Search Results (3,659)

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

This study investigates the structural and functional enhancement of corn zein–chitosan composites via mild alkaline treatment to develop biodegradable protein-polysaccharide materials for diverse applications. Films with varying zein-to-chitosan ratios were fabricated and characterized using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). Both untreated and sodium hydroxide (NaOH)-treated films were evaluated to assess changes in physicochemical properties. FTIR analysis revealed that NaOH treatment promoted deprotonation of chitosan’s amine groups, partial removal of ionic residues, and increased deacetylation, collectively enhancing hydrogen bonding and resulting in a denser molecular network. Simultaneously, partial unfolding of zein’s α-helical structures improved conformational flexibility and strengthened interactions with chitosan. These molecular-level changes led to improved thermal stability, reduced degradation, and the development of porous microstructures. Controlled NaOH treatment thus provides an effective strategy to tailor the physicochemical properties of zein–chitosan composite films, supporting their potential in sustainable food packaging, wound healing, and drug delivery applications. Full article

In gas–liquid two-phase flows, diverging channels such as diffusers often develop low-pressure separation zones where gas can accumulate, hindering pressure recovery and reducing system performance. This issue is particularly critical in centrifugal pumps, where it leads to efficiency losses. Unlike pumps, diffusers without rotating components allow for more precise experimental studies. This research investigates a passive control method using upstream cross-flow steps to reduce gas accumulation in a horizontal diverging channel. Thin metallic sheets with toothed geometries of 2 mm, 5 mm, and 8 mm heights were installed upstream to interact with the flow. These features aim to enhance turbulence, break up larger gas pockets, and promote vertical bubble dispersion, all while minimizing additional flow separation. The diffuser was intentionally designed with an expanding angle to encourage flow separation and gas accumulation. The experiments covered various two-phase flow conditions (liquid Reynolds number 59,530–78,330; gas Reynolds number 3–9.25), and high-speed imaging captured detailed phase interactions. The results show that the steps significantly reduce gas accumulation, especially at higher water flow rates. These findings support the development of more accurate computational models and offer insights for optimizing centrifugal pump designs by minimizing gas buildup in separated flow regions. Full article

Agricultural activities and dry climatic conditions promote the evaporation and salinization of groundwater in arid areas. Long-term irrigation alters the groundwater circulation and environment in arid plains, as well as its hydraulic connection with surface water. A comprehensive assessment of groundwater irrigation suitability and its interaction with surface water is essential for water–ecology–agriculture security in arid areas. This study evaluates the irrigation water quality and groundwater–surface water interaction influenced by agricultural activities in a typical arid plain region using hydrochemical and stable isotopic data from 51 water samples. The results reveal that the area of cultivated land increases by 658.9 km² from 2000 to 2023, predominantly resulting from the conversion of bare land. Groundwater TDS (total dissolved solids) value exhibits significant spatial heterogeneity, ranging from 516 to 2684 mg/L. Cl⁻, SO₄²⁻, and Na⁺ are the dominant ions in groundwater, with a widespread distribution of brackish water. Groundwater δ¹⁸O values range from −9.4‰ to −5.4‰, with the mean value close to surface water. In total, 86% of the surface water samples are good and suitable for agricultural irrigation, while 60% of shallow groundwater samples are marginally suitable or unsuitable for irrigation at present. Groundwater hydrochemistry is largely controlled by intensive evaporation, water–rock interaction, and agricultural activities (e.g., cultivated land expansion, irrigation, groundwater exploitation, and fertilizers). Agricultural activities could cause shallow groundwater salinization, even confined water deterioration, with an intense and frequent exchange between groundwater and surface water. In order to sustainably manage groundwater and maintain ecosystem stability in arid plain regions, controlling cultivated land area and irrigation water amount, enhancing water utilization efficiency, limiting groundwater exploitation, and fully utilizing floodwater resources would be the viable ways. The findings will help to deepen the understanding of the groundwater quality evolution mechanism in arid irrigated regions and also provide a scientific basis for agricultural water management in the context of extreme climatic events and anthropogenic activities. Full article

Background: Empowerment during pregnancy is linked to improved maternal and infant health outcomes and greater maternal well-being. Group Antenatal Care (GANC), a participant-centered model of care, promotes empowerment, active engagement, and the deconstruction of hierarchy between participants and care providers. It combines health assessment, interactive learning, and community building. While empowerment is a core concept of GANC, the ways it manifests and the elements that facilitate it remain unclear. Method: We conducted a generic qualitative study across four organizations in Brussels, using multiple data collection methods. This included interviews with 13 participants and 21 observations of GANC sessions, combining both the insider and outsider perspective. An adapted version of the Pregnancy-Related Empowerment Scale (PRES) guided the interviews guide and thematic analysis. Results: We identified seven themes that capture how empowerment occurs in GANC: peer connectedness, provider connectedness, skillful decision-making, responsibility, sense of control, taking action, and gaining voice. Several aspects of GANC contribute to empowerment, particularly the role of facilitators. Conclusions: This study highlights how GANC enhances empowerment during pregnancy through interpersonal, internal, and external processes. Important components within GANC that support this process include the group-based format and the interactive nature of the discussions. The presence of skillful GANC facilitators is an essential prerequisite. In a diverse and often vulnerable context like Brussels, strengthening empowerment through GANC presents challenges but is especially crucial. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

Insect pests cause severe damage and yield losses to many agricultural crops globally. The use of chemical pesticides on agricultural crops is not recommended because of their toxic effects on the environment and consumers. In addition, pesticide toxicity reduces soil fertility, poisons ground waters, and is hazardous to soil biota. Therefore, applications of entomopathogenic nematodes (EPNs) and plant growth-promoting rhizobacteria (PGPR) are an alternative, eco-friendly solution to chemical pesticides and mineral-based fertilizers to enhance plant health and promote sustainable food security. This review focuses on the biological and ecological aspects of these organisms while also highlighting the practical application of molecular communication approaches in developing a novel plant health product. This insight will support this innovative approach that combines PGPR and EPNs for sustainable crop production. Several studies have reported positive interactions between nematodes and bacteria. Although the combined presence of both organisms has been shown to promote plant growth, the molecular interactions between them are still under investigation. Integrating molecular communication studies in the development of a new product could help in understanding their relationships and, in turn, support the combination of these organisms into a single plant health product. Full article

Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Alexandrium spp. blooms and paralytic shellfish poisoning pose serious economic threats to coastal communities and aquaculture. This study evaluated the removal efficiency of two Alexandrium minutum strains using natural kaolinite clay (KNAC) and kaolinite with polyaluminum chloride (KPAC) at three concentrations (0.1, 0.25, and 0.3 g L⁻¹), two pH levels (7 and 8), and two cell densities (1.0 and 2.0 × 10⁷ cells L⁻¹) in seawater. PAC significantly enhanced removal, achieving up to 100% efficiency within two hours. Zeta potential analysis showed that PAC imparted positive surface charges to the clay, promoting electrostatic interactions with negatively charged algal cells and enhancing flocculation through Van der Waals attractions. In addition, the study conducted a cost estimate analysis and found that treating one hectare at 0.1 g L⁻¹ would cost approximately USD 31.75. The low KPAC application rate also suggests minimal environmental impact on benthic habitats. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

The phase equilibria of the Si-C-Cu ternary system at 700 °C and 810 °C were experimentally investigated for the first time. Fifteen key alloys were prepared via powder metallurgy and analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and electron probe microanalysis (EPMA). Isothermal sections were constructed based on the identified equilibrium phases. At 700 °C, eight single-phase regions and six three-phase regions—(C)+(Cu)+hcp, (C)+hcp+γ-Cu₃₃Si₇, (C)+γ-Cu₃₃Si₇+SiC, γ-Cu₃₃Si₇+SiC+ε-Cu₁₅Si₄, SiC+ε-Cu₁₅Si₄+η-Cu₃Si(ht), and SiC+(Si)+η-Cu₃Si(ht)—were determined. At 810 °C, nine single-phase regions and seven three-phase regions were identified. The solubility of C and Si/Cu in the various phases was quantified and found to be significantly higher at 810 °C compared to 700 °C. Key differences include the presence of the bcc (β) and liquid phases at 810 °C. The results demonstrate that higher temperatures promote increased mutual solubility and reaction tendencies among Cu, C, and Si. Motivated by these findings, the influence of vacuum hot pressing parameters on SiC-fiber-reinforced Cu composites (SiC_f/Cu) was investigated. The optimal processing condition (1050 °C, 60 MPa, 90 min) yielded a high bending strength of 998.61 MPa, attributed to enhanced diffusion and interfacial bonding facilitated by the high-temperature phase equilibria. This work provides essential fundamental data for understanding interactions and guiding processing in SiC-reinforced Cu composites. Full article

Cardiopulmonary bypass (CPB) is associated with significant neurological complications, yet the mechanisms underlying brain injury remain unclear. This study investigated the role of interleukin-17A (IL-17A) in exacerbating CPB-induced neuronal apoptosis and identified vulnerable brain regions. Utilizing a rat CPB model and an oxygen–glucose deprivation/reoxygenation (OGD/R) cellular model, we demonstrated that IL-17A levels were markedly elevated in the hippocampus post-CPB, correlating with endoplasmic reticulum stress (ERS)-mediated apoptosis. Transcriptomic analysis revealed the enrichment of IL-17 signaling and apoptosis-related pathways. IL-17A-Neutralizing monoclonal antibody (mAb) and the ERS inhibitor 4-phenylbutyric acid (4-PBA) significantly attenuated neurological deficits and hippocampal neuronal damage. Mechanistically, IL-17A activated the Act1-IRE1-JNK1 axis, wherein heat shock protein 90 (Hsp90) competitively regulated Act1-IRE1 interactions. Co-immunoprecipitation confirmed the enhanced Hsp90-Act1 binding post-CPB, promoting IRE1 phosphorylation and downstream caspase-12 activation. In vitro, IL-17A exacerbated OGD/R-induced apoptosis via IRE1-JNK1 signaling, reversible by IRE1 inhibition. These findings identify the hippocampus as a key vulnerable region and delineate a novel IL-17A/Act1-IRE1-JNK1 pathway driving ERS-dependent apoptosis. Targeting IL-17A or Hsp90-mediated chaperone switching represents a promising therapeutic strategy for CPB-associated neuroprotection. This study provides critical insights into the molecular crosstalk between systemic inflammation and neuronal stress responses during cardiac surgery. Full article

Joint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions. Full article

Multimodal sentiment analysis (MSA) benefits from integrating diverse modalities (e.g., text, video, and audio). However, challenges remain in effectively aligning non-text features and mitigating redundant information, which may limit potential performance improvements. To address these challenges, we propose a Hierarchical Text-Guided Refinement Network (HTRN), a novel framework that refines and aligns non-text modalities using hierarchical textual representations. We introduce Shuffle-Insert Fusion (SIF) and the Text-Guided Alignment Layer (TAL) to enhance crossmodal interactions and suppress irrelevant signals. In SIF, empty tokens are inserted at fixed intervals in unimodal feature sequences, disrupting local correlations and promoting more generalized representations with improved feature diversity. The TAL guides the refinement of audio and visual representations by leveraging textual semantics and dynamically adjusting their contributions through learnable gating factors, ensuring that non-text modalities remain semantically coherent while retaining essential crossmodal interactions. Experiments demonstrate that the HTRN achieves state-of-the-art performance with accuracies of 86.3% (Acc-2) on CMU-MOSI, 86.7% (Acc-2) on CMU-MOSEI, and 80.3% (Acc-2) on CH-SIMS, outperforming existing methods by 0.8–3.45%. Ablation studies validate the contributions of SIF and the TAL, showing 1.9–2.1% performance gains over baselines. By integrating these components, the HTRN establishes a robust multimodal representation learning framework. Full article