to report on the design, implementation and evaluation of an innovative pilot intervention aiming to improve the quality of life of the family of students with specific learning disabilities. Method: For the purposes of this study, we used an experimental research design, using two groups (intervention and control). The intervention included four meetings with parents of children from the intervention group. To assess the quality of life of the students' family and the parenting style, the following measuring tools were used. They conducted at times and locations convenient to each participant. The instruments used to measure outcomes were two standard weighted questionnaires, the parenting style questionnaire and the family quality of life questionnaire. Findings: The results showed that the intervention program had significantly positive effects on both parenting style and children’s quality of life. Specifically, a decrease in parental authoritarianism and an increase in parental support were observed. Also, an increase in child-parent interaction, emotional and general well-being was found. Conclusion: The intervention program did improve the quality of life of the children by increasing all the variables related to the measurement of their quality of life.

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.

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.

The assessment of disability related costs among children remains a largely under-researched subject with related questions rarely included in surveys. This paper addresses this issue through a unique mixed methods study conducted in the Philippines combining a nationally representative survey and in-depth interviews with families and health professionals. To quantify the extra costs associated with disability the research used the standard of living approach, whereby expenditure levels of families with children with and without disabilities are compared in relation to different measures of living standards. The results find consistent evidence of high extra costs among households that have children with disabilities and point to health expenses as the leading source. Using an asset index as the indicator of living standards, a child with disability is estimated to require between 40 and 80% extra expenditure to reach the same living standard of other children. However, the size of extra costs is substantially higher when the measure of standard of living relies on a broader set of deprivations. In such cases, higher estimates of extra costs are likely to be the result of the lack of an inclusive environment. Critically, this points to the need not only to provide financial support, but also inclusive services, especially in health and education.

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.

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.

In this paper, we analyze data from the 2012 Encuesta de Integración Social y Salud (Social Integration and Health Survey) of the Instituto Nacional de Estadística (Spanish National Institute of Statistics) to obtain profiles created by combining disability, poverty and social exclusion. We hypothesize that the probability that people will experience social exclusion increases if they have a disability, chronic illness or limitation in conducting everyday activities, and that this probability is greater for women than for men. To conduct our analysis, we constructed a social exclusion model based on a series of social determinants that acts as a dependent variable. In this context, social exclusion is understood to go beyond the concept of financial poverty. We performed bivariate analyses, in which we calculated the Odds Ratios (OR) for certain variables considered to be predictors of social exclusion. We also performed a means comparison test and an ANOVA test to observe differences between individuals with recognized disability and those without. Finally, we conducted logistic regression analysis to determine which vulnerability profiles are most likely to experience a situation of social exclusion. We also discuss the limitations of our study, and suggest areas in, which the relationships between health, social exclusion and disability can be further investigated.

The existence and family living with a sibling with a disability implies a dynamic and a structure with meaning to children and their circumstances. However, a sibling that accompanies, helps, or takes part in family life, usually is lower analysis compared with children with disabilities. The goal of this work is to analyze the behavior of children before siblings with disabilities. Following two children’s opinions in this context. It is intended to make some analysis about the children's behavior towards their sibling, and finally, seek reflection on the structure of family relationships.

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.

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.

: This study investigates the workplace experiences of mothers of children with intellectual disabilities. These mothers may face challenges in balancing motherhood, disability, and employment. Therefore, it is important to understand the challenges they are facing and the mechanisms they use to cope with caregiving and work responsibilities in managing the stress that develops throughout this ongoing experience. The purpose of this research was to investigate the workplace experiences of single mothers of children with intellectual disabilities. A review of the literature was conducted. For this research, a qualitative methodology was adopted to gain an in-depth understanding of the mothers’ experiences. A narrative method was used for data collection. Thematic analysis was utilized to obtain a clear understanding. The findings suggest that mothers of children with intellectual disabilities face difficulties in balancing between caregiving for their child with a disability and their job responsibilities, in addition to showing that there is a lack of flexibility in the workplace, less awareness from colleagues and bosses of the challenges these mothers face, and a lack of organizational legislation that supports these mothers. According to our findings, workplaces should provide assistance to mothers of children with intellectual disabilities in order to ensure that they are not disadvantaged in their employment.

This study examines the effects and durability of postural exercise on gait kinematics in people with intellectual and developmental disabilities (IDD). Thirty-four men with mild IDD were assigned to either the training group (n = 19) or the control group (n = 15). The TG performed the intervention over 8 weeks, 3 days/week, 1 h/day. The results indicated that the intervention program had a significant effect on some variables of gait. Also, in the TG between post-test and follow-up, there was no significant difference, which indicated that gait variables were maintained by the TG one month after the end of the program. This study provides evidence that postural exercises can be used as an effective intervention for improving gait in individuals with IDD.

Students with disabilities are often framed as “the problem” and have limited opportunities to engage in standards based mathematics, leading to persistent underachievement. In this paper, we investigate a research divide between mathematics educational research for students with and without disabilities, a divide with significant differences in the theoretical orientations and research methodologies used to understand learners. Based on an analysis of 149 mathematics educational research articles published between 2013 and 2015, we found significant differences between articles focused on learners with and without disabilities. For those with disabilities, mathematical problem solving was understood primarily from behavioral and information processing theoretical perspectives, while for those without disabilities, problem solving was understood primarily through constructivist and sociocultural perspectives. While 86% of research on problem-solving including students with disabilities was quantitative, only 35% of research on students without disabilities was quantitative. Fifty percent of problem-solving research on students without disabilities was qualitative, compared to only 6% of research on students with disabilities. Problem solving, then, is studied in very different ways for learners with and without disabilities. Students without disabilities are studied through close analysis of learning, often individual. Students with disabilities are most often studied quantitatively, in groups, with little analysis of individual thinking. By offering only a limited range of methods and theoretical orientations, this research divide reifies deficit constructions of students with disabilities.

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.

Background and Aims: Screening and assessment of cognitive changes in adults with Intellectual Disabilities, mainly Down Syndrome (DS), is crucial to offer appropriate services to their needs. We present a systematic review of the existing instruments assessing dementia, aiming to support researchers and clinicians’ best practice. Methods: Searches were carried out in the databases Web of Science; PubMed; PsycINFO in March 2019 and updated in May 2020. Studies were selected and examined if they: (1) focused on assessing age-related cognitive changes in person with ID; (2) included adults and/or older adults; (3) included scales and batteries for cognitive assessment. Results: Forty-eight cross-sectional studies and twenty-six longitudinal studies were selected representing a total sample of 5,851 participants (4,089 DS and 1,801 with other ID). In those studies, we found 38 scales, questionnaires, and inventories, and 14 batteries for assessing cognitive and behavioural changes in adults with DS and other ID. Conclusion: The most used instrument completed by an informant or carer was the Dementia Questionnaire for Learning Disabilities (DLD), and its previous versions. We discuss the strengths and limitations of the instruments and outline recommendations for future use.

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

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.

This study aims to assess several morphofunctional parameters in female teenagers with and without intellectual disability to characterise normality and its disturbances. More precisely, the purpose was to examine the factors contributing to the differences in body composition elements between groups, such as the physical activity level. To evaluate these parameters, we used bioelectrical impedance analysis technology (BIA), most commonly used in studies concerning body composition because it is non-invasive, quick, with high data fidelity; it can be easily moved to various locations and applied straightforwardly among populations with diverse types of intellectual disability. Our research sample comprised 212 subjects (boys and girls) aged 17.1±0.6, divided into six groups by gender and type of intellectual disability. The study found influences of physical activity level on body composition components. In addition, we report a highly significant relationship for p<0.05, body mass index and the independent variable (No. of min./week) in some groups.

Aim: The aim of this study was to assess the performance of the SF-36 health survey (SF-36) in a sample of subjects with physical disabilities. Material and Methods: 305 patients recruited using the convenient sampling method from September 2019 to March 2020 in Kermanshah, Iran. Another similar 300 patients were selected to assess the confirmatory factor analysis (CFA). Results: The Cronbach’s α ranged from 0.70 to 0.93, and intra-class correlation coefficients from 0.71 to 0.88; and with the no ceiling and floor effect for two main subscales. Convergent validity was supported by moderate to good correlation between SF-36 subscales and Moorong self-efficacy subscales (r= 0.25- 0.53). The SF-36 divergently validated with HADS total score and subscales (r= -0.24- -0.65), concurrently validated with its subscales (r=0.49- 0.88), and physically discriminated between persons with different level of disability (t-test: p<0.001). Factorial analysis identified seven factors, confirmed with second-order in another 300 samples (chi-square (χ2/df) = 2.61(p < .001); RMSEA = 0.07 (90% CI = 0.07–0.08); AGFI=0.75; GFI = 0.78; CFI= 0.85; and NFI = 0.78). Conclusions: the SF-36 is a reliable and valid tool in physical disables. However, SF-36 shows insufficient eight-factor validity. Future studies should focus on evaluating other psychometric properties, such as sensitivity to change in subjects with physical disabilities.

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%.

The aim of the paper is to assess the accessibility for people with disabilities of flagship cultural attractions, that is museums located in Krakow, a city with a rich historical heritage entered onto the UNESCO World Cultural and Natural Heritage List and distinguished by the Access City Award. The research shows to what degree the museums have been adapted to receive visitors with disabilities. Assessment was also conducted of access to the museums’ websites, and the possibility for virtual tours of their collections. The research results indicate that Krakow museums are well-prepared for receiving people with physical disabilities and seniors, but are less well adapted to receiving visually impaired and blind people, and hearing impaired and deaf people. Analysis of the museums’ websites shows that they are well-prepared in terms of compatibility and comprehensibility, but that they received a worse assessment for their perceivability and functionality. Visitors to the museums are principally adults, adolescents and seniors. People with disabilities make up 10% of all visitors.

Comprehensive sexuality education (CSE) is an important framework utilized worldwide to provide students and young people with accurate, affirming, and socially conscious sexuality education. However, there is still a lack of CSE curricula in school contexts that is relevant for students with various disabilities. This article takes the Ontario, Canada context as an example of where and how CSE can improve to be more inclusive for students with disabilities. This article reviews the current context of CSE in Ontario, Canada, including its controversies while providing recommendations for meeting the needs of students with various disabilities, including psychological, intellectual, and physical disabilities. This article aims to provide recommendations that are relevant for scholars, researchers, and policymakers in various international contexts for improving CSE for students with disabilities in schooling.

At least 15% of the population in the world has some type of disability. Unfortunately, this population has the problem of facing various accessibility barriers, to which technological barriers are also added. One of the most relevant obstacles is the one that arises as a result of the development of the technology itself when using the Information and Communication Technologies (ICT). Therefore, the objective of this article is to review the main uses of the Semantic Web tools and to group them together in order to be able to propose the design and construction of more personalized and flexible systems, which allows to help people with disabilities to perform some type of activity using ICT, in this way, knowledge can be modeled in different domains related to people who have some type of disability, using ontologies, and some ontological models can be reused for various types of disability depending on the case study. The usefulness of this study is to reveal that with the models presented, it is possible to construct a Meta ontology that includes some or all areas of disability.

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.

The United Nations Convention on the Rights of Persons with Disabilities (hereafter CRPD) has provided a radical imperative for the reform of mental health and capacity legislation around the world. The interpretation of the CRPD has been controversial, ranging from complete abolition of detention, forcible treatment and substitute decision making, to acceptance that elements of these measures need to be retained but based on non-discriminatory criteria, additional safe-guards and a comprehensive shift towards supported decision-making. While the potential ef-fects of the CRPD on mental health social work and social work generally are considerable given their shared commitment towards social justice, to date there has been no review of research evi-dence exploring their relationship. In addressing this knowledge gap, this study held a prelimi-nary discussion with practitioners and academics at the European Association of Social Work Mental Health Special Interest Group in Amsterdam 2022, followed by a scoping literature re-view on the question: What impact, if any, has the CRPD had on social work practice? The review produced four main findings: impact on legislation; positive impact on practice; limited impact on practice; and impact on social work education and research. In sum, while there were some positive indications of social work and mental health social work practice being influenced by the CRPD, these were scant. Barriers to change included tendencies among some social workers to practise substitute decision-making, in part related to resourcing and policy contexts, and under-standings of disability aligned to individualised/medical rather than social perspectives. The results indicate that legal reform on its own is insufficient to impact social work practice, and that realising the potential of the CRPD will necessitate good quality training, as well as improv-ing social workers’ knowledge of the human rights of people with mental impairment.

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.

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.

Worldwide, 200 million children experience disability, with the vast majority living in low and middle-income countries. The United Nations Convention on the Rights of the Child (CRC) places great importance on the rights of all children for the opportunities for survival, growth, health, and development. A subsequent document, the UN Convention on the Rights of Persons with Disabilities (CRPD) identifies children with disabilities as rights bearers, who should be considered in all policies and programming worldwide. Nigeria, in 1991 and 2010 ratified the CRC and the CRPD respectively. Nonetheless, knowledge of the extent to which their disability and child-directed policies are considered by these two key conventions remains limited. This study examined the extent to which Nigeria’s current disability and childhood policies have integrated the two normative frameworks from the UN. Using a structured search of databases and Nigerian federal and state government websites, we conducted a policy review to identify their disability and child-related disability policies. We also included the CRC and CRPD reports submitted by the Nigerian government to the United Nations Office of the High Commissioner for Human Rights (OHCHR) (2008 &amp; 2010 cyclical year). A thematic analysis, based on the CRC and CRPD report, identified the following six themes: participation, support systems, awareness-raising, factors associated with adherence to the CRC, laws &amp; rights, and services. The review showed that the available Nigerian disability policies were federal, with some state policies which aligned with the CRC and CRPD. Also identified was the lack of disability policies specific to children and their families. We concluded that, to ensure proper inclusion of the rights of all children, including those with disabilities, in Nigeria there is a need for a more optimal uptake of recommendations of the CRC and CRPD as laid out by the UN.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

Conclusions: The mortality rate of people with disabilities from all causes did not significantly increase compared to the pre-COVID-19 era. However, COVID-related deaths among people with disabilities increased significantly.

X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing molecular-level data, gaining attention for high-resolution imaging. However, increased sensitivity with benchtop X-ray sources raises radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized SCUNet model for noise reduction in low-concentration XRF images. Compared to higher-dose images, our method’s effectiveness is evaluated. While various denoising techniques exist for X-ray and CT, few address to XFCT. The DL model is trained and assessed using the augmented data, focusing on background noise reduction. Image quality is measured using PSNR and SSIM, comparing outcomes with 100% X-ray dose images. Results show the proposed algorithm achieves high-quality images from low-dose and low-contrast agents, with a maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms BM3D, NLM, DnCNN, and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.

Every subset S of a Cartesian spaces Rd, endowed with differen- tial structure C∞(S) generated by restrictions to S of functions in C∞(Rd), has a canonical partition M(S) by manifolds, which are or- bits of the family X(S) of all derivations of C∞(S) that generate local one-parameter groups of local diffeomorphisms of S. This partition satisfies the frontier condition, Whitney’s conditions A and B. If M(S) is locally finite, then it satisfies all definitions of stratification of S. This result extends to Hausdorff locally Euclidean differential spaces.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

(1) Background: This paper details the development process of two assistive technology products (ATs) implemented in the PAID project, aiming to promote the social, educational, and labor inclusion of people with disabilities across 10 municipalities of Antioquia, Colombia. (2) Methods: Applying Design Thinking (DT) methodology, including stages of empathizing, defining, ideating, prototyping, and testing, alongside a demographic study where specific needs of people with disabilities (PwDs) were identified. (3) Results: As a result, the construction process of two specific cases of assistive technology products (ATs) is detailed, including finite element simulation, mechanical testing, and delivery to end-users. Additionally, workshops for assistive product fabrication conducted in select municipalities of the project are highlighted. (4) Conclusions: the information gathered guided the development of the ATs, focusing on their features on accessibility and ease of replication. The primary objective is to facilitate the participation of PwDs in educational environments, thus promoting inclusion and accessibility. This article describes the results of two of these ATs; however, it is worth noting that this process was performed for 112 ATs delivered through the duration of the project.

Access to maternal healthcare services is a challenge in most low and middle-income countries. South Africa is one of the countries striving to improve the accessibility of maternal healthcare services. Although South Africa has put some interventions to improve the accessibility of maternal healthcare services, vulnerable women including women with disabilities are still facing numerous challenges when trying to access maternal healthcare services. The aim of the study was to explore the experiences of women with disabilities in the province of KwaZulu-Natal in South Africa in accessing public maternal healthcare services. Twelve women with disabilities (four with physical impairments, four with hearing impairments and four with visual impairments) were interviewed for this study. Data were transcribed verbatim and analysed utilising the Framework of Assessing Access to Maternal Healthcare Services by Peters et al. 2008. The study found that narrow passages and information in inaccessible formats were a challenge for women with visual impairments. The women with hearing impairments had challenges in communication as most facilities did not have sign language interpreters, negative attitudes of health care workers and being ignored when they asked for help. The women with physical impairments encountered inaccessible buildings, narrow passages, small consultation rooms and equipment which is not adjustable such as beds and scales.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

The present study comprises the second part of our previous work that dealt mainly with the phytochemical and physicochemical characterization of green coffee, clove, cinnamon/clove and nutmeg ethanolic extracts of grape origin. In the present study, we focused on producing a discriminating model concerning green coffee, clove, cinnamon, cinnamon and clove, and nutmeg fine powders based on multivariate analysis of variance and supervised learning on data of volatile compounds analysis, carried out with solid phase dynamic extraction in combination with gas chromatography/mass spectrometry. Results showed that 7 volatile compounds namely ethylene, methanol, 3-methylpentane, ethyl acetate, 9-hexadecen-1-ol, toluene, and methyl acetate could differentiate the investigated samples resulting in a 100% classification rate using the cross-validation method of linear discriminant analysis. Results were further confirmed using partial least squares regression analysis. The study contributes to the typification of green coffee, cinnamon, clove, cinnamon and clove and nutmeg, based on selected volatile compounds, and provides further support to the literature by means of possible adulteration given the statistical models developed.

Children are inherently curious about everything they encounter, making the early years a perfect time for STEAM (science, technology, engineering, arts, and math) investigations. An outdoor learning environment can influence STEAM concept formation of preschoolers with intentional design that offer STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3-5 years old). This scoping review intends to evaluate the existing knowledge regarding the physical factors that contribute to STEAM learning affordances in an outdoor environment for children aged 3 to 5. The review included studies from the last twenty years. This scoping review was conducted in accordance with the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. Through the synthesis of information from those 31 studies, a list of STEAM learning behaviors of children and STEAM activity-supportive settings were identified that may positively influence preschoolers' STEAM concept development.

Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

We explored the impacts of the remote and return to in-person work periods on sleep and well-being as reported by faculty (n = 22) and non-teaching staff (n=21) with and without disabilities. Our results show that contrary to expectations, the Covid-19 remote teaching/working period resulted in better sleep, as well as greater well-being, than the return to in-person. With respect to sleep, faculty members had slightly more negative outcomes than staff, most evident in heightened anxiety and work aspects. Faculty with disabilities had somewhat worse sleep and well-being during the remote period than faculty without disabilities. During the return to in-person work, both faculty and non-teaching staff reported more negative than positive sleep and well-being outcomes. In particular, during the in-person period faculty members experienced slightly more negative sleep outcomes related to anxiety and work, while staff members experienced slightly more negative sleep outcomes related to the need to commute and lifestyle. Our findings show that there were benefits and disadvantages to both remote and in-person work periods, suggesting a hybrid work schedule should be considered in more detail, particularly as an optional reasonable accommodation for faculty and staff with disabilities. Our study highlights that training to keep faculty abreast of the latest technological innovations, ways to promote work-life balance and steps to remedy classroom size and building ventilation to prevent the spread of disease all need urgent attention.

Coronavirus disease 2019 restrictions impacted Canadians' daily living, especially those at higher risk of compromised health conditions. This study aimed to describe the physical, psychological, and social well-being of adults with disabilities, and older adults from May to June 2020. An online survey was used to administer standardized measures of mobility, anxiety and depression, boredom, resilience, technology readiness, social support, social networks, and participation. Seventy-two participants were recruited, with a mean age (SD) of 61.2 (13.8). 69.4% of participants had a disability, and 51.4% were older adults. 27.8% and 16.7% of participants exceeded the anxiety and depression cut-off scores, respectively. Boredom and restriction in participation were experienced by 76.4% and 80.1% of participants, respectively. Participants' mean (SD) resilience and life space scores were 72.4 (14.0) and 51.9 (24.0), respectively. Individuals with disabilities have comparatively higher mean anxiety (5.5 to 3.3), depression (4.8 to 2.9), and boredom (92.2 to 72.3) scores than those without. Individuals with a disability had lower resilience (69.9 to 78.0) and felt more life space restrictions (45.4 to 67.6). Our findings revealed issues with anxiety, boredom, participation, and life space activity. This information may provide supporting evidence when creating policies to mitigate existent health and social inequities.

The aims of this study were: (1) to analyze the level of agreement of a sample of Italian people with the rights of people with physical and psychical disabilities (PwPHDs and PwPSYDs) to have satisfying sexuality, to marry, to adopt a child; (2) to inquire if PwPSYDs were subject to less recognition than PwPHDs; (3) to verify if socio-demographic characteristics, such as age, sex, education, occupation, geographical origin, relational status, sexual orientation, and religiosity, associated with being against these sexual and parenting rights (SPRs). An online anonymous questionnaire inquired the level of agreement or disagreement with statements regarding the SPRs of PwPHDs and PwPSYDs. 973 participants, aged 18 – 84 years (71.1% females) were considered for analyses; At least 7 out of 10 participants declared in favor of the SPRs of PwPHDs, while the SPRs of PwPSYDs were always subjected to higher underrecognition. Religiosity almost invariably associated to being against the SPRs of PwDs. Being male, of higher age and lower education also associated with lower recognition. A better identification of the less tolerant respondents and of the less recognized categories may allow for specific strategies for promoting the recognition of the SPRs for PwDs.

Classical Arabic poetry is a form of Arabic literature, and almost every piece of Arabic poetry follows one of the 16 meters. The rhythmic framework of the poems or the verses is called meters. The deep learning (DL) model was implemented using TensorFlow in this work using a large dataset of Arabic poetry. The character level encoding was used to convert text to integers for classifying the full-verse and half-verse data. The study evaluates the data without removing diacritics from the text. The train-test-split method with a 70-15-15 split was employed for classification. 15% of the total data was considered unseen test data for all models. The work was conducted with multiple deep learning models, including the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional LSTM (Bi-LSTM). The Bi-LSTM model shows the best accuracy of all the models specified, with 97.53% for full-verse and 95.23% for half-verse without removing diacritic data.

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

This paper used the decolonial theory to analyse provision of Assistive Technology and assistive devices at an institution of higher education in South African. It was an empirical study, in which data were collected through interviews with students with disabilities and the Disability Rights Centre staff members. The paper sought to understand the invisible hidden implications of provision of Assistive Technology and assistive devices. The finding was that it is students with disabilities who were provided with Assistive Technology and assistive devices at the institution. The institution provided them through the Centre, to support their learning. However, this way of provision was found to be stigmatising and segregative. Furthermore, while the provision on one hand enabled students with disabilities’ learning, on the other, it constrained it. The argument of the paper is that when provision of Assistive Technology and assistive devices is for a particular group of students it defeats the whole purpose for it is intended, and could hinder rather than promote learning. It is hoped that the paper will contribute to contemporary debate on provision of Assistive Technology and support services for people with disabilities in low resource settings, from a South African context specifically, and in higher education broadly.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.