Global Demand Research Articles

Building Excellence: How Transformational Leadership Elevates Madrasa Graduate Quality and Prepares Students for Global Competence

This study explores the impact of transformational leadership on the quality of graduates in madrasa education, emphasizing the role of leadership in fostering educational excellence through innovation, empowerment, and holistic development. Utilizing a qualitative approach, the research examines how transformational leadership practices contribute to student engagement, skill enhancement, and overall graduate preparedness for future challenges. Key transformational leadership elements—such as inspirational motivation, individualized consideration, and intellectual stimulation—are analyzed to understand their influence on students' academic and personal growth. Findings indicate that transformational leadership within madrasas not only enhances academic performance but also equips students with essential competencies and values aligned with global demands. This study highlights the importance of strategic leadership in education, offering insights into how madrasas can elevate graduate quality through effective leadership practices.

The Genomic SSR Millets Database (GSMDB): enhancing genetic resources for sustainable agriculture.

The global population surge demands increased food production and nutrient-rich options to combat rising food insecurity. Climate-resilient crops are vital, with millets emerging as superfoods due to nutritional richness and stress tolerance. Given limited genomic information, a comprehensive genetic resource is crucial to advance millet research. Whole-genome sequencing provides an unprecedented opportunity, and molecular genetic methodologies, particularly simple sequence repeats (SSRs), play a pivotal role in DNA fingerprinting, constructing linkage maps, and conducting population genetic studies. SSRs are composed of repetitive DNA sequences where one to six nucleotides are repeated in tandem and distributed throughout the genome. Different millet species exhibit genomic variations attributed to the presence of SSRs. While SSRs have been identified in a few millet species, the existing information only covers some of the sequenced genomes. Moreover, there is an absence of complete gene annotation and visualization features for SSRs. Addressing this disparity and leveraging the de-novo millet genome assembly available from the NCBI, we have developed the Genomic SSR Millets Database (GSMDB; https://bioinfo.icgeb.res.in/gsmdb/). This open-access repository provides a web-based tool offering search functionalities and comprehensive details on 6.747645 million SSRs mined from the genomic sequences of seven millet species. The database, featuring unrestricted public access and JBrowse visualization, is a pioneering resource for the research community dedicated to advancing millet cultivars and related species. GSMDB holds immense potential to support myriad studies, including genetic diversity assessments, genetic mapping, marker-assisted selection, and comparative population investigations aiming to facilitate the millet breeding programs geared toward ensuring global food security. Database URL: https://bioinfo.icgeb.res.in/gsmdb/.

Research on Microgrid Optimal Scheduling Based on an Improved Honey Badger Algorithm

As global energy demands continue to grow and environmental protection pressures increase, microgrids have garnered widespread attention due to their ability to effectively integrate distributed energy sources, improve energy utilization efficiency, and enhance grid stability. Due to the complexity of internal structure, variety of energy sources, and uncertainty of load demand, the optimal scheduling problem of microgrids becomes extremely complicated. Traditional optimization methods often perform poorly in complex and dynamic microgrid environments, and it is assumed that the complexity is low or that more simplification is needed, which leads to poor convergence and local optimality when dealing with uncertainty and nonlinear problems, making intelligent optimization algorithms a crucial solution to this problem. To address the shortcomings of the traditional honey badger algorithm, such as the slow convergence speed and a tendency to fall into local optima in complex microgrid optimal scheduling problems, this paper proposes a multi-strategy improved honey badger algorithm. During the population initialization phase, a combined opposition-based learning strategy is introduced to enhance the algorithm’s exploration and exploitation capabilities. Additionally, the introduction of variable spiral factors and a linearly decreasing strategy for parameters improves the overall efficiency of the algorithm and reduces the risk of local optima. To further enhance population diversity, a hunger search strategy is employed, providing stronger adaptability and global search capabilities in varying environments. The improved honey badger algorithm is then applied to solve the multi-objective optimal scheduling problem in grid-connected microgrid modes. The simulation results indicate that the improved honey badger algorithm effectively enhances the economic and environmental benefits of microgrid operations, improving system operational stability.

Progress in MOFs and MOFs-Integrated MXenes as Electrode Modifiers for Energy Storage and Electrochemical Sensing Applications

The global energy demand and environmental pollution are the two major challenges of the present scenario. Recently, researchers focused on the preparation and investigation of catalysts for their capacitive properties for energy storage devices. Thus, supercapacitors have received extensive interest from researchers due to their promising energy storage features and decent cyclic stability/performance. The performance of the supercapacitors are significantly influenced by the physicochemical properties of the electrocatalyst. In this review article, we have compiled the previous reports on the fabrication of MOFs-based composite materials with MXenes for energy storage and electrochemical sensing applications. The metallic and bimetallic MOFs and MOFs/MXenes materials for supercapacitor applications are reviewed. In addition, MOFs/MXenes-based hybrid composites are also compiled towards the determination of various toxic/hazardous materials, such as metal ions like copper ions, mercury ions, and picric acid. We believe that present review article may benefit the researchers working on the preparation of MOFs-based catalysts for supercapacitor and electrochemical sensing applications.

Educação Inteligente e Imersiva e Educação para o Desenvolvimento Sustentável

This article investigates the transformative potential of Smart and Immersive Education in promoting Education for Sustainable Development (ESD), connecting technological innovations to global sustainability demands. Through a conceptual model, it explores the contributions of emerging Technologies — such as Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI) — for the transformation of education, making it adaptive, engaging, and aligned with the Sustainable Development Goals (SDGs). The argument is made that these technologies enhance learning and empower both managers and students to take on leading roles in addressing socio-environmental challenges. The study provides a basis for new empirical research and educational practices, suggesting pathways to turn environmental and social challenges into opportunities for innovation and positive impact.

Optimizing Energy Efficiency in Smart Home Automation through Reinforcement Learning and Iot

This thesis explores an innovative approach to optimizing energy efficiency in smart home environments by leveraging reinforcement learning (RL) and Internet of Things (IoT) technologies. As global energy demand rises and concerns over environmental sustainability intensify, smart homes offer a promising solution to reduce residential energy consumption while enhancing user comfort. The study presents a comprehensive architecture integrating IoT devices with RL algorithms, allowing for real-time monitoring and intelligent energy management. Through data collected from smart sensors, RL agents continuously learn and adapt to occupant behaviors and environmental changes, making optimal decisions to minimize energy usage without compromising user comfort. A real word-based analysis demonstrates that the proposed system achieves significant energy savings compared to traditional rule-based methods. The results underscore the effectiveness of combining RL and IoT for adaptive energy management, paving the way for scalable solutions that could extend to smart cities and renewable energy systems. This research provides valuable insights into how emerging technologies can contribute to sustainable energy practices in the residential sector.

State-led Maritime Governance, Education and Training Nexus in South Africa

The maritime environment has a significant socio-economic impact on nations, regional and global economies. However, it also experiences many challenges related to safety, security, Education and Training (E & T) which stakeholders such as businesses and government through State institutions play a crucial role in addressing. In addition to high global labour demands, E & T challenges have related to the decreasing number of youth taking up maritime E & T. In addition, technological advancements affect costs, standards, and quality of maritime ET. The Republic of South Africa (RSA) has not been immune to these challenges. Public higher E & T institutions in RSA are the primary providers of tertiary maritime E & T but remain confronted by numerous challenges tied to organisational resources, State funding and the growing demand for increased internal and external capacity. This study’s objective was to examine maritime State-led interventions and challenges in selected public higher E & T institutions offering maritime studies in the coastal province of KwaZulu-Natal (KZN), RSA. Guided by a Systems Thinking approach, this empirical study adopted a qualitative research methodology utilizing interviews with key knowledge holders and secondary data. It was found that there exists a consensual view that higher E & T institutions offering maritime studies in the province are well positioned to play a more meaningful role in addressing the country’s high unemployment rate. However, the current conditions in maritime E & T have undermined this and resulted in the pursuit of international partnerships to address local deficiencies. These findings are significant to stakeholders involved in maritime higher E & T, State-led maritime interventions, policymaking, governance, management, leadership and administration. The findings of the study also point to the need for a holistic understanding of maritime issues and challenges as maritime E & T is a complex global social space. This could eventually facilitate innovation, enhance leadership and contribute to meaningful and crucial partnerships between developed and more developing nations.

An Economic Analysis of Maize Production across Major Producing States of India

National Food Security Mission has been taken by Government of India to increase coarse cereal Maize production in the country to meet food, feed for livestock and poultry and industrial raw material, domestic as well as global demand. The traditional and modern industrial uses have made it one of the fastest growing cash crops in the world. Considering its global as well as national importance, In this article, we have analysed the relative change, growth rate and CDVI (cuddy-della valle-index) in cost, returns, profitability and break-even production of maize during the study period 2000-01 to 2018-19 of Maize across major Maize growing states of India. The study found that Cost A1, cost A2, cost B1, cost B2, cost C1 and cost C2 of maize was found to be increased higher in Madhya Pradesh with highly significant annual growth rate during the study period than other states. These cost concepts showed low to medium instability for almost all the leading states. The findings revealed that maize was more profitable in Bihar, followed by Andhra Pradesh and Karnataka, owing to higher net income and B:C ratios than in other states. Bihar states had higher differential yield and a greater discrepancy between the cost of production and the minimum support price. In some states, actual yields were lower than break-even, and production costs were greater than the minimum support price, resulting in losses for maize growers. The study's findings would help policymakers and governments devise appropriate development strategies and effective policies for less lucrative states.

Variability of drinking water for pigs and poutry the southern region of Brazil over a twelve – month period

The increase in global food demand has led to intensified production of pigs and poultry. In this context, water quality on farms needs to be monitored to ensure maximum production and animal welfare. This study aimed to evaluate the quality and variability of drinking water for animals over twelve months in properties in the Southern Region of Brazil. Physical, chemical, microbiological parameters, and the average rainfall index of nine water sources were observed. Variations in pH were noted among the evaluated water sources, as well as within the same source over monthly collections. A relationship between average rainfall and elevated iron levels was also demonstrated. Nitrate and nitrite levels exceeded Brazilian legislation at certain sampling points, posing risks to human and animal health. In 67.59% of samplings, total coliforms were present, and 59.26% showed the presence of Escherichia coli. The study highlighted the variability of water sources in the Southern Region of Brazil, reflecting the need for constant monitoring and treatment measures, such as water treatment stations, chlorination systems, and acidification in rural properties.

Using data-driven models to simulate the performance of surfactants in reducing heavy oil viscosity.

There is a substantial body of literature exploring the challenges associated with exploring and exploiting these underground resources. Unconventional resources, particularly heavy oil reservoirs, are critical for meeting ever-increasing global energy demand. By injecting surfactants into heavy oil, chemically enhanced oil recovery (EOR) may enable emulsification, which may reduce the viscosity of heavy oil and facilitate extraction and transportation. In this work, a large experimental dataset, containing 2020 data points, was extracted from the literature for modeling oil-in-water (O/W) emulsion viscosity using machine learning (ML) methods. The algorithms used pressure, temperature, salinity, surfactant concentration, type of surfactant, shear rate, and crude oil density as inputs. For this purpose, five ML algorithms were selected and optimized, including adaptive boosting (AB), convolutional neural network (CNN), ensemble learning (EL), artificial neural network (ANN), and decision tree (DT). A combined simulated annealing (CSA) method was utilized to optimize all algorithms. With AARE, R2, MAE, MSE, and RMSE values of 8.982, 0.996, 0.004, 0.0002, and 0.0132, respectively, the ANN predictor exhibited higher accuracy in predicting O/W emulsion viscosity for total data (train and test subsets combined). A Monte-Carlo sensitivity analysis was also performed to determine the impact of input features on the model output. By using the proposed ML predictor, expensive and time-consuming experiments can be eliminated and emulsion viscosity predictions can be expedited without the need for costly experiment.

Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

Research Progress of Dual‐Site Tandem Catalysts in the Preparation of Multi Carbon Products by Electro Reduction of CO2

AbstractThe era of an energy economy driven by “carbon neutrality” is putting forward stricter requirements for the use of carbon resources and the governance of CO2. Electrochemical reduction of carbon dioxide reaction (CO2RR), driven by renewable energy, is a practical energy storage technology with broad application prospects. It can reduce CO2 into carbon‐based fuels and chemical products. Among them, multi‐carbon (C2+) products have higher energy density and larger market size, and can significantly reduce the global demand for fossil fuels and close the artificial carbon cycle. Introducing additional active sites into Cu‐based catalysts to prepare dual‐site tandem catalysts can regulate the electronic and geometric structure of the catalysts, break linear scale relationships, reduce reaction potential barriers, and bring superb and stable catalytic performance. Various types of dual‐site tandem catalysts are developed, and the understanding of the tandem effect is pushed to a higher level. This paper reviews several typical dual‐site tandem catalysts: atom–atom dual‐site tandem catalysts, atom‐particle dual‐site tandem catalysts, particle–particle dual‐site tandem catalysts, and heterogeneous interface dual‐site tandem catalysts. It then deeply analyzes the reaction mechanism and research progress of these advanced catalysts in CO2RR. In addition, the challenges and opportunities faced by such catalysts are also discussed.

Exploring Artificial Intelligence and Machine Learning in Precision Agriculture: A Pathway to Improved Efficiency and Economic Outcomes in Crop Production

This review analyzes secondary data from academic databases, research articles, and case studies to explore the role of new technologies for precision agriculture (PA) and investigates the value addition that Artificial Intelligence (AI) and Machine Learning (ML) provide to resource use, crop yield, and economic performance. Accordingly, the most of the key applications of AI in PA were related to crop yield prediction, disease detection, and effective water usage. Operating models through AI will analyze much data in real time, thus providing insight into informed decision making by farmers for proactive action against crop challenges like drought or pest attack. Furthermore, IoT devices and remote sensing support continuous monitoring in the delivery of correct data about optimizations of resources with minimal environmental impact. AI-driven robotics further automates all tasks related to planting, harvesting, and pesticide application, improving labor productivity and operational efficiency. This would involve in other issues like implementation costs, data privacy, and general unawareness among farmers of developing areas. Equally important will be ethical issues like ownership of data and loss of jobs. Various case studies in India, China, the United States, and Africa reveal how AI could transform the future of agriculture if integrated into agricultural systems properly to gain higher productivity and sustainability. Improvements in data quality and ethical issues, and increased access by smallholder farmers, will also be part of future research. Eventually, integrating AI with IoT, robotics, and big data analytics could provide high potential to meet global food demand in a sustainable manner.

Harnessing Artificial Intelligence for Wildlife Conservation

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

Interprofessional collaboration between hospital-based palliative care teams and general healthcare workers: A realist review protocol.

Palliative care, vital for patients with advanced, life-limiting or life-threatening illnesses, faces an increasing global demand due to aging populations and rising non-communicable diseases. Specialized palliative care teams (PCTs) within hospitals significantly impact patient outcomes, which requires effective interprofessional collaboration with general healthcare workers. Therefore, We will conduct a realist review to explore the contextual factors, mechanisms and outcomes related to the interprofessional collaboration between PCTs and general healthcare workers. Grounded in the third generation Cultural-Historical Activity Theory, this review will follow 5-step iterative process. First, a preliminary literature search will define the review scope. Second, based on the preliminary searches an initial program theory will be developed. Third, systematic searches across PubMed, Embase, CINHAL, Web of Science, and Scopus will be caried out. Fourth, data extraction of included studies will be conducted. Simultaneously, relevance and rigour of individual studies will be evaluated. Lastly, data analysis and synthesis will be conducted in which identified individual Context-Mechanism-Outcome (CMO) configurations will be combined in chains of inference through which hypotheses can be formulated. In summary, this realist review will refine an initially developed program theory, producing a framework elucidating how interprofessional collaboration works between PCTs and general healthcare workers. This review aims to provide crucial insights into interprofessional collaboration between PCTs and general healthcare workers, informing optimized palliative care delivery in acute care hospitals for diverse stakeholders.

The modern role of paraprofessionals in delivering brief psychological interventions

AbstractThe increasing global demand for mental health services, compounded by the COVID‐19 pandemic, has spurred innovative approaches. Among these, the utilisation of paraprofessionals for delivering brief psychological interventions (BPIs) is gaining significant traction across diverse settings and populations. Paraprofessionals (individuals equipped with specialised mental health training but lacking formal qualifications) have emerged as a valuable resource. They have the potential to offer cost‐effective, culturally sensitive and readily accessible support. The integration of paraprofessionals into various healthcare settings, such as inpatient psychiatric units and primary care clinics, underscores their potential to enhance holistic well‐being and early intervention. This integration not only lightens the load on licensed therapists but also broadens access to services while reducing treatment costs. At a global scale, paraprofessionals, armed with local knowledge and community connections, play a pivotal role in improving mental health outcomes among underserved populations. Furthermore, the rise of telemental health, an increasingly crucial aspect of mental healthcare, presents new avenues for paraprofessionals to deliver psychological care. Despite their potential, paraprofessionals encounter challenges related to work‐life balance, boundary‐setting and the stigma attached to their roles. Their integration into healthcare systems requires clear roles and responsibilities, along with continuous support to ensure effective intervention and the well‐being of both providers and recipients. Further research and development are necessary to harness their full potential and meet the increasing demand for accessible mental health services.

High‐Throughput Screening Technologies of Efficient Catalysts for the Ammonia Economy

AbstractDriven by the growing global demand for sustainable energy, ammonia is gaining recognition as a crucial energy carrier with the potential for widespread manufacturing. This has spurred the emergence of a “sustainable ammonia economy”. Efficient catalyst development is paramount to achieving this purpose. Although the traditional trial‐and‐error methods have led to substantial progress, they are inherently time‐consuming. In recent years, high‐throughput screening strategies are revolutionizing catalyst discovery, offering an efficient and cost‐effective approach for identifying high‐performance catalysts across various processes within the ammonia economy. This review explores recent advancements in these rapid catalyst screening techniques for ammonia‐based processes. By outlining the general screening principles and the key steps involved in the computational analysis of catalysts relevant to different stages of the ammonia economy, this review aims to provide fundamental insights for the development of novel catalysts using these accelerated methods.

Design and Optimization of Higher Education Online English Teaching Utilizing Wireless Network Technology in the Context of 5G

The global demand for wireless communication networks is a result of the widespread use of wireless devices and the related surge of wireless service providers. Data transmission speed, coverage areas, cost-effectiveness, resource utilization, security, scalability, and adaptability are all elements expected to be improved in future 5G wireless networks. Since this rapid development of network communication technology affects the delivery of English education in tertiary contexts, English teachers in colleges and universities should make use of network resources—especially the communication tools embedded in the 5G network. File transfer protocol can utilize the advanced artificial bee colony optimization algorithm (AABCOA) to realize classification, and the system proposed in this article is simulated and tested in Matrix Laboratory (MATLAB). The research results indicate that students are unsatisfied with traditional methods of English pedagogy in higher education and are keen to explore the new learning possibilities provided by 5G integrated wireless network technology.

التطور اللوجستي وتحسين الأداء في حجم التجارة والميزان التجاري في المملكة العربية السعودية: دراسة مقارنة بين سنوات (2007-2023)

One of the goals of Vision 2030 is to transform the Kingdom into a leading industrial power and a global logistics hub, and to this end, the Ministry of Transport and Logistics has developed an ambitious strategy aimed at developing the transport and logistics sector in the country, especially in light of the increasing global demand for logistics services. The Vision 2030 plan is leading massive investments and expansions in all sectors of the economy, and logistics infrastructure is being set up to increase private sector participation,. Given that The study aimed to know the extent of development in logistical performance in the Kingdom and its role in improving the trade balance and trade volume during the period (2007-2023) and the most important problems addressed by the research were there an impact on the development of logistical performance and improving the trade balance The volume of trade of the Kingdom of Saudi Arabia, and the most important hypotheses where there is a statistically significant relationship between logistical performance and improving the trade balance of the Kingdom of Saudi Arabia and to reach a solution to the problems that the research was exposed to, the research was addressed in three sections: The nature of logistics services - trade balance - international trade - statistics on the volume of trade and trade exchange (2007-2023)The second topic: the nature of the logistics performance index and the evaluation of the role of the Kingdom's performance in it and the Kingdom's strategy in logistics services. The third topic: statistical analysis of the extent of development of the logistical performance of the Kingdom and its impact on improving the volume of international trade balance and the volume of international trade The results of the statistical analysis showed that there is a positive impact between the development of the Kingdom's logistical performance and the improvement of the trade balance and the volume of trade, which confirms that the trend to rely on the development of logistics services will have a fairly good return on the trade balance and the volume of trade and thus increase the recovery and progress of the economy.

Genetic Variability Studies in Chickpea (Cicer arietinum L.) for Yield and Contributing Traits Through Half-diallel Mating Strategy under Late-sown Conditions

Background: One of the oldest cultivated legumes, chickpeas (Cicer arietinum L.), are essential for world nutrition, especially with climate change. Chickpea output is poor despite rising demand for its nutritional value. Improved genetics can enhance yields to fulfil local and global demands. By selecting better parents and potential hybrids utilising half-diallel mating, breeders can hasten the development of high-yielding, durable cultivars. Methods: This study examined 55 half-diallel mating genotypes, comprising 10 parental lines and 45 F1 hybrids. At the Acharya Narendra Deva University of Agriculture and Technology Genetics and Plant Breeding Farm in Ayodhya, U.P., these were evaluated using a randomised block design with three replications under late-sown circumstances in 2022-23 rabi. Normal agricultural procedures were used. A four-meter row of each genotype was planted with 30 cm between rows and 10 cm between plants. Genetic indices like GCV, PCV, heritability, genetic progress, correlation coefficient and path analysis were evaluated. Result: ANOVA revealed significant genetic variability across all traits. Biological yield plant-1 had the highest PCV and GCV, followed by seed yield plant-1. Heritability and genetic progress were highest for days to 50% flowering and biological yield plant-1, indicating strong genetic control. Days to 50% flowering and maturity inversely linked with most yield-related characteristics. Seed yield was closely associated with primary and secondary branches plant-1, plant height, pods per plant, seeds pod-1, 100-seed weight and biological yield. Path coefficient study showed that all characteristics except days to 50% flowering, days to maturity and plant height influenced grain yield.