bluuebunny/crossref_metadata_2025 huggingface dataset:
FPGA Accelerated Deep Learning for Industrial and Engineering Applications: Optimal Design Under Resource Constraints
Authors: Liu Yanyi , Du Hang , Wu Yin , Mo Tianli
Published: February 2025

In response to the need for deploying the YOLOv4-Tiny model on resource-constrained Field-Programmable Gate Array (FPGA) platforms for rapid inference, this study proposes a general optimization acceleration strategy and method aimed at achieving fast inference for object detection networks. This approach centers on the synergistic effect of several key strategies: a refined resource management strategy that dynamically adjusts FPGA hardware resource allocation based on the network architecture; a dynamic dual-buffering strategy that maximizes the parallelism of data computation and transmission; an interface access latency pre-configuration strategy that effectively improves data throughput; and quantization operations for dynamic bit width tuning of model parameters and cached variables. Experimental results on the ZYNQ7020 platform demonstrate that this accelerator operates at a frequency of 200 MHz, achieving an average computing performance of 36.97 Giga Operations Per Second (GOPS) with an energy efficiency of 8.82 Giga Operations Per Second per Watt (GOPS/W). Testing with a metal surface defect dataset maintains an accuracy of approximately 90% per image, while reducing the inference delay per frame to 185 ms, representing a 52.2% improvement in inference speed. Compared to other FPGA accelerator designs, the accelerator design strategies and methods proposed in this study showcase significant enhancements in average computing performance, energy efficiency, and inference latency.
Comparison of LSTM- and GRU-Type RNN Networks for Attention and Meditation Prediction on Raw EEG Data from Low-Cost Headsets
Authors: Rivas Fernando , Sierra-Garcia Jesús Enrique , Camara Jose María
Published: February 2025

This study bridges neuroscience and artificial intelligence by developing advanced models to predict cognitive states—specifically attention and meditation—using raw EEG data collected from low-cost commercial devices such as NeuroSky and Brainlink. Leveraging the temporal capabilities of recurrent neural networks (RNNs), particularly long short-term memory (LSTM) and gated recurrent units (GRUs), the study evaluates their effectiveness in predicting future cognitive states. These predictions have applications in real-time brain–computer interface (BCI) systems, enhancing responsiveness and adaptability in dynamic environments like robotic control. The proposed LSTM model demonstrated superior predictive accuracy for meditation states, achieving a Root Mean Squared Error (RMSE) of 10.90, while the GRU model excelled in predicting attention states, with an RMSE of 11.79. Both models outperformed the results provided by the proprietary eSense algorithm, reinforcing the potential of raw EEG data in cognitive-state analysis. Notably, inference times were optimized to under 50 milliseconds, making the models suitable for real-time applications. These findings underline the feasibility of using raw EEG signals from affordable devices for robust real-time prediction, offering a significant step forward in applied neuroscience. This research lays the groundwork for further exploration of RNN architectures in BCI applications, enabling safer, more intuitive, and personalized interactions in assistive technologies and beyond.
Integrated Photonic Processor Implementing Digital Image Convolution
Authors: Wang Chensheng , Wu Wenhao , Wang Zhenhua , Zhang Zhijie , Xiong Wei , Deng Leimin
Published: February 2025

Upon the advent of the big data era, information processing hardware platforms have undergone explosive development, facilitating unprecedented computational capabilities while significantly reducing energy consumption. However, conventional electronic computing hardware, despite significant upgrades in architecture optimization and chip scaling, still faces fundamental limitations in speed and energy efficiency due to Joule heating, electromagnetic crosstalk, and capacitance. A new type of information processing hardware is urgently needed for emerging data-intensive applications such as face identification, target tracking, and autonomous driving. Recently, integrated photonics computing architecture, which possesses remarkable compactness, wide bandwidth, low latency, and inherent parallelism, has harvested great attention due to its enormous potential to accelerate parallel data processing, such as digital image convolution. In this study, an integrated photonic processor based on a Mach-Zehnder interferometer (MZI) network is proposed and demonstrated. The processor, being scalable and compatible with complementary metal oxide semiconductors, facilitates mass production and seamless integration with other silicon-based optoelectronic devices. An experimental verification for digital image convolution is also performed, and the result deviations between our processor and a commercial 64-bit computer are less than 2.3%.
A Programmable Gain Amplifier Featuring a High Power Supply Rejection Ratio for a 20-Bit Sigma-Delta ADC
Authors: Li Wenhui , Tian Daishi , Zhu Hao , Sun Qingqing
Published: February 2025

A programmable gain amplifier (PGA) is commonly used to optimize the input dynamic range of high-performance systems such as headphones and biomedical sensors. But PGA is rather sensitive to electromagnetic interference (EMI), which limits the precision of these systems. Many capacitor-less low-dropout regulator (LDO) schemes with high power supply rejection have been proposed to act as the independent power supply for PGA, which consumes additional power and area. This paper proposed a PGA with a high power supply rejection ratio (PSRR) and low power consumption, which serves as the analog front-end amplifier in the 20-bit sigma-delta ADC. The PGA is a two-stage amplifier with hybrid compensation. The first stage is the recycling folded cascode amplifier with the gain-boost technique, while the second stage is the class-AB output stage. The PGA was implemented in the 0.18 μm CMOS technology and achieved a 9.44 MHz unity-gain bandwidth (UGBW) and a 57.8° phase margin when driving the capacitor of 5.9 pF. An optimum figure-of-merit (FoM) value of 905.67 has been achieved with the proposed PGA. As the front-end amplifier of a high-precision ADC, it delivers a DC gain of 162.1 dB, the equivalent input noise voltage of 301.6 nV and an offset voltage of 1.61 μV. Within the frequency range below 60 MHz, the measured PSRR of ADC is below −70 dB with an effective number of bits (ENOB), namely 20 bits.
Improving Energy Efficiency in the Management of Drilling Waste from Trenchless Gas and Power Pipeline Construction Through the Implementation of Photovoltaic Panels and Circular Economy Principles
Authors: Jamrozik Aleksandra , Ziaja Jan , Wysocki Sławomir
Published: February 2025

The modern construction of transmission networks for transporting energy resources (e.g., crude oil, gas, hydrogen) or electricity is increasingly being carried out using trenchless technologies. Trenchless methods significantly reduce the need for extensive earthworks; however, they consequently generate substantial amounts of drilling waste. This waste consists primarily of a mixture of spent drilling fluids and drill cuttings. Due to the volume and composition of the waste, along with the rapidly increasing costs of waste disposal, the trenchless technology industry faces significant economic and environmental challenges related to circular economy principles in waste management. This article presents an analysis of trenchless construction methods for underground transmission networks, with particular emphasis on the quantity and quality of the generated drilling waste. Furthermore, research is conducted to develop a cationic flocculant based on polyvinylamine, designed to eliminate the harmful coagulants in drilling waste treatment technology. Based on the conducted studies, we propose a closed-loop waste management system for trenchless technologies. The implementation of circular economy principles, along with the integration of drilling fluid treatment systems with photovoltaic panels and energy storage units, enhances the energy efficiency of drilling waste treatment processes and aligns with global trends in the adoption of renewable energy sources (RESs).
Integrated Micro- and Nano-Grid with Focus on Net-Zero Renewable Energy—A Survey Paper
Authors: Kadir Nourin , Fung Alan S.
Published: February 2025

An integrated micro- and nano-grid with net-zero renewable energy is a sophisticated energy system framework aimed at attaining optimal efficiency and sustainability. This survey paper examines several contemporary research works in this domain. This document summarizes the latest papers selected for analysis to comprehend the current state-of-the-art, integration process, methodology, and research gaps. The objective of this review is to identify existing trends and ongoing transformations in this domain. At the conclusion of the study, emerging technologies for smart grid integration are offered, emphasizing Transactive Control, Blockchain Technology, and Quantum Cryptography, based on existing research gaps. Microgrids and nano-grids are localized energy systems capable of functioning alone or in tandem with larger power grids, offering resilience and adaptability. By incorporating renewable energy sources like solar, wind, and storage devices, these networks can produce and regulate energy locally, guaranteeing that the generated energy meets or surpasses the energy used. The incorporation of intelligent technology and control systems facilitates optimized energy distribution, real-time monitoring, and load balancing, advancing the objective of net-zero energy use. This strategy not only bolsters energy security but also markedly decreases carbon emissions, rendering it an essential element in the shift towards a sustainable and resilient energy future. The worldwide implementation of interconnected micro- and nano-grids utilizing net-zero renewable energy signifies a pivotal transition towards a sustainable and resilient energy future. These localized energy systems can function independently or in conjunction with conventional power grids, utilizing renewable energy sources like solar, wind, and advanced storage technology. Integrating these resources with intelligent control systems enables micro- and nano-grids to optimize energy production, distribution, and consumption at a detailed level, ensuring that communities and companies globally can attain net-zero energy usage. This method not only diminishes greenhouse gas emissions and reliance on fossil fuels but also improves energy security and grid stability in various places. These technologies, when implemented globally, provide a scalable answer to the issues of energy access, environmental sustainability, and climate change mitigation, facilitating a cleaner and more equal energy landscape worldwide.
Reducing Environmental Impacts of Water Electrolysis Systems by Reuse and Recycling: Life Cycle Assessment of a 5 MW Alkaline Water Electrolysis Plant
Authors: Hoppe Annika C. , Minke Christine
Published: February 2025

The circular economy offers a vital avenue for sustainable development by optimizing resource utilization through reusing and recycling materials. This study focuses on the lifecycle assessment (LCA) of a 5 MW alkaline water electrolysis (AWE) system, emphasizing end-of-life (EoL) strategies, material recovery, and their environmental implications. Focusing on the recycling and reuse of critical materials—including stainless steel and nickel—we argue that enhancing material efficiency in AWE systems can lead to significant reductions in global warming potential (GWP). Our LCA reveals that manufacturing an AWE system from recycled materials results in a 50% decrease in GWP compared to virgin materials. Despite the operational focus of previous studies, our research uniquely incorporates comprehensive EoL considerations, assessing realistic recycling scenarios that highlight potential material recovery and component reuse after the system’s 20-year lifespan. Notably, 77% of materials in the AWE system can be recycled or reused, though the substantial environmental impacts of certain components, particularly the inverter and nickel, necessitate ongoing research and improved recycling technologies. This study underscores the critical role of systematic recycling and the strategic selection of materials to enhance the sustainability profile of hydrogen production technologies. By bridging the gap between operational efficiency and EoL management in AWE systems, our findings contribute to the broader aim of advancing circular economy principles in clean energy transitions. Ultimately, the research emphasizes the need for integrating innovative recycling methods and material reuse strategies to lower carbon footprints and enhance resource security, aligning with sustainable industrial practices and future energy demands.
From Waste to Functional Material—Carbon Aerogels from Citrus Biomass Infiltrated with Phase Change Materials for Possible Application in Solar-Thermal Energy Conversion and Storage
Authors: Suchorowiec Katarzyna , Bieda Martyna , Szatkowska Martyna , Sieradzka Małgorzata , Kuźnia Monika , Ziąbka Magdalena , Pielichowska Kinga
Published: February 2025

Green energy conversion and storage materials have become a focal point of research in recent times, especially in energy-consuming buildings. Phase change materials (PCMs) have gained more and more attention not only for energy storage but also in composites for solar energy conversion. This research investigates a sustainable method for converting orange biomass waste (OBW) into advanced porous carbon aerogel (PCA) composites, designed for solar-thermal energy harvesting and storage in building applications. Using potato starch as a binder, the research develops a process for producing a uniform and lightweight carbon matrix that could be scalable. The best results were found for PCA obtained with 2.5% starch, where the lowest mass loss (8.2, 0.5, 11.2% pt) was observed during the leakage test. This study highlights the suitability of OBW-derived aerogels as effective matrices for PCM impregnation and shape stabilization, indicating their future potential in solar-thermal energy conversion and storage and potentially lowering energy consumption in buildings. By repurposing agricultural waste, this work contributes to sustainable material development and advances the application of renewable energy technologies.
Pulsar’s Application in Energy Systems: Review of Current Status, Challenges, and Opportunities
Authors: Zhang Yongxin , Wu Yuru , Liu Yu , Sun Biao , Yang Jiahui , Thotakura Naga Lakshmi , Ahmed Kazi Ishrak , Liu Qian , Shen Chong , Liu Yilu
Published: February 2025

To accelerate progress toward the realization of advanced energy systems, this review explores the potential of pulsar technology to create a more stable, economical, and environmentally friendly energy infrastructure. Pulsars, with their precise and reliable timing characteristics, have emerged as a promising tool for enhancing energy systems. This review begins by examining the development history of pulsar technology, shedding light on its evolution and the milestones achieved. It then provides a comprehensive summary of the current state of research, highlighting recent advancements and breakthroughs in this field. It also explores transformative pulsar applications in energy systems, including improved grid stability, advanced energy synchronization, and efficient energy storage management. However, implementing pulsar-related technologies presents significant technical, economic, and operational challenges. This review examines these hurdles and proposes strategies to overcome them, emphasizing the need for innovation, interdisciplinary collaboration, and supportive policies to fully integrate pulsar technologies into sustainable energy systems.
A Knowledge Graph-Based Framework for Smart Home Device Action Recommendation and Demand Response
Authors: Chen Wenzhi , Sun Hongjian , You Minglei , Jiang Jing , Rivera Marco
Published: February 2025

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset.
Energy Cooperatives as an Instrument for Stimulating Distributed Renewable Energy in Poland
Authors: Brodzińska Katarzyna , Błażejowska Małgorzata , Brodziński Zbigniew , Łącka Irena , Stolarska Alicja
Published: February 2025

In Poland, the development of renewable energy primarily focuses on solar energy, especially through household prosumer installations. One emerging solution for promoting distributed renewable energy is energy cooperatives. The main aim of the research was to identify the legal and socioeconomic factors influencing the formation and growth of energy cooperatives in Poland and to provide recommendations for their development. The research, conducted in 2024, covered 47 energy cooperatives. The data gathered included information from the National Court Register (KRS), the National Support Centre for Agriculture (KOWR), and qualitative research through in-depth individual interviews using the CATI technique (computer-assisted telephone interview). The findings highlight the need to streamline legal regulations concerning energy cooperatives, particularly the dual registration system (KRS and KOWR), which delays operational start-up. They also suggest reviewing restrictions on the area and power capacity for renewable energy distribution. Proper training for cooperative managers and network operator staff is essential. Given the early stage of Poland’s energy cooperative development, ongoing monitoring is necessary. Further research will aid in creating effective tools to foster the growth of distributed renewable energy, especially through energy cooperatives.
A Survey on Green Designs for Energy Harvesting Backscatter Communications to Enable Sustainable IoT
Authors: Zeng Jiawang , Zhang Tianyi , Mishra Deepak , Yuan Jinhong , Seneviratne Aruna
Published: February 2025

The majority of Internet of Things (IoT) devices operate with limited energy resources, making it essential to prioritize sustainable carbon emissions and the adoption of energy-efficient IoT solutions. For this reason, backscatter communication (BackCom) devices are widely deployed because they are mostly passive devices that harvest energy from RF signals and modulate the information onto reflected signals by adjusting the impedance of the load. BackCom devices have a simple structure, low cost, and easy deployment. Although BackCom plays a positive role in improving energy efficiency, IoT systems that deploy many EH BackCom devices and connect numerous peripherals still face difficulties in terms of power limitations because the energy required for their operation is almost all harvested from the outside. This paper comprehensively reviews the approaches to solving the energy efficiency issues in energy harvesting (EH) BackCom-enabled IoT systems, which mainly include high-efficiency EH and energy conversion designs for the BackCom tag, renewable energy harvesting, waveform design, and resource allocation for readers. We also investigate various green designs for cooperative EH BackCom systems. Finally, we indicate the new applications and open challenges of green BackCom IoT systems, as well as future research directions.
Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir
Authors: Fan Dongyan , Lai Sicen , Sun Hai , Yang Yuqing , Yang Can , Fan Nianyang , Wang Minhui
Published: February 2025

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security.
Building a Novel Electromechanical-Thermal Model for Semi-Solid-State Batteries
Authors: Wang W. W. , Zhi S. T. , Xu X. T. , Liu X. Y. , Zhen H. S.
Published: February 2025

This article develops an electromechanical-thermal model for semi-solid-state batteries using Software COMSOL Multi-physics. The battery’s three-dimensional structure is firstly simplified into a one-dimensional electrochemical model (P2D), which combines the solid heat transfer module and the solid mechanics module. The total power consumption of the battery, obtained from the P2D model, is used to calculate the battery temperature and the lithium concentration. Then, stress analysis of the anode active particles is conducted, and the battery temperature is fed back into both the electrochemical and mechanical models. To validate the model, constant current charge/discharge cycling experiments, as well as tests on the basic electrical parameters and temperature of the battery, are conducted. The electromechanical-thermal model developed in this study serves as an effective tool for simulating semi-solid-state lithium-ion batteries, which can predict the battery’s performance under various operating conditions. The simulation results from the numerical model are consistent with experimental data at low charge/discharge rates, while slightly larger discrepancies are observed at high charge/discharge rates, with the accuracy remaining over 90%. Further, the thermal expansion behavior of the batteries with silicon-carbon anodes during the charge-discharge process can be examined using the developed model.
Critical Concerns Regarding the Transition from E5 to E10 Gasoline in the European Union, Particularly in Poland in 2024—A Theoretical and Experimental Analysis of the Problem of Controlling the Air–Fuel Mixture Composition (AFR) and the λ Coefficient
Authors: Warguła Łukasz , Wieczorek Bartosz , Gierz Łukasz , Karwat Bolesław
Published: February 2025

The RED II Directive requires European Union member states to increase the share of renewable energy in the transport sector to at least 14% by 2030. In January 2024, Poland replaced E5 gasoline (95 octane) with E10, which contains up to 10% bioethanol derived from second-generation sources such as agricultural residues. The transition to E10 raises concerns about the ability of engine management systems to adapt to its different air–fuel ratio (AFR) requirements. The AFR for E10 (13.82) is 1.98% lower than for E5 (14.25) and 3.88% lower than for pure gasoline (14.7). Research conducted on a spark-ignition engine (with AFR regulation) using an exhaust gas analyzer demonstrated that during the combustion of E5 and E10 fuels with correctly adjusted AFR and operation at λ = 1, the use of E10 potentially increases CO2 and NOx emissions despite reductions in CO and HC. However, when calibrated for E5 and operated with E10 fuel, an increase in CO2 and HC concentrations in the exhaust gases is observed, along with a reduction in CO and NOx. This phenomenon is attributed to operation with lean mixtures, at λ = 1.02. This study investigates both the theoretical and experimental impact of this fuel transition. Fuel systems typically adjust engine operation based on exhaust gas analysis but cannot recognize fuel type, leading to incorrect λ values when the AFR differs from the ECU’s programming. Effective adaptation would require additional fuel composition sensors and editable ECU mappings. For older vehicles or small non-road engines, manual adjustments to injection or carburetor systems may be necessary.
Energy Release in Deuterium–Constantan Interactions
Authors: Alexandrov Dimiter
Published: February 2025

A significant energy release over a short time is achieved in replicable experiments involving the interaction of deuterium gas with constantan specimens. The experiments were carried out in a gas chamber where the injected deuterium interacted with heated specimens: (i) Many replicable experiments were performed at initial temperatures in the range of 666–681 °C. The temperatures of the specimens began to increase ~8 s after the beginning of deuterium injection as additional increases of 358–382 °C reached after ~30 s. The released excess power was in the range of 183–209 W, its density ranged from ~114–130 W/g, and the ratio of (output power)/(input power) was ≈ 3.76–3.91. (ii) Several replicable experiments were performed at initial temperatures of 950 °C. In all these experiments, explosive evaporation of the wires occurred immediately after the beginning of deuterium injection. The released excess momentary power was greater than 3400 W, its density was 2280 W/g, and the ratio of (output power)/(input power) was ≈ 16 and greater. The outcomes found were as follows: (a) the released excess power was not of electrical origin; (b) the released excess power of chemical origin was less than ~0.18% of the total released excess power; (c) the significant density of the released excess power; and (d) helium release, correlating with the energy release, was observed. The conclusion that the released energy is of nuclear origin was drawn.
Methods for the Investigation and Mitigation of Conducted Differential-Mode Electromagnetic Interference in Commercial Electrical Vehicles
Authors: Widek Per , Alaküla Mats
Published: February 2025

One of the main challenges as the market for fully commercial electrified vehicles quickly expands is predicting the electromagnetic interference (EMI) in traction voltage systems (TVSs) in differential mode (DM) and common mode (CM). The number of subsystems connected to vehicle TVSs is increasing, and thus, so are the electromagnetic compatibility (EMC) requirements. These requirements should make sure that neither the function nor lifetime of any source or load is affected by another, but experience shows that they are often insufficient. The purpose of this article is to show how circuit simulations can complement these requirements and that a generalized artificial network/line impedance stabilization network (LISN) is insufficient to correctly predict the EMI situation of a real vehicle. This article presents a method for complexity reduction in TVS DM simulations and a comparison with the usage of LISN to predict the EMI between subsystems; the article also addresses how to mitigate the EMI with DM filters for the subsystems. The proposed method creates a foundation for a faster and safer development process. The simulation model’s development includes a traction battery and TVS subsystems. It is found that a standardized LISN does not reflect the behavior of a commercial TVS and cannot be used solely to judge if a subsystem will operate as intended within a TVS without creating EMI. A change in switching frequency in the DUT can cause severe resonance between TVS subsystems, but this is not seen with a LISN. The conclusion of the article is that LISN can provide a false sense of security and that calibrated simulation models of a complete TVS are necessary to predict the behavior in that TVS; this study also highlights the importance of using DM filters to ensure protection against resonance frequencies.
Evaluation of the Ignition Properties of Fuels Based on Oil Diesel Fuel with the Addition of Pyrolytic Oil from Tires
Authors: Chybowski Leszek , Szczepanek Marcin , Pusty Tomasz , Brożek Piotr , Pełech Robert , Borowski Paweł
Published: February 2025

The results of testing the ignition properties of fuels in the form of blends of diesel oil with pyrolysis oil produced from tires, used as an additive at concentrations of 0, 5, 7, 10, 15, and 20% m/m, are presented in this paper. The experiment included the preparation of distillation curves and the determination of the flash points, derived cetane number, and calculated cetane ratios. The results are related to the limits indicated in selected standards and regulations on requirements for marine- and land-based compression ignition engine fuels. The obtained results show the suitability of pyrolysis oil and the possibility of its use as an additive to fossil fuels, which fits in with the requirements of the policies currently being developed for reducing the use of fossil fuels and building a circular economy.
An Approach to Multiclass Industrial Heat Source Detection Using Optical Remote Sensing Images
Authors: Zeng Yi , Liao Ruilin , Ma Caihong , Wang Dacheng , Lv Yongze
Published: February 2025

Industrial heat sources (IHSs) are major contributors to energy consumption and environmental pollution, making their accurate detection crucial for supporting industrial restructuring and emission reduction strategies. However, existing models either focus on single-class detection under complex backgrounds or handle multiclass tasks for simple targets, leaving a gap in effective multiclass detection for complex scenarios. To address this, we propose a novel multiclass IHS detection model based on the YOLOv8-FC framework, underpinned by the multiclass IHS training dataset constructed from optical remote sensing images and point-of-interest (POI) data firstly. This dataset incorporates five categories: cement plants, coke plants, coal mining areas, oil and gas refineries, and steel plants. The proposed YOLOv8-FC model integrates the FasterNet backbone and a Coordinate Attention (CA) module, significantly enhancing feature extraction, detection precision, and operational speed. Experimental results demonstrate the model’s robust performance, achieving a precision rate of 92.3% and a recall rate of 95.6% in detecting IHS objects across diverse backgrounds. When applied in the Beijing–Tianjin–Hebei (BTH) region, YOLOv8-FC successfully identified 429 IHS objects, with detailed category-specific results providing valuable insights into industrial distribution. It shows that our proposed multiclass IHS detection model with the novel YOLOv8-FC approach could effectively and simultaneously detect IHS categories under complex backgrounds. The IHS datasets derived from the BTH region can support regional industrial restructuring and optimization schemes.
Research on the Design and Application of Multi-Port Energy Routers
Authors: Zhu Xianping , Li Weibo , Huang Kangzheng , Cao Shuai , Lin Boyu , Li Rentai , Xu Wei
Published: February 2025

At present, the development of the global energy internet is occurring in depth and the construction of a distributed power supply is rapid, and the energy router (ER), as a key device for integrating energy flow and information flow, has important application value in microgrids. In this paper, a multi-port energy router based on a 710 V DC bus is designed and developed with a modular structure design, including core components such as a total controller, a power converter, a hybrid energy storage system, and an auxiliary power supply. Flexible access and the management of multiple-voltage-level ports (690 V AC, 380 V AC, 220 V DC, and 24 V DC) are realized through rational topology design. The test results of the device show that the system performance indexes meet the design requirements. The operation is stable and reliable, displaying strong practical engineering value, and at the same time provides a technical solution that can be borrowed for other special scenarios such as the microgrid system.
Assessment of the Solar Potential of Buildings Based on Photogrammetric Data
Authors: Jaczewska Paulina , Sybilski Hubert , Tywonek Marlena
Published: February 2025

In recent years, a growing demand for alternative energy sources, including solar energy, has been observed. This article presents a methodology for assessing the solar potential of buildings using images from Unmanned Aerial Vehicles (UAVs) and point clouds from airborne LIDAR. The proposed method includes the following stages: DSM generation, extraction of building footprints, determination of roof parameters, map solar energy generation, removing of the areas that are not suitable for the installation solar systems, calculation of power per each building, conversion of solar irradiance into energy, and mapping the potential for solar power generation. This paper describes also the Detecting Photovoltaic Panels algorithm with the use of deep learning techniques. The proposed algorithm enabled assessing the efficiency of photovoltaic panels and comparing the results of maps of the solar potential of buildings, as well as identifying the areas that require optimization. The results of the analysis, which had been conducted in the test areas in the village and on the campus of the university, confirmed the usefulness of the above proposed methods. The analysis provides that the UAV image data enable generation of solar potential maps with higher accuracy (MAE = 8.5 MWh) than LIDAR data (MAE = 10.5 MWh).
A Bi-Level Method for Flexibility Feature Extraction and User Clustering Based on Real-World Data from Independent Smart Meters of Residential Electric Vehicle Users
Authors: Zhang Jian , Li Shujun , Li Lili , Zu Guoqiang , Wang Yongchun , Yang Ting
Published: February 2025

Residential electric vehicle (EV) chargers in China are typically paired with independent smart meters. A bi-level method for extracting flexibility features and clustering residential EV users is proposed based on real-world smart meter data, offering significant advantages in terms of data accessibility and implementation feasibility. First, real-world smart meter data from over 5000 residential EV users are partitioned into charging segments, which are then filtered and grouped. At the charging segment level, three charging features are extracted using the Pearson correlation method, and six categories of charging segments are clustered using a Gaussian Mixture Model (GMM). Subsequently, at the user level, two flexibility features are extracted based on the charging segment clustering results, and five categories of residential EV users are clustered using the K-means algorithm. The results of the bi-level method are presented and analyzed, with its effectiveness validated by comparing the trends of flexibility features across different user categories between the training dataset and the validation dataset. In the concluding section, the limitations of the current research are discussed, and potential directions for further research are outlined.
Decarbonization Pathway for Train Systems Using a Supercapacitor Energy Storage Charged by Distributed Solar PV Systems: A Case Study for Saudi Arabia
Authors: Alqahtani Bandar Jubran , Aleraij Mussab , Alajmi Abdulhadi
Published: February 2025

The study aims to introduce a novel system that powers a passenger train using supercapacitor energy storage that is charged by a solar carport system located at each train stop station. The system’s detailed design and its techno-economic analysis have been carried out and applied to a case study of a supercapacitor-based train (SC-Train) that connects an international airport with five major cities in the eastern region of Saudi Arabia. The objective is to reduce CO2 emissions from Saudi Arabia’s transportation sector utilizing an electric-operated train energized by a solar carport system. The solar carport system is designed to have a capacity equal to the train’s energy consumption. Additionally, the supercapacitor has been selected as a storage device to utilize the regenerative braking system feature to enhance the train’s energy efficiency, which results in energy savings equivalent to 44.9% of total energy consumption. Finally, the project’s feasibility has been determined via the benefit–cost analysis approach, which yields a positive net present value of USD 367 million over 30 years.
Solar Selective Absorber Coating with Ag Infrared Reflector for Receiver Tubes Operating at 550 °C
Authors: Esposito Salvatore , D’Angelo Antonio , Diletto Claudia , Rossi Gabriella , Volpe Raffaele , Guglielmo Antonio , De Girolamo Del Mauro Anna , Prestigiacomo Claudia , Lanchi Michela
Published: February 2025

The present work introduces a new solar selective absorber coating (SSAC) for the receiver tube of Concentrated Solar Power (CSP) systems, proposing silver as an infrared reflector for application at 550 °C. In the past, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) has developed SSACs suitable for applications at 550 °C, featuring solar absorbers based on graded multilayer cermet of WN-AlN and W-Al2O3 and an infrared reflector of tungsten. Although these coatings ensured properly stable photothermal performance at 550 °C, due to the low tungsten diffusivity, their hemispherical emittance could be reduced by using metals with higher reflectance in the infrared region, like silver. However, the high diffusivity of silver compromises its use at high temperatures. This last drawback has been addressed by foreseeing two stabilizing layers enclosing the Ag infrared reflector. One W stabilizing layer was placed between the substrate and the Ag infrared reflector, whereas a second stabilizing layer, selected among aluminum nitride deposited with a low and high nitrogen flow and aluminum oxide deposited at a low oxygen flow, was placed between the Ag infrared reflector and the solar absorber. Accelerated aging tests revealed a negligible (not detectable) degradation of the solar absorptance for the new SSACs. Furthermore, the hemispherical emittance at 550 °C increased by 0.75% and 0.42% for solar coatings with aluminum nitride stabilizing layers deposited through a high and low nitrogen flow, respectively. Differently, the increase was evaluated as being equal to 0.08% for the solar coating with an aluminum oxide stabilizing layer deposited through a low oxygen flow. The manufactured solar coating with a stabilizing layer of aluminum nitride deposited with a low nitrogen flow exhibited a solar absorptance of 95%, comparable to ENEA coatings incorporating a W infrared reflector for applications at 550 °C, whereas the estimated hemispherical emittance at 550 °C was 2% lower than that of the best ENEA coating with a W infrared reflector for the same temperature.
A Novel Filtering Observer: A Cost-Effective Estimation Solution for Industrial PMSM Drives Using in-Motion Control Systems
Authors: Dursun Cagatay , Ozcira Ozkilic Selin
Published: February 2025

This paper presents a cost-efficient estimation method, the filtering observer (FOBS), which provides a smooth estimation through prior estimation, enhancing the field-oriented control (FOC) performance of motion control systems by estimating the angular rotor position, angular rotor velocity, and disturbance torque of permanent magnet synchronous motors (PMSMs). The cost-effective FOBS demonstrates characteristics akin to optimal estimating methods and employs arbitrary pole placement, facilitating more straightforward adjustment of the FOBS gain. The non-linear characteristics of low-resolution and low-cost encoders, the computation of angular rotor velocity using traditional techniques, and disturbances over broad frequency ranges in the servo drive system impair the efficacy of the motion control system. As a cost-effective solution, the FOBS minimizes the deficiencies of the low-cost encoder, reduces oscillations and measurement delays in the speed feedback signal, and provides smooth estimation of disturbance torque. Based on the results from experiments, the FOBS was compared against traditional approaches and the performance of the motion control system was examined. Also, the performance of the motion control system was investigated. The results indicate that these enhancements were achieved with low processing power and an easily implementable estimate technique suitable for low-cost industrial systems.
A Review Study of Fuzzy Cognitive Maps in Engineering: Applications, Insights, and Future Directions
Authors: Karatzinis Georgios D. , Boutalis Yiannis S.
Published: February 2025

Fuzzy Cognitive Maps (FCMs) have emerged as powerful tools for addressing diverse engineering challenges, leveraging their cognitive nature and ability to encapsulate causal relationships. This paper provides a comprehensive review of FCM applications across 15 engineering sub-domains, categorizing 80 studies by their learning family, task type, and case-specific application. We analyze the methodological advancements and practical implementations of FCMs, showcasing their strengths in areas such as decision-making, classification, time-series, diagnosis, and optimization. Qualitative criteria are systematically applied to classify FCM-based methodologies, highlighting trends, practical implications of varying complexity, and human intervention across task types and learning families. However, this study also identifies key limitations, including scalability challenges, reliance on expert knowledge, and sensitivity to data distribution shifts in real-world settings. To address these issues, we outline key areas and directions for future research focusing on adaptive learning mechanisms, hybrid methodologies, and scalable computational frameworks to enhance FCM performance in dynamic and evolving contexts. The findings of this review offer a structured roadmap for advancing FCM methodologies and broadening their application scope in both contemporary and emerging engineering domains.
Late 20th Century Hypereutrophication of Northern Alberta’s Utikuma Lake
Authors: Walsh Carling R. , Grey Fabian , Patterson R. Timothy , Ralchenko Maxim , Patterson Calder W. , Reinhardt Eduard G. , Grey Dennis , Grey Henry , Thunder Dwayne
Published: February 2025

Eutrophication in Canadian lakes degrades water quality, disrupts ecosystems, and poses health risks due to potential development of harmful algal blooms. It also economically impacts the general public, industries like recreational and commercial fishing, and tourism. Analysis of a 140-year core record from Utikuma Lake, northern Alberta, revealed the processes behind the lake’s current hypereutrophic conditions. End-member modeling analysis (EMMA) of the sediment grain size data identified catchment runoff linked to specific sedimentological processes. ITRAX X-ray fluorescence (XRF) elements/ratios were analyzed to assess changes in precipitation, weathering, and catchment runoff and to document changes in lake productivity over time. Five end members (EMs) were identified and linked to five distinct erosional and sedimentary processes, including moderate and severe precipitation events, warm and cool spring freshet, and anthropogenic catchment disturbances. Cluster analysis of EMMA and XRF data identified five distinct depositional periods from the late 19th century to the present, distinguished by characteristic rates of productivity, rainfall, weathering, and runoff linked to natural and anthropogenic drivers. The most significant transition in the record occurred in 1996, marked by an abrupt increase in both biological productivity and catchment runoff, leading to the hypereutrophic conditions that persist to the present. This limnological shift was primarily triggered by a sudden discharge from a decommissioned sewage treatment lagoon into the lake. Spectral and wavelet analysis confirmed the influence of the Arctic Oscillation, El Niño Southern Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation on runoff processes in Utikuma Lake’s catchment.
A Resilience-Augmented Approach to Compound Threats and Risk Governance: A Systems Perspective on Navigating Complex Crises
Authors: Klasa Katarzyna , Trump Benjamin D. , Dulin Sam , Smith Madison , Jarman Holly , Linkov Igor
Published: February 2025

Compound threats—two or more relatively rare and high-consequence events that co-occur in time and space, amplifying their effects—present difficult-to-predict events that can impose potentially grave consequences. While there has been increasing attention placed on modeling the probabilities and outcomes of compounding threats, there are no proposed governance models for compound threats, limiting the ability of policymakers and decisionmakers to manage such crises in the future. We visualize resilience for compound threats to understand how critical functioning and system utility to contain hazards, to absorb losses, and to recover from stressors shifts over time. Using North Carolina as a case study, we conduct a compound threats assessment for disaster risk to showcase its effectiveness in more accurately predicting disaster risk areas, as well highlight the limitations of existing risk models used by policymakers. We propose a resilience-augmented conceptual framework to rethink risk governance for compound threats that allows for speed (specifically flexibility and adaptability) in situations of high uncertainty while working within the rigid, slow-moving boundaries of government and bureaucracy. Finally, we discuss strategies for key actors to apply a resilience-augmented governance approach to compound threats into operational decision-making during crisis situations.