Search Results (1,299)

Desalination is a vital solution to global water scarcity, yet its substantial energy demand persists as a major challenge. As the core energy-consuming components, pumps are fundamental to both membrane and thermal desalination processes. This review provides a comprehensive analysis of renewable energy source (RES)-driven pumping systems for desalination, focusing on the integration of solar photovoltaic and wind technologies. It examines the operational principles and efficiency of key pump types, such as high-pressure feed pumps for reverse osmosis, and underscores the critical role of energy recovery devices (ERDs) in minimizing net energy consumption. Furthermore, the paper highlights the importance of advanced control and energy management systems (EMS) in mitigating the intermittency of renewable sources. It details essential control strategies, including maximum power point tracking (MPPT), motor drive control, and supervisory EMS, that optimize the synergy between pumps, ERDs, and variable power inputs. By synthesizing current technologies and control methodologies, this review aims to identify pathways for designing more resilient, energy-efficient, and cost-effective desalination plants, supporting a sustainable water future. Full article

Background and Objectives: Cognitive deficits are common in obstructive sleep apnea (OSA), and both intermittent hypoxemia and cardiovascular comorbidity may contribute to poorer outcomes. Arterial hypertension (HTN) has been suggested as a potential modifier of cognitive function in OSA, but findings remain inconsistent. This study examined whether HTN influences baseline cognition or cognitive improvement after continuous positive airway pressure (CPAP) therapy in moderate-to-severe OSA and identified predictors of poorer post-treatment cognitive status. Materials and Methods: This prospective study involved 71 adults with newly diagnosed moderate-to-severe OSA (AHI ≥ 15). Participants underwent baseline polysomnography, Montreal Cognitive Assessment (MoCA) testing, and P300 assessments. Cognitive impairment was defined as MoCA < 26 and HTN by antihypertensive therapy, documented diagnosis, or repeatedly elevated blood pressure. All participants initiated auto-adjusting CPAP and were reassessed after three months for adherence, residual respiratory indices, MoCA, and P300 parameters. Multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of poorer cognitive outcomes. Results: CPAP therapy significantly improved apnea severity, daytime sleepiness, global cognition, and P300 latency, while P300 amplitude did not change significantly. After three months, hypertensive and normotensive patients showed similar MoCA scores, respiratory outcomes, and P300 amplitude; P300 latency remained marginally longer in hypertensive individuals. Across multivariate models, lower mean nocturnal oxygen saturation and reduced CPAP adherence independently predicted poorer cognitive outcome at follow-up. CPAP adherence demonstrated greater discriminative ability than mean nocturnal oxygenation. Conclusions: In adequately treated moderate-to-severe OSA, HTN did not significantly affect baseline cognition or short-term cognitive recovery with CPAP. Although P300 latency remained slightly prolonged in hypertensive individuals, this difference was marginal and not accompanied by cognitive deficits. Nocturnal oxygenation and CPAP adherence emerged as the strongest predictors of post-treatment cognitive status, underscoring the importance of sustained and effective therapy. These findings suggest that effective CPAP adherence and improved nocturnal oxygenation are crucial for cognitive recovery in OSA patients, regardless of hypertensive status. Full article

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market. Full article

A growing number of fires and explosions in energy storage plants have been triggered by the thermal runaway of lithium-ion batteries. Owing to the complex physicochemical properties of these batteries, their fire safety issues remain unresolved and constitute a major obstacle to the large-scale deployment of energy storage systems. Compared with conventional extinguishing media, liquid nitrogen (LN₂) offers a dual suppression mechanism, i.e., rapid endothermic vaporization and oxygen displacement by inert nitrogen gas, making it highly suitable for lithium-ion battery fire control. However, the key operational parameters governing its suppression efficiency remain unclear, leading to excessive or insufficient LN₂ use in practice. This study established a dedicated experimental platform and designed 10 experimental conditions, each repeated three times, to investigate the propagation of thermal runaway between adjacent batteries and to quantify the suppression performance of LN₂ under varying nozzle diameters and injection strategies. Results demonstrate that under identical injection pressures, larger nozzle diameters significantly outperform smaller ones in cooling and suppression efficiency. The optimal nozzle diameter was found to be 14 mm, achieving a cooling efficiency of 40%. Furthermore, intermittent LN₂ injection of equal total mass outperformed continuous injection, with a 45 s intermittent duration achieving a cooling efficiency of 63%, 23% higher than continuous injection. These findings provide quantitative guidance for the design of LN₂-based suppression systems in large-scale lithium-ion battery energy storage modules. Full article

Hydrogen sulfide (H₂S) prevalent in fuel gases such as natural gas and biogas necessitates removal prior to utilization or pipeline distribution. Biological desulfurization is considered a green purification technology employing sulfur-oxidizing bacteria (SOB) under ambient conditions to eliminate sulfur compounds, offering advantages including high efficiency, simplified equipment, and minimal chemical consumption. A highly efficient SOB TYWJ-2 was isolated in this study. Genomic analysis revealed that strain TYWJ-2 possesses a complete set of sulfur metabolism genes, enabling the metabolism of various inorganic sulfides, along with salt-tolerance genes that support adaptation to high osmolarity environments. The optimal conditions for desulfurization were determined through single-factor experiments and Box–Behnken response surface methodology. Long-term desulfurization performance demonstrated stable operational efficiency, with H₂S removal rates consistently reaching 99.72~99.87%. System performance remained robust under varying sulfur loads, elevated salinity, and intermittent operational shutdowns, with no significant decline in desulfurization efficiency observed. These findings indicate that strain TYWJ-2 holds considerable potential for the biological desulfurization of sulfur-containing biogas and natural gas. Full article

The increasing demand for energy combined with depleting conventional energy sources has led to the evolution of distributed generation using renewable energy sources. Integrating these distributed generations with the existing grid is a complicated task, as it risks the stability and synchronisation of the system. Microgrids (MG) have evolved as a concrete solution for integrating these DGs into the existing system with the ability to operate in either grid-connected or islanded modes, thereby improving reliability and increasing grid functionality. However, owing to the intermittent nature of renewable energy sources, managing the energy balance and its coordination with the grid is a strenuous task. The hierarchical control structure paves the way for managing the dynamic performance of MGs, including economic aspects. However, this structure lacks the ability to provide effective solutions because of the increased complexity and system dynamics. The incorporation of artificial intelligence techniques for the control of MG has been gaining attention for the past decade to enhance its functionality and operation. Therefore, this paper presents a critical review of various artificial intelligence (AI) techniques that have been implemented for the hierarchical control of MGs and their significance, along with the basic control strategy. Full article

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions. Full article

This paper considers the two parallel-machine scheduling problem with intree-precedence constraints where machines are subject to non-availability constraints. In the literature, this problem is considered to be an open problem of unknown complexity. The proposed solution proves that the problem under consideration has polynomial complexity. Periods of machine unavailability are predetermined, and both task execution and inter-task communication are modeled as requiring one unit of time. The optimization criterion central to this study is the minimization of the makespan. Such a scheduling challenge is directly applicable to manufacturing environments, where production equipment can be intermittently offline for reasons such as unscheduled repairs or planned preventative maintenance. Adopting a unit-time task model offers a valuable framework for subsequently scheduling larger, preemptable jobs.This work presents a new method, called Scheduling Intrees with Unavailability Constraints (SIwUC), which operates by aggregating tasks into distinct groups. The analysis establishes that the SIwUC algorithm produces optimal schedules and reveals how the underlying problem architecture and its solutions demonstrate a symmetrical property in the distribution of tasks across the two parallel machines. This paper demonstrates that the proposed SIwUC algorithm builds optimal schedules and highlight how the problem structure and its solutions exhibit a form of symmetry in balancing task allocation between the two parallel machines. Full article

Background: Retained products of conception (RPOC) after term delivery are uncommon but may lead to persistent abnormal uterine bleeding and other complications. Hysteroscopic removal is considered the optimal management strategy, and technological advances have increasingly enabled operative procedures to be performed safely in an office setting. Clinical case: We report the case of a 43-year-old woman who presented with intermittent spotting four months after spontaneous vaginal delivery. Transvaginal ultrasound revealed a small, avascular hyperechoic intrauterine lesion consistent with retained amniochorionic tissue. She underwent office hysteroscopic removal using a 16 Fr bipolar mini-resectoscope under nitrous oxide (N₂O) buccal–nasal analgesia. The procedure was performed using a vaginoscopic, no-touch approach without speculum, tenaculum, or cervical dilation. Complete resection was achieved in a seven-minute procedure, with a postoperative pain score of 2/10 on the VAS and no complications. At 30-day follow-up, the patient was asymptomatic, and an ultrasound confirmed complete resolution. Conclusion: This case demonstrates that retained amniochorionic tissue can be safely and effectively treated in a fully ambulatory setting using mini-resectoscopic technology and N₂O analgesia. The combination of minimally invasive instruments, patient-centered procedural strategies, and well-tolerated analgesia supports the growing role of office operative hysteroscopy for selected complex intrauterine conditions. Full article

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Urban helicopter activity is intermittent and route-focused, yet most strategic mapping tools were developed for fixed-wing traffic and long-term averages, leaving urban rotorcraft noise under-represented. In the EU, the Environmental Noise Directive (2002/49/EC) and its CNOSSOS-EU methods require Member States to measure, map, and report aviation noise at major airports (using indicators such as Lden and Lnight), covering helicopter operations as part of overall aviation noise; yet current practice and tooling remain largely fixed-wing oriented. To the authors’ knowledge, no peer-reviewed real-case applications of NORAH2 to urban helicopter operations have yet been published. Therefore, this study demonstrates an end-to-end NORAH2 workflow using Cannes, France, as an urban case study, modelling 556 helicopter operations recorded between 12 and 25 May 2025 over an 8.3 km × 2.5 km analysis grid, and utilising openly available ADS-B/Mode-S trajectories to generate noise-related maps that can be used to support policy-making. Radar trajectories were conditioned to retain sampling while ensuring kinematic plausibility; environmental layers (terrain, land cover, basic meteorology) and rotorcraft representations were configured in NORAH2. Standard indicators were produced on a uniform grid, Lden (day–evening–night) and LAeq, 16 h, alongside event-count metrics (N60/N65/N70) and single-event LAmax footprints. Over a two-week window, outputs exhibited coherent corridor-level structure and event footprints consistent with observed operations, indicating that ADS-B-derived trajectories, after light conditioning, are suitable inputs for urban NORAH2 mapping. The period analysed is short; results are demonstrative for that window and not intended as statutory exposure assessments. The contribution is twofold: (i) the first published demonstration that connects open radar-like data to NORAH2 outputs in a dense urban setting, and (ii) evidence that NORAH2 can provide both energy-average and frequency-of-occurrence views useful for city noise management. Full article

As collaborative robots (cobots) become more prevalent in industry, there is growing need for autonomous cobots that can cooperatively navigate shared workspaces. Reliable navigation and safety become especially critical when intermittent communication failures occur, potentially due to environmental factors or network disruptions. This paper contributes to the development of a navigation scheme for a team of autonomous networked cobots under intermittent communication. In particular, the paper proposes a decentralized control approach enabling cobots to cooperatively transport an object across an industrial environment despite intermittent communication. The navigation scheme is decentralized in the sense that each cobot computes its control actions locally using only information from neighboring cobots, without relying on a central coordinator, and applies actuator commands independently based on local sensor feedback and inter-robot communication. The work presented herein provides a comprehensive framework for autonomous multi-cobot cooperative object transportation tasks, including the design of the control, navigation, and communication systems. The communication network among the cobots is modeled using directed graphs, with the graph Laplacian matrix representing the connectivity among the cobots. The proposed method is first validated using a commercial robot simulator. Its performance is then evaluated on physical cobots operating in an indoor environment with various complexities. Full article

Accelerating the integration of wind and solar power is essential for achieving China’s “Dual Carbon” goals, but their inherent intermittency poses significant challenges for grid stability and renewable energy utilization. This study addresses these challenges by proposing a comprehensive economic benefit optimization model for a combined gas–wind–solar power generation system under a natural gas peaking mode. The model systematically incorporates multidimensional economic indicators—including generation revenue, green certificate revenue, curtailment losses, and carbon emission costs—while accounting for operational constraints and the fluctuating nature of renewables. Simulation results show that the hybrid system achieves a total economic benefit of 9.97 million yuan, with operating costs at 20% of income and curtailment plus carbon penalty costs below 2%. Compared to single-source generation, the hybrid approach reduces wind and solar curtailment by over 90%, and maintains high channel utilization. Sensitivity analysis reveals that lower gas prices and higher green certificate prices significantly enhance both renewable energy integration and economic returns, while balanced output scenarios maximize system benefits. This research provides a quantitative assessment of the economic and environmental outcomes of a gas–wind–solar complementary system, offering practical insights to maximize renewable energy utilization and support China’s low-carbon energy transition. Full article

Cryogenic Energy Storage (CES) has emerged as a promising solution for large-scale and long-duration energy storage, offering high energy density, zero local emissions, and compatibility with intermittent renewable energy sources. This systematic review critically examines recent advances in the numerical modeling of CES systems, with the objective of identifying prevailing methodologies, emerging trends, and existing research gaps. The studies analyzed are classified into three main categories: global thermodynamic modeling, simulation of specific components, and transient dynamic modeling. The findings highlight the continued use of thermodynamic models due to their simplicity and computational efficiency, alongside a growing reliance on high-fidelity CFD and transient models for more realistic operational analyses. A clear trend is also observed toward hybrid approaches, which integrate deterministic modeling with machine learning techniques and response surface methodologies to enhance predictive accuracy and computational performance. Nevertheless, significant challenges persist, including the absence of multiscale integrative models, the scarcity of high-resolution experimental data under transient conditions, and the limited consideration of operational uncertainties and material degradation. It is concluded that the development of integrated numerical frameworks will be critical to advancing the technological maturity of CES systems and ensuring their robust deployment in real-world energy transition scenarios. Additionally, the review also discusses local thermal non-equilibrium (LTNE) conditions, the influence of geometric and operational parameters, and the role of multidimensional and multi-region modeling in predicting thermal and exergy performance of packed-bed TES within LAES cycles. Full article