Search Results (1,038)

A unique V-grooved solar air collector was assessed experimentally and numerically to improve thermohydraulic performance. Experiments were conducted on the collector in Baghdad, Iraq, from 23 March to 3 April 2025, encompassing a mass airflow rate spanning 0.02 to 0.09 kg/s. The proposed design integrates wire mesh layers solely within the upper triangular channels, while the lower triangular channels remain unobstructed. A novel three-dimensional steady-state CFD model was originally developed for evaluating the thermohydraulic performance of a V-grooved solar air heater with and without wire mesh layers. The coupling between fluid flow and heat transfer models was implemented via the finite element method through the COMSOL Multiphysics program. The developed model for the collector incorporating wire mesh layers was validated experimentally, demonstrating strong agreement, with the greatest recorded root mean square error of 1.3597 °C for the outlet air temperature. The findings indicate that the enhanced collector attains a thermal efficiency enhancement of around 1.5 to 2.5 times relative to the baseline design, depending on operating conditions. The thermal efficiency attained levels up to 80%, whereas the thermohydraulic efficiency surpassed 70% at a moderate mass airflow rate. Despite the increase in pressure drops, the performance remained exceptional owing to the substantial enhancement in heat gain supported by the unique architecture. The results illustrate that the proposed design presents a viable solution for efficient solar air heating and can be adequately incorporated into near-zero-energy buildings. Full article

As traditional Korean rice cake products are being developed to reflect changing consumer preferences and market needs, Heugimja Tteok remains a culturally rooted product with functional characteristics. This study empirically examined the relationships among consumers’ choice attributes, purchase behavior, and marketing strategy for Heugimja Tteok, aiming to suggest directions for expanding the traditional rice cake market. Online survey data from 342 adults in the Republic of Korea were analyzed using IBM SPSS Statistics 26.0. Significant positive correlations were identified between choice attributes and both purchase behavior and marketing strategy. Diversity, Quality, and Image were significant positive predictors of marketing strategy, and the regression model explained 59.6% of its variance. These findings indicate that product diversification, quality improvement, functional promotion, distribution channel expansion, standardized production management, and brand image enhancement should be integrated to strengthen the market competitiveness of Heugimja Tteok. By providing empirical evidence on consumer responses to a traditional and functional rice cake product, this study contributes to discussions on the contemporary expansion of the Tteok industry within changing consumer preferences and food market conditions in Korea. Full article

Accurate calibration of Manning’s roughness coefficients is essential for reliable river hydraulic modelling, flood prediction, and water resources management, yet conventional calibration methods often struggle with high-dimensional parameter spaces and nonlinear hydraulic interactions. This study proposes and evaluates a hierarchical artificial intelligence framework for the inverse calibration of spatially distributed Manning’s roughness coefficients across three channel zones (left bank, main channel, and right bank), using a 48 km reach of the Tigris River in Baghdad as a case study. A one-dimensional HEC-RAS hydraulic model based on 30 measured cross-sections generated 18,360 simulations by systematically varying Manning’s roughness coefficients (0.02–0.045). Three calibration strategies were evaluated: (i) a simple Gradient Boosting Regression model based on a weighted composite roughness formula, (ii) conventional machine learning models (Random Forest, Gradient Boosting, and Multi-Layer Perceptron), and (iii) a deep learning framework combining a three-layer neural network (64 → 32 → 16 neurons), Differential Evolution optimisation, and cubic spline interpolation. Calibration accuracy increased with model complexity. The deep learning framework achieved the best performance, reducing the root mean square error by 96.6% (from 1.202 to 0.041 m), with R² = 0.992 and negligible bias (−0.004 m). Conventional machine learning models produced spatially variable Manning’s roughness distributions, with the calibrated main-channel roughness (mean n = 0.0512) being 34.0–57.5% higher than the corresponding bank values. The proposed framework provides an effective approach for calibrating spatially distributed roughness coefficients in one-dimensional hydraulic models, with strong potential to improve river hydraulic simulations and support future applications to flood modelling. Full article

Alpha-latrotoxin (α-LTX) is the principal vertebrate-specific neurotoxin in widow spider (Latrodectus) venom and a powerful presynaptic secretagogue. Although its receptors, pore-forming activity and stimulation of neurotransmitter release have been studied extensively in central and motor neurons, its relationship to the severe, persistent pain of latrodectism remains poorly understood. This focused review re-examines α-LTX from a nociceptive perspective. The available evidence supports a model in which α-LTX binds adhesion G protein-coupled receptor L1 (ADGRL1; latrophilin-1) and neurexin-1α on susceptible sensory neurons; inserts a large cation-permeable pore; and promotes membrane depolarisation, calcium entry and release of pain-associated neuropeptides. The transcript-level gene expression of α-LTX receptor genes in dorsal root ganglion neurons and evidence of toxin-evoked neuropeptide release provide a molecular basis for direct nociceptor activation, although functional validation in defined nociceptor subclasses remains necessary. A comparison with the nociceptive ion channels transient receptor potential vanilloid 1 (TRPV1) and transient receptor potential ankyrin 1 (TRPA1) highlights their convergence on calcium-dependent sensory excitation, while distinguishing α-LTX from toxins that modulate endogenous channels. We propose that the pain of latrodectism is a composite state in which direct nociceptor activation complements muscle spasm and tissue-derived signalling, positioning α-LTX as a distinctive probe of pain pathways. Full article

Industrial water treatment plants are increasingly dependent on cyber–physical systems (CPS) and automated control processes for their operational safety and efficiency. However, the embedding of digital control networks exposes these critical infrastructures to sophisticated cyber–physical attacks, including malicious tampering with chemical dosing units and physical actuators. This paper proposes a robust, AI-enhanced anomaly detection framework designed to identify multi-stage malicious activities in water treatment systems using real-world industrial datasets. The proposed system is developed and validated on the Secure Water Treatment (SWaT) dataset, which contains multivariate sensor and actuator time-series data collected from a fully operational physical testbed under both normal operations and targeted cyber–physical attacks. First, high-frequency sensor noise is filtered, and cross-channel measurement reliability is maximized using a Kalman filter-based sensor fusion module. Subsequently, the fused-state vector is analyzed using an unsupervised Isolation Forest algorithm optimized for high-dimensional boundary isolation. To eliminate false negatives caused by stealthy, low-amplitude data injections that bypass purely statistical models, a deterministic, rule-based verification layer derived from physical process control logic is integrated. By integrating a discrete linear Kalman filter with an unsupervised Isolation Forest and deterministic physical rules, the framework effectively suppresses high-frequency sensor noise, achieving a 67.8% reduction in root mean square error (RMSE), while maintaining high detection accuracy across complex industrial attack scenarios. Experimental results demonstrate that the proposed hybrid framework yields superior detection capability, achieving a Precision of ≈95%, a Recall of ≈93%, a scenario-level F1-score of 94.1 % (alongside a sample-level F1-score of 21.5 %) and an edge inference latency of 0.6 ms, effectively demonstrating its suitability for deployment within simulated real-time industrial edge computing environments. The findings further confirm that combining statistical machine learning, state-space sensor fusion, and invariant physical process logic provides a resilient defense paradigm for securing critical industrial infrastructure against modern cyber–physical threats. Full article

Aquaporins (AQPs) are key channels for water and small molecule transport across membranes, and represent early targets of stress signaling pathways in plants. Salicornia europaea is a typical halophyte adapted to saline-alkaline and nutrient-poor soils. However, studies on AQPs from S. europaea and their precise roles in abiotic stress responses remain limited. In this study, SeTIP2;3 and SePIP1;4 were cloned from S. europaea. Bioinformatics analysis confirmed that they belong to the TIP and PIP subfamilies of AQPs, respectively. Subcellular localization analysis revealed that SeTIP2;3 and SePIP1;4 were localized to the tonoplast and plasma membrane, respectively. Under polyethylene glycol (PEG) treatment, SeTIP2;3 showed increased transcription levels in the roots, and SePIP1;4 showed varied degrees of increased transcription in both roots and shoots. Heterologous expression of both genes in Arabidopsis thaliana was performed to investigate their functions. Under germination assays, the overexpression (OE) lines exhibited faster germination rates and longer primary roots (1.2–1.4 cm) compared to the wild-type (WT) plants (1.2 cm). Under mannitol-simulated drought stress, the transgenic lines showed reduced MDA content (23.8–28.7 nmol/g), increased accumulation of proline (2.5–3.8 mg/g) and soluble sugars (39.4–49.3 mg/g), and higher chlorophyll content (0.7–0.9 mg/g) than WT plants (30.9 nmol/g; 2.4 mg/g; 30.6 mg/g; 0.7 mg/g). Furthermore, under natural drought conditions, the OE lines displayed lower water loss rates and higher survival rates. Collectively, the findings of this study provide a theoretical basis for the potential application of Salicornia aquaporins in enhancing drought tolerance. Full article

Large LNG storage tanks are key shore-based facilities linking maritime transport with receiving-terminal storage and send-out. Their continuous monitoring needs anomaly identification, preservation of physically meaningful deviation directions, and association of abnormal states with potential failure modes. This study proposes an interpretable anomaly-diagnosis and root-cause-analysis framework integrating a signed-deviation-calibrated multi-branch probabilistic autoencoder (SDC-MPAE) with mechanism-consistent evidence reasoning (MCER). SDC-MPAE learns continuous signed states from normal monitoring data through channel-adaptive signed perturbations, normal-manifold-anchored reconstruction, and signed-deviation calibration. MCER combines the mechanism-consistent relation matter-element model (MCREM) with distribution-aware uncertainty-retained Dempster–Shafer fusion (DUR-DS) to map signed states and fuse absolute mechanism support, candidate distributions, and inter-source conflict. A 200,000 m³ full-containment LNG storage tank in Tangshan, China, is the engineering case; its actual temperature, pressure, and liquid-level monitoring data support model training and testing, while parameterized abnormal test scenarios are independently constructed from representative failure mechanisms. SDC-MPAE achieved an accuracy of 0.9704 and a PR AUC of 0.9850, while MCER achieved Top-1 and Top-3 root-cause accuracies of 0.946 and 0.985, respectively, with an MRR of 0.971. Ablation and robustness analyses confirmed improved directional representation, candidate discrimination, and uncertainty expression, providing a feasible pathway for interpretable diagnosis of LNG receiving and storage infrastructure. Full article

Human body static electricity is one of the major hazards in electrostatic-sensitive industrial environments. Existing noncontact measurement methods often rely on explicit physical modeling and generally require accurate prior knowledge of the sensor layout, target motion state, and scene geometry, which limits their applicability in complex dynamic scenarios. To address this issue, this paper proposes a neural network-based data-driven method for estimating human body electrostatic potential from noncontact electrostatic measurements. The proposed method uses four-channel noncontact electrostatic sensor signals as inputs and the synchronously measured reference body potential as the target output. Sixteen neural network architectures, including recurrent neural networks, convolutional neural networks, attention-based networks, and hybrid models, are systematically evaluated. The Gray Wolf Optimizer is further used to optimize key hyperparameters of each model. A 5 m × 5 m experimental scene is established, and 36 groups of synchronized time-series signals are collected from three subjects under two motion states. Training and validation datasets are constructed using a sliding time-window method, and the effects of model architecture, window length, and subject–motion condition on inversion performance are analyzed. The results show that the proposed method can effectively estimate human body electrostatic potential from noncontact measurements. Among the evaluated models, Trans-LSTM achieves the best performance. With a window length of 1 s, it obtains a validation normalized root-mean-square error of approximately 0.139 and a coefficient of determination of approximately 0.72. Compared with a representative existing method under the same coverage area and sensor layout, the proposed approach reduces the NRMSE from 0.22 to 0.139. These results demonstrate that the proposed method provides a feasible approach for remote, real-time, and noncontact monitoring of human body electrostatic potential in complex environments. Full article

This study develops a SCHISM-based hydrodynamic model and an offline Lagrangian virtual-particle workflow for the Zadar Channel, a geometrically complex island–mainland passage in the eastern Adriatic. Independent hourly observations from the MP Zadar tide gauge operated by the Hydrographic Institute of the Republic of Croatia (HHI) were used to evaluate the modelled free-surface response. After exclusion of the first 24 h ramping period, 192 matched hourly pairs gave a Pearson correlation of 0.913, a mean bias of 0.012 m, a mean absolute error of 0.051 m, and a root-mean-square error of 0.070 m; cross-correlation was maximized at zero lag. The model reproduced the timing of the observed oscillations but underestimated their amplitude, with simulated and observed standard deviations of 0.117 and 0.157 m, respectively. The adopted unstructured mesh contains 16,962 triangular elements and 9081 nodes. In four 24 h particle-sensitivity tests, maximum reach ranges from 8.4 to 14.2 km; a 15-fold change in horizontal diffusivity affects reach less than sampling a lower model layer, which reduces reach by 32.8%. In the June 2025 event calculation, cumulative numerical shoreline contact increases from zero to all 1000 particles. The approximately 4 km Copernicus regional product masks the narrow interior passages and is therefore used only to assess spatial representativeness, not to validate channel currents. The tide-gauge comparison supports the modelled sea-level response and its timing at one station, but does not constitute direct validation of local current velocities. The reported trajectories are current-driven passive-particle diagnostics; wave–current coupling, Stokes drift, and material-specific fate processes are not represented. Full article

Floating offshore wind turbines (FOWTs) experience coupled aero–hydro–servo-elastic excitations that produce structurally distinct kinematic responses at different measurement heights. While field monitoring campaigns increasingly deploy multi-level inertial sensors, the quantitative spectral partitioning of response energy across measurement levels and its relationship to operational and environmental conditions remain poorly characterised for operational FOWTs. This study presents a systematic multi-level spectral decomposition of operational FOWT structural response using synchronised tower-base and nacelle strapdown inertial measurements acquired at 8 Hz over a six-day campaign (18–23 April 2023) at a semi-submersible FOWT in Chinese coastal waters. Six kinematic channels—three translational acceleration components and three translational velocity components—from each sensor are decomposed into four physically defined frequency bands: drift (0.005–0.05 Hz), wave (0.05–0.30 Hz), structural (0.30–0.50 Hz), and rotor (0.50–0.80 Hz). Three derived scalar metrics—band energy ratio (BER), Wave-to-Structural Dominance Ratio (WSDR), and Structural Amplification Factor (SAF)—are defined, with their complete computation specifications and parameter sensitivity analysis provided to ensure reproducibility. Across 36 ten-minute windows spanning diverse conditions (mean wind 4.2–12.1 m/s, $H_s$ 0.8–3.1 m), results reveal a pronounced and consistent spectral separation: the tower base is strongly wave-dominated (${\mathrm{BER}}_{\mathrm{wave}}$ = 75.9%, coefficient of variation CV = 15.0% across days), whereas the nacelle exhibits substantially elevated structural-band energy (${\mathrm{BER}}_{\mathrm{struct}}$ = 10.3%, 4.72-fold amplification relative to tower base, 95% CI [3.63, 5.81]) and rotor-band energy (11.8%, 3.77-fold amplification). The WSDR at the tower base (mean 200.9, 95% CI [101.4, 300.4]) exceeds that at the nacelle (mean 48.4, 95% CI [13.1, 83.7]) by a factor of 4.1×. One-way analysis of variance (ANOVA) reveals that nacelle structural-band BER is significantly modulated by SCADA operational regime ($F=5.20$, $p=0.024$, ${\eta }^2=0.16$) and by significant wave height ($p=0.031$), while tower-base wave-band BER is primarily driven by $H_s$ ($p=0.018$). Comparison with baseline features—root-mean-square acceleration, spectral peak frequency, and traditional broad-band energy ratio—demonstrates that the band-resolved BER provides finer discrimination between excitation mechanisms than aggregate metrics. Importantly, no structural damage events occurred during the monitoring period; therefore, the reported stability of these features is interpreted as a baseline characterisation under normal operational conditions, which could support future anomaly detection efforts but does not constitute validation of damage detection capability. A comprehensive limitations assessment is provided, covering single-turbine validation, frequency-band sensitivity, regime sample imbalance, and generalisability constraints. Full article

Rapid and non-destructive assessment of sweet potato quality is important for postharvest management, processing suitability evaluation, and market quality control. In this study, a 12-channel visible/near-infrared (Vis/NIR) spectral sensing system was applied to predict multiple physicochemical quality attributes of postharvest sweet potatoes. Sixty independent sweet potato storage roots were measured at three representative positions, producing 180 position-specific observations; measurements from the same root were retained within the same validation group. The measured attributes included dry matter content (DMC), starch content (SC), soluble solids content (SSC), and the CIE 1976 L*a*b* (CIELAB) color coordinates L*, a*, and b*. Four spectral treatment conditions, including original spectra, normalization, standardization, and first-derivative transformation, were combined with partial least squares regression (PLSR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF), generating 16 prediction strategies for each quality attribute. Root-grouped five-fold cross-validation showed that the optimal models achieved coefficients of determination for cross-validation (R²CV) ranging from 0.9083 to 0.9190 and residual predictive deviation (RPD) values ranging from 3.3112 to 3.5230. Repeated grouped cross-validation produced mean R²CV values of 0.9113–0.9176, and root-block Y-scrambling yielded empirical p values of 0.005 for all six attributes. PLSR provided the highest cross-validated performance for all six quality attributes, although MLR showed comparable performance for several targets. These results provide preliminary evidence that discrete Vis/NIR spectral sensing can support simultaneous non-destructive estimation of multiple sweet potato quality attributes. External multi-batch and multi-cultivar validation is required before the models can be considered robust for practical deployment. Full article

In multi-hop UAV ad hoc networks employing the Statistical Priority-based Multiple Access (SPMA) protocol, the HELLO broadcast interval cannot be arbitrarily shortened due to the inherent upper bound on the per-slot transmission probability of each node in saturated networks, which fundamentally limits the estimation accuracy of Channel Occupancy Statistics (COS). To address this problem, this paper proposes a spatial correlation-aided multi-source asynchronous Kalman filtering method, abbreviated as SMA-KF. On the basis of conventional COS broadcasting, SMA-KF introduces two complementary observation sources: COS measurements piggybacked on data packets, and spatially correlated observations from common neighbors compensated by historical biases. These three types of observations are integrated into a unified Kalman filtering framework, and a state-space model suitable for asynchronous intermittent observations is constructed. Theoretical analysis verifies the convergence of the algorithm. Simulation results demonstrate that the proposed algorithm significantly outperforms the EWMA and TW algorithms across all test scenarios, and achieves overall lower error than BiLSTM. Under the extremely sparse observation condition with a HELLO broadcast interval of 600 slots, the Normalized Root Mean Square Error (NRMSE) of SMA-KF is 28.28%, which is 32.8% and 26.2% lower than those of EWMA (42.11%) and TW (38.33%), respectively. In the heavy-traffic scenario with an average data packet arrival interval of 20 slots, the NRMSE of SMA-KF is as low as 4.80%, whereas those of EWMA and TW are 17.49% and 15.40%, respectively, corresponding to reductions of 72.6% and 68.8%. In comparison with BiLSTM, SMA-KF achieves lower NRMSE in five out of seven traffic configurations, while BiLSTM exhibits only marginal and statistically insignificant advantages in the remaining two configurations. Link interruption experiments show that SMA-KF maintains NRMSE between 5.68% and 10.40% across the entire meaningful interruption coverage range of 0% to 53%, consistently outperforming all benchmark algorithms. Moreover, SMA-KF consistently achieves the lowest estimation error under varying node mobility speeds. Parameter sensitivity analysis confirms that SMA-KF maintains stable performance across a wide range of parameter values. These results validate the effectiveness of multi-source observation fusion and spatial cooperative estimation in improving both the accuracy and robustness of COS estimation. Full article

Deterministic channel models are essential for high-frequency communication system design in complex urban environments, where multiple propagation mechanisms including reflection, diffraction, and vegetation scattering coexist. This paper proposes a hybrid forward–backward ray tube tracing (HFB-RTT-3D) approach that extends the established ray tracing fusion with multiple diffuse scattering (RT-MDS) framework from natural terrain to urban scenarios by introducing pyramid-shaped diffraction and vegetation scattering ray tubes. A vegetation scattering model based on a leaf-level bidirectional scattering distribution function (BSDF) is established, enabling computationally feasible representation of vegetation effects in deterministic channel prediction. The framework thereby covers buildings, vegetation, and terrain within a unified ray tube data structure. Simulation analyses quantify vegetation modulation of multipath structure and received power across frequencies from 5 to 15 GHz and different canopy sizes. Measurement validation in a campus tree-lined avenue scenario at 2.3 to 5.9 GHz demonstrates that incorporating vegetation scattering reduces the root mean square error (RMSE) of the prediction to 6.11 to 6.30 dB, an improvement of 0.57 to 1.67 dB over the case without vegetation, confirming the effectiveness of the proposed method. Full article

To address the slow response, low pointing accuracy, and degraded stability caused by the coexistence of fractional-order dynamics, dual-axis coupling, and system delay in liquid crystal optical phased array (LCOPA) beam control, a fractional-order composite control method is proposed. First, the LCOPA is modeled as a two-input two-output system, and a fractional-order time-delay dynamic model is established. A composite control architecture integrating a fractional-order controller, a diagonal decoupling compensator, and a Smith predictor is then designed to improve dynamic response, suppress cross-axis coupling, and mitigate delay-induced performance degradation. A multi-strategy improved sparrow search algorithm is used to optimize the dual-channel fractional-order controller parameters, and closed-loop stability is verified using characteristic roots and the Matignon criterion. Simulation results show that the settling times of both axes are 11.50 ms, reduced by 62.54%/62.30%, 57.09%/56.11%, and 51.68%/48.89% compared with the results for PID, FOPID, and ADRC, respectively. The maximum cross-axis deviations are reduced to 0.0036° and 0.0030°, respsectively. Experiments further show that the mean absolute pointing errors of the X- and Y-axes decrease from 0.0466° and 0.0362° to 0.0057° and 0.0045°, while the RMSEs under external disturbances are 0.0060° and 0.0044°, respectively. These results demonstrate improved response speed, pointing accuracy, decoupling performance, and disturbance rejection. Full article

Spacecraft and launch-vehicle structures require lightweight multipoint monitoring under combined mechanical, thermal, and environmental loads. This study presents an analytical fiber Bragg grating (FBG) sensor-network workflow integrating reference-grating temperature compensation, regional strain assessment, opposite-surface curvature sensing, wavelength-division-multiplexing allocation, and strain-to-shape reconstruction. The deterministic compensation case is used only as a self-consistency check, whereas practical robustness is assessed through 10,000 Monte Carlo trials incorporating packaged-coefficient mismatch, temperature nonuniformity, wavelength noise, strain-transfer variation, drift, and calibration uncertainty. The calibrated estimator achieved a median strain mean absolute error of 1.73 με and a 95th-percentile error of 4.22 με. The defined finite-element benchmarks produced a maximum engine-mount truss strain of 456.2 με under the defined loads and a median full-field panel-reconstruction normalized root-mean-square error of 1.29% for 18 sensing locations with 2 με noise. Conservative WDM analysis yielded 54, 13, and 16 channels for three operating envelopes, and the prescribed random-vibration spectrum produced 6.78 g_rms. These results demonstrate a reproducible numerical proof of concept and define practical limits for compensation, spectral allocation, curvature interpretation, and inverse reconstruction; they do not constitute experimental validation or flight qualification. Full article