Search Results (547)

The depletion of water resources caused by climate change and human activities is a pressing global issue. Lake Ereen is one of the ten natural landmarks of the Gobi-Altai of western Mongolia is included in the list of “important areas for birds” recognized by the international organization Birdlife. However, the construction of the Taishir Hydroelectric Power Station, aimed at supplying electricity to the western provinces of Mongolia, had a detrimental effect on the flow of the Zavkhan River, resulting in a drying-up and pollution of Lake Ereen, which relies on the river as its water source. This study assesses the pollution levels in Ereen Lake and determines the feasibility of its rehabilitation by redirecting the flow of the Zavkhan River. Field studies included the analysis of water quality, sediment contamination, and the composition of flora. The results show that the concentrations of ammonium, chlorine, fluorine, and sulfate in the lake water exceed the permissible levels set by the Mongolian standard. Analyses of elements from sediments revealed elevated levels of arsenic, chromium, and copper, exceeding international sediment quality guidelines and posing risks to biological organisms. Furthermore, several species of diatoms indicative of polluted water were discovered. Lake Ereen is currently in a eutrophic state and, based on a water quality index (WQI) of 49.4, also in a “polluted” state. Mass balance calculations and box model analysis determined the period of pollutant replacement for two restoration options: drying-up and complete removal of contaminated sediments and plants vs. dilution-flushing without direct interventions in the lake. We recommend the latter being the most efficient, eco-friendly, and cost-effective approach to rehabilitate Lake Ereen. Full article

This study focuses on the evaluation of electrical stress limit capability for 3D-packaged memory (256 M × 72-bit DDR3 SDRAM) (Shanghai Fudan Microelectronics Group Co., Ltd., Shanghai, China). Guided by Reliability Enhancement Theory, this study presents a meticulously designed comprehensive test profile that incorporates critical stress parameters, including supply voltage, input clock frequency, electrostatic discharge (ESD) sensitivity, and electrical endurance. Explicit criteria for stress selection, upper/lower bounds, step increments, and duration are established. A dedicated test platform is constructed, integrating automated test equipment (ATE) and ESD sensitivity analyzers with detailed specifications on device selection criteria and operational principles. The functional performance testing methodology is systematically investigated, covering test platform configuration, initialization protocols, parametric testing procedures, functional verification, and acceptance criteria. Extreme-condition experiments—including supply voltage margining, input clock frequency tolerance, ESD sensitivity characterization, and accelerated electrical endurance testing—are conducted to quantify operational and destructive limits. The findings provide critical theoretical insights and practical guidelines for the design optimization, quality control, and reliability enhancement of 3D-packaged memory devices. Full article

In light of the persistent environmental degradation driven by fossil fuels, developing new energy sources is essential for achieving sustainability. The recent surge in electric vehicle adoption has underscored the significance of new energy batteries. However, the supply chains of new energy battery manufacturers face multiple sustainability risks, which impede sustainable practice adoption. To tackle these challenges, leanness philosophy is an effective tool, and Industry 5.0 enhances its efficacy significantly, further mitigating sustainability risks. This study integrates the supply chain, leanness philosophy, and Industry 5.0 by applying quality function deployment. A novel four-phase hybrid MCDM model integrating the fuzzy Delphi method, DEMATEL, AHP, and fuzzy VIKOR, identified five key sustainability risks five core leanness principles, and eight critical Industry 5.0 enablers. By examining a Chinese new energy battery manufacturer as a case study, the findings aim to assist managers and decision-makers in mitigating sustainability risks within their supply chains. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article

Electricity theft remains a persistent challenge, particularly in developing economies where infrastructure limitations and socioeconomic disparities contribute to illegal connections. This study analyzes the determinants influencing electricity theft in Kinshasa, the Democratic Republic of Congo, using a logistic regression model applied to 385 observations, which includes random bootstrapping sampling for enhanced stability and power analysis validation to confirm the adequacy of the sample size. The model achieved an AUC of 0.86, demonstrating strong discriminatory power, while the Hosmer–Lemeshow test (p = 0.471) confirmed its robust fit. Our findings indicate that electricity supply quality, financial stress, tampering awareness, and billing transparency are key predictors of theft likelihood. Households experiencing unreliable service and economic hardship showed higher theft probability, while those receiving regular invoices and alternative legal energy solutions exhibited lower risk. Lasso regression was implemented to refine predictor selection, ensuring model efficiency. Based on these insights, a multifaceted policy approach—including grid modernization, prepaid billing systems, awareness campaigns, and regulatory enforcement—is recommended to mitigate electricity theft and promote sustainable energy access in urban environments. Full article

With the continuous expansion of users, businesses, and services in electricity retail trading systems, the demand for personalized recommendations has grown significantly. To address the issue of reduced recommendation accuracy caused by insufficient data in standalone recommendation systems, the academic community has conducted in-depth research on distributed recommendation systems. However, this collaborative recommendation environment faces two critical challenges: first, how to effectively protect the privacy of data providers and power users during the recommendation process; second, how to handle the potential presence of malicious data providers who may supply false recommendation data, thereby compromising the system’s reliability. To tackle these challenges, a blockchain-based lightweight reputation-aware electricity retail trading service recommendation (BLR-ERTS) system is proposed, tailored for electricity retail trading scenarios. The system innovatively introduces a recommendation method based on Locality-Sensitive Hashing (LSH) to enhance user privacy protection. Additionally, a reputation management mechanism is designed to identify and mitigate malicious data providers, ensuring the quality and trustworthiness of the recommendations. Through theoretical analysis, the security characteristics and privacy-preserving capabilities of the proposed system are explored. Experimental results show that BLR-ERTS achieves an MAE of 0.52, MSE of 0.275, and RMSE of 0.52 in recommendation accuracy. Compared with existing baseline methods, BLR-ERTS improves MAE, MSE, and RMSE by approximately 13%, 14%, and 13%, respectively. Moreover, the system exhibits 94% efficiency, outperforming comparable approaches by 4–24%, and maintains robustness with only a 30% attack success rate under adversarial conditions. The findings demonstrate that BLR-ERTS not only meets privacy protection requirements but also significantly improves recommendation accuracy and system robustness, making it a highly effective solution in a multi-party collaborative environment. Full article

This study synthesized boron-doped diamond (BDD) thin films using hot-filament chemical vapor deposition at different carbon-to-hydrogen (C/H) ratios in the range of 0.3–0.9%. The C/H ratio influence, a key parameter controlling the balance between diamond growth and hydrogen-assisted etching, was systematically investigated while maintaining other deposition parameters constant. Microstructural and electrochemical analysis revealed that increasing the C/H ratio from 0.3% to 0.7% led to a reduction in sp2-bonded carbon and enhanced the crystallinity of the diamond films. The improved conductivity under these conditions can be attributed to effective substitutional boron doping. Notably, the film deposited at a C/H ratio of 0.7% exhibited the highest electrical conductivity and the widest electrochemical potential window (2.88 V), thereby indicating excellent electrochemical stability. By contrast, at a C/H ratio of 0.9%, the excessively supplied carbon degraded the film quality and electrical and electrochemical performance, which was owing to the increased formation of sp² carbon. In addition, this led to an elevated background current and a narrowed potential window. These results reveal that precise control of the C/H ratio is critical for optimizing the BDD electrode performance. Therefore, a C/H ratio of 0.7% provides the most favorable conditions for applications in advanced oxidation processes. Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

The African catfish, Clarias gariepinus, is among the most farmed fish species in Uganda’s rapidly growing aquaculture sub-sector. The enhanced growth performance, increased survival, and resilience to environmental stressors have driven a rising demand among farmers for improved African catfish broodstock and seed. Until recently, most studies of this species have focused on nutrition, physiology, and culture systems, with little known about the genetics, broodstock, and hatchery management of the cultured C. gariepinus populations. This knowledge gap has led to inbreeding depression, resulting in poor seed quality and reduced performance of the broodstock. To enhance catfish aquaculture production, a survey was conducted across multiple catfish hatcheries and farms in Uganda. Using semi-structured questionnaires, the study assessed broodstock management practices, hatchery propagation methods, the suitability of various populations, demographics, challenges, and prospects of hatchery operators. Responses were coded, and descriptive statistics such as frequency, percentages, and means were calculated. Results indicate that there are farmers who continue to source their broodstocks from the natural water bodies in addition to acquiring them from fellow farmers. The estimated effective population size (Ne) for the majority of the respondents was 133.33 and 178.22, with an average breeding coefficient of 0.4% and 0.3%, respectively. There is also a continuous use of shooters (fast-growing cannibalistic fish) by the farmers who hatch and select them to be used as broodstocks later, under the assumption that they have superior genetic traits. The reported hatchability rate was above 70%, with an average survival rate of 60% from larvae to fry. The study identified diseases, inadequate water supply, and electricity as the primary challenges for catfish breeding. While Uganda’s African catfish aquaculture industry is expanding rapidly, certain hatchery practices pose significant risks to its sustainability if not properly addressed. Full article

Unmanned systems provide significant prospects for improving the efficiency of electromagnetic geophysical exploration in mineral prospecting and geological mapping, as they can significantly increase the productivity of field surveys by accelerating the movement of the measuring system along the site, as well as minimizing problems in cases where the pedestrian walkability of the site is a challenge. Lightweight and cheap UAV systems with a take-off weight in the low tens of kilograms are unable to carry a powerful current source; therefore, semi-airborne systems with a ground transmitter (an ungrounded loop or grounded at the ends of the line) and a measuring system towed on a UAV are becoming more and more widespread. This paper presents the results for a new generation of semi-airborne technology SibGIS UAV-TEMs belonging to the “line-loop” type and capable of realizing the transient/time-domain (TEM) electromagnetics method used for studying a uranium object of the paleovalley type. Objects of this type are characterized by a low resistivity of the ore zone located in relatively high-resistivity host rocks and, from the position of the geoelectric structure, can be considered a good benchmark for assessing the capabilities of different electrical exploration technologies in general. The aeromobile part of the geophysical system created is implemented on the basis of a hexacopter carrying a measuring system with an inductive sensor, an analog of a 50 × 50 m loop, an 18-bit ADC with satellite synchronization, and a transmitter. The ground part consists of a galvanically grounded supply line and a current source with a transmitter creating multipolar pulses of quasi-DC current in the line. The survey is carried out with a terrain drape based on a satellite digital terrain model. The article presents the results obtained from the electromagnetic soundings in comparison with the reference (drilled) profile, convincingly proving the high efficiency of UAV-TEM. This approach to pre-processing UAV–electrospecting data is described with the aim of improving data quality by taking into account the movement and swaying of the measuring system’s sensor. On the basis of the real data obtained, the sensitivity of the created semi-airborne system was modeled by solving a direct problem in the class of 3D models, which allowed us to evaluate the effectiveness of the method in relation to other geological cases. Full article

China’s leading development of a complete battery value chain for electric vehicles (EVs) is restructuring the global automotive sector. In contrast with the normal point of view, which emphasizes the role of industrial policy, this article argues that the competitive advantage of China’s EV battery industry lies in firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork carried out at an EV battery cluster in south China, this paper identifies the Chinese EV battery industry’s core competency and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability, which allows them to supply vehicle manufacturers (OEMs) with lithium-ion batteries of stable and consistent quality at competitive prices. This competency is acquired by firms through technological learning at the workshop level while making use of the experiences they have accumulated while mass producing batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge of battery manufacturing on a large scale is also facilitated by the local industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings have significant implications for battery and car makers in global competition as well as for national and local governments which aim to promote EV battery development. Full article

Designing a sustainable supply chain network for perishable products is challenging due to their short shelf life and sensitivity to environmental conditions. These factors necessitate strict quality control and efficient logistics. The emergence of Internet of Things (IoT) technology has significantly improved supply chain operations by enabling real-time monitoring of environmental conditions. This helps maintain product quality and ensures timely deliveries. Additionally, using mixed fleets—comprising both electric and conventional vehicles—can reduce carbon emissions without compromising operational reliability. While previous studies have explored the application of IoT to enhance delivery efficiency and the use of mixed fleets to address environmental concerns, few have examined both technologies within a unified modeling framework. This study proposes a sustainable multi-period supply chain network for perishable products that integrates IoT technology and mixed fleets into an optimization framework. We develop a multi-objective location-inventory-routing model. The first objective minimizes total costs, including production, facility operation, inventory, transportation, carbon emissions, IoT deployment, and energy use. The second objective aims to maximize service levels, which are measured by product quality and on-time delivery. The model is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A case study based on real-world data demonstrates the model’s effectiveness. Sensitivity analysis indicates that balancing the emphasis on quality and delivery reliability leads to improved cost and service performance. Furthermore, while total costs steadily increase with higher demand, service levels remain stable, showcasing the model’s robustness. These results provide practical guidance for managing sustainable supply chains for perishable products. Full article

This paper presents a thorough examination of the technical requirements for a new Distribution System Operation (DSO) scheme in Colombia. This study contextualizes these requirements to consider local particularities by looking at national and international standards and models relevant to DSO. This study aims to align the technical requirements to the DSO technologies that offer the greatest advantages (real-time data readings to automate commercial cycle, suspension and reconnection of the service, improving reliability and quality of power supply, and environmental benefits) and the fewest implementation obstacles. Today, an electrical operator can become more proactive by integrating technologies such as advanced metering infrastructure (AMI), distributed energy resources (DER), microgrids, and advanced distribution automation (ADA). This study will provide a structured framework for the implementation of a cutting-edge DSO technology in order to assist Colombia’s energy sector in becoming more dynamic and efficient with a smarter and more active electricity distribution system. Full article

This study evaluates the feasibility of repurposing produced water—an abundant byproduct of hydrocarbon extraction—for green hydrogen production in Wyoming, USA. Analysis of geospatial distribution and production volumes reveals that there are over 1 billion barrels of produced water annually from key basins, with a general total of dissolved solids (TDS) ranging from 35,000 to 150,000 ppm, though Wyoming’s sources are often at the lower end of this spectrum. Optimal locations for hydrogen production hubs have been identified, particularly in high-yield areas like the Powder River Basin, where the top 2% of fields contribute over 80% of the state’s produced water. Detailed water-quality analysis indicates that virtually all of the examined sources exceed direct electrolyzer feed requirements (e.g., <2000 ppm TDS, <0.1 ppm Fe/Mn for target PEM systems), necessitating pre-treatment. A review of advanced treatment technologies highlights viable solutions, with estimated desalination and purification costs ranging from USD 0.11 to USD 1.01 per barrel, potentially constituting 2–6% of the levelized cost of hydrogen (LCOH). Furthermore, Wyoming’s substantial renewable-energy potential (3000–4000 GWh/year from wind and solar) could sustainably power electrolysis, theoretically yielding approximately 0.055–0.073 million metric tons (MMT) of green hydrogen annually (assuming 55 kWh/kg H₂), a volume constrained more by energy availability than water supply. A preliminary economic analysis underscores that, while water treatment (2–6% LCOH) and transportation (potentially > 10% LCOH) are notable, electricity pricing (50–70% LCOH) and electrolyzer CAPEX (20–40% LCOH) are dominant cost factors. While leveraging produced water could reduce freshwater consumption and enhance hydrogen production sustainability, further research is required to optimize treatment processes and assess economic viability under real-world conditions. This study emphasizes the need for integrated approaches combining water treatment, renewable energy, and policy incentives to advance a circular economy model for hydrogen production. Full article

The rapid development of prosumer renewable energy sources (RESs) observed in Poland in recent years causes problems in distribution networks such as current amplitude and voltage asymmetry increases, power and energy loss increases, and reverse power flows, and related are voltage control problems, deterioration of energy quality, etc. Moreover, in the case of planned repair/maintenance works in the network and the need to supply energy consumers in an islanded MV grid, the problem of the correct operation of such a subsystem appears. This occurs when the power production by the prosumers’ energy sources at a given moment exceed the power consumption. In such a case, reverse power flows occur in MV/LV transformers, i.e., from the LV network to the MV network. This causes reverse power flow to the diesel generator, leading to its shutdown and, in extreme cases, to damage. The solution to this problem is to use a mobile system equipped with energy storage in addition to a diesel generator and an LV/MV transformer. An additional problem in the case of using a mobile system (diesel generator) to power an MV island is the islanded MV network grounding. Grid islanding changes the earth fault current and electric shock voltages. In general, MV networks in Poland operate as compensated, i.e., grounding transformers are used, the star point of which is grounded by a compensation choke. Unfortunately, in the case of powering an MV island from a mobile system, there is no real possibility of grounding the star point of the LV/MV transformer used there. This article proposes an algorithm of a diesel generator with an energy storage selection, including electric shock protection requirements verification, for the use in suppling energy via an islanded MV network. Full article