Efficient Resource Utilization Research Articles

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.

Hydrothermal synthesis of high crystallinity ZSM-5 zeolite from coal gasification coarse slag and mother liquor circulation for efficient coal chemical wastewater purification.

A hydrothermal synthesis method was developed to produce high crystallinity ZSM-5 zeolite using coal gasification coarse slag (CGCS) as the raw material. Instead of the expensive NaOH(s.), Na2SiO3(s.) was utilized to activate, depolymerize, and recombine Si and Al elements in the CGCS. The mother liquor circulation technology was employed to recover and reuse raw materials and residual reagents (Na2SiO3(aq.) and TPABr), reducing waste emissions and enhancing resource utilization efficiency. The synthesized ZSM-5 had a specific surface area of 455.675 m2g-1, pore volume of 0.284 cm3g-1, and pore diameter of 2.496nm. The influence of various factors on the morphology and crystallinity of ZSM-5 was investigated, resulting in the production of ZSM-5 with higher specific surface area and pore volume. Adsorption experiments showed that WU-ZSM-5 exhibited a removal efficiency of 85% for ammonia nitrogen (NH4+-N(aq.)), validating its effectiveness in coal chemical wastewater purification. The mother liquor recycling technology enabled zero-emission utilization of solid waste resources and improved the utilization rate of alkali and template to 90%. These results demonstrate the potential application of the developed method in the efficient treatment of coal chemical wastewater.

Dynamic microservice placement in multi-tier Fog networks

Fog computing extends Cloud-like infrastructure to the Edge, advancing Industrial Internet-of-Things (IIoT) applications in which parts of business-oriented enterprise systems, decoupled via fine-grained microservices, can be deployed in Fog networks in proximity to sensor devices. It boosts the responsiveness to IIoT events significantly where timely decision-making and actions are required, particularly for problematic trends and anomalies. However, microservice placement is a non-trivial problem in IIoT settings, given the dynamic occurrence of different IIoT events and variable resource needs of microservices against resource-constrained, heterogeneous, and distributed Fog devices. The cost of matching and deploying microservices in real-time, highly dynamic, and contentious settings can easily exceed the benefit of just-in-time Edge deployments. Current studies largely focus on managing the microservice placement from Cloud servers and offloading to the Cloud when Fog devices are resource-deficient. Yet, there is a lack of research focusing on efficient Fog resource use and managing the placement at the edge to improve the responsiveness. The complexity increases when multiple inter-dependent microservices with similar priorities are required to be placed on the same Fog devices. To mitigate this, we develop a tiered framework for dynamic microservice placement, in which costly resource decision-making for microservices is dedicated to Master Fog devices and Citizen Fog devices are exclusively assigned to executing microservice requests. Firstly, we implement a priority-based algorithm in each Master Fog device to identify the high-priority Edge-required microservices. Secondly, we sort the Fog devices according to their resource availability and place the microservices based on their priority and dependencies. Thirdly, we implement the strategies for microservice placement, scaling and request escalation in each Master Fog, managing a small number of Citizen Fog devices. These exempt the Citizen Fog devices from common resource and communication management responsibilities and enable them to allocate more resources to Edge-required microservices with a higher number of instances. Finally, we implement and evaluate our framework in a simulated Fog environment. The results outperform state-of-the-art frameworks in terms of efficient resource utilisation, resulting in reduced Cloud dependency by one-third compared to other approaches and average application execution time by 65–70%.

Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms.

Deficit Irrigation-Based Improvement in Growth and Yield of Quinoa in the Northwestern Arid Region in China

Given the current global water scarcity issues, which particularly affect arid regions such as northwestern China, it is crucial to find crop planting patterns that result in efficient water resource utilization. Quinoa, as a drought-resistant and highly nutritious crop, has garnered significant attention from agricultural researchers in recent years. From 2019 to 2020, a series of experimental studies were conducted under non-mulching drip irrigation conditions to investigate the growth adaptability and the response to different irrigation levels of quinoa in an arid region in northwestern China. A comparative analysis of quinoa’s dry matter accumulation, yield, thousand-grain weight, harvest index, and water use efficiency under varying irrigation levels revealed that increasing irrigation significantly enhanced quinoa’s dry matter accumulation and yield. By optimizing the irrigation strategies, we found that the water-saving practice of initiating moderate irrigation in the sensitive water-demanding stages (flowering and fruiting) of quinoa increased the yield. The experiment results showed that, in 2020, the optimal irrigation amount was 3675 m3·ha−1 during a 14-day irrigation cycle, meeting quinoa’s growth requirements while improving water resource utilization efficiency. This study not only provides a scientific basis for the efficient cultivation of quinoa in the arid regions of northwestern China, but also offers new insights into and technical support for agricultural water resource management in the region, contributing to the sustainable development of agriculture in arid areas.

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures.

Green conversion of carbon disulfide to degradable polydithiourethanes through catalyst-free multicomponent polymerizations of diamines, carbon disulfide and diacrylates

In the pursuit of advanced functional polymer materials, sulfur-containing polymers have received tremendous attention owing to their superior chemical stability, high refractive indices, various supramolecular interactions, and robust dynamic covalent chemistry. However, their synthesis usually involves either expensive monomers or toxic byproducts and requires catalysts, thus hindering their widespread application. In this work, we present a catalyst-free multicomponent polymerization in organic solvents for the synthesis of various polydithiourethanes with excellent yields (up to 98 %) and high molecular weights (up to 112 kg/mol). This reaction involves the direct polymerization of carbon disulfide, an abundant one-carbon feedstock, with commercially available diamines and diacrylates. This polyaddition process demonstrates high atom economy and no byproduct, occurring at room temperature without the need for a catalyst, thus exhibiting excellent green chemistry metrics. By utilizing the dynamic covalent chemistry of dithiourethane groups, these polymers can be efficiently degraded via aminolysis and used to fabricate reprocessable vitrimers. This multicomponent polymerization opens up a new avenue for the efficient utilization of carbon disulfide resources and accelerates the exploration of diversified sulfur-containing functional polymer materials.

Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution

Agricultural water resource utilization efficiency in China is facing significant challenges due to the dual constraints of carbon emissions and water pollution. The inefficiency in water usage in agriculture not only impacts the sustainability of water resources but also contributes to environmental degradation through increased carbon emissions and water pollution. Agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution has been a critical issue in China from 2005 to 2022. This study employs the Quantile Autoregressive Distributed Lag (QARDL) method to comprehensively assess and analyze the complex relationship that exists between agricultural water usage, carbon emissions, and water pollution. By analyzing distinct quantiles of the data distribution, the research investigates how different levels of water resource utilization efficiency relate to carbon emissions and water pollution under various conditions. The findings reveal nuanced insights into the dynamic interactions among these components within the agricultural sector. This research project focuses on the efficiency of water resource utilization in agriculture while considering the constraints of carbon emission and water pollution. Given the dynamic and time-dependent character of these components, the QARDL methodology makes it possible to get a detailed knowledge of how they interact within the framework of agriculture. The study aims to give significant insights and policy suggestions to improve agricultural practices while minimizing environmental concerns linked to carbon emissions and water pollution.

Enhancing integrated resource management through remote sensing and GIS

Integrated Resource Management plays a crucial role in sustainable development by ensuring efficient allocation and utilization of natural resources. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as powerful tools for collecting, analyzing, and managing spatial data, enabling comprehensive and integrated decision-making processes. This review article uniquely focuses on Integrated Resource Management (IRM) and its role in sustainable development. It specifically examines the application of RS and GIS in IRM across various resource management domains. The article stands out for its comprehensive coverage of the benefits, challenges, and future directions of this integrated approach.

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Research on Sugarcane Recognition based on Joint Spatial and Spectral Information from Satellite-Based Hyperspectral Imagery

Abstract. The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%.

Design of intelligent agricultural landscape planning and ecological balance control system based on remote sensing images

Abstract With the modernization of agricultural production and the acceleration of urbanization, agricultural landscape planning and ecological balance control have become important issues in promoting sustainable development. Traditional agricultural production methods have problems such as resource waste, environmental pollution, and ecological damage. This article aimed to achieve scientific planning and management of agricultural landscapes, and promote the efficiency of agricultural production, rational utilization of resources, and protection of the ecological environment, thereby promoting sustainable agricultural development. This study first focused on denoising and feature extraction of agricultural remote sensing data images, using remote sensing technology to obtain agricultural landscape information. After that, artificial intelligence was adopted to achieve intelligent agricultural landscape planning, with the goal of maintaining ecological balance and promoting efficient, eco-friendly, and sustainable agricultural production. This study took the landscape of a certain city as an example to test the processing effect of remote sensing images, and it was proved that the effect was good. We tested the performance of an intelligent agricultural landscape planning and ecological balance control system based on remote sensing images. According to the experimental results, it can be concluded that the resource utilization efficiency of farmland A monitored by the intelligent agricultural landscape planning system using remote sensing image technology was 82%, while the traditional one was only 60%. This indicated that the collection effect of remote sensing image technology was much better than that of traditional technology. This article comprehensively and timely monitored and evaluated farmland through remote sensing image technology, including crop growth status, soil moisture, nutritional status, etc. The system can provide precise planting and management suggestions for farmers based on this information, and help them optimize farmland layout, crop selection, and irrigation management, thereby improving the production efficiency of farmland and crop yield.

INFLUENCE OF HUMAN RESOURCE MANAGMENENT STRATEGIES IN CONSTRUCTION PROJECTS

In the dynamic landscape of project management understanding the significant relationship between human resource management functions and project health has become an essential aspect. This research has invested in a primary quantitative investigation focused on the INDIAN construction sector through which the relationship between human resource management functions and project health would be analysed and their contribution to enhancing project success rate would also be evaluated. With the growing emphasis on efficient resource utilisation and project Optimisation, the study would help into the impact of HRM practices on project teams. Scrutinisation of HRM Strategies and their Correlation with Project Success, this research has aimed at offering significant insights that can revolutionise HRM approaches within the construction sector that can ultimately enhance project health and ensure high success rates in INDIAN construction projects.

The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.

Task Scheduling in a Cloud Environment: A comparative Study

In this study, we explore the application of Monarch Butterfly Optimization (MBO) algorithms for task scheduling in cloud computing, comparing its performance against widely used optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).Task scheduling in the cloud is a critical aspect influencing resource utilization, turnaround time, and overall system efficiency. MBO, known for its effective exploration- exploitation balance, is examined for its suitability in addressing the complexities of cloud computing environments. The study investigates MBO's advantages, such as enhanced adaptability to dynamic conditions, effective handling of multi-objective optimization, and its consideration of bandwidth as a critical resource. Comparative analyses with ACO and PSO highlight MBO's superior performance in achieving near-optimal task schedules, emphasizing its potential to offer innovative solutions to the challenges posed by task scheduling in dynamic and resource-constrained cloud environments. This research contributes valuable insights into the strengths of MBO, paving the way for advancements in optimization methodologies tailored for cloud computing systems.

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.

Optimizing Educational Efficiency: Exploring the Relationship Between Bulk and Clinical Rotation Group Size in Medical Education Students’ Perspective in Busitema University Uganda

Clinical rotation group size plays a pivotal role in shaping the quality, efficiency, and overall student experience in medical education. Despite its significance, determining the ideal group size remains a daunting task. This research endeavors to delve into the correlation between group size and what is termed as "bulk," representing the comprehensive efficiency and productivity of clinical rotation groups. By examining this relationship, the study aims to offer valuable insights into potential strategies for optimizing group size, thereby enhancing the educational experience for medical students. The study employed a mixed-methods approach, combining quantitative analysis of productivity metrics with qualitative assessments of student and faculty perceptions. Surveys, interviews, and observational data were utilized to capture the nuances of group dynamics, learning outcomes, and resource utilization across different group sizes. Statistical techniques such as regression analysis and ANOVA were employed to identify significant correlations and differences. Data were collected from medical students undergoing clinical rotations across multiple specialties. Group sizes varied from small (2-3) and large (4 and more). The study measured bulk using indicators such as patient encounters per student, learning outcomes, and faculty-student interaction. Results indicated a nonlinear relationship between bulk and group size. Smaller groups showed higher individual student engagement, personalized learning experiences, and increased faculty-student interaction. Conversely, larger groups exhibited greater overall productivity due to economies of scale and enhanced peer learning. However, beyond a certain threshold, further increases in group size led to diminishing returns, compromising individualized attention and educational quality. The findings underscore the importance of balancing group size to optimize educational outcomes in clinical rotations. While larger groups may offer efficiency gains, they must be carefully managed to mitigate potential drawbacks such as reduced individualized attention and faculty-student interaction. Smaller groups foster a more personalized learning environment but may lack the resource utilization efficiency of larger cohorts. Institutions should consider contextual factors such as specialty, learning objectives, and available resources when determining optimal group sizes. In conclusion, the relationship between bulk and clinical rotation group size is complex, with both benefits and limitations associated with different group sizes. Institutions should strive to strike a balance between efficiency and educational quality by tailoring group sizes to specific contexts and objectives. Further research is needed to explore additional factors influencing bulk and to develop targeted interventions for optimizing clinical rotation experiences.

Dynamic event-triggered fixed-time consensus control for coupled multi-permanent magnet synchronous motors stochastic system with prescribed performance

With the ubiquity of permanent magnet synchronous motors (PMSM), multi-PMSMs are increasingly being elastically coupled to achieve greater output power. The authors establish a general mathematical model of coupled multi-PMSMs system. The dynamic analysis of the coupled multi-PMSMs system is analyzed using coupled three-PMSMs system as an example, and a dynamic event-triggered fixed-time consensus controller with prescribed performance is constructed for coupled PMSMs stochastic system. In addition, Interval Type-3 Fuzzy System (IT3FS) is employed to estimate unknown functions, the computing cost is reduced by updating the weights based on only one adaptive law per PMSM. To enhance the efficiency of communication resource utilization, a dynamic event-triggered mechanism is introduced, where the triggering condition depends on a dynamic auxiliary variable. Moreover, fixed-time control technical is applied to guarantee the setting time and a novel fixed-time prescribed performance function (PPF) is constructed to constrain consensus errors. To cope with the “complexity explosion”, a fixed-time tracking differentiator (TD) is introduced and the corresponding filtered error compensation signals is designed. Finally, the authors demonstrate the system is fixed-time stabilized in probability by applying fixed-time stabilization Lyapunov stability criterion, and the simulation results of a coupled three-PMSMs stochastic system show that the presented algorithm is feasible.

Advances in Catalysts for Urea Electrosynthesis Utilizing CO2 and Nitrogenous Materials: A Mechanistic Perspective.

Electrocatalytic urea synthesis from CO2 and nitrogenous substances represents an essential advance for the chemical industry, enabling the efficient utilization of resources and promoting sustainable development. However, the development of electrocatalytic urea synthesis has been severely limited by weak chemisorption, poor activation and difficulties in C-N coupling reactions. In this review, catalysts and corresponding reaction mechanisms in the emerging fields of bimetallic catalysts, MXenes, frustrated Lewis acid-base pairs and heterostructures are summarized in terms of the two central mechanisms of molecule-catalyst interactions as well as chemical bond cleavage and directional coupling, which provide new perspectives for improving the efficiency of electrocatalytic synthesis of urea. This review provides valuable insights to elucidate potential electrocatalytic mechanisms.

Extraction and In Vitro Skincare Effect Assessment of Polysaccharides Extract from the Roots of Abelmoschus manihot (L.).

Obtaining high-added value compounds from agricultural waste receives increasing attention, as it can both improve resource utilization efficiency and reduce waste generation. In this study, polysaccharides are extracted from the discarded roots of Abelmoschus manihot (L.) by the high-efficiency ultrasound-assisted extraction (UAE). The optimized condition was determined as solid-liquid ratio SL ratio = 1:20, temperature T = 30 °C and time T = 40 min, achieving an extraction yield of 13.41%. Composition analysis revealed that glucose (Glc, 44.65%), rhamnose (Rha, 26.30%), galacturonic acid (GalA, 12.50%) and galactose (Gal, 9.86%) are the major monosaccharides of the extract. The extract showed a low degree of esterification (DE) value of 40.95%, and its Fourier-transform infrared (FT-IR) spectrum exhibited several characteristic peaks of polysaccharides. Inspired by the wide cosmetic applications of polysaccharides, the skincare effect of the extract was evaluated via the moisture retention, total phenolic content (TPC) quantification, 2,2-Diphenyl-1-picrylhydrazyl (DPPH)-free radical scavenging activity, anti-hyaluronidase and anti-elastase activity experiments. The extract solutions demonstrated a 48 h moisture retention rate of 10.75%, which is superior to that of commercially available moisturizer hyaluronic acid (HA). Moreover, both the TPC value of 16.16 mg GAE/g (dw) and DPPH-free radical scavenging activity of 89.20% at the concentration of 2 mg/mL indicated the strong anti-oxidant properties of the extract. Furthermore, the anti-hyaluronidase activity and moderate anti-elastase activity were determined as 72.16% and 42.02%, respectively. In general, in vitro skincare effect experiments suggest moisturizing, anti-oxidant, anti-radical and anti-aging activities of the A. manihot root extract, indicating its potential applications in the cosmetic industry.