Search Results (317)

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

In this study, a simple and effective approach was developed for the quantitative detection of serotonin. Hexagonal copper selenide nanostructures (CuSe) were employed to modify a disposable screen-printed carbon electrode (SPCE), and their ability to electrochemically detect serotonin in serum samples was investigated. The fabricated CuSe nanostructures exhibited an interconnected, cluster-like morphology composed of irregularly shaped particles with a distinct hexagonal crystal structure. The electrochemical results revealed that the CuSe/SPCE sensor showed better electrochemical activity and good analytical sensing performance towards serotonin detection. The sensor exhibited a linear response in the concentration range of 10 to 1000 nM, with an excellent correlation coefficient (R² = 0.9998) and a low detection limit of 3 nM. Furthermore, the CuSe/SPCE showed better selectivity, impressive sensitivity (12.45 µM/µA cm⁻²), and good reproducibility toward serotonin detection, making it a promising electrochemical biosensor for serotonin detection in various real biological samples. Full article

Earth fill dams are susceptible to internal erosion and instability when founded over cavity-prone formations such as gypsum or karstic limestone. Subsurface voids can significantly compromise dam performance, particularly under seismic loading, by altering seepage paths, raising pore pressures, and inducing structural deformation. This study examines the influence of cavity presence, location, shape, and size on the behavior of zoned earth dams. A 1:25 scale physical model was tested on a uniaxial shake table under varying seismic intensities, and seepage behavior was observed under steady-state conditions. Numerical simulations using SEEP/W and QUAKE/W in GeoStudio complemented the experimental work. Results revealed that upstream and double-cavity configurations caused the greatest deformation, including crest displacements of up to 0.030 m and upstream subsidence of ~7 cm under 0.47 g shaking. Pore pressures increased markedly near cavities, with peaks exceeding 2.7 kPa. Irregularly shaped and larger cavities further amplified these effects and led to dynamic factors of safety falling below 0.6. In contrast, downstream cavities produced minimal impact. The excellent agreement between experimental and numerical results validates the modeling approach. Overall, the findings highlight that cavity geometry and location are critical determinants of dam safety under both static and seismic conditions. Full article

The mechanical characteristics of soil–rock mixtures (S-RMs) are essential for ensuring geotechnical engineering stability and are significantly influenced by the microstructure’s contact network configuration. Due to the irregularity of particle shapes and the variability in particle grading with S-RMs, their macro-mechanical characteristics and mesoscopic contact skeleton distribution exhibit increased complexity. To further elucidate the macro-mesoscopic mechanical behavior of S-RMs, this study employed the DEM to develop a model incorporating irregular specimens representing various states, based on CT scan outlines, and applied flexible boundary conditions. A main skeleton system of contact force chains is an effective methodology for characterizing the dominant structural features that govern the mechanical behavior of soil–rock mixture specimens. The results demonstrate that the strength of S-RMs was significantly influenced by gravel content and consolidation state; however, the relationship is not merely linear but rather intricately associated with the strength and distinctiveness of the contact force chain skeleton. In the critical state, the mechanical behavior of S-RMs was predominantly governed by the characteristics of the principal contact force skeleton: the contact force skeleton formed by gravel–gravel, despite having fewer contact forces, exhibits strong contact characteristics and an exceptionally high-density distribution of weak contacts, conferring the highest shear strength to the specimens. Conversely, the principal skeleton formed through gravel–sand exhibits contact characteristics that are less distinct compared to those associated with strong contacts. Simultaneously, the probability density distribution of weak contacts diminishes, resulting in reduced shear strength. The contact skeleton dominated by sand–sand contact forces displays similar micro-mechanical characteristics yet possesses the weakest macroscopic behavior strength. Consequently, the concept of the main skeleton of contact force chains utilized in this study presents a novel research approach for elucidating the macro- and micro-mechanical characteristics of multiphase media. Full article

A properly functioning capillary microcirculation is essential for sufficient oxygen and nutrient delivery to the central nervous system. The physical mechanisms governing the transport of red blood cells (RBCs) inside the narrow and irregularly shaped capillary lumen are complex, but understanding them is essential for identifying the root causes of neurological disorders like cerebral ischemia, Alzheimer’s disease, and other neurodegenerative conditions such as concussion and cognitive dysfunction in systemic inflammatory conditions. In this work, we conducted numerical simulations of three-dimensional capillary models, which were acquired ex vivo from a mouse retina, to characterize RBC transport. We show how the spatiotemporal velocity of the RBCs deviates in realistic capillaries and equivalent cylindrical tubes, as well as how this profile is affected by hematocrit and red cell distribution width (RDW). Our results show a previously unprecedented level of RBC velocity fluctuations in capillaries that depends on the geometric features of different confinement regions and a capillary circularity index $\left(I_{cc}\right)$ that represents luminal irregularity. This velocity fluctuation is aggravated by high hematocrit conditions, without any further effect on RDW. These results can provide a better understanding of the underlying mechanisms of pathologically high capillary transit time heterogeneity that results in microcirculatory dysfunction. Full article

The You Only Look Once (YOLO) object detection model has been widely applied to electric power operation violation identification, owing to its balanced performance in detection accuracy and inference speed. However, it still faces the following challenges: (1) insufficient detection capability for irregularly shaped objects; (2) objects with low object-background contrast are easily omitted; (3) improving detection accuracy while maintaining computational efficiency is difficult. To address the above challenges, a novel real-time object detection model is proposed in this paper, which introduces three key innovations. To handle the first challenge, an edge perception cross-stage partial fusion with two convolutions (EPC2f) module that combines edge convolutions with depthwise separable convolutions is proposed, which can enhance the feature representation of irregularly shaped objects with only a slight increase in parameters. To handle the second challenge, an adaptive combination of local and global features module is proposed to enhance the discriminative ability of features while maintaining computational efficiency, where the local and global features are extracted respectively via 1D convolutions and adaptively combined by using learnable weights. To handle the third challenge, a parameter sharing of a multi-scale detection heads scheme is proposed to reduce the number of parameters and improve the interaction between multi-scale detection heads. The ablation study on the Ali Tianchi competition dataset validates the effectiveness of three innovation points and their combination. EAP-YOLO achieves the mAP@0.5 of 93.4% and an mAP@0.5–0.95 of 70.3% on the Ali Tianchi Competition dataset, outperforming 12 other object detection models while satisfying the real-time requirement. Full article

Precision seeding plays a critical role in optimizing crop yield and resource efficiency. This study evaluates the application of a Seed Miss Prevention System (SMPS) integrated with a spoon-wheel precision metering device to mitigate seed misses and enhance its performance. A combination of Discrete Element Method (DEM) simulations, electrical hardware design, mechanical retrofitting, software development and laboratory experiments was employed to assess the effectiveness of the system across multiple seed cultivars and operating speeds. Experimental results demonstrated that the SMPS significantly reduced seed misses at lower operational speeds (3–10 rpm), with the implementation of a dual-sensor configuration further improving detection accuracy by filtering out false positives. At higher speeds (≥15 rpm), however, seed miss rates increased, particularly for irregularly shaped seeds like white beans ‘Great Northern’, due to the mechanical limitations of the metering device. Statistical analyses, including Tukey’s HSD test, confirmed the effectiveness of the SMPS in reducing miss rates across different seed types. Despite these improvements, complete elimination of seed misses was not achieved, highlighting the need for further optimization in seed miss detection. Future research should explore adaptations for higher-speed metering devices and field-scale validations. The findings underscore the potential of SMPS technology in advancing precision agriculture by improving seeding accuracy and operational efficiency. Full article

Object detection in complex road scenes is vital for autonomous driving, facing challenges such as object occlusion, small target sizes, and irregularly shaped targets. To address these issues, this paper introduces DGSS-YOLOv8s, a model designed to enhance detection accuracy and high-FPS performance within the You Only Look Once version 8 small (YOLOv8s) framework. The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. These components collectively target the aforementioned challenges in road environments. Evaluations on the Berkeley DeepDrive 100K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets demonstrate the model’s effectiveness. Compared to baseline YOLOv8s, DGSS-YOLOv8s achieves mean Average Precision (mAP)@50 improvements of 2.4% (BDD100K) and 4.6% (KITTI). Significant gains were observed for challenging categories, notably 87.3% mAP@50 for cyclists on KITTI, and small object detection (AP-small) improved by up to 9.7% on KITTI. Crucially, DGSS-YOLOv8s achieved high processing speeds suitable for autonomous driving, operating at 103.1 FPS (BDD100K) and 102.5 FPS (KITTI) on an NVIDIA GeForce RTX 4090 GPU. These results highlight that DGSS-YOLOv8s effectively balances enhanced detection accuracy for complex scenarios with high processing speed, demonstrating its potential for demanding autonomous driving applications. Full article

An aqueous leaf extract of Egeria densa was used to green-synthesize iron (II) and iron (III) oxide nanoparticles from ferrous sulphate and ferric chloride, respectively. The successful green synthesis of the nanoparticles was confirmed through UV–visible spectroscopy, and the colour of the mixtures changed from light-yellow to green-black and reddish-brown for FeO–NPs and Fe₂O₃–NPs, respectively. The morphological characteristics of the nanoparticles were determined using an X-ray diffractometer (XRD), a Fourier transform infrared spectrophotometer (FTIR), a transmission electron microscope (TEM), and energy-dispersive X-ray spectroscopy (EDX). The UV–Vis spectrum of the FeO–NPs showed a sharp peak at 290 nm due to the surface plasmon resonance, while that of the Fe₂O₃–NPs showed a sharp peak at 300 nm. TEM analysis revealed that the FeO–NPs were oval to hexagonal in shape and were clustered together with an average size of 18.49 nm, while the Fe₂O₃-NPs were also oval to hexagonal in shape, but some were irregularly shaped, and they clustered together with an average size of 27.96 nm. EDX analysis showed the presence of elemental iron and oxygen in both types of nanoparticles, indicating that these nanoparticles were essentially present in oxide form. The XRD patterns of both the FeO–NPs and Fe₂O₃–NPs depicted that the nanoparticles produced were crystalline in nature and exhibited the rhombohedral crystal structure of hematite. The FT-IR spectra revealed that phenolic compounds were present on the surface of the nanoparticles and were responsible for reducing the iron salts into FeO–NPs and Fe₂O₃–NPs. Conclusively, this work demonstrated for the first time the ability of Elodea aqueous extract to synthesize iron-based nanoparticles from both iron (II) and iron (III) salts, highlighting its versatility as a green reducing and stabilizing agent. The dual-path synthesis approach provides new insights into the influence of the precursor oxidation state on nanoparticle formation, thereby expanding our understanding of plant-mediated nanoparticle production and offering a sustainable route for the fabrication of diverse iron oxide nanostructures. Furthermore, it provides a simple, cost-effective, and environmentally friendly method for the synthesis of the FeO–NPs and Fe₂O₃–NPs using Egeria densa. Full article

Conveyor belts are critical in various industries, particularly in the barrier eddy current separator systems used in recycling processes. However, hidden issues, such as belt misalignment, excessive heat that can lead to fire hazards, and the presence of sharp or irregularly shaped materials, reduce operational efficiency and pose serious threats to the health and safety of personnel on the production floor. This study presents an intelligent monitoring and protection system for barrier eddy current separator conveyor belts designed to safeguard machinery and human workers simultaneously. In this system, a thermal camera continuously monitors the surface temperature of the conveyor belt, especially in the area above the magnetic drum—where unwanted ferromagnetic materials can lead to abnormal heating and potential fire risks. The system detects temperature anomalies in this critical zone. The early detection of these risks triggers audio–visual alerts and IoT-based warning messages that are sent to technicians, which is vital in preventing fire-related injuries and minimizing emergency response time. Simultaneously, a machine vision module autonomously detects and corrects belt misalignment, eliminating the need for manual intervention and reducing the risk of worker exposure to moving mechanical parts. Additionally, a line-scan camera integrated with the YOLOv11 AI model analyses the shape of materials on the conveyor belt, distinguishing between rounded and sharp-edged objects. This system enhances the accuracy of material separation and reduces the likelihood of injuries caused by the impact or ejection of sharp fragments during maintenance or handling. The YOLOv11n-seg model implemented in this system achieved a segmentation mask precision of 84.8 percent and a recall of 84.5 percent in industry evaluations. Based on this high segmentation accuracy and consistent detection of sharp particles, the system is expected to substantially reduce the frequency of sharp object collisions with the BECS conveyor belt, thereby minimizing mechanical wear and potential safety hazards. By integrating these intelligent capabilities into a compact, cost-effective solution suitable for real-world recycling environments, the proposed system contributes significantly to improving workplace safety and equipment longevity. This project demonstrates how digital transformation and artificial intelligence can play a pivotal role in advancing occupational health and safety in modern industrial production. Full article

In practical dielectrophoretic cell interaction experiments, cells do not always exhibit circular or rod-like shapes, making the study of dielectrophoretic interactions among irregularly shaped particles of significant importance. We established a mathematical model for curved particles to analyze their mutual dielectrophoretic interactions, incorporating particle deformability by varying their shear modulus, and employed the arbitrary Lagrangian–Eulerian method to describe particle motion and deformation. The results demonstrate that under the influence of a direct current electric field, curved particles undergo rotation, deformation, and mutual attraction due to dielectrophoresis, eventually forming a stable alignment parallel to the applied electric field. Adjusting the electric field strength effectively modulates the interaction intensity and movement velocity between particles. This study elucidates the fundamental principles governing dielectrophoretic interactions among deformable curved particles in DC electric fields, providing theoretical guidance for dielectrophoretic manipulation experiments involving biological cells, metallic particles, and other entities under DC electric fields. Full article

Micro- and nanoplastic pollutants are among the major environmental challenges, and are exacerbated by the continuous degradation of growing amounts of plastic debris in the aquatic environment. The purpose of this study was to investigate the morphology of micro/nanoplastics (M/NPs) formed from polyethylene terephthalate (PET) by mechanical degradation in an aquatic environment, which mimics the processes in the natural environment well, and to determine the impact of these particles on model aquatic organisms. To this end, M/NPs were obtained by ball milling in an aqueous medium and the effect of milling length on particle size and shape was investigated. The particles obtained in an environment simulating natural conditions were irregularly shaped, and those of nanometric size tended to form aggregates of various shapes. The ingestion and toxicity of PET M/NPs to freshwater zooplankton were then assessed. Daphnia magna and Thamnocephalus platyurus were used in a series of acute ecotoxicity tests, by exposure to M/NP dispersions at environmentally realistic concentrations (0.01–1.0 mg/L), as well as at very high concentrations (100–1000 mg/L). A significant uptake of PET particles by both types of invertebrates was observed, and the M/NPs were mainly concentrated in the digestive tracts of the crustaceans. However, they did not cause acute toxicity to the tested organisms or a reduction in their swimming activity, even at concentrations as high as 1000 mg/L. Full article

This paper addresses the maximum coverage location problem in a generalized setting, where both facilities (service areas) and regional demand are modeled as continuous entities. Unlike traditional formulations, our approach allows for arbitrary shapes for both service areas and demand regions, with additional constraints on facility placement. The key novelty of this work is its ability to handle complex, irregularly shaped service areas, including approximating them as unions of centrally symmetric shapes. This enables the use of an analytical approach based on spatial symmetry, which allows for efficient estimation of the covered area. The problem is formulated as a nonlinear optimization task. We analyze the properties of the objective function and leverage the Shapely library in Python 3.13.3 for efficient geometric computations. To improve computational efficiency, we develop an extended elastic model that significantly reduces processing time. This model generalizes the well-known quasi-physical, quasi-human algorithm for circle packing, extending its applicability to more complex spatial configurations. The effectiveness of the proposed approach is validated through test cases in which service areas take the form of circles, ellipses, and irregular polygons. Our method provides a robust and adaptable solution for various settings of practically interesting continuous maximum coverage location problems involving irregular regional demand and service areas. Full article

This study posits that soil particles in the filter layer are ellipsoidal. The effective pore radius of the filter material was calculated for various particle-shape parameters and distributions. The relationship between the porosity of the filter material and the ratio of the long axis to the short axis of ellipsoidal particles in a loose arrangement was also examined. The results indicate that the porosity of the filter material initially decreases and subsequently increases with increases in the ratio of the long axis to the short axis; however, the rate of increase progressively slows. A method for transforming irregularly shaped particles into ellipsoidal forms is proposed. The particle-shape parameter, S, is introduced to characterize the shape of irregular particles. The relationship between particle-shape parameters and the ratio of the long axis to the short axis was investigated specifically for ellipsoidal particles. It was found that the particle-shape parameters exhibit an approximately linear relationship with the ratio of the long axis to the short axis within a specific range. The discrete element method was employed to investigate the impact of particle shape on the filtration characteristics of the filter layer and was complemented by comparative experimental analysis. By analyzing the pore structures of spherical and ellipsoidal particles, this study predicts the relationship between pore structure and particle-shape parameters for any irregularly shaped natural-particle filter layer. Full article

Electric power operation violation recognition (EPOVR) is essential for personnel safety, achieved by detecting key objects in electric power operation scenarios. Recent methods usually use the YOLOv8 model to achieve EPOVR; however, the YOLOv8 model still has four problems that need to be addressed. Firstly, the capability for feature representation of irregularly shaped objects is not strong enough. Secondly, the capability for feature representation is not strong enough to precisely detect multi-scale objects. Thirdly, the localization accuracy is not ideal. Fourthly, many violation categories in electric power operation cannot be covered by the existing datasets. To address the first problem, a deformable C2f (DC2f) module is proposed, which contains deformable convolutions and depthwise separable convolutions. For the second problem, an adaptive multi-scale feature enhancement (AMFE) module is proposed, which integrates multi-scale depthwise separable convolutions, adaptive convolutions, and a channel attention mechanism to optimize multi-scale feature representation while minimizing the number of parameters. For the third problem, an optimized complete intersection over union (OCIoU) loss is proposed for bounding box localization. Finally, a novel dataset named EPOVR-v1.0 is proposed to evaluate the performance of the object detection model applied in EPOVR. Ablation studies validate the effectiveness of the DC2f module, AMFE module, OCIoU loss, and their combinations. Compared with the baseline YOLOv8 model, the mAP@0.5 and mAP@0.5–0.95 are improved by 3.2% and 4.4%, while SDAP@0.5 and SDAP@0.5–0.95 are reduced by 0.34 and 0.019, respectively. Furthermore, the number of parameters and GFLOPS are shown to have slightly decreased. Comparison with seven YOLO models shows that our DAO-YOLO model achieves the highest detection accuracy while achieving real-time object detection for EPOVR. Full article