Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is therefore critical for both proactive mitigation planning and real-time emergency response. Most existing outage prediction models operate in open-loop or event-level settings and cannot update forecasts as storm conditions and system states evolve. To address this limitation, we propose the SpatioTemporal Outage ForeCAST (STO-CAST) model, a spatiotemporal deep learning framework that performs state-dependent, observation-updated rolling inference throughout TC events. STO-CAST enables outage forecasts to evolve in response to updated meteorological projections and newly observed outage information during runtime. The model integrates static environmental and infrastructure attributes with dynamic meteorological and outage sequences and produces hourly outage forecasts at a 4 km by 4 km resolution. STO-CAST supports dual-horizon forecasting, providing short-term nowcasting with a 6-hour lead time for real-time situational awareness and long-term forecasting with a 60-hour lead time to inform proactive planning and resource staging. A case study of Typhoon Muifa (2022), evaluated under a Leave-One-Storm-Out framework, demonstrates the model's operational value, including its ability to track evolving outage hotspots and to provide diagnostic insight through error decomposition that distinguishes the effects of model limitations, meteorological uncertainty, and observation gaps. Overall, STO-CAST offers a scalable and interpretable framework to support risk-informed emergency response and enhance power system resilience under intensifying TC threats.
To minimize the health impacts of power outages, which have steadily increased with climate change and aging infrastructure, it is crucial to recognize their range of effects and identify the most vulnerable individuals and communities. Here, we investigate the health consequences of outages among those with asthma residing in New York City. We combined administrative emergency department (ED) visit data from 2018 to 2022 with two granular power outage data sources: the New York State Department of Public Service (locality-level) and the New York City Housing Authority (NYCHA, building-level). We applied an augmented synthetic control approach to four large-scale outages in the New York State Department of Public Service data to assess their association with asthma-related ED visits. We used a case-crossover design to assess the impact of same-day, building-level outage exposure in the NYCHA data on asthma-related ED visits. A large, localized outage on 21 July 2019 was associated with 0.20 (95% confidence interval: 0.00, 0.34) per 1000 sub-daily increases in asthma-related ED visits the same evening of the outage start. For NYCHA residents, in the summer months, outage days were associated with elevated odds of asthma-related ED visits (2.23, 95% confidence interval: 0.92, 5.37), and these effects were particularly relevant for children and for lag days 0-1. Climate change impacts communities through severe weather events and their subsequent disruptions to critical infrastructure, such as electricity service. We add to the limited evidence that these disruptions carry health risks, in this case acute care asthma visits.
Hurricane-related outages affect millions, yet who loses power, how severely, and for how long remains poorly measured. Here we use satellite nighttime radiance to measure outage burden across 30 Atlantic hurricanes (2012-2024) and 156,032 tract-hurricane observations in 18 states. Decomposing outage burden into occurrence, severity and recovery, we find that occurrence disparities were the most robust: minority-status vulnerability was associated with 3.47 percentage points higher outage probability per 10-percentile-point increase. Severity disparities were positive and stronger among tracts that lost power, but attenuated after land-cover adjustment, indicating dependence on built-environment characteristics. Housing and transportation vulnerability was associated with slower recovery. Outages co-occurred with dangerous heat more often in high-minority tracts. Satellite monitoring can provide a utility-independent framework for tracking whether grid resilience investments reduce outage disparities.
Hospital information technology (IT) outages severely disrupt clinical workflows and use of electronic medical records, threatening patient safety and operational continuity. Traditional disaster response training faces limitations including high resource requirements, restricted repeatability, and inability to be conducted without interrupting 24/7 hospital operations. Digital twin technology enables realistic, repeatable simulation training in virtual environments, avoiding operational disruption. This study developed and implemented a digital twin-based virtual hospital platform for Level 1 IT outage disaster response training and evaluated its feasibility through quantitative performance metrics and participant survey feedback. A digital twin-based virtual hospital platform modeling 317 clinical spaces and 6 building entrances of Yongin Severance Hospital, South Korea, was developed to simulate a hospital information system failure (Code White Level 1 IT outage) with 7 patient cases of varying complexity levels, covering complete outpatient workflows from registration through payment. Sixty multidisciplinary participants (physicians, nurses, laboratory technicians, pharmacists, and administrative staff) were recruited through purposive sampling from clinical departments and support services directly involved in outpatient IT outage response. Emergency prescription and patient information lookup systems were integrated into the training. Performance evaluation included scenario completion rates, prescription accuracy, completion times, and operational readiness scores. Training outcomes were compared with 2023 conventional training records from the same institution using descriptive metrics. Open-ended survey responses were analyzed using structured content summarization with text mining and word cloud techniques. A 7-item operational readiness checklist was assessed by a panel of 5 training facilitators to evaluate system functional completeness. In July 2024, 60 multidisciplinary participants completed the training exercise. All 7 patient scenarios achieved 100% completion rates with perfect accuracy in medical billing concordance and prescription entry timeliness. Scenario completion times ranged from 25 to 47 minutes, with variations reflecting testing wait times and workflow complexity. The overall operational readiness score was 80% (with 70% for digital twin platform operational readiness and 90% for emergency prescription program operational readiness). Training reduced resource consumption by 70 minutes compared to the 2023 conventional training approach, decreasing full-time equivalent requirements from 0.072 to 0.038. Open-ended survey feedback yielded five content categories (target extensions, mock training ideas, drug/prescription system improvement, operations and evaluation systems, and process improvement), with realism, collaboration, and prescription as the most frequently cited keywords. This proof-of-concept study demonstrates that a simulation-oriented digital twin platform can support multi-department IT disaster response training with complete workflow execution. Identified technical gaps in user permissions and prescription classification provide a concrete development roadmap for institutional deployment.
Hospitals are increasingly dependent on interconnected digital infrastructures, which are essential for clinical and administrative operations. Outages of IT systems can significantly jeopardize patient care and impact business continuity. While frameworks provide methodological guidance for IT outage management, their practical implementation remains challenging. Therefore, this work presents a Minimum Viable Product (MVP) guideline developed through an iterative design process. It is structured according to different time phases, processes, governance structures, and tools for dealing with an IT outage in hospitals.
Hospitals are critical infrastructures where power continuity is paramount. This study presents a techno-economic and resilience analysis of a grid-connected hybrid microgrid for a medium-sized hospital, comprising solar photovoltaics (PV), a battery energy storage system (BESS), and a diesel generator. Using a mixed-integer linear programming (MILP) model via NREL's REopt® platform, we optimized the system design to minimize the Net Present Cost (NPC) while ensuring an uninterrupted power supply to critical loads during grid outages. The analysis evaluated a wide range of outage scenarios, varying in duration (7-24 h), timing, season, and critical load level (50-100%). This study shows that a design of a microgrid for enhanced resilience is both technically and economically beneficial. The microgrid design with optimization achieves a net present cost savings of 14% for the financial optimization scenario and 9% to 14.2% for all the resilience-constrained scenarios compared to the business-as-usual case of 100% grid dependence. Most importantly, compared to the financial optimum rather than the business-as-usual case, the cost of including resilience constraints is a mere 0.4% to 2.4% of net present cost, showing that increased energy resilience can be delivered at a minimal cost. A key finding is that systems designed for summer outages yield higher savings due to greater solar availability, and a strategic deep-discharge protocol for the battery during emergencies is crucial for cost-effectiveness. This work provides an actionable framework for hospital administrators to enhance energy resilience without incurring a financial penalty, and in many cases, while realizing significant long-term cost savings.
The escalating demand for enhanced coverage and high data rates in wireless networks is driving the adoption of advanced technologies like unmanned aerial vehicles (UAVs). Integrating UAVs with non-orthogonal multiple access (NOMA) has emerged as a promising solution to boost spectral efficiency and user connectivity. However, the practical performance of these UAV-assisted NOMA systems is critically constrained by real-world imperfections, including hardware impairments, inaccurate channel state information (CSI), and non-ideal successive interference cancellation (SIC). To address this, a reliable system design necessitates a precise outage probability analysis, which quantifies the impact of these impairments on both reliability and user experience. This work derives closed-form expressions for the outage probability of a multi-user UAV-assisted NOMA system operating over Rician fading channels, explicitly incorporating the effects of the aforementioned impairments. Analytical results are obtained for a two-user UAV-assisted NOMA system by considering the detrimental effect of hardware impairments along with imperfect CSI and SIC on system performance. These analytical results are further validated by simulated results.
Device-to-Device (D2D) communication constitutes that closely spaced nodes communicate with each other without the use of a centralized base station that carries the communication data. This helps in increasing the data rate, decreasing latency, and expanding the bandwidth and capacity which promotes it to play an important part in next-generation wireless technology. Regardless, the efficient utilization of the available resources is a crucial challenge in D2D systems especially in limited areas; considering that the spectrum is shared across the cell. This paper investigates the spectrum sharing strategies for D2D communication in the context of a single cell by presenting a simulation study of D2D communication performance under different resource allocation scenarios. The first scenario divides the coverage area into inner and outer regions, and the spectrum as well; one for each region, the second scenario assigns resources to nearby users within a range of 10 meters and reuses the resources by other distant users if the separation is more than 50 meters. The variations in the simulation parameters helps to analyze the impact of user location, density, and resource allocation in outdoor urban environments. The simulation results are evaluated in terms of spectrum utilization, and outage probability. These results are analyzed to understand the impact of resource allocation on the performance of the system and thereby show that the second scenario improves the system performance in terms of the outage probability providing useful insights for designing and optimizing D2D communication systems in an outdoor urban environment especially in next-generation-networks.
We examine the linkages between power reliability, economic growth, and income inequality in the United States. Specifically, we use the two-step System Generalized Method of Moments (GMM) estimator to assess the impact of power interruptions on state-level GDP and the Gini Index. Our findings reveal that a 1 percent increase in power interruptions, measured in terms of duration (SAIDI) and frequency (SAIFI), is associated with a 0.07 to 3.7 percent decrease in real GDP and a modest increase in income inequality of approximately 0.17 to 0.20 percent relative to the mean Gini Index. Moreover, the marginal effects of power interruptions are substantial, with frequent outages resulting in GDP losses exceeding $2 trillion in the long run. We also use machine learning models to support the predictive relevance of the power reliability metrics. Overall, the results highlight the significant role that both the frequency and duration of power interruptions play in shaping regional economic performance and the importance of improving power reliability to foster economic stability and equity.
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Stable access to electricity is increasingly recognized as a social determinant of health due to its essential role in daily functioning, safety, and access to services. In Cuba, prolonged and recurrent power outages have become a persistent aspect of daily life and may function as chronic stressors with important mental health consequences. This study examined the association between prolonged power outages, blackout-related functional disruption, and symptoms of depression, anxiety, and stress among adults living in Cuba. A cross-sectional online survey was conducted between July and November 2025 with 415 Cuban adults. Mental health symptoms were assessed using the Depression, Anxiety, and Stress Scale-21. Blackout exposure was measured using a Power Outage Severity Index, and functional disruption was assessed with an Outage Functional Impact Index. Hierarchical multiple linear regression analyses were performed to evaluate independent and incremental associations beyond sociodemographic factors. Extremely severe levels of depression, anxiety, and stress predominated across the sample. Both blackout severity and functional impact were positively associated with all mental health outcomes. Functional impact emerged as the strongest predictor, explaining substantial additional variance in stress, anxiety, and depression beyond demographic variables and blackout severity. When functional impact was included, associations between outage severity and stress and anxiety were attenuated, remaining significant only for depression. Younger age was associated with higher stress and depression. Prolonged power outages in Cuba are associated with adverse mental health outcomes, particularly when daily functioning is disrupted, highlighting energy instability as a critical public health concern.
To address the critical technical challenge of positioning failure in unmanned mining trucks induced by global navigation satellite system (GNSS) signal attenuation in complex open-pit coal mine environments, particularly the significant trajectory oscillation during cornering in IMU-LiDAR integrated navigation under the simulated GNSS outage condition (with a focus on the challenging scenario of GNSS outage), this study proposes a collaborative positioning method integrating inertial measurement unit (IMU) and LiDAR based on the extended Kalman filter (EKF). A nonlinear system fusion positioning model was established, where IMU high-frequency motion estimation serves as the prediction equation and LiDAR point cloud matching (adopting the normal distribution transformation, NDT, algorithm) acts as the observation equation. The EKF algorithm performs optimal estimation on both the position estimation results of IMU and the pose information of LiDAR obtained via NDT, effectively suppressing the cumulative errors of IMU and the matching jitter of LiDAR under feature degradation scenarios. To verify the algorithm's performance comprehensively, a standardized experimental vehicle platform was constructed, and tests were conducted under multi-route, multi-speed, and repeated trial conditions. To simulate a representative and controllable weak-GNSS condition, the GNSS signal was programmatically blocked to emulate a complete outage scenario. Results indicate that while individual positioning algorithms exhibit significant tracking jitter in curved sections, the proposed IMU-LiDAR fusion trajectory closely matches the preset path. Compared to standalone IMU/LiDAR algorithms, the fusion method reduces average offset by up to 24.51% and standard deviation by up to 34.15%; when compared with mainstream open-source integrated navigation algorithms such as FAST-LIO2 and LIO-SAM, it also demonstrates superior positioning accuracy and trajectory stability. These research findings provide reliable technical support for intelligent construction in open-pit coal mines.
Doppler velocity log (DVL) outages can rapidly degrade loosely coupled inertial/Doppler navigation for autonomous underwater vehicles, especially in deep water or over complex seabed terrain. This study presents an attention-guided temporal convolutional method for generating pseudo-DVL velocity measurements during such outages. The model uses recent inertial measurement unit outputs and strapdown inertial navigation system (SINS)-derived attitude, position and velocity as sequential inputs, and learns the mapping to horizontal DVL velocity measurements from DVL-available periods. During DVL-unavailable intervals, the trained model predicts pseudo-velocity measurements at the original DVL update instants, which are then used in an extended Kalman filter measurement update. Causal dilated convolutions are used to extract temporal dependencies without future information, while the attention module weights motion segments that are more informative for velocity prediction. Simulations using 16 autonomous underwater vehicle trajectories show that the proposed method reduces velocity and trajectory errors compared with multilayer perceptron (MLP), temporal convolutional network (TCN) and gated recurrent unit with attention (GRU-Attention) baselines under continuous DVL outage. In a closed-loop turning task, the eastward and northward velocity root mean square error (RMSE) values are reduced by 14.69% and 14.74%, respectively, compared with GRU-Attention.
Climate extremes increasingly threaten energy infrastructure, yet whether disparities in energy resilience persist within cities under comparable hazard exposure and how distributed energy resources may reshape them remain largely unquantified. By integrating climate and energy projections, socio-demographic data, and an optimization-based power outage metric that captures initial outages, recovery, and distributed energy resource support, this study reveals evident energy resilience disparities shaped by intersectionality across income, race, and ethnicity in New York City. These disparities are projected to be exacerbated under future climates. Middle-income households exhibit the lowest levels of energy resilience, with their outage risk increasing by 1.5-2 times compared to the wealthiest households under severe events. Low- and middle-income Asian and high-income Black households experience up to twice the average outage risk increase compared to others within the same income groups. While distributed energy resources can partially mitigate disparities, their impact remains limited under business-as-usual growth. Our findings identify climate-vulnerable communities and inform efforts to promote energy justice in a changing climate.
The population of children with medical complexity (CMC) is increasing globally. Caregivers must provide highly individualized care, and there may be "information asymmetry" between families and support providers. Infrastructure disruptions during disasters, such as power outages and communication failures, threaten the survival of CMC. This study aimed to estimate the independent effect (after adjusting for daily confounding factors) of requiring advanced respiratory support (ARS) on three types of disaster-related anxiety. A cross-sectional online survey was conducted from August to October 2025, targeting caregivers of CMC via a nationwide support network in Japan. Participants were categorized into an ARS group (requiring invasive/non-invasive ventilation, high-flow nasal cannula, or frequent suctioning) and a non-ARS group. Anxiety was assessed across three domains: (1) accurate transmission of information to medical professionals met for the first time, (2) transmission of information in situations where communication methods are unavailable, and (3) continuity of care during power outages. With ARS as the primary independent variable, binomial logistic regression was performed to calculate adjusted odds ratios (AOR), controlling for covariates, including caregiver and child ages, evacuation method difficulty, and daily information-sharing satisfaction. Data from 279 caregivers were analyzed (child mean age: 9.5 ± 7.0 years). Binomial logistic regression showed that requiring ARS had a significant independent effect on anxiety about the accurate transmission of information to medical professionals met for the first time (AOR: 2.60; 95% confidence interval [CI]: 1.13-5.99; p = 0.025) and continuity of care during power outages (AOR: 5.87; 95% CI: 2.99-11.52; p < 0.001), but not for situations where communication methods were unavailable. Caregivers of CMC requiring ARS experience significantly greater anxiety during disasters, caregivers of CMC requiring ARS experience significantly greater anxiety about communicating information to medical professionals and continuity of care. To ensure CMC safety, transitioning to digital personal health records to complement oral and paper-based methods should be considered. Bridging these information gaps may reduce caregiver anxiety and facilitate rapid triage during emergencies.
This paper proposes a radio frequency (RF)/free space optics (FSO) communications hybrid system that will improve security and reliability of future sixth generation (6G) wireless communication network links with practical channel conditions. The system uses the composite Weibull-Lognormal (WLN) turbulence model for modeling the free-space-optics (FSO) link and incorporates the effects of both local fade events and global weather phenomena; it also uses Nakagami-m/Rayleigh fading to model the RF link. A hybrid link selection algorithm (HLA) is used to select the best available transmission link as a function of real-time channel characteristics. The performance of the proposed hybrid system is analyzed from three perspectives: secrecy capacity, bit-error-rate (BER), and outage probability under different fading/turbulence conditions through an exhaustive Monte-Carlo simulation process and supported by analytical results. These analyses show that this hybrid system has significant advantages over single-link systems employing either RF or FSO alone; these advantages include reduced outage probability, improved BER performance, and higher secrecy capacity especially when operating under high-turbulence conditions. These results show that a composite-fading architecture provides a reliable and secure framework for the development of fifth-generation (5G)-like communication systems which can be used in future sixth-generation (6G) wireless communication networks.
Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent diagnosis method based on pulse-coupled injection. At the hardware level, a semi-ring capacitive coupling sensor is developed and designed, which realizes non-contact injection of high-frequency pulse signals and high-SNR extraction without a power outage. The reliability of the system under complex working conditions is verified by field experiments on multiple actual 110 kV transformers. At the algorithm level, an innovative MSCNN-Transformer-PGA deep composite model fused with prior electromagnetic physical knowledge is constructed and combined with the transformer equivalent circuit model. The model uses a multi-scale convolution to extract local details of frequency response signals, adopts Transformer to establish the global sequence dependence, and introduces a Physics-Guided Attention mechanism (PGA) to adaptively focus on the key fault physical frequency bands. The experimental results show that the proposed method effectively overcomes electromagnetic noise interference, and the fault classification accuracy of single-modal pulse frequency response data reaches 97.6%, providing a high-precision online monitoring solution for the safe operation and maintenance of transformers.
Nowadays, farmers across the globe are gradually adopting intelligent farming, which is facilitated by a variety of cutting-edge technologies. The advancement of intelligent farming applications is greatly aided by the internet of farming things (IoFT). Massive IoFT devices generally possess constrained resources, making it challenging to meet the battery and computational requirements of intelligent farming applications through local computation. RF energy harvesting enabled mobile edge computing (RFE-MEC) addresses this issue by harvesting RF energy from an access point, offloading and computing tasks at the edge in a nearby access point. In the proposed scheme, multiuser nonorthogonal multiple access allows the IoFT devices to simultaneously offload computationally intensive tasks to the MEC server for processing. The delay outage probability closed-form expression is formulated for the RFE-NOMA-MEC intelligent farming system under a Rayleigh fading channel. The impact of imperfect channel state information on the RFE-NOMA-MEC is considered. Tunicate enhanced northern goshawk optimization algorithm (TNGO) has been proposed to discover the optimal parameter set to minimize delay outage probability. The results indicate that the system performance is enhanced using TNGO when the optimal time switching factor, power allocation coefficient and task allocation ratio are utilized.
Autonomous vehicles at level 3 and above must maintain high navigation accuracy, particularly in global navigation satellite system (GNSS)-denied environments. The main innovations of this work are threefold. First, we integrate visual inertial odometry (VIO) and light detection and ranging (LiDAR) inertial odometry (LIO) as external updates to mitigate the rapid drift of micro-electromechanical system (MEMS)-based industrial-grade inertial measurement units (IMUs) during long-term GNSS outages. Second, we adopt a redundant IMU (RIMU) approach that fuses multiple low-cost IMUs to reduce sensor noise and improve reliability. Third, we propose a system calibration methodology using both static and dynamic vehicle motion to estimate extrinsic parameters (boresight angles and lever arms) of the sensors, achieving an overall boresight angle root-mean-square error of 0.04 degrees in the simulation. Experiments were conducted under a 7 min GNSS-denied scenario in an underground parking lot, allowing for comparison of the error characteristics of multi-sensor fusion schemes against a navigation-grade reference. The INS/GNSS/LIO framework achieved a two-dimensional root-mean-square position error of 1.22 m (95% position error within 2.5 m), meeting the lane-level (1.5 m) accuracy requirement under a GNSS outage exceeding 7 min without prior maps. In contrast, the RINS/GNSS/VIO framework yielded a 4.71 m 2D mean position error under the same conditions. This paper provides a quantitative comparison of the baseline error characteristics of VIO-, LIO-, and RIMU-assisted INS/GNSS fusion under a GNSS-denied navigation scenario.
This study proposes a novel framework for the optimal allocation of reclosers and the coordinated operation of protective devices in electrical distribution networks. The main objective is to enhance system reliability and reduce outage related indices by improving the operational performance of reclosers and cutout fuses. Protection settings and Time Current Characteristic (TCC) curves are configured to ensure fast fault detection and effective isolation of faulty sections enabling reclosers to sectionalize the network properly and prevent miscoordination among protection devices. One of the key contributions of the proposed method is the integration of a fuse saving strategy. By assigning priority to reclosers for fault clearing unnecessary fuse operations are avoided which in turn reduces permanent outages. In addition coordination between reclosers and cut out fuses is incorporated into the optimization model in the form of a penalty term within the objective function. This formulation ensures proper selectivity among protective devices and minimizes the network fault response time. To identify the optimal locations and settings of protective equipment a hybrid optimization approach based on graph theory and genetic algorithms has been developed. The proposed method simultaneously considers coordination constraints and fuse-saving requirements. Simulation studies carried out on the IEEE 33-bus test system indicate a considerable improvement in reliability indices. In the second scenario, with four cutout fuses and two reclosers installed, ENS is 50.41% and SAIFI and SAIDI are 46.60% improved compared with the base case. The results demonstrate that reclosers play a fundamental role in mitigating the adverse impacts of faults in distribution systems. Their coordinated operation with cutout fuses together with appropriate adjustment of protection characteristics, significantly enhances network reliability and improves service continuity. Moreover accurate tuning of protective device parameters optimizes fault clearing time and prevents unnecessary operations ultimately leading to improved overall protection system performance.