Search Results (36,657)

The reliable operation of grid-connected distributed generation converters is challenged by severe unbalanced conditions and stringent fault ride-through requirements. To address these issues, this paper presents a sensorless flexible predictive direct power control (SF-PDPC) strategy for converters operating under severe asymmetrical grid faults. The proposed approach combines a frequency-adaptive neural network quadrature signal generator (FANN-QSG)-based virtual-flux estimator with a flexible power-reference generation scheme, enabling predictive control without grid-voltage sensors, conventional synchronization units, or cascaded filtering stages. The key feature of the proposed method lies in a flexible power-reference formulation that exploits the degrees of freedom associated with positive- and negative-sequence power components, allowing continuous regulation of the trade-off among current quality, active-power oscillations, and reactive-power oscillations under unbalanced grid conditions. This enables a unified control framework adaptable to different grid support objectives. The effectiveness of the proposed strategy is validated under a severe type-C voltage sag, grid frequency deviation, and harmonic distortion. Compared with the conventional PDPC, the proposed method reduces current total harmonic distortion from 57.78% to 0.44% while maintaining satisfactory active power tracking performance. Furthermore, the FANN-QSG-based estimator and the overall control structure demonstrate strong robustness under highly disturbed operating conditions. The proposed SF-PDPC enhances the operational flexibility of predictive power control for grid-connected converters operating under highly disturbed and unbalanced grid conditions. Full article

Person re-identification (re-ID) aims to match pedestrian images across disjoint camera views. Existing multi-source unsupervised domain adaptation (UDA) re-ID methods still face two critical issues: they fail to effectively balance domain-invariant feature learning and domain-specific style preservation and cannot adequately model the implicit correlations among diverse source domains, resulting in limited cross-domain generalization performance. To address these challenges, this paper proposes a novel multi-source UDA re-ID framework equipped with a Mixture of Experts feature extraction (MEFE) network and a Graph-Based Relation (GBR) module. Specifically, the MEFE network integrates mixed Instance and Batch Normalization (MIBN) to extract robust domain-invariant features, while the embedded domain-specific style information (DSI) module compensates for lost domain-specific style details at the feature level. Furthermore, the cascaded Graph Attention and Graph Convolution Networks (GATs/GCNs) in the GBR module adaptively explore implicit feature correlations and achieve effective multi-source feature fusion. Center maximum mean discrepancy loss is adopted to further reduce cross-domain distribution discrepancies. Extensive experiments on large-scale datasets demonstrate that the proposed method achieves state-of-the-art performance and substantially outperforms mainstream UDA re-ID approaches. Full article

Huanglongbing, a destructive citrus disease of global importance that is also present in Saudi Arabia, is associated with Candidatus Liberibacter asiaticus (CLas) and remains a major threat to citrus production. Although previous studies have documented sequence variation and prophage polymorphism in CLas, broader comparisons of prophage-associated gene content remain limited. In particular, comparative orthogroup analysis of prophage gene-content conservation across geographically structured CLas populations has rarely been explored. In this study, we analyzed 42 CLas prophage genomes from Saudi Arabia and other geographic regions using a comparative orthogroup framework. OrthoFinder assigned 99.1% of predicted proteins (1825 of 1841) to 64 orthogroups, with only 16 genes remaining unassigned. A small number of rare orthogroups restricted to only a few genomes were identified, and no orthogroup was detected in all genomes. Presence–absence analyses supported a predominantly conserved prophage gene repertoire together with a small accessory component, while also indicating that apparent absences should be interpreted in light of mixed assembly status and prophage-region completeness. Saudi Arabian genomes were distributed within the broader global framework and exhibited generally similar gene-content profiles rather than a deeply separated lineage. Functional interpretation of representative orthogroups identified conserved prophage-associated genes related to replication, helicase activity, and phage packaging, whereas variable orthogroups were primarily associated with hypothetical or accessory prophage-related functions. Overall, these results are consistent with a model in which CLas prophage diversification is associated more with sequence-level variation and localized structural differences than with extensive gain or loss of prophage genes. These findings further refine current understanding of CLas genome evolution and highlight conserved prophage-associated targets that may support molecular diagnostics and epidemiological surveillance. Full article

As an important port in the Beibu Gulf of the South China Sea, Tieshangang Bay is potentially at risk of PTE pollution, yet systematic research integrating multi-hydrological period data remains limited. By applying pollution indices (C_f, WQI, I_geo, RI) combined with PCA, and PMF, we investigated PTE distribution characteristics, risk assessment, and source apportionment across different hydrological seasons. The results indicate that average PTE concentrations in surface seawater meet Class II standards of the Sea Water Quality Standard, with Zn and As showing relatively high concentrations compared to other PTEs. High-concentration areas were mainly located in the inner and middle bay. In sediments, concentrations of Zn and Cr were relatively high, with values generally higher inside the bay than outside. Both C_f and WQI values for seawater PTEs were below 1, indicating an overall low pollution risk. However, Cd and Hg in sediments presented a moderate potential ecological risk. Source apportionment revealed that seawater PTEs primarily originated from an industrial–aquaculture composite source (44.60%), while sediment PTEs were mainly attributed to composite terrestrial inputs (53.16%). These findings provide a scientific basis for PTE pollution management and sustainable development in Tieshangang Bay. Full article

Subtitles often attract visual attention even when they are not necessary for comprehension. In the present eye-tracking experiment, we examined whether attention to subtitles in instructional videos varies as a function of audio–subtitle language–script pairing in Hindi–English bilinguals with an English-medium instruction (EMI) background. Native Hindi participants viewed videos in three conditions: English audio with English subtitles (L2–L2), Hindi audio with Hindi subtitles (L1–L1), and English audio with Hindi subtitles (L2–L1). In the L2–L2 condition, gaze was distributed similarly across speakers’ faces and subtitles. In contrast, in both Hindi-subtitle formats, viewers allocated more dwell time to the speakers’ faces than to the subtitles. Comprehension scores did not differ significantly across conditions. These findings suggest that subtitle engagement among EMI bilinguals is not solely determined by the presence of subtitles but is also modulated by the properties and perceived utility of the written channel. More generally, our results caution against the view that subtitle engagement is uniformly automatic across multilingual instructional settings. Full article

Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into $64\times 9$ grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

A single user-invariant tensor, kinematically impossible for any human finger to produce, bypasses bidirectional long short-term memory (BiLSTM) continuous-authentication defenders with numerically identical structure across four independently trained generators. We arrive at this finding by training generative adversarial networks against BiLSTM defenders on 51 users across three independent random seeds, with the data partition held fixed, to test the prevailing assumption that successful generative attacks must reproduce the victim’s kinematic behavior. Aggregate attack success rate varies from 31.4% to 45.1% across seeds, a 13.7 percentage-point spread arising purely from optimization stochasticity, demonstrating how unreliable single-seed reporting is as an estimator of the true attack surface. A four-group descriptive stratification shows that 8% of users are attacked across all three seeds, 31% are consistently safe, and 61% exhibit seed-dependent outcomes. Classifier accuracy on zero-effort impostors does not predict adversarial vulnerability (Spearman $\rho =-0.058$, permutation $p=0.688$), whereas intra-user behavioral variance does ($\rho =+0.351$, permutation $p=0.012$, Bonferroni-corrected). The mechanism is not behavioral emulation but convergence to an Adversarial Skeleton Key, a tensor located in an unregularized region of the BiLSTM’s decision surface that the network reliably maps to acceptance, despite lying many standard deviations outside any genuine human distribution. The mimicry-centric evaluation paradigm underestimates the real threat surface. Input-space plausibility must be treated as a defensive layer rather than a preprocessing concern. Full article

Although sustainability has consolidated as a central criterion of value and performance in project management, a deep gap persists between its conceptual recognition and its effective application, making it difficult to structure and measure its real scope. Faced with this complexity, this study aims to dimension sustainable project management in productive sectors by analyzing its strategic alignment and operational trends. Methodologically, the research relies on a meta-aggregative review of 124 articles, integrating qualitative synthesis with quantitative structural analysis to decipher how the field is operationalized. Qualitatively, the results reveal that sustainability redefines project success, shifting toward the integral generation of long-term economic, social, and environmental value, contingent upon its anchoring in corporate strategy, governance, and the project lifecycle. However, quantitative analysis exposes an inherent thematic multidimensionality. The Latent Dirichlet Allocation (LDA) model identifies multiple simultaneous dimensions (entropy = 0.74), and the Principal Component Analysis (PCA) explains 27.24% of the cumulative variance. While these values align with the standard benchmarks for high-dimensional textual data, they empirically represent a highly complex and distributed knowledge structure rather than a unified theoretical framework. Consequently, while consolidated nuclei exist around management and governance, critical empirical gaps persist regarding risk integration, performance metrics, and, particularly, the circular economy. It is concluded that, although the discipline enjoys high theoretical legitimacy and growing measurement capabilities, its integration into operational decision-making remains partial. The ultimate challenge lies in articulating conceptual knowledge, tangible metrics, and strategic governance, ensuring that sustainability evolves from a declarative ideal into the inescapable, cross-cutting operational framework of project management. Full article

The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints, leading to infeasible or unsafe operation plans. To address this issue, this paper proposes a source-grid-load-storage aggregation optimization method that explicitly incorporates both distribution network power flow constraints and district heating network hydraulic–thermal coupling constraints. The network constraints are integrated into the optimization objective as penalty terms, and the dispatch problem is formulated as a Markov decision process. A deep reinforcement learning framework, combining twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms, is employed to solve the sequential decision-making problem. Simulation results demonstrate that the proposed method effectively ensures distribution network security and heating quality while maintaining economic efficiency, providing a feasible and safe dispatch strategy for VPPs in coupled electricity–heat systems. Full article

Self-organized criticality (SOC) describes a class of dynamical systems that may evolve toward statistically critical states characterized by scale-free avalanche-like events. In this work, we study an SOC-inspired discrete toy model and examine the avalanche-size statistics generated by local stochastic interactions. The aim is to explore whether a minimal avalanche model can reproduce statistical features that are formally reminiscent of multiscale turbulent phenomenology. We present a mathematical formulation of the toy model, analyze its numerical avalanche-size distribution, and discuss its relation to concepts of scaling, intermittency, and energy cascades in turbulence. The comparison with Navier–Stokes turbulence is therefore interpreted as a qualitative and statistical analogy, not as a physically complete correspondence. The results suggest that SOC-inspired toy models can provide a useful exploratory framework for understanding heavy-tailed activity and multiscale organization. Full article

Background/Objectives: Bitter taste receptors (T2Rs), specifically T2R38, are present in the respiratory epithelium and react with bacterial quorum-sensing molecules to induce an innate immunity response. Although TAS2R38 polymorphisms have been correlated with susceptibility to chronic rhinosinusitis (CRS), they have not yet been explored in odontogenic rhinosinusitis (ORS), a distinct form of CRS with particular microbial and inflammatory features. We aim to establish a proof-of-concept methodology for investigating TAS2R38 genetic variants in ORS using direct maxillary sinus tissue analysis and demonstrate the feasibility of this translational approach. Methods: We conducted a prospective pilot case–control study of 36 ORS patients and 37 controls undergoing septoplasty without sinonasal disease. Maxillary sinus mucosal biopsies were obtained intraoperatively with informed consent. Genomic DNA was extracted using the PureLink Genomic DNA Mini Kit and quantified via NanoDrop spectrophotometry. TAS2R38 haplotypes were determined and classified as taster (PAV/PAV), non-taster (AVI/AVI), or intermediate (PAV/AVI) phenotype. Results: Among fully classifiable canonical TAS2R38 phenotypes (32 ORS patients, 28 controls), distributions were: tasters 12.5% vs. 25.0%, non-tasters 31.3% vs. 25.0%, and intermediate 56.3% vs. 50.0%. AVI/AVI non-taster status was not significantly associated with ORS susceptibility (OR = 1.36, 95% CI: 0.44–4.25; Fisher’s exact p = 0.775). Conclusions: This proof-of-concept study demonstrates that genotyping-grade genomic DNA can be recovered from acutely inflamed maxillary sinus mucosa, validating this substrate for future tissue-based expression, functional, and microbiome analyses not obtainable from peripheral samples; germline genotyping itself does not require sinus tissue. The observed difference in non-taster prevalence (31.3% vs. 25.0%) did not reach statistical significance and is reported descriptively. This directional trend is hypothesis-generating only and, given the limited statistical power, does not constitute evidence for an association. The demonstrated feasibility, together with the established biological rationale, supports an adequately powered confirmatory study and lays the foundation for future investigation of taste receptor genetics in ORS pathogenesis, and potentially personalized therapeutic strategies. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Horizontal nystagmus is still commonly interpreted at the bedside through a pragmatic peripheral-versus-central dichotomy. Although this heuristic is often clinically useful, it may be misleading because distributed brainstem–cerebellar disorders can generate peripheral-appearing phenotypes. This paper presents a narrative clinical–conceptual review proposing that a substantial subset of horizontal nystagmus patterns may be understood more coherently as expressions of dysfunction within a coupled vestibulo-ocular integrative network rather than as direct signatures of a single lesion site. Within this framework, two core dynamical domains are separated conceptually: a vestibular nuclei (VN)-centered velocity-storage process and an NPH-centered gaze-holding integrator. These processes are proposed to operate under cerebellar regulatory influence, with the nodulus–uvula (NU) acting as a plausible regulator of storage gain, temporal persistence, adaptation stability, and oscillatory behavior. Clinically, the velocity-storage domain is expressed through low-frequency vestibulo-ocular reflex behavior and optokinetic after-nystagmus-related dynamics, whereas the gaze-holding domain is expressed through eccentric gaze stability, gaze-evoked nystagmus, and post-saccadic drift. This framework carries a clinically relevant implication: horizontal nystagmus phenotypes may be interpreted more effectively by asking which functional process is predominantly abnormal—gaze holding, storage-related vestibular persistence, or cerebellar regulatory stability—rather than by relying solely on a binary peripheral–central label. On this basis, we outline a clinician-facing workflow linking gaze dependence, periodicity, direction reversals, head-shaking behavior, and Alexander-law mismatch to operational bedside criteria and candidate quantitative readouts. The proposed model is intended as a clinical–conceptual framework rather than a deterministic localization tool. Its main value lies in organizing discordant vestibular findings, strengthening the mechanistic interpretation of bedside and instrumented observations, and identifying testable directions for future validation studies in acute dizziness and ocular motor disorders. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article