Search Results (5,420)

Granite formations provide suitable geological conditions for building gravity dams. However, the presence of intruding granite creates a fractured zone. The interaction of this fractured zone with structural planes and faults can create geological conditions that are unfavorable for the anti-sliding stability of gravity dams. This paper identifies the dominant structural planes that affect the anti-sliding stability of dams by studying the three-dimensional intersection relationships between groups of structural planes, faults, and fracture zones. The three-dimensional distribution and occurrence of the dominant structural planes directly impact the anti-sliding stability and sliding failure mode of gravity dams. Through comprehensive field investigations and systematic analysis of engineering geological data, the spatial distribution characteristics of structural planes and fracture zones were quantitatively characterized. Subsequently, the potential for deep-seated sliding failure of the gravity dam was rigorously evaluated and conclusively dismissed through application of the rigid body limit equilibrium method. It was established that the sliding mode of the foundation of the dam under this combination of structural planes is primarily shallow sliding. Additionally, based on the engineering geological data of the area around the dam, a three-dimensional finite element numerical model was developed to analyze stress–strain calculations under seepage stress coupling conditions and compared with calculations made without considering seepage stress coupling. The importance of seepage in the anti-sliding stability of the foundation of the dam was determined. The research findings provide engineering insights into enhancing the anti-sliding stability of gravity dams in granite distribution areas by (1) identifying critical structural planes and fracture zones that control sliding behavior, (2) demonstrating the necessity of seepage-stress coupling analysis in stability assessments, and (3) guiding targeted reinforcement measures to mitigate shallow sliding risks. Full article

During the process of oil and gas drilling, due to the existence of pores or micro-cracks, drilling fluid is prone to invade the formation. Under the action of hydration expansion of clay in the formation and liquid pressure, wellbore instability occurs. In order to reduce the wellbore instability caused by drilling fluid intrusion into the formation, this study proposed a method of forming a dynamic hydrogen bond cross-linked network weak gel structure with modified nano-silica and P(AM-AAC). The plugging performance of the drilling fluid and the performance of inhibiting the hydration of shale were evaluated through various experimental methods. The results show that the gel composite system (GCS) effectively optimizes the plugging performance of drilling fluid. The 1% GCS can reduce the linear expansion rate of cuttings to 14.8% and increase the recovery rate of cuttings to 96.7%, and its hydration inhibition effect is better than that of KCl and polyamines. The dynamic cross-linked network structure can significantly increase the viscosity of drilling fluid. Meanwhile, by taking advantage of the liquid-phase viscosity effect and the physical blocking effect, the loss of drilling fluid can be significantly reduced. Mechanism studies conducted using zeta potential measurement, SEM analysis, contact angle measurement and capillary force assessment have shown that modified nano-silica stabilizes the wellbore by physically blocking the nano-pores of shale and changing the wettability of the shale surface from hydrophilic to hydrophobic when the contact angle exceeds 60°, thereby reducing capillary force and surface free energy. Meanwhile, the dynamic cross-linked network can reduce the seepage of free water into the formation, thereby significantly lowering the fluid loss of the drilling fluid. This research provides new insights into improving the stability of the wellbore in drilling fluids. Full article

Pretesting and posttesting have long been implemented in classrooms as methods of testing and improving learning. Prior research has been mixed on the relative benefits of pretesting versus posttesting, with some studies finding pretesting to be more beneficial, and others finding posttesting to be more beneficial. True/False testing is a particularly easy-to-implement method and is regularly used in classrooms. However, relatively little is known about how these tests affect learning. Three experiments address the effects of true/false pre- and posttests on learning correct information and intrusion rates of false information. We find consistent benefits of both pretesting and posttesting but significantly higher intrusion rates for posttesting relative to pretesting, a finding that persisted despite inclusion of simple True/False feedback (Experiment 2) and substantive feedback (Experiment 3). Although the difference between pretesting and posttesting intrusion rates was still significant with the addition of substantive feedback, overall intrusion rates were greatly reduced. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

UAV-based inspections are an effective way to ensure railway safety and have gained significant attention. However, images captured during complex weather conditions, such as rain, snow, or fog, often suffer from severe degradation, affecting image recognition accuracy. Existing algorithms for removing rain, snow, and fog have two main limitations: they do not adaptively learn features under varying weather complexities and struggle with managing complex noise patterns in drone inspections, leading to incomplete noise removal. To address these challenges, this study proposes a novel framework for removing rain, snow, and fog from drone images, called WeatherClean. This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. It also employs a hierarchical multi-scale cropping strategy to enhance the recovery of fine noise and edge structures. Additionally, it incorporates a degradation synthesis method based on atmospheric scattering physical models to generate training samples that align with real-world weather patterns, thereby mitigating data scarcity issues. Experimental results show that WeatherClean outperforms existing methods by effectively removing noise particles while preserving image details. This advancement provides more reliable high-definition visual references for drone-based railway inspections, significantly enhancing inspection capabilities under complex weather conditions and ensuring the safety of railway operations. Full article

Deep coalbed methane (CBM) reservoirs are characterized by high hydrocarbon content and are considered an important strategic resource. Due to their inherently low permeability and porosity, horizontal well drilling is commonly employed to enhance production, with the length of the horizontal section playing a critical role in determining CBM output. However, during extended horizontal drilling, wellbore instability frequently occurs as a result of drilling fluid invasion into the coal formation, posing significant safety challenges. This instability is primarily caused by the physical intrusion of drilling fluids and their interactions with the coal seam, which alter the mechanical integrity of the formation. To address these challenges, interpenetrating and semi-interpenetrating network (IPN/s-IPN) hydrogels have gained attention due to their superior physicochemical properties. This material offers enhanced sealing and support performance across fracture widths ranging from micrometers to millimeters, making it especially suited for plugging applications in deep CBM reservoirs. A self-degradable interpenetrating double-network hydrogel particle plugging agent (SSG) was developed in this study, using polyacrylamide (PAM) as the primary network and an ionic polymer as the secondary network. The SSG demonstrated excellent thermal stability, remaining intact for at least 40 h in simulated formation water at 120 °C with a degradation rate as high as 90.8%, thereby minimizing potential damage to the reservoir. After thermal aging at 120 °C, the SSG maintained strong plugging performance and favorable viscoelastic properties. A drilling fluid containing 2% SSG achieved an invasion depth of only 2.85 cm in an 80–100 mesh sand bed. The linear viscoelastic region (LVR) ranged from 0.1% to 0.98%, and the elastic modulus reached 2100 Pa, indicating robust mechanical support and deformation resistance. Full article

The Varnous Mt. area in the northern Pelagonian Nappe is characterized by the intrusion of an Early Permian pluton, with its tectonic setting and igneous petrology well constrained in earlier studies. The metamorphic basement rocks warrant further detailed investigation due to their complex history. These rocks are polymetamorphosed, preserving a sequence of overprinting metamorphic and deformational events. The metapelitic rocks have undergone an initial, pre-Carboniferous regional metamorphism of unknown grade before or during Hercynian Orogeny, followed by a thermal metamorphic event associated with the intrusion of the Varnous pluton at 297 Ma. The assemblage attributed to this event is And + Crd + Bt + Ms (west), while the first assemblage identified at the eastern part is Sil + Bt + Gt. Additionally, three regional tectonometamorphic events occurred during the Alpine Orogeny. For the Alpine events, the assemblages are as follows: first, the development of St + Gt + Chl + Kfs + Pl + Qtz at 150–130 Ma; second, retrograde metamorphism of these assemblages with Cld + Gt + Ser + Mrg + Chl ± Sil (Fi) at 110–90 Ma; and finally, mylonitization of all previous assemblages at 90–70 Ma with simultaneous annealing and formation of Cld + Chl + Ms. Full article

The utilization of recycled concrete aggregate presents an effective solution for construction waste mitigation. However, concrete service in sulfate environments leads to sulfate ion retention in recycled aggregates, substantially impairing their quality and requiring modification approaches. A critical question remains whether traditional recycled aggregate modification techniques can effectively enhance the performance of these sulfate-containing recycled aggregates (SRA). Cement paste wrapping in various proportions was used in this investigation to enhance SRA. The performance of both SRA and modified aggregates was systematically assessed through measurements of apparent density, water absorption, crushing value, and microhardness. Microstructural analysis of the cement wrapping modification mechanism was conducted by scanning electron microscopy coupled with mercury intrusion porosimetry. Results revealed that internal sulfate addition decreased the crushing value and increased the water absorption of recycled aggregates, primarily due to micro-cracks formed by expansion. Additionally, the pores were occupied by erosion products, leading to a slight increase in the apparent density of aggregates. The performance of SRA was effectively enhanced by cement paste wrapping at a 0.6 water–binder ratio, whereas it was negatively impacted by a ratio of 1.0. The modifying effect became even more effective when 15% fly ash was added to the wrapping paste. Scanning electron microscopy observations revealed that the interface of SRA was predominantly composed of gypsum crystals. Cement paste wrapping greatly enhanced the original interface structure, despite a new dense interface formed in the modified aggregates. Full article

In this case study, we used the Delta Simulation Model II (DSM2) to study the salt balance at the land–water interface in the river delta of California’s San Francisco Estuary. Drainage, a source of water and salt for adjacent channels in the study area, is affected by channel salinity. The DSM2 approach has been adopted by several hydrodynamic models of the estuary to enforce water volume balance between diversions, evapotranspiration and drainage at the land–water interface, but does not explicitly enforce salt balance. We found deviations from salt balance to be quite large, albeit variable in magnitude due to the heterogeneity of hydrodynamic and salinity conditions across the study area. We implemented a procedure that approximately enforces salt balance through iterative updates of the baseline drain salinity boundary conditions (termed loose coupling). We found a reasonable comparison with field measurements of drainage salinity. In particular, the adjusted boundary conditions appear to capture the range of observed interannual variability better than the baseline periodic estimates. The effect of the iterative adjustment procedure on channel salinity showed substantial spatial variability: locations dominated by large flows were minimally impacted, and in lower flow channels, deviations between baseline and adjusted channel salinity series were notable, particularly during the irrigation season. This approach, which has the potential to enhance the simulation of extreme salinity intrusion events (when high channel salinity significantly impacts drainage salinity), is essential for robustly modeling hydrodynamic conditions that pre-date contemporary water management infrastructure. We discuss limitations associated with this approach and recommend that—for this case study—further improvements could best be accomplished through code modification rather than coupling of transport and island water balance models. Full article

The reliability of passive samplers in measuring volatile organic compounds (VOCs) in indoor air depends on whether the uptake rate is constant given the environmental conditions and sampler exposure duration. The first phase of this study evaluated the performance of charcoal-based, solvent-extracted passive samplers (e.g., Radiello^® 130 passive samplers with white diffusive bodies) over exposure periods ranging from 1 week to 1 year in a test house with known vapor intrusion (VI). Chloroform %Bias values exceeded the ±30% acceptance criterion after 4 weeks exposure. Benzene, hexane, and trichloroethylene (TCE) concentrations were within the acceptance criterion for up to three months. Toluene and tetrachloroethylene (PCE), the two least volatile compounds, demonstrated uniform uptake rates over one year. In the second phase of this study, testing of the longer exposure times of 6 months and 1 year were evaluated with three additional passive samplers: Waterloo Membrane Sampler^TM (WMS^TM), SKC 575 with secondary diffusive cover, and Radiello^® 130 passive samplers with yellow diffusive bodies. The SKC 575 and Radiello^® 130 passive samplers produced acceptable results (%Bias ≤ 30%) over the 6-month exposure period, while the WMS^TM sampler results favored petroleum hydrocarbon more than chlorinated solvent uptake. After the 1-year exposure period, the passive sampler performances were acceptable under specific conditions of this study. The results suggest that all three samplers can produce acceptable results over exposure time periods beyond 30 days and up to a year for some compounds. Full article

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

Recent studies on network intrusion detection using deep learning primarily focus on detecting attacks or classifying attack types, but they often overlook the challenge of attributing each attack to its specific source among many potential adversaries (multi-adversary attribution). This is a critical and underexplored issue in cybersecurity. In this study, we address the problem of attacker attribution in complex, multi-step network attack (MSNA) environments, aiming to identify the responsible attacker (e.g., IP address) for each sequence of security alerts, rather than merely detecting the presence or type of attack. We propose a deep learning approach based on Transformer encoders to classify sequences of network alerts and attribute them to specific attackers among many candidates. Our pipeline includes data preprocessing, exploratory analysis, and robust training/validation using stratified splits and 5-fold cross-validation, all applied to real-world multi-step attack datasets from capture-the-flag (CTF) competitions. We compare the Transformer-based approach with a multilayer perceptron (MLP) baseline to quantify the benefits of advanced architectures. Experiments on this challenging dataset demonstrate that our Transformer model achieves near-perfect accuracy (99.98%) and F1-scores (macro and weighted ≈ 99%) in attack attribution, significantly outperforming the MLP baseline (accuracy 80.62%, macro F1 65.05% and weighted F1 80.48%). The Transformer generalizes robustly across all attacker classes, including those with few samples, as evidenced by per-class metrics and confusion matrices. Our results show that Transformer-based models are highly effective for multi-adversary attack attribution in MSNA, a scenario not or under-addressed in the previous intrusion detection systems (IDS) literature. The adoption of advanced architectures and rigorous validation strategies is essential for reliable attribution in complex and imbalanced environments. Full article

Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained on such datasets. This issue is crucial given the application of such a model to actual internet traffic because high-performance measures obtained on datasets do not necessarily imply similar efficiency on the real traffic. We propose a procedure consisting of cross-validation using various sets sharing some standard traffic classes combined with the t-SNE visualization. We apply it to investigate four well-known and widely used datasets: UNSW-NB15, CIC-CSE-IDS2018, BoT-IoT, and ToN-IoT. Our investigation reveals that the high accuracy of a model obtained on one set used for training is reproducible on others only to a limited extent. Moreover, benign traffic classes’ generalizability differs from harmful traffic. Given its application in the actual network environment, it implies that one needs to select the data used to train the ML model carefully to determine to what extent the classes present in the dataset used for training are similar to those in the real target traffic environment. On the other hand, merging datasets may result in more exhaustive data collection, consisting of a more diverse spectrum of training samples. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

We present mmHSE, a two-stage framework for human skeleton estimation using dual millimeter-Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar signals. To enable data-driven model design and evaluation, we collect and process over 30,000 range–angle maps from 12 users across three representative indoor environments using a dual-node radar acquisition platform. Leveraging the collected data, we develop a two-stage neural architecture for human skeleton estimation. The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. A cross-modal attention fusion module is then used to generate initial estimates of 21 skeletal keypoints. The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. Experiments show that mmHSE achieves a Mean Absolute Error (MAE) of 2.78 cm. In cross-domain evaluations, the MAE remains at 3.14 cm, demonstrating the method’s generalization ability and robustness for non-intrusive human pose estimation from mmWave FMCW radar signals. Full article