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Abstract In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving.

To fill a gap in current literature, this paper analyses the impact of human factors on the operational performance of innovation hubs in the SAARC Nations and the European Union. Upon conducting an extensive examination of academic literature, four primary classifications of human factors were determined: organizational factors, individual attributes, the nature of a task and working environment. This research offers a cross-sectional analysis of these factors and their implications on innovation centres in the culturally and economically diverse domain of SAARC Nations and the European Union. The findings suggest that organizations functioning in the context of Innovation hubs in SAARC Nations, where centralized authority and value correctness is mostly emphasised on, face challenges in order to foster employee engagement and creativity. However, the innovation hubs of the European Union receive treatment from a more flexible and tolerant approach which is backed by progressive technology and is incredibly sensitive to concerns touching on ergonomics and safety of the working environment. This research highlights High performing, High committing, and High involvement management, management techniques to manage human factors to enhance operation performance. Adaptive and individual approach to the management of human variables, based on the differences in cultural and economic conditions of each region, is essential for effective development of the innovative substance and competitive advantage, the paper states. The results obtained from this study offer important guidance for the policymakers, managers and practitioners who are involved in managing and creating innovation hubs. It has provided a framework of how the organisation’s operation performance may be improved through effective management of human resources.

The rapid development of digital technologies, digital transformation, business innovation, and the emergence of such technologies as artificial intelligence, blockchain, the Internet of Things provides significant potential for transforming business processes and creating new innovative models. The trend analysis conducted in this article (using the Google Trends tool) around the world for 2009-2023 showed that public interest in “business innovation” was almost constant until 2021 (according to the GT Scale, it ranged from 0.25 to 0.5). After 2021, it increased (according to the GT Scale, it was more than 0.5). At the same time, public interest in “digital transformation” has significantly increased since 2014, reaching almost maximum values in 2020-2023 (0.75 - 1.00 on the GT Scale). This confirms a growing need in society to acquire new knowledge related to the ability of businesses to adapt to digital transformation, including through creating innovative developments. Unlike trend analysis, bibliometric and comparative analyses identify trends in developing interest in this topic not among ordinary citizens - users of the Google search platform ‒ but among scientists who publish the results of their research either in monographs or in authoritative peer-reviewed scientific journals indexed by leading scientometric databases. The comparative analysis conducted in the article using the Google Books Ngram Viewer (Google) tool, which displays the frequency of references to relevant terms in English-language books for 2009-2019 worldwide, showed similar trends: the persistence of scholars' attention to business innovation, while their interest in digital transformation is growing: in 2009-2013 - a slight increase, in 2013-2019 ‒ a significant increase. In the bibliometric analysis, a five-level mechanism for limiting the research sample was applied: only English-language and most recent (for 2018-2022) scientific articles indexed by the Scopus database in the subject area of Business, Management and Accounting were subject to analysis. Based on these limitations, the research sample for the keyword “digital transformation” was reduced from 12131 to 1700 papers, and for “business innovation” from 865 to 70. The analysis of the published articles, their views and citations, carried out using the SciVal tool, fully confirmed the conclusions drawn from the trend and comparative analysis results. In terms of digital transformation, the leaders in terms of the number of papers and the number of views are Germany, Italy, the UK and the US; in terms of citations - the UK, Germany, the US, and in terms of individual scientists - Brazil and Germany. As for the topic of business innovation, the United States is the undisputed leader in all these parameters, followed by China and the United Kingdom. Some scientists from Germany and Spain also attract the attention of the academic community (in terms of views and citations). Changes in trends and research priorities in each of the studied topics were identified and described using SciVal (analysis of the reference dynamics to the top 50 keywords in articles for 2018-2023). For example, in 2023, compared to 2022, scientific interest in issues related to “value creation” (+61.5%), "incumbents" (+66.7%), “service economy” (+83.3%), “digital marketing” (+75.0%), and “design thinking” (+150.0%) increased the most. VOSviewer software made it possible to cluster the scientific work by thematic areas. The largest cluster on digital transformation relates this concept to small and medium-sized businesses and technologies, and the largest cluster on business innovation relates it to the development of small and medium-sized enterprises and the protection of property rights.

This research paper conducts an extensive exploration of onshore, offshore, and floating offshore wind turbines, pivotal components in the landscape of sustainable energy generation. The study thoroughly investigates various wind energy systems, shedding light on their distinct attributes, advantages, and limitations. The examination of onshore wind turbines encompasses aspects such as installation procedures, size considerations, wind dynamics, accessibility challenges, and visual impact assessments. Similarly, the analysis of offshore wind turbines covers considerations such as location selection, installation complexities, size variations, wind conditions, environmental impacts, and visual aesthetics. The paper also delves into the emerging technology of floating offshore wind turbines, highlighting their unique features, advantages, and technological advancements. A thorough comparative examination is conducted to assess the economic, environmental, and social aspects of onshore, offshore, and floating offshore wind turbines. The research paper concludes by offering insights into policy considerations, addressing research gaps, and outlining prospects for the seamless integration and advancement of these diverse wind energy systems. This comprehensive review aspires to contribute to the existing knowledge base in sustainable energy generation, fostering informed decision-making for a greener and more sustainable future.

PURPOSE: This study aims to identify the differences and similarities in the innovation systems of developing vs. developed countries that influence SMEs’ adoption of Fourth Industrial Revolution (4IR) technologies. There is a notable absence of comparative research between National Innovation Systems (NIS) of developing and developed countries. Additionally, the current scholarly conversation lacks a holistic view of NIS. Our study aims to fill these gaps by employing Lundvall’s framework to explore both developed and developing countries’ systems comprehensively. METHODOLOGY: The data was collected through a Systematic Literature Review, identifying a total of 695 publications from SCOPUS, Web of Science (WoS), and ProQuest. The PRISMA process was adhered to, resulting in 32 papers undergoing quality evaluation using Gough’s ‘weight of evidence’ guidelines. Twenty-nine primary papers were selected, comprising twelve from developed countries, another twelve from developing countries, and the remainder from both categories. Using Qualitative Meta-synthesis (QMS) with ATLAS.ti, a systematic alignment of codes with research inquiries pertaining to NIS ensued, revealing a multifaceted spectrum of findings across these scholarly investigations. FINDINGS: We found that there are similarities and differences between the innovation systems of developed and developing nations. The similarities include the intra-firm interactions taking place between managers and workers, inter-firm relations between the SMEs and Academia and other SMEs, as well as the role of the government in providing funding and regulation (albeit at significantly varying degrees). The most significant differences observed were in the funding mechanisms, the role of the government, and the R&D systems. It was found that governments in developed countries provided SMEs with substantial incentives, tax credits, and subsidies to adopt 4IR technologies, which appears to positively impact the adoption rate. We conclude by developing a conceptual framework for the NIS necessary for the adoption of SMEs’ 4IR technologies in developing countries. IMPLICATIONS: This study contributes to the literature on innovation systems by examining the NIS of both developed and developing countries. This analysis allows us to gain deeper insights into how specific aspects of each country (developed or developing) affect (positively or negatively) SMEs’ adoption of 4IR technologies. Practically, it informs governments in developing countries on which aspects to focus on in their NIS to increase the rate of the adoption of 4IR technologies by SMEs. ORIGINALITY AND VALUE: A distinctive aspect of this study lies in the creation of a comprehensive conceptual model delineating the essential components of the innovation system pivotal for the successful integration of 4IR technologies within SMEs. This model is designed to serve as a practical tool for governments in developing countries, providing a structured framework to facilitate and enhance the strategic development of their innovation landscapes.

The purpose of this paper is to conduct a comprehensive study of economic security in the EU Member States and Ukraine, to identify differences and to propose targeted policy interventions based on these differences. Methodology. The study uses a quantitative strategy that assigns numerical ratings to countries based on their performance against predefined thresholds for various economic security indicators. These indicators include industrial security, demographic security, energy security, foreign trade security, investment and innovation security, macroeconomic security, food security, social security and financial security. The study uses data from a variety of sources and applies a ranking system to reflect the level of economic security in each category. Results. The analysis revealed significant differences in the levels of economic security between EU Member States and Ukraine. The main findings include differences in agricultural efficiency, healthcare system efficiency, renewable energy consumption, trade balance, research and development expenditure, and socio-economic factors. For example, countries such as Belgium and Ireland have high yields, while Cyprus and Romania have much lower yields. Life expectancy and the quality of healthcare also vary: Spain and Sweden lead the way, while Bulgaria and Ukraine lag behind. Practical implications. The findings suggest the need for targeted policies and technological advances to improve economic security. For the younger generation, marketing strategies based on brand equity and exclusivity can continue even during economic downturns. For the older generation, strategies that focus on value for money or lower prices may be more effective. Understanding these metrics can help businesses align their strategies with national economic priorities and identify potential areas for investment and growth. Value / Оriginality. This study contributes to the existing literature by providing a detailed comparative analysis of economic security in the EU Member States and Ukraine, filling a gap in the understanding of the multifaceted aspects of economic security in these regions.

This article provides a timely intervention to debates and scholarship about the professional regulation of social work and social care. In Ireland, the recent commencement of the statutory regulation of social care by CORU – Ireland’s multi-professional health regulator – offers a watershed opportunity for learning. Social work has been separately regulated by CORU for over a decade, while the social care work register opened in November 2023. We conduct a comparative content analysis of the standards of proficiency for social work and for social care work. Albeit with different historical origins, regulation has now provided these professions with distinctive, as well as shared professional ‘benchmarks’, that may shape the trajectories of their future roles and training. We consider the approach CORU takes to regulation, in terms of the standards and how these differentiate or align the two professions. The novel contribution of the paper lies in its key findings. Namely, that: the format of the standards of proficiency framework itself warrants further consideration; insufficient attention is paid to empathy and emotions across standards; there is an absence of a considered approach to the influence of socio-economic factors on practice; there are variations in the emphasis placed on relational versus socio-political dimensions in practice; and finally, there are differences in the importance paid to ‘critical understanding’ across the standards. These areas particularly illuminate how CORU frames and interprets the nature of both professions. It is concluded that the standards of proficiency for both social care work and social work have much shared terrain, interspersed with infrequent but striking differences, indicative of the many commonalities and overlap in occupational spheres for both professions.

The primary objective of this research paper is to conduct a comprehensive investigation into the process of developing briefs for Public–Private Partnership (PPP) projects. The study aims to outline the fundamental stages, standard processes, and critical decision points involved in this process. This is achieved through a comparative analysis of briefing frameworks used in three countries that are among the top in the PPP Market Maturity chart: The United Kingdom, Australia, and Canada. The paper discusses the stages and decision points within these countries’ briefing processes, highlighting their challenges, similarities, and differences. It emphasizes the fundamental role of effective communication and coordination in PPP projects, which involve multiple stakeholders. By examining these interconnected activities and analyzing the phases, stages, and key processes constituting the briefing process, the study provides valuable insights into the PPP briefing processes in these nations. The paper concludes by proposing a conceptual process framework for brief development in PPP projects, which will be further tailored for adaptation in the PPP market in the United Arab Emirates.

This paper presents an experimental comparison of two types of Li-ion battery stacks for low-voltage energy storage in small urban Electric or Hybrid Electric Vehicles (EVs/HEVs). These systems are a combination of lithium battery cells, a battery management system (BMS), and a central control circuit—a lithium energy storage and management system (LESMS). Li-Ion cells are assembled with two different active cathode materials, nickel–cobalt–aluminum (NCA) and lithium iron phosphate (LFP), both with an integrated decentralized BMS. Based on experiments conducted on the two assembled LESMSs, this paper suggests that although LFP batteries have inferior characteristics in terms of energy and power density, they have great capacity for improvement.

Relevance. Enhancing the development of the machinery manufacturing sector significantly contributes to realizing sustainable economic development goals, thereby fostering sustainable growth opportunities across various scales, ranging from local to global. The active advancement of the machine-building sector, which serves as the foundation for sustainable societal progress, demands careful consideration of key factors for the effective operation of machinery manufacturing enterprises with investments being one of the foremost. This highlights the imperative for comprehensive research into the tangible impact of investments in securing the sustainable growth of machine-building businesses. Objective. The paper targets the conduct of analytical research on investment practices and approaches adopted by machine-building enterprises in European Union countries to specify the key investment factors for their sustainable development. Methods. The research is based on both general scientific and specialized methods of economic theory, including methods of theoretical synthesis and comparative analysis. Throughout the research, statistical data from the European Union regarding the operation and performance of machinery manufacturing enterprises were utilized and processed using analysis methods, including comparison, grouping, calculation of averages, and absolute growth. Furthermore, a rating methodology was implemented to identify European Union member states exhibiting the most favorable indicators about the status and dynamics of the machine-building industry. Results. A comprehensive analysis of contemporary scholarly advancements regarding the examination of the role of investments and effective methodologies for their implementation within the machine manufacturing industry was conducted. A grouping of 22 European Union member states was undertaken based on the criteria of the number of machine-building enterprises and indicators of its absolute growth over a ten-year temporal span. The 22 EU member states are grouped by the parameters of the number of machine-building enterprises and their absolute growth over a tenyear period. The average revenue and gross income per enterprise in the selected countries of the European Union’s machinery sector were studied, and based on the results, a ranking was conducted. Utilizing the established ranking of the top selected countries, a comprehensive analysis of key investment indicators was conducted, focusing on parameters that include investments in tangible assets, machinery and equipment, as well as human resources. An overview of investment practices by machinery manufacturing enterprises was undertaken, resulting in the identification of their respective strengths and weaknesses.

Abstract Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

Dariah.lab is a research infrastructure created for digital humanities, consisting of state-of-the-art hardware and dedicated software tools. One of the tools developed for digital musicology is Timbra, a web-based application for conducting research on sound timbre. The aim was to create an easy-touse online tool for non-programmers. The tool can be used to calculate, visualise, and compare different timbre characteristics of uploaded audio files and to export the extracted parameters in CSV format for further processing, e.g. by classification tools. The application offers extraction and visualisation of scalar features such as zero crossing rate, fundamental frequency, spectral centroid, spectral roll-off, spectral flatness, band energy ratio, as well as feature vectors (e.g. chromagram, spectral contrast, spectrogram, and MFCCs). An interested user can compare selected sound characteristics using various types of plots and run dissimilarity analysis of timbre parameters by means of 2D or 3D multidimensional scaling (MDS). The paper showcases potential applications of the tool based on presented case studies. In terms of implementation, the calculations are performed at the backend Django server using Librosa and standard Python libraries. Dash library is used for the frontend. By offering an easy-to-use tool accessible anytime and anywhere through the Internet, we want to facilitate timbre analysis for a broader group of researchers, e.g. sound engineers, luthiers, phoneticians, or musicologists.

The paper deals with a new method of innovative assessment of the supported effects and ecosystem services of forests using the actual amount of their increased costs. Among the most important research findings and significance of the new method is that it contributes to the body of knowledge on valuation and payment for ecosystem services. The focus of the research task is to help address the current difficult economic situation of forest owners resulting from the impacts of climate change. The study aims to determine what work quantifies the amount of compensation for the provision of supported ecosystem services that is sufficient for owners by using hypothesis testing. The evaluation of the supported effects was carried out in a case study in the area of the Chrudim-Podhůra Recreational Forests in the Czech Republic. The results of the evaluation were quantified in two experimental spreadsheets of the results, a comparative analysis of the evaluation results was performed, and hypothesis testing was conducted. The innovative methodological approach to the assessment of supported effects can be easily modified and used to assess the actual amount of increased costs of other ecosystem services of the forest.

Facial emotion recognition (FER) is the technology or process of identifying and interpreting human emotions based on the analysis of facial expressions. It involves using computer algorithms and machine learning techniques to detect and classify emotional states from images or videos of human faces. Further, FER plays a vital role in recognizing and understanding human emotions to better interpret someone’s feelings, intentions, and attitudes. In the present time, it is widely used in various fields such as healthcare, human–computer interaction, law enforcement, security, and beyond. FER has a wide range of practical applications across various industries including Emotion Monitoring, Adaptive Learning, and Virtual Assistants. This paper presents a comparative analysis of FER algorithms, focusing on deep learning approaches. The performance of different datasets, including FER2013, JAFFE, AffectNet, and Cohn–Kanade, is evaluated using convolutional neural networks (CNNs), deep face, attentional convolutional networks (ACNs), and deep belief networks (DBNs). Among the tested algorithms, DBNs outperformed other algorithms, reaching the highest accuracy of 98.82%. These results emphasize the effectiveness of deep learning techniques, particularly DBNs, in FER. Additionally, outlining the advantages and disadvantages of current research on facial emotion identification might direct future research efforts in the direction of the most profitable directions.

This study comprehensively compares and analyzes eight representative global frameworks for teacher Information and Communication Technology (ICT) competencies, each reflecting regional educational priorities and philosophies. The research aims to transcend geographical boundaries by identifying consensus on key areas of teacher ICT competencies and exploring unique characteristics of each framework within specific educational contexts and challenges. The findings indicate that despite diversity, global teacher ICT competencies represent an integration of knowledge, skills, and values, particularly in digital society building, digital career development, digital communication and collaboration, digital assessment, digital teaching and learning, and development of students’ digital competencies. The study further highlights varying emphasizes among frameworks in core areas and competencies, accompanied by varying levels of implementation support. Ultimately, the paper provides recommendations to assist educators, policymakers, and digital leaders in understanding global standards for teacher ICT competencies, developing effective and inclusive frameworks, and exploring best practices for advancing teacher ICT competencies.

This research focuses on a comprehensive analysis of the energy efficiency of the Renesas DA16200 microcontroller. The investigation adopts a comparative approach, directly contrasting the power consumption of the DA16200 with the widely used ESP8266 controller under identical operating conditions. The primary metric employed to assess energy efficiency is average battery life. Additionally, a detailed examination of current consumption is conducted across various operational modes, encompassing active states like data exchange, reception, and transmission, as well as low-power sleep mode. This analysis extends beyond simply measuring peak current draw. Transient current profiles are captured, providing time-resolved insights into how current consumption fluctuates throughout different operational phases. This granular data enables a deeper understanding of the microcontrollers' energy utilization patterns. Furthermore, the research explores and evaluates techniques for minimizing energy consumption specifically in the ESP8266. These findings are then juxtaposed against the inherent energy-saving features of the DA16200 microcontroller. To facilitate a precise and verifiable comparison, a custom test bench accommodating both the DA16200 and ESP8266 is designed and implemented. This controlled environment ensures consistency in operating conditions and minimizes external variables that could influence the results. The culmination of this research is the presentation of a comprehensive analysis, detailing the comparative energy consumption profiles of the studied microcontrollers. This data forms the foundation for objectively evaluating their suitability for various low-power the Internet of Things applications.

The paper presents an analysis of a 100-meter wind measurement guyed mast located in the north-western part of the United States, in the state of Oregon. Using the RFEM software [1], the influence of the wind on the mast was analyzed according to the guidelines of two standards: American TIA-222-H [2] and European EN 1993-3-1 [3]. The purpose of this work is to show the differences between the results of static computations of the mast in terms of the considered standards. Due to the limited content of the work, the icing load on the structure was ignored in the analysis and the focus was on determining the response of the mast only in terms of wind action. The author tried to describe the differences in this respect between the standard guidelines [2] and [3]. The comments and conclusions regarding the analysis of guyed masts presented in the article have some practical aspects and can be used in design practice.

Abstract Background Non-invasive prenatal testing (NIPT), which can screen for aneuploidies such as trisomy 21, is being implemented in several public healthcare systems across Europe. Comprehensive communication and information have been highlighted in the literature as important elements in supporting women’s reproductive decision-making and addressing relevant ethical concerns such as routinisation. Countries such as England and France are adopting broadly similar implementation models, offering NIPT for pregnancies with high aneuploidy probability. However, we do not have a deeper understanding of how professionals’ counselling values and practices may differ between these contexts. Methods In this paper, we explore how professionals in England and France support patient decision-making in the provision of NIPT and critically compare professional practices and values. We draw on data from semi-structured interviews with healthcare professionals. Results Both English and French professionals emphasised values relating to patient choice and consent. However, understandings and application of these values into the practice of NIPT provision differed. English interviewees placed a stronger emphasis on interpreting and describing the process of counselling patients and clinical care through a “principle” lens. Their focus was on non-directiveness, standardisation, and the healthcare professional as “decision-facilitator” for patients. French interviewees described their approach through a “procedural” lens. Their focus was on formal consent, information, and the healthcare professional as “information-giver”. Both English and French professionals indicated that insufficient resources were a key barrier in effectively translating their values into practice. Conclusion Our findings illustrate that supporting patient choice in the provision of NIPT may be held as an important value in common on a surface level, but can be understood and translated into practice in different ways. Our findings can guide further research and beneficially inform practice and policy around NIPT provision.

Abstract Background The COVID-19 pandemic, extreme weather events, and the Russian invasion of Ukraine have highlighted global food system vulnerabilities and a lack of preparedness and prospective planning for increasingly complex disruptions. This has spurred an interest in food system resilience. Despite the elevated interest in food system resilience, there is a lack of comparative analyses of national-level food system resilience efforts. An improved understanding of the food system resilience landscape can support and inform future policies, programs, and planning. Methods We conducted a cross-country comparison of national-level food system resilience activities from Australia, Aotearoa New Zealand, Sweden, and the United States. We developed upon and adapted the resilience framework proposed by Harris and Spiegel to compare actions derived from thirteen national food system resilience documents. We coded the documents based on the actions taken by the governments including: the food system resilience attributes utilized, the part of the food supply chain, the specific shocks or stressors, the implementation level, the temporal focus of action, and the expected impact on food security. We analyzed and compared countries’ coded categories and subcategories, and category combinations. Results The results showed that these countries are addressing some of the same issues, are using multi-pronged policy actions to address food system resilience issues, and are focused on both retrospective reviews and prospective models of disruptive events to inform their decisions. Some work has been done towards preparing for climate change and other natural disasters, and less preparing has been done for other shocks or stressors. Conclusions This paper develops and applies a framework rooted in literature to understand the content of national-level food system resilience documents. The analysis identified potential gaps, concentrations, and themes in national food systems resilience. The framework can be applied to augment existing policy, create new policy, as well as to supplement and complement other existing frameworks.

Optimization algorithms exist to find solutions to various problems and then find out the optimal solutions. These algorithms are designed to reach desired goals with high accuracy and low error, as well as improve performance in various fields, including machine learning, operations research, physics, chemistry, and engineering. As technology continues to advance, optimization algorithms are increasingly needed to address complex real-world challenges and drive innovation across all disciplines. Quantitative leaps have been achieved in improving the efficiency of optimization algorithms through the diversity of sources of information feeding these algorithms according to the type of optimization problem, based on scientific and organized foundations. The objectives of this paper are to discuss the most important optimization algorithms, classify the scientific fields involved in their application, and optimize problems involved in this regard, in addition to providing a brief overview for comparison among these algorithms.

In this paper, a comparative analysis of numerical results of the neutron transport and diffusion theories for steady-state and transient multigroup problems is presented. The neutron transport equation is known as the one that best describes the behavior of the neutron population in a nuclear reactor. However, due to the difficulty of working with its complete form, other models are considered as approximations to this equation. One such approximation is the neutron diffusion equation, which uses the Fick's Law. It is well known, however, that the diffusion model may not work well under specific conditions. The objective of this work is to establish a quantitative comparison of numerical results obtained for the K dominant eigenvalue and for the scalar fluxes from the two theories and to analyze the influence of the model on the results.

Abstract Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study.

This paper presents the design and analysis of PRSEUS structures with no openings, small openings, and mid-sized openings. For the typical panel structure of the small-opening PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure), five reinforcement schemes are developed using different types, such as mouth frames and convex platforms. Simulation analyses of tensile and compressive load are conducted. Compared to the non-opening PRSEUS panel, the small-opening PRSEUS panel’s maximum von Mises stress increases by 4.57 times under uniaxial tensile load, and the maximum von Mises stress increases by 2.55 times under uniaxial compressive load. The reinforcement efficiency of the small-opening PRSEUS panel in all five schemes exceeded 30%, Scheme E exhibits the highest load-bearing efficiency under both tensile and compressive load conditions, at 673.63 MPa/kg and 503.38 MPa/kg, respectively. For the typical PRSEUS panel structure with a mid-sized opening, three reinforcement schemes are proposed based on different methods, such as grid and cross-shaped reinforcement. These address the impact of mid-sized openings on the bearing capacity of the PRSEUS pane by analyzing the bearing efficiency of the three schemes. Compared to the non-opening PRSEUS panel, the maximum von Mises stress increases by 15.22 times under a uniaxial tensile load and by 32.45 times under a uniaxial compressive load. The reinforcement efficiency of the mid-sized-opening PRSEUS panel in these three schemes is greater than 50%, with Scheme III providing the highest bearing efficiency under a tensile load at 20.74 MPa/kg and 50.66 MPa/kg under a compressive load.

In the intricate landscape of the global food system, a nuanced understanding of dynamic evolution patterns and driving mechanisms of food trade network is essential for advancing insights into the African food trade and maintaining the food security of Africa. This paper constructs a framework for analyzing the food trade network from a comparative perspective by comparing and analyzing the evolution of food trade networks in China, the United States, Russia, the European Union, and African countries. The development trend of food trade between China, Russia, the United States, the European Union, and African countries is relatively good. China, the United States, Russia, and the European Union export far more food to African countries than they import, and bilateral food trade plays an important role in alleviating food supply shortages in Africa. The food trade networks between China, the United States, Russia, the European Union, and African countries exhibit a butterfly-shaped structure centered in Africa, and the overall intensity of bilateral trade linkages is gradually increasing. France has the greatest control over the food trade network between China, the United States, Russia, the European Union, and African countries, and the influence of the United States on the food trade network between China, the United States, Russia, the European Union, and African countries is increasing. China’s independence in the food trade network between China, the United States, Russia, the European Union, and African countries is enhanced, but its control ability is limited. The impact of differences in total population, differences in food production, and geographical borders on the trade network between China, the United States, the European Union, and African countries tends to decrease, while the influence of differences in the proportion of agricultural employment, differences in the arable land available for food production, and institutional distance tends to increase.

Maize (Zea mays L.) is known to be one of the current crops with wide adaptability and the potential to grow in various agroecological zones. It has been titled as “queen of cereals” group owing to its high genetic yield capability and abundance among the cereal crop. This study highlights the nutritive composition, phytochemical composition, pharmaceutical properties, and the unconventional use (like ethanol production) of maize plant parts such as the husk, silk, and cob, along with their utilisation in the food sector and pharmaceutical industries. Apart from the kernels, bulk of the harvest, if not used as manure, is majorly treated as waste and is usually discarded. Maize can be incorporated and utilised in the waste management of crop residues. The industrial significance of the maize crop is unmatched when compared to other cereal crops and it is used as a raw material for over 3,000 products in various sectors namely, sweeteners, cosmetics, textiles, gum, alcoholic beverages, films, package, and paper industries. Each part of the maize plant is rich in macronutrients (carbohydrates and proteins) and micronutrients (vitamins and minerals) along with other phytochemical constituents due to which it has an immense scope to be used in value-added products providing various pharmacological properties.

In an era dominated by digital advancements, cybersecurity plays a pivotal role in safeguarding information and systems from evolving threats. The escalating sophistication of cyber threats necessitates a critical examination of the efficacy of contemporary defenses. Recognizing the limitations and gaps in current solutions, this research introduces a pioneering framework aimed at fortifying cyber defenses. Motivated by a comprehensive exploration of research articles, surveys, online media, and practical studies, this study scrutinizes the intricacies of cyber threats and assesses the strengths and weaknesses of existing solutions. The proposed frameworks emerge from a meticulous feasibility and practicality study, leveraging insights garnered from diverse online sources. The 'how' encompasses a comparative analysis, evaluating the novel framework against established solutions to delineate their respective merits and shortcomings. The impetus behind this research lies in offering valuable insights to researchers, practitioners, and policymakers grappling with the multifaceted challenges of cybersecurity. By navigating through the complexities of existing solutions and introducing innovative frameworks, this paper aims to guide efforts in bolstering cyber defenses. Ultimately, this research envisions a continuous cycle of improvement and evolution in the realm of cybersecurity, as stakeholders collectively strive to adapt to the ever-changing digital threat landscape.

This paper introduces a rigorous and comprehensive approach to the assessment of marine shafting systems through the utilization of an advanced π-Theorem-based scaling methodology. Integrating journal-bearing similarity assessment and shaft-line scaling methodology with advanced dimensional analysis, the study aims to provide a methodology foundation for systematic replication and analysis of marine shafting systems through scaled models. The proposed scaling methodology ensures geometric and mechanical similarity in terms of shaft-line deflection, considering key scaling parameters such as shaft length, diameter, weight, loads, rotational speed, material properties, bearing locations, and offsets. The advanced dimensional analysis computes specific non-dimensional ratios to guarantee a close resemblance between a real-size system and a scaled lab model. The methodology is analytically derived and validated with numerical simulations for a case study, conducting comparative analysis, evaluating discrepancies, and utilizing the integrated framework for experimentation.

This paper first studies the basic characteristics of college student’s mental health and constructs an innovative model of college student’s mental health education based on positive psychology. Then, it explores the basic situation of college student’s mental health and analyzes the differences in the mental health levels of students of different grades and genders. The mental health status and positive psychological quality changes of students before and after teaching were compared. The study showed that more than 30% of the students had mental health problems, and there was a significant difference in the mental health level of students before and after mental health education (t=3.133, p=0.002, p