Integration Of Technologies Research Articles

A standard network selection and resource allocation mechanism in 5G heterogeneous networks using hybrid heuristic algorithm with multi-objective constraints

The integration of various Random Access Technologies (RATs) within 5G Heterogeneous Networks (HetNets) to satisfy the diverse communication needs of the Internet of Things (IoT) causes significant challenges in network topology selection and resource allocation. Traditional approaches do not handle network congestion and poor user experience which necessitates the development of more efficient and intelligent network management strategies. The main novelty of the research work is to combine the two intelligent optimization algorithms to address the complexities of resource management for enhancing system performance. The integrated optimization algorithm also aims to reduce the latency and communication costs while enhancing resource utilization within the network. The novel Hybrid Snow Leopard and Dark Forest Algorithm (HSL-DFA) combines the strengths of the Snow Leopard Optimization Algorithm (SLOA) and the Dark Forest Algorithm (DFA) to optimize network performance based on the multiple objectives including resource utilization, makespan, Quality of Service (QoS), energy consumption, communication cost, congestion control, and latency. The HSL-DFA algorithm integrates the SLOA and DFA to leverage their respective advantages in solving complex optimization problems. It worked based on a position-updating process using current and mean fitness values. Here, the SLOA is used for the position-updating process if the current fitness exceeds the mean fitness and DFA is used for the opposite scenario. This approach ensures higher convergence rates and optimal solutions for complex network optimization problems. By addressing multi-objective constraints, the algorithm significantly improves network performance and provides a promising solution for the management of a 5G network. Various metrics are utilized to confirm the effectiveness of the proposed model. The results showed that the throughput of the proposed model was 93 at the 200th node.

Innovative Technologies for Smarter and Efficient Operating Room Scheduling.

An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., which developed a predictive model of surgical case durations. The model appears to adopt a pragmatic approach by analyzing tangible variables and undergoing validation across various types of surgical procedures, which suggests potential avenues for enhancing efficiency and sustainability in healthcare practices. However, we have some observations, particularly regarding the feasibility and practical implementation of the proposed model. A key limitation of the model is the precise definition of surgical duration, which requires further specification. To effectively translate the model into a practical scheduling approach, it is essential to consider total Operating Room (OR) occupancy time as a critical determinant of surgical planning and resource allocation. This includes not only the actual procedural time but also preoperative preparation, anesthesia induction and recovery, cleaning, and material restocking, all of which significantly impact overall scheduling efficiency. Another critical aspect concerns the quality and reliability of the input data, which is fundamental for ensuring the accuracy and effectiveness of the model. Furthermore, the adoption of new technologies should be regarded not merely as an innovation but as a means to develop high-performance, efficient tools that enhance current clinical practice. In this context, machine learning models should not only serve as analytical instruments but also as actionable tools, enabling the transition from predictive insights to strategic planning and optimized scheduling, ultimately improving decision-making and resource allocation. While making accurate predictions is a good starting point, maintaining an active AI model requires investment in resources, such as an increase in the number of surgical cases compared to the current organizational system. It may be beneficial to consider the creation of a multidisciplinary group that could promote the integration of AI with other emerging technologies.

Identifying critical factors in higher education studies: combining necessary condition and importance-performance map analyses

ABSTRACT This paper presents a combination of necessary condition analysis (NCA) and importance-performance map analysis (IPMA) as a suitable analytical approach for studies in higher education. NCA identifies necessary conditions for achieving specific outcomes, while IPMA identifies factors according to their importance and performance. The combined approach is illustrated using data from the 2023 Norwegian Studiebarometer survey, focusing on identifying key factors influencing overall student satisfaction. The results reveal that the organization of study programs is the most critical factor influencing overall student satisfaction, followed by teaching quality and the academic and social environment. Interestingly, feedback and guidance ranked as the least significant factor, raising questions about students’ perceptions of its importance. The study proposes several potential educational research topics that can be addressed using NCA combined with IPMA. Such issues include factors related to inclusive education and accessibility, student engagement and motivation, and technology integration in education.

Rotten to the core: Challenges with postharvest disease management of fruit crops.

Postharvest losses are attributed to fungal and bacterial diseases that contribute significantly to food loss and waste. In addition, some of the pathogens produce mycotoxins which contaminate processed food products made from culls and fruit that are not fit for the fresh market. Since consumers expect fresh fruit year-round, coupled with demands for high quality and low residue levels, new research and tools are needed to combat decay. Hence, this review focuses on the latest advances in detection, control, and cultural practices that can be integrated or stand alone to help thwart rot caused by fungal and bacterial postharvest pathogens of fruit. While we have chosen a holistic, integrated approach to cover various aspects of postharvest decay control, the manuscript focuses on specific high value crops like citrus, apple, avocado and grapes. In these different crops, we discuss the significant impacts that have been made to understand aspects of pathogen biology, epidemiology, and control. Notwithstanding, postharvest disease management options for both conventional and organic markets is projected to continually increase and will require innovative solutions. We envision the integration of standard, conventional and novel technologies with the help of artificial intelligence to move the pace of discovery forward that manifest in paradigm shifting, long-term management solutions.

Strategizing agriculture produce logistics resilience: a study of N-WINGS-ISM framework for managing disruptions

PurposeAgriculture logistics networks are vulnerable to several disruptions. Disruptions impeding agriculture produce logistical flows often result into distorted food supplies, excessive logistics lead times and higher food prices. The strategies for enabling agriculture produce logistics resilience (APLR) are becoming crucial for managing logistical disturbances. The purpose of this study is to devise strategic implementation framework for APLR managing disruptions.Design/methodology/approachThe factors contributing APLR are identified utilizing literature review and discussions with various stakeholders of agro-logistics sectors. The integrated N-WINGS-ISM approach is developed to explore causal interrelationship among APLR factors. Geographical Indication (GI) tagged Nashik grapes from Nashik, Maharashtra, India have been specifically chosen to demonstrate application of the developed model to devise the strategic framework for managing disruptions.FindingsThe study identifies 16 APLR factors imparting logistics resilience. N-WINGS provide categorical clustering of APLR into – Priority, Contingency, Autonomous and Long-term factors. ISM model structures the hierarchy of the implementation strategies highlighting three levels – Depth, Transition and Surface level factors. N-WINGS-ISM combined results are utilized to devise APLR implementation strategies.Originality/valueThe study argues that the need for developing proactive resilience systems incorporating resilience culture. Moving beyond technology integration, developing “Human-Technology-Systems” ecosystem is the primary imperative for systemic resilience.

Research Progress and Ecological Implications of Soil Nematode Metabolic Footprint

Soil nematodes, as a key functional group in soil ecosystems, have a profound impact on carbon and nitrogen cycles, microbial community dynamics, and soil health. Metabolic footprint, as a core indicator for quantifying nematode ecological effects, integrates processes such as respiration, decomposition of excreta, and microbial regulation. Recent breakthroughs in molecular biology and stable isotope tracing technologies have significantly enhanced the accuracy of nematode metabolic pathway analysis. Studies have shown that interspecific differences in nematodes lead to significant variations in carbon and nitrogen transformation efficiency, for example, the organic carbon mineralization rate of fungivorous nematodes can reach 60%, while that of bacterivorous nematodes is only 30-45%. At the same time, environmental factors (such as temperature, pH) and biological interactions (such as root signaling of plants, control by predators) further affect the ecological function contributions of nematodes by regulating their metabolic activity. However, existing research still has shortcomings in the refinement of metabolic mechanisms, long-term effect assessment, and the construction of climate change response models. This article systematically reviews the research progress of nematode metabolic footprint, reveals its ecological regulatory mechanisms, and proposes that future research should focus on the integration of omics technologies and the development of cross-scale models to provide theoretical support for soil resource management and the achievement of carbon neutrality goals.

Enhancing additive manufacturing for multi-layer samples: experimental analysis of aluminum sheet thickness and perforation geometry via fused filament fabrication

The widespread integration of 3D printing technologies across diverse sectors, including prototyping and automotive industries, underscores their growing importance. This study aims to investigate the influence of aluminium sheet parameters (specifically, thickness, number, and perforation diameter) on the mechanical properties of multi-layer specimens fabricated using Fused filament fabrication (FFF) 3D printing. Polylactic acid sheets were deposited on both sides of aluminium sheets, creating a sandwich structure to enhance strength and flexibility. Aluminium sheets of varying thickness (1 mm and 2 mm) with perforations of 5 mm and 7 mm diameters were examined. Mechanical testing included flexural, compression, and impact assessments. Results from the flexural tests demonstrated that specimens reinforced with 2 mm thick aluminum sheets exhibited significantly higher force at failure. Compression testing revealed a notable increase in maximum compressive force for samples incorporating aluminium sheets. Charpy impact testing indicated an 882% improvement in impact strength for samples with a 2 mm aluminium sheet compared to those without. These findings highlight the critical role of aluminium sheet parameters in enhancing the mechanical performance of FFF-printed multi-layer structures. The study provides valuable insights for optimizing the design and fabrication of composite materials in additive manufacturing applications. The enhanced mechanical properties observed underscore the potential for these materials in sectors requiring robust and resilient components, such as aerospace and automotive industries.

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative potential of AI in veterinary diagnostic imaging and radiation oncology. This position statement outlines the guiding principles for the ethical development and integration of AI technologies to ensure patient safety and clinical effectiveness. Artificial intelligence systems must adhere to good machine learning practices, emphasizing transparency, error reporting, and the involvement of clinical experts throughout development. These tools should also include robust mechanisms for secure patient data handling and postimplementation monitoring. The position highlights the critical importance of maintaining a veterinarian in the loop, preferably a board-certified radiologist or radiation oncologist, to interpret AI outputs and safeguard diagnostic quality. Currently, no commercially available AI products for veterinary diagnostic imaging meet the required standards for transparency, validation, or safety. The ACVR and ECVDI advocate for rigorous peer-reviewed research, unbiased third-party evaluations, and interdisciplinary collaboration to establish evidence-based benchmarks for AI applications. Additionally, the statement calls for enhanced education on AI for veterinary professionals, from foundational training in curricula to continuing education for practitioners. Veterinarians are encouraged to disclose AI usage to pet owners and provide alternative diagnostic options as needed. Regulatory bodies should establish guidelines to prevent misuse and protect the profession and patients. The ACVR and ECVDI stress the need for a cautious, informed approach to AI adoption, ensuring these technologies augment, rather than compromise, veterinary care.

Bio-nanoparticles sensor couple with smartphone digital image colorimetry and dispersive liquid–liquid microextraction for aflatoxin B1 detection

A novel nanobiosensor-based colorimetric method was developed by integrating ZnO nanoparticles functionalized with curcumin, dispersive liquid–liquid microextraction (DLLME), and smartphone digital image colorimetry for the sensitive detection of aflatoxin B1 (AFB1) in baby food samples. The unique combination of biologically-derived ZnO nanoparticles with curcumin created a sensing platform, while DLLME provided efficient pre-concentration of the target analyte. A custom-designed portable colorimetric box enabled standardized image capture and analysis using a smartphone camera and colorimetric software. Under optimized conditions using chloroform as the extraction solvent and acetonitrile as the disperser solvent, the method achieved a remarkable limit of detection of 0.09 μg/kg within linear concentration range of 0–1 μg/L. The calibration curves demonstrated excellent linearity (R2 > 0.9906) with high precision (RSD < 5.52%). The method was successfully validated using baby food samples, achieving high recoveries (89.8–94.2%). This innovative integration of nanobiosensing, microextraction, and smartphone technology offers a rapid, highly sensitive, and cost-effective platform for on-site AFB1 detection in food safety applications, particularly beneficial for resource-limited settings.

IMPACTO DO PROTOCOLO ATLS NO ATENDIMENTO AO POLITRAUMATIZADO: REVISÃO INTEGRATIVA DAS EVIDÊNCIAS E IMPLICAÇÕES NA MORTALIDADE HOSPITALAR

Polytrauma remains one of the leading causes of morbidity and mortality worldwide, requiring systematized emergency approaches to optimize patient survival. The Advanced Trauma Life Support (ATLS) protocol is widely used to ensure rapid assessment and stabilization of polytraumatized patients, prioritizing the identification and treatment of life-threatening injuries through the ABCDE approach. This integrative review analyzed the literature on the impact of ATLS in polytrauma care, focusing on its effectiveness in reducing hospital mortality and improving clinical outcomes. The studies reviewed indicate that ATLS implementation leads to shorter response times, greater diagnostic accuracy, and improved coordination among multidisciplinary teams. Moreover, the continuous training of healthcare professionals and the proper structuring of hospital services emerge as critical factors for the protocol’s success. However, challenges such as disparities in ATLS implementation across different healthcare settings, the need for constant updates, and the integration of new technologies highlight the necessity for further research. It is concluded that ATLS plays an essential role in reducing hospital mortality, requiring the strengthening of professional training and hospital infrastructure to ensure its large-scale effectiveness.

Digital Health Literacy Questionnaire for Older Adults: Instrument Development and Validation Study.

The integration of digital technology into older adult health and care has enhanced the intelligence of health and older adult care products and services while also transforming how seniors acquire and share health information. Assessing older adults' digital health literacy (DHL) is crucial for developing targeted interventions. This study aims to develop and validate a DHL assessment questionnaire for older adults. It also seeks to evaluate the questionnaire's scientific validity and feasibility among community-dwelling older adults in China, providing a reliable tool for assessing their level of DHL. A literature review, focus group discussions, and the Delphi method were used to construct the questionnaire item pool and perform item screening. Item analysis was conducted for comprehensive evaluation, and questionnaire validity was assessed through construct validity (exploratory factor analysis, confirmatory factor analysis, convergent validity, and discriminant validity), content validity, and criterion-related validity. Reliability was analyzed using Cronbach alpha coefficient, split-half reliability, and test-retest reliability. The study included 710 participants. Item analysis indicated that the questionnaire had strong discriminant validity. Correlation coefficient analysis showed that the item-total correlation coefficients ranged from 0.497 to 0.920 (P<.01). After multiple exploratory factor analyses, 6 common factors were extracted, with a cumulative variance contribution rate of 73.745%. Confirmatory factor analysis demonstrated a good model fit (χ2/df=2.803, root-mean-square error of approximation=0.071, comparative fit index=0.907, goodness-of-fit index=0.773, incremental fit index=0.908, Tucker-Lewis index=0.901, normed fit index=0.863). The questionnaire demonstrated favorable convergent validity, content validity, and criterion-related validity. The total Cronbach α coefficient was 0.976, with dimension-specific Cronbach α coefficients ranging from 0.819 to 0.952, indicating satisfactory internal consistency. Additionally, the test-retest reliability coefficient for the total questionnaire was 0.925, demonstrating good stability over time. This study developed a questionnaire specifically designed to assess DHL in older adults through a scientifically rigorous and systematic process. The questionnaire demonstrates strong psychometric properties and can serve as an empirical tool for health professionals to design personalized intervention policies and enhance health service delivery.

THE IMPORTANCE OF UTILIZING BIG DATA ANALYTICS IN U.S. HEALTHCARE SUPPLY CHAIN MANAGEMENT

This study investigates the importance of utilizing Big Data Analytics (BDA) in U.S. Healthcare Supply Chain Management (HCSCM) to enhance operational efficiency, decision-making, and resource management. A literature review was conducted, analyzing 15 studies that explored the application of BDA in supply chain management, providing insights into research methods, data collection techniques, and the effectiveness of BDA in healthcare. The findings revealed that there is an increasing trend in the use of Big Data Analytics (BDA) in Supply Chain Management (SCM), particularly in the healthcare sector. The study identified several key enablers: Effective technological infrastructure, such as high-speed internet and cloud-based platforms is identified to be essential for storing and processing large volumes of healthcare data. The study also highlighted the importance of a skilled workforce, particularly data scientists and healthcare logistics professionals, to manage and analyze data effectively. Leadership support, adequate financial resources, and compliance with regulatory requirements were also key enablers found in the study. However, significant challenges were identified, including concerns over data privacy and security under HIPAA regulations, inconsistent data quality across systems, high implementation costs, and organizational resistance to change. These challenges hinder the optimal utilization of BDA in healthcare supply chains. In conclusion, the study emphasizes the transformative potential of BDA in improving healthcare supply chain management by enhancing decision-making, efficiency, and resource optimization. However, overcoming barriers such as data privacy concerns, high costs, and workforce resistance is important to fully leverage the benefits of BDA in SCM. Effective technological integration, leadership support, and investment in skilled personnel are essential for successful implementation and achieving improved operational outcomes in healthcare supply chains. KEYWORDS: Big Data Analytics, Healthcare Supply Chain, Predictive Analytics, Resource Optimization, Real-Time Monitoring, Logistics Optimization

Expert System for Automation in the Implementation of COBIT 5 Case study: Teacher evaluation at UPPachuca

Information technology (IT) governance and management are critical to organizational success inthe digital age. The COBIT (Control Objectives for Information and Related Technologies) frameworkhas been widely recognized as an effective tool for aligning IT with strategic objectives, managing risksand ensuring regulatory compliance. However, COBIT implementation and monitoring can becomplex and resource-demanding, which has generated growing interest in its automation. Thisarticle explores how COBIT model automation, through the integration of technology, can optimizeIT management processes, improve the accuracy of controls, and provide real-time information.Additionally, the challenges inherent to this automation are addressed, such as adaptation tospecific contexts and integration with pre-existing systems. Based on a theoretical analysis and a casestudy, a reference framework is offered for the successful implementation of COBIT automation,highligh its potential to transform IT governance in organizations in various sectors

Impact of open and distance learning on agricultural education: A systematic review and bibliometric analysis

Open and distance learning (ODL) plays a crucial role in agricultural education due to its potential to transform knowledge dissemination. It enhances agricultural productivity and addresses educational accessibility challenges. This study employs systematic literature review and bibliometric analysis to examine the evolution and impact of ODL in agricultural education. A total of 759 relevant studies were identified from the Web of Science database and analysed using VOS viewer and R Studio to map research trends and thematic clusters. The selection process was systematically documented following the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The analysis highlights the growing scholarly interest in this field, with significant research output emerging between 2020 and 2024 and peaking at 90 articles in 2022. The bibliometric analysis reveals diverse authorship with 3789 contributors and a strong collaborative focus, as evidenced by a 36.76 % international co authorship rate. The thematic analysis underscores four major clusters focusing on the impact of COVID-19, training and technology integration, educational frameworks and curriculum design. Geographical contexts indicate varied implementation across regions, with countries such as the USA, the UK, Germany and Italy emerging as central nodes in research networks. Future research should explore low-tech solutions, assess long-term impacts, integrate emerging technologies and establish robust evaluation frameworks. The findings highlight ODL's vital role in creating an inclusive, knowledge driven agricultural sector aligning with global development goals.

Implementation of Good Corporate Governance in Madrasah Cooperatives as a Strategic Management Approach

This study aims to explore change management strategies in Islamic education to adapt to contemporary educational reforms and trends. The focus of the study covers three main areas: integration of digital technology as a catalyst for change, application of collaborative learning as an innovative approach, and implementation of inclusive and differentiated learning to enhance learning effectiveness. The methods used include in-depth interviews, participant observation, documentation analysis, and Focus Group Discussion (FGD). The data collected were analyzed using coding techniques, thematic analysis, and triangulation to ensure the validity and reliability of the findings. The results showed that the application of digital technology, such as e-learning platforms and interactive applications, significantly increased student engagement and enriched the learning experience. Collaborative learning proved effective in developing social skills, cooperation, and deeper understanding, while inclusive and differentiated strategies ensured that all students, including those with special needs, had access to equitable and effective education. In addition, innovations in teaching and assessment methods helped create a dynamic learning environment that was relevant to students' needs. The conclusion of this study shows that the strategies implemented not only made Islamic education more adaptive and relevant to the digital era but also contributed to improving the overall quality of learning. This study contributes to the scientific field by enriching the literature on change management in Islamic education, although it is still limited to a certain scope. Further research is needed to explore the implementation of this strategy in various contexts and assess its long-term impact.

Transforming E-Voting with Blockchain: A Review of Technologies and Practical Implementations

The integration of blockchain technology into electronic voting systems promises to address critical challenges in electoral processes, such as security, transparency, and trust. Traditional voting systems, whether manual or electronic, face concerns related to privacy, data integrity, and vulnerability to manipulation. Blockchain, with its decentralized and immutable nature, offers a transformative solution to these challenges by ensuring end-to-end verifiability and tamper-resistant record-keeping. This review paper explores the current landscape of blockchain-based e-voting systems, evaluates existing technologies, and highlights open challenges that need to be addressed for large-scale adoption. It also presents insights from practical implementations, including an overview of a blockchain-based e-voting project. The paper aims to provide a comprehensive understanding of how blockchain can reshape the future of secure and transparent voting systems.

A Blockchain-Based Digital Product Passport System Providing a Federated Learning Environment for Collaboration Between Recycling Centers and Manufacturers to Enable Recycling Automation

As the global emphasis on sustainable resource management intensifies, the need for efficient recycling automation becomes critical. This paper addresses the integration of blockchain technology and federated learning to enhance the automation and efficiency of recycling processes. The authors propose a novel digital product passport system that utilizes blockchain for secure data sharing and federated learning for continuous improvement of recycling automation models. Manufacturers develop baseline models using synthetic data, which are then refined by recycling centers using real-world data. The updated models are aggregated through a federated averaging algorithm. Our experimental results demonstrate that the integrated system not only enhances the accuracy and efficiency of recycling processes but also ensures the privacy and security of data across different stakeholders. The performance of the federated models shows significant improvement over traditional recycling methods. This paper provides a scalable and secure framework that supports the circular economy by enabling enhanced collaboration between manufacturers and recycling centers. The blockchain-based digital product passport system ensures data integrity and transparency, contributing to more sustainable recycling practices.

Students' Insights into the Libraries

This article presents a forward-looking perspective on the evolution of libraries over the next century, focusing on the integration of advanced technology and the preservation of traditional library elements. Drawing from personal experiences and anecdotes from students at the University of Florida (UF), it highlights the multifaceted role libraries play in academic life, serving as essential spaces for studying, collaboration, and resource borrowing. It anticipates a future where libraries are equipped with automated systems for book retrieval and checkout, alongside a plethora of digital tools such as 3D printers and high-tech devices, while also maintaining the classic library ambiance cherished by users. The reflections of students underscore the libraries' impact on productivity, community, and the discovery of unique resources, revealing a deeper appreciation for libraries as dynamic hubs of learning and collaboration.

Innovations and Technological Interventions for Sustainability: Emerging Challenges

This research explores the integration of sustainable practices and technology in the manufacturing industry, highlighting areas for improvement and strategies to overcome implementation challenges. The study examines the Barriers hindering the widespread adoption of sustainable technologies, the current state of sustainable practices in manufacturing Perceived effectiveness of technological interventions on sustainability, Leadership and organizational culture's impact on sustainability, Employee engagement and training in sustainable practices, Insights gathered from 100 participants across five manufacturing enterprises. Key findings indicate are Significant obstacles to adopting sustainable practices exist, Variability in current sustainability initiatives, favorable perceptions of technological interventions, Leadership's crucial role in promoting sustainability, and Essential need for employee engagement and training. This study contributes significantly to enhancing sustainable practices in the dynamic manufacturing sector, providing valuable insights for industry stakeholders.

Advancing Somali agriculture through digitalization: Assessing the impacts of ICT and foreign direct investment on food production

Abstract The integration of information and communication technology (ICT) into Somali agriculture has the potential to transform traditional practices, which enhances efficiency and increases production. Despite ICT’s transformative potential, comprehensive empirical studies analysing its combined influence with foreign direct investment (FDI) on food production in Somalia remain scarce. Employing the autoregressive distributed lag (ARDL) technique and Kernel-based regularised least squares (KRLS) methodology, this study examines these relationships using time series data from 1990 to 2022. The findings reveal that agricultural labour, cropped land area, and trade openness significantly enhance food production in the short- and long-run. Remarkably, mobile phone usage exhibits a positive association with food production in Somalia, which reinforces the critical role of digital communication. Conversely, internet usage negatively affects food production, potentially reflecting challenges in integrating technology with traditional practices. Capital and FDI demonstrate adverse but statistically insignificant effects. By reinforcing the ARDL findings further, the KRLS analysis further demonstrates the heterogeneous effects of these variables. Specifically, there is an increasing marginal impact of agricultural labour, land, FDI, trade openness, and mobile usage on food production, while capital and internet usage exhibit decreasing marginal effects. Based on these insights, this study suggests optimising land use, fostering ICT adoption, addressing inefficiencies in capital investments, and enhancing trade openness to support sustainable agricultural growth.