Efficient Resource Utilization Research Articles

Adaptive load balancing in distributed cloud environment: Hybrid Kookaburra-Osprey optimization algorithm

A distributed cloud environment is characterized by the dispersion of computing resources, services, and applications across multiple locations or data centres. This distribution enhances scalability, redundancy, and resource utilization efficiency. To optimize performance and prevent any single node from becoming a bottleneck, it is imperative to implement effective load-balancing strategies, particularly as user demands vary and certain nodes experience increased processing requirements. This research introduces an Adaptive Load Balancing (ALB) approach aimed at maximizing the efficiency and reliability of distributed cloud environments. The approach employs a three-step process: Chunk Creation, Task Allocation, and Load Balancing. In the Chunk Creation step, a novel Improved Fuzzy C-means clustering (IFCMC) clustering method categorizes similar tasks into clusters for assignment to Physical Machines (PMs). Subsequently, a hybrid optimization algorithm called the Kookaburra-Osprey Updated Optimization Algorithm (KOU), incorporating the Kookaburra Optimization Algorithm (KOA) and Osprey Optimization Algorithm (OOA), allocates tasks assigned to PMs to Virtual Machines (VMs) in the Task Allocation step, considering various constraints. The Load Balancing step ensures even distribution of tasks among VMs, considering migration cost and efficiency. This systematic approach, by efficiently distributing tasks across VMs within the distributed cloud environment, contributes to enhanced efficiency and scalability. Further, the contribution of the ALB approach in enhancing the efficiency and scalability of distributed cloud environments is evaluated through analyses. The KBA is 1189.279, BES is 629.240, ACO is 1017.889, Osprey is 1147.300, SMO is 1215.148, APDPSO is 1191.014, and DGWO is 1095.405, respectively. The resource utilization attained by the KOU method is 1224.433 at task 1000.

Simultaneous preparation of lignin-containing cellulose nanocrystals and lignin nanoparticles from wood sawdust by mixed organic acid hydrolysis

In the global search for sustainable and environmentally friendly materials, the efficient utilization of biomass resources has become a hot research topic. Due to the high specific surface area, nanocellulose crystals (CNCs) have attracted considerable attention. However, CNC preparation often requires the separation of lignin and hemicellulose, which wastes resources. In this study, lignin-containing cellulose nanocrystals (LCNCs) and lignin nanoparticles (LNPs) were prepared from p-toluenesulfonic acid (p-TsOH)/formic acid (FA) pretreated poplar wood in one-step. The process was performed in a mixed acid system with a mass ratio of p-TsOH, FA, and deionized water (DI) of 3:5:2, and mechanical stirring at 80 ºC for 3 h. Under these conditions, the lignin removal rate was about 70 %, and the LCNCs yield was more than 40 %. The p-TsOH is regained by vacuum distillation and recrystallization, and the recovery rate can reach 60 %. The prepared LCNCs were then carbonized and used as an electrode for electrochemical testing. The electrode obtained by carbonizing LCNC exhibits an areal capacitance of 156.4 F g−1 (0.5 A g−1). The study provided a new prospect for the efficient utilization of lignocellulosic nanomaterials.

A sustainable one-pot synthesis of 2,5-diformylfuran from carbohydrates using tetracationic ionic liquid

A plausible solution to the current energy crisis is the development of cost-effective and inherently secure methods for accelerating the sustainable production of value-added compounds from biomass. In this study, we have developed a highly efficient catalytic system for the conversion of fructose into 2,5-diformylfuran (DFF) using tetracationic ionic liquids with dual Brønsted acid sites and four bromide ion sites. The presence of Brønsted acid sites promotes the production of 5-hydroxymethylfurfural (HMF), while the bromide anions react to form a DFF, which undergoes further transformation via the Kornblum mechanism. Under optimized conditions, a remarkable DFF yield of 99.6 % was attained in dimethyl sulfoxide (DMSO) at 140 °C for 6 h. Moreover, the catalyst exhibited excellent stability and reusability, indicating its potential for industrial application in the synthesis of DFF from fructose. With the help of this effort, Br- anion may be able to throw some light on the oxidation of HMF to DFF. Overall, this study highlights the promising application of tetracationic ionic liquid catalyst systems, showcasing the potential for the efficient utilization of renewable biomass resources and the synthesis of high-value-added compounds.

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

Green innovation and natural resource efficiency: The role of environmental regulations and resource endowment in Chinese cities

Achieving natural resource utilization efficiency (NRUE) is a critical objective in addressing the pressing challenges of climate change and the risks associated with the finite availability of mineral resources. Enhancing NRUE can mitigate environmental impacts and support sustainable development for future generations. In this context, green innovation emerges as a pivotal driver of environmental sustainability and resource conservation. However, its potential role in advancing the NRUE has not been fully explored. To fill this gap, this study conducts an empirical analysis of 268 Chinese cities from 2010 to 2022, examining the impact of green innovation on NRUE. This study also incorporates the mediating role of environmental regulations in shaping this impact. The findings demonstrate that green innovation significantly enhances NRUE, primarily by facilitating industrial structural upgrading and reducing resource dependence. Additionally, environmental regulations strengthen the positive effects of green innovation, further amplifying its contribution to NRUE. Moreover, geographical location and differences in resource endowments among cities lead to variations in the effectiveness of green innovation on NRUE. These findings underscore the complex interplay between green technological progress, proactive government policies, and NRUE, offering valuable insights for policymakers and researchers alike.

Research progress on operation control and optimal scheduling of irrigation canal systems

AbstractOpen canals are a common water transfer method used in water transfer projects and agricultural irrigation and drainage projects. With the emergence of drawbacks in traditional canal control models and the increasingly severe shortage of water resources, accurate transport and distribution of water in the canal system of the irrigation district, rational allocation of water resources, reduction in water loss, improvement in the efficiency and benefit of water resource utilization, and satisfaction of the water demand of different water users are needed. Many scholars have conducted extensive research on canal operation control and optimal scheduling. This paper systematically reviews and summarizes the relevant research progress, including the theory of unsteady flow in open canals, the operation mode of the canal system, the operation control model and algorithm of canal systems, and the optimization of water distribution in canal systems. By summarizing the research progress already achieved, the existing problems and future development directions are identified according to actual needs, providing a reference for the ongoing modernization of irrigation districts and the research and application of digital twin irrigation district technology.

Multi-polar group cationic collector HTTPD: Mechanistic insights into selective flotation of bastnaesite

To address the challenge of separating bastnaesite rare earth minerals from gangue minerals, this study independently synthesized a novel multi-polar group cationic surfactant,2,2-bis(hydroxymethyl)-N1,N1,N3,N3-tetramethyl-N1,N3-bis(3-tridecanamidopropyl)propane-1,3-diaminium bromide (HTTPD), as a collector. The flotation results showed that HTTPD could obtain REO grade of 69.71% and recovery of 89.33%, significantly outperforming conventional collectors and proving to be an excellent collector. Density functional theory (DFT) calculation displayed that two quaternary ammonium groups and –NH/OH polar groups were main active sites with strong reactivity. The quaternary ammonium groups of HTTPD strongly adsorbed on the bastnaesite surface through electrostatic attraction, thereby enhancing the flotation selectivity from calcite. The –NH/–OH polar groups adsorbed with bastnaesite by hydrogen bonding interaction. Multi-polar group synergistic adsorption, formed through electrostatic attraction and hydrogen bonding interaction, was the primary mechanism for the efficient separation of bastnaesite and calcite. This research not only provided new insight and theoretical guidance for the design and application of novel collectors but also promoted the efficient development and utilization of rare earth mineral resources.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

Enhanced removal of organic dyes by piezoelectric-Fenton-like treatment with Fe-MOF@MoS2 catalysts

The risks associated with dye wastewater to the ecological environment are becoming increasingly significant. The efficient utilization and effective protection of water resources have become a persistent challenge. Employing coupling technologies to enhance the wastewater treatment process is an effective strategy for achieving sustainable water resource utilization. Herein, composite catalysts with iron-based metal-organic frameworks loaded with molybdenum disulfide (Fe-MOF@MoS₂) were prepared by a hydrothermal approach. The Fe-MOF@MoS2 composite catalysts demonstrated superior degradation efficiencies towards various organic dyes like methylene blue, rhodamine B, and Congo red, especially in conditions of low hydrogen peroxide concentration and ultrasonic vibration. Results indicated that after 30 minutes of piezoelectric-Fenton-like treatment, the removal efficiency of the above organic dyes exceeded 90 %. The piezoelectric effect led to efficient cyclic conversion of Fe2+/Fe3+ ions, while the inclusion of MoS2 enhanced H2O2 activation efficiency. Moreover, the Fe-MOF@MoS2 catalyst displayed remarkable recycling capabilities, retaining 90 % of its initial catalytic activity even after six degradation cycles. Thus, the development of composite catalysts with combined piezoelectric and Fenton-like functions holds great promise for improving organic dye wastewater treatment and safeguarding environmental and water resources.

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

Optimizing Ridge–Furrow Ratio to Improve Water Resource Utilization for Wheat in the North China Plain

The shortage of water resources seriously limits sustainable production in agriculture, and the ridge–furrow planting pattern is an effective water-saving cultivation pattern. However, the mechanism of the ridge–furrow planting pattern that drives the efficient utilization of field water resources in the North China Plain (NCP) is still unclear. A two-year field experiment was conducted in the NCP from 2021 to 2023. The ridge–furrow planting patterns followed a randomized block design as follows: ridge–furrow ratios of 50 cm:50 cm (M2), 75 cm:50 cm (M3), and 100 cm:50 cm (M4). A traditional planting pattern was used as the control (M1). These were used to investigate the effects of different treatments on water use and roots. The results showed that M3 reduced the amount of irrigation, improved water distribution after irrigation, increased water use efficiency (WUE), and promoted root growth. Compared with other treatments, M3 increased soil water consumption at a 0–100 cm soil depth by 6.76–21.34% (average values over two years), root length density by 8.46–20.77%, and root surface area density by 7.87–22.13%. On average, M3 increased grain yields by 3.96–9.80%, biomass yields by 5.32–10.94%, and WUE by 4.5–9.87%. In conclusion, M3 is an effective planting pattern for improving the yield and WUE of wheat in the NCP.

Spatial spillover effects and correlation network analysis of green construction development efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.

Reporting on the establishment of a Privacy Preserving Record Linkage to Facilitate an Ongoing Crosswalk Between Research and Health Administrative Databases

IntroductionThe Ontario Brain Institute (OBI) is a provincially funded, not-for-profit organization that accelerates discovery and innovation, benefiting both patients and the economy (Stuss, 2014). OBI has established a large-scale neuroinformatics platform - Brain-CODE - to support the collection, storage, federation, sharing, and analysis of different data types across several brain disorders (Behan et al., 2023; Vaccarino et al., 2018). A privacy preserving record linkage protocol was developed to allow for the linkage of research data at Brain-CODE with health administrative data holdings at the Institute for Clinical Evaluative Sciences (ICES) (Gee et al., 2018). Objective and ApproachThe methodology related to an ongoing crosswalk linkage between OBI and ICES, to allow for more seamless integration between the respective data holdings, has been previously presented (Behan et al., 2020). This methodology has since been operationalized leading to the establishment of an ongoing crosswalk linkage that is updated on an annual basis. ResultsSince the initial development of this ongoing crosswalk, two updates have been successfully completed leading to the linkage of over 7,000 study participants between the two platforms. This has led to a more efficient utilization of human and computational resources, compared to earlier data linkage projects completed on a project-by-project basis. Analysis projects in the areas of neurodegeneration, concussion, and neurodevelopmental disorders have already leveraged this crosswalk linkage process. Conclusions/ImplicationsThe establishment of this ongoing crosswalk linkage has supported a more streamlined approach of data linkage activities between OBI and ICES allowing for enhanced neuroscience-focused research activities.

Parcel-Locker-Sharing Model for E-Commerce Logistics Service Providers

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators’ parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.

High efficiency and cost-effective innovative self-oxidation in-situ removal scheme for the preparation of battery-grade manganese sulphate

As an essential crucial intermediate in the green new energy, achieving sufficient global supply of battery-grade manganese sulfate holds significant strategic importance. However, high efficiency and cost-effective preparation of battery-grade manganese sulfate and efficiently removing impurity ions pose highly challenging. Herein, an innovative self-oxidation in-situ removal scheme successfully prepared battery-grade MnSO4 from low-grade pyrolusite. Specifically, during the purification of qualified MnSO4, abundant Fe3+ was generated in situ through the self-oxidation of pyrolusite without the need for additional oxidants. This significantly facilitated the formation of jarosite (XFe3[SO4(OH)3]2, X=K/Na), greatly enhancing the removal efficiency of K, Na, and Fe (99.69 %, 99.82 % and 99.99 %), and promoted the removal of subsequent impurity ions. Subsequently, the dissolved impurity ions were successively removed in-situ using industrial reagents, achieving technical indices (Cu 99.93 %, Co 99.73 %, Cd 93.26 %, Zn 99.50 %, Ni 98.42 %, Al 99.71 %, Si 98.67 %, Ca 99.82 %, Mg 97.90 %, and F 99.78 %) that are far superior to those of battery-grade MnSO4.This fruitful innovative scheme has promising commercia prospects and is conducive to the efficient and low-cost comprehensive utilization of global pyrolusite resource.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

Recycling of waste glass powder: Effects on microstructure, mechanical properties and sustainability of seashell powder calcined sludge cement

The massive accumulation of waste seashells, waste sludge and waste glass not only occupies a large amount of land resources, leading to a shortage of land resources, but also causes serious soil-water-air composite pollution over a long period of time with the role of the surrounding environment, which poses a serious hazard to the ecological environment and public health. In this study, the effect patterns of waste glass powder (WGP) on the workability, mechanical properties, microstructure and carbon emission of seashell powder calcined sludge cement (SCSC) slurries prepared using waste sludge and waste seashells as supplementary cementitious materials in place of part of the cement clinker were investigated. The hydration process and microstructure of the materials were characterized by heat of hydration tests, thermogravimetry (TG-DTG), infrared Fourier transform (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that the addition of WGP improved the fluidity of SCSC slurries and reduced the shear stress of SCSC slurries without changing the flow pattern of SCSC slurries, and all the slurries conformed to the power law model. The compressive strength of SCSC slurries increased by 25.26 % with 5 % WGP addition. The CO2 emissions per cubic meter of SCSC slurries were reduced by 4.43 %, 8.81 %, 13.5 % and 18.23 % for WGP additions of 5 %, 10 %, 15 % and 20 %, respectively. These results can provide a new way for the efficient resource utilization of waste seashells, waste sludge and waste glass, and reduce the CO2 emission during the cement production process, promoting the clean production of cement.

Measurement and Influencing Factors of Total Factor Water Resources Utilization Efficiency in the Yellow River Basin

This paper focuses on the total factor water resources utilization efficiency (WRUE) as the research object, employing the Stochastic Frontier Analysis (SFA) model based on the transcendental logarithmic production function to measure the WRUE of nine provinces in the Yellow River Basin of China from 2012 to 2021, obtains WRUE values for each province. And then, we perform a regression analysis of influencing factors using the Tobit model. The results indicate that: (1) The average value of WRUE of nine provinces in the Yellow River Basin is 0.541, which deviates greatly from the fully effective frontier of SFA, with an overall downward trend during the study period. (2) There is a significant polarization of WRUE among the provinces. Inner Mongolia exhibits the highest WRUE at 0.978, close to the SFA efficient frontier, while Ningxia has the lowest WRUE at only 0.314; the remaining provinces display efficiency values between 0.4 and 0.6. (3) Among the influencing factors affecting the WRUE, the value added of the primary industry, per capita disposable income, and the number of reservoirs have a significant positive impact on the WRUE. In contrast, the value added of the secondary industry, local financial environmental protection expenditures, comprehensive water supply production capacity, and per capita daily water consumption negatively affect the WRUE.