Optimal Resource Research Articles

Evaluation of Farming System Modules for Improving Profitability of Small Farmers in Coastal Odisha, India

The study evaluated the profitability, sustainability and environmental impact of integrated farming system (IFS) modules, including cropping systems, horticulture, dairy, poultry and fishery in an area of 1.2 ha, in coastal Odisha, India. This research project was part of the sixth production cycle under the All India Coordinated Research Project on IFS and conducted at the Central Research Station, Odisha University of Agriculture and Technology, Bhubaneswar, during the 2022-23 agricultural year. The experiment integrated five major modules: cropping systems (0.32 ha), horticulture (0.31 ha), dairy (2 cross-bred cows), poultry (500 coloured chicken for meat) and fishery (0.40 ha). Productivity was assessed using rice equivalent yield (REY) and economic returns were calculated for each module. Sustainability was evaluated through sustainable yield indices and carbon footprint assessments, while soil quality parameters, such as bulk density, pH, organic carbon and available macronutrients, were monitored over six years. Dairy emerged as the most productive module, yielding the highest REY (15.14 t) and gross return (Rs.3,27,040). Horticulture recorded the highest net return (Rs.1,40,627) and benefit-cost ratio (3.61). The cost of production was mainly influenced by external inputs (52%), followed by labour (33%) and system-recycled inputs (15%). The system generated 399 man-days annually, with over half contributed by dairy. Environmental sustainability was achieved through agroforestry and compost application, making the model carbon-negative. Dairy was the largest greenhouse gas emitter, while horticulture and biomass contributed significantly to carbon sequestration. Over six years, soil quality improved, with reduced bulk density, increased water-holding capacity and enhanced organic carbon levels. Sustainable yield indices for four modules were above 0.6, with dairy showing the highest sustainability. Poultry performance required further improvement to enhance overall system sustainability. In conclusion, the integrated farming system enhanced overall productivity, profitability and environmental sustainability for smallholder farmers in coastal Odisha, showcasing its potential for resource optimization and resilience.

The Role of Remote Sensing and GIS in Sustainable Development and National Resilience

Purpose: This study explores the critical role of Remote Sensing (RS) and Geographic Information Systems (GIS) in supporting Sustainable Development Goals (SDGs) and strengthening national resilience. It aims to highlight the originality and significance of RS and GIS as strategic tools for spatial data collection, analysis, and visualization. Study Design/Methodology/Approach: This research employs a qualitative methodology based on a literature review and case studies. The study evaluates the application of RS and GIS in various sectors, including disaster mitigation, environmental preservation, and resource optimization. Findings: The findings indicate that RS and GIS significantly contribute to SDGs by facilitating actions on climate change, biodiversity conservation, equitable resource distribution, and poverty alleviation. Additionally, RS and GIS strengthen national resilience through economic optimization, ecological disaster mitigation, conflict resolution, and strategic area monitoring. Originality/Value: This study underscores the integrative role of RS and GIS in aligning SDGs and national resilience objectives, offering practical implications for decision-making across multiple sectors. The results provide a foundation for policymakers and practitioners to leverage RS and GIS for holistic and sustainable development.

Integrating Artificial Intelligence in agricultural higher education: Transforming learning and research

The integration of Artificial Intelligence (AI) in agricultural higher education is transforming the landscape of agricultural practices and research. This paper explores the multifaceted applications of AI technologies in the curriculum and pedagogical approaches of agricultural institutions. By enhancing data analysis, predictive modeling, and decision-making processes, AI empowers students and researchers to tackle complex agricultural challenges such as crop management, pest control, and resource optimization. Furthermore, the study examines the implications of AI on student engagement, skill development, and interdisciplinary collaboration. As agricultural sectors increasingly rely on data-driven solutions, the incorporation of AI in higher education not only prepares future professionals with essential competencies but also fosters innovation in sustainable agricultural practices. This article underscores the critical role of AI in shaping the future of agricultural education and its potential to revolutionize the industry.

Satellite Edge Computing for Mobile Multimedia Communications: A Multi-agent Federated Reinforcement Learning Approach

The rapid expansion of satellite mega-constellations has highlighted the potential of satellite edge computing as a promising solution for mobile multimedia communications. While reinforcement learning has been explored in satellite communication systems, significant challenges remain, including high latency and limited resources. This study addresses these challenges by focusing on the joint optimization of communication, computing, and caching resources in satellite edge computing to support mobile multimedia applications. A mixed-integer nonlinear programming (MINLP) problem is formulated with the objective of minimizing the total delay experienced by mobile users, subject to multidimensional resource capacity constraints, which is NP-hard and computationally intractable to solve in polynomial time. To address this complexity, we propose a multi-agent federated reinforcement learning (MAFRL) approach as an efficient solution. In this framework, each satellite operates as an autonomous learning agent equipped with an actor-critic network structure. The proposed MAFRL method demonstrates superior performance, achieving lower delays compared to all baseline approaches. It effectively optimizes delay-sensitive mobile multimedia communications by minimizing total delay and improving task offloading ratios. To the best of the authors’ knowledge, this study is the first to introduce a MAFRL-based approach for resource allocation in satellite edge computing, marking a significant contribution to the field.

Toward Enhanced Seed Potato Yield: Ultrasonication Techniques for Sustainable Agricultural Development

The study aimed to explore the potential of ultrasonication techniques in seed potato production as a sustainable agricultural innovation. By improving seed potato efficiency and promoting resource optimization, this research aligns with the goals of sustainable agricultural and rural development, addressing challenges such as food security, environmental preservation, and economic viability in rural farming communities. The study was conducted over three years in the central–eastern region of Poland, utilizing a randomized block design with a split-split-plot approach. The main experimental factor was the cultivation technology, which included (a) an innovative ultrasonic pre-sowing treatment method and (b) a traditional cultivation method without such treatment. The secondary factor was the potato varieties. The ultrasonic treatment of tubers was performed using an ultrasonic tub-type device equipped with piezoelectric transducers. Cultivation technology, potato varieties, and weather conditions had a significant impact on the yield of tubers in the seed potato fraction size, the number of tubers in this fraction, and the multiplication coefficient. Additionally, the genetic characteristics of the studied varieties and random environmental factors significantly influenced the weight of a single seed potato tuber and the number of shoots produced by each plant.

Mathematical optimisation model for searching duplicate string objects in the memory snapshot

The purpose of this paper is to identify the increased memory usage of software appli-cations. The modern software development cycle focuses on functionality and often ignores aspects of optimal resource usage. Limited physical scaling sets an upper limit on the system's capacity to process requests. The presence of unchanged objects with the same information is a sign of increased memory consumption. Avoiding duplicate objects in memory allows for a more rational use of the existing resource and an increase in the amount of information proc-essed. Existing scientific publications focus on the study of memory leakage problems, and limit their attention to excessive memory usage due to the lack of a unified model for finding excessive memory usage. It is worth noting that existing programming templates contain the ‘object pool’ template, but leave the conclusion about the feasibility of its implementation to engineers without providing a mathematical basis. The paper presents the development of a mathematical model for the process of detecting duplicate objects with the immutability prop-erty of the String type in a memory snapshot. Industrial systems that require hundreds of GB of RAM to operate and contain millions of objects in RAM are analysed. Given this scale of data, there is a need to optimise the duplicate detection process. The research method is to analyse memory snapshots of highly loaded systems using the software code developed on .NET technology and the ClrMD library. The memory snapshot reflects the state of the proc-ess under study at a given time, contains all objects, threads and operations performed. The ClrMD library allows you to programmatically examine objects, their types, get field values, and build graphs of relationships between objects. Based on the results of the study, an opti-misation was proposed that allows to speed up the process of finding duplicates several times. The scientific contribution of the study is the creation of a mathematically sound approach that significantly reduces the use of memory resources and optimises computing processes. The practical usefulness of the model is confirmed by the optimisation results achieved through the recommendations, reduced hosting costs (which provides greater cost-effectiveness in the deployment and use of software systems in industrial environments), and increased data processing.

Climate-adaptive optimal water resources management: A multi-sectoral approach for the Munneru river basin, India.

Climate change profoundly affects water resource allocation by disrupting the availability, distribution, and quality of water across various regions. Optimal allocation of water resources represents a comprehensive strategy for water resource management by addressing the intricate connections between water allocation systems and their repercussions on the environment, society, and economy. In this study, an Optimal Water Resources Management (OWRM) framework was developed, focusing on the optimal allocation of water resources and crop planting structures across various sectors. The Munneru river basin, located in the lower Krishna River region of India, was selected as the study area to validate the proposed framework. Five distinct water-demanding sectors-irrigation, domestic, livestock, industrial, and irrigation water requirements for major agricultural seasons-were identified in the study area, and their sectoral water demands were calculated at the basin level. The crop water and irrigation water requirements for various crops were estimated using the CROPWAT tool, while the framework also optimized crop planting structures to maximize returns and resource efficiency. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was applied, with two objectives focused on equity and economic value. Superior solutions were then identified using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The OWRM framework was applied after identifying critical cases of water availability in future periods under climate change scenarios. Through this integrated approach, an average annual increase of 52.6% in agricultural sector returns was achieved for the simulation period (2016-17 to 2022-23). For a condition of providing at least 90% water supply to each sector, the optimal crop patterns led to revenue increases of 136.4%, 59.2%, and 74.7% compared to actual revenues for the water years 2020-21, 2021-22 and 2022-23 respectively. The developed OWRM methodology can be applied to other basins across the world that are impacted by climate change.

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features

BackgroundAccurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting influenza outbreaks, aiming to achieve real-time and accurate influenza prediction.MethodsWe proposed a GNN-based approach with dual topology processing, capturing both geographical and socio-economic associations among counties/cities. The model inputs consist of weekly matrices of influenza-like illness (ILI) rates at city level, along with geographical topology and functional topology. The model construction involves temporal feature extraction through 1-dimensional gated causal convolution, spatial feature embedding through graph convolution, and additional adjustments to enhance spatiotemporal interaction exploration. Evaluation metrics include four commonly used measures: root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and Pearson correlation (Corr).ResultsOur approach for predicting influenza outbreaks achieves competitive performance on real-world datasets (Corr = 0.8202; RMSE = 0.0017; MAE = 0.0013; MAPE = 0.0966), surpassing established baselines. Notably, our approach exhibits excellent capability in accurately and timely capturing short-term influenza outbreaks during the flu season, outperforming competitors across all evaluation metrics.ConclusionThe incorporation of dual topology processing and the subsequent fusion mechanism allows the model to explore in-depth spatiotemporal feature interactions. Demonstrating superior performance, our approach shows great potential in early detection of flu trends for facilitating public health decisions and resource optimization.

Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

Research on Strategies for Promoting the Education Management of Higher vocational College Students’ Sustainable Career Planning Based on the SCCT Theory and SWOT Analysis

Sustainable career planning among higher vocational college students plays a significant role in personal development, career planning, resource optimization, social harmony, and sustainable development. This study investigates the current status, anticipated scenarios, and influencing factors of sustainable career planning among higher vocational students based on Social Cognitive Theory (SCT) and Social Cognitive Career Theory (SCCT). The findings indicate that the level of sustainable career planning among the surveyed students is moderate and is influenced by multiple factors. The study develops educational management strategies to promote sustainable career planning among higher vocational students, utilizing SWOT analysis and focus group discussions. High-level experts evaluate these strategies for their adaptability and feasibility. Based on the consensus of these experts, it was determined that there is agreement on “Strategies for Promoting the Education Management of Higher Vocational College Students’ Sustainable Career Planning Based on the SCCT Theory and SWOT Analysis.

Strategic evaluation of European Inland transport companies for European green deal compliance: A multi-criteria decision making-analysis.

The purpose of this study is to evaluate the readiness of European inland transport companies for the European Green Deal by analyzing the causal relationships between key factors essential for EGD implementation. The research identifies six critical factors: Investment in Green Technologies, Policy Alignment and Compliance, Stakeholder Collaboration, Resource Management, Employee Training and Engagement, and Emission Reduction Initiatives. This research offers the first integrated evaluation of EGD readiness utilizing a combination of fuzzy-DEMATEL, multiple regression analysis, and Structural Equation Modeling aimed at uncovering systemic interactions among the factors in the system. The study revealed Resource Management and Employee Training as significant drivers in enhancing the readiness, highlighting the role of human capital and resource optimization in environmental sustainability. Expert panel, consisting of 19 industry representatives selected through purposive sampling, provided insights on the effective relationships which offer practical strategies for managers to prioritize resource management and employee engagement to achieve Green Deal compliance and guiding more strategic and efficient allocation of resources to enhance alignment with European Green Deal stipulations.

Advances in the selective c-MET kinase inhibitors: Application of fused [5,6]-Bicyclic nitrogen-containing cores for anticancer drug design.

Over the past two decades, small molecules bearing [5,6]-bicyclic nitrogen-containing cores have emerged as one of the most extensively studied structures for the development of selective c-MET kinase inhibitors. Structure-activity relationship (SAR) studies have demonstrated that modifying these cores can significantly impact the biological properties of c-MET inhibitors, including safety/toxicity, potency, and metabolic stability. For example, although c-MET kinase inhibitors containing the [1,2,4]triazolo[4,3-b][1,2,4]triazine scaffold (core P) exhibit high inhibitory potency, they often face challenges due to metabolic stability defects. Alternatively, compounds containing [1,2,3]triazolo[4,5-b]pyrazine (core K) and [1,2,4]triazolo[4,3-b]pyridazine (core I) scaffolds demonstrate lower potency but improved metabolic stability, allowing some of them to progress into clinical trials and even be approved as novel anticancer drugs. Fortunately, X-ray crystallography studies have well elucidated key interactions between [5,6]-bicyclic nitrogen-containing cores and crucial amino acid residues within the c-MET active site. These insights emphasize the significance of π-π stacking interactions with Tyr1230 and hydrogen bonding with Asp1222, providing valuable guidance for the targeted design and optimization of selective c-MET kinase inhibitors. Following the identification/introduction of sixteen distinct [5,6]-bicyclic nitrogen-containing cores (cores A-P) utilized in the design of selective c-MET kinase inhibitors over the past two decades, this manuscript offers a comprehensive review of their roles in drug development of anticancer agents, and describes the various synthesis methods employed. The insights presented herein can serve as a resource for better structural optimization of c-MET kinase inhibitors in the future research.

Optimization of hybrid manufacturing thermal energy resources and simulation of enterprise economic benefits based on blockchain technology

Optimization of hybrid manufacturing thermal energy resources and simulation of enterprise economic benefits based on blockchain technology

Joint optimization trajectory and resource allocation for UAV-assisted semantic communications

Semantic communication can effectively save bandwidth, and enhance communication capabilities by transmitting semantic features. However, semantic communication has certain limitations, such as limited application scenarios and inflexible deployment. To this end, we investigate an unmanned aerial vehicle (UAV)-assisted semantic communication system in this paper. An UAV serves as a mobile base station to service users in designated area. Each user has different requirements for transmission delay and performance, and the UAV has a limited maximum flight time. We need to achieve the communication goals of all users in the shortest possible time, that is, to ensure that the information received by each user meets latency and quality requirements. This is a non-convex optimization problem, which is very complicated to solve using traditional methods. In order to solve this problem, we propose a deep reinforcement learning algorithm based on Proximal Policy Optimization 2. The simulation results confirm the effectiveness of our proposed algorithm.

Optimal task partitioning to minimize failure in heterogeneous computational platform

The increased energy consumption by heterogeneous cloud platforms surges the carbon emissions and reduces system reliability, thus, making workload scheduling an extremely challenging process. The dynamic voltage- frequency scaling (DVFS) technique provides an efficient mechanism in improving the energy efficiency of cloud platform; however, employing DVFS reduces reliability and increases the failure rate of resource scheduling. Most of the current workload scheduling methods have failed to optimize the energy and reliability together under a central processing unit - graphical processing unit (CPU-GPU) heterogeneous computing platform; As a result, reducing energy consumption and task failure are prime issues this work aims to address. This work introduces task failure minimization (TFM) through optimal task partitioning (OTP) for workload scheduling in the CPU-GPU cloud computational platform. The TFM-OTP introduces a task partitioning model for the CPU-GPU pair; then, it provides a DVFS- based energy consumption model. Finally, the energy-load optimization problem is defined, and the optimal resource allocation design is presented. The experiment is conducted on two standard workloads namely SIPHT and CyberShake workload. The result shows that the proposed TFA-OTP model reduces energy consumption by 30.35%, reduces makespan by 70.78% and reduces task failure energy overhead by 83.7% in comparison with energy minimized scheduling (EMS) approach.

Problematics of Domestic Violence Case Handling of PPA Unit of Pangkalpinang Police: Analysis of Case Arrears

This research examines the problems in handling Domestic Violence (KDRT) cases at the Women and Children Protection Unit (PPA) of the Pangkalpinang City Police, with a focus on the backlog of cases that occurred during the 2021-2023 period. This study aims to identify the factors causing the backlog of KDRT cases and analyze its implications for the performance of the PPA Unit. The research method used is empirical legal research with a qualitative approach, through in-depth interviews, document studies, and observations. The results show that the backlog of cases is caused by three main factors: the uncooperative behavior of victims, limitations in tracing perpetrators, and institutional resource constraints. The implications of the case backlog include a decrease in the effectiveness of case handling, psychological pressure on personnel, a decrease in public trust, and obstacles in inter-agency coordination. This study produces theoretical propositions in the form of "Integrated KDRT Case Management Theory" and "Integrated Case Management and Resource Optimization Theory" as solutions to address the problem of the KDRT case backlog.

AI-based processing of future prepared foods: Progress and prospects.

The prepared foods sector has grown rapidly in recent years, driven by the fast pace of modern living and increasing consumer demand for convenience. Prepared foods are taking an increasingly important role in the modern catering industry due to their ease of storage, transportation, and operation. However, their processing faces several challenges, including labor shortages, inefficient sorting, inadequate cleaning, unsafe cutting processes, and a lack of industry standards. The development of artificial intelligence (AI) will change the processing of prepared foods. This review summarizes the progress and prospects of AI applications in the sorting/classification, cleaning, cutting, preprocessing, and freezing of prepared foods, encompassing techniques such as mathematical modeling, chemometrics, machine learning, fuzzy logic, and adaptive neuro fuzzy inference system. For example, AI-powered sorting systems using computer vision have improved accuracy in ingredient classification, while deep learning models in cleaning processes have enhanced microbial contamination detection with high spectral imaging techniques. Despite challenges like managing large-scale data and complex models, AI has shown significant potential to inspire both industry practice and research. AI applications can enhance the efficiency, accuracy, and consistency of prepared foods processing, while also reducing labor costs, improving hygiene monitoring, minimizing resource waste, and decreasing environmental impact. Furthermore, AI-driven resource optimization has demonstrated its potential in reducing energy consumption and promoting sustainable food production practices. In the future, AI technology is expected to further improve model generalization and operation precision, driving the food processing industry toward smarter, more sustainable development. This study provides valuable insights to encourage further innovation in AI applications within food processing and technological advancement in the food industry.

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

Transformative Approaches to Agricultural Sustainability: Automation, Smart Greenhouses, and AI

Agricultural sustainability is continually undermined by climate change, resource depletion, and the increasing worldwide need for food. Fundamental technologies, including automation, smart greenhouses, and artificial intelligence (AI), are changing modern agricultural methods by providing novel ways to improve sustainability in farming. This review study examines the significance of these technologies in advancing sustainable agricultural systems, particularly their effects on resource optimization, environmental conservation, and economic efficiency. Automation technologies, such as robots, drones, and autonomous vehicles, enhance farm management by enhancing efficiency and minimizing resource waste. Intelligent greenhouses, fitted with IoT sensors and temperature regulation systems, provide precise management of environmental conditions, therefore improving agricultural output while minimizing water and energy use. AI-driven technologies, including machine learning and predictive analytics, enhance crop health monitoring, pest management, and yield prediction, enabling data-informed decision-making. The research analyses the combination of various technologies, focusing their synergies in developing comprehensive smart agricultural systems that promote enduring sustainability. Despite the apparent promise, challenges like substantial initial investment, technological intricacy, and scalability persist. This review continues by addressing future directions, policy implications, and research requirements for promoting the use of these technologies to enhance global agricultural sustainability.

Dual sensory loss and cognitive impairment: A study in elderly users of gerontological centers in a Galician urban area.

Several studies indicate that dual sensory loss (DSL) is related to cognitive impairment. However, there are still no conclusive data on the role of each individual deficit on cognitive performance when both co-occur in the same person. This study aimed to analyze the possible association between sensory deficit and cognitive impairment in a sample of older adults and whether this association differs in subjects with DSL compared with those with single or no sensory impairment. Participants of this study were older adult users of gerontological centers in an urban area of Galicia (Spain). All subjects underwent visual and auditory screening tests to detect the presence of DSL. The Spanish version of the Mini-Mental State Examination test was used to identify cognitive impairment. Hearing loss was defined according to Ventry and Weinstein's criteria. Visual impairment was defined as a binocular presenting visual acuity <0.5 (20/40). The presence of DSL was established on the basis of the definitions of the single deficits. A total of 534 subjects were examined. Older adults with DSL had the worst results in the Mini-Mental test, and those without sensory impairment had the best. The likelihood of cognitive impairment was greater in older adults with DSL than in those without sensory impairment (odds ratio, 6.06 [3.14 to 11.65]). When only visual impairment was present, the likelihood of cognitive impairment was 3.73 times higher (0.95 to 3.09) than that of subjects with no sensory impairment. Hearing loss alone seems not to be significantly associated with a higher risk of cognitive impairment. The results of this study highlight the importance of continuing research in DSL for the prevention of cognitive impairment, the design of intervention plans, and the optimization of health resources.