Search Results (85)

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

The transformative trajectory of urban development in the contemporary era has engendered a substantial escalation in construction waste generation, particularly in China, where it constitutes approximately 40% of the total solid waste stream. Traditional landfill disposal methodologies pose formidable ecological challenges, encompassing soil contamination, groundwater pollution, and significant greenhouse gas emissions. Furthermore, the unsustainable exploitation of natural sandstone resources undermines energy security and disrupts ecological balance. In response to these pressing issues, an array of scholars and researchers have embarked on an exploratory endeavor to devise innovative strategies for the valorization of construction waste. Among these strategies, the conversion of waste into recycled aggregates has emerged as a particularly promising pathway. However, the practical deployment of recycled aggregates within the construction industry is impeded by their inherent physico-mechanical properties, such as heightened water absorption capacity and diminished compressive strength. To surmount these obstacles, a multitude of enhancement techniques, spanning physical, chemical, and thermal treatments, have been devised and refined. This paper undertakes a comprehensive examination of the historical evolution, recycling methodologies, and enhancement strategies pertinent to recycled aggregates. It critically evaluates the efficacy, cost–benefit analyses, and environmental ramifications of these techniques, while elucidating the microstructural and physicochemical disparities between recycled and natural aggregates. Furthermore, it identifies pivotal research gaps and prospective avenues for future inquiry, underscoring the imperative for collaborative endeavors aimed at developing cost-effective and environmentally benign enhancement techniques that adhere to the stringent standards of contemporary construction practices, thereby addressing the intertwined challenges of waste management and resource scarcity. Full article

As a core component of the photovoltaic-storage microgrid systems, three-port DC–DC converters have attracted significant attention in recent years. This paper proposes a multi-vector modulated model predictive control (MVM-MPC) method based on vector analysis for a non-isolated three-port DC–DC converter formed by paralleling two bidirectional DC–DC converters. The proposed modulated MPC method utilizes three basic vectors to calculate the optimal switching sequence for minimizing the error vector. It can significantly minimize voltage ripple while maintaining the nonlinear and dynamic performance characteristics of a traditional MPC. MATLAB/Simulink R2024a simulations and hardware-in-loop (HIL) experimental results demonstrate that, compared with finite control set MPC and traditional two-vector modulated MPC methods, the proposed approach achieves remarkable reductions in current ripple and voltage ripple, along with excellent dynamic performance featuring smooth mode-switching. Full article

The tumor microenvironment (TME) plays a pivotal role in shaping immunometabolism in prostate cancer, influencing disease progression and therapeutic response. This review examines the dynamic interactions between tumor cells and immune cells within the prostate cancer TME, focusing on how metabolic reprogramming of both tumor and immune cells drives immunosuppression. Key immune players, including T-cells, macrophages, and myeloid-derived suppressor cells, undergo metabolic adaptations influenced by hypoxia, nutrient deprivation, and signaling from tumor cells. Additionally, we discuss the metabolic pathways involved, such as glycolysis and oxidative phosphorylation, and how these processes are exploited by cancer cells to evade immune surveillance. Furthermore, this review highlights potential therapeutic strategies targeting immunometabolism, including metabolic inhibitors and their combination with immunotherapies. A deeper understanding of the complex role of immunometabolism in prostate cancer will not only provide insights into the tumor’s immune evasion mechanisms but also facilitate the development of novel treatment approaches that enhance the efficacy of current therapies. Full article

Background/Objectives: The emergence and transmission of the tigecycline resistance efflux pump gene cluster tmexCD-toprJ among humans, animals and the environment have posed a serious threat to public health. The objective of this study was to characterize Pseudomonas strains carrying multiple copies of tmexC6D6-toprJ1b from a pig farm and illustrate the genetic context of tmexC6D6-toprJ1b in the NCBI database. Methods: The characterization of Pseudomonas strains FJFQ21PNM23 and FJFQ21PNM24 was determined by antimicrobial susceptibility testing, whole-genome sequencing, and RT-qPCR. Results: The tmexCD-toprJ-positive P. mendocina strains FJFQ21PNM23 and FJFQ21PNM24 were isolated from nasal swabs in a pig farm. Sequence analysis showed that the two P. mendocina strains harbored multiple antimicrobial resistance genes, including tigecycline resistance gene tmexC6D6-toprJ1b. WGS analysis indicated that tmexC6D6-toprJ1b gene was located on a classical transferable module (int1-int2-hp1-hp2-tnfxB-tmexCD-toprJ) and a multiresistance region in FJFQ21PNM24 and FJFQ21PNM23, respectively. Further analysis revealed that 39 additional tmexC6D6-toprJ1b genes in the NCBI database were all identified in Pseudomonas spp., and the genetic features of tmexC6D6-toprJ1b were summarized into three distinct structures. Conclusions: This study is the first to identify and report the tigecycline resistance gene tmexCD-toprJ in a swine farm. Our findings summarize the three structures in the genetic context of tmexC6D6-toprJ1b and reveal that Pseudomonas serves as the only known reservoir of tmexC6D6-toprJ1b. Full article

China, with its complex geology and diverse climate, is highly prone to landslides, endangering public safety and infrastructure. To address disaster prevention needs, this study comprehensively assesses national landslide susceptibility. We divided China into 37 geomorphic districts, diverging from traditional methods. By using a 2018–2022 surface deformation dataset, we introduced a rarely—considered dynamic aspect for more accurate mapping of landslide—prone areas. Nine key environmental factors were carefully considered, including terrain, geology, meteorology, hydrology, seismic activities, and engineering activities. Based on these innovative methods and data, we created a 40 m—resolution landslide susceptibility map (LSM) for the whole country. Our assessment showed high accuracy, with an AUC of 0.927, precision of 0.859, recall of 0.815, F1—score of 0.828 and Matthews correlation coefficient of 0.773. Seven high—risk regions, like the Tianshan Mountains and the southern Tibetan valleys, were analyzed. The study revealed regional differences in landslide occurrences and key influencing factors. The LSM and findings enrich landslide susceptibility theory and offer a valuable resource for engineering, disaster management, and mitigation in China, helping reduce potential landslide losses. Full article

Deep-sowing tolerance (DST) is a key trait for the field germination of sorghum (Sorghum bicolor L.) seeds, especially in arid and semi-arid regions. However, the mechanisms of DST are poorly understood in sorghum. In this study, we compared two sorghum lines with contrasting tolerance to deep sowing for morphological, biochemical, and cytological changes during germination from deep soil (15 cm). The deep-sowing-tolerant (DT) line (Daluochui) showed 79% seedlings establishment (SE), while the deep-sowing-sensitive (DS) line (Xiaobailiang) showed no established seedlings at 7 days after sowing. Mesocotyl elongation is a key morphological change that accounted for the difference in seedling establishment between DT and DS. The mesocotyl elongation in DT was jointly established by both cell division and expansion. The levels of ethylene, auxin, and spermidine were markedly higher in DT than DS and were also supported by enzyme activity and qPCR, indicating that phytohormones play an important role in seed emergence from deep soil. Furthermore, α-amylose activity, soluble sugar, and ATP contents in DT were markedly higher than in DS, suggesting that there was a better energy supply in DT during deep-sowing emergence. The activities of endo-1,4-β-xylanase and endo-β-mannanase, as well as the expression of the corresponding genes, were higher in DT than DS. This study identified potential key regulatory factors that may control sorghum DST and yield potential, thus, providing new insights into the molecular mechanism of sorghum DST. Full article

Background/Objectives: Monkeypox, twice declared a public health emergency of international concern by the WHO, currently lacks approved targeted therapeutics. This study focused on the development of monkeypox virus (MPXV) E8-specific human monoclonal antibodies (mAbs) derived from recipients of the recombinant vaccinia vaccine (rTV), with subsequent evaluation of their cross-neutralizing activity against orthopoxviruses, including the vaccinia virus (VACV) and MPXV. Methods: Three mAbs (C5, C9, and F8) were isolated from rTV vaccinees. Structural mapping characterized their binding domains on the MPXV E8 and VACV D8 proteins. Neutralization potency was assessed against the VACV TianTan strain and MPXV clade IIb. A combo was further evaluated in a VACV-infected mice model for clinical recovery and viral load reduction. Complement-dependent enhancement mechanisms were also investigated in vitro. Results: C9 targets the virion surface region of E8 and both the virion surface region and intravirion region of D8, showing cross-neutralization activity against the MPXV (IC₅₀ = 3.0 μg/mL) and VACV (IC₅₀ = 51.1 ng/mL) in vitro. All three antibodies demonstrated potent neutralization against the VACV in vitro: C5 (IC₅₀ = 3.9 ng/mL), C9 (IC₅₀ = 51.1 ng/mL), and F8 (IC₅₀ = 101.1 ng/mL). Notably, complement enhanced neutralization against the VACV by >50-fold, although no enhancement was observed for the MPXV. In vivo administration accelerated clinical recovery by 24 h and achieved significant viral clearance (0.9-log reduction). Conclusions: E8-targeting mAbs exhibited broad-spectrum neutralization against orthopoxviruses, demonstrating therapeutic potential against both historical (VACV) and emerging (MPXV) pathogens. However, MPXV’s resistance to complement-dependent enhancement highlights the necessity for pathogen-adapted optimization. These findings establish E8 as a critical conserved target for pan-poxvirus VACV and MPXV countermeasure development. Full article

The oxidative coupling of methyl chloride (CH₃Cl) to vinyl chloride (C₂H₃Cl) (MCTV) represents a promising yet challenging direct conversion route for C₂H₃Cl production. In this study, a novel catalyst, cristobalite silica, supported Na₂WO₄ nanoclusters, was fabricated by calcining an intermediate composite composed by β-zeolite and sodium tungstate (Na₂WO₄). The pore structure of this β-zeolite possesses a regular shape and suitable size distribution, providing an accurate geometric matching effect for Na₂WO₄ to homogeneously distribute in the entire β-zeolite matrix with high loading. Accordingly, the excellent dispersity of Na₂WO₄ nanocluster active sites is well maintained even after calcining at 750 °C, and the microporous β-zeolite matrix is completely converted to dense cristobalite phase silica after the calcination. The high-loading and well-dispersed Na₂WO₄ nanocluster leads to a superior performance in MCTV with a CH₃Cl conversion of 81.5%, a C₂H₃Cl selectivity of 42.4%, and a C₂H₃Cl yield of 34.6%. Notably, the catalyst exhibits remarkable stability during the catalytic process. Full article

In this work, magnetic biochars (MBCs) were produced with the chemical coprecipitation method. The resulting materials were dried at 50 °C for 12 h and characterized via SEM-EDS, XRD, FT-IR, BET, TGA, and VSM techniques to evaluate their efficacy in removing Congo red (CR). The effects of solution pH, CR concentration, MBC1:1 mass, and a variety of ions on the adsorption performance were systematically examined. According to the experimental results, for 200 mL of 50 mg/L CR, the highest adsorption capacity of 20 mg MBC1:1 was 172.88 mg/g in a 2 h period at pH 7. Additionally, the pseudo-second-order (PSO) model-based kinetic analysis exhibited that the process of adsorption adhered to this model. Furthermore, the interaction between MBC1:1 and CR was best described by Langmuir multilayer adsorption, according to isotherm analysis. All of these theoretical and practical findings point to the great potential of MBC1:1 as adsorbents for the applications of wastewater treatment. Full article

Doping lithium cobalt oxide (LiCoO₂) cathode materials is an effective strategy for mitigating the detrimental phase transitions that occur at high voltages. A deep understanding of the relationships between cycle capacity and the design elements of doped LiCoO₂ is critical for overcoming the existing research limitations. The key lies in constructing a robust and interpretable mapping model between data and performance. In this study, we analyze the correlations between the features and cycle capacity of 158 different element-doped LiCoO₂ systems by using five advanced machine learning algorithms. First, we conducted a feature election to reduce model overfitting through a combined approach of mechanistic analysis and Pearson correlation analysis. Second, the experimental results revealed that RF and XGBoost are the two best-performing models for data fitting. Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R² values of 0.8882 and 0.8318, respectively. Experiments focusing on ion electronegativity design verified the effectiveness of the optimal combined model. We demonstrate the benefits of machine learning models in uncovering the core elements of complex doped LiCoO₂ formulation design. Furthermore, these combined models can be employed to search for materials with superior electrochemical performance and processing conditions. In the future, we aim to develop more accurate and efficient machine learning algorithms to explore the microscopic mechanisms affecting doped layered oxide cathode material design, thereby establishing new paradigms for the research of high-performance cathode materials for lithium batteries. Full article

In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named NP-net, focusing on non-predictive variables, and the other named T-net, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques. Full article

Inorganic phosphorus (P) is a key component of soil P pools, influencing their availability and mobility. Although studies on biochar’s effect on inorganic P fractions in various soils are growing, a critical review of these findings is lacking. Herein, we conducted a quantitative meta-analysis of 74 peer-reviewed datasets, drawing general conclusions and confirming the absence of publication bias through funnel plot statistics. The results showed that biochars can influence soil inorganic P fractions, with their effects depending on biochar (i.e., feedstock, pyrolysis temperature and time, C:N ratio, pH, ash and P content) and soil-related properties (i.e., pH, texture, P content). Specifically, the addition of biochar significantly enhanced the diverse soil inorganic P fractions and P availability (as indicated by Olsen-P). Only biochars produced from wood residues and having high C/N ratios (>200) did not significantly increase the labile P fractions (water extracted soil phosphorus (H₂O-P), Olsen-P, and soil calcium compounds bound phosphorus (Ca₂-P)). The application of biochars derived from crop residues significantly increased the soil P associated with iron and aluminum oxides, while there was no significant effect on manure- and wood residue-derived biochars. In addition, applications of low temperature biochars and manure residue-derived biochars could increase the proportions of soil highly stable P. We identified knowledge gaps in biochar production and its potential for soil phosphorus regulation. Due to the complex processes by which biochar affects soils, more systematic evaluations and predictive methods (e.g., modeling, machine learning) are needed to support sustainable agriculture and environmental practices. Full article

In the context of deep mining, the uncertainty of gas emission levels presents significant safety challenges for mines. This study proposes a gas emission prediction model based on Kernel Principal Component Analysis (KPCA), an Improved Crow Search Algorithm (ICSA) incorporating adaptive neighborhood search, and Support Vector Regression (SVR). Initially, data preprocessing is conducted to ensure a clean and complete dataset. Subsequently, KPCA is applied to reduce dimensionality by extracting key nonlinear features from the gas emission influencing factors, thereby enhancing computational efficiency. The ICSA is then employed to optimize SVR hyperparameters, improving the model’s optimization capabilities and generalization performance, leading to the development of a robust KPCA-ICSA-SVR prediction model. The results indicate that the KPCA-ICSA-SVR model achieves the best performance, with RMSE values of 0.17898 and 0.3071 for the training and testing sets, respectively, demonstrating superior robustness and generalization capability. Full article

Energy-efficient and cost-effective localization systems are attractive for large-scale tracking and localization of goods. In this paper, we propose a room-level localization system using energy-harvesting BLE tags to track the targets. We introduce the Dempster–Shafer (D–S) evidence theory combined with fingerprinting technology for location estimation. To reduce the estimation complexity, we divide the indoor environment into clear areas and fuzzy areas. The D–S algorithm is employed to locate the target in the clear areas when the targets are only detected by the anchor nodes within a single room. Conversely, fuzzy areas are characterized by RSSI signals detected by anchor nodes across multiple rooms. Then, the system integrates fingerprint matching to ensure superior positioning accuracy across the deployment. Extensive experiments demonstrate that the proposed system maintains a room-level positioning accuracy above 99% under standard test conditions within an area of approximately 2000 m² with lots of rooms. Full article