Molecular insights into the migration of organic contaminants during purification of epoxy resin waste salt: A comparative study of interfacial wetting and ethanol/water extraction processes.

Collaborative processing of steelmaking slag and red mud for efficient phosphorus recovery and resource utilization.

A comprehensive assessment of a product's economic viability, along with a value chain analysis, is essential to identify opportunities for improvement, ascertain actual costs and margins, understand the interrelationships between activities, and enhance production efficiency. This study evaluates the economic viability and value chain of summer tomato cultivation, a sector gaining importance due to rising off-season demand and price premiums. A total of 50 summer tomato growers and 45 traders were selected using multi-stage sampling techniques from the Netrokona and Kishoreganj districts in Bangladesh. The results indicates that the adoption levels of ploughing, planting periods, seedling usage, pesticide application, and hormone use were high, whereas the use of urea, gypsum, MoP, boron, and zinc was low. The total cost of summer tomato cultivation was Tk. 2,75,606 per acre, while the average net return per acre was Tk. 3,43,364. The Benefit-Cost Ratio (BCR) of summer tomato cultivation was found to be 2.25, indicating that producing summer tomatoes is economically viable. Seven marketing channels were identified, with retailers contributing 42.58% of the overall value addition, marking the most significant contribution to the gross marketing margin. The summer tomato value chain in Bangladesh is sustainable due to its capacity to create income, encourage efficient resource utilization, enhance food security, and adapt to climatic changes. By maintaining investments in technology, training, and market systems, this value chain has the potential to flourish and contribute to a more resilient and inclusive agricultural economy.

The study aims to derive a conceptual model of an ecosystem of a patient in their home environment, integrating principles from biological ecosystem theory to enhance holistic, home-based care for patients with non-communicable diseases. A concept derivation approach was employed due to the absence of any clear definition of the patient ecosystem at home concept. As a conceptual study, ethical considerations were minimal. Ethical standards were upheld during the literature review, and ethical approval was not required. Concept derivation was used to derive the concept from biology and apply it to the patient's home. The process involved four steps: a literature review, cross-disciplinary exploration, selection of the parent concept, and a redefinition of the concept in the context of patients at home. The conceptual clarity, structure, and applicability of the derived model of the patient ecosystem served as measures of the outcomes. These include the identification and integration of the ecosystem's key components, such as the environment, living elements, processes, and structure, and their relevance for supporting patients at home. The main outcome is a conceptual model of the patient ecosystem at home that highlights dynamic interactions within the ecosystem and stresses patient agency and collaboration between informal, formal, and virtual support networks. As ecosystem managers, nurses play a crucial role in supporting the patient ecosystem's capability to provide homeostasis. As a conceptual model not yet empirically validated, its applicability may vary across cultural and socioeconomic contexts. The proposed patient ecosystem model provides a novel framework for understanding and supporting patients at home, facilitating efficient resource utilisation and adding to the quality of life for patients and family members. While offering theoretical insight, further validation and cross-sector collaboration are essential before implementing the model in practice.

Cysteine conversion inhibitor drives humic substances formation by regulating aromatic amino acid metabolism of microbial community.

The banking sector plays a significant role ensuring efficient utilization of financial resources, particularly in the post-merger period where effective capital management becomes essential for operational stability and growth. The present study focused on the analysis of capital productivity of State Bank of India during post-merger period from 2017-18 to 2024-25. It is based on secondary data and the analysis carried out using various capital productivity ratios as well as descriptive statistics & Compound Annual Growth Rate. The overall results suggest that SBI maintained balanced utilization of capital resources and stable capital productivity performance during post-merger period.

The massive CO2 emission arising from fossil fuel combustion has exacerbated climate crises and disrupted ecological balance, driving the need for sustainable processes for the efficient utilization of renewable resources. Lignocellulose represents as a promising alternative to fossil-based feedstock, while the inherent structural recalcitrance significantly hinders its efficient conversion. Mechanical ball milling is considered as a potential strategy for lignocellulose valorization for its intensive mechanical energy to disrupt the physical architecture of lignocellulose. Currently, the existing reviews mostly focus on the application of mechanical ball milling for biomass-based materials preparation, lacking integrated discussions on the application for lignocellulose separation and depolymerization. This review emphasized the synergistic effect of the mechanochemical strategy for structural disruption and chemical activation, which includes the effective separation of lignin and cellulose and their depolymerization to obtain value-added monomers and platform chemicals. Furthermore, the potential and energy advantages of mechanical ball milling for industrial-scale applications were elaborated by comparing different mechanochemical strategies, combined with energy consumption comparison and process scaling. Finally, future perspectives on the precise optimization of mechanical ball-milling systems are proposed. This review offers promising guidance on the valorization of lignocellulosic biomass.

Fuzzy logic-based median filters (FLBMF) are widely applied in medical imaging to suppress impulse noise while preserving diagnostically relevant structures. Floating-point implementations, although precise, impose substantial computational and memory demands, limiting their deployment on embedded, portable, or low-power imaging systems. This study presents a systematic evaluation of integer-only FLBM and floating-point FLBMF implementations, analyzing trade-offs between denoising performance, computational cost, and hardware efficiency. The integer-only FLBM design leverages quantized membership functions, fixed-point arithmetic, and optimized rule evaluation to reduce arithmetic complexity while maintaining perceptual fidelity, with the floating-point version serving as a precision benchmark. Evaluations were conducted on noisy mammography datasets across multiple noise densities (ρ = 0.3-0.7) using an ARM Cortex-M7 microcontroller and a Xilinx Zynq-7020 FPGA. Performance metrics include image quality (PSNR, SSIM, VIF, FOM), computational efficiency, memory usage, energy consumption, and hardware resource utilization. Experimental results indicate that integer-only FLBMF reduces memory usage by up to 40% and execution time by up to 55% on embedded processors, while maintaining perceptually equivalent denoising quality (ΔPSNR ≤ 0.5dB, ΔSSIM ≤ 0.01). On FPGA platforms, integer-only FLBM implementations achieve lower DSP and LUT utilization, reduced power consumption, and higher operating frequency compared to floating-point versions. Comparisons with classical median, adaptive median, and TV-based filters show that both integer-only FLBM and floating-point FLBMF substantially outperform these baselines. These findings establish integer-only FLBMF as a hardware-friendly, energy-efficient, and clinically faithful solution for real-time, low-power medical imaging, representing the first systematic evaluation of such implementations across MCU and FPGA platforms.

This study addresses the integrated collection–disassembly–distribution problem under fuzzy demand. The problem provides a roadmap for effectively managing reverse supply chains under uncertain conditions. It involves collecting end-of-life products, disassembling them, and distributing the components obtained from the disassembly process to demand centers under fuzzy demand. The reuse of valuable components not only provides cost savings but also supports sustainable supply chain practices. A nonlinear mathematical model is developed to represent the problem and is tested through various scenario analyses. The results show that the proposed model provides a realistic decision-support framework that accounts for the vagueness faced by decision-makers and manufacturers. Moreover, the findings indicate that it is possible to achieve cost-effectiveness and efficient resource utilization even in the presence of demand ambiguity, highlighting the model’s applicability to industries operating in volatile and unpredictable market environments.

Task scheduling has become a significant research focus in cloud computing because of the growing need for efficient resource utilization. This paper explores an innovative scheduling approach harnessing Human Memory Optimization (HMO) and Fuzzy Adaptive Human Memory Optimization (FAHMO). Such techniques are inspired by human cognitive principles and employ an adaptive search strategy that maintains an effective trade-off between exploration and exploitation. By maintaining a history of successful and unsuccessful scheduling decisions, HMO enables continuous enhancement of scheduling efficiency. Integrating fuzzy logic into FAHMO improves the decision-making process by effectively managing uncertainty and ambiguity in task scheduling, thereby producing more flexible and efficient solutions. Comparative analysis demonstrates that HMO and FAHMO outperform conventional metaheuristic algorithms, including PSO-PGA, in terms of convergence speed and task completion time. The results confirm that the proposed approach significantly reduces makespan and enhances overall cloud task scheduling performance. Specifically, FAHMO achieved up to 67.46% improvement in makespan and 63.18% in convergence accuracy compared to PSO-PGA.

With the rapid development of urban underground space, the large volume of muck generated from shield tunneling has posed significant environmental and engineering challenges in terms of disposal and stockpiling. To promote the resource utilization of shield tunneling muck and reduce the consumption of natural sand, this study employs tunneling muck as a fine aggregate to partially or fully replace natural river sand, while fly ash, ground granulated blast-furnace slag, steel slag, and silica fume are used as cementitious components to prepare an alkali-activated synchronous grouting material. The effects of muck replacement ratio (0%-100%) on the workability, mechanical properties, durability, and microstructural characteristics of the grouting slurry were systematically investigated.The results indicate that as the muck replacement ratio increases from 0% to 100%, the flowability of the slurry decreases from 385mm to 144mm, while the setting time is shortened from 118min to 30min; notably, the bleeding rate remains zero throughout the entire replacement range. In terms of mechanical performance, both 3-day and 28-day compressive strengths exhibit a trend of initial increase followed by a decline with increasing replacement ratio, reaching peak values of 6.98MPa and 11.54MPa, respectively, at a replacement ratio of 40%. Durability tests reveal that resistance to salt attack and freeze-thaw cycles gradually deteriorates with increasing muck content. The mixture with a 20% replacement ratio (M1) demonstrates the best durability performance, exhibiting corrosion resistance coefficients of 0.916 and 0.905 after immersion in 5% NaCl and 5% MgSO₄ solutions for 56 days, respectively, and a compressive strength loss of only 8.76% after 20 freeze-thaw cycles.However, when the replacement ratio exceeds 40%, the slurry flowability deteriorates sharply, accompanied by pronounced reductions in mechanical properties and water-dispersion resistance. SEM and XRD analyses reveal that at muck contents of 40% or lower, muck particles are effectively encapsulated by C-A-S-H gels, forming a dense microstructure; at higher replacement levels, insufficient cementitious phases lead to weakened interfacial bonding and increased porosity. Considering the overall performance and resource utilization efficiency, it is recommended that the muck replacement ratio be controlled within 40% under the investigated mix design to achieve an optimal balance between engineering performance and sustainable resource utilization.

The highly dynamic topology, large scalability, and rapid node mobility in mobile ad-hoc networks (MANETs) pose major challenges to achieving efficient routing and sustaining a long network lifetime. Geographic routing, also referred to as position-based routing, enables data transmission between source and destination nodes based on their spatial coordinates. While such techniques enhance resource utilization and coverage efficiency, they often struggle to deliver optimal routing performance with prolonged network longevity. To overcome these limitations, this study proposes an improved multi-layer perceptron-based golf optimization algorithm for geographic routing (IMLP-GOA-GR) in MANETs. The proposed method exploits each mobile node's locally optimal position in the search space to guide routing decisions, movement, and communication, thereby reducing unnecessary energy expenditure. Simulation results across varying node densities show that IMLP-GOA-GR improves network lifetime by approximately 18-25% and reduces total energy consumption by 20-27% compared to baseline methods. Further, it lowers the average end-to-end delay by 44.44% and improves the packet delivery ratio (PDR) by 37.5%. These outcomes clearly demonstrate the effectiveness of the proposed framework in significantly improving routing efficiency and overall MANET performance.

Service delivery in Kenya’s county referral hospitals remains inconsistent despite ongoing reforms aimed at strengthening fiscal accountability and budgetary discipline. Procurement practices have increasingly emerged as a key determinant of service delivery, influencing the availability of medical supplies, efficiency of resource utilization, and responsiveness to patient needs. This study examined the effect of procurement practices on service delivery in Level 5 county referral hospitals in Kenya, while assessing the moderating role of leadership styles. The study was anchored on the Resource-Based View and Transformational Leadership theories and adopted a descriptive quantitative research design. The target population comprised all 47 county referral hospitals, stratified across Kenya’s eight administrative regions. A sample of 148 respondents drawn from hospital management teams was selected using stratified random sampling. Data were collected through structured questionnaires, which were subjected to pilot testing to ensure validity and reliability. Data analysis was conducted using descriptive statistics, Pearson correlation, and multiple regression, alongside moderation analysis. The findings indicate that procurement practices exert a strong, statistically significant influence on service delivery. However, leadership styles did not demonstrate a significant moderating influence on this relationship. The study concluded that strengthening procurement practices is critical in enhancing service delivery in county referral hospitals.

Ultra-high-speed railway communication systems face several technical challenges due to extremely high mobility, including Doppler-induced channel variations, frequent handovers, and increasing network traffic. These challenges not only degrade communication reliability but also negatively affect the efficiency of network resource utilization. In this paper, we review the key technical challenges in ultra-high-speed railway communication environments and investigate artificial intelligence (AI)-based intelligent network control techniques to address these issues. In particular, we examine mobility management approaches focusing on AI-based predictive handover schemes and intelligent network control architectures based on the Open Radio Access Network (O-RAN). In addition, network resource management strategies are discussed through mobile edge computing (MEC)-enabled traffic offloading and task migration techniques. Through this analysis, we discuss the potential applicability of intelligent network control technologies for improving communication reliability and enhancing network resource utilization efficiency in ultra-high-speed railway communication environments.

Biomass gasification technology is a crucial pathway for obtaining clean syngas and achieving efficient utilization of carbon resources. However, tar is one of the main factors restricting the industrialization of biomass gasification technology. Among various solutions, catalytic steam reforming is regarded as the most promising solution. Currently, natural minerals and Ni-based catalysts have been demonstrated to be effective and economically viable for tar removal, which are widely used in industrial fluidized beds. Therefore, the basic reaction principles of tar steam reforming were briefly introduced. The development of tar steam reforming catalysts, focusing mainly on natural minerals and Ni-based catalysts, have been studied in this review. The catalytic cracking mechanisms of natural minerals such as dolomite and limestone, as well as the steam reforming mechanism of Ni-based catalysts, have been thoroughly summarized. In addition, the active sites of the catalysts, reaction pathways, and the essence of catalyst deactivation are discussed. Based on this, the catalytic effect of these two catalysts for steam reforming of tar in the fluidized bed was summarized. Further, the engineering challenges (such as mass transfer, wear, and continuous regeneration) and the corresponding process optimization measures were comprehensively reviewed, and future perspectives are discussed.

ABSTRACT Currently, financial personnel in the power grid are mainly responsible for basic accounting and reimbursement, and financial data is used only for simple storage and query. Power grid fund planning and operations are at risk due to inefficient financial budget control brought on by increasing data quantities. In order to improve supervision and efficiency, this work has established a power grid financial management platform by combining the ID3 decision tree algorithm with the TFP (FP-growth with traffic optimization) algorithm for financial expense forecast and risk analysis. The suggested financial expense prediction model outperformed benchmarks with an accuracy of above 0.9. With a response time of 1.54 seconds, below the 2-second requirement, the integrated power grid platform demonstrated high efficiency, guaranteeing efficient processing. At the same time, the central processing unit and memory usage of the platform were both controlled below 50%, demonstrating excellent resource utilization efficiency and providing efficient and stable financial management services for enterprises. The proposed financial management method for the power grid can monitor the financial status promptly, enhance the efficiency and accuracy of financial processing for power grid enterprises, and offer strong support for their strategic planning and operational management.

Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive dynamic programming.

HRB: A backfilling algorithm for heterogeneous clusters with job prioritization

The rapid advancement of Internet of Medical Things (IoMT) technologies and the growing demand for ubiquitous healthcare services have created an urgent need for intelligent networking solutions that can seamlessly integrate space, air, and ground communication infrastructures. This paper presents a novel intent-based networking framework designed for IoMT communications within space-air-ground integrated systems. The proposed framework addresses the complex requirements of healthcare applications by introducing an intelligent intent understanding and translation mechanism that can automatically configure network resources based on high-level medical service requirements. Our approach incorporates four core components: medical intent construction for IoMT scenarios, intelligent intent classification using enhanced BERT-CNN models, sophisticated intent parsing through GlobalPointer-based entity extraction, and dynamic intent translation for real-time network policy generation. Experimental results demonstrate that our framework achieves 94.37% accuracy in intent classification and 83.76% F1-score in entity extraction. Experimental validation across space-air-ground network simulations demonstrates substantial improvements in resource utilization efficiency (23.4% increase), bandwidth allocation optimization, and latency reduction (18.6% improvement), directly enhancing patient care capabilities and clinical decision-making reliability in distributed healthcare environments.

The emergence of Internet of Things (IoT) devices in healthcare environments presents unprecedented opportunities for remote patient monitoring, telemedicine, and intelligent medical data processing. Current network management approaches fail to accommodate the unique characteristics of medical environments, where emergency cardiac monitoring systems require guaranteed millisecond response times while routine administrative systems can tolerate moderate delays. Existing solutions lack the domain knowledge to automatically distinguish between life-critical and standard medical traffic, resulting in inefficient resource allocation and potential patient safety risks. Therefore, this paper proposes a novel intent-based networking framework integrating knowledge-driven policy generation with multi-path network slicing for healthcare IoT ecosystems. Our approach introduces a multi-stage intent translation mechanism that leverages external medical knowledge graphs and deep learning techniques to decompose high-level healthcare service intents into executable network configurations automatically. Results show that the proposed framework achieves 85-98% resource utilization efficiency across varying network conditions, maintains 99.5% quality of service achievement for emergency response systems, and exhibits superior scalability with sub-150 millisecond response times when supporting 1000 IoT devices, enabling hospitals to reduce network configuration complexity by 78% while maintaining critical system uptime.