Search Results (18,144)

The reprocessing of wood–plastic composites (WPCs) significantly affects their structural integrity and thermal behavior. Despite this, the effect of reprocessing on PVC-based WPCs has not been extensively investigated, and the mechanism is not well understood. This study evaluated the effect of reprocessing on the properties of a PVC-based WPC. Small pieces of extruded WPC boards (2–4 mesh) were first milled to a granulation of 50 mesh, and then the material was reprocessed by compression molding, with part of the samples reinforced with glass- and carbon-fiber fabric. The physical and mechanical properties of the reprocessed material were analyzed, and the chemical and thermal characteristics of the reprocessed WPC were compared with the virgin WPC. The results of the mechanical and physical property tests showed that the reprocessed WPC had satisfactory properties compared with the virgin WPC. Samples reinforced with carbon-fiber fabric showed a statistically significant increase in tensile and flexural strength in comparison with unreinforced reprocessed WPC samples. Fourier-transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) showed that partial dehydrochlorination, thermal degradation and a decrease in thermal stability occurred. Overall, the results of this study show that although chemical degradation and a decrease in thermal stability were present in the reprocessed WPC, it retained satisfactory mechanical and physical properties that could be improved by reinforcing it with carbon-fiber fabric. Full article

Background: Soil mulching is a widely adopted agricultural practice known to regulate soil microclimate and enhance crop productivity; yet the biochemical mechanisms by which intact plastic and biodegradable mulch films influence soil nitrogen (N) cycling at the metabolic pathway level remain largely unexplored. Understanding these nitrogen transformation pathways is critical for assessing the long-term impacts of mulching materials on soil microbial communities, soil health, and sustainable agricultural management. This study focuses on the biochemical effects of intact mulch film application on soil N metabolism. Methods: N cycle-related soil metabolites were profiled using GC‒MS/MS and MALDI TOF/TOF MS and then integrated with multivariate statistical modelling and pathway-level metabolic network perturbation analysis to compare conventional plastic and biodegradable plastic mulch film application against unmulched controls. Results: A panel of 62 KEGG-annotated N-cycle metabolites was profiled, and material-dependent metabolome separation was confirmed by OPLS-DA (R²Y 0.893–0.956; Q² 0.546–0.786). Both mulching materials significantly perturbed soil N-metabolite pools but differed in terms of pathway identity, magnitude, and directionality. Conventional plastic mulching caused the greatest disruption—near-complete suppression of N-storage and stress-adaptation pools (NES of −1.16; impact score of 10.01) and severe impairment of aspartate-centred metabolism—with L-aspartate identified as a critical stoichiometric hub. Biodegradable mulching material imposed a distinct profile dominated by inhibition of branched-chain amino acid catabolism and lysine degradation, with L-pipecolate as a treatment-specific critical impact node. Conclusions: These findings support that mulching material choice is a primary determinant of soil N-cycling biochemistry. The observed metabolite-level perturbations are suggestive of potential consequences for nitrogen retention. Though this inference is based on metabolite pool size differences and network topology metrics rather than directly measured process rates, it should therefore be interpreted with appropriate caution. Full article

To address electric bicycle detection in confined indoor environments affected by occlusion and image degradation, this study proposes DCM-YOLO, a robustness-oriented detection framework designed to improve detection accuracy under complex indoor visual conditions. First, the Dual-branch Adaptive Fusion (DAF) module combines lightweight feature generation with adaptive modulation to preserve local structures and channel diversity when target appearances are incomplete. Second, the Spatial–Channel Synergistic Attention (SCSA) mechanism sequentially refines informative regions and semantic channels, allowing the detector to suppress background interference more effectively. Third, the Multi-Scale Group-Aware Head (MSGA-Head) introduces multi-branch receptive-field modeling and grouped refinement to improve scale-sensitive classification and localization. These components form a coordinated backbone–attention–head design, reducing detection ambiguity caused by partial visibility and degraded image quality, including underexposure, overexposure, low contrast, and blur. Experimental results on a public dataset collected from representative indoor environments indicate that DCM-YOLO achieves 87.6% Precision, 83.7% Recall, 86.2% mAP50, and 65.1% mAP50-95, exceeding the baseline model by 2.5, 2.9, 2.8, and 1.7 percentage points, respectively. Additional evaluations on public benchmark datasets further verify the effectiveness and robustness of DCM-YOLO. Full article

The prevention and control of forest fires are of vital importance for ecological security. The efficiency and environmental friendliness of fire-extinguishing agents remain the core focus of current research. This paper reviews the research progress and fire extinguishing mechanisms of three types of forest-fire-extinguishing agents, namely, foam extinguishing agents, gel extinguishing agents, and fire-resistant barrier materials. These three types of extinguishing agents work together to extinguish fires through three principles: isolating combustibles, reducing the oxygen concentration, and lowering the temperature. This paper systematically summarizes the performance evaluation methods, covering the cooling rate, fire extinguishing time, and re-ignition rate, and combines numerical simulation and field experiments to build a multi-scale verification system. The environmental assessment focuses on biodegradability, the ecological toxicity to soil and water systems, and the impact on plant germination and biodiversity. It clearly indicates that degradability, low toxicity, and low residue are key development directions. The current research still needs to further deepen in aspects such as long-term stability, adaptability to complex terrains, and ecological risk assessment during the life cycle. In the future, priority should be given to promoting green, multi-functional, and precise application technologies to provide solid support for scientific forest fire prevention and ecological protection. Full article

Polyhydroxyalkanoates (PHAs) are bio-based, biodegradable polyesters increasingly explored as sustainable biomaterials for regenerative medicine. This review summarizes recent advances in PHA-based bone substitute materials, highlighting their properties, fabrication methods, and biological performance. PHAs combine biocompatibility, tunable mechanical behavior, and degradation into non-toxic metabolites, while copolymerization and monomer selection modulate the stiffness, crystallinity, and resorption rate. Processing techniques such as solvent casting, electrospinning, and additive manufacturing allow the production of porous architectures that mimic bone extracellular matrix. Electrospinning is particularly suitable for nanoscale fibrous matrices, whereas 3D printing enables patient-specific scaffolds with controlled geometry and interconnected porosity. Scaffold performance can be further improved through the incorporation of osteoconductive fillers, including hydroxyapatite, β-tricalcium phosphate, bioactive glasses, graphene oxide, and carbon nanotubes, as well as through drug-delivery and pro-angiogenic functionalization. In vitro and in vivo studies consistently report favorable cytocompatibility, enhanced osteogenic differentiation, vascularization, and effective repair of bone defects in animal models. However, clinical translation remains limited by production costs, variability in polymer quality, thermal processing constraints, and regulatory challenges. Future progress will rely on more efficient biosynthesis, medical-grade purification, multifunctional scaffold design, and stronger collaboration between academia, industry, and clinicians to unlock the full potential of PHAs in regenerative bone therapies. Full article

The rapid localisation of survivors in disaster-affected areas remains a critical challenge due to complex terrains and limited communication infrastructure. Existing multi-UAV cooperative search methods suffer from insufficient perception of target distribution, inefficient use of scarce communication resources, and redundant coverage that degrades exploration efficiency. To address these issues, this paper proposes a multi-agent deep reinforcement learning framework based on the Multi-Agent Proximal Policy Optimisation (MAPPO) algorithm under centralised training with the decentralised execution (CTDE) paradigm. A detection-augmented observation mechanism is designed to encode target distribution information without introducing additional trainable parameters. A lightweight mean-pooling communication strategy is developed to optimise communication resource utilisation, reducing per-agent bandwidth to 64 bytes per step while preserving effective inter-agent coordination. Furthermore, a composite reward function is constructed to balance target detection, exploration, and redundancy suppression. Simulation results demonstrate that the proposed method achieves a recall of 0.70 and improves flight efficiency by 28% compared with the Grid Search baseline, while consuming over two orders of magnitude less communication bandwidth than representative feature-sharing approaches. The results support the use of the framework as a communication-resource-efficient solution for cooperative UAV swarm perception in bandwidth-constrained emergency rescue scenarios. Full article

Background: Heterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak alignment mechanisms, limiting their robustness and practical applicability. Objectives: This study aims to develop and evaluate a genomics-guided multimodal representation learning framework that enables robust heterogeneous data fusion, reliable cross-modal correspondence, and accurate prediction under incomplete-data conditions. Methods: We propose a multimodal learning architecture that models genomics as the primary biological anchor and learns conditional projections to imaging modalities, including multiparametric MRI and whole-slide histopathology (WSI). The framework formulates multimodal fusion as a genomics-guided contrastive learning problem, incorporates domain-specific optimization constraints, and learns a latent shared-state representation to support inference without requiring fully paired datasets. Evaluation was conducted using public datasets, including TCGA-PRAD and TCIA, across low-risk versus higher-risk/clinically significant prostate cancer (csPCa) discrimination, Gleason-based risk stratification, and clinically significant outcome prediction tasks under realistic multimodal and missing-modality scenarios. Results: In the adequately powered $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{W}\mathrm{S}\mathrm{I}$ cohort (n = 486), the framework achieved an AUROC of 0.985 ± 0.005 for low-risk versus higher-risk/csPCa discrimination (p < 0.001). Exploratory analysis in a small, matched $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{M}\mathrm{R}\mathrm{I}$ cohort (n = 28) yielded an AUROC of 0.980 ± 0.006 for the same endpoint; these findings are reported descriptively with bootstrap confidence intervals due to limited sample size. Because the negative reference group consisted of low-risk prostate cancer cases rather than cancer-free controls, results are interpreted as within-cancer risk discrimination rather than de novo cancer detection. The framework achieved weighted accuracy up to 92.1%, Cohen’s κ up to 0.86, and reduced critical decision errors by 58%. Calibration remained strong (ECE 0.021–0.024), and decision-curve analysis indicated improved utility with reduced unnecessary invasive workups in retrospective modeling. Robustness analysis demonstrated AUROC degradation below 0.04 under domain shifts. Single-modality inference using genomics alone maintained AUROC > 0.90. Interpretability analysis revealed feature attributions aligned with domain-relevant genomic markers. Conclusions: The proposed framework provides a scalable and generalizable solution for heterogeneous multimodal data fusion, supporting reliable prediction, robustness to missing modalities, and applicability to complex information systems beyond the studied domain. Full article

Biofilms are structured communities of microorganisms embedded in a self-produced extracellular polymeric substance (EPS) matrix, whose development significantly enhances microbial resistance to antibiotics, disinfectants, and host immune defenses, posing major challenges in clinical, industrial, and environmental settings. Compared with planktonic cells, biofilm-associated microorganisms can exhibit up to 10- to 1000-fold increased tolerance to antimicrobial agents, contributing to the persistence of biofilm-associated infections (BAIs). These infections remain difficult to eradicate due to reduced penetration, altered metabolic states, and the presence of dormant or persister cells. Anti-biofilm strategies can be broadly classified into physical approaches (e.g., ultrasound, mechanical stress, and light-based approaches) that target biofilm structure; chemical and enzymatic methods (e.g., EPS-degrading enzymes) that destabilize the matrix; and biological and molecular strategies (e.g., quorum-sensing (QS) inhibitors, anti-virulence agents, bacteriophages, phage-derived antimicrobial molecules, antimicrobial peptides, and natural bioactive compounds) that modulate biofilm development and integrity by targeting regulatory pathways and matrix stability through distinct mechanisms of action. Natural compounds, including lactoferrin, lactoferrin-derived peptides, and probiotic and postbiotic fractions of lactic acid bacteria (LAB), as well as plant-derived metabolites, have shown promising anti-biofilm effects, with efficacy often enhanced through complementary or potentially synergistic interactions. However, despite these advancements, clinical translation remains limited. For example, BAIs account for approximately 80% of chronic infections, with high recurrence rates and therapeutic failure reported in device-associated infections and chronic wounds. These limitations highlight the need for clinically translatable, multimodal approaches that integrate structural biofilm disruption, antimicrobial targeting, and host response modulation to design more effective and sustainable anti-biofilm strategies. Full article

The microstructure of as-cast Al-xSi (x = 4, 7, 10) alloys solidified under various cooling rates was characterized using scanning electron microscopy (SEM). To overcome the multicollinearity among eutectic silicon parameters, Principal Component Regression (PCR) analysis was employed to quantitatively evaluate the effects of silicon morphology, scale, and content on the electrical conductivity of the alloys. The results demonstrate that rapid solidification significantly refines the plate-like eutectic silicon and reduces its volume fraction, leading to improved electrical conductivity. The PCR model shows that a hierarchical mechanism: volume fraction (PC1) acts as the principal determinant, increasing baseline resistance primarily by truncating the electron mean free path (MFP); meanwhile, within identical alloy systems, morphological parameters (PC2) play a dominant regulatory role. A semi-quantitative electron drift path model was established, confirming that the morphological deviation of eutectic silicon from a spherical shape (i.e., increased aspect ratio) causes a non-linear increase in the amplitude of electron detours. This geometric elongation significantly degrades electrical conductivity, providing theoretical guidance for the microstructural design of high-conductivity Al-Si alloys, which can be practically applied to the manufacturing and optimization of lightweight, heat-dissipating enclosures for new energy vehicle (NEV) motors and power distribution systems. Full article

Discoidin domain receptor 1 (DDR1) has been implicated in fibrotic progression in multiple organs, including the kidney. However, its role in regulating cytoskeletal organization and matrix remodeling in renal fibroblasts remains unclear. Here, we investigated how DDR1 expression is regulated by profibrotic stimulation and extracellular matrix stiffness, and how DDR1 influences cytoskeletal organization and collagen remodeling. Single-cell RNA sequencing of murine kidneys subjected to unilateral ureteral obstruction (UUO) revealed enrichment of Ddr1 expression in transitional fibroblast populations during early activation. In vitro, transforming growth factor-β1 (TGF-β1) increased DDR1 expression, but DDR1 depletion did not affect canonical myofibroblast marker expression. Instead, DDR1 depletion suppressed stress fiber assembly while promoting actin-rich podosome formation associated with matrix degradation. Functionally, DDR1-deficient cells exhibited impaired focal adhesion maturation, enhanced collagen degradation, reduced gel contraction, and decreased collagen matrix stiffness as measured by atomic force microscopy. Furthermore, extracellular matrix stiffness dynamically regulated DDR1 expression, suggesting a bidirectional relationship between DDR1 expression and matrix mechanics. Together, these findings identify DDR1 as a modulator of cytoskeletal remodeling that governs the balance between matrix-degradation and contractile remodeling programs in renal fibroblasts. Full article

The first line of defense against infection is provided by the innate immune system, which is able to recognize molecular patterns in a variety of infectious agents through the action of different families of pattern recognition receptors (PRRs). These effectors detect the invading agent and trigger powerful inflammatory responses that help fight the infection from the very beginning. However, inflammatory reactions can be damaging for the host and must be properly controlled to prevent pathological consequences. Here we provide a comprehensive review of the important role of autophagy, a catabolic pathway that degrades cellular components for quality control and regulatory purposes, in the regulation of innate immune responses, and the underlying mechanisms involved. Inflammatory pathways discussed in this review include those triggered by Toll-like receptors (TLRs), Retinoic acid-Inducible Gene (RIG)-I-like receptors (RLRs), Nucleotide-binding Oligomerization Domain (NOD)-like receptors (NLRs), and the receptor for cyclic GMP–AMP Stimulator of Interferon Genes (STING). Finally, we also consider examples where autophagy plays context-dependent or even pro-inflammatory roles, reflecting a complex involvement that remains to be fully characterized. Full article

Focusing on how environmental stressors intersect with socioeconomic vulnerabilities to shape migratory patterns, this study examines the relationship between climate change and youth (im)mobility in Ethiopia. It examines how climate shocks—including droughts, unpredictable rainfall, and land degradation—heighten household insecurity and shape young people’s migration decisions. Using mixed-methods data, including surveys and interviews conducted in Chencha, Dugna Fango, and Kebribeyah, the research shows that youth mobility serves both as a proactive adaptation and a reactive coping mechanism. Some young people migrate to pursue education, employment, and independence, while others move to meet immediate livelihood needs. Mobility pathways such as stepwise, return, seasonal, and rural-urban migration are shaped by social networks, local ecological conditions, and perceived opportunities. Kebribeyah emerges as the most vulnerable location according to the Household Susceptibility Index (HVI), highlighting regional disparities. By demonstrating that migration reflects both agency and structural constraints, the study challenges simplified push–pull models and advocates for policies that address spatial variations in vulnerability, support youth aspirations, and recognize migration as a legitimate adaptation strategy. It also offers insights for designing inclusive, context-sensitive interventions that bolster resilience and expand opportunities amid climate uncertainty, promoting a more nuanced understanding of climate-related mobility rooted in adolescent experiences. Full article

The construction industry contributes significantly to global CO₂ emissions, primarily due to the production of ordinary Portland cement (OPC). As a sustainable alternative, geopolymer concrete, utilizing industrial by-products, such as ground granulated blast furnace slag (GGBFS) and fly ash (FA), has attracted increasing attention. However, studies on the post-fire behavior of high-strength slag–fly ash-based geopolymer concrete (HSSFGC) remain limited. In this study, two HSSFGC mixtures with FA contents of 10% and 30% were prepared and exposed to elevated temperatures of 100 °C, 300 °C, 450 °C, and 600 °C. After natural cooling, mass loss, ultrasonic pulse velocity (UPV), residual compressive strength, and microstructural evolution were investigated using XRD, FTIR, TGA, SEM, and EDS techniques. The results show that as temperature increases, mass loss and internal defects also increase, accompanied by deterioration of the interfacial transition zone (ITZ). At 100–300 °C, specimens with higher FA content exhibited improved residual compressive strength due to secondary geopolymerization of unreacted FA. However, above 300 °C, all specimens experienced significant strength degradation, with residual compressive strength at 600 °C reduced to 57% for FA-10 and 49% for FA-30 of their respective room-temperature values. This mix-specific difference, attributed to higher pore connectivity and more severe dehydroxylation in FA-30. These findings reveal the temperature-dependent degradation mechanisms of HSSFGC and provide a theoretical basis for post-fire assessment and sustainable engineering applications. Full article

Accurate detection of Campylobacter in chicken liver is hindered by strong matrix inhibition. This study evaluated sample-processing strategies to improve droplet digital PCR (ddPCR) quantification of Campylobacter coli and Campylobacter jejuni in chicken liver. Mechanical homogenization (Stomacher) and enzymatic/mechanical dissociation (gentleMACS), with and without 8 μm filtration, were compared. Particle-size analysis showed that filtration, especially following gentleMACS treatment, produced smaller, more uniform particles and reduced variability. Percent-degradation assays confirmed that gentleMACS achieved substantially greater tissue disruption than Stomacher homogenization. The multiplex ddPCR assay, which simultaneously targets C. coli and C. jejuni, produced droplet counts comparable to single-target reactions, indicating minimal interference between targets under the conditions tested. In inoculated liver samples, gentleMACS processing yielded droplet counts similar to those obtained from pure cultures, whereas unprocessed liver caused severe matrix interference and inconsistent quantification. Furthermore, gentleMACS-treated samples exhibited strong log-to-log linearity for quantifying C. coli and C. jejuni, enabling detection near 1 genome copy equivalent per reaction. Overall, the results indicate that enzymatic/mechanical dissociation combined with fine-pore filtration improves ddPCR detection of Campylobacter species in chicken liver. Full article

A high quality factor, denoted as the Q-factor, is crucial for micromachined disk resonator gyroscopes, commonly referred to as DRGs, to suppress thermomechanical noise and improve bias stability. However, the coupled energy dissipation mechanisms under low-pressure conditions impose significant limitations on further Q-factor enhancement. This paper establishes a rigorous multiphysics damping analysis framework for DRGs and quantitatively investigates the contributions of air damping, thermoelastic damping, and anchor loss. A free-molecular squeeze-film damping model is derived based on kinetic gas theory and molecular energy transfer mechanisms, avoiding the continuous fluid assumption of the classical Reynolds equation, which fails in low-pressure regimes. Due to the highly symmetric ring structure and central anchor design, finite element method simulations reveal an extremely high anchor-loss-limited quality factor, Q_anchor, of approximately 1.85 × 10¹², indicating negligible anchor-induced dissipation. Under an operating pressure of 0.1 Pa, air damping is validated as the absolute dominant energy dissipation mechanism with a gas quality factor, Q_air, of approximately 1.105 × 10⁵, which is significantly lower than the thermoelastic damping quality factor, Q_TED, evaluated at 8.98 × 10⁵. To break the classical trade-off between squeeze-film damping suppression and capacitive drive efficiency, a decoupled gap optimization strategy is proposed. By maintaining the drive electrode gap, gap_e, at 7.2 µm while increasing only the parasitic ring-to-suspended-mass gap, gap_m, to 12 µm, the squeeze-film-damping-limited Q-factor is improved by approximately 25% to 1.381 × 10⁵ without degrading electromechanical coupling efficiency. In addition, the optimal anchor radius is determined to be approximately 160 µm. The proposed framework provides practical design guidance for high-Q DRGs and other MEMS resonant inertial sensors. Full article