Search Results (2,174)

As stability is one of the most important property of any system, studying it is paramount when performing a power-hardware-in-the-loop simulation in an experimental setup. To guarantee the proper operation of such a system, a thorough understanding of the critical issues regarding the dynamics of the power amplifier, the real-time simulated system and the hardware under test is required. Thus, this paper provides a detailed analysis of the correct design of the real-time simulation modeling for the secure and reliable execution of power-hardware-in-the-loop simulations involving power electronic devices in an experimental setup. Specifically, the stability region of a power-hardware-in-the-loop simulation in an experimental AC microgrid setup involving two parallel three-phase grid-following inverters with LCL filters is studied. Through experimental testing, the stability boundaries of the power-hardware-in-the-loop simulation in the experimental setup is determined, demonstrating a direct relationship between the short-circuit ratio of the utility grid and the cutoff frequency of the feedback current filter. Experimental evidence confirms the capability of the AC microgrid setup to achieve smooth transitions between diverse operating conditions and determine stability boundaries with parameter variations. This research provides practical design guidelines for modeling and the real-time simulation to ensure stability in the power-hardware-in-the-loop simulations in experimental setups involving actual grid-following inverters, specifically using an Opal-RT platform with a voltage-source ideal transformer model and parameter variations in the short-circuit ratio from 2 to 20, the line impedance ratio $X/R$ from 7 to 10, and the feedback-current-filter cutoff frequency from 100 to 1000 kHz. Full article

Short-form video platforms have shifted advertising from standalone, time-bounded spots to feed-embedded, swipeable stimuli, creating a high-velocity processing context that can penalize casting complexity. We ask whether a “one face, many roles” casting strategy (a single actor playing multiple characters) outperforms multi-actor executions, and why. A two-phase pretest (N = 3500) calibrated a realistic ceiling for “multi-actor” casts, then four experiments (total N = 4513) tested mechanisms, boundary conditions, and alternatives. Study 1 (online and offline replications) shows that single-actor ads lower cognitive load and boost account evaluations and purchase intention. Study 2, a field experiment, demonstrates that Need for Closure amplifies these gains via reduced cognitive load. Study 3 documents brand-type congruence: one actor performs better for entertaining/exciting brands, whereas multi-actor suits professional/competence-oriented brands. Study 4 rules out cost-frugality and sympathy using a budget cue and a sequential alternative path (perceived cost constraint → sympathy). Across studies, a chain mediation holds: single-actor casting reduces cognitive load, which elevates brand authenticity and increases purchase intention; a simple mediation links cognitive load to account evaluations. Effects are robust across settings and participant gender. We theorize short-form advertising as a context-embedded persuasion episode that connects information-processing efficiency to authenticity inferences, and we derive practical guidance for talent selection and script design in short-form campaigns. Full article

Polyethylene microplastics (PE-MPs) and glyphosate (GLY) are widespread aquatic contaminants, but their combined effects on microalgae remain poorly understood. This study assessed the individual and joint toxicity of GLY and PE-MPs to the model microalga Chlorella vulgaris. Acute (3-day) and chronic (7-day) exposures were performed using GLY at 1–40 mg/L, alone or combined with PE-MPs (10 mg/L). A four-parameter log-logistic (4PL) model was applied to estimate median effect concentrations (EC₅₀). After 72 h, the EC₅₀ values were 9.77 mg/L for the GLY single system and 2.31 mg/L for the GLY-PE combined system, confirming enhanced toxicity in combined exposures. Co-exposure reduced pigment levels (chlorophyll a, chlorophyll b, and carotenoids) by up to 65% and significantly increased oxidative stress markers, including reactive oxygen species production and malondialdehyde accumulation, compared with single treatments. Antioxidant enzymes (superoxide dismutase and catalase) showed concentration- and time-dependent responses, indicating activation of cellular defense mechanisms. Scanning Electron Microscopy revealed PE-induced aggregation and structural damage to algal cells, particularly at higher GLY concentrations. These findings demonstrate that PE-MPs can amplify the toxic effects of GLY on microalgae and highlight the need for further studies at environmentally relevant concentrations and with different polymer types. Full article

This study investigates the influence of heavy-layer thickness on shock-accelerated interfacial instabilities in single-mode stratifications using high-order discontinuous Galerkin simulations at a fixed shock Mach number ($M_s=1.22$). By systematically varying the layer thickness, we quantify how acoustic transit time, shock attenuation, and phase synchronization modulate vorticity deposition, circulation growth, and interface deformation. The results show that thin layers ($d=2.5$–5 mm) generate strong and early baroclinic vorticity due to frequent reverberations, leading to rapid circulation growth, vigorous Kelvin–Helmholtz roll-up, and early jet pairing. In contrast, thick layers ($d=20$–40 mm) attenuate and dephase shock returns, producing weaker baroclinic reinforcement, delayed shear-layer growth, and smoother interfaces with reduced small-scale activity, while the intermediate case ($d=10$ mm) exhibits transitional behavior. Integral diagnostics reveal that thin layers amplify dilatational, baroclinic, and viscous vorticity production; sustain stronger circulation and enstrophy growth; and transfer bulk kinetic energy more efficiently into interface deformation and small-scale mixing. Full article

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

We present a theoretical model to explain how tubulin dimers in neuronal microtubules might achieve collective quantum coherence, resulting in wavefunction collapses that manifest as avalanches within a self-organized criticality (SOC) framework. Using the Orchestrated Objective Reduction (Orch-OR) theory as inspiration, we propose that microtubule subunits (tubulins) become transiently entangled via dipole–dipole couplings, forming coherent domains susceptible to sudden self-collapse. We model a network of tubulin-like nodes with scale-free (Barabási–Albert) connectivity, each evolving via local coupling and stochastic noise. Near criticality, the system exhibits power-law avalanches—abrupt collective state changes that we identify with instantaneous quantum wavefunction collapse events. Using the Diósi–Penrose gravitational self-energy formula, we estimate objective reduction times $T_{\mathrm{OR}}=\hslash /E_g$ for these events in the 10–200 ms range, consistent with the Orch-OR conscious moment timescale. Our results demonstrate that quantum coherence at the tubulin level can be amplified by scale-free critical dynamics, providing a possible bridge between sub-neuronal quantum processes and large-scale neural activity. Full article

NO₂ is a toxic gas mainly generated by combustion processes, such as vehicle emissions and industrial activities. It is a key contributor to smog, acid rain, ground-level ozone, and particulate matter, all of which pose serious risks to human health and the environment. Conventional resistive gas sensors, typically based on metal oxide semiconductors, detect NO₂ by resistance modulation through surface interactions with the gas. However, they often suffer from low responsiveness and poor selectivity. This study investigates NO₂ detection using nanoporous zinc oxide thin films integrated into a resistor structure and floating-gate field-effect transistor (FGFET). Both Silvaco-Atlas simulations and experimental fabrication were employed to evaluate sensor behavior under NO₂ exposure. The results show that FGFET provides higher sensitivity, faster response times, and improved selectivity compared to resistor-based devices. In particular, FGFET achieves a detection limit as low as 89 ppb, with optimal performance around 400 °C, and maintains stability under varying humidity levels. The enhanced performance arises from quantum well effects at the floating-gate Schottky contact, combined with NO₂ adsorption on the ZnO surface. These interactions extend the depletion region and confine charge carriers, amplifying conductivity modulation in the channel. Overall, the findings demonstrate that FGFET is a promising platform for NO₂ sensors, with strong potential for environmental monitoring and industrial safety applications. Full article

The detection of allergens is essential for ensuring food safety, protecting public health, and providing accurate information to consumers. Wheat (Triticum aestivum L.) and maize (Zea mays L.) are recognized as important food allergens. In this study, novel PCR methods were developed for the reliable detection of wheat and maize allergens, including wheat high-molecular-weight glutenin subunit (HMW-GS) and low-molecular-weight glutenin subunit (LMW-GS), as well as three maize allergens, namely, Zea m 14, Zea m 8, and zein. Wheat and maize genomic DNA, as well as allergen genes, were examined during 60 min of baking at 180 °C and 220 °C. Agarose gel electrophoresis revealed degradation of genomic DNA and amplified PCR fragments in correlation with increasing baking temperature and time. For each target gene, the best primers were identified that could detect HMW-GS and LMW-GS genes in wheat samples and Zea m 14, Zea m 8, and zein genes in maize samples after baking at 220 °C for 60 min and 40 min, respectively. The results indicate that these PCR methods can be used for the reliable and sensitive detection of wheat and maize allergens in processed foods. Full article

This article explores the voices of sailors across time, focusing on how song and prayer animate the nautical cult of St. Nicholas of Myra from the Middle Ages to the present. Drawing on hagiography, poetry, and music, it examines how medieval sources portray sailors’ cries to St. Nicholas during storms at sea, often depicting univocal, affective pleas that provoke divine response. These representations—especially in Latin sequences such as Congaudentes exultemus—highlight the cultural weight of the literal and metaphorical voice within miracle narratives. The article then bridges medieval and modern devotional soundscapes through nineteenth- and twentieth-century ethnographic collections from Apulia, Italy, particularly through the work of folklorists Saverio La Sorsa and Alfredo Giovine. Their records of Barese sailors’ songs and prayers to St. Nicholas—still sung today—provide embodied counterpoints to the mediated voices of medieval texts. Through this transhistorical lens, I argue that voice operates as connective tissue in the devotional lives of seafarers: an expression of fear, faith, and communal identity. By amplifying sailors’ voices in text, song, and performance, both medieval and modern traditions construct a vivid aural archive that affirms the enduring relationship between St. Nicholas and those who navigate the dangers of the sea. Full article

The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman filter (with total-variation prior), a Hidden Markov Model (HMM) for state stabilization, and a lightweight Artificial Neural Network (ANN) for learned temporal refinement, enabling real-time crash detection from monocular video. Evaluated on simulated traffic in CARLA and real-world driving in Istanbul, the full temporal stack achieves the best precision–recall balance, yielding 83.47% F1 offline and 82.57% in real time (corresponding to 94.5% and 91.2% detection accuracy, respectively). Ablations are consistent and interpretable: removing the HMM reduces F1 by 1.85–2.16 percentage points (pp), whereas removing the ANN has a larger impact of 2.94–4.58 pp, indicating that the ANN provides the largest marginal gains—especially under real-time constraints. The transition from offline to real time incurs a modest overall loss (−0.90 pp F1), driven more by recall than precision. Compared to strong single-frame baselines, YOLOv10 attains 82.16% F1 and a real-time Transformer detector reaches 82.41% F1, while our full temporal stack remains slightly ahead in real time and offers a more favorable precision–recall trade-off. Notably, integrating the ANN into the HMM-based pipeline improves accuracy by 2.2%, while the time-variant Kalman configuration reduces detection lag by approximately 0.5 s—an improvement that directly addresses the human reaction time gap. Under identical conditions, the best RCNN-based configuration yields AP@0.50 ≈ 0.79 with an end-to-end latency of 119 ± 21 ms per frame (~8–9 FPS). Overall, coupling deep learning with probabilistic reasoning yields additive temporal benefits and advances deployable, camera-only crash detection that is cost-efficient and scalable for intelligent transportation systems. Full article

The accurate assessment and management of Blue Mackerel (Scomber australasicus) and Chub Mackerel (Scomber japonicus) resources in the high seas of the Northwest Pacific are constrained by the persistent issue of data misreporting in catch records, which arises from their high morphological similarity. This study integrates fishery logbooks and field sampling data from Chinese purse seine fleets (2014–2023), along with key oceanographic factors—six of which were finally selected after correlation analysis. We introduce, for the first time, a Zero-One Inflated Beta Model (ZOIBM) to analyze the spatiotemporal distribution of the relative abundance of these two mackerel species. Furthermore, a Generalized Additive Model (GAM) was employed to reveal the environmental mechanisms driving their niche differentiation. The results show that the ZOIBM demonstrates excellent performance (R² = 0.63, RMSE = 0.305), effectively quantifying the proportional composition of the two species in mixed catches. Spatially, high-abundance areas of Blue Mackerel were concentrated within 35–44° N, 145–160° E, with its proportion decreasing at higher latitudes. In contrast, Chub Mackerel exhibited an opposite latitudinal pattern, with its high-abundance areas covering a broader latitudinal range (35–47.5° N). The analysis of environmental drivers indicated that SST was the most critical factor for differentiation, while Chla and VO further amplified the divergence in resource utilization strategies between the species. From 2014 to 2023, the distribution centroids of both mackerel species showed significant northward and eastward shifts, and their spatial overlap has been continuously increasing. This research provides a methodological reference for the fine-scale assessment of co-occurring fish resources and offers a scientific basis for the sustainable management of the North Pacific mackerel fishery. Full article

This work presents a low-power photoplethysmography (PPG) readout integrated circuit (IC) that achieves a wide dynamic range (DR) through the direct integration of a voltage-controlled oscillator (VCO)-based quantizer into the photodiode driver. Conventional PPG readout circuits rely on either transimpedance amplifier (TIA) or light-to-digital converter (LDC) topologies, both of which require auxiliary DC suppression loops. These additional loops not only raise power consumption but also limit the achievable DR. The proposed design eliminates the need for such circuits by embedding a linear regulator with a mirroring scale calibrator and a time-domain quantizer. The quantizer provides first-order noise shaping, enabling accurate extraction of the AC PPG signal while the regulator directly handles the large DC current component. Post-layout simulations show that the proposed readout achieves a signal-to-noise-and-distortion ratio (SNDR) of 40.0 dB at 10 µA DC current while consuming only 0.80 µW from a 2.5 V supply. The circuit demonstrates excellent stability across process–voltage–temperature (PVT) corners and maintains high accuracy over a wide DC current range. These features, combined with a compact silicon area of 0.725 mm² using TSMC 250 nm bipolar–CMOS–DMOS (BCD) process, make the proposed IC an attractive candidate for next-generation wearable and biomedical sensing platforms. Full article

The medical imaging domain frequently encounters the dual challenges of annotation scarcity and class imbalance. A critical issue lies in effectively extracting information from limited labeled data while mitigating the dominance of head classes. The existing approaches often overlook in-depth modeling of sample relationships in low-dimensional spaces, while rigid or suboptimal dynamic thresholding strategies in pseudo-label generation are susceptible to noisy label interference, leading to cumulative bias amplification during the early training phases. To address these issues, we propose a semi-supervised medical image classification framework combining labeled data-contrastive learning with class-adaptive pseudo-labeling (CLCP-MT), comprising two key components: the semantic discrimination enhancement (SDE) module and the class-adaptive pseudo-label refinement (CAPR) module. The former incorporates supervised contrastive learning on limited labeled data to fully exploit discriminative information in latent structural spaces, thereby significantly amplifying the value of sparse annotations. The latter dynamically calibrates pseudo-label confidence thresholds according to real-time learning progress across different classes, effectively reducing head-class dominance while enhancing tail-class recognition performance. These synergistic modules collectively achieve breakthroughs in both information utilization efficiency and model robustness, demonstrating superior performance in class-imbalanced scenarios. Extensive experiments on the ISIC2018 skin lesion dataset and Chest X-ray14 thoracic disease dataset validate CLCP-MT’s efficacy. With only 20% labeled and 80% unlabeled data, our framework achieves a 10.38% F1-score improvement on ISIC2018 and a 2.64% AUC increase on Chest X-ray14 compared to the baselines, confirming its effectiveness and superiority under annotation-deficient and class-imbalanced conditions. Full article

The evaporation of sessile water drops involves coupled heat and mass transfer and is influenced by temperature, relative humidity, and the nature of the surface on which the drop rests. This work investigates the possibility of using vibration to enhance evaporation kinetics. For this purpose, experiments were conducted with vertical vibration near the resonant frequency. An original experimental device was designed, including a shaker controlled by a signal generator and an amplifier, a high-speed camera, and an adapted lighting system. The amplitude–frequency relationship was first examined to select the resonance frequency. As expected, the evaporation kinetics of two drops—one with vibration at the resonance frequency and the other without vibration—demonstrate that vibration accelerates evaporation and reduces drying time by 20.6% on PTFE substrate and by 23.5% on glass substrate. Full article

The intensification of extreme hydrologic events, such as flash floods and landslides, has amplified the challenges of ensuring timely and effective emergency response. A key factor in the efficiency of such interventions is the accessibility of affected areas, which often becomes compromised during hazard events. In this context, the present study focuses on the Vărbilău River catchment in Romania, a region highly exposed to frequent flash floods and terrain instability. The research evaluates the spatial accessibility of emergency intervention units. Four major intervention centers were assessed under both normal and constrained scenarios. Accessibility was quantified through travel-time thresholds, incorporating variables such as road quality, network density, topography, and hazard-induced disruptions. Findings indicate that southern localities enjoy relatively short intervention times (less than 10 or between 10 and 20 min) due to favorable terrain and proximity to well-equipped centers. In such cases, the speed on main roads is 50–60 km/h, while the accessibility index is 5. Conversely, northern areas and villages like Lutu Roşu face elevated isolation risks, as single-road access and weak connectivity heighten their vulnerability during floods or landslides. In such cases, speeds reduce to 10 km/h and accessibility is very low, with the accessibility index of 1. Scenario modeling further demonstrated that the loss of key hubs (e.g., Ploieşti or Văleni) severely undermines coverage efficiency, particularly in high-risk zones, where the access times increases over 40 min. These results emphasize the need for dynamic intervention planning, infrastructure reinforcement, and the systematic integration of hazard-prone areas into emergency response strategies. Moreover, the methodological framework developed here can be adapted to other regions exposed to hydrologic hazards. Full article