Search Results (108)

High-efficiency design solutions for cogeneration steam power plants are studied for different steam consumer requirements (steam pressures between 3.6 and 40 bar and heat flow rates between 10 and 40% of the fuel heat flow rate into the steam generators). Using a genetic algorithm, optimum designs for schemes with extraction-condensing steam turbines, reheat, and supercritical parameters were found considering four objective functions (high global efficiency, low specific investment in equipment, high exergetic efficiency, and high power-to-heat ratio in full cogeneration mode). A second Pareto front was computed from the prior solutions, considering the first two objective functions, resulting in the high-efficiency cogeneration schemes with a primary energy savings (PES) ratio higher than 10%. The results showed that the PES ratio depends strongly on the steam consumer requirements, rising from values under 10% for low heat flow rates and few preheaters to over 25% for a higher number of preheaters, high heat flow rates, and low steam pressures to the consumer. At the same heat flow rate to the consumer, the power-to-heat ratio in full cogeneration mode increases with the decrease in the required steam pressure to the consumer. Full article

Passion fruit (Passiflora edulis Sims), belonging to the Passifloraceae family, is an economically important plant in tropical and subtropical regions. The advances in functional genomics research of passion fruit have been significantly hindered by its recalcitrance to regeneration and stable transformation. This study establishes the first efficient Agrobacterium rhizogenes-mediated hairy root transformation system for passion fruit. Utilizing the eGFP marker gene, transformation efficiencies of 11.3% were initially achieved with strains K599, MSU440, and C58C1, with K599 proving most effective. Key transformation parameters were systematically optimized to achieve the following: OD₆₀₀ = 0.6, infection duration 30 min, acetosyringone concentration 100 μM, and a dark co-cultivation period of 2 days. The system’s utility was further enhanced by incorporating the red visual marker RUBY, enabling direct, instrument-free identification of transgenic roots via betaxanthin accumulation. Finally, this system was applied for functional analysis using PeMYB123, which may be involved in proanthocyanidin accumulation. Overexpression of PeMYB123 produced a higher content of proanthocyanidin in hairy roots. Additionally, the PeANR gene involved in the proanthocyanidin pathway was strongly activated in the transgenic hairy roots. This rapid and efficient visually simplified hairy root transformation system provides a powerful tool for functional gene studies in passion fruit. Full article

Sweet cherry (Prunus avium L.) is becoming increasingly popular in China, but its postharvest quality deteriorates significantly during harvest storage and transport. Here, we investigated the efficiency of different modified atmosphere packaging (MAP) treatments on the quality and physiology of ‘Meizao’ sweet cherry during 60 days of cold storage (0 ± 0.5 °C). Fruits were sealed in four types of MAP low-density polyethylene (LDPE) liners (PE20, PE30, PE40, and PE50), with unsealed 20 μm LDPE packaging bags used as the control. Our findings demonstrated that PE30 packaging established an optimal gas composition (7.0~7.7% O₂ and 3.6~3.9% CO₂) that effectively preserved ‘Meizao’ sweet cherry quality. It maintained the fruit color, firmness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc) content while simultaneously delaying deteriorative processes such as weight loss, pedicel browning, and fruit decay. These results indicate that PE30 was the most suitable treatment for preserving the quality of ‘Meizao’ sweet cherries during cold storage. Furthermore, physiological research showed that significant inhibition of respiration rate was achieved by PE30, accompanied by maintained activities of antioxidant enzymes (CAT, POD, and SOD), which consequently led to reduced accumulations of ethanol and malondialdehyde (MDA) during cold storage. To date, no systematic studies have investigated the physiological and biochemical responses of ‘Meizao’ to different thickness-dependent LDPE-MAP conditions. These observations highlight the power of the optimized PE30 packaging as an effective method for extending the fruit storage life, delaying postharvest senescence, and maintaining fruit quality of ‘Meizao’ sweet cherry. Full article

Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate status evaluation and proactive risk management. This paper proposes TransQwen, a domain-adapted LLM tailored for transformer fault diagnosis and maintenance strategy generation. Built upon the Qwen-7B-Chat architecture, TransQwen is fine-tuned on a domain-specific corpus encompassing transformer fault cases aligned with technical standards and operational procedures. It integrates DoRA for efficient parameter adaptation and RoPE to enhance positional encoding during training. The model is evaluated in three core tasks: fault type classification, fault severity grading, and strategy generation. The results show significant improvements—over 10 percentage point gains in standard conditions and up to 30 percentage points in F1 score under extreme low-sample settings (e.g., 100 samples), demonstrating robust generalization. In the maintenance strategy generation experiment, all the evaluation results of the TransQwen model reached the optimal. Through a knowledge-driven approach, the model can perform question-and-answer tasks involving professional knowledge in the power vertical field, and customize and generate accurate maintenance strategies for specific fault scenarios. Full article

This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources “AVR”, Technological Support “TS”, and Facilitating Conditions “FC” significantly impact AI adoption intentions. At the same time, Competitive Pressure “COP” and Government Regulations “GOR” do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience. Full article

Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (Edge-BS-RoFormer), a novel, lightweight band-split rotary position encoding transformer. While existing deep learning methods face limitations in dynamic UAV noise suppression under such constraints, including insufficient harmonic modeling and high computational complexity, the proposed Edge-BS-RoFormer distinctively synergizes a band-split strategy for fine-grained spectral processing, a dual-dimension Rotary Position Encoding ($\mathrm{RoPE}$) mechanism for superior joint time–frequency modeling, and $\mathrm{FlashAttention}$ to optimize computational efficiency, pivotal for its lightweight nature and robust ultra-low-SNR performance. Experiments on our self-constructed DroneNoise-LibriMix (DN-LM) dataset demonstrate Edge-BS-RoFormer’s superiority. Under a −15 dB SNR, it achieves Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) improvements of 2.2 dB over Deep Complex U-Net (DCUNet), 25.0 dB over the Dual-Path Transformer Network (DPTNet), and 2.3 dB over HTDemucs. Correspondingly, the Perceptual Evaluation of Speech Quality (PESQ) is enhanced by 0.11, 0.18, and 0.15, respectively. Crucially, its efficacy for edge deployment is substantiated by a minimal model storage of 8.534 MB, 11.617 GFLOPs (an 89.6% reduction vs. DCUNet), a runtime memory footprint of under 500MB, a Real-Time Factor (RTF) of 0.325 (latency: 330.830 ms), and a power consumption of 6.536 W on an NVIDIA Jetson AGX Xavier, fulfilling real-time processing demands. This study delivers a validated lightweight solution, exemplified by its minimal computational overhead and real-time edge inference capability, for effective speech enhancement in complex UAV acoustic scenarios, including dynamic noise conditions. Furthermore, the open-sourced dataset and model contribute to advancing research and establishing standardized evaluation frameworks in this domain. Full article

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar_a. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO₂e, 11,184 tCO₂e, and 19,913 tCO₂e of greenhouse gases, respectively, would be released into the atmosphere. Full article

Among different kinds of renewable energy sources, ocean wave energy offers a promising source of low-carbon electricity. However, despite this potential, ocean wave energy systems can have notable environmental impacts, which remain underexplored. Environmental life cycle assessment (LCA) is a method that can be used to evaluate the environmental impact of these systems. But few LCAs have been conducted for wave energy converters (WECs), and no prior studies specifically address onshore oscillating water column (OWC) devices, leaving a clear gap in this field. This research provides a cradle-to-gate LCA for an OWC device, using the 500 kW LIMPET OWC plant, located on the Isle of Islay in Scotland, as a case study. The assessment investigated the environmental impacts of the plant across 19 impact categories. OpenLCA 2.0 software was used for the analysis, with background data sourced from the Ecoinvent database version 3.8. The ReCiPe 2016 Midpoint (H) and Cumulative Energy Demand (CED) methods were used for the impact assessment. The results revealed a Global Warming Potential (GWP) of 56 kg CO₂ eq/kWh and a carbon payback period of 0.14 years. The energy payback period is significantly higher at 196 years, largely due to the plant’s inefficient energy capture and recurring operational failures reported. These findings highlight that although ocean wave energy is a renewable energy source, WEC’s efficiency and reliability are key factors for sustainable electricity generation. Furthermore, the findings conclude the need for selecting eco-friendly construction materials in OWC construction, namely chamber construction, and the advancement of energy-harnessing mechanisms, such as in Power Take-off (PTO) systems, to improve energy efficiency and reliability. Moreover, the importance of material recycling at the end-of-life stage, which was not accounted for in this cradle-to-gate analysis yet, is underscored for offsetting a portion of the associated environmental impacts. This research contributes novel insights into sustainable construction practices for OWC devices, offering valuable guidance for future wave energy converter designs. Full article

Power electronics (PE) technology has become integral across various applications, playing a vital role in sectors worldwide. The integration of renewable energy (RE) into modern power grids requires highly efficient and reliable power conversion systems, especially with the increasing demand for grid controllability and flexibility. Advanced control and information technologies have established power electronics converters as essential enablers of large-scale RE generation. However, their widespread use has introduced challenges to conventional power grids, including reduced system inertia and stability issues. This article studies the critical role of power electronics in the grid integration of RE systems, addressing key technical challenges and requirements. A special focus is given to the integration of wind energy, solar photovoltaic, and energy storage systems. This paper reviews essential aspects of energy generation and conversion, including the control strategies for individual power converters and system-level coordination for large-scale energy systems. This article additionally includes grid codes that pertain to wind and photovoltaic systems, as well as power conversion and control technologies. Finally, it outlines the future research directions, aimed at overcoming emerging challenges and advancing the seamless integration of RE systems into the grid, thereby contributing to the development of more sustainable and resilient energy infrastructure. Full article

Wood heat treatment is considered by many to be an eco-friendly wood modification method, given that only heat is applied during the treatment. However, it is essential to recognize that energy consumption can give rise to various environmental challenges. Quantitative evaluation of the environmental performance of a wood modification technology is always a challenge faced by the wood processing industry. To perform a comprehensive assessment, it is imperative to adopt a life-cycle-based approach, which is still very limited for heat-treated wood in China. This study investigated the mass and energy consumption of heat-treated radiata pine lumber in life cycle stages from forest management in New Zealand to wood heat treatment in East China and calculated its environmental impacts using the ReCiPe method. Two heat supply scenarios, i.e., on-site wood-fired boilers and off-site coal-fired power plants, were compared to evaluate the influence of national policy on environmental performance. Transoceanic shipping and lumber drying were found to be the life cycle stages dominating the environmental impacts level, and human-health-related impacts, mainly fine particulate matter, photochemical ozone formation, human toxicity, and global warming, were the major environmental impacts of heat-treated radiata pine lumber. With on-site heat supply, more heat and electricity were consumed due to a lower boiler efficiency and more energy demands. However, the impact assessment showed lower environmental impacts in this scenario. The non-fossil and carbon-neutral nature of wood is the key to the environmental advantages of this heat supply scenario. Full article

Power equipment maintenance work orders are vital in power equipment management because they contain detailed information such as equipment specifications, defect reports, and specific maintenance activities. However, due to limited research into automated information extraction, valuable operational and maintenance data remain underutilized. A key challenge is recognizing unstructured Chinese maintenance texts filled with specialized and abbreviated terms unique to the power sector. Existing named entity recognition (NER) solutions often fail to effectively manage these complexities. To tackle this, this paper proposes a NER model tailored to power equipment maintenance work orders. First, a dataset called power equipment maintenance work orders (PE-MWO) is constructed, which covers seven entity categories. Next, a novel position- and similarity-aware attention module is proposed, where an innovative position embedding method and attention score calculation are designed to improve the model’s contextual understanding while keeping computational costs low. Further, with this module as the main body, combined with the BERT-wwm-ext and conditional random field (CRF) modules, an efficient NER model is jointly constructed. Finally, validated on the PE-MWO and five public datasets, our model shows high accuracy in recognizing power sector entities, outperforming comparative models on public datasets. Full article

The accuracy of hydro-turbine fault diagnosis directly impacts the safety and operational efficiency of hydroelectric power generation systems. This paper addresses the challenge of low diagnostic accuracy in traditional methods under complex environments. This is achieved by proposing a signal preprocessing method that combines complete ensemble empirical mode decomposition with adaptive noise and multiscale permutation entropy (CEEMDAN-MPE) and that is optimized with the crested porcupine optimizer algorithm for the bidirectional long- and short-term memory network (CPO-BILSTM) model for hydro-turbine fault diagnosis. The method performs signal denoising using CEEMDAN, while MPE extracts key features. Furthermore, the hyperparameters of the CPO-optimized BILSTM model are innovatively introduced. The extracted signal features are fed into the CPO-BILSTM model for fault diagnosis. A total of 150 sets of acoustic vibrational signals are collected for validation using the hydro-turbine test bench under different operating conditions. The experimental results demonstrate that the diagnostic accuracy of the method is 96.67%, representing improvements of 23.34%, 16.67%, and 6.67% over traditional models such as LSTM (73.33%), CNN (80%), and BILSTM (90%), respectively. In order to verify the effectiveness of the signal preprocessing method, in this paper, the original signal, the signal processed by CEEMDAN, CEEMDAN-PE, and CEEMDAN-MPE are input into the CPO-BILSTM model for controlled experiments. The results demonstrate that CEEMDAN-MPE effectively denoises hydro-turbine acoustic vibrational signals while preserving key features. The method in this paper integrates signal preprocessing and deep learning models and, with the help of intelligent optimization algorithms, significantly enhances the model’s adaptive ability, improves the model’s applicability under complex operating conditions, and provides a valuable supplement for hydro-turbine fault diagnosis. Full article

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 × 512-sized SAR images per second. Full article

[011] poled relaxor-PT single crystals provide superior piezoelectric constants and electromechanical coupling factors in the 32 crystal directions, and also exhibit high electrical stability under compressive stresses and temperature changes. In particular, Mn-doped Pb(In_1/2Nb_1/2)O₃-Pb(Mg_1/3Nb_2/3)O₃-PbTiO₃ (Mn:PIN-PMN-PT) single crystals show a superior coercive field (E_C ≥ 8.0 kV/cm) and mechanical quality factor (Q_m ≥ 1030), making them suitable for high-power transducers. The high-power characteristics of [011] poled single crystals have been verified from a material perspective; thus, further investigation is required from a transducer perspective. In this study, the high-power characteristics of piezoelectric transducers based on [011] poled PIN-PMN-PT and [011] poled Mn:PIN-PMN-PT single crystals were investigated. To analyze the driving limits of the single crystals, the polarization–electric field (P–E) curves, as a function of the driving electric field, were measured. The results showed that [011] poled Mn:PIN-PMN-PT single crystals demonstrate lower energy loss and THD (Total Harmonic Distortion), directly relating to the driving efficiency and linearity of the transducer. Additionally, [011] poled Mn:PIN-PMN-PT crystals provide excellent stability under the compressive stress and temperature changes. To analyze the high-power characteristics of [011] poled single-crystal transducers, two types of barrel-stave transducers, based on [011] poled PIN-PMN-PT and [011] poled Mn:PIN-PMN-PT, were designed and fabricated. The changes in the impedance and transmitting voltage response with respect to the driving electric fields were measured, and the energy loss and THD of the transducers with respect to the driving electric fields were examined to assess the driving limit of the [011] poled single-crystal transducer. The high-power characteristic tests confirmed the stability of [011] poled Mn:PIN-PMN-PT single crystals and verified their potential for high-power transducer applications. Full article

Low-color-temperature candlelight organic light-emitting diodes (OLEDs) offer a healthier lighting alternative by minimizing blue light exposure, which is known to disrupt circadian rhythms, suppress melatonin, and potentially harm the retina with prolonged use. In this study, we explore the integration of transition metal dichalcogenides (TMDs), specifically molybdenum disulfide (MoS₂) and tungsten disulfide (WS₂), into the hole injection layers (HILs) of OLEDs to enhance their performance. The TMDs, which are known for their superior carrier mobility, optical properties, and 2D layered structure, were doped at levels of 0%, 5%, 10%, and 15% in PEDOT:PSS-based HILs. Our findings reveal that OLEDs doped with 10% MoS₂ exhibit notable enhancements in power efficacy (PE), current efficacy (CE), and external quantum efficiency (EQE) of approximately 39%, 21%, and 40%, respectively. In comparison, OLEDs incorporating 10% of WS₂ achieve a PE of 28%, a CE of 20%, and an EQE of 35%. The enhanced performance of the MoS₂-doped devices is attributed to their superior hole injection and balanced carrier transport properties, resulting in more efficient operation. These results highlight the potential of incorporating 2D TMDs, especially MoS₂, into OLED technology as a promising strategy to enhance energy efficiency. This approach aligns with environmental, social, and governance (ESG) goals by emphasizing reduced environmental impact and promoting ethical practices in technology development. The improved performance metrics of these TMD-doped OLEDs suggest a viable path towards creating more energy-efficient and health-conscious lighting solutions. Full article