Maximize Resource Utilization Research Articles

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Innovations in Aquaponics Technology and Building Sustainable Infrastructure for Agriculture

Aquaponics, a symbiotic integration of aquaculture and hydroponics, presents a promising approach to sustainable agriculture by maximizing resource utilization and minimizing environmental impact. This study explores recent innovations in aquaponics technology and the development of sustainable infrastructure to support its widespread adoption. A systematic literature review is conducted, showing the fundamentals of aquaponics and its key components such as fish tanks, grow beds, and biological filters. Innovations in technology are examined, including advanced monitoring and control systems, integration of renewable energy sources, automation, and novel growing techniques. Sustainable infrastructure for aquaponics is analyzed, focusing on water management strategies, energy efficiency measures, material selection, and integration with urban agriculture. Furthermore, the alignment of aquaponics technology and sustainable infrastructure development with the United Nations Sustainable Development Goals (SDGs) is studied, particularly in achieving goals related to zero hunger, clean water and sanitation, responsible consumption and production, and climate action.

Lean Management and use of AI in healthcare: living in the present

This study explores the possible relationship between Lean Management and generative artificial intelligence (Generative AI), which are seemingly distant but with common objectives namely to reduce waste and increase efficiency. Our analysis reveals that Generative AI models can support “lean” principles by facilitating just-in-time manufacturing, increasing flexibility, promoting waste reduction, and rapid prototyping. However, solutions are needed for the technological challenges and the quality of the content generated. Effective implementation requires a focus on optimizing operations and automating data analysis, as well as aligning goals between stakeholders. The joint use of cross-functional teams can maximize resource utilization and drive informed decisions. Generative AI is therefore a powerful complement to Lean Management in business processes. A focus was then placed on the impact that AI and technological advancement may have in various areas of the pharmaceutical sector. The state of the art was assessed by referring to publications in the literature and by interviewing experts not only with a corporate profile, but also community pharmacists, students, and professionals in the pharmaceutical sector.

Exploring Low-Grade Iron Ore Beneficiation Techniques: A Comprehensive Review

The beneficiation of low-grade iron ores is a key research and development topic in the mineral processing industry. The gradual exhaustion of high-grade iron ore reserves, and rising consumer iron and steel demand globally necessitate efficient low-quality iron ore beneficiation to meet steelmaking quality requirements. This comprehensive review explores various beneficiation techniques for low-quality iron ore, focusing on conventional methods including comminution, froth flotation and gravity separation. This article discusses the principles, processes, and equipment used in these techniques and highlights recent advancements and research efforts in the field. This review also emphasizes the importance of effective beneficiation processes in enhancing economic viability, sustainable resource management, and environmental conservation. Furthermore, it presents a case study of iron ore deposits in Botswana, highlighting the potential economic growth and sustainable development that can be achieved by maximizing resource utilization through reductive roasting, followed by magnetic separation of iron ore using semi-bituminous coal as a reductant. Overall, this review provides valuable insights into low-grade iron ore beneficiation techniques and their significance in meeting the growing demand for high-quality iron and steel products.

Location Optimization Strategies for Corn Production and Distribution towards Sustainable Green Supply Chain

Background: The corn supply chain is vital for food security and economic stability regionally and globally. This study integrates sustainable supply chain management with location optimization to address trade-offs from climate change, economic viability, and environmental impact while assuming the constant social obligation inherent in the supply chain structure. Methods: This study employs a mixed-integer programming (MIP) framework to optimize facility locations in North Dakota, including corn production zones as suppliers and ethanol plants as consumers. Primary objectives include cost minimization and greenhouse gas reduction, enabling the prioritization of economic or environmental goals as per organizational strategies and regulations. This approach ultimately maximizes resource utilization by ensuring efficient production and distribution practices. Results: The case study results highlight the optimal selection of 20 out of 30 corn production zones to meet statewide ethanol plant demand efficiently. Using compressed natural gas (CNG) instead of diesel could potentially save USD 2 million annually and cut carbon emissions by up to 1148 thousand tons per year, demonstrating meaningful progress toward economic and environmental sustainability within the supply network. Conclusions: The presented work offers a systematic methodology for designing sustainable supply chains for various agricultural products, aligning with the broader goal of promoting sustainability and resilience for efficient agricultural production and distribution systems.

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.

Green flexible production and intelligent factory building structure design based on improved ant colony algorithm

Traditional factory production has limitations such as energy waste, emission pollution, etc., and there are also certain unreasonable structural aspects. Therefore, this article proposes a method that can make the factory building structure more green, flexible, and intelligent. It is hoped that by improving the ant colony algorithm and applying it to factory design and production, the goal of reducing energy consumption, maximizing resource utilization, and reducing environmental impact can be achieved. Transforming the design problem of factory building structure into an optimization problem using ant colony algorithm, that is, finding the optimal path and layout. In this process, the ant colony algorithm simulates the behavior of ants by continuously iterating and updating the concentration of pheromones, in order to achieve the global optimal solution. In order to achieve the combination of green design principles and flexible production needs, corresponding constraint conditions and objective functions were introduced, taking into account the flexibility and adjustability within the factory to meet different production needs. Ant colony algorithm can provide the best structural design solution for factory buildings by optimizing search, combining green design principles and flexible production requirements, and promoting the development of green and sustainable factory buildings.

Penilaian Tingkat Kesehatan Keuangan Berdasarkan Rasio Likuiditas, Solvabilitas, dan Profitabilitas Pada PT. Wilmar Cahaya Indonesia

The aim of this research is to assess the financial condition of PT. Wilmar Cahaya Indonesia in the 2019-2023 period based on liquidity, solvency and profitability ratios. The methodology used is quantitative descriptive analysis using secondary data from the company's financial reports. The research results show that although the company's liquidity is said to be liquid, a turnover ratio of more than 500% is considered too high. This indicates inadequate capital utilization. The solvency ratio shows a company's healthy financial position, identifying the company as being able to fulfill its long-term obligations. However, the profitability ratio shows poor financial health with the ratio far below standard, tends to decline, and has low ROA and ROE values. The company's inability to use resources efficiently to generate profits is the cause of this decline. Overall, even though PT Wilmar Cahaya Indonesia is liquid and solvable, its financial health for the 2019-2023 period still needs to be improved, especially in terms of profitability. The analysis shows that to increase profitability, companies must improve the effectiveness of current asset management and maximize resource utilization. This study provides an overview of the company's financial situation and can be used as a reference for future management.

Epidemic dynamics of complex networks under pulse treatment and vaccination

The dynamics of a new Susceptible-Vaccinated-Infected-Recovered (SVIR) epidemic model in complex networks under constant and pulse control are investigated. Firstly, the epidemic model in complex networks under constant control is proposed. Utilizing the basic reproduction number, the sufficient conditions for stabilizing the epidemic under constant control are established. Since constant control is not conducive to resource conservation, it has been replaced by pulse control in epidemic model. The paper demonstrates the global attractiveness of the disease-free periodic solution of the model under pulse treatment and pulse vaccination. In addition, a sufficient condition is put forward to make sure that the system persists under pulse control, indicating periodic outbreaks of the epidemic. Furthermore, the epidemic is uniformly persistent when the pulse period is greater than the pulse period threshold or the vaccination rate is less than the vaccination threshold. Finally, several numerical simulations prove the feasibility of the theoretical results. By comparing the pulse period with a critical value, the strategy that most contributes to controlling the epidemic propagation is chosen from constant control and pulse control to achieve maximum utilization of resources. Moreover, it shows that decreasing the fraction of pulse treatment and pulse vaccination or prolonging the pulse period is beneficial to epidemic propagation. The topology of complex networks has a non-negligible influence on the epidemic propagation under pulse control, which is demonstrated both theoretically and numerically.

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

Co-pyrolysis behavior of municipal solid waste and food waste residue: A thermogravimetric study to discern synergistic effect

Effective solid waste disposal necessitates suitable combination and clean conversion technologies to maximize resource utilization. Thus, this study investigates the behavior of co-pyrolysis in municipal solid waste (MSW) and food waste (FW) via thermogravimetric analysis, aiming to discern synergistic effects and optimize waste to energy conversion. The impacts of heating rate and blending ratios were evaluated under an inert N2 atmosphere, within a temperature range of 30–900℃, during single and combined pyrolysis processes respectively. Results revealed slight variations in weight loss patterns and thermal decomposition behavior. When heating rate increased from 10 to 30℃.min−1, decomposition process delayed, resulting in higher initial (Ti), peak (TP), and final (Tf) temperatures due to inadequate heat transfer. As food waste increased, mass loss rose and favorable synergistic effect was observed at 20 % FW during medium temperatures, while other blends showed inhibitory effects. The pyrolytic index (D) rose nonlinearly from 6.29 × 10−7 to 7.72 × 10−7 with a higher FW proportion, peaking at 20 % FW, suggesting an optimal MSW and FW mixing ratio. This blend also exhibited the lowest activation energy, indicating a more efficient thermal decomposition process. Those outcomes offer crucial insights for optimizing co-pyrolysis systems and provide essential information for co-disposal of MSW and FW, potentially reducing pollutant emissions and enhancing waste to energy conversion.

High Discretion Slack: Penentu Keberhasilan Kinerja Organisasi Badan Usaha Miliki Desa (Bumdesa)

This research focuses on high discretion slack in achieving good organizational performance in Village-Owned Enterprises (BUM Desa). The type of data used in this research is primary data obtained from interviews with informants. This research will be conducted with a descriptive qualitative approach with a single case study on BUM Desa in Jombang district. The results showed that in BUM Desa there are unbound resources in the form of cash, working capital that can be utilized by managers in improving BUM Desa performance. However, these resources are not directly able to determine the success of BUM Desa performance. These unbound resources require the ability of managers, in this case human resources, to utilize these resources. The results of this study also show the limited ability of human resources in the village to drive BUM Desa, in addition to the low level of community participation in assisting BUM Desa management makes BUM Desa difficult to develop, even though the resources owned are high. Thus, human resource capability and village community participation are important factors to be used as benchmarks in maximizing resource utilization for the successful performance of BUM Desa.

INNOVATIONS IN SCHOOL ROOM RESERVATION MANAGEMENT: TOWARDS SMART AND SUSTAINABLE SOLUTIONS

Abstract The efficient management of school room reservations is crucial for maximizing resource utilization and ensuring smooth operations within educational institutions. Traditional room reservation systems often suffer from inefficiencies, leading to underutilization of spaces, scheduling conflicts, and increased administrative burden. This research paper explores the development and implementation of smart and sustainable solutions for school room reservation management. The suggested approach makes use of contemporary technologies to expedite the classroom reservation process, giving teachers, administrators, and students effective means of scheduling and overseeing room reservations. The technology has significantly improved classroom utilization rates and overall operational efficiency in our institution, according to preliminary tests. The school room reservation system provides a scalable solution for improving the management of educational facilities by lowering scheduling conflicts, minimizing downtime between courses, and enabling better resource allocation. Future improvements could incorporate support for mobile platforms, integrate with current school management systems, and introduce new capabilities in response to user feedback and changing needs. In general, the establishment of this school room reservation system is a step toward the modernization of facilities management procedures in academic establishments, which will ultimately lead to the creation of a more efficient and cooperative learning environment. Index Term : Frontend development HTML, CSS, React, Backend NodeJS, Express, MongoDB

Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

Financial Management Strategies for Implementing Lean Manufacturing in the Footwear Industry

The concept of lean manufacturing, initially developed to maximize resource utilization by minimizing waste, has evolved to address the needs of a rapidly changing and competitive business environment. Footwear industries face significant challenges and complexities that require a systematic and continuous response to maintain and enhance product value. Implementing lean manufacturing practices has become essential for these organizations to sustain and thrive in such a dynamic landscape. However, successful implementation is not solely dependent on operational adjustments but also on strategic financial management. This research examines the financial management strategies necessary for the effective implementation of lean manufacturing in the footwear industry. It emphasizes the importance of integrating financial planning with lean principles to ensure comprehensive and sustainable improvements. Key lean elements such as Value Stream Mapping (VSM), Cellular Manufacturing (CM), U-line systems, Line Balancing, Inventory Control, Single Minute Exchange of Dies (SMED), Pull Systems, Kanban, and Production Levelling are explored in detail. The study demonstrates how a focused approach to financial management can support these lean elements, enabling footwear companies to optimize resource allocation, reduce costs, and improve efficiency. By aligning financial strategies with lean manufacturing goals, organizations can achieve significant economic and operational benefits. This includes better cash flow management, investment in continuous improvement initiatives, and a robust framework for measuring the financial impact of lean practices. Through a comprehensive analysis of these financial strategies, the research provides valuable insights for footwear companies aiming to implement lean manufacturing successfully. It highlights the need for an integrated approach that combines financial acumen with lean methodologies to drive value creation, enhance competitiveness, and ensure long-term sustainability in the industry.

Crop Recommendation System Using Machine Learning

Abstract: This study describes how machine learning techniques were used to successfully construct a crop recom- mendation system. With an emphasis on maximizing resource utilization and advancing agricultural sustainability, the system is a useful tool for helping farmers choose their crops. The system delivers customized crop suggestions by ana- lyzing multiple aspects, including soil conditions and climate, by utilizing machine learning algorithms and incorporating real-time data. This novel strategy seeks to improve farming methods by giving farmers customized insights that help them make more effective and sustainable farming decisions

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis.

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS. Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%). The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain. A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.

The impact of CEO's green experience on digital transformation

According to resource orchestration theory, key human capital can allocate resources to the critical paths of projects to maximize resource utilization efficiency. This study examines how the green experience acts as an important organization capital and affects a company's digital and green transformation processes. Based on the organization capital theory, we study the performance of China's A-share listed companies from 2013 to 2021. A CEO's green experience inhibits a company's digital transformation while promoting its green transformation. Based on organizational theory, our conclusion is consistent with signal and information asymmetry theories. Executives with green experience have a better understanding of ways to improve the credibility of the public's green environmental practices. This agency cost often has a negative impact on the financial performance of enterprises, which is not conducive to their digital transformation. Companies should carefully recruit executives with green experiences, based on future digital or sustainable development strategies, to maximize the organization capital.

Leveraging Digital Transformation and ERP for Enhanced Operational Efficiency in Manufacturing Enterprises

Purpose: The process of making the most of digital tools in the workplace to support an organization's success is known as "leveraging on technology." By automating manual tasks more quickly, modern ERP systems free up time and funds for other important projects. Data can be aggregated by contemporary ERP systems for simpler management, access, and analysis. Method: Systems for enterprise resource planning, or ERPs, have become a vital component of contemporary manufacturing processes. These integrated software solutions are essential for improving, streamlining, and controlling many aspects of manufacturing, including financial control, customer service, production and inventory management. Manufacturers can effectively manage vital resources like labor, equipment, and materials with the help of ERP systems. ERP helps eliminate waste, minimize production delays, and maximize resource utilization by offering real-time visibility into resource allocation and availability. Result and Conclusion: Manufacturing process synchronization and automation are made easier by ERP software. Work orders, production schedules, and quality control can all be managed by it, resulting in seamless and effective manufacturing processes. In manufacturing, keeping the proper amount of inventory on hand is a difficult task. ERP systems give producers insights into demand trends, order lead times, and reorder points, which helps them optimize inventory levels. In addition to lowering carrying costs and preventing stockouts, this minimizes superfluous inventory. Implication of Research: An organization's operational model is altered by digital transformation. This process involves systems, workflow, processes, and culture. Every level of an organization is impacted by this transition, which unifies data from different departments to collaborate more successfully. Originality/Value: Enhanced Efficiency: Businesses are able to allocate resources more effectively thanks to automation and streamlined processes, which also lower operational costs and increase productivity.

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.