Search Results (979)

Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

For high-voltage signal detection applications, an auto-zero and chopper operational amplifier (OPA) is proposed in this paper. With the auto-zero and chopper technique, the OPA adopts an eight-channel Ping-Pong mechanism to reduce the high-frequency ripple and glitch generated by chopper modulation. The main transconductor effectively suppresses low-frequency noise and offset by combining input coarse and output fine auto-zero. A common-mode voltage tracking circuit is presented to ensure constant gate-source and gate-substrate voltages of the chopper, which reduces the charge injection caused by threshold voltage drift of their transistors and improves output signal resolution. The OPA is implemented using CMOS 180 nm BCD process. The post-simulation results show that the unit gain bandwidth (UGB) is 2.5 MHz and common-mode rejection ratio (CMRR) is 137 dB when the power supply voltage is 5–55 V. The noise power spectral density (PSD) is 6.6 nV/√Hz, and the offset is about 47 µV. The overall circuit consumes current of 960 µA. Full article

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

Transformers are critical assets for power system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impacts on multiple aspects. Given the aging transformer fleet, supply chain challenges, and constrained investment capacity, the adoption of risk-based strategies is essential to support long-term maintenance planning and investment. This paper proposes a multicriteria framework to assess the probability and impact of transformer failure, enabling a more comprehensive and data-driven risk evaluation. The method was applied to a sample fleet, enabling the identification and prioritization of the most critical units through a risk plot. The framework enhances asset management by identifying critical units within a transformer fleet, promoting efficiency, reliability, and long-term planning based on objective risk indicators. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic power to a three-phase residential inverter supplying a clean 50 Hz load and another mode that uses a DC–DC buck–boost converter to integrate a battery storage unit for single-phase load supply. The system allows independent control of each harmonic component and maintains a clean sinusoidal voltage at the load side through DC-link isolation. The LVDC link functions as a frequency-selective barrier to suppress non-standard harmonic signals on the load side, effectively isolating the multi-frequency power grid from standard-frequency household loads. The proposed solution fills the gap between the multi-frequency power systems and the single-frequency loads because it allows the transfer of total multi-frequency grid power to the traditional household loads with pure fundamental frequency. Experimental results and simulation outcomes demonstrate that the system achieves high efficiency, robust harmonic isolation, and dynamic adaptability when load conditions change. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

The intensifying global competition for cobalt resources and the increasing likelihood of trade decoupling and disruption are profoundly impacting the global energy transition. In a globalized trade environment, a decline in cobalt supply from exporting countries can spread through the trade network, negatively affecting demand countries. Quantitative analysis of the negative impacts of export supply declines in various countries can help identify early risks in the global supply chain, providing a scientific basis for energy security, industrial development, and policy responses. This study constructs a trade network using trade data on metal cobalt, cobalt powder, cobalt concentrate, and ore sand from the upstream (mining, selection, and smelting) stages of the cobalt industry chain across 155 countries and regions from 2000 to 2023. Based on this, an impact diffusion model is established, incorporating the trade volumes and production levels of cobalt resources in each country to measure their resilience to shocks and determine their direct or indirect dependencies. The study then simulates the impact on countries (regions) when each country’s supply is completely interrupted or reduced by 50%. The results show that: (1) The global cobalt trade network exhibits a ‘one superpower, multiple strong players’ characteristic. Congo (DRC) has a far greater destructive power than other countries, while South Africa, Zambia, Australia, Russia, and other countries have higher destructive power due to their strong storage and production capabilities, strong smelting capabilities, or as important trade transit countries. (2) The global cobalt trade network primarily consists of three major risk areas. The African continent, the Philippines and Indonesia in Southeast Asia, Australia in Oceania, and Russia, the United States, China, and the United Kingdom in Eurasia and North America form the primary risk zones for global cobalt trade. (3) When there is a complete disruption or a 50% reduction in export supply, China will suffer the greatest average demand loss, far exceeding the second-tier countries such as the United States, South Africa, and Zambia. In contrast, European countries and other regions worldwide will experience the smallest average demand loss. Full article

Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

Implementing energy-efficient solutions and developing energy complexes to decentralise power supply are key objectives for enhancing national security in Ukraine and Eastern Europe. This study compares the design, numerical, and experimental parameters of a channel-type jet-reaction turbine. A steam turbogenerator unit and a pilot industrial experimental test bench were developed to conduct full-scale testing of the unit. The article presents experimental data on the operation of a steam turbogenerator unit with a capacity of up to 475 kW, based on a channel-type steam jet-reaction turbine (JRT), and includes the validation of a computational fluid dynamics (CFD) model against the obtained results. For testing, a pilot-scale experimental facility and a turbogenerator were developed. The turbogenerator consists of two parallel-mounted JRTs operating on a single electric generator. During experimental testing, the system achieved an electrical output power of 404 kW at a turbine rotor speed of 25,000 rpm. Numerical modelling of the steam flow in the flow path of the jet-reaction turbine was performed using ANSYS CFX 25 R1 software. The geometry and mesh setup were described, boundary conditions were defined, and computational calculations were performed. The experimental results were compared with those obtained from numerical simulations. In particular, the discrepancy in the determination of the power and torque on the shaft of the jet-reaction turbine between the numerical and full-scale experimental results was 1.6%, and the discrepancy in determining the mass flow rate of steam at the turbine inlet was 1.34%. JRTs show strong potential for the development of energy-efficient, low-power turbogenerators. The research results confirm the feasibility of using such units for decentralised energy supply and recovering secondary energy resources. This contributes to improved energy security, reduces environmental impact, and supports sustainable development goals. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article