Search Results (656)

High-throughput microfluidic deformation assays enable label-free single-cell mechanophenotyping by quantifying how cells deform under controlled hydrodynamic loading. These approaches commonly extract deformation-related observables, such as projected area, axis ratio, and deformation index, and use them as indicators for cellular mechanical properties. However, deformation is not solely determined by stiffness; it is a coupled outcome of cell size, local hydrodynamic stress, and intrinsic mechanical response. Therefore, we present a simulation-based microfluidic framework for estimating region-dependent apparent Young’s modulus (E, a quantitative indicator characterizing cellular mechanical stiffness) from diameter–deformation measurements at the single-cell level. A three-region microfluidic channel is designed to impose distinct hydrodynamic loading conditions, while numerical simulations establish quantitative maps linking cell diameter, deformation, and E. Based on these results, region-specific nonlinear surface models are constructed to invert experimental diameter–deformation measurements into E values. Finally, application to primary T cells and K562 cells demonstrates clear region-dependent differences in E, highlighting the influence of local loading conditions on inferred mechanical properties. Overall, this work provides a simplified but practical route for transforming deformation-based phenotypes into quantitative, loading-aware mechanical parameters for single-cell analysis. Full article

Three layered rare-earth hydrogenselenite–selenite hydrates, Ln(HSeO₃)(SeO₃)·2H₂O (Ln = Yb, Dy, Eu), were synthesized under hydrothermal conditions and systematically characterized. Single-crystal and powder X-ray diffraction show that the compounds are isostructural and crystallize in the non-centrosymmetric orthorhombic Sohncke space group P2₁2₁2₁, featuring LnO₈ polyhedra and SeO₃/HSeO₃ units assembled into hydrogen-bonded layered frameworks. Two-component inversion-twin refinements gave Flack x values of 0.06(4), 0.27(3), and 0.22(3) for the Yb-, Dy-, and Eu-containing crystals, respectively; the Yb crystal is dominated by one inversion domain, whereas the Dy and Eu crystals contain appreciable inverted-domain fractions. Because L/D/DL descriptors conventionally refer to the absolute configuration of chiral molecular entities, they are not assigned to these extended inorganic frameworks. Under the present achiral synthesis conditions, crystals dominated by the opposite, inversion-related framework hand cannot be excluded. Photoluminescence measurements reveal characteristic Dy³⁺ and Eu³⁺ emissions, while the Yb analogue exhibits a broad visible band tentatively related to host-framework states. Magnetic measurements show no long-range ordering above 2 K; the Yb and Dy phases display dominant antiferromagnetic correlations, whereas the Eu phase is governed mainly by Van Vleck paramagnetism. These results identify Ln(HSeO₃)(SeO₃)·2H₂O as a layered Sohncke-symmetry platform with lanthanide-dependent optical and magnetic behavior. The observed lanthanide emissions and non-centrosymmetric framework suggest prospective photonic and nonlinear-optical applications, although device-level performance remains to be established. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

Radiomics features are strongly sensitive to image acquisition, and separating that sensitivity from biological signal usually requires repeated patient scans that cannot be shared. We present an open, fully synthetic framework (radiomics-phantom) that maps and, as a proof of concept, corrects radiomics feature instability without any patient data. Deterministic three-dimensional texture phantoms are generated as anisotropic Gaussian random fields with known ground truth and an optional embedded lesion. An independently implemented feature core aligned with the Image Biomarker Standardization Initiative (IBSI) covers all eleven IBSI-1 feature families and matched all 482 published digital-phantom benchmark values within the applicable tolerances. An image-domain acquisition simulator applies point-spread blur, slice-profile averaging, dose-scaled correlated noise, resampling, and quantisation. Per-feature reproducibility across a sweep of fifteen textures (varying correlation length, anisotropy, and intensity scale) by nine acquisition conditions, with five independent noise realisations per stochastic setting, is summarised by the absolute-agreement intraclass correlation ICC(2,1), with a realisation-aware percentile-bootstrap 95% confidence interval for every estimate; constant features are excluded from estimation. Values span nearly the full range (median 0.13, 95% CI 0.03–0.19), and a hierarchical variance decomposition attributes a median 77% of per-feature variance to the acquisition condition and under 1% to stochastic realisation; the values are interpreted as exploratory rankings within this acquisition envelope. As a proof of concept, intensity variance and grey-level co-occurrence contrast under additive Gaussian noise were normalised using calibrated, invertible response models, returning them to their noiseless values on held-out data (median error below 4% across five textures and repeated noise realisations, and about 11% when the noise level is estimated from the degraded image itself), while features the models cannot describe are refused rather than corrected. All code and a 716-test suite are released openly and archived on Zenodo. The result is a reproducible, patient-data-free testbed for radiomics feature stability. Full article

On-board three-phase two-level uninterruptible power supply systems serve as vital energy interfaces, ensuring high-fidelity power distribution for critical payloads in heavy-duty unmanned aerial vehicles. Therefore, this paper introduces a robust current-sensorless discrete-time sliding-mode control (DSMC) strategy in the stationary $\alpha \beta$ frame to simplify the system structure while maintaining high-quality dynamic voltage performance. A discrete-time extended-state observer (DESO) is implemented to precisely estimate the filter capacitor current, effectively addressing the voltage regulation issues stemming from load fluctuations and the absence of sensors. Furthermore, the DESO-based current estimate is incorporated as feedforward compensation into the DSMC architecture to significantly bolster the disturbance rejection and fault-tolerance capabilities of system. The simulation results verify that the proposed method outperforms typical cascaded proportional–resonant control, delivering superior voltage tracking accuracy and robust performance. Specifically, compared to the typical cascaded strategy, the proposed method reduces the steady-state RMS voltage tracking error by approximately 1.5 V across all load types, and decreases the THD by 0.08% under balanced loads and 0.15% under nonlinear loads. Full article

This work presents a novel method known as the Red Deer Algorithm-Based Artificial Neural Network (RDA-ANN) for managing real-time voltage and harmonic control in a cascade H-bridge multilevel inverter (CHB-MLI) that is fed by photovoltaic (PV) panels. The RDA-ANN technique proposed here computes the switching angles in real-time for selective harmonic elimination (SHE) on the output voltage of a multilevel inverter (MLI). In the proposed approach, a comprehensive lookup table containing 7776 permutations of switching angles was first generated offline using RDA optimization for a three-phase, 11-level CHB-MLI with five PV panels operating across a voltage range of 30 V to 35 V. This dataset was subsequently used to train a feed-forward ANN model capable of predicting optimal switching angles corresponding to any real-time voltage measurements from the panels. The SHE-PWM approach based on RDA-ANN targets the elimination of the 5th, 7th, 11th, and 13th order harmonics. This algorithm guarantees that the intended fundamental voltage is achieved, even during fluctuations in the voltages of the panels caused by varying irradiation and temperature conditions, while effectively removing the unwanted harmonics. The findings, validated under multiple environmental scenarios, illustrate that the RDA-ANN-based SHE-PWM technique successfully eliminates the selected harmonics from the load voltage with a fundamental voltage error not exceeding 0.18%, and results in a low total harmonic distortion (THD) value that complies with the IEEE 519-2022 standard across all tested conditions. Full article

As artificial intelligence and digital human technologies become increasingly integrated into livestream commerce, AI anchors are becoming important marketing agents. Yet prior research has focused primarily on their visual characteristics, leaving dynamic vocal cues largely unexplored. Drawing on social response theory and perceived authenticity research, this study examines the nonlinear association between AI anchor pitch variability and consumer engagement, together with a proposed psychological pathway and boundary condition. Study 1 analyzes 4322 product-presentation segments nested within 330 AI-anchored livestreams and 85 independent accounts on Douyin. Negative binomial models, formal boundary-slope tests, and additional specifications using account and livestream-session fixed effects, a correlated-random-effects decomposition, and viewer-minutes exposure provide robust evidence of an inverted U-shaped association between pitch variability and real-time danmaku engagement. Evidence concerning appearance-realism moderation is conditional and specification-sensitive across alternative pitch operationalizations, exposure definitions, and within-account specifications. Study 2 uses a preregistered multi-stimulus mixed design with four AI anchors, four products, and three between-participants pitch-variability conditions. Correctly scaled planned contrasts show that moderate pitch variability produced greater perceived authenticity and engagement intentions than the average of the two endpoint conditions. A 2-1-1 multilevel analysis yielded an indirect-effect pattern consistent with the proposed role of perceived authenticity. Models allowing treatment effects to vary across the 16 included anchor-product combinations showed a positive average moderate-pitch advantage, although its magnitude varied across stimuli. These findings extend livestream-commerce research from human streamers to AI-mediated communication while indicating that appearance-realism moderation, stimulus-level generalization, and causal mediation require further replication. Full article

Building-applied photovoltaics (BAPV) offer a practical retrofit pathway for the prefabricated social housing estates of Central and Eastern Europe. This study assesses the solar and photovoltaic potential of post-WW II housing districts in Poznań, Poland, through a multi-scale workflow spanning the city, district, and building levels. Measured municipal rooftop data for 336 residential buildings in four districts were combined with tree-inclusive parametric solar simulations, calibrated against the rooftop solar cadastre (normalized mean bias error of +0.4% after calibration), to select South Winogrady and two representative buildings. Three PV design scenarios were then evaluated in roof, facade, and combined configurations using a techno-economic model that incorporates manufacturer-warranted degradation, maintenance, inverter replacement, and a ±25% electricity price and installation cost sensitivity envelope. Roof configurations pay back in 6.6–7.2 years (30-year return on investment of 286–321%), facade systems are economically defensible only on the best-exposed surfaces (8.9–13.8 years), and all configurations remain profitable even under the pessimistic bounding case. ENVI-met simulations of the same scenarios show localized pedestrian-level reductions in the Universal Thermal Climate Index of up to 2.41 °C near the PV-equipped buildings, persisting under 2050 climate projections. The results provide a transferable evidence chain for prioritizing BAPV retrofits in standardized post-war housing stock. Full article

The growing share of photovoltaic generation in power grids intensifies the need for converter architectures capable of combining efficient energy conversion, DC-bus stability, and ancillary service provision at the grid coupling point. This paper presents the modeling, implementation, and simulation-based evaluation of a 91 kWp three-phase photovoltaic (PV) system integrated with a battery energy storage system (BESS), developed in the PLECS environment. The proposed architecture comprises three interleaved Boost stages for maximum power point tracking (MPPT), a DC bus regulated at 600 V, three independent bidirectional buck–boost converters for LiFePO₄ bank management, and a two-level three-phase voltage source inverter (VSI) with an LC output filter. The control is organized in cascade voltage–current loops for the DC–DC stages and in vector control within the synchronous reference frame (SRF) for the inverter, with synchronization via SRF-PLL. A C-Script supervisory block integrates the Perturb and Observe (P&O) MPPT algorithm, independent state of charge (SOC) estimation per bank via coulomb counting, and Volt–VAR reactive power reference generation with a dead band of 0.90–1.10 pu. Five scenarios are analyzed for validation: DC-bus regulation under irradiance transients; reactive power support during undervoltage and overvoltage events (0.80–0.85 pu and 1.15–1.20 pu); BESS operation as an active DC-link support element; and PV curtailment with fully charged banks. All five scenarios were additionally corroborated on a Typhoon HIL402 Pro 2 hardware-in-the-loop platform, reproducing the PLECS waveforms within the amplitude and timing resolution of the oscilloscope captures. Across all scenarios, the DC bus is held within $\pm 15$ V (2.5%) of the 600 V reference, with the worst-case transient recovering in 80–100 ms; under a sustained 9 s bidirectional disturbance, redirecting PV surplus to BESS charging in both the undervoltage and overvoltage segments—with no externally imposed active-current limit—keeps the current-vector magnitude $\sqrt{i_d^2+i_q^2}$ below the 335 A rating throughout (≈271 A and ≈242 A, respectively), while the available reactive margin $Q_{disp}$ reaches ≈78–80 kVAr in both segments and the bank SOC advances by ≈$0.03$ pu; and supervisory curtailment under a sustained overvoltage ride-through with a saturated bank keeps the per-bank SOC dispersion within $4\times {10}^{-5}$ pu while expanding the available reactive margin $Q_{disp}$ from ≈50 to ≈90 kVAr. Full article

This study investigates the impact of real effective exchange rate (REER) volatility on foreign direct investment (FDI) inflows in three major Central and Eastern European (CEE) economies—Hungary, Poland, and Romania—using quarterly data spanning from 2007-Q1 to 2024-Q4. The exchange rate volatility is modeled using a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework, and country-specific relationships are estimated through Autoregressive Distributed Lag (ARDL) bounds testing and Toda–Yamamoto causality analysis. Our research indicates that a uniform relationship does not exist across the region. In Hungary, the utilization of directional FDI data excluding Special Purpose Entities (SPEs), in conjunction with structural breaks and quarterly seasonal controls, reveals a statistically significant nonlinear (inverted U-shaped) relationship between long-run exchange rate volatility and FDI inflows. In addition, domestic financial development exerts a substantial buffering effect on the transmission of volatility in Hungary by bypassing SPE flows that previously obscured this effect. In Poland and Romania, a stronger currency consistently discourages investment by reducing cost competitiveness. Romania shows a distinct pattern: volatility initially attracts FDI, and while deeper financial markets meaningfully dampen this effect, the net relationship remains positive, unlike Hungary, where sufficiently deep credit markets fully reverse it. These results suggest that policymakers should look beyond short-term exchange rate stabilization and instead prioritize structural reforms, competitive exchange rate levels, transparent FDI reporting standards, and deeper domestic financial markets to sustain FDI inflows. Full article

Three-level neutral-point-clamped (NPC) inverters are widely used in medium- and high-power drives and grid-connected applications due to their reduced device voltage stress, improved output power quality, and lower switching losses relative to conventional two-level topologies. Among the potential failure modes, clamping diode open-circuit faults are difficult to detect because the clamping diodes conduct only during the zero-voltage states, and their failure produces only subtle distortions in the phase current waveform. This paper proposes a fault diagnosis method for clamping diode open-circuit faults in three-level NPC inverters. The method is based on a current asymmetry index defined as the ratio of the per-cycle mean phase current to the per-cycle mean absolute phase current. During healthy operation, this index is approximately zero in all phases. A fault causes the index to deviate markedly from zero, while the polarity of this deviation identifies the failed diode. The method requires only phase-current measurements already available in the inverter control system. Consequently, no additional sensors, hardware modifications, or changes to inverter operation are required. Simulation results obtained for a 10 kW three-level NPC inverter demonstrate successful fault detection within approximately one to two fundamental cycles for open-circuit failures of both the upper and lower clamping diodes in all three phases. Full article

A major barrier to developing adaptive human–machine systems is the lack of interpretable physiological markers for characterizing physiological responses associated with changes in task demands. The aim of this study was to evaluate the feasibility of machine-learning classification of physiological responses during simple and complex task stages and two standardized task-demand transitions—baseline/rest-to-simple and simple-to-complex—in cognitive and motor-cognitive protocols. Sixty-nine healthy volunteers completed cognitive tasks involving normal and 180° inverted-text reading and motor-cognitive tasks involving simple and complex movement sequences performed with the non-dominant hand. Each protocol comprised a 1 min baseline followed by three consecutive blocks, each including a 1 min simple task, a 1 min complex task, and a 1 min rest period. Photoplethysmography (PPG), skin conductance (SC), and abdominal respiration were recorded continuously. Pulse-to-pulse (PP) intervals were extracted from the PPG signal, and conventional heart rate variability (HRV) indices, including mean RR interval, HR range, SDNN, RMSSD, Total Power, and SD2, were calculated in Kubios HRV from the PPG-derived interval series. Logistic Regression, Random Forest, and Support Vector Machine models were trained using either absolute physiological values or dynamic features (Δ%) calculated for the baseline/rest-to-simple and simple-to-complex transitions. Models based on dynamic features consistently demonstrated higher classification performance than those based on absolute physiological values. For the motor-cognitive protocol, Random Forest achieved an accuracy of 0.726 and a ROC-AUC of 0.730, whereas for the cognitive protocol, Support Vector Machine achieved an accuracy of 0.655 and a ROC-AUC of 0.722 on the held-out participant-level test set. The most informative features included mean RR interval (derived from PPG), Total Power, RMSSD, SDNN, SD2, and HR range in both protocols. Comparable feature-importance patterns across protocols suggest overlap in the physiological variables contributing to classification under the standardized experimental conditions. These findings are limited to the fixed transition sequence used in the present study and require confirmation in randomized or counterbalanced designs. Full article

The increasing integration of power electronic converters in hybrid AC/DC microgrids introduces significant challenges in maintaining DC-link voltage stability during mode transitions, where uncoordinated actions cause large voltage deviations. This paper proposes a coordinated hierarchical control strategy incorporating a supervisory finite state machine (FSM) and a slew-rate-limited reference shaping mechanism to ensure smooth transitions in a microgrid interfaced through a bidirectional DC–DC converter and a three-level T-type inverter. The supervisory layer coordinates the sequencing of subsystem activation and routes all mode changes through a dedicated transition state in which the power reference is gradually shaped to suppress DC-link disturbances, while a dedicated resynchronization state manages reconnection to the grid after a sustained outage. The strategy is validated through detailed switching-level simulations across five operating scenarios, including islanded load energization, grid blackout, discharging-to-charging transitions, state-of-charge limit management, and grid restoration through reclosing and resynchronization, and is further compared against a droop-based coordination scheme. Simulation results demonstrate that the proposed approach reduces the transient DC-link voltage deviation from approximately 18–20% to below 7%, and to as low as 2.6%, without introducing steady-state error, confirming its effectiveness in enhancing the dynamic stability of the system during mode transitions. Full article

As global environmental challenges intensify and the imperative to mitigate extreme climate change grows increasingly urgent, the sustainable development of economic and social systems has emerged as a fundamental pathway for addressing environmental challenges. Smart manufacturing, as a core technology of the new round of technological revolution, can drive green transformation and enhance sustainable development performance. Using a sample of A-share listed manufacturing corporations from 2009 to 2023, this study systematically investigates the impact, underlying mechanisms, and heterogeneity of smart manufacturing on the sustainable development performance of manufacturing corporations from the perspective of local environmental regulation stringency. The results show that corporate smart manufacturing significantly improves sustainable development performance. This conclusion remains robust after addressing endogeneity concerns and conducting a series of robustness checks. Mechanism analysis reveals that corporate smart manufacturing operates via three primary channels: the data asset accumulation effect, the green technology innovation effect, and the information environment improvement effect. Furthermore, the moderating role of local environmental regulation stringency follows an inverted U-shaped trajectory, suggesting that regulatory stringency facilitates the smart manufacturing–performance nexus only up to a certain threshold, beyond which it becomes counterproductive. Heterogeneity analysis further shows that the positive effect is particularly pronounced among private corporations, large-scale corporations, those located in the eastern region, and those situated in key environmental protection cities. By uncovering the micro-level mechanisms through which smart manufacturing affects sustainable performance, this study offers novel theoretical insights and actionable policy implications for leveraging intelligent technologies to advance manufacturing sustainability, both in China and globally, in support of the nation’s carbon peaking and carbon neutrality commitments. Full article

The large-scale integration of inverter-interfaced renewable generation has made the steady-state short-circuit current of renewable energy stations increasingly important for setting protections, planning, and fault analysis. Traditional single-unit multiplication methods are computationally efficient but often ignore the effects of internal electrical distance and active power output dispersion among generation units, which can lead to non-negligible errors at the station level. To address this issue, this paper proposes an improved single-unit multiplication method and a corresponding dimension-reduction framework for centralized renewable energy stations considering short-circuit current fitting characteristics. A unified three-segment positive-sequence current control model is first adopted to represent the low-voltage ride-through behavior of photovoltaic (PV), direct-drive wind turbine, and battery energy storage system (BESS) stations. The upper and lower voltage breakpoints are selected as 0.9 p.u. and 0.2 p.u., respectively, and the linear support coefficient was determined as 1.5 according to Chinese national standards. On this basis, the effects of electrical distance and active power output dispersion on the calculation error of the traditional single-unit multiplication method are analyzed. A grouping criterion based on the average access-point voltage and critical active power is then established, and the resulting two-group equivalent method is used to estimate the total short-circuit current of renewable energy stations. Electromagnetic transient simulations in PSCAD are conducted for PV, direct-drive wind, and BESS stations. The results show that, compared with the traditional single-unit multiplication method, the proposed method more accurately captures the steady-state short-circuit current characteristics under different voltage dips and output-dispersion conditions while retaining high engineering practicality. Full article