Search Results (8,323)

Air pollution negatively impacts human health and environmental sustainability, particularly in areas with high industrial activity. This study comparatively evaluated deep learning-based models for estimating PM₁₀ and SO₂ pollutants in Dilovası and Ereğli (Turkey), industrial areas with high pollutant loads. The study utilized Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), an RNN–GRU stacked hybrid model, an attention-based hybrid model, and the proposed GRU–LSTM dual-path hybrid model. The proposed method consists of four main stages: data conversion into a time-series format, data preprocessing and feature generation, model architecture development, and model training and performance evaluation. The dataset consisted of 365 daily PM₁₀ and SO₂ observations obtained from the Air Monitoring Center for the Dilovası and Ereğli monitoring stations. Model performance was evaluated using the coefficient of determination (R²), training time, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) metrics. The findings showed that the hybrid models provided higher accuracy compared to the single-track models. Specifically, the proposed GRU–LSTM dual-path hybrid model produced the highest R² and lowest error values for both pollutant parameters in both the Dilovası and Ereğli regions. In Dilovası, this model achieved R² = 0.97 for SO₂ and R² = 0.96 for PM₁₀; in Ereğli, it reached R² = 0.92 for SO₂ and R² = 0.98 for PM₁₀. Thus, it has been shown that the GRU–LSTM dual-path hybrid model, which models short-term and long-term temporal dependencies in parallel, is an effective and reliable method for air pollutant forecasting in industrial areas. These findings demonstrate the potential of the proposed model to support air quality monitoring, early warning systems, and environmental decision-making in industrial regions. Full article

Plant-based food systems increasingly rely on heat-induced gelation of protein–starch mixtures, yet no focused synthesis has linked legume protein composition to mixed gel structure and function. This review critically analyses heat-induced gelation mechanisms in legume protein–starch systems, using the legumin-to-vicilin (L:V) ratio and starch origin as integrating design parameters. Legume storage proteins range from legumin-rich faba bean and Lupinus angustifolius, which form dense, disulfide-stabilised networks with high storage moduli, to vicilin-dominated mung bean, which produces weaker gels reliant on starch reinforcement. Pulse starches, characterised by high amylose content (24–45%), C-type crystallinity, and rapid amylose retrogradation upon cooling, act as a parallel gel-forming phase whose contribution scales inversely with protein network strength. Four protein–starch interaction modes, namely segregative phase separation, water competition, granule filler effects, and molecular complexation, jointly determine microstructure and rheological behaviour. A three-axis compositional framework defined by the L:V ratio, starch amylose content, and protein-to-starch ratio maps the gel design space. Variables favouring plant-based meat analogue performance, including high elastic modulus, yield stress, and hardness, are systematically opposed by dysphagia food requirements, including low yield stress, adequate lubrication, and soft fracture. This demonstrates that both application domains traverse the same compositional space in opposite directions. Critical research gaps include chickpea and lentil performance in meat analogue systems, mechanistic modelling of protein-matrix-mediated starch digestibility, and retrogradation kinetics during food storage. Full article

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by β-amyloid (Aβ) accumulation and a breakdown of mitochondrial homeostasis. Vitamin K2 (VK2) has emerged as a potential neuroprotective agent, yet the specific molecular cascades linking its intervention to the restoration of mitochondrial integrity remain poorly understood. This study utilizes an AD Drosophila model to investigate the efficacy of VK2 and elucidates its multidimensional regulatory mechanisms. Behavioral analysis showed that VK2 significantly rescued locomotor impairments, improving both vertical climbing and horizontal walking performance. Crucially, VK2 intervention achieved a systemic rescue of mitochondrial health: transmission electron microscopy (TEM) confirmed the preservation of mitochondrial ultrastructure and cristae density, while biochemical assays demonstrated a robust recovery of bioenergetic markers, including ATP levels and the NAD⁺/NADH ratio. Furthermore, VK2 treatment stabilized the mitochondrial membrane potential (MMP) and effectively attenuated the accumulation of reactive oxygen species (ROS). To identify the molecular drivers of this recovery, an unbiased integration of human clinical transcriptomic data and network pharmacology prioritized the EGFR-Ras-ERK signaling axis as a central hub. In vivo validation confirmed that VK2 suppresses the pathological overactivation of this cascade. VK2 reduced EGFR phosphorylation in parallel with the effects observed for the EGFR inhibitor Gefitinib. Collectively, our findings show that VK2 ameliorates locomotor deficits and mitochondrial dysfunction in Aβ42-expressing flies and that these effects are associated with suppression of the EGFR-Ras-ERK signaling axis. Further studies are required to establish direct target engagement and pathway causality. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article

Bipolar vessel sealing systems are widely used in surgery, yet their effectiveness varies depending on the histological composition of the target vessel. In particular, the influence of elastin on seal stability is not well understood. Porcine carotid arteries, which show a pronounced proximal–distal elastin gradient, provide an ideal model for systematic analysis. In this study, fresh porcine carotid arteries were divided into three segments based on vessel diameter (<5 mm, 5–7 mm, >7 mm). Histological EvG staining was used to quantify elastin and collagen content. All vessels (n = 8 per group) were sealed using a bipolar marSeal^® 5 plus device, followed by burst pressure testing and peel force measurements. Elastin content increased significantly from peripheral to central segments (9% → 25% → 42%; p < 0.001), while collagen content remained constant (22 ± 2%). In parallel, seal stability decreased markedly: burst pressures dropped from 723 mmHg to 240 mmHg and to 31.5 mmHg (p < 0.001). Peel forces showed the same trend (1.75 ± 0.07 N → 0.65 ± 0.03 N → 0.26 ± 0.11 N; p < 0.001). Wall thickness showed no proportional relationship to seal quality. Interestingly, the sealing performance of bipolar systems seems to be greatly influenced by the histological structure of the vessel wall. A high elastin content—rising from 9% to 42% along the carotid artery—was associated with a reduction in burst pressure and peel strength. These findings highlight the need to consider tissue composition when selecting sealing methods and support the development of adaptive energy delivery technologies. Full article

Shoulder injuries due to labor-related lifting tasks are widespread in manufacturing and logistics companies. Prolonged shifts and repetitive motions lead to muscle fatigue, significantly elevating the risk of both acute accidents and chronic musculoskeletal disorders. Many passive exoskeletons which use springs to provide lifting assistance have been commercialized, and many active exoskeletons have been researched. The drawback to passive exoskeletons is the larger the lifting force that they produce, the larger the force required to lower the arms. This contributes to tiring the user. Conversely, active exoskeletons require substantial energy to provide meaningful torque. Furthermore, they pose a safety risk; a sudden power failure could result in an instantaneous loss of support, potentially causing the user to drop a heavy load and sustain injury. This research project proposes a hybrid exoskeleton with a parallel elastic actuator that uses a motorized helical actuator which can be tuned to improve lifting performance. This paper evaluates the kinematics and statics of the proposed exoskeleton, details the design and implementation of the electrical control system, shows mechanism optimization of the mechanical advantage profile, and validates the concept through the construction and experimental testing of a functional prototype. Full article

With the continuous expansion and increasing topological complexity of modern power grids, achieving high-precision fault localization under sparse measurement conditions has become a core challenge in the operation and maintenance of smart grids. Existing methods based on deep graph networks generally face complex spatiotemporal coupling between fault types and fault localization. To address this, this paper proposes a recognition method for fault localization based on sparse measurements and spatial configuration. A reinforcement learning algorithm with a Checking-Action mechanism, termed DQN-CA, is adopted to identify optimal PMU installation buses. In parallel, a dual-path spatio-temporal multi-task graph convolutional network, termed ST-MTGCN, is developed to decouple fault-type-related features from topology-sensitive fault-Localization features through a global feature dimensionality-reduction path and a K-hop spatial graph convolution path, thereby accomplishing the fault localization task. Experimental results on the IEEE 39-bus system show that ST-MTGCN achieves 99.68% fault type accuracy, 89.94% fault localization accuracy, and 88.62% accuracy for 185 joint fault type-Localization classes under the OPT13 configuration. Comparative experiments, PMU configuration sensitivity analysis, and ablation studies further demonstrate the effectiveness of the proposed framework under sparse measurement conditions. Full article

This study investigates a polymorphic underwater vehicle designed to combine long-range cruising with stable underwater operation, reducing dependence on surface support vessels. By introducing a foldable polymorphic structure, the vehicle can switch configurations, including serial and parallel. However, underwater environments often contain obstacles, and the vehicle may collide with them during the folding process. To prevent collisions between the vehicle and surrounding obstacles during the folding process, this paper investigates the motion envelope of the vehicle and examines how motion parameters and mass distribution influence the motion envelope. In this work, the polymorphic underwater vehicle is modeled as a multibody system operating under a neutrally buoyant condition. Based on space robot modeling methodologies and the linear and angular momentum theorems, the equations of motion of the polymorphic underwater vehicle are derived and verified using the Adams software 2020. In summary, the present study establishes a clear relationship between motion parameters, mass distribution, hydrodynamic effects, and the resulting motion envelope of a polymorphic underwater vehicle. The results show that the attitude of the vehicle during the folding process is uniquely determined by the joint angles, and a larger relative speed between the outer and inner folding motions produces a more compact attitude during the folding process. Mass distribution further influences the motion envelope of the vehicle: concentrating mass toward the center of the vehicle shifts the overall motion envelope upward, whereas concentrating mass toward both ends of the vehicle shifts it downward. In addition, hydrodynamic forces introduce an upward velocity component of the vehicle in the vertical direction during the folding process, which leads to an upward shift in the overall center of mass of the vehicle. Full article

This work presents the kinematic analysis of a redundant planar parallel manipulator within the framework of screw theory. The main contribution of this work is the introduction and kinematic modeling of a novel redundant planar parallel manipulator topology composed exclusively of revolute joints. The proposed architecture is motivated by the search for structurally simple mechanisms with favorable analytical properties for screw-theoretic formulation and potential applications in robotic systems requiring compact and efficient planar motion. For completeness, the displacement analysis is included. Thanks to the simple topology of the otherwise complex mechanism, the inverse–forward displacement problem is resolved through straightforward quadratic equations. The velocity input–output relationship is derived without reliance on passive joint rate velocities, and the acceleration input–output equation is obtained independently of passive joint rate accelerations. These simplifications are achieved by exploiting reciprocal line properties. Numerical examples are provided to illustrate the robustness and effectiveness of the proposed kinematic analysis method across the main topics addressed in this contribution. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

This study investigates the potential, limitations, and pedagogical implications of applying a parallel multi-model AI evaluation workflow, using ChatGPT, DeepSeek, and Uizard, to assess student design projects in higher education. Because design assessment involves both formal criteria and subjective creative interpretation, the study first established a human expert baseline based on three independent university professors. The human inter-rater reliability was low to moderate, with a mean pairwise Spearman’s ρ of 0.36 and Cronbach’s α of 0.60 for packaging design, and ρ of 0.43 and α of 0.69 for web design. This finding is central to the study, as it shows that the human benchmark in creative design assessment is itself variable and interpretive. Against this baseline, AI–human alignment remained limited and task-dependent. For packaging design, the AI ensemble showed only a weak positive association with the human expert baseline (Spearman’s ρ = 0.30, p = 0.031), which should be interpreted cautiously given the Bonferroni-adjusted significance threshold used in the study. For web design, no significant AI–human association was observed. Qualitative analysis of AI-generated rationales identified recurring limitations, including hallucination, aesthetic shield effects, and missed context, where visually polished work was rewarded despite deeper conceptual or structural weaknesses. The findings suggest that current AI systems can provide useful formative feedback on visible formal features, but they are not reliable as autonomous grading tools for complex creative work. AI-assisted assessment is therefore best understood as a supervised formative support mechanism, while final evaluation should remain grounded in human pedagogical judgment. Full article

Platform algorithms are widely used to personalize content and organize users’ everyday social media experiences. Yet they may also become objects of resistance when algorithmic recommendations are perceived as intrusive, repetitive, or difficult to escape. Drawing on the critical theory of technology, this study develops a parallel mediation model to explain why users resist algorithm-driven social media platforms. Focusing on algorithmic power and algorithmic technicality as two perceived characteristics of platform algorithms, the model examines whether these perceptions are associated with algorithmic resistance through fairness evaluations and negative emotions. Based on survey data from users of Chinese algorithm-driven social media platforms, the results show that both algorithmic power and algorithmic technicality are associated with stronger algorithmic resistance through lower fairness evaluations and stronger negative emotions. These findings suggest that algorithmic resistance is not merely a response to inaccurate or opaque recommendations, but also reflects users’ reactions to algorithms experienced as systems of platform control and data-driven inference. By identifying fairness evaluations and negative emotions as parallel cognitive and affective pathways, this study shifts attention from algorithmic acceptance to algorithmic resistance and provides a more critical understanding of user agency in human–algorithm relations. Full article

Buried parallel pipelines are increasingly common, and unlike single-line systems, adjacent pipelines exhibit mutual interactions. This study investigated their behavior under symmetric and asymmetric conditions, considering empty pipeline, water filling, and normal working, as well as the effects of diameter-to-thickness ratio, spacing, and burial depth. The results indicate that the pipeline–soil interaction differs significantly from single pipelines and is highly dependent on working conditions. Under symmetric conditions, vertical and horizontal deformations differ by 3.0–4.3 mm; contact pressure is nearly circular under empty pipeline and water filling conditions, but elliptical under normal working condition; tangential force follows a cloverleaf pattern; and soil pressure at the pipeline top and vertical soil support lose axial symmetry, with unequal horizontal resistance on either side. Under asymmetric operations, the largest differences occur under the water filling—normal working condition, with the soil pressure at the top of the #1 pipeline being 36.7% lower than that of the #2 pipeline. Moreover, smaller diameter-to-thickness ratios reduce sensitivity to working conditions, while greater burial depth linearly increases deformation and soil pressure, amplifying inter-pipeline differences. Pipeline spacing has only limited effects. These findings reveal the mechanical properties of parallel pipelines under various operating scenarios, providing a reference for the design of multi-pipeline systems. Full article

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. This review provides a comprehensive and critical synthesis of AI-assisted SERS platforms for cardiovascular diagnostics, integrating advances in plasmonic materials, biomolecular recognition, and intelligent spectral analysis within a unified framework. It further examines key translational barriers, including data variability, model interpretability, and scalability, and outlines future directions for developing standardized, edge-deployable, and clinically validated SERS-AI systems. Full article