bluuebunny/crossref_metadata_2025 huggingface dataset:
Cosine Distance Loss for Open-Set Image Recognition
Authors: Li Xiaolin , Chen Binbin , Li Jianxiang , Chen Shuwu , Huang Shiguo
Published: January 2025

Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggles to improve recognition performance caused by open space risk. Therefore, we introduce a cosine distance loss function (CDLoss), which exploits the orthogonality of one-hot encoding vectors to align known samples with their corresponding one-hot encoder directions. This reduces the overlap between the feature spaces of KCs and UCs, mitigating open space risk. CDLoss was incorporated into both Softmax-based and prototype-learning-based frameworks to evaluate its effectiveness. Experimental results show that CDLoss improves AUROC, OSCR, and accuracy across both frameworks and different datasets. Furthermore, various weight combinations of the ARPL and CDLoss were explored, revealing optimal performance with a 1:2 ratio. T-SNE analysis confirms that CDLoss reduces the overlap between the feature spaces of KCs and UCs. These results demonstrate that CDLoss helps mitigate open space risk, enhancing recognition performance in open-set image classification tasks.
Ultra-Wideband High-Power GaN Rectifier with Extended Input Power Range Based on a Terminal Matching Network
Authors: Huo Shudong , Liu Huining , Dang Kui , Cui Yuxuan , Min Xianghao , Qiu Zhilin , Zhang Yachao , Zhou Hong , Ning Jing , Zhang Jincheng , Hao Yue
Published: January 2025

This paper proposes a terminal matching network (TMN) technology, which can realize wideband matching of microwave rectifiers in a wide input power range. At the same time, it is proposed to realize ultra-wideband microwave rectifiers by connecting two TMN branches of different frequencies in parallel. In order to verify this theory, two rectifiers using single TMN and dual TMN branches are designed and realized based on high-power GaN Schottky barrier diodes (SBDs). The single TMN GaN rectifier achieves a peak efficiency of 72.3% and a conversion efficiency of more than 70% in the frequency range of 1.8–2.7 GHz at 1 W of input power, while being more than 50% efficient in the input power range of 16–35 dBm. Benefitting from the power combination of different frequency TMNs, the dual TMNs GaN rectifier achieves 75.8% peak efficiency and over 70% conversion efficiency at 1.1–3.1 GHz frequency and 1 W input power with a relative bandwidth over 95.2% and maintains high efficiency of over 50% in the input power range of 15–35 dBm. The advantages of the ultra-wideband, wide input power range, high power, and high efficiency make the GaN rectifier with TMNs expected to play an important role in microwave power transmission (MPT).
Multi-Agent Reinforcement Learning for Efficient Resource Allocation in Internet of Vehicles
Authors: Wang Jun-Han , He He , Cha Jaesang , Jeong Incheol , Ahn Chang-Jun
Published: January 2025

The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm.
Partial Discharge Recognition of Transformers Based on Data Augmentation and CNN-BiLSTM-Attention Mechanism
Authors: Fu Zhongjun , Wang Yuhui , Zhou Lei , Li Keyang , Rao Hang
Published: January 2025

Partial discharge (PD) is a commonly encountered discharge-related fault in transformers. Due to the unique characteristics of the environment where PD occurs, challenges such as difficulty in data acquisition and scarcity of samples arise. Convolutional neural networks (CNNs) are widely used in pattern recognition because of their strong feature extraction capabilities. To improve the recognition accuracy of PD models, this paper integrates CNN, bidirectional long short-term memory (BiLSTM), and an attention mechanism. In the proposed model, CNN is employed to extract local spatial and temporal features, BiLSTM is utilized to extract global bidirectional spatial and temporal features, and the attention mechanism assigns adaptive weights to the features. Additionally, to address the issues of sample scarcity and data imbalance, an improved GAN is introduced to augment the data. The experimental results demonstrate that the CNN-BiLSTM-attention method proposed in this paper significantly improves the prediction accuracy. With the help of GAN, the proposed method achieves a recognition accuracy of 97.36%, which is 1.8% higher than that of the CNN+CGAN(Conditional Generative Adversarial Network) method and 5.8% higher than that of thetraditional recognition model, SVM, making it the best-performing method among several comparable methods.
How Self-Regulated Learning Is Affected by Feedback Based on Large Language Models: Data-Driven Sustainable Development in Computer Programming Learning
Authors: Sun Di , Xu Pengfei , Zhang Jing , Liu Ruqi , Zhang Jun
Published: January 2025

Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context, large language models (LLMs), with their ability to use human language and continuously interact with learners, not only provide personalized feedback but also offer a data-driven approach to sustainable development in education. By leveraging real-time data, LLMs have the potential to deliver more effective and interactive feedback that enhances both individual learning experiences and scalable, long-term educational strategies. Therefore, this study utilized a quasi-experimental design to examine the effects of LLM-based feedback on learners’ SRL, aiming to explore how this data-driven application could support learners’ sustainable development in computer programming learning. The findings indicate that LLM-based feedback significantly improves learners’ SRL by providing tailored, interactive support that enhances motivation and metacognitive strategies. Additionally, learners receiving LLM-based feedback demonstrated better academic performance, suggesting that these models can effectively support learners’ sustainable development in computer programming learning. However, the study acknowledges limitations, including the short experimental period and the initial unfamiliarity with LLM tools, which may have influenced the results. Future research should focus on refining LLM integration, exploring the impact of different feedback types, and extending the application of these tools to other educational contexts.
Dynamics Modeling Dedicated to the Operation and Control of Underwater Vehicles
Authors: Jarzębowska Elżbieta , Ładyżyńska-Kozdraś Edyta , Kamieniecki Konrad
Published: January 2025

The paper addresses the dynamics modeling of underwater vehicles that are inertia propelled, i.e., they can move based upon the change of the amount of water in their water tanks and the motion of an internal mass, enabling maneuvers. Underwater vehicles of this type can be successfully applied in ocean scientific reconnaissance and exploration missions or for water pollution monitoring. Usually, dynamics modeling methods for them are based upon the Newton–Euler or Lagrange approaches modified to encompass variable mass. The main motivation of this research is to explore other modeling methods and compare them to those traditionally used. In this paper, modeling methods based on the Maggi and Boltzmann–Hamel approaches are presented and discussed with respect to their effectiveness in modeling, operation, and control applications. The resulting comparisons indicate that the traditional approaches are sufficient for the analysis of vehicle operation and performance in the realization of simple tasks; however, they become of limited application when the variable mass or constraints on vehicle dynamics or motion are added or complex maneuvers are required. In this regard, the Maggi or Boltzmann–Hamel approaches are more effective for dynamics modeling. The theoretical development is illustrated by examples of vehicle dynamics developed using the approaches we propose.
Invisible Backdoor Learning in Transform Domain with Flexible Triggers and Targets
Authors: Sun Yuyuan , Lu Yuliang , Yan Xuehu , Pang Zeshan
Published: January 2025

The high demands on datasets and computing resources in deep learning make the models vulnerable to a range of security threats such as backdoor learning. The study of backdoor learning also helps to improve the understanding of model security. In order to ensure the attack effect, the triggers and targets in the existing backdoor learning methods are usually fixed and single, so a single defense will lead to the failure of the attack. This paper proposes an invisible backdoor learning scheme in the transform domain with flexible triggers and targets. By adding different offsets of different frequencies in the transform domain, multiple triggers and multiple targets are controlled. The generated poisoning images are added to the training dataset and the model is fine-tuned. Under the conception, two modes of backdoor learning enable flexible triggers and targets. One mode is multi-triggers and multi-targets (MTMT), and it can implement multiple triggers corresponding to different activation targets. The other mode is multi-triggers and one-target (MTOT), and it can realize multiple trigger sets to activate the target together. The experimental results show that the attack success rate reaches 95% and the accuracy of the model decreases within 3% under the premise that the trigger is not visible. This scheme can resist the common defense methods and has a good sample of the visual quality.
Dynamic Imaging of Projected Electric Potentials of Operando Semiconductor Devices by Time-Resolved Electron Holography
Authors: Wagner Tolga , Çelik Hüseyin , Gaebel Simon , Berger Dirk , Lu Peng-Han , Häusler Ines , Owschimikow Nina , Lehmann Michael , Dunin-Borkowski Rafal E. , Koch Christoph T. , Hatami Fariba
Published: January 2025

Interference gating (iGate) has emerged as a groundbreaking technique for ultrafast time-resolved electron holography in transmission electron microscopy, delivering nanometer spatial and nanosecond temporal resolution with minimal technological overhead. This study employs iGate to dynamically observe the local projected electric potential within the space-charge region of a contacted transmission electron microscopy (TEM) lamella manufactured from a silicon diode during switching between unbiased and reverse-biased conditions, achieving a temporal resolution of 25 ns at a repetition rate of 3 MHz. By synchronizing the holographic acquisition with the applied voltage, this approach enables the direct visualization of time-dependent potential distributions with high precision. Complementary static and dynamic experiments reveal a remarkable correspondence between modeled and measured projected potentials, validating the method’s robustness. The observed dynamic phase progressions resolve and allow one to differentiate between localized switching dynamics and preparation-induced effects, such as charge recombination near the sample edges. These results establish iGate as a transformative tool for operando investigations of semiconductor devices, paving the way for advancing the nanoscale imaging of high-speed electronic processes.
Research on Low-Latency TTP–TSN Cross-Domain Network Planning Problem
Authors: Peng Yifei , Jiang Tigang , Tu Xiaodong , Huang Bolin , Guo Zheng , Xu Du
Published: January 2025

With services becoming increasingly complex and network scales expanding, hybrid network architectures that combine bus and switch networks while supporting deterministic transmission will play a crucial role in future networks. Time-Triggered Protocol (TTP) and Time-Sensitive Networking (TSN), as key protocols currently ensuring time determinism in bus and switch networks, respectively, are of significant importance for research on hybrid network architectures and ensuring deterministic communication across protocols. In this paper, firstly, we analyzed the causes of latency in TTP–TSN hybrid networks. To reduce latency, we designed a cross-protocol time-synchronization algorithm. And, based on network-wide time synchronization, we constructed a joint TTP–TSN low-latency scheduling model, using Integer Linear Programming (ILP), which was then solved by an ILP solver. Based on this scheduling model, we proposed a heuristic fast scheduling algorithm and proofs of its schedulability and approximation ratio. Finally, we designed simulation and prototype systems for verification. Our experimental results demonstrate that the time-synchronization algorithm proposed in this paper achieves a synchronization error of no more than 1 µs. Compared to the case without applying the joint scheduling model, the fast heuristic algorithm can reduce end-to-end latency by at least 50%, and shorten the solving time by thousands of times compared to an ILP solver, all while ensuring schedulability.
Impedance Reshaping Strategy for Battery Energy Storage Systems Based on Partial Power Conversion
Authors: Li Ming , Wu Yucheng , Xi Xiangxin , Liu Haibo , Xu Baizheng , Jing Long
Published: January 2025

To avoid additional component losses while significantly improving the energy conversion efficiency of battery energy storage systems, the application of series-connected partial power converter (S-PPC) technology in battery energy storage systems is investigated in this study. In the S-PPC battery energy storage system configuration, coupling effects exist between the dc-link side and the battery-series side. The impedance modeling of a battery energy storage system is performed while taking these coupling effects into consideration. To address the instability observed during battery discharge conditions, an impedance reshaping control strategy that is suitable for the S-PPC battery energy storage system is proposed. The proposed method focuses on adjusting the input impedance of the load converter within a limited frequency band centered on the system’s oscillation frequency. This targeted approach significantly improves the stability of the system while ensuring ease of implementation and maintaining high reliability. Finally, the experimental results validate the theoretical analysis.
Renewable Energy for Sustainable Development: Opportunities and Current Landscape
Authors: Atstāja Dzintra
Published: January 2025

Energy is often described as the lifeblood of a nation’s economy, and the world energy trilemma calls for collaboration and innovative solutions at the national level. This is where Education for Sustainable Development (ESD) plays a crucial role, helping integrate the achievement of the United Nations Sustainable Development Goals (SDGs) while addressing the challenges posed by the energy trilemma. Europe’s strong commitment to transitioning to sustainable energy is evident in its response to geopolitical changes and climate targets. Notably, the Baltic States have taken decisive action in response to the war in Ukraine, choosing to completely halt electricity imports from Russia and Belarus. This shift was supported by increased energy imports via interconnectors from Finland, Sweden, and Poland, with electricity imports rising to 13,053 GWh—an increase of 2.6% in 2023 compared to the previous year. Latvia, which holds the highest green energy potential in the Baltic Sea region, has nevertheless lagged behind its Baltic counterparts in terms of implementation. In 2021, Latvia ranked third among European Union (EU) countries for renewable energy share in final energy consumption, with 42.1%, significantly higher than the EU average of 21.8%. However, further progress is needed to meet Latvia’s 2030 target of 14% renewable energy use in transport. The Baltic States aim to produce 98–100% of their electricity from renewable sources by 2050. The Baltic States should be regarded as a unified energy system, with a coordinated strategy for achieving sustainable energy development through collaboration and joint planning. This analysis highlights the complexities of managing energy markets amidst global and regional challenges, emphasizing the importance of well-designed public interventions to secure long-term benefits. The study concludes with a call for enhanced interagency cooperation to reform ESD and create a new interdisciplinary sector dedicated to “Sustainable Development”.
BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance
Authors: Tsikas Panagiotis , Chassiakos Athanasios , Papadimitropoulos Vasileios , Papamanolis Antonios
Published: January 2025

The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values.
A Digital Twin of Hot Pumping Waxy Oil Through a Main Pipeline
Authors: Zhapbasbayev Uzak , Bekibayev Timur , Ramazanova Gaukhar , Akasheva Zhibek
Published: January 2025

This article presents a digital twin of hot pumping waxy oil through a main pipeline. Digital copies of the original object data were identified through sensor measurements from SCADA and ECMAS, forming the basis of the SmartTranPro 1.7.1 Software. The mathematical model of the software describes the process of hot pumping waxy oil regarding heat exchange with the environment. The intelligent algorithms of the SmartTranPro 1.7.1 Software were used to determine the actual dependencies of the digital twins of the objects, hydraulic parameters, and heat transfer for the Kassymov–Bolshoi Chagan hot main pipeline, which has a length of 450 km. The results of the thermal–hydraulic calculations for the hot pumping of waxy oil are in good agreement with the actual sensor data from SCADA and ECASM. The optimization calculations of the heating temperature for waxy oil show an economic efficiency of 38.9% for the hot pumping method.
Multi-Objective Optimization of the Energy, Exergy, and Environmental Performance of a Hybrid Solar–Biomass Combined Brayton/Organic Rankine Cycle
Authors: Valencia-Ochoa Guillermo , Duarte-Forero Jorge , Mendoza-Casseres Daniel
Published: January 2025

This research proposes integrating a combined system from a supercritical Brayton cycle (SBC) at extremely high temperatures and pressures and a conventional ORC cycle. The ORC cycle was evaluated with three working fluids: acetone, toluene, and cyclohexane. Of these, the cyclohexane, thanks to its dry fluid condition, obtained the best result in the sensitivity analysis for the energetic and exergetic evaluations with the most relevant (net power and exergy destruction) for the variation in the most critical performance parameter of the system for both the configuration with reheat and the configuration with recompression. Between the two proposed configurations, the most favorable performance was obtained with a binary system with reheat and recompression; with reheat, the SBC obtained first- and second-law efficiencies of 45.8% and 25.2%, respectively, while the SBC obtained values of 54.8% and 27.9%, respectively, with reheat and recompression. Thus, an increase in overall system efficiency of 30.3% is obtained. In addition, the destroyed exergy is reduced by 23% due to the bypass before the evaporation process. The SBC-ORC combined hybrid system with reheat and recompression has a solar radiation of 950 W/m2 K, an exhaust heat recovery efficiency of 0.85, and a turbine inlet temperature of 1008.15 K. The high pressure is 25,000 kPa, the isentropic efficiency of the turbines is 0.8, the pressure ratio is 12, and the pinch point of the evaporator is initially 20 °C and reaches values of 45 °C in favorable supercritical conditions.
Thermal Performance Analysis of Nanofluids for Heat Dissipation Based on Fluent
Authors: Xu Junqiang , Shang Zemin , Qing Shan
Published: January 2025

With the increasing demand for thermal management in electronic devices and industrial systems, nanofluids have emerged as a research hotspot due to their superior thermal conductivity and heat transfer efficiency. Among them, CuO-H2O demonstrates excellent heat transfer performance due to its high thermal conductivity, Fe3O4-H2O offers potential for further optimization by combining thermal and magnetic properties, and Al2O3-H2O exhibits strong chemical stability, making it suitable for a wide range of applications. These three nanofluids are representative in terms of particle dispersibility, thermal conductivity, and physical properties, providing a comprehensive perspective on the impact of nanofluids on microchannel heat exchangers. This study investigates the heat transfer performance and flow characteristics of various types and volume fractions of nanofluids in microchannel heat exchangers. The results reveal that with increasing flow rates, the convective heat transfer coefficient and Nusselt number of nanofluids exhibit an approximately linear growth trend, primarily attributed to the turbulence enhancement effect caused by higher flow rates. Among the tested nanofluids, CuO-H2O demonstrates the best performance, achieving a 4.89% improvement in the heat transfer coefficient and a 1.64% increase in the Nusselt number compared to pure water. Moreover, CuO-H2O nanofluid significantly reduces wall temperatures, showcasing its superior thermal management capabilities. In comparison, the performance of Al2O3-H2O and Fe3O4-H2O nanofluids is slightly inferior. In terms of flow characteristics, the pressure drop and friction factor of nanofluids exhibit nonlinear variations with increasing flow rates. High-concentration CuO-H2O nanofluid shows a substantial pressure drop, with an increase of 7.33% compared to pure water, but its friction factor remains relatively low and stabilizes at higher flow rates. Additionally, increasing the nanoparticle volume fraction enhances the convective heat transfer performance; however, excessively high concentrations may suppress heat transfer efficiency due to increased viscosity, leading to a decrease in the Nusselt number. Overall, CuO-H2O nanofluid exhibits excellent thermal conductivity and flow optimization potential, making it a promising candidate for efficient thermal management in MCHEs. However, its application at high concentrations may face challenges related to increased flow resistance. These findings provide valuable theoretical support and optimization directions for the development of advanced thermal management technologies.
Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman
Authors: Baloch Mazhar , Honnurvali Mohamed Shaik , Kabbani Adnan , Ahmed Touqeer , Chauhdary Sohaib Tahir , Saeed Muhammad Salman
Published: January 2025

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R2, and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R2 values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman.
Distribution Characteristics of Swirling-Straight Sprinklers Inside a Nuclear Power Pressurizer
Authors: Bi Jinghao , Xu Xiao
Published: January 2025

Droplet size and distribution uniformity of sprinklers significantly affect production safety in the processes of steam temperature and pressure reduction within nuclear power, and other high-temperature, high-pressure industries. In industrial sprays with high flow rates and low pressure drops, reducing droplet size poses additional challenges, making improved spray uniformity essential for enhancing heat transfer. This study designed and produced a set of swirling-straight sprinklers, tested their flow characteristics and liquid distribution, and proposed a highly uniform spray mode involving swirl jet interaction mixing. The discharge coefficient (Cd) changes indicated that enlarging the jet channel area diminishes the amplification effect, suggesting a trade-off in industrial high flow sprinkler design. A detailed evaluation and analysis method of the spray process, which is superior to the use of a single uniformity parameter, is proposed based on Gaussian function peak fitting method. It has been observed that the relationship between the Gaussian fitting parameters and the pressure drop of the sprinkler tends to be linear. This discovery provides a new basis for designing nozzles with low pressure drop, high flow rates, and uniform distribution. The findings contribute to the optimization of spray performance and provide valuable data for computational fluid dynamics model verification.
New Advances in Bioelectrochemical Systems in the Degradation of Polycyclic Aromatic Hydrocarbons: Source, Degradation Pathway, and Microbial Community
Authors: Feng Yimeng , Zhu Xuya , Huang Xiulin , Li Fengxiang
Published: January 2025

Because of their high persistence, polycyclic aromatic hydrocarbons (PAHs) are found in a wide range of settings and pose a health risk to both humans and other organisms. Degradation of PAHs is an essential part of environmental management. By combining biological metabolism and electrochemical processes, bioelectrochemical systems (BESs) can degrade PAHs and provide important applications by converting the chemical energy of pollutants into electrical energy for energy conversion and recovery. This review provides a comprehensive introduction to PAH degradation by BESs, including PAH sources, degradation effects of BESs, performance enhancement methods, degradation pathways, and dominant microorganisms. By focusing on the relevant research in recent years, the main innovative research focuses on the optimization of the configuration, the electrode preparation, and the media additions to improve the removal performance of PAHs. It demonstrates the potential of BESs in the field of environmental remediation, especially their effectiveness in treating difficult-to-degrade pollutants such as PAHs, by concentrating on the application and mechanism of BESs in PAH degradation. This review is intended to provide the inexperienced reader with an insight into this research area and to point out directions for future research, especially in the design optimization of BESs and microbial community analysis.
Robust Wireless Power Transfer for EVs by Self-Oscillating Controlled Inverters and Identical Single-Coil Transmitting and Receiving Pads
Authors: Eikani Alireza , Amirkhani Mohammad , Farmahini Farahani Ehsan , Pickert Volker , Mirsalim Mojtaba , Vaez-Zadeh Sadegh
Published: January 2025

Inductive wireless power transfer (IWPT) with stable output power and high efficiency is a major challenge for charging electric vehicles (EVs). This paper, for the first time, develops a robust IWPT system using a circular pad (CP) and double-D pad (DDP) with a self-oscillating controlled inverter (SOCI), which offers high steady output power and transfer efficiency under magnetic coupling variations simply with feedback from the transmitter-side current. The compact 2CP and 2DDP magnetic couplers with single identical coils are robust to self- and mutual-inductance variations, so the IWPT system exhibits greater robustness at increased transfer distances (air gaps), as well as in the presence of lateral and rotational misalignments between the two magnetic pads, compared to couplers using nonidentical transmitting primary (TP) and receiving secondary (RS) pads and numerous decoupled coils on the RS pad. Based on a thorough analysis and experimental study, the proposed 1 kW IWPT system with 2CP and 2DDP couplers with up to a 20 cm air gap achieves constant output power with 93% and 92% constant transfer efficiency, respectively. The 2CP with a 15 cm air gap and the 2DDP with a 20 cm air gap, with up to 12 cm lateral misalignment, can tolerate coupling variations.
Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
Authors: Fevgas Georgios , Lagkas Thomas , Sarigiannidis Panagiotis , Argyriou Vasileios
Published: January 2025

The rapid development of nanosatellite technologies, their low development cost, and their economical launching due to their small size have made them an excellent option for Earth Observation (EO) and remote sensing. Nanosatellites are widely used in generic applications, such as education, vegetation monitoring, natural disasters, oceanography, and specialized applications, such as disaster response, and they serve as an Internet of Things (IoT) communications platform. This paper presents a review of the latest public nanosatellite EO missions, their applications, and their propulsion systems. Furthermore, we discuss specialized applications of the nanosatellites and their use in remote sensing for EO. Likewise, we aim to present the limitations of the nanosatellites in remote sensing, a comprehensive taxonomy according to propulsion systems, and directions for future research.
Reducing Plate Waste in Latvian Schools: Evaluating Interventions to Promote Sustainable Food Consumption Practices
Authors: Lonska Jelena , Kodors Sergejs , Deksne Juta , Litavniece Lienite , Zvaigzne Anda , Silicka Inese , Kotane Inta
Published: January 2025

Food waste (FW) threatens food security, environmental sustainability, and economic efficiency, with about one-third of global food production lost or wasted. Schools play a crucial role in addressing FW, representing lost resources and missed educational opportunities. The present research assessed three interventions to reduce plate waste (PW) in Rezekne City schools, namely (S1) a plate waste tracker, (S2) an awareness and educational campaign, and (S3) organizational changes, including larger plates, extended lunch breaks, and teacher supervision. Implemented in three schools with a fourth as a control, PW was measured at three intervals, at pre-intervention, short-term, and long-term post-intervention. The PW data analysis utilized two models (day view and class view) and a Wilcoxon signed-rank test. While the plate waste tracker initially reduced PW, sustained impact required continuous reinforcement. The awareness and educational campaign alone proved insufficient, highlighting the need for complex strategies. The organizational changes unexpectedly increased PW, underscoring FW’s complexity. The research has concluded that reducing FW requires tailored and multi-faceted approaches. According to the MOA framework, the school catering model in Rezekne City lacks essential “Opportunities” for effective FW reduction, as students have limited flexibility in portion sizes and food choices, which hinders the interventions’ effectiveness. Future research should explore adaptable FW-reducing interventions suited to specific school contexts.
Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer’s Disease Therapy
Authors: Zhou Junyu , Li Chen , Kim Yong Kwan , Park Sunmin
Published: January 2025

Alzheimer’s disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC50 (pIC50) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), microtubule-associated protein tau (MAPT), presenilin-1 (PSEN1), tumor necrosis factor (TNF)-α, and valosin-containing protein (VCP). Their activities were then validated through a molecular docking analysis using Autodock Vina. Predictions by the deep neural analysis identified 166 NCs with potential effects on AD across seven proteins, demonstrating outstanding recall performance. The top five food sources of these predicted compounds were black walnut, safflower, ginger, fig, corn, and pepper. Statistical clustering methodologies segregated the NCs into six well-defined groups, each characterized by convergent structural and chemical signatures. The systematic examination of structure–activity relationships uncovered differential molecular patterns among clusters, illuminating the sophisticated correlation between molecular properties and biological activity. Notably, NCs with high activity, such as astragalin, dihydromyricetin, and coumarin, and medium activity, such as luteolin, showed promising effects in improving cell survival and reducing lipid peroxidation and TNF-α expression levels in PC12 cells treated with lipopolysaccharide. In conclusion, our findings demonstrate the efficacy of combining bioinformatics with deep neural networks to expedite the discovery of previously unidentified food-derived active ingredients (NCs) for AD intervention.
Global Insights into Cultured Meat: Uncovering Production Processes, Potential Hazards, Regulatory Frameworks, and Key Challenges—A Scoping Review
Authors: Zandonadi Renata Puppin , Ramos Maíra Catharina , Elias Flavia Tavares Silva , Guimarães Nathalia Sernizon
Published: January 2025

This scoping review aims to understand the cell-based meat production process, including the regulations, potential hazards, and critical points of this production. This review includes studies on cultured meat production processes, health hazards, and regulatory guidelines, excluding those without hazard analysis, incomplete texts, or studies published before 2013. The search was performed in eight electronic databases (MEDLINE, Web of Science, Embase, Cochrane Library, Scopus, LILACS, and Google Scholar) using MeSH terms and adaptations for each database. The search for local studies on regulations and guideline documents was complemented by a manual search on the websites of governments and regulatory agencies from different regions (e.g., FDA, FAO, EFSA, USDA, Health Canada, EC, EU, ANVISA/Brazil, MAPA/Brazil, FSANZ, and SFA). This step involved reading full texts to confirm eligibility and extract key data, including author, year, country, study design, objectives, results, cultured meat protocols, health hazards, and hazard control measures, followed by data analysis. A comprehensive search of the databases yielded 1185 studies and 46 regulatory or guidance documents. After removing duplicate studies and applying eligibility criteria to titles, abstracts and full texts, 35 studies and 45 regulatory or guidance documents were included. The cultured meat production protocols are well-established, highlighting potential hazards and critical control points. Although guidance documents and regulations are limited, they are expanding globally. The development and commercialization of cultured meat require clear, and up-to-date regulations and supervision, which are being studied and formulated by regulatory agencies worldwide. Cultured meat production presents some potential hazards (chemical, biological, and physical) that require food safety considerations: (i) genetic stability of cells/cell lines; (ii) microbiological hazards related to cell lines; (iii) exposure to substances used in the production process; (iv) toxicity and allergenicity of the product or its component for the population; (v) post-harvest microbiological contamination; (vi) chemical contamination/residue levels; and (vii) nutritional aspects/risks. Currently, no standardized testing approach exists for cultured meat. However, effective hazard and safety assessment strategies, such as HACCP combined with best practices, should be implemented throughout the production process.
Molecular Detection of Acetobacter aceti and Acetobacter pasteurianus at Different Stages of Wine Production
Authors: Mitina Irina , Grajdieru Cristina , Sturza Rodica , Mitin Valentin , Rubtov Silvia , Balanuta Anatol , Behta Emilia , Deaghileva Angela , Inci Fatih , Hacıosmanoğlu Nedim , Zgardan Dan
Published: January 2025

Acetobacter aceti and Acetobacter pasteurianus belong to acetic acid bacteria (AAB), associated with wine spoilage. The timely detection of AAB, thought essential for their control, is however challenging due to the difficulties of their isolation. Thus, it would be advantageous to detect them using molecular methods at all stages of winemaking and storage. In this paper, we analyzed wines, musts and grapes of 13 varieties grown in different regions with Protected Geographical Indication of the Republic of Moldova for the presence of AAB, Acetobacter aceti and Acetobacter pasteurianus by real-time PCR and measured wine volatile acidity. Overall, the AAB content in the mature wine explained 33.7% of the variance in the volatile acidity of the mature wine, while the A. pasteurianus content in the mature wine alone explained 59.6% of the variability in the volatile acidity in the wine, and its content in the grapes, must and wine explained about 70% of the variance in the the volatile acidity. This makes A. pasteurianus a good candidate to be a potential predictor of wine volatile acidity.
The Electrochemical Detection of Bisphenol A and Catechol in Red Wine
Authors: Wang Chao , Wu Xiangchuan , Lin Xinhe , Zhu Xueting , Ma Wei , Chen Jian
Published: January 2025

The use of nanozymes for electrochemical detection in the food industry is an intriguing area of research. In this study, we synthesized a laccase mimicking the MnO2@CeO2 nanozyme using a simple hydrothermal method, which was characterized by modern analytical methods, such as transmission electron microscope (TEM), X-ray diffraction (XRD), and energy dispersive X-ray spectroscopy (EDX), etc. We found that the addition of MnO2 significantly increased the laccase-like activity by 300% compared to CeO2 nanorods. Due to the excellent laccase-like activity of the MnO2@CeO2 nanozyme, we developed an electrochemical sensor for the detection of hazardous phenolic compounds such as bisphenol A and catechol in red wines by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). We used the MnO2@CeO2 nanozyme to develop an electrochemical sensor for detecting harmful phenolic compounds like bisphenol A and catechol in red wine due to its excellent laccase-like activity. The MnO2@CeO2 nanorods could be dispersion-modified glassy carbon electrodes (GCEs) by polyethyleneimine (PEI) to achieve a rapid detection of bisphenol A and catechol, with limits of detection as low as 1.2 × 10−8 M and 7.3 × 10−8 M, respectively. This approach provides a new way to accurately determine phenolic compounds with high sensitivity, low cost, and stability.
Development and Assessment of a Color-Variable Chlorine Dioxide Slow-Releasing Card for Litchi Preservation
Authors: Guo Li , Wu Guang , Yin Qingchun , Luo Lin , Deng Hao
Published: January 2025

Chlorine dioxide (ClO2) gas has attracted considerable attention due to its safety and efficiency. In this study, we successfully developed a color-variable ClO2 slow-releasing card for postharvest litchi. The optimal ClO2 slow-releasing card was prepared as follows: Card A was soaked in 2.5 mol/L NaClO2 and 0.3 mol/L CaCl2. Card B was soaked in 1 mol/L oxalic acid, 0.3 mol/L CaCl2, and natural pigment. Finally, cards A and B were dried and adhered using 60% gelatin. The ClO2 releasing time of the card was more than 120 h at 5–25 °C, and it could change color from dark yellow to white. The qualities of 3 kg litchi treated with no pieces (CK), half a piece (T1), one piece (T2), two pieces (T3), and three pieces (T4) of ClO2 slow-releasing card were compared. The results showed that litchi of T1 had significantly (p < 0.05) higher L* and a* values but a lower respiration rate and relative conductivity than CK after 7 days of storage, showing the best pulp qualities and pericarp color. Further correlation analyses revealed a significant positive correlation (R2 = 0.971) between Cya-3-O-gal-1 and a*, indicating a sharp decline in Cya-3-O-gal-1 and strong pericarp browning in CK. On the contrary, the low-releasing ClO2 of T1 effectively inhibited Cya-3-O-gal-1 degradation. This could be one of the reasons for the superior pericarp color of T1. This study provides a visual, efficient, and economical solution for postharvest litchi.
Genome-Wide microRNA Expression Profiling in Human Spermatozoa and Its Relation to Sperm Quality
Authors: Cassuto Nino-Guy , Boitrelle Florence , Mouik Hakima , Larue Lionel , Keromnes Gwenola , Lédée Nathalie , Part-Ellenberg Laura , Dray Geraldine , Ruoso Léa , Rouen Alexandre , De Vos John , Assou Said
Published: January 2025

Background: Sperm samples are separated into bad and good quality samples in function of their phenotype, but this does not indicate their genetic quality. Methods: Here, we used GeneChip miRNA arrays to analyze microRNA expression in ten semen samples selected based on high-magnification morphology (score 6 vs. score 0) to identify miRNAs linked to sperm phenotype. Results: We found 86 upregulated and 21 downregulated miRNAs in good-quality sperm (score 6) compared with bad-quality sperm samples (score 0) (fold change > 2 and p-value < 0.05). MiR-34 (FC × 30, p = 8.43 × 10−8), miR-30 (FC × 12, p = 3.75 × 10−6), miR-122 (FC × 8, p = 0.0031), miR-20 (FC × 5.6, p = 0.0223), miR-182 (FC × 4.83, p = 0.0008) and miR-191 (FC × 4, p = 1.61 × 10−6) were among these upregulated miRNAs. In silico prediction algorithms predicted that miRNAs upregulated in good-quality sperm targeted 910 genes involved in key biological functions of spermatozoa, such as cell death and survival, cellular movement, molecular transport, response to stimuli, metabolism, and the regulation of oxidative stress. Genes deregulated in bad-quality sperm were involved in cell growth and proliferation. Conclusions: This study reveals that miRNA profiling may provide potential biomarkers of sperm quality.
The Destabilizing Effect of Glacial Unloading on a Large Volcanic Slope Instability in Southeast Iceland
Authors: Ben-Yehoshua Daniel , Erlingsson Sigurður , Sæmundsson Þorsteinn , Hermanns Reginald L. , Magnússon Eyjólfur , Askew Robert A. , Helgason Jóhann
Published: January 2025

Since the turn of the 20th century, glacial thinning has been exposing volcanic mountain slopes around Iceland’s outlet glaciers. In the early 2000s, several slope instabilities appeared around the Svínafellsjökull outlet glacier in Southeast Iceland. The largest of these is located on a slope called Svarthamrar and is defined by a more than 2 km-long fracture system that separates the northernmost part of the mountain, south of Svínafellsjökull. Here we present updated glacier bed topography, a stratigraphical and structural assessment of the Svarthamrar slope, and quantify the destabilizing effect of glacial unloading from 1890 to deglaciated. Our results show that the slope was predisposed to instability by structural discontinuities and a strongly overdeepened glacial trough. Glacial unloading likely controlled the slope destabilization, potentially exacerbated by temporarily steeper hydraulic gradients due to rapid glacier thinning in the late 1990s and 2000s. The load of older landslide deposits on the glacier acts stabilizing on the slope. We propose that future glacial thinning will reduce the slope stability further, making it more susceptible to external triggers, and resulting in reactivation of the deformation and potential failure. Similar trends of destabilization can be expected for many slopes in Iceland and elsewhere.