Search Results (554)

The ceramic industry relies on several energy-intensive processes that currently use natural gas as their primary energy source. In line with the European Union’s objective of achieving carbon neutrality by 2050, alternative energy sources are being explored to replace natural gas. This study evaluates several potential pathways for decarbonizing ceramic production. The alternatives considered include the use of green hydrogen for combustion, the electrification of processes, the combustion of biomethane (produced from biogas), and the deployment of a small modular reactor (SMR), capable of supplying either thermal or electrical energy from nuclear power. A fifth option involves a hybrid approach combining hydrogen and electrification, with each technology applied according to the requirements of the specific process being decarbonized. The results of this study indicate that electrification is currently the most suitable option for immediate implementation. In contrast, SMRs appear to offer the most economically attractive long-term solution, although the technology is still under development, and political, environmental and societal concerns need to be accounted for. Full article

The integration of high-penetration renewable energy creates new requirements for cross-timescale peak shaving and for system robustness under extreme meteorological conditions. This study develops a dual-timescale capacity allocation method for pumped hydro storage (PHS), combining 8760 h chronological production simulation with monthly typical-day retrospective analysis. The model represents the operating limits of conventional units, nuclear power, hydropower, wind power, photovoltaic generation, tie-line exchange, and PHS energy shifting. On this basis, a stepwise capacity-sensitivity framework is established to minimize annualized comprehensive system cost while controlling renewable energy curtailment within a predefined planning threshold, rather than treating zero curtailment as an unconditional monthly hard constraint. Using long-term planning data from a coastal provincial power grid in southeastern China, the study compares the 2035 and 2040 planning scenarios. The results show that isolated typical-day models tend to overestimate PHS requirements because they disconnect chronological continuity and cross-day reservoir buffering. In 2035, the system presents a two-level seasonal capacity structure: 15,000 MW can support normalized operation in stable months, whereas the rigid boundary rises to 19,000 MW under extreme autumn high-wind conditions. In 2040, wind and photovoltaic capacity increase by approximately 20.01 GW compared with 2035, deepening low-net-load valleys and compressing seasonal regulation margins. Under the assumed planning boundary, the recommended PHS capacity converges to 23,000 MW. The proposed framework provides a practical reference for flexible resource planning in coastal power grids with deep renewable energy integration. Full article

Global food production systems are increasingly challenged by population growth, climate change, water scarcity, and environmental degradation, necessitating the adoption of sustainable, resource-efficient food production strategies. Aquaponic systems integrate recirculating aquaculture with hydroponic crop cultivation, enabling nutrient recycling and improved water-use efficiency. Simultaneously, CRISPR/Cas9 genome-editing technology has emerged as a powerful tool for precise genetic improvement of economically important aquaculture traits. This review critically evaluates current progress in CRISPR/Cas9 applications in aquaculture, with emphasis on Nile tilapia (Oreochromis niloticus). Evidence from peer-reviewed studies indicates that targeted modification of genes associated with growth regulation, disease resistance, nutrient metabolism, feed efficiency, and stress tolerance can significantly enhance fish productivity and physiological resilience. Genes involved in hypoxia adaptation and nitrogen metabolism may further improve environmental performance in intensive recirculating systems by reducing ammonia accumulation and enhancing nutrient utilization. However, most genome-editing studies have been conducted under laboratory or conventional aquaculture conditions, with limited information available regarding the long-term performance, ecological interactions, microbial dynamics, and biosafety of genome-edited fish in aquaponic environments. Technical limitations including off-target effects, mosaicism, delivery efficiency, regulatory uncertainty, and public acceptance continue to constrain large-scale implementation. In the short term, CRISPR/Cas9 applications are likely to focus on practical trait enhancement under controlled aquaculture systems, whereas longer-term research may explore fish lines specifically optimized for nutrient cycling, environmental resilience, and integrated aquaponic sustainability. Overall, CRISPR/Cas9-mediated genome editing represents a promising but still emerging strategy for improving sustainable aquaculture and aquaponic food production systems. Full article

Hydrogen (H₂) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H₂-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet 24.82% of demand, although at costs approximately 3.9 times higher than power grid (PG) electricity, while producing 3.44 MtCO₂/year compared to 65.96 MtCO₂/year from the PG. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H₂ costs decline, and their share rises from 19% to 35%, while electricity costs decrease from 408 USD/MWh to 170 USD/MWh. These findings suggest that H₂-based electricity supply can support long-term sustainability and resilience objectives in regional energy planning. Full article

Microwave (MW) ablation is a minimally invasive technique used to destroy pathological tissues through localized heating generated by a needle applicator. Internally cooled applicators using water circulation have long been the standard for high-power applications; however, water cooling introduces significant mechanical complexity. This work investigates the feasibility of a novel air-cooled coaxial thermal-ablation needle operating at 2.45 GHz up to 70 W. The system uses two concentric metal tubes—an outer 14 G stainless steel shaft (OD 2.1 mm) and an inner copper capillary (OD 1 mm, ID 0.7 mm)—serving simultaneously as the MW transmission line and cooling conduit, with dry air at room temperature (25 °C) flowing at 11 L/min under 5 bar input pressure. Experimental cooling efficiency tests demonstrated 78% efficiency for the shaft section in air and 32% for the section embedded in tissue. Electromagnetic and thermal simulations predicted ablation dimensions in a non-perfused liver of 35 mm short axis with ellipticity of 0.65 for the basic applicator, improving to 0.88 with an advanced PEEK-shaft design featuring a cancelling slot. A prototype was built and tested on exvivo bovine liver, achieving input matching better than −24 dB at 2.44 GHz and ablation dimensions (average of 5 tests) of 31 mm short axis and 45 mm long axis. Results confirm the feasibility of air cooling as a simpler, safer, and lower-cost alternative to water cooling for medium-power MW ablation. Full article

CO₂ gas-line absorption is emerging as a major L-band impairment in low-loss hollow-core fiber (HCF) links. We compare two transponder-side mitigation strategies—spectral pre-emphasis and spectral avoidance—over span lengths of 100–300 km and transmission reach of up to 3000 km. The preferred strategy depends on reach, launch power, span length, and the stability of the live-link absorption comb. Pre-emphasis is favored at short reach and for short spans, whereas spectral avoidance is superior at moderate to long reach, with a peak capacity gain of about 4 Tb/s. Pre-emphasis is also more sensitive to mismatch between the design-time and live-link absorption combs: increasing the live absorption peak from 0.10 to 0.35 dB/km reduces capacity by up to 8.5 Tb/s, while tripling the CO₂ absorption linewidth reduces capacity by up to 10.3 Tb/s. We further review implementation options for both methods: DGFF-based pre-emphasis at the WSS sites, and DSP-based avoidance via digital subcarrier multiplexing (DSCM) or entropy-loaded orthogonal frequency-division multiplexing (OFDM). These results provide a concise framework for selecting mitigation strategy under realistic operating conditions. Full article

This paper presents a comprehensive review of voltage stability challenges in South Africa’s constrained power grid, particularly in the context of rising hybrid renewable energy integration. With the growing deployment of inverter-based resources (IBRs) like solar PV, wind, and battery energy storage systems (BESS), especially under programmes through the Independent Power Procurement Office, voltage stability has emerged as a key concern, particularly in weak grid areas like the Northern Cape Province. We highlight how weak grids characterized by low short-circuit capacity, long transmission lines, and limited reactive power support are more susceptible to voltage instability, especially with high penetration of non-synchronous generation. Using a modified IEEE 14-bus system with hybrid generation, the study simulates a weak grid scenario. Findings point to significant reactive power losses and capacitive over-voltages in long and lightly loaded lines, mirroring some of the weak-grid-transmission challenges experiences in an area of the South African power grid. The study underscores the importance of dynamic load modelling (e.g., ZIP and exponential models) and inverter behaviour in stability analysis. It concludes that hybrid systems, when optimally designed and integrated with storage, can help support grid stability. However, proactive planning, advanced modelling, and compliance with evolving grid codes remain essential for securing reliable renewable integration. Full article

The growing demand for energy-efficient urban rail transit has led to the increasing deployment of reversible substations (RS) in traction power supply systems. These substations, equipped with bidirectional converter devices (BCDs), involve high initial costs and complex parameter optimization challenges. This paper presents a coordinated optimization method for BCD-equipped RS using a two-layer model. In the upper layer, the model determines the siting of RS and the capacity of BCD to minimize life-cycle cost (LCC). In the lower layer, it adjusts the control parameters of BCDs to reduce annual operating cost. An improved salp swarm algorithm (ISSA), incorporating Tent chaotic mapping and Levy flight, is developed to solve the model. A case study based on an 18.2 km subway line shows that the optimized configuration reduces overall cost by 5.12% and electricity cost by 10.53% compared with a conventional rectifier system. Moreover, it achieves a 1.19% reduction in electricity cost over a system with fixed control parameters, while maintaining rail potential and catenary voltage within safe limits. These findings demonstrate that the proposed method strikes an effective balance between initial investment and long-term operational benefits, contributing to improved energy efficiency and economic performance. Full article

Low-refresh-rate driving is an effective way to reduce the power consumption of active-matrix organic light-emitting diode (AMOLED) displays. However, in conventional low-temperature polycrystalline silicon (LTPS) thin-film transistor (TFT) pixel circuits, leakage current through switching TFTs can disturb the stored gate voltage of the driving TFT during the long emission period. This causes the time-dependent variation in driving current and visible flicker. In this study, a novel pixel circuit for leakage suppression in low-refresh-rate driving is presented. Bias aging was first applied to reduce the leakage current of the LTPS TFT, and a device model was then built from the characteristics measured at 60 °C. Based on this model, the leakage-induced instability of a conventional 7T1C pixel circuit was analyzed. To suppress this effect, a new 8T1C pixel circuit was proposed. The key idea is to reduce the source–drain voltage of the leakage-sensitive switching TFT during the emission period by raising the initial line potential to a level close to the storage node potential. Simulation results show that the proposed circuit greatly reduces the time-dependent variation in both the driving TFT gate voltage and the driving current compared with the conventional 7T1C circuit. Perceptual evaluation based on human visual sensitivity also confirms stable low-refresh-rate operation down to 20 Hz over the practical gray range. These results show that the proposed circuit is an effective solution for moderate low-refresh-rate operation without relying on low-temperature polycrystalline silicon and oxide (LTPO) technology. Full article

Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors, while the frontend electronics use pulse-height and pulse-length information to suppress non-neutron background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments is validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, low-power and cost-efficient alternative for the next generation of CRNS monitoring networks. Full article

Background: Extremely low-frequency electromagnetic fields (ELF-EMFs), generated mainly by power infrastructure and household devices, have raised scientific interest due to their potential impact on the endocrine system. Animal research consistently shows effects on melatonin secretion, stress hormone levels, thyroid activity, and reproductive function—largely mediated by oxidative stress and calcium ion imbalance. In contrast, human studies remain inconsistent, often hindered by methodological limitations and insufficient exposure characterization. Objective: This review synthesizes experimental and epidemiological studies examining low-frequency electromagnetic field exposure (≤100 kHz) and its influence on hormonal regulation. Methods: A bibliometric analysis highlights focused interest on specific endocrine targets, particularly the pineal gland. Importantly, many experimental studies use field strengths above those found near high-voltage power lines, limiting direct applicability. Conclusions: While a definitive causal link has not been established, the widespread exposure to low-frequency electromagnetic fields justifies precautionary considerations. Several important research gaps remain, many of which are identified in this review. The topic of low-frequency electromagnetic field effects on the endocrine system requires more rigorous, long-term human studies with accurate exposure assessment. Full article

To address the challenges of insufficient small target recognition, difficulty in edge information extraction, and high computational overhead in insulator defect detection, this paper proposes a lightweight detection method based on the YOLO-MLSL model for transmission line insulator defect detection. First, a C2f-LRFCA module is introduced, effectively enhancing feature interaction through a long-range convolutional attention mechanism, thereby improving the perception of fine-grained defects. Second, an MEUM multi-scale feature enhancement module is designed to achieve more efficient contextual information fusion during upsampling, improving the detection performance for multi-scale targets. Third, the ShapeIoU loss function is employed to improve the bounding box regression accuracy in complex backgrounds, and LAMP pruning technology significantly reduces the model’s computational and storage overhead. Experimental results show that the improved algorithm achieves an mAP@0.5 of 85.4%, a 4.1% improvement compared to the original YOLOv8n, while maintaining a low parameter count and computational complexity, demonstrating both high accuracy and efficiency. This research provides a valuable reference for the design and application of lightweight target detection models in the intelligent inspection of power equipment. Full article

Long-duration animal trajectories are central to computational ethology, yet constructing large rodent cohorts remains costly, time-intensive, and constrained by animal-use considerations. We present a sensor-aware generative framework that separates latent behavioral dynamics from sensing-induced observation distortion to synthesize observed-domain trajectories that are behaviorally plausible while reproducing proxy-referenced observation distortions. The framework combines a run-level semi-Markov ethology model, occupancy calibration, and state-conditioned kinematic generation with a regime-dependent Ultra-Wideband observation channel that explicitly captures Line-of-Sight and Non-Line-of-Sight sensing conditions. Using four UWB sessions, this proof-of-concept study models three states—exploring, feeding, and burrowing—and evaluates realism through state occupancy, state-conditioned kinematic divergence, residual-domain agreement, and mean-squared displacement across time lags. We further assess whether sensor-aware conditioning improves robustness under LoS/NLoS domain shift in downstream trajectory classification. Sensor-aware conditioning yields stable mixed-domain performance with AUC = 0.995, whereas condition-agnostic baselines decline to AUC = 0.974 and AUC = 0.901. These results support the feasibility of sensor-aware in silico ethology as a proof-of-concept framework for controlled robustness studies and algorithm evaluation under proxy-referenced observation distortion. Because the present evaluation is based on four UWB sessions and uses a smoothed UWB-derived reference trajectory rather than independent ground truth, broader applications to synthetic-cohort generation, disease modeling, and statistical power-analysis workflows should be considered future directions requiring validation in larger datasets. Full article

This paper presents an initial estimate of the energy demand and charging power requirements for the initial replacement of internal combustion public transport buses by battery electric buses in Chillán and Chillán Viejo, Ñuble Region, Chile. The analysis is based on an operational baseline derived from route lengths, service frequencies, departures, and fleet data, combined with official energy and transport demand projections. A case study is conducted for the introduction of 10 battery electric buses on Line 13 of public transport, comparing 100% overnight depot charging and 80% overnight charging complemented by opportunity charging. Results show that the initial 10-bus deployment would require an installed depot charging power between 300 and 450 kW, depending on the charging strategy, with an annual delivered energy of 1149.75 MWh. Long-term scaling scenarios suggest that bus-dedicated charging infrastructure could require between 4.52 MW and 22.35 MW by 2050. Although the numerical results are specific to the case study, the main contribution of this study lies in an operationally grounded planning framework that links the sizing of pilot routes with long-term charging demand scenarios and charging infrastructure projections, providing a transferable basis for preliminary electric bus planning in other mid-size or emerging regions facing similar infrastructure constraints. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article