Search Results (11,992)

An ultrasonic-enhanced hydrogen peroxide water-washing process was developed to recover lead from raw flue dust (RFD) under neutral conditions. At optimal parameters (40 °C, 30 min, 4 mL H₂O₂, liquid-to-solid ratio 2:1, 240 W ultrasound), the Pb mass fraction in the solid residue increased from 41.68% in the RFD to 68.11%, accompanied by a Pb recovery rate of 97.1%. These values are significantly higher than those obtained under identical conditions without ultrasound (64.07% and 95.93%, respectively). Ultrasound promotes de-agglomeration and generates •OH radicals that accelerate the oxidation of PbSO₃ to insoluble PbSO₄ while concurrently removing impurity cadmium. This research offers a green and efficient alternative to traditional lead recovery methods, fostering sustainable development in the metallurgical industry. Full article

The ataxia–telangiectasia-mutated (ATM) protein plays a crucial role in the DNA damage response, particularly in the homologous recombination (HR) pathway. This study aimed to assess the impact of deleterious ATM variants on homologous recombination deficiency (HRD) and response to PARP inhibitors (PARPi) in melanoma patients, using a cell line established from melanoma tissue of a patient carrying the c.5979_5983del germline ATM variant. Despite proven loss of heterozygosity, lack of ATM activation, and HRD, our model did not show sensitivity to PARPi. We assessed the potential contribution of the Schlafen family member 11 (SLFN11) helicase, whose expression is inversely correlated with PARPi sensitivity in other cancers, to the observed resistance. The ATM mutant cell line lacked SLFN11 expression and featured hypermethylation-mediated silencing of the SLFN11 promoter. While sensitive to the ATR inhibitor (ATRi), the addition of ATRi to PARPi was unable to overcome the resistance. Our findings suggest that ATM mutational status and HRD alone do not adequately account for variations in sensitivity to PARPi in our model. A comprehensive approach is essential for optimizing the exploitation of DNA repair defects and ultimately improving clinical outcomes for melanoma patients. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. Full article

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

The interaction between a shock wave and a boundary layer promotes corner separation and prevents performance enhancement in a supersonic compressor cascade. Different vortex generator (VG) designs are presented to control corner separation in a supersonic compressor cascade, including endwall VGs (EVG), suction surface VGs (SVG), and combined endwall and suction surface VGs (E-SVGs). It is demonstrated that EVG and coupled E-SVGs reduce losses in the supersonic compressor cascade. For an optimal EVG, the total loss is reduced by 24.6% and the endwall loss is reduced by 33.6%. The coupled E-SVG better controls corner separation and reduces endwall losses by 56.9%. The suppression mechanism is that vortices alter the direction of the separated flow, allowing it to overcome the chordwise pressure gradient. Moreover, the VGs change the shock structure near the endwall. For the EVG, clockwise vortices are effective in controlling corner separation due to their minor effect on the shock structure near the endwall. However, anticlockwise vortices are not suitable for controlling corner separation in the supersonic compressor because they increase the shock strength induced by the VG. The control mechanism of the coupled E-SVG on corner separation is also discussed. Full article

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

The cigarette butt (CB) recycling process yields several byproducts, including cleaned filters, solid debris (mainly paper and tobacco), and wastewater. This study aimed to assess, for the first time, the long-term suitability of these recycled byproducts for turfgrass cultivation. Under controlled conditions, Paspalum vaginatum Swartz was grown in sand–peat substrate, either unmodified (control) or amended with small pieces of uncleaned CBs or solid byproducts from CB recycling at concentrations of 25% or 50% (v/v). In additional tests, turfgrass grown in unmodified substrate received wastewater instead of tap water once or twice weekly. Over 7 weeks, physiological and biometric parameters were assessed. Plants grown with solid debris showed traits comparable to the control. Those grown with intact CBs or cleaned filters had similar biomass and coverage as the control but accumulated more carotenoids and antioxidants. Wastewater significantly enhanced plant growth when applied once weekly, while becoming toxic when applied twice, reducing biomass and coverage. After scalping, turfgrass recovered well across all treatments, and in some cases biomass improved. Overall, recycled CB byproducts, particularly wastewater used at optimal concentrations, can be a sustainable resource for promoting turfgrass growth. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

This investigation examines the influence of P. dabryanus density on the growth performance of P. nigromaculatus and the structural and functional dynamics of paddy soil microbial communities within a rice–frog–loach integrated aquaculture system. Field experiments were conducted with five density gradients of P. dabryanus (0.5, 1.0, 1.5, 2.0, and 2.5 × 10⁴ individuals/667 m²), designated as RFLS0.5, RFLS1.0, RFLS1.5, RFLS2.0, and RFLS2.5, respectively. Control treatments included rice monoculture (RM) and rice–frog co-culture (RFS). These findings demonstrated that as the density of loach increased, the weight gain ratio of P. nigromaculatus showed a unimodal pattern, reaching its peak in RFLS1. Metagenomic analysis on paddy soil revealed that the RFLS1 facilitated the enrichment of nitrogen-fixing bacteria (Proteobacteria), while concurrently suppressing proliferation of the potential pathogen Pseudomonas aeruginosa and microbial markers in metal-contaminated environments of Usitatibacter rugosus. Further, functional profiling indicated that RFLS1 group reached a peak activity in amino acid metabolism (14.52 ± 0.09%) and carbohydrate metabolism (14.44 ± 0.06%) and showed a higher proportion of glycosyltransferase (GT) abundance (41.93 ± 0.02%) than other groups. In summary, the optimal stocking density of P. dabryanus in rice–frog–loach integrated systems was determined to be 1.0 × 10⁴ individuals/667 m². This density not only promotes the growth of P. nigromaculatus but also improves the structure of paddy soil microbial communities. Full article

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

Tidal seashell waste represents an abundant, underutilized marine resource that poses environmental disposal challenges but offers potential as a sustainable bio-filler in epoxy composites. This study investigates its incorporation into bio-based epoxy systems to reduce reliance on non-renewable materials and promote circular economy objectives. Processed seashell powder was blended into epoxy formulations, and response surface methodology was applied to optimize filler loading and resin composition. Comprehensive characterization included tensile strength, impact resistance, hardness, density, and thermal conductivity testing, along with microscopy analysis to evaluate filler dispersion and interfacial bonding. The optimized composites demonstrated improved hardness, density, and thermal stability while maintaining acceptable tensile and impact strength. Microscopy confirmed uniform filler distribution at optimal loadings but revealed agglomeration and void formation at higher contents, which can reduce interfacial bonding efficiency. These findings highlight the feasibility of valorizing marine waste as a reinforcing filler in sustainable composite production, supporting environmental goals and offering a scalable approach for the development of durable, lightweight materials suitable for structural, coating, and industrial applications. Full article

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article

As an important direction in industrial evolution, the synergistic agglomeration of manufacturing and productive service industries has become a key path to promote the green transformation of the economy. Based on China’s provincial panel data, this study utilizes a variety of econometric methods to explore in depth the mechanisms, spatial effects and regional differences in the impact of the synergistic agglomeration of manufacturing and productive service industries on the green, low-carbon and recycling development of the economy. The empirical results show that the synergistic agglomeration of manufacturing and productive services not only directly promotes the green, low-carbon and recycling development of the economy, but also generates an indirect impact through the intermediary channel and exhibits significant spillover characteristics in the spatial dimension. This conclusion holds firm after a series of robustness tests. In addition, environmental regulations and the level of regional industrialization play a moderating role on the impact of industrial synergistic agglomeration and green, low-carbon and recycling development of the economy, and the effect of the role varies across regions and levels of economic development. This paper provides a decision-making reference for further optimizing the regional layout of China’s industries and enhancing the green, low-carbon and recycling development of the economy in each province. Full article

Soil salinity adversely affects crop growth and development, leading to reduced soil fertility and agricultural productivity. The indigenous salt-tolerant plant growth-promoting rhizobacteria (PGPR), as a sustainable microbial resource, do not only promote growth and alleviate salt stress, but also improve the soil microecology of crops. The strain H5 isolated from saline-alkali soil in Bachu of Xinjiang was studied through whole-genome analysis, functional annotation, and plant growth-promoting, salt-tolerant trait gene analysis. Phylogenetic tree analysis and 16S rDNA sequencing confirmed its classification within the genus Halomonas. Functional annotation revealed that the H5 genome harbored multiple functional gene clusters associated with plant growth promotion and salt tolerance, which were critically involved in key biological processes such as bacterial survival, nutrient acquisition, environmental adaptation, and plant growth promotion. The pot experiment under moderate salt stress demonstrated that seed inoculation with Halomonas sp. H5 not only significantly improved the agronomic traits of tomato seedlings, but also increased plant antioxidant enzyme activities under salt stress. Additionally, soil analysis revealed H5 treatment significantly decreased the total salt (9.33%) and electrical conductivity (8.09%), while significantly improving organic matter content (11.19%) and total nitrogen content (10.81%), respectively (p < 0.05). Inoculation of strain H5 induced taxonomic and functional shifts in the rhizosphere microbial community, increasing the relative abundance of microorganisms associated with plant growth-promoting and carbon and nitrogen cycles, and reduced the relative abundance of the genera Alternaria (15.14%) and Fusarium (9.76%), which are closely related to tomato diseases (p < 0.05). Overall, this strain exhibits significant potential in alleviating abiotic stress, enhancing growth, improving disease resistance, and optimizing soil microecological conditions in tomato plants. These results provide a valuable microbial resource for saline soil remediation and utilization. Full article

This study evaluated the effects of low-dose recombinant human bone morphogenetic protein-2 (rhBMP-2) coating in combination with vacuum plasma treatment on titanium implants, aiming to enhance osseointegration and bone regeneration while minimizing the adverse effects associated with high-dose rhBMP-2. In vitro analyses demonstrated that plasma treatment increased surface energy, promoting cell adhesion and proliferation. Additionally, it facilitated sustained rhBMP-2 release by enhancing protein binding to the implant surface. In vivo experiments using the four-beagle mandibular defect model were conducted with the following four groups: un-treated implants, rhBMP-2–coated implants, plasma-treated implants, and implants treated with both rhBMP-2 and plasma. Micro-computed tomography (micro-CT) and medical CT analyses revealed a significantly greater volume of newly formed bone in the combined treatment group (p < 0.05). Histological evaluation further confirmed superior outcomes in the combined group, showing significantly higher bone-to-implant contact (BIC), new bone area (NBA), and inter-thread bone density (ITBD) compared to the other groups (p < 0.05). These findings indicate that vacuum plasma treatment enhances the biological efficacy of low-dose rhBMP-2, representing a promising strategy to improve implant integration in compromised conditions. Further studies are warranted to determine the optimal clinical dosage. Full article