Efficient maximum power extraction in variable-speed wind energy conversion systems (WECSs) remains challenging because of nonlinear turbine dynamics, continuously varying wind conditions, measurement disturbances, and mechanical-parameter uncertainties. This study presents a robust nonlinear integral backstepping control (BC) strategy for generator-speed regulation within a Tip-Speed Ratio (TSR)-based Maximum Power Point Tracking (MPPT) framework. The proposed controller combines nonlinear backstepping stabilization with integral compensation to improve reference tracking and reduce persistent tracking errors. The turbine-generator mechanical inertia is explicitly incorporated into the control formulation, providing a physically consistent representation of the mechanical dynamics and enabling systematic evaluation of parameter uncertainty. A comprehensive comparative assessment is conducted in MATLAB/Simulink using four control strategies: proportional-integral (PI), integral-proportional (IP), sliding-mode control (SMC), and the proposed integral BC. The controllers are evaluated under five complementary scenarios: variable wind speed, measurement noise, abrupt stepwise wind-speed variations, ±20% mechanical-inertia uncertainty, and a 10-ms rotor-speed measurement delay. Performance is assessed using the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE), together with statistical measures across the five scenarios. Under the baseline variable-wind condition, BC achieves ISE = 24.4164, IAE = 1.539, and ITAE = 0.716, outperforming PI, IP, and SMC in all three indices. Under abrupt stepwise wind-speed variations, BC further achieves ISE = 0.00110, IAE = 0.0056, and ITAE = 0.0529, demonstrating rapid transient error suppression. The proposed controller remains stable under ±20% mechanical-inertia variations and a 10-ms measurement delay. Across the five scenarios, BC achieves the lowest mean ISE, IAE, and ITAE values of 19.353, 1.231, and 2.642, respectively, as well as the lowest standard deviations for ISE and IAE. SMC exhibits particularly consistent performance under measurement noise and the lowest standard deviation for ITAE. Overall, the results demonstrate that the proposed integral BC provides the most favorable balance of tracking accuracy, transient performance, and robustness among the investigated strategies. The improved rotor-speed regulation supports operation near the optimal TSR and effective aerodynamic power extraction. The findings highlight the potential of the proposed approach for robust MPPT control of variable-speed WECSs.
Energy systems are crucial to residential life and industrial production. During practical operation, these systems may experience various anomalies that disrupt the stability of system operation. Recent years have witnessed remarkable progress in power system anomaly detection. However, existing methods still suffer from two limitations. First, detection algorithms neglect privacy protection, although privacy security is also a critical issue in energy systems. Second, existing studies have difficulty characterizing latent dependencies and topology changes, which limits detection performance. To bridge these gaps, we present a power system anomaly detection method that integrates physics-informed sparse graph temporal modeling with homomorphic encryption, enabling anomalous-event identification and anomalous-bus localization under privacy-preserving conditions. Specifically, we construct a sparse graph using the power-grid topology and normal measurement residuals. We then obtain system-state predictions through polynomial graph temporal prediction and physics-guided affine correction and use anomaly scores to diagnose anomalous conditions. Furthermore, we employ homomorphic encryption to perform ciphertext computation for the affine prediction model without exposing historical measurement data, thereby enabling privacy-preserving remote anomaly detection. We conduct experiments on IEEE bus benchmarks to verify the effectiveness of the proposed method under multiple anomaly scenarios.
The integration of unmanned aerial vehicles (UAVs) as aerial base stations has emerged as a key enabler for next-generation wireless networks, particularly in disaster recovery, temporary events, and infrastructure-deficient regions. However, multi-UAV deployments introduce severe co-channel interference due to spectrum reuse and overlapping coverage areas, while existing spectrum allocation methods either rely on centralized optimization with limited scalability or on reinforcement learning frameworks that lack spatial awareness of interference sources. To address these challenges, this paper proposes a Hybrid DeepMUSIC-assisted Cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for intelligent spectrum allocation and interference management in multi-UAV 6G networks. The proposed framework integrates a hybrid interference localization module, which fuses the classical MUltiple SIgnal Classification (MUSIC) algorithm with a deep neural network to accurately estimate the direction of arrival (DoA) of interference sources, into a DeepMUSIC-enhanced state representation used by cooperative Deep Q-Network (DQN) agents trained under a Centralized Training and Decentralized Execution (CTDE) paradigm, enabling coordinated yet fully distributed spectrum allocation decisions. Extensive simulations demonstrate that the proposed Hybrid DeepMUSIC module reduces the mean DoA estimation error to approximately 0.105°, more than an order of magnitude better than classical MUSIC and standalone DeepMUSIC estimators. Compared with seven baseline algorithms spanning heuristic, optimization-based, single-agent, and cooperative multi-agent reinforcement learning approaches, the proposed framework achieves the highest network throughput, SINR, spectrum efficiency, and energy efficiency, together with the fastest and most stable training convergence, reaching a stable cooperative reward of 76.246 within approximately 371 training epochs. The framework further maintains near-linear computational scaling with the number of UAV agents, confirming its suitability for real-time deployment in dense, AI-native multi-UAV 6G wireless communication systems.
Overlapping cells in plant suspension-culture microscopy pose a particular challenge, for instance, segmentation because a single pixel may belong to more than one cell. Most standard instance-segmentation methods are not designed for this setting and tend to treat overlapping objects as mutually exclusive regions. We instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding. This allows multiple predicted masks to occupy the same image region. We further introduce a mask-level multiplicity-consistency loss that encourages the predicted number of masks covering a pixel to agree with the underlying cell occupancy. We evaluate MC-SlotNet on a newly annotated dataset of 53 Siraitia grosvenorii suspension-culture micrographs containing 4131 full-cell instances acquired at 4×–40× magnification. Using grouped five-fold cross-validation and an overlap-preserving evaluation protocol, we compare the method with Mask R-CNN, SOLOv2, and Mask2Former. MC-SlotNet achieves the best performance on AP50 (0.800), mAP50:95 (0.565), F150 (0.842), all-ground-truth Dice (0.761), AJI+ (0.754), overlap-region Dice (0.705), and overlap-instance recall (0.828). Its AP75 (0.649) is comparable to Mask2Former’s (0.651). MC-SlotNet also has the lowest inference time among the evaluated methods, at 1.113 s/image. These results indicate that decoding full-cell masks independently, rather than enforcing an exclusive partition of image pixels, is well-suited to instance segmentation in plant suspension-culture microscopy images with substantial cell overlap.
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.
This study evaluated the effects of uniform and symmetric step-gradient infill architectures on the density, mechanical properties, Shore D hardness, and dry-sliding coefficient of friction of FFF-printed thermoplastic polyurethane (TPU) 95A. Uniform triangular infills at 50%, 70%, and 90% were compared with a surface-dense architecture (GI-1: 90–70–50–70–90%) and a core-dense architecture (GI-2: 50–70–90–70–50%). The experimental density of GI-2 (0.988 g/cm3) did not differ significantly from that of the uniform 70% configuration (0.990 g/cm3). GI-2 nevertheless exhibited significantly higher tensile strength (20.3 vs. 18.4 MPa) and a higher work of deformation to failure (48.3 vs. 32.3 MJ/m3). GI-1 had a lower experimental density than the uniform 90% configuration yet higher tensile strength and work of deformation to failure; the two configurations were not density-matched. Both gradient architectures exhibited lower mean compressive stresses at 20% strain than the uniform 70% and 90% specimens. GI-1 displayed a Shore D hardness comparable to the uniform 90% configuration and the lowest mean coefficient of friction (0.50 ± 0.01). Overall, step-gradient infill can improve specific tensile properties while allowing compressive compliance and the contact response of the outer shell to be tailored separately. However, the effects of gradient order and material content could not be fully separated because GI-1 and GI-2 differed in nominal mean infill density.
AI-generated and locally manipulated images can support impersonation, forged evidence, identity-document abuse, and other forms of digital fraud, making reliable content-authenticity analysis increasingly important. This paper presents STeREx-Net, a three-class forensic framework for distinguishing real, fully synthetic, and locally tampered images using frozen diffusion-derived residual evidence, spatially aligned RGB features, multi-task prediction heads, and reviewable visual evidence. On the official balanced 60,000-image SID-Set test, the original model achieved 96.24% accuracy, 96.26% macro F1, and a multiclass Matthews correlation coefficient of 0.944. Separate matched three-seed ablations showed that diffusion-residual evidence is materially useful relative to RGB-only input; however, residual-only and simple-fusion controls outperformed the proposed fusion on the clean SID-Set, so fusion superiority is not claimed. Frozen robustness testing further revealed strong condition dependence: SID-Set macro F1 decreased from 0.9655 on clean images to 0.7987 under JPEG Q75 and 0.6330 under JPEG Q50, while generator-stratified AIS-4SD results also varied substantially. Zero-shot transfer to FantasyID failed to detect tampered samples, whereas leakage-safe restricted adaptation partially recovered tampered recall to 0.3447 and reduced the expected calibration error from 0.7815 to 0.2077, at the cost of lower real-image recall. A validation-selected localization intervention increased Dice from 0.2817 to 0.3413 but remained precision-biased and non-uniform across manipulation sizes. Quantitative explanation analysis supported in-domain decision faithfulness and benign-transformation stability, but these properties did not transfer consistently to external data. STeREx-Net should therefore be viewed as a human-supervised forensic research framework with explicitly characterized robustness, localization, and generalization boundaries rather than as a universally robust detector.
The main scope of this research was to complete and validate the analysis of stator winding topologies of six-phase AC machines using the winding quality factor by determining fault tolerance and validating it through experimental tests. This paper proposes a unified and practical methodology for evaluating the performance of stator windings in six-phase induction machines, with emphasis on magnetic field quality and fault-tolerant operation. The approach combines analytical modeling of the magnetomotive force (MMF) with its graphical representation using the MMF polygon, enabling an efficient assessment of harmonic content through a global indicator, referred to as the winding quality factor. Several representative winding topologies are analyzed within a common framework, including single-layer and double-layer configurations with full-pitch and short-pitch coils, suitable for generating homologous series of six-phase machines. The study considers both normal operating conditions and post-fault regimes, particularly operation with a single three-phase set. The results reveal the strong influence of winding topology on harmonic distortion and overall machine performance, highlighting the trade-offs between magnetic field quality and fault tolerance. It is shown that appropriate winding design can reduce spatial harmonics and improve robustness under degraded operating conditions. The theoretical findings are validated through experimental investigations, demonstrating good agreement between analytical predictions and measured data. A parallel analysis was performed between the theoretical findings and experimental data, and the results conform to the authors’ expectations.
Repeated dyadic interactions are characterized not only by average multimodal signal levels but also by how cross-modal consistency evolves over time. This study investigated whether the joint temporal organization of within-person face–body cross-modal consistency and probabilistic cross-modal affective agreement, quantified by the Consistency Index for Affective Synchrony (CIAS) and the Probabilistic Multimodal Consistency Index (PMCI), yielded reproducible session-level dynamic representations associated with repeated dyadic interaction. The primary analysis included 24 improvised and 17 naturalistic sessions, with 12 dyads in each condition. Time-varying signals from both partners were summarized through dynamic-state occupancy, dwell structure, transition characteristics, and temporal variability and evaluated using relational permutation analysis with shared-participant and technical controls. In naturalistic interaction, repeated sessions of the same dyad were significantly more similar (β = −2.0180, FDR q = 0.0114), with significant dyad-specificity beyond shared-participant similarity (Δβ = −2.4670, q = 0.0033). Representation ablations showed that PMCI provided the dominant state-separation structure, whereas CIAS contributed modest and condition-dependent complementary information. Most notably, frozen participant-disjoint evaluation on 73 naturalistic sessions from six dyads and 11 previously unseen participants retained significant dyad-associated structure and achieved session-weighted nearest-dyad accuracy of 0.8356 and macro-dyad accuracy of 0.8512. Additional robustness analyses showed that the principal pattern persisted across state-count specifications, temporal-window settings, partner-order reversal, and explicit technical-quality adjustment. Overall, the findings support the existence of reproducible session-level multimodal dynamic structure associated with repeated dyadic interaction and its transfer to previously unseen participants, while not implying direct interpersonal synchrony or psychologically defined interaction states.
Concentrated solar power systems can provide dispatchable renewable energy, but their application in distributed cogeneration is constrained by high costs and thermal losses associated with indirect heat transfer. This study proposes a direct vapour generation cascade organic Rankine cycle (DVG-CORC) for combined heat and power production. A biphenyl–diphenyl oxide (BDO) mixture is used as both the solar-collection fluid and the high-temperature cycle working fluid, while thermal storage and four operating modes are incorporated to accommodate variations in solar irradiance and enable continuous operation. Thermodynamic and economic models are developed to evaluate system performance under different evaporation and condensation temperatures. At an evaporation temperature of 400 °C, the maximum thermal efficiencies are 37.67%, 35.38%, and 33.11% at condensation temperatures of 60 °C, 80 °C, and 100 °C, respectively. At a condensation temperature of 60 °C, the cogeneration system generates an estimated annual revenue of USD 929,637, which is USD 284,269 higher than that of the power-generation-only configuration operating at a condensation temperature of 30 °C. These results demonstrate that direct vapour generation, cascade energy utilization, and heat recovery can improve the thermodynamic and economic performance of distributed solar cogeneration systems.
The application of artificial intelligence (AI) and the Internet of Things (IoT) is transforming smart hotel services by simultaneously creating technological benefits and privacy-related concerns. Although previous studies have extensively examined the effects of AI and IoT on tourist experiences, they have predominantly adopted an individual perspective, with limited attention to potential interdependence among co-present guests. This study examines how technological benefits (AI usefulness, IoT convenience, and personalization) and technological risk (perceived privacy risk) are associated with tourists’ satisfaction and electronic word-of-mouth (eWOM) intention, while also investigating whether these outcomes exhibit dependence across social, spatial, and temporal structures of guest co-presence. The study draws on survey data collected from tourists staying in AI-and IoT-enabled hotels in Hungary, Croatia, and Serbia. The findings show that AI usefulness, IoT convenience, and personalization are positively associated with satisfaction and eWOM intention, whereas perceived privacy risk is negatively associated with both outcomes. Furthermore, technological benefit constructs exhibit significant positive indirect associations across guest co-presence structures, whereas privacy risk exhibits less consistently statistically significant indirect associations, particularly for eWOM intention. These findings indicate the coexistence of positive technology evaluations and privacy concerns and reveal different patterns of co-presence-associated interdependence rather than demonstrating that technological benefits statistically dominate privacy risks. These findings contribute to smart hospitality research by extending the benefit–risk perspective beyond exclusively individual-level evaluations and highlighting the importance of considering conditional interdependence among co-present guests when evaluating AI- and IoT-enabled hotel services.
Impulsivity is a common trait and is understood as a symptom of various disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). We previously proposed ImpulsivityBank protocol, which is a standardized discourse protocol for retrieving speech samples that enables the identification of the speech features related to the impulsivity trait in children and adolescents. ImpulsivityBank protocol presents three elicitation methods (recall task, storyboard, picture description) and quantifies general language abilities using a battery of linguistic assessments. In this paper, we analyze the current data from the Impulsivity corpus, which is a corpus that consists of speech samples collected using our protocol. We performed an automated linguistic-feature analysis of the Impulsivity corpus to examine speech features related to the impulsivity trait in children and adolescents. We present the results of both the classification and prediction systems considering diverse experimental scenarios. Our results indicate that the speech features from the storyboard consistently yielded the best-performing classification and prediction systems among the evaluated elicitation methods. We conclude that ImpulsivityBank protocol facilitates the collection of speech samples from which a range of linguistic features can be extracted and explored in relation to the impulsivity trait in children and adolescents. The consistency observed across the classification and prediction models suggests that multiple speech features jointly contributed to the fitted models’ outputs. Our main contribution lies in speech analysis, particularly in the extraction and study of speech features that may be associated with impulsivity.
Existing wind power optimization control strategies often lack a qualitative analysis of the relationship between wind power energy and system frequency, which may lead to insufficient or excessive frequency support, thereby reducing the effectiveness of wind power frequency support or triggering a secondary frequency drop. To address this issue, a receiving-end system frequency optimization control strategy based on the available rotor kinetic energy of sending-end wind farms is proposed. First, the mathematical expression of the approximately first-order response in the initial stage of optimized frequency dynamics is clarified. The quantitative relationship between the available rotor kinetic energy of wind farms and the frequency support level is derived, and a target frequency design method is developed by combining a conservative evaluation of effective frequency regulation energy. Second, according to the deviation between the target frequency and the measured frequency, the total frequency regulation demand calculation, the approximate calculation of synchronous generator mechanical power variation, and the wind farms’ frequency regulation command calculation are dynamically executed within each control step. In this way, the outputs of wind farms and synchronous generators are coordinated to regulate the frequency close to the target value. Finally, a simulation model is built in MATLAB/Simulink to verify the effectiveness of the proposed frequency control target design method and control strategy under different operating conditions, as well as their robustness against parameter acquisition errors, communication delays, and power disturbance estimation errors.
This work presents a reproducible YOLO-based pipeline for bottle detection in sandy environments, emphasizing dataset integrity, leakage-free evaluation, and deployment-oriented model selection. A one-class dataset of 1585 images and 3167 annotated bottles was audited to identify annotation-format defects and near-duplicate contamination between training and validation partitions. Sequence membership was reconstructed through perceptual-image similarity and used to assign complete image components to a sequence-aware train/validation split, eliminating the near-duplicate pairs found in the initial random partition. A controlled ablation holding model, seed, and corrected labels fixed showed that the random split reports 0.040 higher mAP@0.5:0.95 than the sequence-aware split (0.787 vs. 0.747), quantifying the leakage risk directly rather than only asserting it. Five YOLO configurations were then benchmarked under three independent seeds each; the observed mAP@0.5:0.95 differences among models (0.004–0.008) were small in absolute magnitude and, given only three seeds per model, are interpreted descriptively rather than as evidence of statistical equivalence or significance, so yolo11n_bottle was selected through a joint accuracy-parity, compactness, and exportability criterion (precision 0.982, recall 0.985, mAP@0.5 0.992, mAP@0.5:0.95 0.748), using approximately ten times fewer parameters than the largest configuration and producing a 5.2 MB checkpoint. ONNX export preserved detection geometry closely (100% count agreement, mean matched IoU ≥0.9998), without meeting strict metric-parity tolerances. A stratified sample of 108 frames from operational RealSense BAG footage was manually annotated by an independent reviewer and evaluated quantitatively: mAP@0.5 remained close to the internal validation figure (0.927 vs. 0.992), while mAP@0.5:0.95 fell substantially (0.483 vs. 0.747), revealing a localization gap between the curated benchmark and operational conditions that this manuscript reports transparently. Together, these results show that dataset auditing, sequence-aware partitioning, multiseed benchmarking, and manually annotated operational evidence are each necessary to interpret a detection benchmark built from continuous video acquisition, providing a traceable, reproducible workflow for selecting and evaluating compact visual-perception models for resource-constrained environmental applications.
Accurate and robust object detection in complex disaster scenes is essential for effective emergency response; however, severe occlusion, dense overlap, and cluttered backgrounds pose significant challenges to conventional single-model detectors. To address these limitations, this study proposes a novel rescue-oriented detection framework that integrates a fine-grained disaster dataset, a cross-generational YOLO ensemble, and a consensus-based fusion strategy using Weighted Boxes Fusion (WBF). A dataset of 2323 images was constructed by re-annotating CDNIC19k with instance-level labels for four rescue-critical roles, enabling more precise evaluation in real-world scenarios. Heterogeneous YOLO models spanning multiple architectural generations were jointly exploited within a unified ensemble framework to leverage complementary representations. Meanwhile, a consensus-driven fusion strategy based on WBF was adopted to improve prediction aggregation in dense and occluded scenes. Experimental results showed that the proposed method outperformed single-model baselines and NMS-based approaches, improving mAP@0.5 from 0.696 to 0.756 (+6.0%) while maintaining strong recall and robustness. Analysis of the YOLOv12 family reveals an accuracy–efficiency trade-off, where lightweight models enable real-time inference while high-capacity models provide more reliable detection. Overall, these findings demonstrate that cross-generational architectural diversity combined with consensus-based fusion constitutes a generalizable and effective paradigm for high-precision disaster scene understanding under diverse deployment constraints.
This paper focuses on the binary task of parking-slot occupancy detection (vacant vs. occupied) from UAV aerial imagery. Accurate parking-slot detection from UAV imagery is challenged by small target sizes, highly regular rectangular shapes, large-scale variations, complex backgrounds, and perspective distortions. Compared with fixed surveillance cameras, UAV-based detection offers flexible deployment, wide-area coverage, and no requirement for pre-installed infrastructure, making it especially suitable for large open-air parking lots and temporary parking scenarios. To address this, this study proposes a task-specific framework for UAV-based parking-slot detection, with improvements in backbone design, attention modeling, and bounding-box regression. A lightweight MnasNet-inspired backbone is used to improve multi-scale feature extraction at low computational cost. An enhanced EMA module with adaptive grouping, FFT-based frequency enhancement, and gated fusion is introduced to better model the structured patterns of parking lot scenes. In addition, a UIoU+ loss tailored to rectangular geometry is proposed to improve localization quality. Sensitivity analysis and repeated experiments show that the method is stable and statistically reliable. All main metrics are evaluated on an independent held-out test set to ensure generalization. Extensive experiments demonstrate that each component brings consistent performance gains. The proposed model achieves 99.44 ± 0.12% mAP@0.5, 90.31 ± 0.27% mAP@0.5:0.95, 99.27 ± 0.15% precision, and 99.00 ± 0.18% recall on the self-built UAV Parking Lot dataset. Its mAP@0.5:0.95 is 26.01 percentage points higher than the YOLOv11n baseline. Consistent performance improvements are also validated on two additional public benchmarks (CARPK and PKLot), confirming the generalization of the proposed method beyond the self-built dataset. Most importantly, the method supports real-time inference on embedded UAV platforms and achieves state-of-the-art performance among lightweight detectors, making it an ideal solution for practical intelligent parking management. Ablation studies further confirm the complementary synergy between the proposed backbone, attention module, and loss function. Full implementation code, pre-trained weights, and detailed reproduction guidelines are publicly available to ensure research reproducibility.
Urban air pollution driven by road traffic poses a significant public health challenge in cities with high vehicle density and frequent congestion, particularly in topographically constrained environments such as Almaty, Kazakhstan. This study presents a web-based digital twin prototype for the integrated monitoring and analysis of traffic flow and air quality in Almaty, Kazakhstan. The system autonomously collects data from the TomTom Traffic, OpenWeather Air Pollution, and WAQI APIs and official population statistics for five fixed monitoring stations, computing traffic density, vehicles per hour, road congestion, estimated CO2 emissions, an air pollution index, and a population exposure index, and providing real-time dashboard visualization alongside longitudinal data accumulation. Over a 50-day deployment (26 May–16 July 2026), 4961 real co-located observations across 18 active days were analyzed; records generated by the prototype’s fallback mechanism during API outages were excluded from the scientific analysis. During this summer period, PM2.5 was low (mean ≈ 6 µg/m3) and spatially uniform, and showed no statistically significant association with traffic intensity (r ≈ −0.03). Traffic indicators were instead weakly but significantly correlated with the vehicle-emitted gases NO2 (r ≈ 0.16) and CO (r ≈ 0.10), which they preceded by up to about one hour. A short-horizon PM2.5 nowcasting task, evaluated across temporal resolutions with time-series cross-validation, was dominated by temporal persistence, with traffic-derived features contributing negligibly. The absence of a summer traffic–PM2.5 association does not preclude such a relationship during the heating season, when particulate levels are higher. The results indicate that the traffic–air-quality relationship in Almaty is season- and pollutant-dependent, and demonstrate a lightweight, reproducible platform suitable for longitudinal monitoring and future heating-season assessment.
In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions.
Sn-3Ag-0.5Cu-xBi alloys (SAC305-xBi) represent promising lead-free alternatives for low-temperature soldering. Low Bi concentrations can strengthen SAC-based solders through solid-solution strengthening, refining β–Sn grains and transforming needle-like Ag3Sn phases into equiaxed morphologies. However, excessive Bi alloying may induce precipitation of brittle Bi particles, cause microstructural instability and interfacial degradation, thereby weakening the solder joint performance. As such, the concentration of Bi in the SAC305 alloys should be carefully controlled. In this work, the microstructure and corrosion behavior of Sn-3Ag-0.5Cu-xBi solder alloys (SAC305-xBi, where x = 0, 1, 2 and 4 wt. %) were investigated. Attention has been paid to the influence of low Bi concentration on the microstructure, morphology, and chemical composition of the phases present in the solder alloys before and after corrosion exposure. The alloys were prepared by induction melting of Sn, Ag, Cu and Bi lumps under Ar gas. The microstructure of the SAC305 and SAC305-1Bi alloys represented a hypoeutectic microstructure with dendritic (Sn) grains and the ternary eutectic, consisting of (Sn), Cu6Sn5 and Ag3Sn, located in inter-dendritic regions. In the SAC305-2Bi and SAC305-4Bi alloys, a segregation of (Bi) particles was observed in addition to dendritic (Sn) and ternary eutectic. The (Bi) particles were located at the (Sn)Ag3Sn interface in the inter-dendritic spaces of the (Sn) solid solution. The corrosion resistance of the as-cast alloys was studied in aqueous NaCl electrolyte (3.5 wt. %) using electrochemical methods. Open circuit potentials of the alloys were found to increase with increasing concentration of Bi. The highest corrosion current was found for the SAC305-1Bi alloy. It was observed that micro-galvanic cells at the Sn-Ag3Sn interface were the initiating factors of corrosion in the SAC305-1Bi alloy. The corrosion activity of the SAC305-1Bi alloy is related to the high density of fine Ag3Sn particles. The higher fraction of Ag3Sn particles provided a dense network of local galvanic interaction sites, leading to the acceleration of the corrosion rate. The presence of discrete Bi precipitates in the SAC305-2Bi and SAC305-4Bi alloys, on the other hand, partially reduced the risk of galvanic corrosion. Since Bi has a higher standard electrode potential compared to Sn, the Bi/Ag3Sn and Bi/Cu6Sn5 couples were less prone to corrosion. The corrosion mechanism of the SAC305-xBi alloys is discussed, and results are compared to previously studied SAC-Bi alloys.
Mixed Reality (MR) serious games combine immersive technologies with game-based learning to support training, skill development, and decision-making across diverse disciplines in university education. Although previous research has demonstrated improvements in engagement, usability, and learning outcomes, less attention has been devoted to developing design recommendations and methodological approaches for studying MR serious games. A theory-derived Meta Quest 3-based MR serious game for diagnostic classification was developed by integrating learning theories, gameplay mechanics, and gamification features while functioning as both an educational intervention and a research instrument. The system was evaluated through two complementary studies involving undergraduate students: the first compared learning outcomes across instructional approaches, whereas the second examined learner performance and transfer using a mixed-method approach incorporating objective metrics, questionnaires, and interviews. The proposed system achieved learning outcomes comparable to expert-guided field training while significantly outperforming classroom instruction and self-directed study. Significant transfer to real-world diagnostic tasks was demonstrated, and complementary evidence was triangulated to derive preliminary design recommendations and a multi-dimensional methodological framework. These contributions provide an initial foundation for the systematic design and study of MR serious games in higher education.
