Optimal Resource Research Articles

Enhanced resource optimization with the unevenness coefficients use in regular and irregular graph-based networks

The paper presents a simulation-based study of the impact of transmission resource reallocation on the performance of a communication network. The study considers the unevenness coefficients use to control network resources allocation. The results show that adjusting the transmission resource allocation based on the above-mentioned parameter can significantly improve network performance, leading to a equalization of network characteristics, and all of this without increasing the resources pool. The study’s findings provide valuable insights for network designers and operators seeking to optimize network performance in communication systems.

A circular economy integration approach into vertical farming with computer-based simulation model for resource optimization and waste reduction

Integrating circular economy into vertical farming holds promises to transform the current economic landscape and bolster environmental sustainability. However, knowledge gaps persist regarding the efficiency of vertical farming methods within the circular economy context, particularly in the development of a simulation model aimed at resource optimization and waste reduction. This study aimed to analyze the vertical farming methods in the circular economy framework and introduce a zero-waste circular economy model both experimentally and through simulation. In the study, a circular economy model was proposed and implemented, utilizing waste generated from hydroponics and aquaponics, alongside vermicompost tea and aerobic compost tea/quick compost, as nutrients for six distinct halophyte plants in the secondary farming setup. The model serves as a valuable tool for identifying and assessing critical factors influencing the circular economy principle, as well as for optimizing resource allocation and management. Over six weeks, plant growth was monitored and compared across the vertical farming methods. Results show that both hydroponic and aquaponic waste proved superior growth compared to the alternatives, highlighting vertical farming's potential for the circular economy. Using the results from the experimental observation, a simulation model was developed. The simulation model's accuracy was validated by comparing outcomes with actual data. Furthermore, the model was used to optimize for balancing resources and capacities in each stage of vertical farming. The vertical farming circular economy model proposed in this study can serve as a blueprint for agriculturalists and decision-makers to adopt more sustainable and efficient farming practices that not only reduce waste but also generate profits.

No Stakeholder Is an Island in the Drive to This Transition: Circular Economy in the Built Environment

Ensuring optimum utilisation of the Earth’s finite resources engenders the circular economy (CE) concept which has attracted the attention of policymakers and practitioners worldwide. As a bifurcated strategy which involves both scientific knowledge, advanced technologies and behavioural changes, the CE transition is sociotechnical in nature. Yet, prolific studies focus on scientific knowledge and technologies alone, while studies on promoting CE practices or built environment stakeholders’ behaviour are limited. Using Stakeholder Theory, a comprehensive literature review on CE drivers was conducted. Through a questionnaire survey of professionals, key drivers identified were deployed to develop a 20-driver model for CE transition in the built environment. The model is relevant to policymakers and practitioners because it highlights essential drivers for optimum resource allocation. Moreover, the findings apprise policymakers of the drivers that pertain to key stakeholders (i.e., professional and higher educational institutions, society and clients, government and firms), thus stating the requirements for driving each stakeholder to achieve this sociotechnical transition.

A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

This study presents a novel Coupled Human And Natural Systems (CHANS) modelling framework that integrates a hydrodynamic model with an agent-based model at the memory level within a multi-GPU computing environment. This two-way coupled model captures real-time interactions between human activities and flood dynamics, with a focus on the deployment of temporary flood defences during the 2015 Desmond flood in Carlisle, UK. The findings reveal that temporary defences can significantly reduce flood inundation by 30% with early warnings and 15% through real-time decision-making, leading to financial savings of £30 million and £15 million, respectively. The study further explores the decision-making process for effective emergency flood management, emphasising the importance of early warnings and resources optimisation. The new CHANS model provides a valuable tool for testing and optimising emergency flood management strategies, highlighting the necessity of directly incorporating human activities into flood risk management.

Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit

Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Resource and trajectory optimization for UAV-assisted MEC communication systems over unlicensed spectrum band

The new radio unlicensed (NR-U) technology is proposed by 3GPP to extend NR to the unlicensed spectrum because of the shortage of the licensed spectrum. Different from the ground and fixed communication equipment-based unlicensed spectrum access scheme, the unmanned aerial vehicle (UAV) mobile platform-based unlicensed spectrum access scheme is not only related to incumbent users but also its trajectory and resource allocation. Therefore, this paper proposes a hybrid unlicensed spectrum access scheme for the UAV-assisted unlicensed mobile edge computing (MEC) communication (UAUM) system, where each flight time slot of the UAV is divided into two parts: power free (PF) and power controlled (PC) stages. In the PF stage, the transmit power is only restrained by the unlicensed spectrum regulations, and thus the UAV can provide high-rate services for real-time downlink users (RDUs) and uplink computing users (UCUs). In the PC stage, the transmit power of the UAV is mainly restrained by the interference to WiFi devices, and thus UAV can be allowed to provide low-rate services for non-realtime downlink users (NDUs) without affecting WiFi users. Based on the proposed scheme, a multi-variable optimization problem regarding trajectory, bandwidth, transmit power, and duty cycle is formulated to maximize the total offloaded computing bits on the premise of ensuring the quality of experience of RDUs, NDUs, and WiFi users under the maximum energy budget. To solve this problem efficiently, we propose an iterative algorithm based on the block coordinate descent method and successive convex approximation technique to decompose the original problem into four optimization subproblems of trajectory, bandwidth, transmit power and duty cycle, which are then solved alternatively in an iterative manner. A large number of simulation results demonstrate that in terms of spectrum efficiency and total offloaded computing bits, the proposed algorithm outperforms other unlicensed spectrum access schemes and optimization algorithms. The other performances of the proposed algorithm are deeply evaluated to prove its effectiveness and feasibility.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

AI-Powered Administration: The Role of Intelligent Tutoring Systems in Education

The integration of Artificial Intelligence (AI) into education is transforming traditional administrative processes and instructional methodologies. This paper examines the pivotal role of Intelligent Tutoring Systems (ITS) in enhancing education administration through AI-powered solutions. By automating routine tasks, providing real-time analytics, and supporting data-driven decision-making, ITS contribute to more efficient and effective management of educational institutions.We explore several models that illustrate the diverse applications of ITS in administration, including automated workflow management, data-driven decision-making, personalized learning support, resource optimization, and enhanced student support services. Each model demonstrates the potential of ITS to streamline operations, improve resource allocation, and offer personalized educational experiences, thereby reducing administrative burdens and fostering a more adaptive and responsive educational environment.Through a comprehensive analysis of current implementations and case studies, this paper highlights the transformative impact of AI-powered ITS on education administration. The findings underscore the importance of careful implementation and continuous evaluation to address challenges such as data privacy and ethical considerations, ensuring that the benefits of AI are equitably distributed among all stakeholders in the educational ecosystem.

Island Water Management Cyprus Facts Finding Mission and Gap Identifications

Water management on islands faces unique challenges due to limited size, isolation, climate change impacts, and economic development needs. These factors intensify water scarcity, highlighting the need for sustainable resource development through multidisciplinary approaches. Cyprus, the third-largest Mediterranean island, exemplifies these challenges, with the lowest water availability per capita in the region and severe water scarcity exacerbated by climate change. Addressing these issues requires a comprehensive master plan that integrates technical, environmental, social, and economic factors, focusing on climate change impacts on the water cycle. The Energy, Environment, and Water Research Center (EEWRC) plays a pivotal role in global resource management, emphasizing sustainable development goals and Integrated Water Resources Management Policy. EEWRC’s key initiatives include hydrodiplomacy and WEF Nexus and Integrated Water Resource Management IWRM, monitoring sediment transport pollution, water quality monitoring of basins and aquifers, smart water management of conventional and non-conventional water, and nature-based solutions for water and land management integration. To improve water management in Cyprus, establishing a National Commission for Water Experts is recommended. This commission would provide a detailed roadmap for water management, aligning with Sustainable Development Goals (SDGs) and the Climate Change Initiative coordinated by the Cyprus Institute. Additionally, organizing a conference focused on “Cyprus Island Water Management” would help launch this concept. In conclusion, it is crucial to develop a long-term strategy for optimal water resource management in Cyprus, addressing water issues, promoting conservation, launching climate-resilient initiatives, and fostering collaboration with national and international entities for success.

Conservation Agriculture is a Sustainable Approach for Future

Conservation agriculture (CA) embodies a forward-looking and sustainable approach to cultivation. It is characterized by a farming system that prioritizes sustainability through practices such as minimizing soil disturbance, maintaining permanent soil cover, and implementing crop rotations. The overarching goal is to preserve soil health and enhance productivity. This method emphasizes the optimization of natural resources, the reduction of inputs, and the enhancement of nutrient utilization efficiency. The primary focus of this review article around conservation agriculture (CA), characterized by revolutionary practices involving minimal soil disturbance (no-till, NT) and the maintenance of continuous soil cover (mulch). Agriculture heavily relies on cultivation and tillage practices. The review first explores the advantages of conventional cultivation techniques before the introduction of conservation tillage (CT), a method that originated in response to the American dust bowl of the 1930s. Subsequently, the review delves into the benefits of CA, considered an improvement over CT. The incorporation of no-till (NT), mulching, and related innovations significantly enhances soil qualities and various biotic factors within the framework of CA. Nevertheless, the promotion of Conservation Agriculture (CA) technologies encounters several challenges. These include the absence of suitable seeders tailored for small-scale farmers, conflicts between the use of CA and livestock feeding for crop residues, the practice of burning crop residues, a scarcity of skilled manpower, and the challenge of overcoming traditional attitudes towards tillage. Drawbacks of conservation agriculture encompass factors such as limited adoption, insufficient knowledge and skills, reliance on herbicides, and initial investment costs, among others.

A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation

Solar energy prediction poses a challenging task that necessitates robust models and precise data to accurately forecast solar energy yield, especially in grid areas with high photovoltaic shares. Existing methods often rely on statistical or physical models, which have limitations in capturing the complex and non-linear relationships between weather variables and solar power generation. In this paper, we address this issue by comparing and evaluating different learning models, ranging from artificial neural networks (ANNs) and random forest models to long- and short-term memory (LSTM) networks, to predict the PV energy yield based on weather forecast data. A methodology has been developed to evaluate various models using real-world datasets from a large-scale industrial solar project, incorporating historical photovoltaic data, meteorological data, and solar irradiation data. The experimental results showed that the Random Forest Algorithm (RFR) consistently outperforms other algorithms, providing a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.15 when applied to historical meteorological datasets. The accuracy of the learning model was improved by combining meteorological data with a solar irradiation dataset to obtain an MAE of 0.03 % and an RMSE of 0.09 %. Validation analysis showed the proposed model is effective in both forecast accuracy and stability. The proposed methodology can provide valuable information to PV system operators, grid managers, and energy planners, facilitating the optimization of solar energy resources.

A deep reinforcement learning-based scheduling framework for real-time workflows in the cloud environment

Benefiting from the flexibility, scalability and many other advantages of cloud computing, an increasing number of research institutions and enterprises are using cloud platforms to deploy their scientific or commercial workflow applications. From the perspective of cloud service providers, how to schedule workflows reasonably to improve user satisfaction and resource utilization is a big challenge. This challenge becomes more prominent in a dynamic cloud environment, where the workload of user-submitted workflows is constantly changing and unpredictable. Traditional scheduling methods designed for fixed workflows or real-time independent tasks are not applicable in such scenarios. In this paper, we present an online scheduling framework for multiple real-time workflows in a dynamic environment. In this framework, the scheduler employs a deep reinforcement learning based algorithm (R-DQN) to assign each task in workflows to a suitable virtual machine instance according to the current situation. Its goal is to achieve optimal task scheduling and resource allocation, so as to minimize workflow makespan and maximize resource utilization. We provide a comprehensive overview of the design and implementation of our approach, and we conduct experiments using various workflow benchmarks with different structures and scales on the WorkflowSim platform. The experimental results demonstrate that our algorithm significantly outperforms the compared algorithms in term of makespan and other metrics. Moreover, our algorithm demonstrates strong performance in generality, robustness and scalability.

Transforming waste into beauty: Sustainable clay mask formulations using ornamental stone processing waste

Waste generated from ornamental stone processing presents a significant opportunity for repurposing in various technological applications. This study investigates incorporating such waste with calcium bentonite to develop an industrial clay suitable for cosmetology. The chemical composition and mineralogical properties of the mixtures were evaluated through comprehensive analysis, such as X-ray fluorescence (XRF) and diffraction (XRD), and scanning electronic microscopy with energy dispersive X-ray spectroscopy (SEM-EDS). Specifically, we investigated a 70/30 mixture of ornamental stone processing waste and calcium bentonite. The results revealed a diverse range of beneficial elements. Additionally, the fine granulometry of the sample renders them ideal for cosmetic applications. Microbiological analyses demonstrate the absence of harmful microbial populations, ensuring product safety. By repurposing waste materials, this research offers valuable insights into sustainable practices in cosmetics and environmental science, underscoring the importance of resource optimization in modern industry.

An efficient algorithm for resource optimization in TWDM passive optical network using a C-RAN

The traditional base station in C-RAN is divided into three parts: a pool of centralized baseband units (BBUs), a fronthaul network that links the BBUs and remote radio units (RRUs), and RRUs. This paper proposes a novel cooperative algorithm for resource optimization in a time-wavelength division multiplexed (TWDM) passive optical network (PON) incorporating a cloud radio access network (C-RAN). First, a joint collaborative strategy is deployed to optimize cooperative caching and transmission in the wireless and optical domains. Then, the quality of experience (QoE) is improved by bandwidth configuration and caching. Simulation results show that the average throughput of the proposed QoE-aware video cooperative caching and transmission mechanism (QACCTM) algorithm is approximately 30% higher than that of other algorithms. Compared with the relative average residual clutter power (RARCP) and quality-aware wireless edge caching (QAWEC) algorithms, the proposed QACCTM algorithm reduces the access delay by approximately 27.1% and 15.9%, respectively.

An intelligent task scheduling approach for the enhancement of collaborative learning in cloud computing

Cloud computing is the foremost technology that reliably connects end-to-end users. Task scheduling is a critical process affecting the performance enhancement of cloud computing. The scheduling of the enormous data results in increased response time, makespan time, and makes the system less efficient. Therefore, a unique Squirrel Search-based AlexNet Scheduler (SSbANS) is created for adequate scheduling and performance enhancement in cloud computing suitable for collaborative learning. The system processes the tasks that the cloud users request. Initially, the priority of each task is checked and arranged. Moreover, the optimal resource is selected using the fitness function of the squirrel search, considering the data rate and the job schedule. Further, during the scheduled task-sharing process, the system continuously checks for overloaded resources and balances based on the squirrel distribution function. The efficacy of the model is reviewed in terms of response time, resource usage, makespan time, and throughput. The model achieved a higher throughput and resource usage rate with a lower response and makespan time.

Sustainability and health promotion actions in Brazil

This study explores sustainable programs or actions implemented within the work environments of students enrolled in a Postgraduate Program in Health Promotion in Brazil. Seventy participants, primarily healthcare professionals, were tasked with identifying the sustainable actions during the Health Promotion and Sustainability course. Responses were analyzed using the IraMuTeQ V. 0.7 alpha 2 program. Of these participants, 15 individuals did not identify any actions, while 55 highlighted various initiatives. Key strategies included waste management, health education, promotion of physical exercise, optimization of food resources, composting, and conscientious water usage. These actions not only offer potential cost savings but also contribute to a more responsible utilization of natural resources and the attainment of Sustainable Development Goals.

Evaluating Bank Efficiency with Risk Management by Optimal Common Resource and Three-Parallel Two-Stage Dynamic DEA Model

Evaluating Bank Efficiency with Risk Management by Optimal Common Resource and Three-Parallel Two-Stage Dynamic DEA Model

Impacts of the Middle Route of the South-to-North Water Diversion Project on Land Surface Temperature and Fractional Vegetation Coverage in the Danjiang River Basin

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin.

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism