Search Results (21,387)

Rubberized concrete is a novel green building material that enhances many features when rubber particles are incorporated into cement mortar, simultaneously yielding economic benefits through the recycling of waste tires. This study applies styrene–butadiene latex (SBL) for toughening treatment. The investigation delves into the mechanism by which SBL improves the interface between rubber and cement, encompassing macroscopic mechanical properties, microscopic structural characteristics, and nano-scale interfacial interactions. Macroscopic mechanical tests reveal a significant increase in flexural strength, shear strength, and compressive strength of the composite concrete upon the introduction of SBL and rubber. Specifically, the compressive strength improved by 8.8%, shear strength by 13.7%, and flexural strength by 18.9% at 28 days. Through electron microscopy observation of corresponding polymer cement concrete sections, observations reveal that SBL reinforces both interfaces and elucidates its bonding impact at the micro-level interface. Molecular dynamics (MD) modeling of SBL/rubber/CSH is employed at the nanoscale to compute and examine the local structure, dynamic behavior, and binding energy of the interface. The findings indicate that SBL mitigates interface impacts, enhances interface hydrogen bonds, van der Waals interactions, $\mathrm{C}\mathrm{a}-\mathrm{H}$ coordination bonds, and stability, consequently improving interfacial adhesion and fortifying the feeble interface bonding between organic polymers (rubber) and inorganic silicates (CSH). Full article

This study introduces a modular, behaviorally curated malware dataset suite consisting of eight independent sets, each specifically designed to represent a single malware class: Trojan, Mirai (botnet), ransomware, rootkit, worm, spyware, keylogger, and virus. In contrast to earlier approaches that aggregate all malware into large, monolithic collections, this work emphasizes the selection of features unique to each malware type. Feature selection was guided by established domain knowledge and detailed behavioral telemetry obtained through sandbox execution and a subsequent report analysis on the AnyRun platform. The datasets were compiled from two primary sources: (i) the AnyRun platform, which hosts more than two million samples and provides controlled, instrumented sandbox execution for malware, and (ii) publicly available GitHub repositories. To ensure data integrity and prevent cross-contamination of behavioral logs, each sample was executed in complete isolation, allowing for the precise capture of both static attributes and dynamic runtime behavior. Feature construction was informed by operational signatures characteristic of each malware category, ensuring that the datasets accurately represent the tactics, techniques, and procedures distinguishing one class from another. This targeted design enabled the identification of subtle but significant behavioral markers that are frequently overlooked in aggregated datasets. Each dataset was balanced to include benign, suspicious, and malicious samples, thereby supporting the training and evaluation of machine learning models while minimizing bias from disproportionate class representation. Across the full suite, 10,000 samples and 171 carefully curated features were included. This constitutes one of the first dataset collections intentionally developed to capture the behavioral diversity of multiple malware categories within the context of Internet of Things (IoT) security, representing a deliberate effort to bridge the gap between generalized malware corpora and class-specific behavioral modeling. Full article

The present study explores the potential improvement of the mechanical properties of bio-based polyamide 11 (PA11) for demanding industrial application using natural and surface-treated mica at 1, 2 and 5 wt%. Suppressed water uptake by up to 4% was revealed with an unfavorable effect of the surface treatment. Impact strength decreased with filler content from 39.6 kJ m⁻² to between 22–10 kJ m⁻², while stiffness and resistance towards deformation improved: flexural modulus rose from 518.5 MPa to 596 MPa at 5 wt%-treated small particle, and elastic modulus changed from 542.7 MPa to 705.6 MPa. Particle size dependent trends were observed in crystallinity by Differential Scanning Calorimetry (DSC). Surface treatment promoted the presence of a mesophase form, which was also presented by Scanning Electron Microscopy (SEM). Dynamic Mechanical Analysis (DMA) revealed increased internal friction, temperature-dependent modifications in the elastic properties and a glass transition temperature of 36.6 °C. X-ray Diffraction (XRD) proved an unusual decrease in basal spacing of mica from 9.92 to 9.82 Å due to silanization; however, the compounding process provoked some increase again up to 10.03 Å. Results highlight a viable pathway to modify the properties of PA11 with a primarily role in the filler concentration and dimensions over the surface characteristics. Full article

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

This study investigates the dynamic response characteristics of the tunnel bottom structure, focusing on a heavy-haul railway tunnel. To assess the condition of the tunnel bottom, geological radar and drilling core techniques were employed, along with on-site dynamic testing. The dynamic stress and acceleration response characteristics of the tunnel bottom structure, situated in grade V surrounding rock, were analyzed under axle loads of 25 t, 27 t, and 30 t. Both time-domain and frequency-domain analyses were conducted to explore the impact of structural defects on the dynamic response of the tunnel bottom. The results indicate that the dynamic response of the tunnel bottom structure increases linearly with increasing train axle load. In the presence of void-related defects at the tunnel bottom, the dynamic response of the structure is amplified, with an observed growth rate of up to 26.3%. Furthermore, the load exerted by heavy-duty trains on the tunnel bottom structure is predominantly a low-frequency effect, concentrated within the range of 0–20 Hz. Analysis of the 1/3 octave band reveals that the maximum difference in acceleration levels occurs at a center frequency of 31.5 Hz. Additionally, as the distance between the measurement point and the vibration source increases, the dynamic response induced by the void defect on the tunnel bottom structure weakens. Full article

A seawall settlement is a critical concern in marine engineering, as an excessive or uneven settlement can undermine structural stability and diminish the capacity to withstand marine hydrodynamic actions such as storm surges, waves, and tides. Accordingly, accurate settlement prediction is vital to ensuring seawall safety. To address the lack of clustering methods that capture the time-series characteristics of monitoring points and the limitations of hyperparameter sensitivity of conventional LSTM models, this study proposes a hybrid model integrating Dynamic Time Warping-based Hierarchical Clustering (DTW-HC) and an Adaptive Joint Search Optimization-enhanced Long Short-Term Memory Model (AJSO-LSTM). First, DTW-HC is employed to cluster monitoring points according to their time series characteristics, thereby constructing a spatial panel data structure that incorporates both temporal evolution and spatial heterogeneity. Then, an AJSO-LSTM model is developed within each cluster to capture temporal dependencies and improve prediction performance by overcoming the weaknesses of a conventional LSTM. Finally, using seawall settlement monitoring data from a real engineering case, the proposed method is validated by comparing it with a statistical model, a back-propagation Neural Network (BP-ANN), and a conventional LSTM. Results demonstrate that the proposed model consistently outperforms these three benchmark methods in terms of prediction accuracy and robustness. This confirms the potential of the proposed framework as an effective tool for seawall safety management and long-term service evaluation. Full article

Heavy metal ions (HMIs) threaten ecosystems and human health due to their carcinogenicity, bioaccumulativity, and persistence, demanding highly sensitive, low-cost real-time detection. Electrochemical sensing technology has gained significant attention owing to its rapid response, high sensitivity, and low cost. Molybdenum disulfide (MoS₂), with its layered structure, tunable bandgap, and abundant edge active sites, demonstrates significant potential in the electrochemical detection of heavy metals. This review systematically summarizes the crystal structure characteristics of MoS₂, various preparation strategies, and their mechanisms for regulating electrochemical sensing performance. It particularly explores the cooperative effects of MoS₂ composites with other materials, which effectively enhance the sensitivity, selectivity, and detection limits of electrochemical sensors. Although MoS₂-based materials have made significant progress in theoretical and applied research, practical challenges remain, including fabrication process optimization, interference from complex-matrix ions, slow trace-metal enrichment kinetics, and stability issues in flexible devices. Future work should focus on developing efficient, low-cost synthesis methods, enhancing interference resistance through microfluidic and biomimetic recognition technologies, optimizing composite designs, resolving interfacial reaction dynamics via in situ characterization, and establishing structure–property relationship models using machine learning, ultimately promoting practical applications in environmental monitoring, food safety, and biomedical fields. Full article

China has witnessed extensive construction of underground powerhouses for pumped storage power stations. With the continuous increase in unit capacity, vibration problems have become particularly pronounced. Intense vibrations may not only disrupt the normal operation of hydropower units but also compromise the overall structural safety of the powerhouse. Moreover, in dynamic analyses of powerhouse structures, different parameters exert varying degrees of influence on the results, making it essential to systematically examine their impacts. This study focuses on a large-scale underground powerhouse, establishing a three-dimensional finite element model of Unit #1 to investigate its dynamic characteristics and parametric sensitivity. Through modal and harmonic response analyses, the effects of key parameters—including the zone of surrounding rock, elastic modulus of surrounding rock, dynamic elastic modulus of concrete, and damping ratio—were systematically evaluated. Results indicate that an expanded surrounding rock zone reduces natural frequency and increases dynamic displacement, with a zone twice the span length offering an optimal balance between accuracy and computational efficiency. Increasing the elastic modulus of the surrounding rock raises the natural frequency and slightly reduces displacement, while having a limited impact on dynamic stress. The dynamic elastic modulus of concrete shows a square-root relationship with natural frequency and an inverse correlation with dynamic displacement. The damping ratio has negligible influence on natural frequency, dynamic displacement, and dynamic stress. These findings provide a theoretical basis and practical guidance for parameter selection in the dynamic analysis of underground powerhouse structures, enhancing the reliability of numerical simulations. Full article

In the processes of deep hole drilling and boring, tool deflection and chatter are prevalent problems that significantly affect the quality and efficiency of deep hole part machining. This paper designs a Helical-Type Vibration-Damping and Deflection Correction Device for BTA (boring and trepanning association) deep hole drilling based on the principles of fluid dynamic pressure lubrication and squeeze film damping. By leveraging the flow field characteristics of cutting oil during machining, the device achieves vibration-damping, deflection correction, and enhanced support for the tool system throughout the drilling operation. Through theoretical analysis, this research examines the oil film pressure distribution and stability of the Designed Vibration-Damping and Deviation Correction Device. It also explores the influence patterns of factors such as cutting parameters, device structure, minimum film thickness, film thickness ratio, and length-to-diameter ratio on its vibration-damping, deviation correction, and stability performance. Taking a $\varphi 29.35$ deep hole as the research object, an experimental platform was designed and constructed to measure and verify the device’s vibration-damping and deviation correction effects under different operating conditions. Deep hole drilling tests were carried out on 10 conventional gun steel specimens ($\varphi 29.35$ × 3000 $\mathrm{m}\mathrm{m}$). The results indicate that, when the minimum oil film gap of the Vibration-Damping and Deflection Correction Device is 0.08$\mathrm{m}\mathrm{m}$, the axis deviation range is 0.27~0.45$\mathrm{m}\mathrm{m}$, with a surface roughness of 0.589 to 0.677$\mathsf{\mu }\mathrm{m}$. Compared to similar conditions without the device, these represent reductions of 55~73% and 47.07~53.95%, respectively. It allows for a reduction of over 10% in blank material allowance and an increase of 5–15% in tool feed rates. Full article

The increasing penetration of renewable energy, such as photovoltaic generation, makes it essential to enhance power system dynamic performance through improved grid-forming control strategies. In the grid-forming control system, the virtual synchronous generator control (VSG) is currently widely used. However, the inertia (J) and damping (D) in the traditional VSG are fixed values, which can result in large overshoots and long adjustment times when dealing with disturbances such as load switching. To address these issues, this paper proposes an adaptive virtual synchronous generator (VSG) control strategy for grid-side inverters, which is accomplished by adaptively adjusting the VSG’s inertia and damping. Firstly, we established a photovoltaic-storage VSG grid-forming system; here, the photovoltaic power is boosted through a DC-DC converter, and the energy storage is connected to the common DC bus through a bidirectional DC-DC converter. We analyzed how J and D shape the system’s output characteristics. Based on the power-angle characteristic curve, the tanh function was introduced to design the control function, and a JD collaborative adaptive control (ACL) strategy was proposed. Finally, simulation experiments were conducted under common working conditions, such as load switching and grid-side voltage disturbance, to verify the results. From the results shown, the proposed strategy can effectively improve the response speed of the system, suppress system overshoot and oscillation, and, to a certain extent, improve the dynamic performance of the system. Full article

This paper presents the development of a 224-channel radio frequency (RF) receiver operating in the 0.6–1.8 GHz band, intended for a wide-field phased array radio telescope. The system employs a cost-effective architecture that combines the flexibility of phased array feeds with the low-cost characteristics of reflector systems, enabling high gain, rapid scanning, and multi-beam observation. The receiver achieves low-noise amplification, dynamic gain control, and filtering through a modular design. The system provides a total gain of 80–85 dB with a noise temperature of less than 35.1 K. Full article

The development of tunable optical filters in the mid-infrared (MIR) region is crucial for a variety of applications, including environmental monitoring, medical diagnostics, and communication systems. This paper presents the design, fabrication, and characterization of a novel Twisted Liquid Crystal (TLC) electro-tunable optical cavity filter for the MIR region 3–5 μm. The filter is based on a Fabry–Perot interferometer configuration, which includes a polarization-independent TLC to achieve electrical control over the filter’s transmission characteristics. Two distinct filters were fabricated, differing in their substrate materials: silicon and glass. The silicon-based filter demonstrated an impressive 80% transmission with a tuning range of ∼13.6 nm and ∼14.64 nm in two separate bands, achieved by varying the applied voltage from 0 to 20 V. In contrast, the glass substrate filter exhibited a slightly higher transmission of 82% with tuning ranges of ∼10.5 nm and ∼7.2 nm across the spectral band when the voltage was adjusted from 0 to 27 V. Experimental validation showed a strong alignment between the simulations and results, demonstrating the feasibility of integrating tunable liquid crystals into mid-infrared optical cavities. This advancement highlights their potential for applications that require precise and dynamic control of the mid-infrared spectrum. Full article

With the rapid development of solar thermal power generation technology, the structural stability of the dish solar concentrator system under complex wind environments has become a critical limiting factor for its large-scale application. This study investigates the flow field distribution and structural response under fluctuating wind loads using computational fluid dynamics (CFD). A three-dimensional model was developed and simulated in ANSYS Fluent under varying wind angles and speed cycles. The results indicate that changes in the concentrator’s orientation significantly influence the airflow field, with the most adverse effects observed at low elevation angles (0°) and an azimuth angle of 60°. Short-period wind loads (T = 25 s) exacerbate transient impact effects of lift forces and overturning moments, markedly increasing structural fatigue risks. Long-period winds (T = 50 s) amplify cumulative drag forces and tilting moments (e.g., peak drag of −73.9 kN at β = 0°). Key parameters for wind-resistant design are identified, including critical angles and period-dependent load characteristics. Full article

To address the challenges in concrete vibration during the construction of concrete-faced rockfill dams, this study proposes a multi-objective-driven lightweight and high-frequency vibrating robotic arm (VRA). The proposed system aims to improve adaptability and performance under harsh site conditions, such as inclined slab surfaces and confined rebar layouts. Based on the geometric structure and task characteristics of the VRA, a multi-objective topology optimization framework was established, integrating compromise programming and average frequency strategies. This method simultaneously achieves mass reduction, stiffness enhancement, and modal frequency improvement to avoid resonance during high-frequency operations. The workspace of the VRA was verified using kinematic modeling and Monte Carlo sampling, and a critical physical posture—where the arm is fully extended horizontally, producing maximum span and joint loads—was identified to extract dynamic load boundaries. Finite element analysis was then conducted under worst-case conditions, and the optimization results were validated by modal analysis and flexibility metrics. The optimized VRA demonstrated substantial improvements in structural performance, reducing overall mass, lowering flexibility, and increasing modal frequencies. The proposed framework provides a transferable approach for designing high-frequency robotic arms in vibration-intensive scenarios, supporting intelligent construction in concrete-faced rockfill dams and similar complex environments. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article