Search Results (1,855)

Optogenetic manipulation is pivotal in basic neuroscience research and in studying neuropsychiatric disorders, enabling the activation or inhibition of neuronal populations with millisecond precision. However, this technique can induce artificial neuronal hypersynchronization that is rarely observed under physiological conditions. Given that epileptic seizures arise from abnormally synchronized neuronal discharges, the potential for optogenetic stimulation to trigger seizures and confound experimental outcomes warrants close examination. Here, using electrophysiological and behavioral recordings, we demonstrate that even single-trial optogenetic stimulation of CaMKII-positive neurons in the hippocampal CA1 region, anterior piriform cortex (APC), or lateral/medial entorhinal cortex (LEnt or MEnt) can induce seizure-like discharges and behaviors in adult male C57BL/6 mice. Repeated stimulation in the APC, LEnt, or MEnt elicited more severe seizure-like activity. Furthermore, stimulation protocols characterized by high power, long duration, high frequency, and medium pulse width were more prone to inducing such events. Additionally, we found that CA1 stimulation could impair subsequent contextual fear memory. These findings provide critical cautions and practical guidelines for the design and implementation of in vivo optogenetic experiments in neuroscience research. Full article

Background: Medication errors (MEs) are an important cause of preventable harm but remain insufficiently quantified in emergency care. This study assessed the frequency, characteristics, and clinical impact of MEs among adverse drug reaction (ADR)–related emergency department (ED) admissions in Germany. Methods: We conducted a prospective multicenter study across six EDs between 2015 and 2021 (n = 7967). ADRs and MEs were classified using standard causality (World Health Organization-Uppsala Monitoring Centre (WHO-UMC)) and preventability criteria (Schumock). Patient, medicine, symptom, and outcome characteristics were compared between ADRs caused by MEs and ADRs without documented MEs. Regression models assessed factors associated with ME involvement among ADR cases and length of hospital stay. Results: 20.1% of ADR-related cases involved an ME. Clinical presentation, symptom burden, triage severity, and discharge outcomes were similar between groups. MEs clustered around commonly used chronic medications, including pantoprazole, torasemide, metoprolol, ramipril, phenprocoumon, and ibuprofen. Schumock analysis showed preventability as primarily linked to dosing errors (30%), non-adherence (28%), contraindications (26%), and monitoring (20%). Medicine-specific symptom clusters mirrored expected pharmacological effects but were not specific to ME involvement. Multimorbidity was associated with slightly lower odds of ME attribution, although the estimate was borderline (OR 0.84, 95% CI 0.71–1.00); no robust associations were identified for the other patient-level characteristics examined. Adjusted length of hospital stay was slightly longer in cases involving MEs (+4.8%; p = 0.025), corresponding to an adjusted difference of 0.33 days. Conclusions: MEs were identified in a substantial proportion of ADR-related ED admissions. Most MEs arose from routine prescribing and monitoring processes involving commonly used medicines, suggesting that preventive efforts should focus on upstream safeguards, including medication reviews, electronic prescribing support, pharmacist involvement, and adherence support, rather than detection at emergency presentation. Full article

Natural diversion channels offer a promising solution for flood mitigation within the context of natural flood management; however, their design must balance hydraulic performance and intervention scale. This study presents an integrated framework for the optimal design of modified natural diversion channels by coupling two-dimensional hydrodynamic modelling with surrogate modelling and multi-objective optimisation. A Hazard Estimation Index (HEI) is derived from 2D flood hazard maps by aggregating the spatial distribution of flood hazard within the township and agricultural floodplain areas, providing a quantitative measure in which higher values indicate greater flood hazard. Multiple Linear Regression (MLR) models are developed to approximate HEI as a function of flood peak discharge and channel cross-sectional area. Separate HEI formulations are defined for township and floodplain areas to reflect differing mitigation priorities. The surrogate models are integrated within a Multi-Objective Particle Swarm Optimisation (MOPSO) framework to minimise flood hazard in both domains and channel size, with channel cross-sectional area used as a geometric proxy for excavation cost rather than as a direct estimate of construction cost. Three independent optimisation systems are developed for minor-to-moderate, major, and very major flood regimes based on flood-frequency analysis. Application to Moree Plains, Australia, shows that minor-to-moderate floods can be addressed with an optimal channel size of approximately 425 m², achieving HEI values of approximately 0.18 in the township and 0.07 in the floodplain. For major floods, a larger channel of approximately 1016 m² results in HEI values of approximately 0.35 and 0.26, respectively. For very major floods, a substantially larger channel of approximately 1620 m² is required, while township HEI remains relatively high (>0.5), indicating substantial residual risk. The results demonstrate diminishing hazard-reduction benefits with increasing channel size for larger floods and highlight the value of a regime-specific optimisation approach for supporting risk-informed natural flood management planning. Full article

Online partial discharge monitoring is increasingly complementing periodic offline testing in medium-voltage switchgear, particularly through the use of embedded and edge-computing platforms. This review critically examines systems based on artificial intelligence and machine learning for partial discharge detection and classification, considering the complete diagnostic chain from sensing to field deployment. The analyzed literature is organized into five interdependent layers: sensors and analog front-ends, data acquisition and triggering architectures, phase-synchronized signal representations, machine learning models, and target embedded hardware. Sensing techniques based on high-frequency current transformers, transient earth voltage, and ultra-high-frequency sensors are compared in terms of bandwidth, sensitivity, installation requirements, and immunity to interference. Particular attention is given to phase-resolved partial discharge patterns, time–frequency representations, event-driven acquisition, hardware-assisted data reduction, and synchronization mechanisms. The analysis demonstrates that high classification accuracy obtained under offline laboratory conditions does not, by itself, indicate deployment readiness. Practical implementations must also satisfy constraints related to analog-to-digital converter bandwidth, buffering, memory usage, inference latency, energy consumption, quantization, thermal performance, and field noise. Lightweight neural networks, optimized object detectors, input dimensionality reduction, quantized inference, and multimodal data fusion are identified as promising development directions. However, current research remains limited by laboratory-scale validation, incomplete hardware reporting, and insufficient long-term field datasets. Full article

Centralized sewer networks are rarely feasible for scattered mountain villages across China. Their construction costs stay high, and uneven terrain easily triggers pipe blockages and infiltration. Most existing rural wastewater treatment devices run on fixed operating schedules. They maintain full aeration even during low water inflow, wasting electricity and destabilizing effluent quality. This study reports a full-scale summer field assessment of an integrated attached-growth biofilm reactor deployed at the sewage treatment station serving Miaodong and Miaoxi Villages, Ruyang County, Henan Province, China. The system combines hydrolysis acidification, two-stage biological contact oxidation, sedimentation, post-sedimentation polishing, and a cloud-connected monitoring and control module. The control system adjusts influent pumping, aeration, internal reflux, and sludge discharge in response to measured hydraulic and dissolved-oxygen signals. The design treatment capacity was 850 m³ d⁻¹. During the 25-day monitoring period, the packing filling ratio was 70%, dissolved oxygen was maintained at 2.0–4.0 mg L⁻¹, and water temperature was 20 ± 5 °C. Average COD removal reached 91.1%, ammonium nitrogen (NH₄⁺-N) removal reached 88.9%, total nitrogen (TN) removal reached 83.5%, and total phosphorus (TP) removal achieved 81.7%. The average unit electricity consumption was 0.195 kWh·m⁻³. Because no fixed-frequency reference operation was conducted under identical influent and environmental conditions, the specific energy-saving contribution of the adaptive control module could not be quantitatively isolated. The reported value should therefore be interpreted as system-level field performance rather than as a verified percentage reduction attributable exclusively to intelligent control. The average TP concentration after polishing was 0.59 mg L⁻¹, exceeding the 0.5 mg L⁻¹ Class A limit of GB 18918-2002. The results characterize summer operation under the investigated loading and temperature conditions and should not be extrapolated directly to year-round compliance, winter operation, or heavy-rainfall events. Full article

Aromatic polyimide films were treated by pulsed electrical discharges formed in water containing different quantities of hexagonal boron nitride (h-BN). The relative permittivity was measured across wide ranges of frequencies and temperatures by broadband dielectric spectroscopy. Slightly increased values of the dielectric constant for the samples treated in water containing 10 mg and 20 mg of h-BN were observed. The sample treated by pulsed electrical discharge in water containing 30 mg of h-BN remained unchanged. The electrical conductivity decreased with increasing BN concentration. The plasma treatment affected the dielectric behavior of the films without causing major modifications to the polymer structure. Full article

How suspended grains traverse energy-dissipating micro-passages determines whether sediment-laden water can be used without rapid emitter deterioration. A novel cylindrical asteroid-shaped drip emitter was investigated through laboratory anti-clogging tests and two-way coupled computational fluid dynamics–discrete element method (CFD–DEM) simulations. The laboratory tests comprised 20 intermittent irrigation cycles at five pressures ranging from 60 to 140 kPa, with relative discharge used to characterize hydraulic performance. The simulations tracked particle motion, wall collisions, and mass transmission over a 0.20 s observation window to examine the effects of operating pressure, injected particle mass, and flow-path radius. Numerical cases isolated hydraulic forcing and, at 100 kPa, changes in solids dose and cavity radius. Measured discharge retention occupied a narrow 96.45–97.83% interval, whereas the fraction of particulate mass leaving the domain spanned 81.3–92.7%. Despite representing different responses, both indices followed pressure in a closely associated manner (Pearson r = 0.975, p = 0.0046). Stronger forcing extended the high-speed portion of individual trajectories and brought the final wall contacts forward in time, although contact totals did not follow a monotonic sequence. Changing the dose between 1.0 and 2.0 × 10⁻⁶ kg altered the selected upper-speed statistics by only +1.8% and −2.1% but reshaped the contact histories. Expanding the radius from 0.65 to 0.85 mm produced much larger reductions of 28.1% and 91.3%; the latter record then showed sustained near-stagnation with relatively few impacts. Thus, low collision frequency cannot independently demonstrate effective sediment passage. Combining effluent mass balance with trajectory and contact information provides a mechanistic basis for diagnosing retention in irrigation microchannels. Full article

Multi-temporal satellite data were used to examine changes in aufeis area and spatial configuration in the Chuluut River valley, Mongolia, and to compare seasonal vegetation dynamics among sites with different return frequencies of aufeis. Annual aufeis masks were derived from Landsat imagery for 1986–2025, while vegetation phenology was assessed using Harmonized Landsat–Sentinel-2 (HLS) data for 2016–2025. Mean aufeis area was 11.98 km² and declined significantly by approximately 0.084 km² per year, accompanied by spatial reorganization. The strongest climatic association was found with April–June precipitation in the preceding year. Hydrologically, aufeis acts as a temporary seasonal water store, retaining part of winter discharge and releasing meltwater during spring. Its decline and redistribution may alter the timing and pathways of seasonal water release, reduce delayed moisture inputs to floodplain surfaces, and modify water availability for riparian and meadow ecosystems. In meadows, the start of season (SOS) occurred later under frequent than infrequent aufeis recurrence. Peak and mean summer normalized difference vegetation index (NDVI) values remained high after aufeis melt-out. The highest integrated seasonal NDVI values occurred under intermittent aufeis recurrence. Full article

Background/Objective: Diagnostic safety is a major challenge in emergency departments (ED), where clinicians frequently make decisions under time pressure and with incomplete information. Prospective data on diagnostic discrepancies in Swiss EDs remain limited. To determine the frequency of diagnostic discrepancies between the initial ED diagnosis and the final Internal Medicine (IM) discharge diagnosis, identify associated patient-, physician-, and context-related factors, and evaluate clinical outcomes associated with diagnostic discrepancy. Methods: We conducted a prospective observational study including 515 patients admitted from the ED to an IM ward and managed by 44 physicians at a Swiss tertiary non-university hospital. The initial ED diagnosis was compared with the IM discharge diagnosis (or diagnosis on day 28 if the patient remained hospitalized). Diagnostic discrepancies were classified using a predefined algorithm with independent expert opinion where required. Generalized linear mixed-effects models were used to assess associations between diagnostic discrepancy and mortality, in-hospital transfers and length of stay; a multivariable logistic regression model was used to identify factors associated with diagnostic discrepancy. Results: Diagnostic discrepancies were identified in 10.1% of patients (n = 52). These patients had longer hospital stays (9.3 ± 9.2 vs. 6.9 ± 5.8 days; p = 0.069) and were more frequently transferred within the hospital (17% vs. 5.8%; p = 0.006). After adjustment, diagnostic discrepancies were associated with higher odds of in-hospital transfer (OR 3.35; 95% CI 1.47–7.66; p = 0.004) and a 20% longer stay (exp β = 1.20; 95% CI 1.00–1.45; p = 0.049), although the latter was attenuated after adjustment for comorbidity. Specialist involvement in the ED was independently associated with lower odds of diagnostic discrepancy (OR 0.30; 95% CI 0.10–0.72; p = 0.015), whereas each one-point increase in physician-perceived diagnostic difficulty was associated with higher odds (OR 1.42; 95% CI 1.07–1.90; p = 0.016). Conclusions: Diagnostic discrepancies occurred in approximately one in ten patients admitted from the ED to IM and were associated with increased in-hospital transfer and, less robustly, with prolonged hospitalization. Prospective multicenter studies should evaluate strategies to reduce diagnostic discrepancies and improve diagnostic safety. Full article

Defibrillators require high-performance power electronic converters capable of rapidly charging a high-voltage capacitor and delivering controlled therapeutic waveforms while ensuring patient safety. This paper presents a predictive control strategy for the flyback converter of a defibrillator, including both its charging and discharging stages, together with the design and verification workflow for its implementation. The proposed system integrates the FPGA implementation of the charging-stage control with a hardware-in-the-loop (HIL) emulation of the flyback converter and discharge stage within a unified model-based design framework. The charging stage consists of a flyback converter regulated by a Finite Control Set Model Predictive Control (FCS-MPC) strategy, while the discharge stage employs a full-bridge converter to generate truncated exponential biphasic (BTE) waveforms. To regulate the switching frequency without sacrificing the fast dynamic response of predictive control, the Period Control Approach (PCA) is incorporated into the FCS-MPC. The proposed solution is benchmarked against conventional FCS-MPC and a hysteresis controller, highlighting the advantages of PCA-based predictive control in terms of switching-frequency regulation while preserving accurate current tracking. The proposed control system and the corresponding defibrillator model are developed in MATLAB/Simulink and automatically translated into synthesizable VHDL using HDL Coder. This approach enables FPGA implementation of the control strategy and HIL emulation of the power converters without manual HDL programming. The proposed methodology covers the entire workflow, from simulation to real-time FPGA implementation and HIL emulation. Simulation results demonstrate accurate current tracking, proper BTE waveform generation, and improved switching-frequency regulation compared with both conventional FCS-MPC and hysteresis-based control. HIL experiments on a Xilinx Artix-7 FPGA confirm the real-time operation of the implemented predictive controller interacting with the emulated flyback converter. The experimental results are consistent with the simulation results. This work provides a solid foundation for the development and validation of digitally controlled defibrillators based on advanced predictive control techniques. The results demonstrate the feasibility of the proposed approach in both simulation and reconfigurable hardware. Full article

In battery energy storage systems (BESSs) in microgrids, traditional proportional–integral (PI) controllers remain a popular choice for management. The PI-based system often fails during sharp transients and under sudden shifts in solar irradiance or load demands. PI-based systems typically exhibit sluggish recovery times and pronounced voltage overshoots. To overcome these limitations, this article develops a flatness-based control (FBC) framework designed to optimize the dynamic response and stability of an islanded hybrid microgrid powered by photovoltaic (PV) arrays and wind turbines. The core mechanism directly regulates the battery charging and discharging currents. This mechanism ensures that the DC-bus voltage strictly tracks its reference command regardless of fluctuations in load or weather profiles. Crucially, the structural resilience of this control architecture was rigorously assessed, with the simulation model subjected to severe 20% mismatches in physical parameters, specifically the main DC-bus capacitance and battery inductance, alongside continuous high-frequency Gaussian white noise injected into the measurement feedback channels. Three scenarios have been implemented in MATLAB/Simulink: variable weather conditions, realistic weather conditions, and parameter uncertainties with measurement noise. The comparison shows that the new FBC controller cuts the settling time down from 0.47 s with the regular PI controller to just 0.02 s, which is a 95.7% decrease. In addition, the proposed controller substantially mitigates transient voltage deviations and eliminates the 2.6% voltage overshoot observed with the PI controller. The rise time is also reduced by approximately 35%. These results demonstrate that the proposed FBC provides faster, overshoot-free, and more stable DC-bus voltage regulation under the investigated operating conditions. Full article

Partial discharge (PD) generates transient acoustic-vibration signals that can be used for the passive monitoring of cable insulation. Unlike previous studies that separately addressed DFB-FL sensitivity, polymer-property effects, or acoustic attenuation in a particular cable, this study establishes an application-oriented cross-scale assessment that links material-level propagation loss with cable-scale detection by a distributed feedback fiber laser (DFB-FL)/fiber Bragg grating (FBG) system. This study compared the Young’s moduli of polypropylene (PP), cross-linked polyethylene (XLPE), and methyl silicone rubber (MQ), and measured their frequency-dependent attenuation coefficients along with those of insulating oil in the range of 50 to 300 kHz. Subsequently, a 250 mm-long, 220 kV XLPE cable specimen containing a needle-induced artificial cavity was tested at sensing distances of 0–7 cm and at four circumferential positions. For the three tested materials, the extrapolated initial response amplitude decreased as the Young’s modulus increased. The measured polymer attenuation coefficients were approximately 0.052–0.192 Np/cm, compared with approximately 0.018–0.033 Np/cm for insulating oil. Under the stated laboratory criteria, the lowest time-correlated apparent-charge event detected by the DFB-FL system was 16 pC. The DFB-FL peak-to-peak response decreased from 5.04 Vpp at 0 cm to 1.00 Vpp at 7 cm, crossing below the background threshold between 6 and 7 cm, which experimentally verifies the intrinsic connection among material attenuation, internal cable propagation paths, and sensor-placement constraints. Full article

Background: The characterisations of highly diluted preparations impregnated onto solid carriers remains analytically challenging, particularly when residual material is present at very low levels. Electrophotonic analysis (EPA), based on corona-discharge imaging and quantitative image analysis, has recently been explored as a possible complementary approach, but its applicability to impregnated pilules requires further investigation. Purpose: This exploratory study examined whether EPA-derived image parameters could reveal measurable differences among size-6 pilules impregnated with stepwise diluted and dynamised preparations of Cuprum metallicum and Gelsemium sempervirens and whether such differences might vary according to source material and manufacturing protocol. Methods: Size-6 pilules were impregnated with Hahnemannian and Korsakovian preparations of Cuprum metallicum and Gelsemium sempervirens. Control samples included non-impregnated pilules, pilules impregnated with pure solvent, and pilules impregnated with simply diluted, non-dynamised preparations. Images were acquired in randomised and blinded conditions using a prototype EPA device. Image intensity, contrast, entropy, and fast Fourier transform (FFT)-derived period/spatial-frequency parameters were analysed. Results: The EPA images showed patterns that may indicate differences between impregnated pilules and controls, between the two source materials, and between manufacturing protocols under the experimental conditions used here. FFT-based analysis suggested that some spatial-period/spatial-frequency indices may contribute to describing these preliminary differences. Among the parameters examined, the smallest diameter of the selected high-intensity FFT-image regions remained statistically significant after correction for multiple comparisons, whereas other parameters should be interpreted cautiously. Observations of aged samples also suggested possible differences from reference preparations, but these findings remain preliminary and require confirmation. Conclusions: These results suggest that EPA combined with FFT-based image analysis may provide exploratory information on EPA-derived image patterns in potentised preparations impregnated onto solid carriers. However, the present data are not sufficient to establish EPA as a reliable discriminatory analytical tool. Further measurements, larger sample sets, independent replication, improved control of environmental variables, and comparison with complementary analytical methods are required before firm conclusions can be drawn about its analytical value. Full article

Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine–cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment. Full article

Partial discharge (PD) detection is a crucial means for the early warning of incipient insulation defects in arresters. However, under strong electromagnetic interference and background noise, PD signals are prone to distortion, making it difficult to accurately determine the pulse onset front and thus severely degrading the accuracy of time-delay estimation. To address the difficulty of time-delay estimation under low signal-to-noise ratio (SNR) and multi-channel aliasing conditions, this paper proposes a method for arrester PD detection and high-precision time-delay estimation based on joint denoising and improved cross-correlation. First, a joint denoising strategy that integrates singular value decomposition (SVD), variational mode decomposition adaptively optimized by the sparrow search algorithm (SSA-VMD), and the Teager energy operator (TEO) is constructed. This strategy suppresses white noise and periodic narrowband interference while effectively extracting the oscillatory onset characteristics of PD pulses. Second, an enhanced time-delay estimation method based on HB-weighted generalized quadratic cross-correlation is introduced. By employing the dual mechanisms of HB frequency-domain weighting and amplitude weighting to sharpen the correlation peak, the estimation robustness under low SNR is improved. Simulation results show that the proposed method attains an accuracy of 99.9911%, significantly outperforming conventional cross-correlation, PHAT-SCOT, and NLMS methods. Finally, experiments are conducted on a needle-plate discharge platform. In multiple comparative experiments with different spatial distance differences (ranging from <30 cm to >50 cm), the maximum relative error is kept within 0.6%, verifying the reliability and accuracy of the proposed algorithm under controlled laboratory conditions. This method can provide a new approach for online monitoring and accurate fault location of arresters in power systems. Full article