Search Results (1,080)

Efficient drying is crucial for the preservation and high-value utilization of tricholoma matsutake (TM). Traditional hot-air drying is inefficient, energy-intensive, and prone to quality degradation. This study investigates the application of microwave drying for TM, systematically analyzing its dielectric properties and moisture states, and elucidating the dielectric response mechanisms during drying. Response surface methodology (RSM) was employed to optimize key process parameters, including microwave power, drying time, and sample mass, and to validate the feasibility of the optimized process for industrial applications. Results revealed that the dehydration process of TM comprises three distinct stages, with free water evaporation contributing 69.8% of the total weight loss. Dielectric properties correlated strongly with apparent density and temperature, with the loss tangent ($\mathit{tan}\delta$) increasing by 213.0% at higher temperatures, confirming dipole loss as the primary heating mechanism. Under optimized drying conditions (power: 620.00 W, time: 2.70 min, mass: 13.2 g), a dehydration rate (DR) of 85.41% was achieved, with a 1.50% deviation from the model-predicted values. The optimized process effectively maintained the relative integrity of the microstructure of TM, with the C/O ratio increasing from 1.03 to 1.31. Steam pressure-driven moisture migration was identified as the primary mechanism facilitating microwave-enhanced dehydration. Pilot-scale experiments scaled up the processing capacity to 15 kg/h and confirmed that the new process reduced total costs by 38% compared to traditional hot-air drying. The study developed an efficient and reliable microwave drying model, supporting industrial-scale TM processing. Full article

To address the issues of link mismatch and channel impairment in wireless optical communication across atmospheric-oceanic media, this paper proposes a two-hop relay transmission architecture based on the multiple-input multiple-output (MIMO)-enhanced multi-level hybrid multiplexing. The system implements decode-and-forward operations via maritime buoy/ship relays, achieving physical layer isolation between atmospheric and oceanic channels. The transmitter employs coherent orthogonal frequency division multiplexing technology with quadrature amplitude modulation to achieve frequency division multiplexing of baseband signals, combines with orthogonal polarization modulation to generate polarization-multiplexed signal beams, and finally realizes multi-dimensional signal transmission through MIMO spatial diversity. To cope with cross-medium environmental interference, a composite channel model is established, which includes atmospheric turbulence (Gamma–Gamma model), rain attenuation, and oceanic chlorophyll absorption and scattering effects. Simulation results show that the multi-level hybrid multiplexing method can significantly improve the data transmission rate of the system. Since the system adopts three channels of polarization-state data, the data transmission rate is increased by 200%; the two-hop relay method can effectively improve the communication performance of cross-medium optical communication and fundamentally solve the problem of light transmission in cross-medium planes; the use of MIMO technology has a compensating effect on the impacts of both atmospheric and marine environments, and as the number of light beams increases, the system performance can be further improved. This research provides technical implementation schemes and reference data for the design of high-capacity optical communication systems across air-sea media. Full article

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy. Full article

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This system innovatively integrates a patented evaporator-pump module and an optimized miniature rotary compressor, achieving a 151 W cooling capacity at 55 °C ambient temperature, surpassing existing portable systems in compactness and performance. Human trials with eight male participants at 35 °C (walking) and 40 °C (sitting) demonstrated that the liquid cooling garment system significantly improved thermal comfort. The mean thermal comfort vote decreased from 2.63 (uncomfortable) to 1.13 (slightly uncomfortable) while walking and from 3.88 (very uncomfortable) to 1.25 (slightly uncomfortable) while sitting. The mean skin temperature in the final stable state was reduced by 0.34 °C in walking trials and 1.09 °C in sitting trials, and heart rate decreased by up to 10.2 bpm in sedentary conditions. Comprehensive human trials under extreme heat further validate this system’s efficacy. This lightweight, efficient system offers a practical solution for personal thermal management in extreme high-temperature environments, with potential applications in industrial safety, military operations, and emergency response. Full article

Natural dynamical systems can often display various long-term behaviours, ranging from entirely predictable decaying states to unpredictable, chaotic regimes or, more interestingly, highly correlated and intricate states featuring emergent phenomena. That, of course, imposes a level of generality on the models we use to study them. Among those models, coupled oscillators and cellular automata (CA) present a unique opportunity to advance the understanding of complex temporal behaviours because of their conceptual simplicity but very rich dynamics. In this contribution, we review the work completed by our research team over the last few years in the development and application of an alternative information-based characterization scheme to study the emergent behaviour and information handling of nonlinear systems, specifically Adler-type oscillators under different types of coupling: local phase-dependent (LAP) coupling and Kuramoto-like local (LAK) coupling. We thoroughly studied the long-term dynamics of these systems, identifying several distinct dynamic regimes, ranging from periodic to chaotic and complex. The systems were analysed qualitatively and quantitatively, drawing on entropic measures and information theory. Measures such as entropy density (Shannon entropy rate), effective complexity measure, and Lempel–Ziv complexity/information distance were employed. Our analysis revealed similar patterns and behaviours between these systems and CA, which are computationally capable systems, for some specific rules and regimes. These findings further reinforce the argument around computational capabilities in dynamical systems, as understood by information transmission, storage, and generation measures. Furthermore, the edge of chaos hypothesis (EOC) was verified in coupled oscillators systems for specific regions of parameter space, where a sudden increase in effective complexity measure was observed, indicating enhanced information processing capabilities. Given the potential for exploiting this non-anthropocentric computational power, we propose this alternative information-based characterization scheme as a general framework to identify a dynamical system’s proximity to computationally enhanced states. Furthermore, this study advances the understanding of emergent behaviour in nonlinear systems. It explores the potential for leveraging the features of dynamical systems operating at the edge of chaos by coupling them with computationally capable settings within machine learning frameworks, specifically by using them as reservoirs in Echo State Networks (ESNs) for time series forecasting and predictive modeling. This approach aims to enhance the predictive capacity, particularly that of chaotic systems, by utilising EOC systems’ complex, sensitive dynamics as the ESN reservoir. Full article

This paper proposes a novel quantum image transmission framework to address the limitations of existing single-qubit time domain systems, which struggle with noise resilience and scalability. The framework integrates frequency domain processing with multi-qubit (1 to 8 qubits) encoding to enhance robustness against quantum noise. Initially, images are source-coded using JPEG and HEIF formats with rate adjustment to ensure consistent bandwidth usage. The resulting bitstreams are channel-encoded and mapped to multi-qubit quantum states. These states are transformed into the frequency domain via the quantum Fourier transform (QFT) for transmission. At the receiver, the inverse QFT recovers the time domain states, followed by multi-qubit decoding, channel decoding, and source decoding to reconstruct the image. Performance is evaluated using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Results show that increasing the number of qubits enhances image quality and noise robustness, albeit at the cost of increased system complexity. Compared to time domain processing, the frequency domain approach achieves superior performance across all qubit configurations, with the eight-qubit system delivering up to a 4 dB maximum channel SNR gain for both JPEG and HEIF images. Although single-qubit systems benefit less from frequency domain encoding due to limited representational capacity, the overall framework demonstrates strong potential for scalable and noise-robust quantum image transmission in future quantum communication networks. Full article

Because the interfacial Cu⁰/Cu⁺ in Cu-based electrocatalyst promotes CO₂ electroreduction activity, it would be highly desirable to physically separate Cu-based nanoparticles through coating shells and load them onto porous carriers. Herein, multilayered graphene-coated Cu (Cu@G) nanoparticles with tailorable core diameters (28.2–24.2 nm) and shell thicknesses (7.8–3.0 layers) were fabricated via lased ablation in liquid. A thin Cu₂O layer was confirmed between the interface of the Cu core and the graphene shell, providing an interfacial Cu⁰/Cu⁺. Cu@G cross-linked biochars (Cu@G/Bs) with developed porosity (31.8–155.9 m²/g) were synthesized. Morphology, crystalline structure, porosity, and elemental chemical states of Cu@G and Cu@G/Bs were characterized. Cu@G/Bs captured CO₂ with a maximum sorption capacity of 107.03 mg/g at 0 °C. Furthermore, 95.3–97.1% capture capacity remained after 10 cycles. Cu@G/Bs exhibited the most superior performance with 40.7% of FE_C2H4 and 21.7 mA/cm² of current density at −1.08 V vs. RHE, which was 1.7 and 2.7 times higher than Cu@G. Synergistic integration of developed porosity for efficient CO₂ capture and the fast charge transfer rate of interfacial Cu₂O/Cu enabled this improvement. Favorable long-term stability of the phase/structure and CO₂ electroreduction activity were present. This work provides new insight for integrating Cu@G and a biochar platform to efficiently capture and electro-reduce CO₂. Full article

The advancement of high-performance sodium-ion batteries (SIBs) necessitates cathode materials that exhibit both structural robustness and long-term electrochemical stability. Na₃V₂(PO₄)₃ (NVP), with its NASICON-type framework, is a promising candidate; however, its inherently low electronic conductivity restricts full capacity utilization. In this study, carbon-coated and Cu-substituted Na₃V₂(PO₄)₃ (NVCP) composites were synthesized via a solid-state reaction using agarose as a carbon source. Structural and morphological analyses confirmed the successful incorporation of Cu²⁺ ions into the rhombohedral lattice without disrupting the crystal structure and the formation of uniform conductive carbon layers. The substitution of Cu²⁺ induced increased carbon disorder and partial oxidation of V³⁺ to V⁴⁺, contributing to enhanced electronic conductivity. Consequently, NVCP exhibited excellent long-term cycling performance, maintaining over 99% of its initial capacity after 500 cycles at 0.5 C. Furthermore, the electrode demonstrated outstanding high-rate capabilities, with a capacity recovery of 97.98% after cycling at 20 C and returning to lower current densities. These findings demonstrate that Cu substitution combined with carbon coating synergistically enhances structural integrity and Na⁺ transport, offering an effective approach to engineer high-performance cathodes for next-generation SIBs. Full article

This article aims to analyze the conditions that contribute to the sustainability of violence against women in Bulgaria, an EU member state with high rates of this phenomenon. The analysis is based on data obtained through qualitative and quantitative methods, including in-depth interviews and focus groups with experts from state institutions (the police, prosecutors, courts, and social services), politicians, journalists, and from non-governmental organizations working with victims, as well as a nationwide sample survey of the adult population of Bulgaria. The qualitative data were analyzed through thematic analysis. The article demonstrates that cultural, ideological, and structural conditions in Bulgarian society facilitate the sustainability of violence against women. These include patriarchal norms prevailing in the family, specific characteristics of the popular culture, the neoliberal ideology of extreme individualism, the withdrawal of the state from its obligations, and ineffective institutional response. These conclusions point to the need to enhance the state’s capacity to counteract the phenomenon as well as the need to address ingrained cultural norms of conduct. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging–discharging conditions) and shallow charging–discharging conditions prevalent in real-world scenarios. To address these challenges, this study proposes a self-supervised learning framework for SOH estimation. The framework employs a dual-time-scale collaborative pre-training approach via masked voltage sequence reconstruction and interval capacity prediction tasks, enabling automatic extraction of cross-time-scale aging features from unlabeled data. Innovatively, it integrates domain knowledge into the attention mechanism and incorporates time-varying factors into positional encoding, significantly enhancing the capability to extract battery aging features. The proposed method is validated on two datasets. For the standard dataset, using only 10% labeled data, it achieves an average RMSE of 0.491% for NCA battery estimation and 0.804% for transfer estimation between NCA and NCM. For the shallow-cycle dataset, it achieves an average RMSE of 1.300% with only 2% labeled data. By synergistically leveraging massive unlabeled data and extremely sparse labeled samples (2–10% labeling rate), this framework reduces the labeling burden for battery health monitoring by 90–98%, offering an industrial-grade solution with near-zero labeling dependency. Full article

Non-powered dams represent 97% of dams in the United States and their energy generation potential has not been fully realized. The use of an Archimedes screw turbine to generate power at non-powered dams offers a dual benefit; producing electricity, and acting as downstream fish passage, helping to reconnect previously separated ecosystems. In this study, we assess the technical, environmental, social, and economic feasibility of generating power at non-powered U.S. dam sites using Archimedes screw turbines by integrating mechanical constraints, social impact metrics, proximity to infrastructure, and environmental sensitivity data. Results account for future precipitation predictions and show, between 2024 and 2050, the number of sites where Archimedes screw turbines are viable decreases by one site, but overall generation capacity increases due to increased flow rates across persisting locations. Our analysis identified 82 non-powered dam sites with a mean generation capacity of 49 kW that meet the mechanical requirements for Archimedes screw turbine technology in 2024. Our analysis presents a framework for considering social, environmental, and economic impacts of specific turbine technologies to convert non-powered dams to generate power. Full article

With the rapid advancement of industrialization and urbanization, the systematic assessment of water environment security in lake-type basins and the identification of key restoration zones have become critical scientific challenges for sustainable watershed management. This study focused on the Dongting Lake Basin, where a comprehensive evaluation system comprising 24 indicators was developed based on the Driving forces–Pressure–State–Impact–Response model. Indicator weights were determined using the entropy method. An obstacle degree model was applied to quantitatively identify the primary factors constraining water environment security. Additionally, spatial autocorrelation analysis was introduced to examine spatial dependency characteristics, enabling the delineation of priority restoration areas. The results demonstrated the following: (1) During 2000–2020, the Dongting Lake Basin exhibited significant spatial heterogeneity, with higher water environment security risks in the southeastern region, while the central-eastern region showed a continuous improvement trend. (2) Quantitative analysis identified the core obstacle factors affecting regional water environment security: wastewater treatment capacity (obstacle degree: 16.87%), ecological water use proportion (12.71%), effective irrigation area ratio (9.29%), environmental protection investment as a percentage of GDP (8.54%), and wastewater treatment rate (7.10%). (3) Based on these key constraints, targeted governance strategies are proposed, including enhancing wastewater treatment capacity, optimizing ecological water allocation, and increasing environmental protection investment. This study established an integrated “diagnosis–restoration–regulation” analytical framework for assessing water environment security and identifying priority restoration zones in lake-type basins, providing both theoretical foundations and practical references for global lake-type basin management. Full article

Optimizing the power allocation and capacity configuration of hybrid energy storage systems (HESS) is crucial for enhancing grid stability, power quality and renewable energy utilization in wind–solar complementary microgrids. However, the conventional configuration methods exhibit inaccuracy and low reliability. To achieve the optimal capacity configuration of HESS in wind–solar complementary microgrids, a power allocation strategy and a capacity optimization configuration model for HESS consisting of vanadium redox flow batteries (VRBs) and supercapacitors (SCs) were proposed based on parameter-optimized variational mode decomposition (VMD). Firstly, the number of mode decomposition (K) and the penalty factor (α) of VMD were optimized using the random walk grey wolf optimizer (RW-GWO) algorithm, and the HESS power signal was decomposed by RW-GWO-VMD. Secondly, an optimal capacity configuration model was formulated, taking into account the whole life cycle cost of HESS, and particle swarm optimization (PSO) algorithm was applied to optimize HESS capacity while satisfying operational constraints on charge/discharge power, state of charge (SOC) range, and permissible rates of load deficit and energy loss. Thirdly, the optimal capacity allocation was obtained by minimizing the whole life cycle cost of HESS, with the frequency division threshold N serving as the optimization parameter. Finally, comprehensive comparison and analysis of proposed methods were conducted through simulation experiments. The results demonstrated that the whole life cycle cost of RW-GWO-VMD was 7.44% lower than that of EMD, 1.00% lower than that of PSO-VMD, 0.72% lower than that of AOA-VMD, and 0.27% lower than that of GWO-VMD. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article