Efficient Resource Utilization Research Articles

Enhancing Distribution Efficiency Through OTIF Performance Evaluation

Today’s market with more frequent but smaller deliveries complicates the realization of logistics processes and activities. Customers expect products within 24 h or 48 h. This poses a logistics challenge for companies in fulfilling the expected time window. In these situations, companies can either perform self-distribution or they can outsource it. When outsourcing, companies must define key performance indicators (KPIs) to measure the efficiency of the distribution process. One such KPI is On-Time In-Full (OTIF). OTIF fulfillment is increasingly becoming a fundamental requirement not only in distribution but also throughout the entire supply chain. Two essential prerequisites for successful outsourcing collaboration are ensuring that orders are delivered on time and in full. A review of the existing literature revealed that no studies simultaneously address this complex issue. This was exactly the main motive for developing a model that simultaneously takes into account the utilization of logistics resources on one hand, and OTIF on the other when calculating the efficiency. The model developed in this paper was tested on the data of a company operating in the Serbian market. The results showed that seven of the company’s warehouses are inefficient. A two-stage matrix analysis with the three most important indicators (operational efficiency, on-time delivery, and in-full delivery) was performed based on the obtained results. Other than the obvious scientific contributions, the proposed model can help companies increase their resource utilization efficiency as well as OTIF fulfillment.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

Serverless Computing: Optimizing Resource Utilization and Cost Efficiency

Serverless computing has emerged as a transformative paradigm in cloud infrastructure, offering organizations the ability to scale their applications dynamically without the burden of managing underlying servers. By abstracting away the provisioning and scaling of infrastructure, serverless computing enables developers to focus on building and deploying their applications, while the cloud provider handles the auto- scaling, load balancing, and fault tolerance. This paper examines the key benefits and challenges of serverless computing, with a particular emphasis on optimizing resource utilization and cost efficiency. The findings suggest that serverless computing can lead to significant improvements in resource utilization and cost savings, but organizations must also address challenges related to cold starts, vendor lock-in, and monitoring complexity to fully realize the potential of this cloud computing paradigm.

Multi-Objective Design and Optimization of Hardware-Friendly Grid-Based Sparse MIMO Arrays

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GBSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The framework explores optimization strategies for the partial sharing of antenna elements, specifically tailored for multi-mode radar applications, utilizing the desirability function to enhance performance across various operational modes. A novel machine learning initialization approach is introduced for rapid convergence. Key observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GBSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study emphasizes the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework. The proposed design framework for grid-spaced sparse arrays stands out for its superior efficiency and applicability in processing hardware compared to both uniform and non-uniform arrays.

Intrusion detection based on concept drift detection and online incremental learning

Purpose The primary purpose of this paper is to introduce the drift detection method-online random forest (DDM-ORF) model for intrusion detection, combining DDM for detecting concept drift and ORF for incremental learning. The paper addresses the challenges of dynamic and nonstationary data, offering a solution that continuously adapts to changes in the data distribution. The goal is to provide effective intrusion detection in real-world scenarios, demonstrated through comprehensive experiments and evaluations using Apache Spark. Design/methodology/approach The paper uses an experimental approach to evaluate the DDM-ORF model. The design involves assessing classification performance metrics, including accuracy, precision, recall and F-measure. The methodology integrates Apache Spark for distributed computing, using metrics such as processed records per second and input rows per second. The evaluation extends to the analysis of IP addresses, ports and taxonomies in the MAWILab data set. This comprehensive design and methodology showcase the model’s effectiveness in detecting intrusions through concept drift detection and online incremental learning on large-scale, heterogeneous data. Findings The paper’s findings reveal that the DDM-ORF model achieves outstanding classification results with 99.96% accuracy, demonstrating its efficacy in intrusion detection. Comparative analysis against a convolutional neural network-based model indicates superior performance in anomalous and suspicious detection rates. The exploration of IP addresses, ports and taxonomies uncovers valuable insights into attack patterns. Apache Spark evaluation attests to the system’s high processing rates. The study emphasizes the scalability, availability and fault tolerance of DDM-ORF, making it suitable for real-world scenarios. Overall, the paper establishes the model’s proficiency in handling dynamic, nonstationary data for intrusion detection. Research limitations/implications The research acknowledges certain limitations, including the potential challenge of DDM detecting only frequency changes in class labels and not complex concept drifts. The incremental random forest’s reliance on memory may pose constraints as the forest size increases, potentially leading to overfitting. Addressing these limitations could involve exploring alternative concept drift detection algorithms and implementing ensemble pruning techniques for memory efficiency. Further research avenues may investigate algorithms balancing accuracy and memory usage, such as compressed random forests, to enhance the model’s effectiveness in evolving data environments. Practical implications The study’s practical implications are noteworthy. The proposed DDM-ORF model, designed for intrusion detection through concept drift detection and online incremental learning, offers a scalable, available and fault-tolerant solution. Leveraging Apache Spark and Microsoft Azure Cloud enhances processing capabilities for large data sets in dynamic, nonstationary scenarios. The model’s applicability to heterogeneous data sets and its achievement of high-accuracy multi-class classification make it suitable for real-world intrusion detection. Moreover, the auto-scaling features of Microsoft Azure Cloud contribute to adaptability, ensuring efficient resource utilization without downtime. These practical implications underscore the model’s relevance and effectiveness in diverse operational contexts. Social implications The DDM-ORF model’s social implications are significant, contributing to enhanced cybersecurity measures. By providing an effective intrusion detection system, it helps safeguard digital ecosystems, preserving user privacy and securing sensitive information. The model’s accuracy in identifying and classifying various intrusion attempts aids in mitigating potential cyber threats, thereby fostering a safer online environment for individuals and organizations. As cybersecurity is paramount in the digital age, the social impact lies in fortifying the resilience of networks, systems and data against malicious activities, ultimately promoting trust and reliability in online interactions. Originality/value The DDM-ORF model introduces a novel approach to intrusion detection by combining drift detection and online incremental learning. This originality lies in its utilization of the DDM-ORF algorithm, offering a dynamic and adaptive system for evolving data. The model’s contribution extends to its scalability, fault-tolerance and suitability for heterogeneous data sets, addressing challenges in dynamic, nonstationary environments. Its application on a large-scale data set and multi-class classification, along with integration with Apache Spark and Microsoft Azure Cloud, enhances the field’s understanding and application of intrusion detection, providing valuable insights for securing digital infrastructures.

Does the Bike-Sharing Platform Share Its Own Private Information or Not in a Reverse Supply Chain?

Bike sharing facilitates the public’s daily lives and possesses substantial market demand, yet its overwhelming presence has triggered some environmental problems. To enhance resource utilization efficiency, we examine the decision-making strategies within a reverse supply chain considering the private information of the bike-sharing platform. Using Stackelberg games, two models are formulated in order to investigate whether the platform should share its own private information when the used shared bikes are recycled. A supply chain without private information is provided as a benchmark for comparison, and we obtain the optimal equilibrium strategies of the benchmark and the proposed model. Furthermore, how the optimal strategies change with the variation in parameters and the impacts of the private information on the optimal strategies are discussed. The results show that information asymmetry could lead to a reduction in supply chain efficiency. Thus, a two-part pricing contract is introduced to incentivize the platform to share information and coordinate the supply chain, which can effectively improve the performance of the integral supply chain and implement a “win-win” strategy for the third-party remanufacturer and the platform.

Comparison of Emergency Department Disposition Times in Adult Level I and Level II Trauma Centers

Introduction: The efficient utilization of resources is a crucial aspect of healthcare, particularly in both Level I and Level II American College of Surgeons (ACS)-verified trauma centers. The effect of resource allocation on emergency department length of stay (ED-LOS) of trauma patients has remained under-investigated. As ED crowding has become more prevalent, especially at quaternary care centers, an evaluation of the potential disparities in ED-LOS between Level I and Level II trauma centers is warranted. We hypothesized a longer ED-LOS at Level I centers compared to Level II centers. Methods: We queried the 2017–2021 Trauma Quality Improvement Process (TQIP) database for trauma patients ≥18 years of age presenting to either a Level-I or -II center. The TQIP defines ED-LOS as the time from arrival until the time an ED disposition (admission or discharge) order is written. We excluded transferred patients and those with missing data regarding ACS trauma center verification level. We performed bivariate analyses, as well as subgroup analyses based on location of disposition. Results: Of 2,225,067 trauma patients, 59.3% (1,318,497) received treatment at Level I centers. No significant differences were found in Injury Severity Scores between patients admitted to the operating room or non-intensive care unit (ICU) locations, or discharged home from Level-I and -II centers (all P < 0.05). The ED-LOS for trauma patients was longer at Level-I centers for all patient categories: overall (198 vs 145 minutes [min], P < 0.001), discharged home (286 vs 160 min, P < 0.001), non-ICU admissions (234 vs 164 min, P < 0.001), and those requiring surgery (126 vs 101 min, P < 0.001). Conclusion: Even when treating patients with similar injury severity, trauma patients at Level I trauma centers had longer ED-LOS compared to Level II centers, irrespective of the patients’ final disposition (surgery, non-ICU admission, or discharge). To optimize resource utilization and alleviate ED saturation, further research must delve into the underlying causes of these discrepancies to identify best practices and solutions.

Emergy-Based Evaluation of the Sustainability of Agricultural Ecosystem in Dazhou, China, from 2002 to 2022

Our aim is to analyze the emergy evaluation indicators of the agricultural ecosystem in Dazhou, northeastern Sichuan, and provide practical and effective recommendations for sustainable agricultural development. Using emergy analysis, the emergy inputs and outputs of an agricultural ecosystem from 2002 to 2022 were calculated. Five emergy indicators were selected for evaluation: emergy yield ratio (EYR), emergy self-sufficiency ratio (ESR), emergy input ratio (EIR), environmental load ratio (ELR), and emergy sustainable indices (ESI). The total emergy input of the agricultural ecosystem showed an upward trend from 2002 to 2017, thus the industrial auxiliary emergy input decreased, somewhat curbing its continued rise from 2017 to 2022. The structure of emergy inputs, in descending order, is as follows: industrial auxiliary > organic emergy > renewable environmental resources > non-renewable environmental resources. The total emergy output of the agricultural ecosystem was highest in 2007, reaching 2.31 × 1022 Sej, and lowest in 2012, at 1.83 × 1022 Sej. The structure of emergy outputs, in descending order, is as follows: livestock > planting > fishery > forestry. The emergy yield ratio fluctuated down from 3.12 to 2.51, with an average of 2.88, below the provincial average of 3.07. The emergy self-sufficiency ratio fluctuated down from 0.30 to 0.26, with an average of 0.27, above the provincial average of 0.13. The emergy input ratio fluctuated up from 2.31 to 2.91, with an average of 2.66, above the provincial average of 1.86. The environmental load ratio fluctuated from 3.8 to 4.75, with an average of 4.40, which is higher than the provincial average of 1.68. The emergy sustainable indices fluctuated down from 0.81 to 0.53, with an average of 0.67, below the provincial average of 1.17. The efficiency of resource utilization in the agricultural ecosystem of Dazhou has decreased, economic inputs have increased, and it is in a consumptive production process. The pressure on the local natural environment is increasing, and the capacity for sustainable development remains at a low level over the long term.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development: A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization. Urban integration is an emerging trend in metropolitan areas. This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development. Rail transit in metropolitan areas offers both technical and economic advantages, improving the efficiency of time and space resource utilization, fostering economic cooperation, and ultimately contributing to an integrated development model. However, the integration of rail transit networks faces several challenges, including road network planning, technical standards, and operational organization. Using the Wuhan metropolitan area as a case study, this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

Construction and Optimization Strategies for Rural Residential Spatial Models Based on the Concept of Resource Metabolism: A Case Study of Rural Areas in the Shandong Plain

Rural communities can be conceptualized as spatial organisms interconnected by optimized resource utilization systems. Investigating the efficient utilization of rural resources and spatial construction methods grounded in resource metabolism is a pivotal step toward achieving the ecological transformation of rural spaces. This paper examined rural settlements in the Shandong Plain as a case study, exploring the relationships among three scales: village, neighborhoods, and courtyards. This analysis was based on elucidating the interaction mechanisms between “space and resource” and the integration of key resources and spatial elements. From the perspective of resource circulation and metabolism, this study aimed to elucidate the equilibrium of each resource element within three resource metabolism subsystems: the agricultural production system (core element), the ecological technology system (technological link), and the human life system (spatial carrier) in Shandong Plain’s villages, considering general climatic conditions. To achieve this, this research utilized the resource production volume, the utilization and transformation volume of resource metabolism technology facilities per unit area, and the average per capita resource consumption as fundamental measurement units. The concept of a rationing relationship is introduced to clarify resource allocation. Combining the aforementioned research on spatial resource metabolism in ecological villages in Shandong Province with the material flow analysis method, this study constructed a bottom–up spatial model of resource metabolism at three scales, courtyards, neighborhoods, and villages, under various resource metabolism scenarios. This study is anticipated to significantly contribute to the theoretical understanding of rural habitat environments, offering novel methods and perspectives for constructing ecological rural settlements.

Effects of land use change on runoff depth in the Songnen Plain, China.

Climate change and human activities both have a considerable impact on runoff depth, which are important parts of a changing ecosystem. Nevertheless, the main focus of hydrological response research has been on investigating the impact of climate change on the depth of runoff. In contrast, there has been limited emphasis on comprehending the precise mechanisms through which changes in land use, in relation to human activities, influence runoff depth. This paper employs the MIKE SHE/MIKE 11 model to simulate surface runoff in the study area from 1980 to 2020, assesses the effects of climate change and land use change on runoff depth using the runoff reduction method, and quantifies the influence of land use change on runoff depth through a spatio-temporal geographically weighted regression model. This study indicates that during the past 40 years, the average runoff depth in the Songnen Plain was 36.26mm, exhibiting a tendency of 'increasing-decreasing-increasing'. The impact of climate change on surface runoff depth is more substantial than that of land use change. During the impact period 1, the runoff depth diminished by 19.07mm, with climate change contributing to a decrease of 15.89mm (83.31% contribution). In the impact period 2, the runoff depth increased by 7.49mm relative to the baseline period, with climate change leading to an increase of 12.73mm (70.84% contribution). Changes in various land types within the watershed can be used to observe the influence of human activities on runoff depth. More precisely, a 10% rise in the rate of change of construction land, dry land, and unoccupied land results in an increase in runoff depth of 6.21mm, 2.45mm, and 1.14mm, respectively. Conversely, a 10% rise in the rate of alteration of marsh, paddy, and forest land leads to a reduction in the depth of runoff by 9.49mm, 6.46mm, and 3.07mm, respectively. This research can contribute to improving the efficiency of water and land resource utilization and optimizing land resource governance.

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

Spontaneous combustion coal gangue-based composite cement: Compressive performance and environmental benefits under grinding kinetics control

This study aims to tackle the environmental pressure of spontaneous combustion coal gangue by using its active silica-alumina content to create eco-friendly composite cement through a low-energy process. The Divas-Aliavden grinding kinetics equation was established, and powder characteristics were analyzed using particle size distribution, the Rosin-Rammler-Bennett model, and fractal dimension theory. The compressive performance of the coal gangue-based composite cement was tested. A comprehensive evaluation model linked grinding time, powder characteristics, compressive performance, and energy consumption, while also analyzing hydration products and environmental impact. Results showed that initial grinding stages had larger particle sizes and higher energy utilization efficiency. As grinding progressed, energy consumption increased. A grinding time of 35 min achieved optimal balance between energy efficiency and compressive performance, improving particle distribution uniformity and complexity. The compressive performance of the composite cement reached 95 % of ordinary Portland cement after 28 days of curing. The evaluation model indicated that specific surface area, d50, and fractal dimension are key factors influencing energy efficiency, determining energy consumption and particle distribution during grinding. Environmental assessment revealed that substituting 30 % of cement with coal gangue could reduce CO2 emissions by 250 kg CO2/t, achieving a reduction rate of 29.5 %. This study supports the large-scale application of spontaneous combustion coal gangue, contributing to efficient resource utilization and environmental protection.

Red Blood Cell (RBC) Antigen Confirmation Workflow – A Positive Impact at an Academic Medical Center

Abstract Introduction/Objective Introduction: Blood group antigens, present on the surface of red blood cells (RBCs), play a crucial role in transfusion medicine. These antigens are recognized serologically and form the basis of RBC phenotyping. Understanding the significance of antigen-negative RBCs is essential for mitigating the risk of hemolytic transfusion reactions and hemolytic disease of the fetus and newborn (HDFN). This study aims to assess the impact of implementing a new RBC antigen confirmation workflow on transfusion service efficiency and patient outcomes. Methods/Case Report Method: The study involved collaboration between our Transfusion Services Department (TS), the Blood Center supplier, and the Apheresis department. A comprehensive review of historical practices and FDA recommendations regarding RBC antigen labeling was conducted. Changes were implemented to streamline antigen confirmation testing and labeling processes. The impact on TS workload, turnaround time, and patient care metrics was evaluated. Results (if a Case Study enter NA) Results: Implementation of the new RBC antigen confirmation workflow led to a significant reduction in antigen confirmation tests performed by TS, with a 607% decrease observed from 2020 to 2023. Concurrently, the workload for reference lab workups increased by 337% over the same period. This shift in workload distribution allowed TS staff to focus more on patient reference workup cases and case review. The improved workflow efficiency resulted in enhanced turnaround times for providing patients with antigen-negative RBCs, ultimately improving the patient experience. Conclusion Conclusion: The adoption of the new RBC antigen confirmation workflow has resulted in compliance with FDA labeling requirements, efficient utilization of TS staff resources, and decreased turnaround times for blood delivery. These improvements have positively impacted patient care by ensuring timely access to compatible blood products. Future steps include developing reports to track turnaround time for patients with special blood product needs and continuing collaboration with the blood supplier and the Apheresis department to optimize inventory management and transfusion processes.

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.

Efficient Toluene Decontamination and Resource Utilization through Ni/Al2O3 Catalytic Cracking.

Volatile organic compounds (VOCs), particularly aromatic hydrocarbons, pose significant environmental risks due to their toxicity and role in the formation of secondary pollutants. This study explores the potential of catalytic pyrolysis as an innovative strategy for the effective remediation and conversion of aromatic hydrocarbon pollutants. The research investigates the high-efficiency removal and resource recovery of the VOC toluene using a Ni/Al2O3 catalyst. The Ni/Al2O3 catalyst was synthesized using the impregnation method and thoroughly characterized. Various analytical techniques, including scanning electron microscopy, X-ray diffraction, and N2 adsorption-desorption isotherms, were employed to characterize the Al2O3 support, NiO/Al2O3 precursor, Ni/Al2O3 catalyst, and the resulting solid carbon. Results indicate that Ni predominantly occupies the pores of γ-Al2O3, forming nano/microparticles and creating interstitial pores through aggregation. The catalyst demonstrated high activity in the thermochemical decomposition of toluene into solid carbon materials and COx-Free hydrogen, effectively addressing toluene pollution while recovering valuable resources. Optimal conditions were identified, revealing that a moderate temperature of 700 °C is most favorable for the catalytic process. Under optimized conditions, the Ni/Al2O3 catalyst removed 1328 mg/g of toluene, generated 915 mg/g of carbon material, and produced 1234 mL/g of hydrogen. The prepared carbon material, characterized by its mesoporous structure and high specific surface area graphite nanofibers, holds potential application value in adsorption, catalysis, and energy storage. This study offers a promising approach for the purification and resource recovery of aromatic volatile organic compounds, contributing to the goals of a circular economy and green chemistry.

Effects of Compost Application of Green Waste on Soil Properties: A Meta-Analysis

Objective: With the accelerating urbanization process, the garden area is gradually expanding, and the production of green waste is also increasing. Composting green waste can not only reduce environmental pollution caused by incineration and landfill and improve the utilization efficiency of resources but also improve the soil and increase soil productivity. The study aims to investigate the comprehensive impact of green waste compost (GWC) application on soil nutrient conditions. Through comprehensive data analysis, the impact of compost application of green waste on soil properties was revealed as a reference for compost application and fertilizer reduction. Methods: Based on meta-analysis, we quantitatively investigated the response of soil properties to the application of green waste, collected published experimental data, and integrated 25 domestic and international literature to analyze the effects of different soil properties on soil nutrients. Literature was used to analyze the impact of different application rates of GWC on the physicochemical properties of soils with varying pH levels. The results were compared to control conditions with no GWC application. Results: The application of significantly improved soil quality by reducing soil bulk density and increasing the levels of soil organic matter, organic carbon, total nitrogen, available phosphorus, available potassium, and dehydrogenase activity. The increases in available potassium and soil organic carbon were consistently significant across all subgroups. However, the effects on available phosphorus and organic matter varied in significance depending on initial soil pH. Soil bulk density was influenced by the GWC content, while dehydrogenase activity showed significant effects only when initial soil pH was ≥8. Total nitrogen levels were significantly impacted by GWC application in soil with an initial pH of <8 and at GWC contents of ≥50%. Conclusion: The application of green waste compost demonstrates a positive effect on soil improvement. This study provides a comprehensive database that supports the use of GWC in enhancing soil quality and reducing the need for chemical fertilizers.

Active Sites on the CuCo Catalyst in Higher Alcohol Synthesis from Syngas: A Review

Higher alcohol synthesis through the Fischer–Tropsch (F–T) process was considered a promising route for the efficient utilization of fossil resources could be achieved. The CuCo catalysts were proven to be efficient candidates and attracted much interest. Great efforts have been made to investigate the active sites and mechanisms of CuCo catalysts. However, the industrialized application of CuCo catalysts in this process was still hindered. The poor stability of this catalyst was one of the main reasons. This short review summarized the recent development of active sites on the CuCo catalysts for higher alcohol synthesis, including CuCo alloy particles, CuCo core–shell particles, and unsaturated particles. The complex active sites and their continual changes during the reaction led to the poor stability of the catalysts. The effect of active sites on catalytic performance was discussed. Furthermore, the key factors in fabricating stable CuCo catalysts were proposed. Finally, reasonable proposals were proposed for designing efficient and stable CuCo catalysts in higher alcohol synthesis.

Sustainable Supply Chain Management and Green Logistics Practice Analysis: Case Study of Wahaha and Patagonia

In globalization and digitization, supply chains have become key for companies to gain competitive advantages and achieve sustainable development. This study provides a detailed analysis of the sustainable supply chain management practices of China's leading food and beverage company, Wahaha, and the American outdoor brand, Patagonia. It explores their successes in green production, logistics, packaging, and environmental governance. The research finds that Wahaha has achieved efficient resource utilization and cost reduction through digital and intelligent transformation, enhanced recycling, and the use of clean energy. On the other hand, Patagonia maintains a strong environmental commitment by using organic cotton and recycled materials, supporting environmental projects, and promoting a green supply chain. Despite their different strategies, both companies have achieved significant sustainable supply chain management results, offering valuable insights for other businesses. Future research should include different regions and industries, explore the challenges and solutions in sustainable development, and optimize evaluation systems through quantitative analysis and model building to enhance practical application value.