Search Results (60,938)

Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article

The role of photogrammetric texturing, understood as a tool to connect information modeling and outputs, jointly oriented toward documentation and dissemination, constitutes the topic of this contribution. Starting from a reality-based survey, an as-built model was developed and applied to the case study of the Fountain of the 99 Cannelle, validated according to the criteria of the Level of Accuracy (LOA) and enriched through a material–chromatic mapping of the surfaces, derived from photogrammetric data. This mapping is intended to make phenomena of decay and traces of intervention immediately legible, while at the same time increasing the informational density of the model. The model was then integrated into real-time visualization environments, in which it can also be explored and interrogated in immersive mode, making it an excellent support for collaborative processes of data management and sharing. In this regard, the results show how the controlled integration of textures within the HBIM workflow makes it possible to produce, in a coordinated manner, outputs for documentation and enhancement, facilitating access to information, including for non-specialist users; however, critical issues remain related to scalability and the complexity of texture management in these environments. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

Natural fungal polysaccharides are increasingly explored as bioactive compounds capable of orchestrating complex regenerative responses during tissue repair. This study aimed to evaluate the in vivo wound-healing efficacy and molecular mechanisms of a topical polysaccharide formulation derived from Bovistella utriformis (Calvatin 2%) using complementary murine, zebrafish, and proteomic approaches. Phylogenetic analysis based on ITS sequences confirmed the taxonomic identity of the Chilean specimen. In a murine full-thickness excisional wound model, Calvatin 2% significantly accelerated wound contraction and re-epithelialization compared to both saline and base-cream controls, achieving near-complete closure by day 10. Label-free quantitative proteomic analysis of wound tissue by UHPLC-HRMS identified 2432 high-confidence proteins, with 171 upregulated and 153 downregulated proteins in the Calvatin versus control comparison (p < 0.01). Functional enrichment revealed strong activation of innate immune response, complement activation, coagulation cascades, and acute-phase response pathways, while lipid metabolism, mitochondrial energy production, and muscle-related processes were significantly downregulated. KEGG pathway analysis further highlighted complement and coagulation cascades and neutrophil extracellular trap formation as the most prominently affected pathways. In a zebrafish laser-induced wound model, Calvatin induced early and sustained regenerative responses, reaching over 93% wound closure by 18 days post-lesion, significantly outperforming both PBS and vehicle-treated groups. Chronic oral administration of polysaccharides did not induce major hepatic inflammatory responses, supporting systemic safety. Overall, these findings indicate that B. utriformis polysaccharides are associated with modulation of immune- and repair-related pathways together with tissue reprogramming processes that may contribute to accelerated cutaneous regeneration, positioning Calvatin as a promising bioactive formulation for wound-healing applications. Full article

Silver films are key building blocks for plasmonic and nanophotonic devices, whose optical performance and device reliability are highly sensitive to particulate contamination introduced during fabrication and operation. Herein, a non-destructive surface cleaning strategy specifically applicable to silver film systems is proposed, based on the synergistic regulation of the mechanical properties of a polymer layer and its interfacial adhesion to the silver film. Such regulation is achieved by tuning hydrogen-bond-mediated interactions within a modified poly(vinyl alcohol) (PVA) layer, enabling effective control over the locus of fracture during peeling, such that fracture preferentially occurs at the polymer/silver interface. Unlike conventional polymer-assisted cleaning methods that suffer from an inherent trade-off between bulk cohesion and interfacial adhesion, this approach decouples the two properties through molecular-level hydrogen-bond redistribution. As a result, particulate contaminants can be efficiently removed from the silver surface while preserving the structural integrity of the silver film. The proposed method achieves a particle removal efficiency of up to 98% for contaminants larger than 30 nm and can be stably applied to silver films with lateral dimensions ranging from 1 inch to 12 inches, demonstrating excellent scalability. By further adjusting the processing parameters and compositional ratios of the polymer layer, this strategy is expected to be adaptable to silver films with different thicknesses and structural configurations, providing a reliable surface cleaning solution for improving the performance and reliability of plasmonic and optoelectronic thin-film devices. Full article

Inflammatory and autoimmune diseases are now understood to be significantly influenced by the intricate interactions between the microbiome and host physiology. This review investigates the function of epigenetic dysregulation in microbiome–host interaction and its consequences for health and disease. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNA-associated regulation, are key mechanisms that control gene expression without altering the underlying DNA sequence. Microbial metabolites and community composition alterations can cause disruptions in these epigenetic processes, resulting in dysregulated immune responses and the initiation of chronic inflammatory conditions. In particular, the diversity of gut microbiota alters host epigenetic patterns, affecting T cell differentiation, inflammatory modulation, and tissue homeostasis. Aberrant epigenetic modifications contribute to the pathogenesis of autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) by promoting inflammation and autoimmunity. Similarly, gut microbiota dysbiosis has been implicated in the development and progression of inflammatory bowel disease (IBD). Identifying the reciprocal interaction between epigenetic alterations and microbiome dynamics provides unique insights into therapeutic options targeted at restoring microbial homeostasis to prevent disease progress. Consequently, understanding the intricacies of epigenetic dysregulation in microbiome–host interactions represents a significant sector in biomedical research and highlights the promise for precision medicine approaches in treating inflammatory and autoimmune diseases. The potential for microbiome-based therapies to affect host epigenetic landscapes requires additional research, paving the way for innovative therapeutic paradigms targeted at improving host resilience and restoring immunological balance. The purpose of this review is to synthesize current knowledge on how epigenetic dysregulation and microbiome–host interactions drive inflammatory and autoimmune diseases and to highlight emerging therapeutic opportunities. Full article

As public demand for health and well-being continues to rise, rural landscapes are increasingly valued as settings for stress reduction and psycho-physiological restoration. Drawing on five “Beautiful Villages” in Jiangning District, Nanjing (China), this study categorizes rural landscapes into three types—farmland production landscapes, rural settlement landscapes, and rural mountain–water landscapes—based on the proportional dominance of key landscape elements. Audio-visual stimuli were developed from on-site photography and field recordings to construct controlled rural audio-visual environments. Using a combination of physiological indicators and self-reported psychological assessments, we systematically compare restorative responses across modalities (visual, auditory, and audio-visual) and across landscape types, and examine how specific landscape elements relate to restorative outcomes. Results show that (1) auditory stimuli generally produce stronger restorative responses than visual stimuli, and audio-visual interactions are evident; (2) restorative benefits vary significantly across the three rural landscape types; and (3) visually natural and structurally rich elements are associated with greater restoration, while auditory cues can direct visual attention and natural sounds are positively linked to restorative outcomes. These findings advance understanding of multi-sensory restorative processes in rural landscapes and provide evidence for sustainable rural landscape planning and design by supporting healthier, more restorative, and more human-centered rural environments. Full article

Ferroptosis is an iron-dependent cell death driven by lipid peroxidation and failure of cellular antioxidant defenses. It is triggered by oxidative stress and can be aggravated by aging, inflammation, and dysregulation of iron homeostasis. In the central nervous system, iron dyshomeostasis, mitochondrial dysfunction, and membrane lipid remodeling can amplify oxidative injury and increase susceptibility to ferroptotic damage, particularly in vulnerable neurons. There is growing evidence that ferroptosis-related processes are linked to Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. This review addresses novel approaches to track ferroptosis in vivo, such as imaging and biomarker techniques, and important molecular mechanisms linking iron metabolism, reactive oxygen species, and PUFA-driven lipid peroxidation to neuronal damage. We also explore upstream transcriptional control via NRF2, iron chelation and iron-handling modulation, inhibition of lipid peroxidation, and reinforcement of the System Xc-GSH-GPX4 and CoQ10-linked defense pathways. Subsequently, we highlight translational issues that need attention to further progress ferroptosis-targeted therapies for neurodegenerative disease. Full article

In order to address the challenges of frequency fluctuations and uneven voltage distributions in islanded microgrids, this paper proposes a distributed model predictive control (DMPC) strategy for secondary frequency and voltage regulation, and it adopts the virtual oscillator control (VOC) grid-forming method for the primary control. Firstly, the prediction model is constructed by integrating VOC dynamic equations with virtual inertia terms. Secondly, a cost function incorporating consensus constraints and tracking error terms is designed within the MPC framework, thereby achieving an optimal balance between dynamic consensus speed and steady-state tracking precision. Thirdly, the quadratic programming formulation strategy is used to solve the cost function optimization problem and update the DMPC outputs. Finally, simulation results verify that the proposed strategy ensures rapid frequency restoration and voltage regulation under sudden load variations and communication topology changes, while maintaining a smooth control process. Full article

In this paper, a triple-clutch power shift transmission is proposed as a means of enhancing the performance of a dual-clutch transmission in a tractor. However, it should be noted that the oil temperature and the wear of the clutch have a detrimental effect on the performance of the proposed transmission. Consequently, an adaptive control strategy is proposed for the clutch in terms of oil temperature and wear. In order to address the issue of the timeliness of temperature compensation, an adaptive fuzzy control strategy is proposed with a view to improving the control of the peak oil filling pressure, pre-filling time, and bonding time. This paper sets out a proposed adaptive iterative control strategy for the compensation of wear, with a view to regulating the pressure at different stages of the filling process. The two proposed control strategies are then subjected to rigorous testing in a test bench to ascertain their effectiveness. The findings indicate that the proposed strategy is capable of effectively mitigating the deviation of clutch binding resulting from fluctuations in oil temperature and wear, thereby enhancing the performance of agricultural machinery transmissions. Full article

Maintaining stable molding quality in hydraulic injection molding machines is difficult because the internal state of molten resin cannot be directly observed and varies with material properties and operating conditions. This difficulty is intensified by variations in hydraulic characteristics caused by oil temperature changes. This study proposes an adaptive feedforward control (AFC) framework that improves injection velocity tracking while utilizing AFC learning parameters as indicators of resin state. AFC is implemented as a multi-frequency feedforward controller whose parameters are updated through repetitive injection cycles. To overcome the limited learning duration within a single injection shot, a shot-to-shot compensation mechanism accumulates and transfers learning results across consecutive shots. Experiments are conducted on a hydraulic injection molding machine using polypropylene materials with different viscosities. The results show that the converged AFC learning parameters vary systematically with material changes and correspond to differences in molded product appearance. Furthermore, by adjusting the cylinder temperature of another material, the AFC parameters converge to values close to those of a reference material, resulting in similar molded products. These findings demonstrate that AFC learning parameters reflect variations in resin state and can serve as practical state indicators for aligning molding conditions. Full article

This study proposes EvoDeep-Quality, a closed-loop hybrid framework integrating deep learning-based perception with multi-objective evolutionary optimization for adaptive quality control in smart manufacturing. The architecture combines a CNN-LSTM network for real-time spatiotemporal quality prediction with an NSGA-III-based optimization unit to balance conflicting objectives of quality, cost, and energy efficiency. A continuous adaptive learning loop addresses concept drift and process variability. Evaluated on an industrial-inspired synthetic dataset of textile blends (N = 5000) and validated on the real-world SECOM semiconductor manufacturing dataset, the framework demonstrates strong predictive capability (R² = 0.947 ± 0.012, MAE = 0.035 ± 0.003) and significant manufacturing performance improvements, including a 23.5% quality enhancement and an 8.7–12.3% operational cost reduction compared to traditional and standalone AI models. Statistical significance testing (paired t-test, p < 0.01) confirms the superiority of the proposed approach. This deep-evolutionary framework advances proactive quality assurance and adaptive process control, offering a scalable solution aligned with Industry 4.0 and 5.0 paradigms. Full article

The Fused Deposition Modeling (FDM) process often produces parts with high surface roughness, limiting their end-use applications, especially in the biomedical field. This paper presents an experimental study on improving the surface finish of 3D-printed polycaprolactone (PCL) samples using a robotic burnishing process. A key innovation is the development of a low-cost sensorless setup using a 5-DOF manipulator, which controls the applied force by correlating a precise robotic displacement with the known stiffness of springs via Hooke’s law. Ten PCL samples were tested using two burnishing directions: 90° (perpendicular) and 0° (parallel) relative to the printing orientation. The as-printed samples showed a highly anisotropic surface. The 90° trajectory (group 1) proved to be more effective in reducing primary roughness ($R_{a_{\perp }}$), lowering the mean $R_a$ from $2.11\mathsf{\mu }\mathrm{m}$ to $1.44\mathsf{\mu }\mathrm{m}$ (a mean reduction of 29.9%). In contrast, the 0° trajectory (group 2) was more effective in reducing roughness $R_{a_{\Vert }}$, lowering its mean $R_a$ from $0.225\mathsf{\mu }\mathrm{m}$ to $0.144\mathsf{\mu }\mathrm{m}$ (a mean reduction of 34.0%). The results demonstrate that the proposed sensorless system is a valid method for surface post-processing of FDM parts when the required forces fall below a specific threshold, ensuring a significant reduction in roughness without damaging the samples. The lower surface roughness obtained with the proposed post-processing strategy may represent a promising approach for improving the surface characteristics of FDM-fabricated polymer scaffolds intended for biomedical applications. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article