Solid waste management and rural energy poverty in developing nations are global issues that demand creative waste-to-energy (WTE) solutions. An experimental and computational evaluation of a micro-scale fluidized bed-fired Stirling engine (~100 W electrical output) designed to convert waste-derived heat into sustainable energy is conducted in this work. Combining a beta-type Stirling engine with fluidized bed combustion (FBC), the system closes significant research gaps in sub-1 kW systems. This study's three novelties are the development and testing of an inexpensive prototype using locally sourced materials, high-fidelity experimental-computational convergence with deviations less than 0.4% and RMSE less than 0.33 W, and the assessment of energy recovery and environmental benefits in rural areas with abundant biomass. Maximum mechanical power output was 113.75 W, real thermal efficiency was 35.2%, 96.69 W of electrical power was generated from 258.2 W of heat input, and the generator efficiency was 85%. The Sankey diagram energy flow study revealed that most of the total losses, 55.8%, were caused by mechanical losses (17.06 W) and heat rejection (144.45 W). Root Mean Square Error was used to validate the model's resistance to temperature changes (ΔT = 258–383°C). The technology has a lot of potential to reduce open burning, greenhouse gas emissions, and energy poverty in rural Nigeria and similar regions. Some potential research objectives include life-cycle assessment, hybrid renewable integration, multi-fuel emission profiling, techno-economic analysis for community-scale deployment, and scaling to 1–5 kW. The project promotes circular economy and sustainable development goals by converting waste into valuable energy source.

Ultra-wide bandgap semiconductors exhibit advantageous electronic properties that make them promising for high-voltage, high-power electronics applications. Building on over a decade of progress in material growth and device fabrication, discrete ultra-wide bandgap devices with power-switching capacities up to the kilowatt level have been recently demonstrated. However, a packaged, multi-die ultra-wide bandgap power module - essential for further power scaling toward industrial, biomedical, grid, and aerospace applications - has yet to be realized. Here, we present a flip-chip packaged gallium oxide power module capable of 1000 A, 1000 V pulsed power switching with fast speed and minimal reverse recovery, advancing the power capacity of ultra-wide bandgap electronics by over two orders of magnitude. To address challenges posed by high electric fields and transient power surges, we employ a high-permittivity interface design enabling device-package electrothermal co-optimization. This optimization maximizes the module's transient thermal performance and enables full exploitation of the high volumetric heat capacity of gallium oxide-a largely untapped advantage in prior device development-alongside its high-temperature stability. The optimized ultra-wide bandgap module achieves over 1.8 MW/cm2 pulsed power capacity density, outperforming silicon and wide-bandgap semiconductor counterparts and suggesting the promise of ultra-wide bandgap electronics in next-generation high-power systems.

This study presents a simulation of electricity generation systems utilizing solar energy, employing TRNSYS and EES software to address the energy needs of isolated areas in Algeria. Two solar energy systems were compared: the CSP-ORC and the Photovoltaic, analyzed across three regions: Adrar, Illizi, and El-Bayadh. The CSP-ORC system was enhanced by incorporating components for optimizing the operation of the pump within the ORC, as well as integrating a solar photovoltaic system with battery storage at a capacity of 50 kW. The estimated electrical power generated by the studied systems is approximately 1 MW. We compared the technical performance of a 1 MW s-ORC system with thermal energy storage against that of a solar photovoltaic system of the same capacity. This analysis underscores the viability of both CSP-ORC and PV systems for electricity generation in isolated arid regions of Algeria. However, the superior performance of the PV system, particularly during the winter months, suggests that while CSP-ORC systems are promising, they may require further enhancements or integrated solutions (such as hybrid systems) to improve output and reliability across all seasons. The economic analysis highlights the cost-effectiveness of PV systems, which have lower investment, maintenance costs, and LCOE than CSP-ORC. The lowest LCOE (0.0311 €/kWh) and fastest payback (3.62 years) were observed in Illizi for PV, while CSP-ORC had the highest LCOE in El-Bayadh. Environmentally, PV reduces 55.2 tons of CO2 emissions in Illizi, whereas CSP-ORC, generating more electricity, prevents 84 tons. Both systems significantly cut emissions compared to diesel generators.

The purpose of this study is to examine the effect of workers’ perceived safety blind spots on near-miss reporting culture in industrial settings and to empirically investigate the mediating role of negative perceptions toward near-miss reporting in this relationship. Industrial accidents should be understood not merely as the result of individual errors or random events, but rather as structural outcomes arising from accumulated management gaps and distorted risk perceptions, despite the formal existence of safety regulations and procedures. From this perspective, this study focuses on the relationship between safety blind spots—areas where risks remain insufficiently recognized and controlled prior to accidents—and near-miss reporting culture. To achieve this objective, a questionnaire survey was conducted among workers in the electric power industry, and a total of 700 valid responses were used for analysis. The collected data were analyzed using SPSS Statistics 26.0 and R 4.5.1. After verifying the internal consistency of the measurement instruments through reliability analysis, descriptive statistics, correlation analysis, regression analysis, and regression-based mediation analysis were conducted to examine the relationships among the variables. As a cross-sectional study based on self-reported data collected at a single point in time, the analysis emphasizes associations among variables rather than causal inference. The results indicate that perceived safety blind spots have a significant negative effect on near-miss reporting culture; higher levels of perceived safety blind spots were associated with a weaker reporting culture. In addition, perceived safety blind spots significantly increased negative perceptions toward near-miss reporting. The mediation analysis further revealed that negative perceptions toward near-miss reporting play a partial mediating role in the relationship between perceived safety blind spots and near-miss reporting culture, indicating that the effect of safety blind spots on reporting culture is partly transmitted through this perceptual pathway. In conclusion, this study empirically demonstrates that the deterioration of near-miss reporting culture cannot be explained solely by individual attitudes but should instead be understood as a structural issue resulting from the combination of perceived safety management gaps in the workplace and negative perceptions toward reporting. These findings suggest that preventing industrial accidents requires moving beyond reactive approaches focused on post-accident responses toward a preventive safety management system that identifies and addresses safety blind spots at an early stage and ensures that near-miss reporting contributes to organizational learning and safety improvement.

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model's feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents.

As a key node in the campus public space system, the small squares in dormitory areas carry important functions for students’ informal learning, social interaction, and leisure relaxation, making them one of the significant public activity spaces on campus. As high-frequency daily public spaces frequently used by students, the current quality of university dormitory area small squares stands in sharp contradiction with students’ growing diversified and individualized needs. Based on demand theory, environmental behavior studies, participatory design, and other theoretical foundations, this study employs methods such as literature analysis, case studies, and field surveys to systematically investigate the existing problems in current dormitory small squares, including underutilized facilities, single-function layouts, insufficient vitality, and lack of humanistic care. The research shows that in the renovation process, priority must be given to responding to the common “basic needs” related to safety, shelter, and wayfinding, while strategically integrating and elevating the differentiated “expectation needs” concerning leisure, socialization, and aesthetic enhancement. The core lies in adopting a student demand-oriented approach and implementing four major strategies—functional compounding, flexible boundaries, natural integration, and smart empowerment—to construct a “dormitory living room” that promotes interaction and stimulates vitality. This study provides a theoretical framework and practical pathway for the “micro-renewal” of existing public activity spaces in university dormitory areas. It holds significant reference value for improving the quality of campus living environments and promoting student community integration.

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines.

Photovoltaic-thermal (PVT) systems, which harness the full solar spectrum, are attracting substantial research attention. By integration of electrical power generation with thermal energy extraction, these systems enhance overall energy efficiency. However, existing studies often focus on individual parameters, lacking a comprehensive investigation of the combined effects of nanofluids, channel geometries, and coolant volumetric flow rates. This study presents a novel PVT system tailored for high-rise residential applications featuring a lightweight aluminum alloy structure and a parallel full-channel collector configuration. Thermoelectric performance and photovoltaic (PV) cell temperature distribution were analyzed experimentally and via CFD-FLUENT simulations using water, a 40% ethylene glycol solution, and a 3% Al2O3-water nanofluid across volumetric flow rates ranging from 0.02 to 0.20 L/s. Experimental results demonstrate that increasing the volumetric flow rate significantly improves the convective heat transfer effectiveness. The 3% Al2O3-water nanofluid exhibits superior thermal conductivity, achieving a combined thermoelectric efficiency of 76.97% at 0.20 L/s, followed by water (74.09%), while the 40% ethylene glycol solution yields the lowest efficiency (70.62%). Simulation results indicate that the collector's flow channel geometry constitutes the intrinsic physical mechanism governing PV cell temperature field uniformity, whereas the volumetric flow rate serves as the key external parameter modulating this mechanism's efficacy. Higher flow rates enhance cell cooling and improve temperature uniformity across the PV module, albeit at the expense of reduced coolant exergy. A significant synergistic effect exists between coolant type and flow rate: under high volumetric flow conditions, the 3% Al2O3-water nanofluid, leveraging intensified turbulence and high thermal conductivity, achieves both the lowest cell temperature and minimal standard deviation in cell temperature. However, its application is limited in subzero environments due to its freezing point (-1 to -2 °C). Although the 40% ethylene glycol solution delivers lower thermoelectric overall efficiency, it offers broader operational temperature tolerance. This work establishes a theoretical foundation for coolant selection and operational optimization of PVT systems, providing valuable insights for advancing renewable energy utilization.

This study explored strategies for effectively addressing reading comprehension difficulties among Standard Six pupils in Tanzania. The study was conducted in Kongwa District in Dodoma Region, and data was collected qualitatively from 37 participants, where nine (9) were English teachers, 10 head teachers, eight (8) Ward Education Officers (WEOs), and 10 External School Quality (SQ) assurers. Data was collected through interviews and documentary review. The findings reveal that teachers employed group reading, repeated reading, vocabulary teaching, and guided reading as interactive strategies to enhance pupils' learning comprehension. It was also found that using motivation, appropriate reading materials, technology, assessment and feedback, teaching and learning materials, and using English as the medium of instruction significantly enhances reading comprehension. However, it was found that a lack of a supportive school environment, Internet connectivity, and limited access to English reading materials hindered effective reading comprehension among pupils. It is argued in this study that teachers need to use interactive methods to improve pupils' reading comprehension and fluency in English, and ESQ assurers need to ensure that assessment and feedback are provided to schools. For this to be a reality, the government has to ensure that schools have reading materials, computers, overhead projectors, televisions (TVs), Internet connectivity, and an electrical power supply. Nevertheless, creating conducive and supportive environments at both school and home for pupils to practice the language and providing resources for ESQ assurers remains imperative.

Abstract Wind disasters often cause widespread power outages, posing a serious threat to the safe operation of cities. This article systematically reviews key advances in early warning research on wind-induced power outages based on the framework of risk source, risk exposure, and risk mitigation. For risk sources, existing studies focus on wind load forecasting and the mechanisms that induce tree failure. For risk exposure, research has matured in analyzing wind-induced transmission and distribution circuit failures, while critical node identification and fault-propagation modeling have garnered extensive interest. For risk mitigation, the application of physical reinforcing, network reconfiguration, emergency power deployment, and intelligent dispatch has markedly strengthened pre-event preparedness and mid-event response. However, several gaps remain in current research. (1) Wind-field modeling often relies on stationary-wind assumptions and single-parameter meteorological inputs, making it difficult to capture the complexity of dynamic processes under evolving, nonstationary typhoon winds. (2) In the urban environment, the cascading faults in the fault propagation mechanism of the power system have not yet been fully analyzed. (3) Power outage prediction models rely on static data and analysis of isolated variables. Mainstream machine learning methods overly focus on historical data fitting while neglecting the physical consistency of the disaster process. (4) Mitigation strategies often emphasize localized hardening and post-event response, lacking pre-disaster resilience planning and system design.

The rapid adoption of electric vehicles (EVs) presents significant challenges regarding the stability of the power grid, involving increasing the peak demand as well as voltage deviation, power quality (PQ) degradation, harmonic distortion, and reactive power mismatch. This paper proposes an integrated artificial intelligence solution to optimize EV charging, utilizing predictive forecasting and adaptive control, to ensure compliance with the power quality standards of grid power. In the artificial intelligence solution, we employ a Temporal Fusion Transformer (TFT) to forecast multi-horizon charging demand, with a Proximal Policy Optimization (PPO)-based deep reinforcement learning agent to coordinate smart charging. The proposed artificial intelligence solution comprises a multi-objective power quality optimizer with Distribution Static Compensator (D-STATCOM) capabilities, allowing for real-time harmonic filtering and reactive power compensation. The proposed artificial intelligence solution was validated using the MATLAB platform, utilizing simulations comprising a 10 MVA distribution feeder with 20 EV chargers, with power ratings ranging from 7 to 150 kW. The simulation results confirmed substantial improvements in the grid’s performance, including a reduction in energy losses by 59.7%, a decrease in instances of load-shedding by 75.4%, and an improved power factor from 0.910 to 0.969. The performance measures used to indicate power quality observed substantial improvements, with the Total Harmonic Distortion (THD) reduced from 6.8% to 4.6%, limited to a maximum of 3.03% (as prescribed by IEEE-519), and an observed 7.2% to 4.1% decrease in voltage deviation. The proposed AI-based strategy optimally balances charging efficiencies, cost minimization, and power quality improvements. The proposed AI-driven approach successfully balances charging efficiency, cost optimization, and power quality enhancement, providing a scalable solution for large-scale EV integration in smart grid environments.

With increasing multifunctional requirements for wearable electronic devices, higher requirements have been placed on the battery life of such devices. Humans generate a large amount of mechanical energy in daily life; efficiently converting this mechanical energy into electrical power to drive sensors is an important approach to addressing the issue of limited battery life. In this study, a hybrid energy harvester combining a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) was developed to collect human limb motion energy and provide continuous power for wearable devices and sensors. The WH-HG (triboelectric-electromagnetic hybrid generator) consists of a central eccentric rotor, copper coils at the top and bottom, and a TENG section at the top. When driven by human limb movement, the eccentric rotor rotates. In the EMG unit, the rotation of the magnets inside the rotor changes the magnetic flux through the surrounding coils, generating electric energy via electromagnetic induction. In the TENG unit, rotation of the eccentric rotor drives periodic contact and separation between polycarbonate (PC) villi and a fluorinated ethylene propylene (FEP) film, producing electrical energy through triboelectrification and electrostatic induction effects. When the energy harvester was worn on a tester's wrist and tested at a running speed of 160 steps per minute, the WH-HG generated 10.61 mW, which was sufficient to light up over 100 LEDs and, after energy storage in capacitors, power a temperature-humidity sensor and a calculator. This research promotes the advancement of human-motion energy harvesting and provides an important reference for further efficient harvesting of human motion energy.

Near the rated wind speed, due to the random variations of wind speed, wind direction, and sea conditions, large-scale floating wind turbines encounter coupled interference between torque control and pitch control. It may result in substantial drops or fluctuations in electrical power. We propose a multi-stage adaptive decoupling control strategy to address the issue of electrical power drops near the full-load operation. It dynamically adjusts the closed-loop input error of PI controllers by correlating the state of the wind turbine with its torque/pitch outputs. The simulation results demonstrate that this strategy can enhance operational stability, significantly increase electrical power generation, and reduce fatigue/extreme loads of key components in large-scale floating wind turbines.

Magnets are essential for mobile consumer electronics, electric motors that power industry and the future of transportation as well as generating and transforming most electric power. Strong magnets reduce the size and weight of motors and generators as well as improve efficiency. The most powerful -based magnets have a complex structure like millions that are expected to exist but have not been made and characterized. With the recent developments of AI materials discovery techniques, which enable data-driven and machine-learning-assisted screening, together with computational approaches that can accurately predict intrinsic magnetic properties of a given structure, and high-throughput autonomous labs, the discovery of new, ultra-powerful magnet materials with saturation magnetization greater than 2.5 Tesla or magnetic energy density ( ) greater than 800 kJ/ is now quitepossible.

Employing integrated energy systems (IESs) with nuclear power plants (NPPs) can improve NPP utilization by leveraging dedicated thermal and electric power delivery, but it may also increase operational safety risks. This paper presents a framework to identify and quantify hazards and risks for such IESs. The framework combines accidentology to review past industrial accidents with failure modes and effects analysis (FMEA) to identify potential future incidents. Hydrogen explosion and toxic chemical release hazards are of particular concern. Explosion consequences are quantified using the Bauwens-Dorofeev (Bauwens) and trinitrotoluene equivalent mass (TNT-EM) methods, while chemical release consequences are computed using the Gaussian atmospheric dispersion method. Operational disturbances from direct electrical and thermal integration that may affect NPP safety are modeled using probabilistic risk analysis (PRA). Hazards and risks are then evaluated for regulatory compliance. The framework is applied to IESs comprising pressurized or boiling water reactors supplying three levels of thermal and electrical power to industrial customers. Case studies include high-temperature steam electrolysis hydrogen plants of varying capacities and a synthetic fuel production plant. Sensitivity analysis examines piping component failures in the PRA model as a precursor to cost estimation for thermal extraction line design. Additionally, Fussel-Vessely (FV) and risk increase importance (RII) measures identify risk-informed design improvements for the thermal extraction system. FMEA highlights hazards such as loss of offsite power, prompt loss of electrical load, loss of thermal output, and immediate steam diversion, in addition to hydrogen explosions and toxic chemical releases. Both Bauwens and TNT-EM methods suggest maintaining several hundred meters of separation between the NPP and hydrogen facility to mitigate explosion risks. PRA results show a maximum initiating event frequency increase of 1.15% and an overall risk increase of 0.28%. Importance measure analysis identifies upstream pipe leak isolation components as critical. Evaluating the results against safety regulations, it is concluded that hazards and risks can be managed to comply with regulations through risk-informed thermal and electrical connection designs, component selection, maintenance programs, and safe separation distances between NPPs and integrated industrial facilities.

The objective of the work is to create a universal, multifunctional uninterruptible power supply with a simple circuit topology of the power path and a wide range of functions. The main purpose of the developed power supply is to provide 220 V AC power to electric loads in-the-field conditions, in case of destroyed infrastructure, in the absence of access to the distribution network, in conditions of man-made and natural disasters. For this purpose, operation from rechargeable batteries is provided, as well as charging them if there is access to an electric power source. When developing the power supply, methods for analyzing switchmode converters in steady-state and transient modes, methods for synthesizing discrete automatic control systems and their implementation using single-chip programmable microcontrollers were used. An original circuit topology solution of an isolated bidirectional resonant DC-AC converter is proposed, on the basis of which a universal power supply with the following functions is implemented: obtaining an alternating volt-age of 220 V from a battery with a voltage from 12 to 29 V; a battery charger; stepless AC voltage regulator; induction heater (powered by either 220 V mains or battery). It is also possible to balance the voltage of lithium battery cells and power USB devices with support for Quick Charge and Power Delivery. The versatility of the developed device makes it possible to reduce the quantity of necessary equipment and increase reliability due to the interchangeability of developed power supplies used to solve different tasks from the list of implemented functions. The developed power supply has been laboratory tested and has shown that the actual technical characteristics correspond to the specified in the project.

All consumers of electric energy (capacity) operating as part of the national energy systems have direct or indirect technological connection to the electric networks of a grid organization and have to pay for the electricity transmission. The cost of electricity transmission is high, and its pricing is different for each industrial consumer. As a result, energy consumers have to plan their energy consumption, monitor the electricity transmission pricing, and manage the costs for transportation services. The author developed a new methodology for calculating the cost of energy transmission services and capacity tariffs. The algorithm relies on the specific calculation parameters, unified regional tariffs for energy transmission, and various types of tariff voltage levels. The formulae refer to the official regulations in single-rate and double-rate terms. The article also provides a method for calculating the payment obligation for the maintenance of grid facilities. The calculations were illustrated by the example of an industrial enterprise connected to the Rosseti Siberia electric grid, Kemerovo Region. The results can be useful for energy companies, industrial enterprises, and state regulators of the electric power industry.

The harvesting of energy from movements is one of the purposes of triboelectric nanogenerators (TENGs). Among the various devices designed to perform this function, floors are one of the primary ones, as they do not need to be individually fitted to each subject and can be manufactured and installed on a large scale. This work classifies previously published TENG-based floors based on their materials, electrical performance in terms of the voltage, current, and power they produce, and their application in different fields. The materials used have been correlated with other important aspects for floors, such as weather or flame resistance, sustainability, recyclability or biodegradability of materials, and price. The synthesis of the variety of TENG-based floor models, which incorporate novel materials, hybrid technologies, or various functionalities, among other characteristics, can enrich and inspire the reader to enhance the performance of future floor designs based on the triboelectric effect. In addition, a novel triboelectric floor design made of nitrile butadiene rubber (NBR) and fluorine kautschuk material is presented, along with the electrical power generated when tribolayers are in contact. For the three floor strips measuring 40 cm long × 4 cm wide and 1 mm thick, electrical current and voltage output was measured, achieving nearly 0.1 W (20 V & 4.5 mA) of electrical power generation.

Microreactors are one promising advanced reactor concept being pursued by the nuclear industry. They are distinguished by a relatively low power output of 20 MW(electric) or less. These microreactors are intended for deployment in applications where conventional small-capacity power solutions, such as diesel generators, are either economically unfeasible or logistically challenging. Such applications include providing electric power and/or heat for remote communities, mining sites, defense installations, and humanitarian and disaster relief missions. An important feature for the successful deployment of microreactors is their capability to be operated remotely. This capability can significantly reduce staffing costs by eliminating the need for licensed operators to be physically present at each reactor site. Instead, operators can be centralized in a single remote operations center placed in an economically advantageous location, thereby optimizing resources by consolidating expertise and enhancing operational efficiency. However, the implementation of a remote operation system for nuclear reactors raises new concerns regarding the security, reliability, and resilience of such a system. One way in which remote operations can be supported in a manner that maintains system security, reliability, and resilience is through the use of digital twins in a framework designed to verify and validate sensor data and commands communicated between the remote operations center and reactor. This framework, known as the digital twin certification system (DTCS), has previously been proposed as an operations architecture that can bring security and resiliency levels of remote nuclear-reactor operations to a level acceptable for commercial deployment. The DTCS makes use of a novel implementation of digital twins that increases a remotely located reactor operator’s situational awareness. This paper moves the proposed DTCS architecture from concept to reality by presenting the implementation and testing of the system. The rationale and implementation of the DTCS using tools such as DeepLynx and Apache Airflow is covered in-depth. This is followed by a demonstration of the DTCS by applying the implemented system architecture to the single primary heat extraction and removal emulator, a small-scale nonnuclear test bed that emulates the thermal behavior of a microreactor. The demonstration illustrates how the DTCS improves the security, reliability, and resilience of remote operations systems in both control and monitoring scenarios. In control scenarios, it is shown how the DTCS both validates that the correct command was received by the reactor facility and that the command was issued based on an accurate understanding of the reactor’s true state, rejecting it otherwise. In the monitoring scenarios, it is shown that the DTCS enhances operator confidence of the true reactor condition by confirming the reactor is in a normal state, and under abnormal conditions, provides alerts and diagnostic support to address unknown anomalies.

With the increasing integration of renewable energy resources into modern electrical power systems, the development of distributed hybrid microgrids is becoming increasingly attractive because of their ability to operate in islanded mode and the coexistence of ac and dc buses. Power electronic converters, as critical technologies for these systems, increasingly require greater standardization and modularity. In this context, the modular universal converter topology emerged with the capability of operating as either a dc-dc or a dc-ac three-phase converter by featuring the same power stage with simple reconfiguration. This paper advances the MUC concept by introducing a modular dual-output hybrid converter that supplies a three-phase four-wire ac port and a regulated dc bus simultaneously using a single power-conversion stage, thereby replacing the conventional dual-output configuration based on separate dc-dc and dc-ac stages. The proposed architecture employs a modular arrangement of identical buck-boost cells in which three cells synthesize dc-biased sinusoidal phase voltages, while a fourth cell regulates the dc output and establishes the ac neutral by directly tying the neutral point to the positive dc terminal, enabling a common ground reference without galvanic isolation. Compared with previous MUC proposals that supply either ac or dc, the proposed converter delivers both simultaneously without reconfiguration. Simulation and laboratory measurements verify the converter operation using a regulated 200V dc bus at a switching frequency of 62.5kHz, demonstrating simultaneous delivery of 1kW to the three-phase ac port and 800W to the dc port with balanced sinusoidal ac waveforms, stable dc regulation, and fast, well-damped responses under both ac and dc load steps. These results demonstrate the feasibility of the proposed converter as a modular, single-stage dual-output solution for hybrid microgrids.