Critical Areas Research Articles

Adaptive Security Paradigms: The Role of Al in Safeguarding Distributed Data Across Multi-cloud Platforms

The proliferation of multi-cloud infrastructures in modern data management strategies has introduced complex security challenges that traditional measures struggle to address effectively. This article investigates the potential of AI-powered security frameworks to enhance distributed data protection across diverse cloud environments. By leveraging advanced machine learning algorithms and predictive analytics, these frameworks offer real-time threat detection, adaptive access controls, and intelligent encryption management. The article examines the key components of AI-driven security systems, including automated anomaly detection and behavior analysis, and their integration with existing security protocols. Through a series of case studies and real-world applications, we demonstrate the efficacy of these frameworks in identifying vulnerabilities, initiating proactive security measures, and maintaining compliance with industry regulations. Our findings indicate that AI-powered security frameworks provide a scalable, adaptive, and robust solution for safeguarding distributed data assets in the dynamic landscape of multi-cloud infrastructures. However, the research also acknowledges potential limitations and ethical considerations, paving the way for future advancements in this critical area of cybersecurity.

New Insights into the Antibacterial Activity of Hydroxytyrosol Extracted from Olive Leaves: Molecular Docking Simulations of its Antibacterial Mechanisms.

This study investigated the biological activities of a hydroxytyrosol-rich extract from Olea europaea leaves, particularly its ability to eradicate severe pathogenic bacteria producing Extended-Spectrum Beta-Lactamases (ESBLs). The latter bacteria are emerging microorganisms that pose significant challenges due to their resistance to a broad range of potent therapeutic drugs. The extract was prepared through an accessible acid hydrolysis method. In vitro and In silico analyses through MIC, MBC analysis and molecular docking were conducted to evaluate the antibacterial properties. The extract showed remarkable antioxidant activity and significant antibacterial potential against reference species and ESBL bacteria. MIC and MBC calculations confirmed the extract's capacity to kill bacteria rather than just inhibit their growth. Further in silico analyzes demonstrated the high binding affinity of HT to the active sites of the gyrase B subunit and the peptidoglycan DD-transpeptidase domain from proteins located in the cytoplasm and the cell wall of the bacteria, respectively. Results confirmed the structure-activity relationship and the ability of HT to disrupt essential bacterial functions. This study validates the debated antimicrobial potential of HT and highlights its importance as a potential therapeutic agent against resistant bacteria, which is a critical area of research given the global challenge of antibiotic resistance.

Extent of Locally Sourced Feed Ingredients and Performance of Feed Mill Entreprises in Southwest, Nigeria

Promoting local sourcing of raw materials for firms in Nigeria has become one of the critical areas of government policies. In achieving this, the government introduced a ban on the importation of grains/cereals, which are critical ingredients in feed production for the feed mill industry. Hence, this study examines the extent (share) of locally sourced feed ingredients and its implications on the performance of feed mills in Southwest, Nigeria. Primary data used for this study was analysed using descriptive statistics and fractional probit regression. Firm’s age, quantity of feed produced and location of the feed mills significantly determined the share of locally sourced feed ingredients in feed production of feed mills in the region while level of education of feed mill operators, firm’s age, quantity of feed produced and proportion of locally sourced feed ingredients had significant effect on the performance (profitability) of feed mill entreprises in the area. It was therefore recommended that policies on improving infrastructure, production of feed ingredients, and addressing insecurity should be prioritised rather than promoting a ban on the importation of critical feed ingredients for the feed mill industry in Nigeria.

A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.

An Analysis of Critical Processes and Employee Challenges at Velmurugan Heavy Engineering Industries (VHEI) Private Limited, Thanjavur

This study investigates the critical processes and challenges faced by employees at Velmurugan Heavy Engineering Pvt. Ltd., located in Sengipatti, Thanjavur. The aim is to identify areas for improvement and provide recommendations to enhance employee performance and organizational effectiveness. Employing a mixed-method approach combining qualitative and quantitative methods, data were collected extensively through surveys, interviews, and observations from a representative sample across departments and hierarchical levels. The findings highlight critical areas such as project management, quality control, communication channels, and performance evaluation. While the organization demonstrates competence in these areas, gaps exist, particularly in streamlined communication and efficient project management. The study also uncovers challenges including unclear role expectations, limited professional development opportunities, inadequate work-life balance, and insufficient recognition and rewards. Recommendations are offered to strengthen communication channels, enhance project management practices, implement employee development programs, promote work-life balance initiatives, and establish a robust recognition and rewards system. Addressing these challenges and implementing recommended strategies has led to improved employee satisfaction, productivity, and overall organizational performance. This study provides valuable insights for Velmurugan Heavy Engineering Pvt. Ltd. to cultivate a positive work environment and foster a highly engaged workforce, contributing to sustainable growth and success

Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions

Artificial Intelligence (AI) is increasingly recognized as a powerful tool for addressing the challenges of climate change. Its ability to process vast amounts of data and generate advanced predictive models positions AI as a key player in efforts to reduce greenhouse gas (GHG) emissions and develop sustainable solutions. This review delves into the multifaceted role of AI in climate change mitigation, highlighting its potential in several critical areas. Firstly, AI is revolutionizing predictive climate modeling by providing more accurate forecasts and simulations, enabling better-informed policy and decision-making. Secondly, it is optimizing energy systems through smart grid management, demand forecasting, and the integration of renewable energy sources, thereby enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, AI is advancing carbon capture and storage technologies by improving the identification of optimal sites and enhancing process efficiency. In environmental monitoring, AI-driven solutions are enabling real-time detection and analysis of environmental data, contributing to more effective conservation efforts. This review also presents case studies and data that demonstrate the tangible impact of AI applications in driving progress towards global emission reduction targets. However, the adoption of AI in this domain is not without challenges. Issues such as data privacy, algorithmic transparency, and the ethical implications of AI deployment need to be carefully addressed. The paper concludes by outlining future research directions and emphasizing the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.

The Role of Artificial Intelligence (AI) and machine learning in social work practice

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.

Assessment of Mortality Attributable to Air Pollution in the Urban Area of Pisa (Central Italy) Characterized by Multi-Source Exposures

Air pollution is one of the main risk factors for human health. The aim of this study was to provide an Integrated Environmental and Health Impact Assessment (IEHIA) tool to estimate the impacts on both environment and human health in Pisa municipality (central Italy). For each pollutant considered (PM2.5, PM10, and NO2), both Population-Weighted Exposure (PWE) and Attributable Deaths (ADs) were calculated considering the difference between the PWE and the latest air quality guidelines suggested by the World Health Organization. The PWEs were 16.1 µg/m3, 24.9 µg/m3, and 25.9 µg/m3 for PM2.5, PM10, and NO2, respectively. The ADs from natural causes due to exposure to PM2.5, PM10, and NO2 were 63, 29, and 51, respectively. The AD distribution was mainly concentrated in urban areas for particulate matter and in urban and suburban areas for NO2. The results highlighted significantly higher levels of air pollution than the reference levels, with a percentage of ADs from natural causes of approximately 6% of the total mortality in Pisa. IEHIA offers support for environmental and health policies and territorial planning. The authors recommend the adoption of prevention measures aimed at mitigating air pollution in critical areas, with a consequent reduction in avoidable mortality.

Knowledge Mapping of the Auxiliary Police: A Basis for Crafting a Capability Training Program

The auxiliary police force, particularly barangay tanods in the Philippines, faces significant challenges in effectively performing their roles due to limited formally training and capacity-building opportunities. This study was conducted to assess the level of knowledge among barangay tanods regarding their duties, responsibilities, roles as first responders, and patrolling and hazard identification and to develop a Capability Training Program tailored to address identified deficiencies. The need for this research arises from the observed gaps in advanced patrolling strategies and emergency response protocols, which hinder their effectiveness as community enforcers. Using a descriptive-quantitative research design, data were analyzed through regression analysis and descriptive statistics, including mean and standard deviation, using SPSS. The study employed adapted questionnaires targeting knowledge in key areas, such as duties and responsibilities, first responder actions, patrolling techniques, and hazard identification. Simple random sampling was applied, resulting in 140 barangay tanods participating as respondents. Findings revealed that although the overall knowledge of the respondents was high, specific gaps were evident in advanced emergency response and proactive patrolling strategies. These insights were instrumental in the development of a comprehensive Capability Training Program aimed at enhancing the effectiveness of police auxiliary services in critical operational areas.

Posterior Basolateral Amygdala is a Critical Amygdaloid Area for Temporal Lobe Epilepsy.

The amygdaloid complex consists of multiple nuclei and is a key node in controlling temporal lobe epilepsy (TLE) in both human and animal model studies. However, the specific nucleus in the amygdaloid complex and the neural circuitry governing seizures remain unknown. Here, it is discovered that activation of glutamatergic neurons in the posterior basolateral amygdala (pBLA) induces severe seizures and even mortality. The pBLA glutamatergic neurons project collateral connections to multiple brain regions, including the insular cortex (IC), bed nucleus of the stria terminalis (BNST), and central amygdala (CeA). Stimulation of pBLA-targeted IC neurons triggers seizures, whereas ablation of IC neurons suppresses seizures induced by activating pBLA glutamatergic neurons. GABAergic neurons in the BNST and CeA establish feedback inhibition on pBLA glutamatergic neurons. Deleting GABAergic neurons in the BNST or CeA leads to sporadic seizures, highlighting their role in balancing pBLA activity. Furthermore, pBLA neurons receive glutamatergic inputs from the ventral hippocampal CA1 (vCA1). Ablation of pBLA glutamatergic neurons mitigates both acute and chronic seizures in the intrahippocampal kainic acid-induced mouse model of TLE. Together, these findings identify the pBLA as a pivotal nucleus in the amygdaloid complex for regulating epileptic seizures in TLE.

Partial Discharge Method for State-of-Health Estimation Validated by Real-Time Simulation

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), remains a critical study area to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that transcends traditional approaches based on estimating the available capacity using the integral of the battery current or estimating the increase in internal resistance. The SOH estimator employs a partial discharge method (PDM) and a linear state-of-charge (SOC) observer based on an equivalent electrical circuit model (ECM), utilizing readily available manufacturer data and designed for real-time applications. The proposed method was tested and validated using three different automotive battery technologies and a real-time simulation on the OPAL-RT platform. The simulations involved voltage and current measurements of pulsed-discharge current profiles under temperature-controlled conditions and an electric vehicle driving profile. The results showed a high accuracy in SOH estimation, with a maximum standard deviation of approximately 0.03497 V for lithium-ion batteries, representing about 7.124% of the mean value of the SOH estimator output. For other technologies, the standard deviations were even lower, all below 0.61% of their respective mean values. These outcomes demonstrate the reliability and accuracy of our method, making it suitable for real-time SOH estimation in EV-BMSs.

Call the experts: identifying stakeholders in the long-term care of youth with hypospadias.

Long-term follow-up for individuals with hypospadias remains a critical area of need, yet evidence-based guidelines for such follow-up are lacking, and the role of involvement of relevant experts is not yet established. Using our hypospadias-specific health-related quality of life conceptual framework and a subsequent qualitative study of prepubertal males and parents of males with hypospadias, we identified potential priorities for long-term follow-up of youth with hypospadias. Using thematic codes from our patient and parent interviews, we searched PubMed for relevant articles and identified the specialties represented by all the authors of these articles. Our search strategy revealed consistent expertise across HRQOL themes and subthemes, including pediatric and adult urology, health psychology, psychiatry, endocrinology, genetics, and social work. Communication experts, as well as patients and families, were also represented in our literature search. Using these findings, we compiled a comprehensive list of potential stakeholders to inform the development of holistic care guidelines for individuals with hypospadias. By engaging these stakeholders, we aim to develop consensus-based, long-term follow-up guidelines and tools to address the evolving physical and psychosocial needs of people with hypospadias over a lifetime. The use of qualitatively derived thematic codes to search for relevant literature is an accessible approach to identifying relevant stakeholders. These findings underscore the importance of involving diverse, multidisciplinary expertise to ensure comprehensive, patient-centered care in complex genitourinary conditions.

Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023)

BackgroundThe incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC.MethodsWe conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis.ResultsOur search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications.ConclusionsRadiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

Coaching Techniques and Team Cohesion in Chinese University Handball

This study looked into how well teaching methods and training techniques worked and how they affected the teamwork and cohesiveness of Chinese university handball teams. Respondents were 428 players and 15 teachers from five Chinese universities. They were chosen through a "stratified sampling" process to ensure that all groups were represented. The study examined six critical areas of coaching: fundamentals, tactical strategies, physical fitness, psychological training, scouting, and personal growth plans. It also looked at six aspects of cohesion: ties between people, team identity, resolving conflicts, commitment, building teams, and support systems. Data showed that coaching methods and team cohesion were ranked low in every area. However, association analysis showed that better teaching techniques were significantly linked to higher team cohesion. Interviews with coaches revealed that putting new strategies into action is hard because people don't like change, there aren't enough resources, teachers don't get enough professional growth, time is limited, and there are cultural barriers.

Assessing Fintech Awareness and Knowledge Among Students at Selected Universities in Somalia

This study examines financial technology awareness among students at four Mogadishu universities (Jamhuriya University of Science and Technology, Banadir University, SIMAD University, and the University of Somalia), using a cross-sectional descriptive research design. The study utilized a structured questionnaire to evaluate 231 students’ understanding and familiarity with fintech concepts, focusing on demographic factors and perceived curriculum gaps. The study’s participants were diverse in gender, age, institution, major, and year of study. The majority were male (63%), with 78% aged between 20 and 25. The study reveals that students’ familiarity and understanding of fintech concepts like blockchain, smart contracts, neo- banking, crowdfunding, cloud computing, big data analytics, robo-advisors, and AI are low. This highlights significant gaps in students’ knowledge of critical areas digital finance, e-payments or e-banking. The study shows a moderate correlation between demographic factors and students’ fintech awareness, with 13.9% of variation explained by these factors. The overall model is statistically significant (F = 7.265, P < 0.001), indicating that these factors collectively impact students’ knowledge and awareness of fintech. Gender, age, and university attended do not significantly influence fintech awareness, suggesting they are not major determinants. However, the study reveals that students’ awareness of fintech increases as they progress through their academic years, with certain majors showing higher levels of awareness. The study recommends universities incorporate fintech topics into their courses, develop accessible options, invest in resources, collaborate with fintech companies, strengthen industry partnerships, and offer cross-disciplinary electives.

The crucial role of education in fostering sustainability awareness and promoting cybersecurity measures

This study examines the critical role of education in fostering sustainability awareness and promoting cybersecurity measures, amidst the evolving challenges of the 21st century. Employing a systematic literature review and content analysis, the research scrutinizes the intersection of education with sustainability and cybersecurity, aiming to uncover innovative pedagogical approaches and the significance of interdisciplinary strategies in enhancing learning outcomes. The methodology encompasses a comprehensive search across major databases, adhering to strict inclusion and exclusion criteria to ensure the relevance and timeliness of the literature reviewed. Key findings reveal a pressing need for educational frameworks to integrate sustainability and cybersecurity comprehensively, highlighting the transformative potential of education in preparing students for a sustainable and secure digital future. The study identifies gaps in current educational practices and proposes solutions, emphasizing the importance of standards and regulatory bodies in shaping effective educational practices. Furthermore, it outlines the consequences for stakeholders, including educators, policymakers, and industry leaders, underscoring the collaborative effort required to address these challenges. Conclusively, the study offers strategic recommendations for enhancing the integration of sustainability and cybersecurity within educational curricula. It calls for a concerted effort among educators, policymakers, and industry leaders to foster a more informed, ethical, and environmentally conscious society. Future research directions are suggested to explore innovative educational technologies, pedagogies, and frameworks that further integrate these critical areas, ensuring the advancement of education for sustainability and cybersecurity.

Artificial Intelligence and Artificial Stupidity: The Inseparables

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.

DenseNet: Assessing Limitations in Connectivity and Memory Efficiency

Abstract—Despite the prevailing trend of introducing skip connections in deep neural networks for enhanced depth and training efficiency, this paper critically examines the limitations of Dense Convolutional Network (DenseNet), a prominent state- of-the-art architecture. The analysis highlights the drawbacks of connecting each layer to every other layer in a feed-forward manner. Additionally, this paper systematically explores the shortcomings and challenges associated with DenseNet, shedding light on its drawbacks. The study culminates in the identification of critical areas for improvement and suggests avenues for optimizing memory utilization during training. Ultimately, this paper aims to provide a comprehensive understanding of the inherent limitations in DenseNet architecture and offers insights into potential advancements in deep neural network design.

Effectiveness of Productive Waqf Management in Improving the Welfare of the Poor: A Bibliometric Review

This bibliometric study examines the evolution and thematic concentration within the field of productive waqf management from 2000 to 2024, employing a comprehensive analysis of academic publications to understand the dynamic interplay between Islamic philanthropy and socio-economic development. Through the utilization of citation metrics and VOSviewer tools, the study reveals significant growth in research focusing on the integration of traditional Islamic charitable mechanisms with modern financial tools, notably micro waqf banks and zakat, aimed at enhancing community welfare and poverty alleviation. Collaborative networks among scholars indicate robust intellectual communities with concentrated expertise in specific thematic areas. The findings emphasize the potential of productive waqf in supporting broader economic and social objectives, including alignment with the sustainable development goals (SDGs), and highlight the emerging trend of leveraging technology to increase the transparency and efficiency of waqf management. Challenges in governance and professional management are identified as critical areas needing attention to maximize the impact of waqf. The study proposes future research directions focused on empirical evaluations and the potential impacts of technological innovations in waqf management.