Search Results (5,698)

The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m³/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application. Full article

Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered decision-making using an Enhanced Hierarchical Holographic Modeling (EHHM) approach for cloud computing security risk assessment. Two methods were used, the Entropy Weight Method (EWM) and Criteria Importance Through Intercriteria Correlation (CRITIC), to provide a multi-factor decision-making risk assessment framework across the different security domains that exist with cloud computing. Additionally, fuzzy set theory provided the respective levels of complexity dispersion and ambiguities, thus facilitating an accurate and objective participation for a cloud risk assessment across asymmetric information. The trapezoidal membership function measures the correlation, rank, and scores, and was applied to each corresponding cloud risk security domain. The novelty of this re-search is represented by enhancing HHM with an expanded security-transfer domain that encompasses the client side, integrating dual-objective weighting (EWM + CRITIC), and the use of fuzzy logic to quantify asymmetric uncertainty in judgments unique to this study. Informed, data-related, multidimensional cloud risk assessment is not reported in previous studies using HHM. The different Integrated Weight measures allowed for accurate risk judgments. The risk assessment across the calculated cloud computing security domains resulted in a total score of 0.074233, thus supporting the proposed model in identifying and prioritizing risk assessment. Furthermore, the scores of the cloud computing dimensions highlight EHHM as a suitable framework to support and assist corporate decision-making in cloud computing security activity and informed risk awareness with innovative activity amongst a turbulent and dynamic cloud computing environment with corporate operational risk. Full article

Polyvinyl chloride (PVC) is a widely used polymer in composite systems due to its versatility and processability, with growing use in advanced engineering applications. This study presents the formulation, processing optimisation, and detailed characterisation of a hot-dip PVC-based plastisol composite coating developed for electrical insulation in electric vehicle (EV) battery systems. A series of plastisol formulations with varying filler contents were prepared and applied via dip-coating at withdrawal speeds of 5, 10, and 15 mm s⁻¹. The 5 mm s⁻¹ withdrawal speed resulted in the most uniform coatings with thicknesses of 890–2100 µm. Mechanical testing showed that lower filler content significantly improved performance: Group 1 (lowest filler) exhibited the highest tensile strength (11.9 N mm⁻²), elongation at break (465%), tear strength (92 N mm⁻¹), and abrasion resistance. SEM and EDX analyses confirmed more homogeneous filler dispersion in Group 1, while FTIR spectra indicated stronger polymer–plasticiser interactions. Contact-angle measurements showed an increase of ${38}^{\circ }$ in low-filler samples, indicating enhanced surface hydrophobicity. Furthermore, Group 1 coatings demonstrated superior dielectric strength (22.1 kV mm⁻¹) and excellent corrosion resistance, maintaining integrity for over 2000 h in salt-spray testing. These findings highlight the importance of filler optimisation in balancing mechanical, electrical, and environmental performance. The proposed PVC-based composite coating offers a durable, cost-effective solution for next-generation EV battery insulation systems and has potential applicability in other high-performance engineering applications. Full article

Cover crop mixes play an important role in enhancing soil health, nutrient turnover, and ecosystem resilience; yet, maintaining even seed dispersion and planting uniformity is difficult due to significant variances in seed physical and aerodynamic properties. These discrepancies produce non-uniform seeding and species separation in drill hoppers, which has an impact on stand establishment and biomass stability. The thousand-grain weight is an important measure for determining cover crop seed quality and yield since it represents the weight of 1000 seeds in grams. Accurate seed counting is thus a key factor in calculating thousand-grain weight. Accurate mixed-seed identification is also helpful in breeding, phenotypic assessment, and the detection of moldy or damaged grains. However, in real-world conditions, the overlap and thickness of adhesion of mixed seeds make precise counting difficult, necessitating current research into powerful seed detection. This study addresses these issues by integrating deep learning-based computer vision algorithms for multi-seed detection and counting in cover crop mixes. The Canon LP-E6N R6 5D Mark IV camera was used to capture high-resolution photos of flax, hairy vetch, red clover, radish, and rye seeds. The dataset was annotated, augmented, and preprocessed on RoboFlow, split into train, validation, and test splits. Two top models, YOLOv5 and YOLOv7, were tested for multi-seed detection accuracy. The results showed that YOLOv7 outperformed YOLOv5 with 98.5% accuracy, 98.7% recall, and a mean Average Precision (mAP 0–95) of 76.0%. The results show that deep learning-based models can accurately recognize and count mixed seeds using automated methods, which has practical applications in seed drill calibration, thousand-grain weight estimation, and fair cover crop establishment. Full article

Static mixers are widely used devices for efficient fluid mixing, homogenization, and enhancement of heat transfer, with applications ranging from chemical processing and pharmaceutical manufacturing to wastewater treatment. This review provides a structured overview of mixing processes and the key metrics used to assess mixing quality in static mixers. Conceptual models such as dispersive versus distributive mixing and the classification into macro-, meso-, and micromixing are introduced as a basis for understanding mixing phenomena. Subsequently, a comprehensive set of quantitative measures, including G-value, residence time distribution, intensity of segregation, coefficient of variation, striation-based descriptors, Lyapunov exponent, extensional efficiency, and shear rate, is discussed in detail. Correlations and relationships among these measures are highlighted to facilitate their application in characterizing mixing quality in static mixers. By systematically summarizing the theoretical background, definitions, and interconnections of mixing quality measures, this review aims to provide researchers and engineers with a clear framework for evaluating and comparing mixing quality in static mixers, thereby supporting both academic studies and practical design considerations. Full article

Zirconia is widely used for customized implant abutments owing to its esthetics, strength, and biocompatibility; however, the optimal surface modification for soft-tissue sealing and bone metabolic remains uncertain. This study evaluated how glass–ceramic spray deposition (GCSD), with or without handheld nonthermal plasma (HNP), alters zirconia surface physiochemistry and cellular responses. Field-emission scanning electron microscopy/energy-dispersive X-ray spectroscopy, surface roughness (Ra), wettability, and surface free energy (SFE) were measured. Human osteoblast-like cells (MG-63) and human gingival fibroblasts (HGF-1) were used to assess attachment and spreading, metabolic activity, cytotoxicity, and inflammatory response (tumor necrosis factor-α, TNF-α) (α = 0.05). GCSD produced an interlaced rod- and needle-like glass–ceramic layer, significantly increasing Ra and hydrophilicity. HNP further reduced surface contaminants, increased SFE, and enhanced wettability. The combination of GCSD and HNP yielded the greatest attachment and spreading for both cell types, without increases in cytotoxicity or TNF-α. GCSD with HNP creates a hydrophilic, micro-textured, chemically activated zirconia surface that maintains biocompatibility while promoting early attachment and bone metabolic activity, supporting its application for zirconia implant abutments. Full article

Laser cladding is an essential method for strengthening and restoring component surfaces. To increase its efficacy and provide a reliable surface treatment technique, it is necessary to optimize process parameters, enhance material adhesion, and guarantee high-quality, reliable coatings. These measures help to extend the lifespan of components. In this study, the surfaces of AISI 904L stainless steel samples were cladded to prepare various Co-based composite coatings with single and multiple layers reinforced with WC–CoCr–Ni powder. The phases within the newly developed layers were investigated using X-ray Diffraction (XRD), while the microstructure was examined using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX). Further tests were performed to assess the hardness, wear resistance and corrosion performance of the deposited coatings. Analyzing and comparing the coatings, it was observed that the coating performance increased with increasing thickness and generally due to a lower amount of Fe present within the microstructure. Full article

This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine—operationalized through planning (rubric-aligned task decomposition), tool use beyond retrieval (tests, static/dynamic analyzers, rubric checker), and self-critique (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters $\eta$ and $\lambda$ that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a Concurrent Programming course ($n=35$), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of what to value and how to assess in AI-rich settings: we elevate process and provenance—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a design model for feedback policy, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization. Full article

Conventional wavefront sensors face challenges when detecting frequency-domain information. In this study, we developed a high-precision, and fast multi-spectral wavefront detection technique based on neural networks. Using an etalon and a diffractive optical element for spectral encoding, the measured pulses were spatially dispersed onto the sub-apertures of the Shack-Hartmann wavefront sensor (SHWFS). We employed a neural network model as the decoder to synchronously calculate the multi-spectral wavefront aberrations. Numerical simulation results demonstrate that the average calculation time is 21.38 ms, with a root mean squared (RMS) wavefront residual error of approximately 0.010 μm for 4-wavelength, 21st-order Zernike coefficients. By comparison, the conventional modal-based algorithm achieves an average calculation time of 102.98 ms and wavefront residuals of 0.090 μm. Remarkably, for 10-wavelength analysis, traditional centroid algorithms fail; this approach maintains high simulation accuracy with the RMS wavefront residual error below 0.016 μm. The proposed approach significantly enhances the measurement capabilities of SHWFS in multi-spectral and single-shot wavefront detection, particularly for single-shot spatio-temporal characterization in ultra-intense and ultra-short laser systems. Full article

The biophysical characteristics of cellular membranes, particularly their electrical properties in the $\alpha$-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these electrical measurements is the confounding effect of morphological changes that inevitably occur during prolonged observation periods. These shape alterations significantly impact measured capacitance values, potentially masking true biological responses to epidermal growth factor (EGF) stimulation that are essential for cancer detection. In this study, we attempted to address this fundamental challenge by developing a deep learning method that establishes a direct computational relationship between cellular morphology and electrical properties. We combined optical trapping technology and capacitance measurements to generate a comprehensive dataset of HeLa cells under two different experimental conditions: (i) DPBS treatment and (ii) EGF stimulation. Our convolutional neural network (CNN) architecture accurately predicts 401-point capacitance spectra (0.1–2 kHz) from binary morphological images at low frequencies (0.1–0.8 kHz, < 10% error rate). This capability allows for the identification and subtraction of morphology-dependent components from measured capacitance changes, effectively isolating true biological responses from morphological artefacts. The model demonstrates remarkable prediction performance across diverse cell morphologies in both experimental conditions, validating the robust relationship between cellular shape and electrical characteristics. Our method significantly improves the precision and reliability of EGFR-based cancer diagnostics by providing a computational framework for a morphology-induced measurement error correction. Full article

In this study, I used regression analysis to examine the relation between stock return dispersion from the market returns and market portfolio returns as a measure of herding behaviour in opportunistic insider traders from 2014 to 2024 in the USA. Opportunistic insiders place a trade in the same calendar month for at least three consecutive years. I found no evidence of statistically significant and negative relationship between return dispersions and market returns, either absolute or squared. This result implies that there is no herding behaviour. The results are robust to large stock price movements and changes in market volatility. My results are important for regulators and investors. Future research may involve the herding behaviour of insiders who follow trading plans. Full article

This study investigates an alternative methodology for incorporating polymeric and steel fibers into concrete. Conventional reinforcement approaches often require complex application techniques and face industrial limitations. In contrast, the present work evaluates the use of short, discontinuous fibers—commercial polypropylene fibers (PFRC), polypropylene fiber braid (PFBRC) and steel fibers (SFRC)—which enable improved dispersion, ease of mixing and potential mechanical benefits. The fibers were randomly oriented and evenly distributed within the cementitious matrix. Mechanical performance was assessed through four-point bending tests combined with displacement measurements, acoustic emission analysis and uniaxial compression tests, while scanning electron microscopy (SEM) confirmed fiber–matrix interaction and fragment retention. The results demonstrated significant improvements, with compressive strength exceeding that of unreinforced concrete, while hybrid fiber systems provided enhanced crack resistance and post-cracking stability. Overall, the findings highlight that the integration of discontinuous fibers may provide tangible mechanical advantages, potentially outweighing the structural benefits of continuous reinforcing bars in applications requiring high strength and reliable mechanical performance. Full article

Green nanotechnology offers a sustainable and eco-friendly approach for nanoframework synthesis. The present study intended to synthesize a novel eco-friendly encapsulated Zeolitic Imidazolate Framework-8 (ZIF-8) in a one-pot method using metabolites from the mangrove plant Conocarpus erectus (CE). Gas Chromatography–Mass Spectrometry (GC-MS) analysis of the extract revealed the presence of important bioactive metabolites. The synthesized material was evaluated by UV-Vis spectroscopy, X-ray diffraction (XRD), particle size analysis (PSA), zeta potential measurement, high-resolution transmission electron microscopy (HR-TEM), and Fourier transform infrared (FT-IR) spectroscopy studies. The environment-friendly mangrove metabolites aided by Zeolitic Imidazolate Framework-8 was found to be crystalline, rhombic dodecahedron structured, and size dispersed without agglomeration. The nanomaterial possessed a broad antimicrobial effect on drug-resistant microorganisms, including Candida krusei, Escherichia coli, Streptococcus Sp., Staphylococcus aureus, Enterococcus Sp., Pseudomonas aeruginosa, Klebsiella pneumoniae, C. propicalis, and C. albicans. Further, its cytotoxicity against MDA-MB-231 cells was found to be efficient. The morphological alterations exhibited by the antiproliferative impact on the breast cancer cell line were detected using DAPI and AO/EB staining. Therefore, ZIF-8 encapsulated mangrove metabolites could serve as an effective biomaterial with biomedical properties in the future. Full article

Although the accidental or intentional explosions produced in industrial facilities or in urban areas are events with low probability, they have a high destructive potential and potential for human injuries and/or fatalities. One of the types of such events is given by detonation of improvised explosive devices (IEDs)—dirty bombs for terrorist purposes—which may produce a high number of metallic fragments. Studying mass and spatial distributions of these fragments is useful for evaluating their lethality and destructive potential and may help to implement adequate protective measures. This work brings a closer insight into the fragment dispersion around the detonation of a steel-enclosed C4 charge with cylindrical symmetry. In this respect a specific approach involving both detonation experiments and numerical simulations performed by home-made and commercial software packages for investigation of the fragmentation process and accompanying angular scattering of the fragments was proposed. Special algorithms, which allow the estimation of the spatial distributions of fragments from the numerical analysis of perforations made by the metallic fragments generated by such IEDs on surrounding material walls, are developed. Further, numerical simulations of a similar IED device provided output parameters related to the statistical distributions of mass, kinetic energy and position of the fragments. Experimental fragmentation generated a recovered mass distribution (94 fragments of 67.5 g) that was compared with that extracted from simulation, revealing a reasonable agreement on the 0.3–1 g range. In the case of simulations, 300 fragments from a total number of 374 showed a mass ranging from 0.004 to 0.3 g. The simulations showed that the middle part of the steel case generated fragments of kinetic energy over 4 kJ and its ends generated fragments of kinetic energy under 1 kJ. Experimental fragment scattering distributions were investigated with specific home-made numerical algorithms, which, based on a set of images, analysed the correlations between spatial coordinates of perforations made by fragments on surrounding special panels and provided histograms that are discussed in relation with the fragment-induced lethality degree. Full article

This study examines the evolution of the Yellow River Basin’s urban corporate network from 2003 to 2023, aiming to understand how intercity connectivity and decision-making authority have developed. Using headquarters–subsidiary linkages of listed firms, we measure connectivity and control of cities within the urban system and employ spatial error models to identify their main determinants. The results show that the network has become denser and more geographically inclusive, especially in the middle and lower reaches. However, a clear hierarchy remains, and upstream integration stays limited. Community structures are anchored by capitals, and multi-core patterns strengthen over time. Coastal hubs in Shandong handle the most significant volumes of ties, while interior capitals such as Zhengzhou, Lanzhou, Xi’an, and Taiyuan concentrate authority—a contrast that has intensified since 2013. Connectivity and control often diverge, and disparities in both have increased. Administrative rank remains the strongest predictor of a city’s position, although its influence has decreased as factors such as openness, development, producer services, and innovation have gained importance. Transportation accessibility and human capital consistently support both connectivity and control, while government intervention initially restricts network roles but becomes less influential over time. These findings suggest that intercity corporate linkages have expanded, yet decision-making authority has not dispersed and remains concentrated in a small set of capitals. Governance that coordinates across provinces is necessary to ensure that increasing linkages translate into shared economic opportunities while protecting the basin’s fragile ecological environment. Full article