Search Results (29,796)

Soil heavy metal contamination has emerged as a global environmental and public health challenge. Among them, cadmium (Cd) is of particular concern due to its high mobility and ecotoxicity. To identify the key limiting factors and their nonlinear threshold effects for Cd accumulation in maize grains (Grain-Cd) in heterogeneous soil environments, a coordinated sampling campaign of soil and maize was conducted at the municipal scale in Chongqing, China. A total of 499 paired soil–maize samples were obtained, and the correlations between Grain-Cd concentrations and soil physicochemical properties, as well as soil Cd pollution characteristics, were quantitatively evaluated using the integrated Random Forest (RF) model and SHAP (SHapley Additive exPlanations) algorithm instead of traditional linear statistical methods. The results showed that the average Cd content in the soil of maize-growing areas in Chongqing City was 0.30 mg·kg⁻¹, with a variation coefficient (CV) of 53%, and the spatial heterogeneity was significant. The average Cd content in maize grains was 0.03 mg·kg⁻¹, with an exceedance rate of 9.6% over the Chinese National Standard (0.10 mg·kg⁻¹), indicating a certain food safety risk. The RF model achieved a high predictive accuracy for Grain-Cd (R² = 0.815, RMSE = 0.028 mg·kg⁻¹, MAE = 0.013 mg·kg⁻¹), which was significantly superior to the traditional linear regression model (R² = 0.526, RMSE = 0.0459 mg·kg⁻¹). The available Cd (avlCd) in the soil was identified as the core controlling factor for the Grain-Cd content, while total soil Cd (SCd) only showed its positive contribution at contents higher than 0.5 mg·kg⁻¹. Soil pH, CEC (cation exchange capacity), and total phosphorus (TP) exerted significant influences on the Grain-Cd by regulating soil avlCd. The dependence of Grain-Cd on these soil factors was typically nonlinear, and an obvious turning point (threshold) existed for each factor with its occurring level in soil, determined by SHAP analyses as avlCd: 0.29 mg·kg⁻¹, pH: 6.58, CEC: 18.9 cmol (+)/kg, and TP: 0.5 g·kg⁻¹, respectively. This study clarifies the nonlinear regulatory mechanisms of key soil factors on Cd accumulation in maize grains in Chongqing, and the established RF-SHAP framework and identified soil factor thresholds lay a scientific foundation for the interpretable quantification of the soil–maize Cd system, while providing a scientific basis for the precise, targeted remediation of Cd-contaminated dryland farmland and the assurance of regional maize production safety. Full article

Optimizing plant density is critical for improving wheat yield and resource-use efficiency, but whether a single density recommendation applies to varieties differing in individual spike productivity under drip fertigation remains unclear. A two-year field experiment (2023–2024 and 2024–2025) was conducted with two winter wheat varieties contrasting in spike type: a multi-spike type (Jimai23, MS) and a large-spike type (Jimai24, LS). Four target densities (200, 300, 400, and 500 plants m⁻²) were evaluated under drip fertigation to quantify yield formation, dry matter production, radiation interception and use, N uptake and nutritional status, and water use. Grain yield responses to density differed markedly between varieties. MS showed an increase–plateau–decline pattern, with the highest yields at 300–400 plants m⁻² (10.13–10.97 t ha⁻¹), whereas LS increased to 400 plants m⁻² and remained relatively stable at 500 plants m⁻² (9.97–10.55 t ha⁻¹). Increasing density increased spike number, LAI, intercepted solar radiation (ISR), and soil water consumption but decreased grains per spike, grain weight, and yield per spike in both varieties. Yield variation was more strongly associated with post-anthesis dry matter production than with grain number. Although MS intercepted more radiation, its radiation use efficiency (RUE), post-anthesis N uptake, N nutrition index (NNI), harvest index, agronomic N-use efficiency, fertilizer N recovery efficiency, and water use efficiency (WUE) declined sharply at high density. In contrast, LS maintained relatively stable RUE, higher NNI, stronger N uptake, and higher WUE at medium-to-high densities. These results demonstrate that optimal density under drip fertigation is variety-dependent and should be determined using a trait-based framework integrating nitrogen–radiation–water use efficiency. Full article

The use of biostimulants in agriculture is increasing; however, their effects on raspberry remain insufficiently understood. The aim of this study was to evaluate the impact of foliar-applied biostimulants on yield and growth in three primocane raspberry cultivars grown under field conditions using multispectral imaging based on unmanned aerial vehicles. An experiment included a control and four foliar biostimulant treatments based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant effects on primocane raspberry were found to vary substantially depending on cultivar, environmental conditions, and formulation type, with measurable impacts on both yield formation and vegetative growth. These responses were further supported and characterized using multispectral UAV-based mutlispectral imaging, which enabled effective detection of treatment-related physiological changes. This approach was based on the analysis of relative percentage changes between consecutive measurements of selected vegetation indices, allowing the identification of dynamic physiological responses over time. These findings highlight the need for a more targeted approach to biostimulant use, taking into account cultivar-specific responses and environmental variability. Future research should extend this framework to a broader range of genotypes, cultivation systems, and biostimulant formulations, while integrating remote sensing with other analytical methods to better understand plant physiological responses. Such developments may support the transition toward data-driven and precision-guided biostimulant application strategies in sustainable crop production. Full article

The reallocation of production factors, particularly labor, is central to understanding economic development and household welfare. This paper investigates how the transfer of farmland, a fundamental shift in factor endowment, affects rural household financial vulnerability, with a specific focus on the mediating role of labor mobility. While factor market liberalization is theorized to enhance efficiency, the micro-level pathways through which land transactions influence financial resilience remain underexplored. Utilizing a unique household survey dataset from Shaanxi Province, China, and employing ordered Probit model alongside propensity score matching (PSM), the impact of farmland transfer-out on the financial vulnerability of rural households is revealed. The results show that farmland transfer-out significantly reduces household financial vulnerability. Mechanism analysis confirms that this effect operates primarily by releasing surplus agricultural labor and promoting its shift into non-farm employment, thereby expanding both the sectoral and geographic scope of household labor supply. Heterogeneity analysis further reveals that the responsiveness of labor mobility to land transfer is more pronounced among households with older heads, higher human capital, and stronger social networks. However, the ultimate mitigating effect on financial vulnerability is consistent across diverse household types. These findings contribute to the literature on factor market integration and household finance in developing economies and offer direct policy implications for designing land institutions and labor policies that synergistically enhance rural economic resilience. Full article

Aggregate production planning (APP) helps medium-term production, manpower, inventory, and subcontracting decisions match expected demand. Deterministic planning models are generally ineffective in manufacturing due to demand and operational variability. Fuzzy linear programming (FLP) has been frequently used to describe imprecision using membership functions and satisfaction levels. Despite its versatility, accurate approaches for solving multi-objective FLP-based APP models become computationally expensive as issue size and complexity increase. Thus, metaheuristic algorithms are widely used, although many still have premature convergence, parameter sensitivity, and restricted scalability. This study investigates the Narwhal Optimization Algorithm (NO) as a population-based metaheuristic framework. It proposes two hybrid variants to improve convergence reliability and constraint-handling capability: NO combined with the Super Modified Simplex Method (SMS) for local refinement and NO integrated with a Runge–Kutta-based optimizer (RK) for search stability. These hybrid techniques are tested for solution quality, convergence behavior, and robustness using eight response-surface benchmark functions and four constrained optimization problems. A real-parameter fuzzy APP problem with three goods and a six-month planning horizon uses the best variations. The Elevator Kinematic Optimization (EKO) algorithm, chosen for its compliance with the same mathematical framework and consistent parameter values, is used to compare the offered solutions fairly and controlled. Fuzzy programming uses a max–min satisfaction framework with linear membership functions from positive and negative ideal solutions. Computational experiments assess solution quality, stability, and efficiency for nominal and ±10% demand disturbances. The hybrid NO variants better resist premature convergence, stabilize solutions, and satisfy users more than the original NO and benchmark approaches. For small and medium-sized organizations in dynamic situations, hybrid narwhal-based optimization appears to be a reliable and scalable decision-support solution for APP problems under uncertainty. Full article

Bacterial cellulose (BC) has emerged as a structurally robust, biologically compatible, and highly adaptable biomaterial with significant potential for next-generation wound-care technologies. Its nanofibrillar, extracellular-matrix-like architecture provides exceptional moisture retention, mechanical stability, and conformability, enabling BC to function as an active scaffold rather than a traditional dressing. Advances in chemical modification, composite engineering, and bioactive functionalization, including antimicrobial metals, chitosan, biosurfactants, enzymes, and growth factors, have expanded BC’s therapeutic capabilities. Emerging smart BC dressings integrate biosensors, stimuli-responsive drug release, and 3D-printed architectures tailored to patient-specific wound geometries. Parallel developments in artificial intelligence (AI) are transforming BC production by optimizing bioprocessing, guiding genetic engineering, reducing culture media costs, and enabling real-time quality control, thereby improving scalability and industrial feasibility. These combined innovations position BC as a multifunctional, immunologically instructive, and digitally integrated platform for advanced regenerative wound care. This review reframes BC within the contemporary pathophysiology of chronic wounds, emphasizing its roles in immunomodulation, macrophage polarization, angiogenesis, mechanotransduction, and the disruption of mixed bacterial–fungal biofilms that characterize diabetic foot ulcers and other non-healing wounds. BC hydrogels typically contain >90–99% water and exhibit tensile strengths exceeding 200 MPa, enabling robust mechanical performance in wound environments. Advances in BC composites have demonstrated antimicrobial reductions of 3–5 log units against common chronic-wound pathogens. Full article

Operating multi-unit combined heat and power (MUCHP) plants involves determining unit commitment (UC) and coupled heat and power dispatch under demand uncertainty and progressive equipment degradation. This paper proposes a reinforcement learning approach to jointly optimize UC, dispatch, and preventive maintenance (PM). Specifically, we develop a Proximal Policy Optimization (PPO)-based policy that shifts the computational burden to offline training, enabling near-real-time decisions during operation. The trained agent is evaluated on an hourly five-unit CHP system model based on operational data from a district heating plant in the Republic of Korea, using a full-year simulation. The robustness of the proposed method is assessed against demand forecast noise and structural system shifts covering reduced, expanded, homogeneous, and heterogeneous unit configurations. The experiments indicate that the proposed approach reduced the total operating cost by 4.69 to 8.35 percent compared to three heuristic baselines across the evaluated scenarios. Moreover, it mitigates supply shortages during high-volatility seasons through proactive pre-commitment and preserves asset health by distributing production loads evenly. These results indicate that integrating PM into operational planning improves both the economic efficiency and operational stability of MUCHP systems. Full article

Ribosomes are widely recognized as large intracellular macromolecular complexes responsible for protein synthesis. However, in recent years, numerous studies have revealed that ribosomal proteins possess non-canonical functions beyond translation, including roles in cell fate regulation, development, and disease. This review outlines emerging concepts surrounding the extracellular functions of ribosomes, with a particular focus on ribosome-induced cellular plasticity and transdifferentiation. Our studies have demonstrated that the incorporation of exogenous ribosomes reprograms somatic cells into a multipotent state and promotes differentiation into multiple lineages. These findings represent an alternative perspective to the conventional view of ribosomes as merely translational components. Furthermore, we discuss the biological significance of factors secreted by ribosome-incorporated cells by integrating the paracrine hypothesis with ribosome-mediated cell fate conversion. Finally, we explore the potential applications of ribosomes in regenerative medicine and cell-cultured food production. By redefining ribosomes as active regulators of cellular identity, this review provides a conceptual framework for understanding ribosome-driven cell fate regulation and its potential applications in sustainable biotechnology. Full article

Polymer films and coatings play an increasingly critical role in extending material functionality across industrial, biomedical, and environmental applications. Recent advances in surface engineering have enabled precise control of interfacial properties, leading to enhanced durability, cleanliness, and protection. This review summarizes state-of-the-art strategies for modifying polymer surfaces, with an emphasis on plasma-based surface modification and plasma-induced polymerization as versatile, solvent-free methods for tailoring wettability, chemical functionality, and adhesion. Furthermore, it examines emerging classes of self-cleaning and self-sterilizing coatings that leverage photocatalytic, hydrophobic, or antimicrobial mechanisms to mitigate contamination, biofouling, and pathogen transmission. Additionally, developments in high-performance barrier films designed to protect food products and electronic devices through improved resistance to gases, moisture, and chemical agents are highlighted. By integrating insights from materials chemistry, surface physics, and nanostructured coating design, this review provides a comprehensive overview of current achievements and future directions in functional polymer films and coatings aimed at anti-pollution, antibacterial, and anti-corrosion performance. Full article

The Historic Urban Landscape (HUL) approach provides a vital and extensive framework for heritage conservation. However, local practices often struggle to spatially translate qualitative assessments into quantitative controls at the urban block level, the most effective basic scale for administrative implementation, thereby limiting effective responses to the Management of Change. By integrating HUL with the theory of Dynamic Integrity, this study constructs a multi-dimensional evaluation index system and proposes a HUL evaluation method based on Character-Based Zoning. Taking the 125 urban block units of the historic urban area of Jingdezhen as a case study, this research integrates historical mapping, GIS spatial analysis, and Co-occurrence Network Analysis to reveal the internal structural logic of the heritage system. The study finds that the HUL of Jingdezhen is a multi-nodal dynamic system driven by four core elements: ritual beliefs, administrative management, production activities, and commercial guilds. Critically, modern visual intrusions severely impact the core heritage components within this system, specifically the Dubang and ritual culture. Based on the three dimensions of Heritage Richness, Landscape Sensitivity and Value Centrality, the study systematically identifies a total of 11 types of urban block units within the plots that characterize distinct historic landscape features and transformation patterns. This research not only deepens the localized application of HUL theory but also provides a scientific basis and methodological support for the Management of Change and periodic assessment in dynamic heritage environments. Full article

Background/Objectives: Rapid, equipment-free molecular detection of bacterial pathogens at the point of care (POC) remains a critical challenge. Staphylococcus aureus is a leading cause of severe infections, necessitating simple and sensitive diagnostic tools. Methods: We developed an integrated assay combining helicase-dependent amplification (HDA) and rolling circle amplification (RCA) in a sequential ‘one-pot’ format. Asymmetric HDA generates short, single-stranded amplicons from S. aureus DNA, enabling specific padlock probe ligation and subsequent exponential RCA. For equipment-free visual detection, biotin-labeled nucleotides are incorporated during RCA, and products are captured on a silica membrane and detected using a streptavidin-HRP conjugate with 3,3′,5,5′-tetramethylbenzidine substrate, producing an unambiguous blue color. Results: The assay detected as few as 10¹ genome copies of S. aureus per reaction. Evaluation against a panel of nine non-target respiratory pathogens and human genomic DNA demonstrated 100% specificity, with no cross-reactivity. The entire procedure is performed isothermally at 65 °C in a single tube with a total assay time of approximately 90 min. Conclusions: This ‘one-pot’ HDA-RCA colorimetric assay combines high sensitivity and specificity for S. aureus in a user-friendly, almost equipment-free format. Its simplicity and robust visual readout make it a promising tool for POC diagnostics in resource-limited settings, enabling rapid clinical decisions without specialized instrumentation. Full article

Prolonged sitting with poor posture is associated with musculoskeletal disorders, reduced productivity, and long-term health risks. Many existing posture monitoring systems predominantly rely on single-modality sensing, such as pressure or vision-based approaches, limiting their ability to capture both static alignment and dynamic micro-movements. This study proposes a multimodal smart-skin system integrating pressure, temperature, and vibration sensors for sitting posture recognition. A total of 42 sensors distributed across 14 anatomical locations were deployed, generating 15,037 samples collected over three independent sessions to evaluate cross-session temporal generalization across nine posture classes under controlled experimental conditions. Two deep learning architectures—Temporal Convolutional Networks with Attention (TCN + Attn) and Convolutional Neural Network–Long Short-Term Memory (CNN − LSTM)—were compared under Leave-One-Session-Out (LOSO) cross-validation. TCN + Attn achieved 85.23% LOSO accuracy, outperforming CNN − LSTM by 2.56 percentage points while reducing training time by 36.7% and inference latency by 33.9%. Ablation analysis revealed that temperature sensing was the most discriminative unimodal modality (71.5% accuracy), and full multimodal fusion improved LOSO accuracy by 22.93% compared to pressure-only configurations. These results demonstrate the feasibility of multimodal smart-skin sensing combined with temporal convolutional modeling for cross-session posture recognition and indicate potential for efficient real-time, privacy-preserving ergonomic monitoring. This study should be interpreted as a controlled, single-subject proof-of-concept, and further validation in multi-subject and real-world environments is required to establish broader generalizability. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article

Methane steam reforming (MSR) is the most widely used industrial process for hydrogen production. However, catalyst deactivation, carbon emissions, and energy inefficiencies limit its sustainable performance. Therefore, improving catalyst selection and optimizing operating conditions are essential for efficient hydrogen generation. This study proposes an artificial intelligence-driven framework to optimize catalyst–condition combinations in MSR systems. The framework integrates Hybrid Golden Beetle Optimization (HGBO), VIKOR-based multi-criteria decision making, and Convolutional Long Short-Term Memory (ConvLSTM) modeling. HGBO explores the solution space and generates Pareto-optimal combinations of catalysts and operating conditions. These solutions are then ranked using the VIKOR method. The ranking considers hydrogen yield, methane conversion, energy efficiency, CO₂ emissions, and catalyst lifetime. Economic feasibility is also included in the decision process. ConvLSTM modeling captures spatiotemporal relationships in catalyst and process data and predicts catalyst degradation under different operating conditions. The framework is evaluated using 620 experimentally reported MSR cases collected from the published literature within industrial ranges of 600–1200 °C, 1–40 bar, and H₂O/CH₄ ratios of 1–6. The optimized configurations achieve hydrogen yields up to 98.5%, energy efficiency approaching 99%, and reduced CO₂ emissions of about 0.85 kg h⁻¹. The results provide practical guidance for catalyst selection and process optimization in industrial hydrogen production systems. Full article

Waste-to-energy technology plays a crucial role in advancing the circular economy framework, a strategy that contributes to achieving the United Nations Sustainable Development Goals on responsible consumption and production, as well as the provision of affordable and clean energy. Hydrothermal co-liquefaction has emerged as a promising technology for addressing waste material challenges by converting them into valuable biofuels. This review focuses on biomass feedstock classification and provides an overview of hydrothermal co-liquefaction for sustainable waste management and improved energy production. Moreover, the article provides details on integrating other waste treatment methods with hydrothermal liquefaction to promote the circular economy. Research publications from 2015 to 2025 were obtained from Web of Science and Scopus to identify research trends and output across countries and map out future research directions. The retrieved data from Web of Science was analysed for mapping research, keyword occurrence, and network analysis using VOSviewer software. The study highlighted that waste treatment techniques not only mitigate environmental pollution but also provide a sustainable pathway for energy production and contribute to global carbon neutrality. The review shows that biocrude yield varies with blending ratio because of differences in the biochemical composition of feedstocks, which affect reaction pathways and lead to synergistic or antagonistic interactions during co-processing. Therefore, careful selection of biomass feedstock is essential to achieve optimal results. Full article