Search Results (2,577)

This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article

Collisions between ships and sea ice pose a significant threat to maritime safety, making it essential to detect sea ice and perform safety-oriented path planning for polar navigation. This paper utilizes an optimized You Only Look Once version 5 (YOLOv5) model, designated as YOLOv5-ICE, for the detection of sea ice in satellite imagery, with the resultant detection data being employed to input obstacle coordinates into a ship path planning system. The enhancements include the Squeeze-and-Excitation (SE) attention mechanism, improved spatial pyramid pooling, and the Flexible ReLU (FReLU) activation function. The improved YOLOv5-ICE shows enhanced performance, with its mAP increasing by 3.5% compared to the baseline YOLOv5 and also by 1.3% compared to YOLOv8. YOLOv5-ICE demonstrates robust performance in detecting small sea ice targets within large-scale satellite images and excels in high ice concentration regions. For path planning, the Any-Angle Path Planning on Grids algorithm is applied to simulate routes based on detected sea ice floes. The objective function incorporates the path length, number of ship turns, and sea ice risk value, enabling path planning under varying ice concentrations. By integrating detection and path planning, this work proposes a novel method to enhance navigational safety in polar regions. Full article

The coloration of shellfish significantly influences both environmental adaptability and economic value. In the Jinjiang oyster (Crassostrea ariakensis), soft-body color varies between individuals, with an orange-yellow phenotype distinct from the milky white coloration of the wild type. To elucidate the compositional differences and molecular mechanisms underlying orange-yellow (designated as CaR) versus milky white (CaW) soft-body color in C. ariakensis, we conducted comparative ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) non-targeted and transcriptomic analyses. A total of 280 differential accumulation metabolites (DAMs) and 691 differentially expressed genes (DEGs) were detected between the CaR and CaW groups. The metabolite set enrichment analysis (MSEA) revealed that DAMs were significantly enriched in pigment metabolism pathways, including tyrosine metabolism, porphyrin metabolism, and lipid metabolism. Furthermore, genes associated with melanin synthesis and carotenoids conversions or transports were upregulated in the CaR vs. CaW group. These genes included Cyp4z1, Cyp4f22, Cyp17a1, Cyp1a5, Cyp2d28a, Lrp4, Aldh, and Tyr-3, potentially driving the accumulation of pheomelanin and carotenoids. This study demonstrates the vital roles of melanin and carotenoid metabolism in Jinjiang oyster body color formation, providing key insights into the molecular mechanisms of color determination in shellfish. Full article

This research paper presents the results of an analysis conducted on a microstrip patch antenna designed to operate within the 1.559–1.591 GHz frequency band, which encompasses three major satellite constellations: GPS, Galileo and BeiDou. The objective of this study is to perform a comparative evaluation of the materials used in the antenna design, assess the geometric configuration and analyze the key performance parameters of the proposed microstrip patch antenna. Prior to the numerical modeling and simulation process, a preliminary assessment was conducted to evaluate how different substrate materials influence antenna efficiency. For instance, a comparison between FR-4 and RT Duroid 5880 dielectric substrates revealed signal attenuation differences of approximately −1 dB at the target frequency. The numerical simulations were carried out using Ansys HFSS design. The antenna was mounted on a dielectric substrate, which was also mounted on a ground plane. The microstrip antenna was fed using a coaxial cable at a single point, strategically positioned to achieve circular polarization within the operating frequency band. The aim of this study is to design and analyze a microstrip antenna that operates within the previously specified frequency range, ensuring optimal impedance matching of 50 Ω with a return loss of S₁₁ < −10 dB at the operating frequency (with these parameters also contributing to the definition of the antenna’s operational bandwidth). Furthermore, the antenna is required to provide a gain greater than 3 dB for integration into GNSS’ receivers and to achieve an Axial Ratio value below 3 dB in order to ensure circular polarization, thereby facilitating the antenna’s integration into GNSSs. Full article

Precast concrete frames integrating ultra-high-performance concrete (UHPC) beams and high-strength concrete (HSC) columns offer exceptional seismic resilience and construction efficiency. However, a performance-based seismic design methodology tailored for this hybrid structural system remains underdeveloped. This study aims to develop and validate a direct displacement-based design (DDBD) procedure specifically for precast UHPC-HSC frames. A novel six-tier performance classification scheme (from no damage to severe damage) was established, with quantitative limit values of interstory drift ratio proposed based on experimental data and code calibration. The DDBD methodology incorporates determining the target displacement profile, converting the multi-degree-of-freedom system to an equivalent single-degree-of-freedom system, and utilizing a displacement response spectrum. A ten-story case study frame was designed using this procedure and rigorously evaluated through pushover analysis. The results demonstrate that the designed frame consistently met the predefined performance objectives under various seismic intensity levels, confirming the effectiveness and reliability of the proposed DDBD method. This work contributes a performance oriented seismic design framework that enhances the applicability and reliability of UHPC-HSC structures in earthquake regions, offering both theoretical insight and procedural guidance for engineering practice. Full article

Background/Objectives: Transforming growth factor-beta (TGF-β) is a multifunctional cytokine involved in immune regulation, extracellular matrix turnover, and tissue repair. Its role in periodontitis remains controversial due to conflicting human studies. This systematic review addressed the PICO-based question: in adults with periodontitis (population), how does the expression and regulation of TGF-β isoforms (intervention/exposure) compare with healthy or post-treatment states (comparator) regarding clinical outcomes (outcomes)? Methods: A systematic search of PubMed and Scopus was conducted on 1 July 2025 for human studies published in English between 2010 and 2025. Eligible studies investigated TGF-β expression, function, or genetic regulation in periodontal tissues or biological fluids. Screening and quality appraisal were performed according to PRISMA guidelines, using design-specific risk-of-bias tools. The review protocol was prospectively registered in PROSPERO (CRD420251138456). Results: Fifteen studies met inclusion criteria. TGF-β1 was the most frequently analyzed isoform and was consistently elevated in diseased gingival tissue and gingival crevicular fluid, correlating with probing depth and attachment loss. Several studies reported post-treatment reductions in TGF-β, supporting its value as a dynamic biomarker. Additional findings linked TGF-β signaling to immune modulation, fibrosis, bone turnover, and systemic comorbidities. Evidence for TGF-β2 and TGF-β3 was limited but suggested isoform-specific roles in epithelial–mesenchymal signaling and scar-free repair. Conclusions: Current evidence supports TGF-β, particularly TGF-β1, as a central mediator of periodontal inflammation and repair, with promise as both a biomarker and therapeutic target. Standardized, isoform-specific, and longitudinal studies are needed to clarify its diagnostic and translational utility. Full article

Synthetic aperture radar (SAR) provides all-weather and all-day imaging capabilities and can penetrate clouds and fog, playing an important role in ship detection. However, small ships usually contain weak feature information in such images and are easily affected by noise, which makes detection challenging. In practical deployment, limited computing resources require lightweight models to improve real-time performance, yet achieving a lightweight design while maintaining high detection accuracy for small targets remains a key challenge in object detection. To address this issue, we propose a novel lightweight network for accurate small-ship recognition in SAR images, named S2M-Net. Specifically, the Space-to-Depth Convolution (SPD-Conv) module is introduced in the feature extraction stage to optimize convolutional structures, reducing computation and parameters while retaining rich feature information. The Mixed Local-Channel Attention (MLCA) module integrates local and channel attention mechanisms to enhance adaptation to complex backgrounds and improve small-target detection accuracy. The Multi-Scale Dilated Attention (MSDA) module employs multi-scale dilated convolutions to fuse features from different receptive fields, strengthening detection across ships of various sizes. The experimental results show that S2M-Net achieved mAP50 values of 0.989, 0.955, and 0.883 on the SSDD, HRSID, and SARDet-100k datasets, respectively. Compared with the baseline model, the F1 score increased by 1.13%, 2.71%, and 2.12%. Moreover, S2M-Net outperformed other state-of-the-art algorithms in FPS across all datasets, achieving a well-balanced trade-off between accuracy and efficiency. This work provides an effective solution for accurate ship detection in SAR images. Full article

The global transition to a circular bioeconomy is accelerating the demand for sustainable, high-performance materials. Filamentous fungi represent a promising solution, as they function as living foundries that transform low-value biomass into advanced, self-assembling materials. While mycelium-based composites have proven potential, progress has been predominantly driven by empirical screening of fungal species and substrates. To unlock their full potential, a paradigm shift from empirical screening to rational design is required. This review introduces a conceptual framework centered on the biochemical programming of the fungal cell wall. Viewed through a materials science lens, the cell wall is a dynamic, hierarchical nanocomposite whose properties can be deliberately tuned. We analyze the contributions of its principal components—the chitin–glucan structural scaffold, the glycoprotein functional matrix, and surface-active hydrophobins—to the bulk characteristics of mycelium-derived materials. We then identify biochemical levers for controlling these properties. External factors such as substrate composition and environmental cues (e.g., pH) modulate cell wall architecture through conserved signaling pathways. Complementing these, an internal synthetic biology toolkit enables direct genetic and chemical intervention. Strategies include targeted engineering of biosynthetic and regulatory genes (e.g., CHS, AGS, GCN5), chemical genetics to dynamically adjust synthesis during growth, and modification of surface chemistry for specialized applications like tissue engineering. By integrating fungal cell wall biochemistry, materials science, and synthetic biology, this framework moves the field from incidental discovery toward the intentional creation of smart, functional, and sustainable mycelium-based materials—aligning material innovation with the imperatives of the circular bioeconomy. Full article

In this paper, a target detection network algorithm based on a multi-branch convolution and attention improvement Cross-Stage Partial-Fusion Bottleneck with Two Convolutions (C2F) module is proposed for the difficult task of detecting small targets in unmanned aerial vehicles. A C2F module method consisting of fusing partial convolutional (PConv) layers was designed to improve the speed and efficiency of extracting features, and a method consisting of combining multi-scale feature fusion with a channel space attention mechanism was applied in the neck network. An FA-Block module was designed to improve feature fusion and attention to small targets’ features; this design increases the size of the miniscule target layer, allowing richer feature information about the small targets to be retained. Finally, the lightweight up-sampling operator Content-Aware ReAssembly of Features was used to replace the original up-sampling method to expand the network’s sensory field. Experimental tests were conducted on a self-complied mountain pedestrian dataset and the public VisDrone dataset. Compared with the base algorithm, the improved algorithm improved the mAP50, mAP50-95, P-value, and R-value by 2.8%, 3.5%, 2.3%, and 0.2%, respectively, on the Mountain Pedestrian dataset and the mAP50, mAP50-95, P-value, and R-value by 9.2%, 6.4%, 7.7%, and 7.6%, respectively, on the VisDrone dataset. Full article

Background/Objectives: This study investigates the dosimetric impact of a 3D-printed applicator integrating multilayer catheter geometry and high-density shielding, designed for contact interventional radiotherapy (IRT) in non-melanoma skin cancer (NMSC) treatment. The aim is to assess its potential to enhance target coverage and reduce doses in organs at risk (OARs). Methods: A virtual prototype of a multilayer applicator was designed using 3D modeling software and realized through fused deposition modeling. Dosimetric simulations were performed using both TG-43 and TG-186 formalisms on CT scans of a water-equivalent phantom. A five-catheter array was reconstructed, and lead-cadmium-based alloy shielding of varying thicknesses (3–15 mm) was contoured. CTVs of 5 mm and 8 mm thickness were analyzed along with a neighboring OAR. Dosimetric endpoints included V95%, V100%, V150% (CTV), D_2cc (OAR), and therapeutic window (TW). Results: Compared to TG-43, the TG-186 algorithm yielded lower OAR doses while maintaining comparable CTV coverage. Progressive increase in shielding thickness led to improved V95% and V100% values and a notable reduction in OAR dose, with an optimal trade-off observed between 6 and 9 mm of shielding. The TW remained above 7 mm across all configurations, supporting its use in lesions thicker than conventional guidelines recommend. Conclusions: The integration of multilayer catheter geometry with high-density shielding in a customizable 3D-printed applicator enables enhanced dose modulation and OAR sparing in superficial IRT. This approach represents a step toward personalized brachytherapy, aligning with the broader movement in radiation oncology toward patient-specific solutions, adaptive planning, and precision medicine. Future directions should include prototyping and mechanical testing of the applicator, experimental dosimetric validation in phantoms, and pilot clinical feasibility studies to translate these promising in silico results into clinical practice. Full article

During prolonged storage of garlic scapes (Allium sativum L.), the proliferation of microorganisms, particularly fungi, frequently results in postharvest rot, which negatively impacts both product quality and market value. Carvacrol, a promising natural food preservative, exhibits broad-spectrum bioactivity against various microorganisms. In this study, a specific pathogenic fungal strain causing postharvest rot in garlic scapes, designated as HQ, was initially isolated from symptomatic garlic scapes. Based on a combination of physiological characteristics and molecular identification techniques, the HQ strain was identified as Aspergillus niger. Our findings further demonstrated that carvacrol exhibits significant in vitro inhibitory effects against Aspergillus niger with an EC₅₀ value of 75.99 μg/L. Moreover, scanning electron microscopy (SEM) observations revealed that carvacrol induces irreversible morphological and structural changes in the hyphae, resulting in deformation and rupture. Additionally, integrated transcriptomic and proteomic analyses indicated that carvacrol primarily targets the cell wall integrity (CWI) signaling pathway within the mitogen-activated protein kinase (MAPK) signaling pathway in Aspergillus niger, thereby compromising cell membrane integrity and stability, which ultimately suppresses fungal growth and proliferation. Full article

Bismuth vanadate (BiVO₄) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic limitations, including poor charge carrier mobility, short diffusion length, and sluggish oxygen evolution reaction (OER) kinetics. This review critically summarizes recent advancements aimed at enhancing BiVO₄ PEC performance, encompassing synthesis strategies, defect engineering, heterojunction formation, cocatalyst integration, light-harvesting optimization, and stability improvements. Key fabrication methods—such as solution-based, vapor-phase, and electrochemical approaches—along with targeted modifications, including metal/nonmetal doping, surface passivation, and incorporation of electron transport layers, are discussed. Emphasis is placed on strategies to improve light absorption, charge separation efficiency (η_sep), and charge transfer efficiency (η_trans) through bandgap engineering, optical structure design, and catalytic interface optimization. Approaches to enhance stability via protective overlayers and electrolyte tuning are also reviewed, alongside emerging applications of BiVO₄ in tandem PEC systems and selective solar-driven production of value-added chemicals, such as H₂O₂. Finally, critical challenges, including the scale-up of electrode fabrication and the elucidation of fundamental reaction mechanisms, are highlighted, providing perspectives for bridging the gap between laboratory performance and practical implementation. Full article

The REPowerEU plan is aimed at a target of 35 bcm of biomethane annually by 2030, up from 4 bcm in 2023, requiring about EUR 37 billion in investment. Food waste is identified as a key feedstock, characterized by discrete homogeneity, although its availability may vary seasonally. In Italy, the Emilia-Romagna region generates approximately 450 kt/y of industrial waste from the food and beverage sector, primarily originating from meat processing (NACE 10.1), fruit and vegetable processing (NACE 10.3), and the manufacture of vegetable and animal oils and fats (NACE 10.4). Of this amount, food and beverage processing waste (EWC 02) accounts for about 302 kt from NACE 10 (food, year 2019) and 14 kt from NACE 11 (beverage, year 2019). This study provides a comprehensive screening of waste streams generated by the local food and beverage industry in Emilia-Romagna, evaluating the number of enterprises, their value added, and recorded waste production. The screening led to the identification of suitable streams for further valorization strategies: a total of ~93 kt/y was selected for the preliminary conceptual design of an integrated process combining anaerobic digestion with hydrothermal treatment, aimed at supporting national biomethane production targets while maximizing material recovery through hydrochar production. Preliminary estimations indicate that the proposed process may achieve a biochemical methane potential of approximately 0.23 Nm³/kg_VS, along with a hydrochar yield of about 130 kg/t_waste. Full article

Human activities have accelerated the extinction of species, driving biodiversity loss and ecosystem degradation. Establishing protected areas (PAs) that encompass habitats of endangered species is essential for achieving biodiversity conservation and ecosystem protection goals. This study aimed to identify and prioritize critical conservation areas for the endangered long-tailed goral (Naemorhedus caudatus) in five regions of Gangwon and Gyeongbuk Provinces, South Korea. The MaxEnt model was applied to predict the potential habitat of the species, considering key environmental factors such as topographic, distance-related, vegetation, and land cover variables. The InVEST Habitat Risk Assessment (HRA) model was used to quantitatively assess cumulative risks within the habitat from the impacts of forest development and anthropogenic pressures. Subsequently, the Zonation software was employed for spatial prioritization by integrating the outputs of the models, and core conservation areas (CCAs) with high ecological value were identified through overlap analysis with 1st-grade areas from the Ecological and Nature Map (ENM). Results indicated that suitable habitats for the long-tailed goral were mainly located in forested regions, and areas subjected to multiple stressors faced elevated habitat risk. High-priority areas (HPAs) were primarily forested zones with high habitat suitability. The overlap analysis emphasized the need to implement conservation measures targeting CCAs while also managing additional HPAs outside CCAs, which are not designated as ENM. This study provides a methodological framework and baseline data to support systematic conservation planning for the long-tailed goral, offering practical guidance for future research and policy development. Full article

Accurate classification of optical communication signal quality is crucial for maintaining the reliability and performance of high-speed communication networks. While existing supervised learning approaches achieve high accuracy on laboratory-collected datasets, they often face difficulties in generalizing to real-world conditions due to the lack of variability and noise in controlled experimental data. In this study, we propose a targeted data augmentation framework designed to improve the robustness and generalization of binary optical signal quality classifiers. Using the OptiCom Signal Quality Dataset, we systematically inject controlled perturbations into the training data including label boundary flipping, Gaussian noise addition, and missing-value simulation. To further approximate real-world deployment scenarios, the test set is subjected to additional distribution shifts, including feature drift and scaling. Experiments are conducted under 5-fold cross-validation to evaluate the individual and combined impacts of augmentation strategies. Results show that the optimal augmentation setting (flip_rate = 0.10, noise_level = 0.50, missing_rate = 0.20) substantially improve robustness to unseen distributions, raising accuracy from 0.863 to 0.950, precision from 0.384 to 0.632, F1 from 0.551 to 0.771, and ROC-AUC from 0.926 to 0.999 compared to model without augmentation. Our research provides an example for balancing data augmentation intensity to optimize generalization without over-compromising accuracy on clean data. Full article