Discharge Patterns Research Articles

A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture

Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. Firstly, a GIS insulation fault simulation experimental platform is constructed, and the PRPD phase data and TRPD timing data under different faults are obtained; secondly, the TRPD timing data are converted into MTF; then the PRPD phase data and MTF timing data are input into the Swin Transformer-AFPN-LSTM model and other deep learning models for performance comparison. The experimental results show that the Swin Transformer-AFPN-LSTM model improves the performance by 14.09–21.23% compared with the traditional CNN model and LSTM model. Moreover, using this model to extract phase features and timing features simultaneously improves the accuracy by 10.67% and 8.66%, respectively, compared with single feature extraction, and the overall accuracy reaches 98.82%, which provides a new idea for GIS insulation fault identification.

Numerical simulation of surface charge accumulation under negative discharge of different humidities and pressures

The surface discharge phenomenon of polymers severely limits their applications in electrical and electronic devices, especially in complex environments. In this study, a drift-diffusion model based on a hydrodynamic approach was developed to investigate the influence of humidity and gas pressure on the negative surface discharge. The results indicate that the discharge pattern did not change under different humidity conditions. The increased humidity accelerated the formation of discharges and increased the discharge pulse current. In particular, as the humidity increased, tiny pulses occurred at the tail of the first pulse, and the number of tiny pulses increased. The appearance of these tiny pulses changed the surface charge distribution from a “ring-like” distribution to a “spot-like” distribution. Meanwhile, the accumulation of surface charges significantly distorted the spatial electric field distribution and suppressed the electron multiplication stage of the subsequent discharges, thus reducing the current in the Trichel pulse discharge stage. It is precisely because the discharge is stronger under high humidity, resulting in more surface charges accumulating on the surface, which is in keeping with the experimental results. The measured charges at different humidities show a similar distinct spot-like distribution, illustrating a constant pattern of discharge. All these results demonstrated the correctness and applicability of the simulation. The surface discharge under different pressures exhibited some similarities with the case of different humidity levels. As the pressure increased, the number of discharge current pulses and the pulse amplitude decreased, resulting in a decrease in the surface charge density.

Simulating the Influence of Rainfall Variability on Discharge in the Upper River Yala Basin, Kenya

Climate variability is significantly altering river flows globally, increasing the risk of floods and droughts. Projections indicate both rising and declining flows across various regions, influenced by the impacts of climate variability and land use changes. Research has shown that climate change, land use, and pollution exacerbate water scarcity for half the global population, impacting ecosystems, especially in vulnerable regions. This study focuses on the Upper Yala River in Kenya, exploring climate variability's influence on discharge in various Land Use contexts using the SWAT model. Existing research highlights the significance of land use, hydrological indicators, and climate data, establishing a framework to analyze stream flow trends. The study analyzed climate and stream flow data from 1990-2020 using the SWAT model for hydrological assessment and predictions for the years 2024 to 2040 was done. The research was guided by Water Balance Theory and employed a descriptive and analytical design. Data collection included meteorological data from weather stations, hydrological data from gauging stations, and land use and land cover (LULC) data from remote sensing and satellite imagery. The Soil and Water Assessment Tool (SWAT) was used to simulate river discharge and assess the impacts of climate variability, integrating climate, land use, soil type, and topographic data. Data analysis involved descriptive statistics to summarize discharge data, correlation analysis to link rainfall variability and discharge patterns, and performance metrics like the Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) to validate the model. Statistical techniques identified long-term trends in climate and streamflow, focusing on inter-seasonal and inter-annual variations. The Upper Yala River Basin experiences significant inter-seasonal and inter-annual streamflow variations, primarily influenced by rainfall fluctuations. A strong correlation between simulated and observed discharge data for the Upper Yala River Basin was demonstrated. The mean observed discharge was 48.69 m³/s, with maximum and minimum values of 163.09 m³/s and 0.328 m³/s, and a standard deviation of 34.28 m³/s. In contrast, the simulated discharge had a mean of 53.56 m³/s, with maximum and minimum values of 174.41 m³/s and 0.360 m³/s, and a standard deviation of 37.87 m³/s. The minimal differences between the observed and simulated values underscore the model's effectiveness in accurately reflecting the impacts of rainfall variability on river flow dynamics. The study concluded that in the Upper River Yala watershed, rainfall variability accounted for 94.2% of the variations in river discharge quantity. The study recommends enhancing climate monitoring by adding weather stations and stream gauges in the basin and utilizing remote sensing for tracking land use and vegetation changes. Improved data availability from these measures will enable better discharge predictions and inform water management decisions to mitigate climate impacts on the river basin and surrounding communities.

Quantifying Thermal Discharges from Nuclear Power Plants: A Remote Sensing Analysis of Environmental Function Zones

Nuclear energy plays a crucial role in global carbon reduction. However, thermal discharges from nuclear power plants can potentially impact marine ecosystems. This study investigates the long-term thermal impact of the Haiyang Nuclear Power Plant on the adjacent marine environment using a decade-long Landsat thermal infrared dataset. Spatial and temporal patterns of thermal discharge were analyzed, focusing on the temperature difference between intake and outlet water, the warming trend in the thermal mixing zone, and the spatial distribution of the thermal plume. Our results indicate the following: (1) Seasonal Variation in Thermal Discharge: The temperature difference between intake and outlet water exhibited significant seasonal variability, with higher values in winter and lower values in summer. The spatial distribution of the thermal plume was influenced by tidal currents, leading to a cyclical pattern. (2) Long-Term Warming Trend: Prolonged thermal discharge resulted in a notable warming trend in the thermal mixing zone, with an average annual increase of 0.3 °C. This warming effect was most pronounced in winter and least in summer. (3) Spatial Distribution of Thermal Plume: The spatial extent and intensity of the thermal plume varied seasonally. Summer exhibited a larger influence range but with lower temperature rises, while winter showed a smaller influence range but with higher temperature rises. In winter, the 4 °C temperature rise area exceeded the designated environmental functional zone boundary in some instances. These findings provide valuable insights into the thermal impact of nuclear power plants and highlight the importance of considering seasonal variations and long-term monitoring to ensure environmental sustainability.

The Influence of Sanitary Infrastructure on Event Nutrient Dynamics in a Headwater Catchment

ABSTRACTMany low‐order streams are recipients of effluents from wastewater treatment plants (WWTPs) and combined sewer overflows (CSOs). Not only do these facilities have to meet fewer requirements compared to their bigger counterparts in more densely populated areas, but they also discharge into smaller, more vulnerable streams, with low dilution potential. Although these local point sources can dictate the local water quality and quantity dynamics, they are barely monitored and often not included in catchment‐wide analyses. In this case study we measured stream water quantity and quality in a first‐order, point source‐influenced stream. We have specifically addressed point sources in our monitoring programme, which included the installation of a low‐budget probe at the CSO outlet to monitor overflow timing. By clustering hysteresis loops and using Principal Component Analysis, we were able to identify hydro‐meteorological drivers and reveal seasonal patterns of discharge and nutrient export dynamics. Mobilisation of nitrate from agricultural sources clearly dominated event dynamics during periods with high soil moisture, while point source dynamics overlaid catchment responses during the rest of the time. Thus, the dilution potential of the stream was found to be a controlling factor for water quality dynamics. Groundwater infiltration into the sewer system probably increased the risk of CSO discharges, especially in winter and spring. In summer, CSO spillages occurred as a result of high‐intensity rainfall. These events were related to an increase of turbidity and a mobilisation of particulate phosphorus. With our novel approach, including urban point sources in our monitoring setup, we were able to show the close relationship between sanitary infrastructure in rural areas and stream nutrient dynamics. Including point sources more closely into monitoring and analysis is essential to improve the process understanding.

KSMNet: Learn the core discriminant features and semantic relationships for partial discharge pattern recognition via Mamba

KSMNet: Learn the core discriminant features and semantic relationships for partial discharge pattern recognition via Mamba

Improving Global Reservoir Parameterizations by Incorporating Flood Storage Capacity Data and Satellite Observations

AbstractAccurate reservoir representation in large‐scale river models remains challenging owing to limited access to data on reservoir operations. We contribute to model development by introducing a global machine‐learning based flood storage capacity (FSC) data set and a satellite‐based target storage reservoir operation scheme (SBTS). The FSC data set for 1,178 flood control reservoirs is constructed using multiple reservoir attributes and reported FSC data. Integrating these FSCs into SBTS enables its global applicability with generic formulations of reservoir zoning. Then, we develop SBTS by introducing monthly median values of satellite storage data as target storage parameters. With these seasonal patterns as constrains, improvements in simulation results are achieved. When simulated with observed inflow, SBTS performed significantly better (median Kling‐Gupta efficiency values of 0.52 and 0.17 for outflow and storage simulations among 289 reservoirs), compared to the previous reservoir operation scheme with linearly interpolated target storage parameter (0.41 and −0.19). Compared to two existing global schemes without seasonal target storages, SBTS demonstrates improved performance for many reservoirs whose inflow seasonal pattern is more regular. When coupled with a global river model, it improved discharge simulations across 293 downstream gauges, with overall performance, peak, and low flow improving at 40%, 21%, and 35% of gauges, respectively, compared to simulations without reservoirs. However, reservoir simulations do not improve notably due to the biases in simulated inflow to reservoirs. We demonstrated that machine‐learning FSC and satellite observations help improve reservoir parameterizations, and found that improvements in other aspects of river modeling are essential for accurately reproducing discharge patterns.

The discharge characteristics of motor units innervating functionally paralyzed muscles.

For individuals with motor complete spinal cord injury (SCI), previous works have shown that spared motor neurons below the injury level can still be voluntarily controlled. In this study, we investigated the behavior of these neurons after SCI by analyzing neural and spatial properties of individual motor units using high-density surface electromyography (HDsEMG) and ultrasound imaging. The dataset for this study is based on motor unit data from our previous work (Oliveira et al., 2024). Eight participants with chronic motor complete SCI and twelve uninjured controls attempted multiple hand movements, guided by a virtual hand, while we recorded forearm muscle activity. We analyzed the common synaptic input to motor neurons with a factorization method and found two dominant motor unit modes in both the SCI and control groups. Each mode was strongly correlated with the virtual hand's flexion or extension movements. The delay between flexion and extension movements and the motor unit modes was similar between groups, suggesting preserved common input to motor neurons after SCI. We classified motor units into task-modulated or non-modulated (i.e., tonic or irregularly firing) based on their discharge patterns and phase difference with virtual hand kinematics and found a higher proportion of non-modulated motor units in the SCI group. At the motor unit action potential level, we found larger motor unit territories after SCI. Finally, we observed distinct movements of paralyzed muscles with concurrent HDsEMG and ultrasound imaging, indicating the presence of highly functional motor units with distinct spared territories after SCI.

Reducing ethnic inequities: Patterns of asthma medication use and hospital discharges in Māori in Aotearoa New Zealand.

In Aotearoa New Zealand (NZ) widespread transition to budesonide/formoterol maintenance and/or reliever regimens in clinical practice is temporally associated with reduced rates of asthma hospitalization. It is unknown whether this association is observed in Māori, the indigenous population of NZ, who experience a disproportionate burden from asthma. We investigated patterns in asthma medication use and hospital admissions in Māori in NZ. Review of NZ national dispensing data for asthma inhaler medications and asthma hospital discharge data from January 2013 to December 2023 in the 12+ age group, with calculation of the relative change in dispensed medication and asthma hospitalization rates for Māori and non-Māori. The most recent six-month period, July to December 2023, is compared with the corresponding six-month period 4 years earlier, July to December 2019. Budesonide/formoterol dispensing increased for both Māori and non-Māori for 2019-2023, with a relative 111% and 115% increase, respectively. Between the two periods, asthma hospital discharges reduced from 142.5 to 97.3 per 100,000, absolute difference 45.2 per 100,000, a 32% reduction for Māori; and 49.4-37.9 per 100,000, absolute difference 11.5 per 100,000; a 23% reduction for non-Māori. The temporal association between a marked increase in dispensing of budesonide/formoterol maintenance and/or reliever regimens and reduced asthma hospitalization was observed for Māori and non-Māori, with a greater reduction in asthma hospitalization for Māori. Despite this reduction in health inequities, asthma hospitalization rates are two and a half times greater for Māori compared to non-Māori.

Towards a quantitative lithostratigraphy of Pleistocene glaciofluvial deposits in the southern Upper Rhine Graben

Abstract. The sediment fills of large terrestrial basins offer an opportunity to study and reconstruct regional-scale landscape history over time. An example of such a basin is the Upper Rhine Graben in central Europe, whose proximal, southern part has so far only sporadically been investigated sedimentologically. Three new drillings in close proximity have recovered the upper ∼ 40–60 m of the Quaternary sedimentary infill near the eastern graben margin north of Freiburg, Germany. Grain sizes, the rounding and shape of clasts, and the petrographic composition of the deposits have been determined and statistically analysed. The cored intervals consist of glaciofluvial gravels derived from and deposited by the Rhine system. While no consistent trends of the morphometric properties could be found, we succeeded in distinguishing two subunits within the topmost stratigraphic unit (Neuenburg Formation, Fm) based on compositional (i.e. gravel petrographic) data. An upper subunit (Hartheim Subformation, Sfm) enriched in lithologies representative of the Alpine orogen could be separated from a lower subunit (Nambsheim Sfm) that is enriched in lithologies of an outer-Alpine origin. We correlate these findings with a shift in the pattern of meltwater discharge from the Rhine glacier lobe that may have delivered more Alpine material into the study region during the last than during the penultimate glaciation and highlight the value of quantitative approaches and appropriate statistical evaluation for gravel petrographic studies.

Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach

ABSTRACTSprings provide critical water resources that are sensitive to changing climate and catchment processes. In many regions, understanding the temporal variability and spatial distribution of spring discharge is therefore crucial for sustainable water management. Knowledge of these discharge characteristics, organised in a coherent framework, is essential for protecting spring water and preventing shortages. To establish such a framework, we conducted a comparative analysis of long‐term discharge records from 96 springs across Austria. Based on discharge seasonality and autocorrelation, we derived a broad‐scale classification through cluster analysis and explored associations between individual clusters. The identified similarities in discharge patterns were grouped into four distinct spring categories, each demonstrating common behaviour. To determine the main factors influencing discharge across these four groups, we compared their spatial and temporal patterns with regional climate and catchment characteristics. They align with physical drivers of spring discharge, including precipitation frequency and intensity, snow cover duration, and dominant aquifer type. As these factors were not included in the classification procedure, their alignment supports the validity of our statistical approach. We conclude that the quantitative information derived from this analysis provides a valuable complement to traditional spring classification schemes, which are often based on qualitative knowledge. Our proposed strategy refines these classification approaches, enhances objectivity and reproducibility, and promotes conformity across hydrological disciplines.

Impact of discharge regulation on zooplankton communities regarding indicator species and their thresholds in the cascade weirs of the Yeongsan River

Discharge regulation in rivers with cascade weirs profoundly alters natural flow dynamics, impacting habitat characteristics and leading to the homogenization of community structure and function. Identifying indicator species and their ecological thresholds is crucial for effectively monitoring and assessing the ecological conditions of regulated river systems. It is also essential for guiding conservation and management efforts. In this study, we investigated the influence of discharge patterns on zooplankton communities in the Yeongsan River, specifically focusing on the effects of flow regulation by cascade weirs. We analyzed the zooplankton community dynamics over a 12-year monitoring period and demonstrated the significance of discharge in shaping river ecosystem dynamics. Species indicator tests and gradient forest modeling were used to identify indicative genera and establish their ecological thresholds under various discharge patterns. Our findings revealed a distinct and significant discharge effect on zooplankton community composition and diversity, independent of water quality and nutrient-related factors. Monsoonal rainfall influenced the discharge patterns, which were categorized into three levels; this classification was further supported by indicator species and their responses. Despite their low abundances limiting clear responses, indicator genera, such as Rotaria and Macrothrix, were shaped by discharge levels. This study highlighted the need to incorporate discharge considerations into river management and conservation strategies to safeguard aquatic biodiversity. Our study provides valuable insights into sustainable river ecosystem management by elucidating the ecological consequences of flow regulation by cascade weirs.

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Dynamical response in an electromechanical arm driven by temperature-dependent neural circuit

The thermosensitive neurons can detect changes in external temperature, and the external physical information is converted into neural signals. The external temperature is translated into an equivalent electrical stimulus, and the excitability of these neurons is controlled by adjusting the external electrical signal. These neurons exhibit a certain degree of adaptability, inducing appropriate discharge patterns only when subjected to external stimuli within a specific temperature range. Therefore, thermosensitive neurons formed by temperature regulated neural circuits can be used to study signal processing of thermosensitive neurons and control of mechanical arm. A temperature sensing mechanical arm system is constructed based on neural circuits in this article, and the influence of temperature on the dynamic behaviors of a mechanical arm system regulated by thermosensitive neurons is studied. Numerical simulations are performed to research the effect of external stimulation current and temperature on pattern formation. The bifurcation analysis of the membrane potential reveals the conversion in electrical activity and pattern selection. The bifurcation analysis of membrane potential reveals the mode switching of electrical activity and its impact on the displacement of the mechanical arm. The results indicate that as the temperature changes, the membrane potential exhibits different discharge patterns, which in turn affect the maximum displacement of the mechanical arm. The coupling between neural circuits and mechanical arm promotes effective signal propagation in the system. It is found that there is an appropriate temperature range that allowed the mechanical arm of the coupled system to move steadily back and forth. The research results confirm that external temperature can affect the firing mode of neurons and control the displacement of the mechanical arm.

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Trends and outcomes in pediatric laparoscopic appendectomy: a NSQIP-P analysis of same-day discharge and readmission rates.

Practice patterns of same-day discharge for pediatric laparoscopic appendectomy for non-perforated appendicitis are not well-analyzed. Our aim is to evaluate current practice patterns and outcomes of same-day discharge for these patients. NSQIP-P retrospective registry identified patients (2017-2021) who underwent laparoscopic appendectomy for non-perforated appendicitis. Annual same-day discharge (SDD) rate was determined. SDD patients were compared to those discharged postoperative days 1-2 (non-SDD). Sub-group analysis was performed on ICD-10 code K35.30. Regression was performed. Overall, 67,214 patients were identified. The SDD rate increased from 33.3% (2017) to 52.5% (2021) with decreased 30-day readmissions between SDD and non-SDD (1.3% vs 2.1%, p < 0.001). Sub-group analysis identified 7,330 patients with SDD rates from 50.7% (2019) to 60.4% (2021) with decreased 30-day readmissions (1.3% vs 2.1%, p < 0.001) for SDD versus non-SDD. No increase in adverse events for SDD occurred in either analysis. Regression identified esophageal/gastric/intestinal diseases increased odds of early readmission or reoperation (OR 1.85, p = 0.042). Same-day discharge after pediatric laparoscopic appendectomy for non-perforated acute appendicitis continues to increase in frequency without a significant increase in adverse outcomes. SDD rates are 20-30% lower than previously published single-center studies, indicating there is a likely a larger percentage of patients that could discharge same-day. Level III.

Transformers partial discharge pattern recognition based on Vision Transform

Abstract The secure and stable operation of modern electricity systems depends on the reliable performance of transformers. Partial discharge (PD), as a common fault phenomenon in transformers, has a significant impact on its performance and lifetime. Traditional PD identification methods rely on manual feature extraction and lack generalization capability. In addition, this paper proposes a partial discharge recognition method based on Vision Transformer (ViT), which utilizes the self-attention mechanism of the visual transformer to accurately recognize partial discharges in transformers. Through the training and testing of the transformer partial discharge mapping dataset, the results show that the method in the present paper outperforms the traditional method in terms of recognition accuracy and stability, and the accuracy is maintained between 96.24% and 97.08%, which has a good prospect for engineering applications.

Hydrological response to climate and land use and land cover change in the Teesta River basin

ABSTRACT The Teesta basin is shared by Bangladesh and India, holds significant importance in the bilateral relationship, and sustains the livelihoods of over 30 million people in Bangladesh. Employing a cellular-automata model (CA), we accurately estimate LULC for the 2020s and projected for the 2050s and 2080s. A semi-distributed hydrological model, Soil Water Assessment Tool (SWAT), is used to generate flow for the base period (1995–2014), the near future (2035–2064), and the far future (2071–2100). SWAT model is forced by eight general circulation models (GCMs) under two socioeconomic pathways (SSP245 and SSP585). The CA-Markov prediction indicates LULC changes, especially increased agriculture and settlements by 76 and 42%, and decreased forest and water by 13 and 36%, respectively, which are expected by 2050s and will influence discharge patterns. This results in additional discharge increases of 4% (–8 to 5%) for SSP245 and 5% (–8 to 10%) for SSP585 scenarios during wet seasons. In the far future, monsoon flow will increase by 13% (0.4 to 23%) for SSP245 and 52% (–29 to 151%) under SSP245 and SSP585. A marginal change in winter flow was shown by –6% (–16 to 4%) reduction under SSP245 and –13% (–64 to 63%) under SSP585 reduction in the 2080s.

Human sympathetic neuronal discharge and recruitment patterns regulate neuropeptide Y bioavailability.

What is the purpose of sympathetic neuronal action potential (AP) discharge and recruitment patterns for human vascular regulation? This study tested the hypothesis that sympathetic neuronal discharge and recruitment patterns regulate neuropeptide Y (NPY) bioavailability. We used microneurography to record muscle sympathetic nerve activity and a continuous wavelet transform to detect sympathetic APs during a baseline condition and intravenous dexmedetomidine infusion (α2-adrenergic agonist, 10-min loading infusion of 0.225 µg·kg-1; maintenance infusion of 0.1-0.5 µg·kg·h-1) in six healthy individuals (5 females, 27 ± 6 yr). Arterial blood samples provided NPY (enzyme-linked immunosorbent assay) and norepinephrine (liquid chromatography-tandem mass spectrometry) levels at baseline and the dexmedetomidine maintenance infusion. Linear mixed-model regressions assessed the relationships between AP discharge, recruitment, and neurotransmitter levels. Across baseline and the dexmedetomidine condition, NPY levels were positively related to mean arterial pressure (β = 1.63 [0.34], P = 0.002), total AP clusters (β = 0.90 [0.22], P = 0.005), and AP frequency (β = 0.11 [0.03], P = 0.003). Norepinephrine levels were not related to mean arterial pressure (β = 0.03 [0.02], P = 0.133) but were positively related to total AP clusters (β = 19.50 [7.07], P = 0.030) and AP frequency (β = 2.66 [0.81], P = 0.014). These data suggest that sympathetic neuronal discharge and recruitment patterns regulate NPY and norepinephrine bioavailability in healthy adults. As such, sympathetic neuronal firing strategies are important for human vascular regulation.NEW & NOTEWORTHY The purpose of sympathetic neuronal discharge and the recruitment of neuronal subpopulations for human circulatory homeostasis remains unknown. This study tested the hypothesis that sympathetic neuronal discharge and recruitment patterns regulate neuropeptide Y (NPY) bioavailability. Across baseline and an intravenous dexmedetomidine infusion (α2-adrenergic agonist) sympathetic action potential (AP) frequency and total sympathetic AP clusters were associated with NPY bioavailability. This is the first study to report that sympathetic neuronal discharge and recruitment patterns regulate NPY bioavailability to support circulatory homeostasis in humans.

Assessment of Emergency Water Sources Using Electrical Resistivity Tomography: A Case Study in the Longmen Shan Fault Zone

The Longmen Shan fold and thrust belt, situated on the eastern margin of the Qinghai–Tibet Plateau, is prone to disasters like earthquakes and debris flows. Thus, applying rapid assessment methods for emergency water sources in disaster-affected areas is crucial for local populations and ensuring an effective response to disasters. In this study, we employed electrical resistivity tomography (ERT) to investigate groundwater resources in post-disaster regions. By integrating the results from ERT profiles with geological and borehole information, we determined the lithology and depth of aquifers. Additionally, we analyzed groundwater recharge and discharge patterns relative to surface water during various precipitation periods and generated a hydrogeological profile for the region. Borehole information confirmed our inferred lithology and aquifer depth, thereby ensuring a reliable water supply during emergencies. This study demonstrates the feasibility of ERT for rapidly identifying water resources in geologically complex environments, providing a scientific foundation for water resource management in disaster-prone regions.