Maximize Resource Utilization Research Articles

Scoping review on the association between early childhood caries and responsible resource consumption and production: exploring Sustainable Development Goal 12

BackgroundThe Sustainable Development Goal 12 (SDG12) promotes patterns that minimize waste and maximize resource utilization. It is therefore plausible that preventing Early Childhood Caries (ECC) and promoting oral health can contribute to sustainable consumption. In addition, sustainable consumption and production can contribute to the control of ECC. This scoping review aimed to explore the possible evidence on the link between ECC and the SDG12 targets.MethodsThis scoping review identified articles on the link between resource consumption and production and caries according to the PRISMA-ScR guidelines. Three electronic databases (PubMed, Web of Science, and Scopus) were systematically searched in August 2023, using specific search terms. Studies written in English, with full text available, addressing dental caries and linked with waste minimization and resource utilization maximisation, with results that could be extrapolated to ECC in children less than 6 years of age) were included. Descriptive statistics were planned to summarize the categories of retrieved papers.ResultsThe initial search yielded 904 articles, with 863 screened for eligibility after the removal of duplicates. No studies were identified that reported data on an association between responsible consumption and production of resources factors and ECC.ConclusionThis scoping review did not identify any articles published in English on evidence of the direct associations between ECC and SDG12 targets. However, there is a plausibility of such a link using minimum intervention dentistry for ECC management as a waste prevention and resource utilisation maximization strategy.

Root spatial distribution and belowground competition in an apple/ryegrass agroforestry system

CONTEXTForestry and fruit industry is a unique and advantageous industry in Xinjiang, China, and it is an important channel for farmers to increase income. With limited land resources and a shortage of forage in southern Xinjiang, farmers began to establish a fruit-grass interplanting model to improve the comprehensive economic benefits of orchards. OBJECTIVEThe objective of this study was to explore the effects of apple/ryegrass intercropping system on root spatial distribution and competition, and to evaluate the performance of field microclimate, soil physical properties and productivity under intercropping. METHODSIn the study, three different planting patterns were designed: sole-cropped apple (The spacing of the rows of apple trees was 4.0 m, and the plant spacing was 1.0 m), sole-cropped ryegrass (Strip sowing, row spacing was 0.25 m), and apple/ryegrass intercropping (The distance from apple row to ryegrass row was 1.0 m). Two-year field experiments were conducted to measure the root length density (RLD), specific root length (SRL), interspecific competitiveness index, soil moisture content, organic matter content, air temperature, relative humidity, wind speed, yield, land equivalent ratio (LER) and aggressivity. RESULTS AND CONCLUSIONCompared with sole cropping, intercropping reduced the RLD of apple and ryegrass and increased the SRL of ryegrass. The competition space between apple and ryegrass was concentrated in the soil area of 100 cm horizontally and 0–20 cm vertically, and apple was more competitive than ryegrass. Intercropping increased the content of soil moisture and organic matter and improved the microclimate of farmland, decreased the air temperature, increased the relative humidity of an orchard, and decreased wind speed. Although intercropping reduced ryegrass and apple yield, land equivalent ratio (LER) was 1.27–1.30, indicating that intercropping improved land use efficiency. Root competition led to an uneven distribution of root length density (RLD) and specific root length (SRL). The plasticity of root morphology and root spatial distribution was key to adapting to competition and maximizing the absorption of water and nutrients in apple/ryegrass agroforestry systems. SIGNIFICANCEApple/ryegrass intercropping system maximizes resource utilization and reduces interspecies resource competition. It improved land use efficiency, improved the comprehensive benefits of fruit and grass production.

Wastewater recycling and groundwater sustainability through self-organizing map and style based generative adversarial networks

Wastewater recycling is a pivotal strategy in sustainable water management, designed to mitigate water scarcity and curb environmental pollution. This research introduces an innovative approach merging Self-Organizing Maps (SOM) and Style-Based Generator Generative Adversarial Networks (StyleGAN) to revolutionize wastewater recycling. SOM optimizes the distribution of treated water by identifying ideal locations for recycling outlets and treatment facilities. Meanwhile, StyleGAN enhances water quality by learning from diverse samples, producing purified water suitable for non-drinking purposes. The combination of SOM and StyleGAN maximizes resource utilization and addressing water scarcity challenges while minimizing environmental impact. Also, this method is not only optimizes treated water distribution but also enhances water quality, showcasing potential benefits for sustainable water practices, including groundwater replenishment. Self-Organizing Maps construct a spatial model of the wastewater treatment system, finding optimal locations for recycling outlets and treatment facilities. This spatial intelligence optimizes the distribution of treated water, channeling it efficiently to high-demand areas, thereby maximizing resource utilization. Simultaneously, Style-Based Generator Generative Adversarial Networks elevate the quality of treated wastewater. By assimilating knowledge from diverse wastewater samples, StyleGAN produces water of superior quality with diminished contaminants and enhanced aesthetics. This transformation renders treated wastewater more versatile for non-potable purposes like irrigation and industrial processes. The SOM-StyleGAN technologies offers a holistic solution for wastewater recycling, addressing both distribution efficiency and water quality enhancement. The SOM method outperforms MLP, DBN, and GAN-ANN in fidelity, diversity, contaminant reduction, and aesthetic quality. With a fidelity of 0.97, substantial improvements over GAN-ANN showcase its effectiveness. These results underscore the method's potential contribution to sustainable water management practices, emphasizing its versatility and quality in treated water generation. These findings contribute significantly to enhancing water quality and underscore its potential practical implications for sustainable water practices, marking a substantial step forward in the field.

An improved task scheduling algorithm for conflict resolution in cloud environment

The performance of the cloud-based systems is directly associated with the resource utilization. The maximum resource utilization indicates the high performance of cloud computing. Further, effective task scheduling is the primary factor that significantly impacts resource utilization. Various researchers have proposed multiple algorithms to achieve optimum utilization, but a high rejection rate of tasks occurred due to conflict among similar tasks during backfilling. A hybrid approach is proposed for selecting the backfilled tasks using a multicriteria decision-making approach (MCDM), namely Distance-based approach with a traditional task scheduling algorithm. The parameters considered to find the suitable task for backfilling are arrival time, start time, deadline time, duration, size, and the number of virtual machines. The proposed algorithm is implemented on different datasets and compared with other state-of-the-art algorithms. The result shows a significant improvement in virtual machine utilization and task acceptance/rejection rate.

Efficient Task Scheduling in Cloud Computing: A Multiobjective Strategy Using Horse Herd–Squirrel Search Algorithm

Cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important difficulties because a solid mapping between available resources and users’ activities is essential to reduce the execution time of users’ jobs (i.e., minimize makespan) and maximize resource utilization. Because the service provider must offer several customers’ benefits at distinct times and from distinct locations, task scheduling is indeed a serious challenge in CC. As a result, in the CC environment, these operations must be scheduled in a more dynamic and timely manner. The objective is to provide an enhanced task scheduling algorithm for allocating the task of the user to different computing resources. The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd–squirrel search algorithm (HO–SSA). The developed HO–SSA assists in introducing a multiobjective optimization for efficiently handling task scheduling issues in the cloud sector. The proposed HO–SSA method for the task scheduling in CC model in terms of cost is 22.22%, 15.73%, and 38.74% better than SSA, HOA, and TSA, respectively. Similarly, the proposed HO–SSA method for the task scheduling in CC model with respect to energy is 9.68%, 5.35%, and 22.50% superior to SSA, HOA, and TSA, respectively. The proposed method outperformed the existing methods like SSA, HOA, and TSA in terms of cost, energy, degree of imbalance, makespan, speedup, and efficiency.

Mathematical Model for Multi-Objective Optimization of Facility Allocation in Flexible Manufacturing Systems with Collaborative Robots

The model proposed in this study addresses the complex task of job-machine assignment in flexible manufacturing systems (FMS) enhanced with collaborative robots (cobots). It aims to optimize three critical performance metrics simultaneously: makespan, energy consumption, and resource utilization. The model formulates these objectives as a multi-objective optimization problem, with each objective weighted to reflect its importance in the manufacturing process. The primary objective is to minimize makespan, representing the total time required to complete all jobs, while also minimizing energy consumption and maximizing resource utilization. By considering these objectives comprehensively, the model provides a valuable framework for manufacturers to make informed decisions about job allocation, leveraging the capabilities of both traditional machines and cobots. This approach enhances the efficiency and sustainability of FMS, aligning with the trend of automation and collaboration between human workers and robotic counterparts. The proposed model contributes to the optimization of manufacturing processes in an era of increasing automation and technological advancements.

A study of applications in archive management empowered by artificial intelligence

Abstract As a critical component of information management, the development of digital and information archive systems enhances both the efficiency and quality of enterprise operations, thereby increasing economic value. This paper employs the ReLU activation function to augment the non-linear capabilities of the neural network model. Additionally, we introduce the MP-CNN model, designed to extract and recognize textual content from archival images. These images are initially processed using the BP neural network algorithm, followed by grayscale conversion for clarity, and subsequently backed up for analytical purposes. The implementation of intelligent archive management resulted in a more than 35% improvement in efficiency. The distribution interval of the Return on Investment (ROI) values from archival data at the test site ranged between [0,5], displaying a concentrated pattern that aligns with the typical economic value distribution. The integration of AI technology in archive management not only streamlines processes but also maximizes resource utilization and enhances returns.

Efficient lung cancer detection using computational intelligence and ensemble learning.

Lung cancer emerges as a major factor in cancer-related fatalities in the current generation, and it is predicted to continue having a long-term impact. Detecting symptoms early becomes crucial for effective treatment, underscoring innovative therapy's necessity. Many researchers have conducted extensive work in this area, yet challenges such as high false-positive rates and achieving high accuracy in detection continue to complicate accurate diagnosis. In this research, we aim to develop an ecologically considerate lung cancer therapy prototype model that maximizes resource utilization by leveraging recent advancements in computational intelligence. We also propose an Internet of Medical Things (IoMT)-based, consumer-focused integrated framework to implement the suggested approach, providing patients with appropriate care. Our proposed method employs Logistic Regression, MLP Classifier, Gaussian NB Classifier, and Intelligent Feature Selection using K-Means and Fuzzy Logic to enhance detection procedures in lung cancer dataset. Additionally, ensemble learning is incorporated through a voting classifier. The proposed model's effectiveness is improved through hyperparameter tuning via grid search. The proposed model's performance is demonstrated through comparative analysis with existing NB, J48, and SVM approaches, achieving a 98.50% accuracy rate. The efficiency gains from this approach have the potential to save a significant amount of time and cost. This study underscores the potential of computational intelligence and IoMT in developing effective, resource-efficient lung cancer therapies.

An Efficient Hybrid Optimizer for Resource Reuse in a Cloud Environment

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre’s enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%.

Modelling and Formal Verification Approach for Microgrid Energy Management Systems under Random Load

This paper presents the modelling and a formal verification approach of energy management control systems for microgrids that are subject to unknown demand and uncertainties in the availability of renewable energy sources. The main objective of this study is to develop a management system for a microgrid composed of renewable and non-renewable energy sources, specifying the different possible modes. A supervisory control method is proposed to autonomously control and monitor a microgrid and maintain more stable energy flows with maximum utilization of available resources. A timed automata network model of the microgrid has been developed by modeling the dynamics of components such as PV, BESS, diesel generator, load, and controller. Possible operating modes in the microgrid between sources and load are derived and classified into allowed and unallowed operating modes. Verification of the designed microgrid models was performed using simulation and formal methods. The advantageous performance of the proposed strategy is illustrated via a numerical example.

Optimization of opportunistic utilization of the ATLAS high-level trigger farm for LHC Run 3

Optimization of opportunistic utilization of the ATLAS high-level trigger farm for LHC Run 3

The role of nutrition-sensitive climate-smart agriculture in ensuring global food security

Climate-Smart Agriculture (CSA) is a sustainable farming approach that attempts to maximize resource utilization, improve long-term agricultural sustainability, and prevent environmental degradation while improving agricultural income and production. Nutrition-Sensitive Agriculture (NSA) revolves around the connections between agriculture, food systems, and health, with the goal of ensuring that agricultural practices contribute to better nutrition and well-being. This research focuses on the deployment of a Nutrition-Sensitive Climate Smart Agriculture (NSCSA) method in order to better understand its potential benefits for global food security and nutrition. The research methodology comprises a thorough analysis and review of existing literature on CSA, NSA, and NSCSA policies and practices. To address the complex concerns of food security and nutrition in the context of climate change, the article emphasizes the significance of incorporating nutrition considerations into CSA policies and initiatives. The findings further highlight the importance of multi-stakeholder collaboration and knowledge sharing in implementing and scaling up nutrition-sensitive CSA initiatives. In conclusion, the study reveals that nutrition-sensitive CSA has the potential to help achieve sustainable development goals such as food security, nutrition, and climate change mitigation and adaptation.

PpNNT: Multiparty Privacy-Preserving Neural Network Training System

By leveraging smart devices (e.g., industrial Internet of Things (IIoT)) and real-time data analytics, organizations such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this paper, we propose the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> reserving deep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> eural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> raining (PpNNT), which is designed to support federated learning in the multi-party setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset.

Controllable Queuing System with Elastic Traffic and Signals for Resource Capacity Planning in 5G Network Slicing

Fifth-generation (5G) networks provide network slicing capabilities, enabling the deployment of multiple logically isolated network slices on a single infrastructure platform to meet specific requirements of users. This paper focuses on modeling and analyzing resource capacity planning and reallocation for network slicing, specifically between two providers transmitting elastic traffic, such during as web browsing. A controller determines the need for resource reallocation and plans new resource capacity accordingly. A Markov decision process is employed in a controllable queuing system to find the optimal resource capacity for each provider. The reward function incorporates three network slicing principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. To efficiently compute the optimal resource capacity planning policy, we developed an iterative algorithm that begins with maximum resource utilization as the starting point. Through numerical demonstrations, we show the optimal policy and metrics of resource reallocation for two services: web browsing and bulk data transfer. The results highlight fast convergence within three iterations and the effectiveness of the balanced three-principle approach in resource capacity planning for 5G network slicing.

Energy crops in Brazil: challenges and perspectives for climate change mitigation

This article explores Brazil's current energy crop cultivation scenario, emphasizing strategies to increase diversity and sustainability in biofuel production. The study underscores the importance of investments in research and development, focusing on the identification of new plant species adaptable to various climatic conditions. It highlights the need for integrated approaches, including agricultural zoning based on soil and climate information, to support crop diversification and sustainable agricultural practices such as crop rotation, species consortium, and agroecological methods to maximize resource utilization and minimize environmental impact. Moreover, it emphasizes the need for integrated approaches, including agricultural zoning based on soil and climate information, to support crop diversification. Sustainable agricultural practices, such as crop rotation, species consortium, and agroecological methods, are underscored to maximize resource utilization and minimize environmental impact. The article also discusses the pivotal role of tax incentives and subsidies in encouraging the adoption of energy crops, particularly within the context of family farming. Additionally, it underscores the potential of utilizing residual biomass from agricultural, industrial, and residential activities to further reduce reliance on specific energy crops. The article delves into the significance of scientific knowledge in policy formulation, particularly addressing biodiversity concerns and ensuring a sustainable approach to biofuel production. Furthermore, it highlights that collaborative efforts are essential among governments, private sectors, and research institutions to forge a resilient, equitable, and sustainable future for bioenergy, thereby promoting a diversified and independent energy matrix of Brazil.

ANALISIS KINERJA RANTAI PASOK AGROINDUSTRI KOPI ARABIKA DI KABUPATEN ACEH TENGAH PROVINSI ACEH

Aceh Province is a leading national coffee producing province, one of the coffee producing centers in Aceh is Central Aceh Regency with the area planted with coffee plants in 2022 reaching 49,997/ha and coffee production reaching 36,060 tons/year. Coffee has economic value from both the farmer level and the final consumer. What often happens is that the value obtained by farmers is not equal to that obtained by other marketing institutions in the supply chain and coffee value chain, so adding value to coffee requires product development and maximum utilization of resources. by generating profits in all lines of institutions involved. The role of the Arabica coffee agroindustry is very helpful in developing coffee into finished products or processed coffee-based products, so that all systems in the coffee supply chain benefit. Apart from that, the role of the Arabica coffee agroindustry will have an impact on the value chain of Arabica coffee produced in Central Aceh Regency. The research method used is descriptive and quantitative analysis using the AHP Matrix. The results obtained show that the performance of the Arabica coffee agroindustry supply chain in 13 agroindustries in Central Aceh is at a below average level, extra improvement is needed to be able to obtain a good or excellent level.

Assessment of Farmers Perception and Problems towards Adoption of Agroforestry Practices in Coastal Area of Bangladesh: A Study in Noakhali District

The economy of Bangladesh largely depends on agriculture. Agroforestry is one of the modern agricultural practices which ensure maximum utilization of resources and offer sustainable environmental and economic development. The present study was carried out to find out farmers’ perceptions and problems towards the adoption of agroforestry practices in the coastal area of Bangladesh. In total, 400 farmers were selected from five upazila of the Noakhali district following a proportionate random sampling technique. Face-to-face interviews, case studies, and focus group discussions (FGD) were used to obtain the data. Statics such as range, mean, standard deviation, Chi-square, and correlation analysis were applied. Most of the respondents were middle-aged (45%) with a low literacy rate and large family size (55%). The majority of them (50%) had small-sized land, and they had limited access to agricultural training. A greater part of the respondents (61%) had a very low level of knowledge of agroforestry practice. Silvopasture and homestead agroforestry system was practiced by most of the farmers (77%) without proper concept. Only 11% of the respondents know and practice multistrata agroforestry, which is one of the best agroforestry practice. The perception of the respondents towards the adoption of agroforestry was found low in most of the cases. They expressed a low level of acuity towards agroforestry, and they were not so interested towards the adoption of these practices. “Agroforestry adoption may be hampered by a lack of institutions and policies to support it” and “Lack of knowledge and awareness on agroforestry practice” was identified most significant problems with a chi-square value of 41.6 and 39.2 at 5% level of significance. Respondents’ knowledge of agroforestry, exposure to training, and farm size were positively and significantly correlated with their adoption of the practice (r = 0.437∗∗, 0.572∗∗, and 0.64∗∗, respectively). Demonstrations, awareness campaigns, financial incentives, and collaborations between government organizations and NGOs may increase the adoption of agroforestry practices in the coastal region of Bangladesh.

Synergizing IFTOPSIS and DEA for Enhanced Efficiency Analysis in Inpatient Units

The pursuit of efficiency in the business sector is a multifaceted endeavor, extending beyond mere cost reduction to encompass a strategic optimization of operational performance. The enhancement of efficiency is not solely for the benefit of investors or proprietors but is also a concerted effort to maximize resource utilization and minimize waste. This study introduces an integrative approach combining IFTOPSIS and DEA methodologies to deliver a robust efficiency evaluation framework.The fusion of IFTOPSIS's qualitative analysis with DEA's quantitative assessments addresses the complexity of operational performance, providing a balanced evaluation that transcends subjective bias with data-driven insights. IFTOPSIS articulates decision-makers' preferences in uncertain scenarios, assigning weights to criteria, while DEA discriminates between efficient and inefficient operational units. This confluence of methods is applied to the assessment of inpatient healthcare units—a sector that has traditionally relied on patient-centric evaluations, neglecting the comprehensive review of resource deployment. The results of this amalgamated approach reveal dimensions of operational efficiency previously unexplored, offering stakeholders a data-enriched foundation for strategic decision-making. The study's findings have significant implications for the healthcare industry, providing a template for resource evaluation that could inform policy and drive improvements in patient care services.

High-efficiency TRNG Design Based on Multi-bit Dual-ring Oscillator

Unpredictable true random numbers are required in security technology fields such as information encryption, key generation, mask generation for anti-side-channel analysis, algorithm initialization, and so on. At present, the true random number generator (TRNG) is not enough to provide fast random bits by low-speed bits generation. Therefore, it is necessary to design a faster TRNG. This work presents an ultra-compact TRNG with high throughput based on a novel extendable dual-ring oscillator (DRO). Owing to multiple bits output per cycle in DRO can be used to obtain the original random sequence, the proposed DRO achieves a maximum resource utilization to build a more efficient TRNG, compared with the conventional TRNG system based on ring oscillator (RO), which only has a single output and needs to build multiple groups of ring oscillators. TRNG based on the 2-bit DRO and its 8-bit derivative structure has been verified on Xilinx Artix-7 and Kintex-7 FPGA under the automatic layout and routing and has achieved a throughput of 550 Mbps and 1,100 Mbps, respectively. Moreover, in terms of throughput performance over operating frequency, hardware consumption, and entropy, the proposed scheme has obvious advantages. Finally, the generated sequences show good randomness in the test of NIST SP800-22 and Dieharder test suite and pass the entropy estimation test kit NIST SP800-90B and AIS-31.

Deep learning and optimization-based task scheduling algorithms for fog-cloud computing environment

Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models.