bluuebunny/crossref_metadata_2025 huggingface dataset:
A Recombinase-Mediated Cassette Exchange Platform for a Triple Independent Inducible Expression System for Human Pluripotent Stem Cells
Authors: Castro-Gutierrez Roberto , Arora Ankita , Vaeth Katherine F. , Taliaferro J. Matthew , Russ Holger A.
Published: January 2025

Human pluripotent stem cells (hPSCs) and their differentiated derivatives represent valuable tools for studying development, modeling diseases, and advancing cell therapy. Recent improvements in genome engineering allow for precise modifications of hPSCs, further enhancing their utility in basic and translational research. Here we describe a Recombinase-Mediated Cassette Exchange (RMCE) platform in hPSCs that allows for the highly efficient, rapid, and specific integration of transgenes. The RCME-mediated DNA integration process is nearly 100% efficient, without negatively affecting the pluripotency or karyotypic stability of hPSCs. Taking advantage of this convenient system, we first established a dual inducible expression system based on the Tet-On and Cumate-On systems, allowing for the inducible expression of two transgenes independently. Secondly, we incorporated a Tet-on inducible system, driving the expression of three genes simultaneously. However, two genes also contain independent degron sequences, allowing for precise control over the expression of each gene individually. We demonstrated the utility of these systems in hPSCs, as well as their functionality after differentiation into cells that were representative of the three germ layers. Lastly, we used the triple inducible system to investigate the lineage commitment induced by the pancreatic transcription factors NKX6.1 and PDX1. We found that controlled dual expression, but not individual expression, biases hPSC embryoid body differentiation towards the pancreatic lineage by inducing the expression of the NeuroD program. In sum, we describe a novel genetic engineering platform that allows for the efficient and fast integration of any desired transgene(s) in hPSCs using RMCE. We anticipate that the ability to modulate the expression of three transgenes simultaneously will further accelerate discoveries using stem cell technology.
Clinical Characteristics and Influencing Factors of Feeding Intolerance After Surgery for Neonatal Necrotizing Enterocolitis
Authors: Hu Mengting , Wu Fan , Fu Zhikai , Zhang Yasi , Ju Xinmin , Chen Zheng , Ma Xiaolu , Zhang Yuanyuan , Shi Wei
Published: January 2025

Background: Feeding intolerance (FI) following surgery for neonatal necrotizing enterocolitis (NEC) can impact recovery and prognosis, making the early identification of FI risk essential for optimizing management and improving outcomes. Methods: We retrospectively collected data from patients who underwent surgery for NEC between January 2013 and December 2023. Multivariate binary logistic regression was performed to identify independent factors influencing postoperative feeding intolerance. Results: Of the 519 infants enrolled in this retrospective study, 155 (29.9%) were diagnosed with feeding intolerance, while 364 (70.1%) were identified as having feeding tolerance. Compared to infants with feeding tolerance, those with feeding intolerance had lower birth weight, smaller gestational age, and lower Apgar scores (all p < 0.01). A 5 min Apgar < 7 (OR 4.794; 95%CI 1.339–17.156), the interval between diagnosis and surgery (OR 0.973; 95%CI 0.947–1.000), and primary anastomosis resection (OR 0.278, 95%CI 0.139–0.555) were identified as significant factors influencing postoperative feeding intolerance. The results remained consistent after performing propensity score matching analysis. Feeding intolerance may result in prolonged hospital stays, and more complications such as retinopathy of prematurity, intestinal failure-associated liver disease, and intraventricular hemorrhage. Conclusions: A lower 5 min Apgar score, shorter interval from diagnosis to surgery and intestine resection with ostomy are associated with a higher incidence of FI after surgery. FI after NEC surgery can prolong recovery and increase family burden.
Volatile Constituents of Four Hypericum Species Native to Greece
Authors: Kakouri Eleni , Daferera Dimitra , Nalbanti Aimilia , Trigas Panayiotis , Tarantilis Petros A.
Published: January 2025

The genus Hypericum (Hypericaceae), comprising approximately 500 taxa classified into 22 sections, has remained largely unexplored in terms of its chemical composition, with existing studies on a limited number of species revealing significant chemical polymorphism. This study investigates the volatile profiles of four Hypericum species (H. rumeliacum subsp. apollinis, H. vesiculosum, H. delphicum, and H. olympicum) through GC-MS analysis. Hypericum rumeliacum subsp. apollinis, collected from Mt. Parnassos, exhibited a high abundance of sesquiterpenes hydrocarbons (32.5%) and oxygenated sesquiterpenes (29.7%). Hypericum vesiculosum collected from Mt. Chelmos was rich in oxygenated monoterpenes (33.5%), followed by benzyl derivatives (25.9%). Hypericum delphicum and H. olympicum, collected from the island of Evvia, showed a predominance of alkanes (35.8%) and oxygenated sesquiterpenes (31.9%) in H. delphicum and sesquiterpenes hydrocarbons (41.2%) and oxygenated sesquiterpenes (29.9%) in H. olympicum. Our findings provide new data on the volatile profile of H. vesiculosum and enhance existing information on other species, highlighting notable chemical diversity within the genus Hypericum.
An Actual Case Study of a Deterministic Multi-Objective Optimization Model in a Defined Contribution Faculty Pension System
Authors: Montufar-Benítez Marco Antonio , Mora-Vargas Jaime , Corona-Armenta José Ramón , Anaya-Fuentes Gustavo Erick , Rivera-Gómez Héctor , Rivera-Anaya Mayra
Published: January 2025

The optimal management of pension funds has become increasingly critical. As the population ages, the effective management of pension funds is essential for the social security system. The primary goal of this paper is to develop a deterministic nonlinear multi-objective optimization model to determine the contribution rates in a defined contribution pension system. The computational optimization model was implemented using the LINGO language. In the first part of this study, three main scenarios were analyzed considering different inflation rates, focusing on the objective function that minimizes the salary percentages workers pay when saving for a specified period while aiming to achieve a certain number of coverage years. The first scenario assumes that the worker desires an economic quality equivalent to their working life, showing that contribution rates range from 10% to 30% (with a 3% inflation rate). The second scenario posits that the worker only requires 80% of their equivalent salary during retirement, resulting in contribution rates directly proportional to those in scenario 1 (using the same parameters). The third scenario speculates that inflation may reach 7% per year, causing contribution rates to rise significantly from 40% to 80%. Finally, the Pareto front illustrates the trade-off between the contribution rate and the coverage years based on scenario 1 parameters.
Fenugreek as a Potential Active Ingredient for the Development of Innovative Cosmetic Formulation
Authors: Abdennebi Ameni Ben , Chaabani Emna , Bourgou Soumaya , Hammami Majdi , Tounsi Moufida Saidani , Merah Othmane , Bettaieb Rebey Iness
Published: January 2025

This study focuses on optimizing the extraction conditions for antioxidants from the fenugreek seeds (Trigonella foenum-graecum L.) through ultrasound-assisted extraction with the aim of creating a stable and effective cosmetic formulation. RSM was used to optimize the extraction parameters to ultrasonic power of 60%, with 50% ethanol concentration for 10 min. Under those conditions, the extract showed a phenolic-rich profile, with a total phenolic content equivalent to 18.56 mg GAE/g DM and a radical scavenging activity of 63.24%. Chromatographic analysis further confirmed the abundance of phenolic compounds, with epicatechin identified as the major compound at a concentration of 22.58 mg/g DM. The extract exhibited considerable antibacterial activity for a number of bacterial strains, and it exhibited no cell toxicity on RAW 267.4 cells, supporting its safe use in cosmetic products. The cosmetic formulation maintained high stability, with pH values from 6.25 to 6.35, viscosity values from 7941.69 to 7956.70 cp, and less color change after 90 days of preservation under varied temperature conditions. These findings validate fenugreek extract’s potential for producing a stable, eco-friendly, and effective cosmetic product, thus bringing skin health benefits and driving sustainable extraction methods in the cosmetic industry.
Numerical Simulation Analysis of Laser Ultrasonic Detection of Defects in Silicon Carbide
Authors: Yin Yuan , Xia Jinbao , Nie Hongkun , Yao Bo , Sun Daofa , Ma Longnan , Li Ruihang , Li Zihao , Zhang Baitao
Published: January 2025

Silicon carbide (SiC) is widely used in power electronic devices and other fields, the defects of which can significantly impact its performance in device fabrication. Laser ultrasonic non-destructive testing (NDT) as a novel and effective approach can detect these defects in real time. This study introduces a numerical model for the SiC NDT that elucidates the dynamic interactions between laser-induced ultrasonic waves and surface defects, and internal defects in SiC, respectively. Results show NDT is an effective way to locate the SiC defect and the ultrasonic waves’ vibration amplitude of detection points at defect edges increases by at least 16% compared to adjacent points, with a maximum of 43%. A comparative assessment between surface and internal defect vibration responses for acoustic is also made. For internal defects, the oscillation time of the acoustic wave at the detection point on the surface away from the edge of the defect at the excitation point exceeds that of surface defects by 100 ns, and the amplitude near the excitation point is more pronounced, reaching 1.44 nm, which is 4.2 times that of corresponding surface defects. Additionally, a linear relationship is observed between the arrival time of transmitted Rayleigh Waves (RSR) and internal defect length, with a correlation coefficient of 0.9878. Similarly, a linear relationship is established between the amplitude of reflected Rayleigh Waves (rR) and defect width, with a correlation coefficient of 0.9976, providing an effective way to quantify the inner defect. Furthermore, transient temperature profiles at distinct positions and transient acoustic fields and the relationship of acoustic vibration amplitude increasing with laser spot size under a fixed laser power density are also analyzed. This model provides a theoretical foundation for laser ultrasonic NDT setup and choice of micro-vibration detection device.
AoI-Minimal Task Assignment and Trajectory Optimization in Multi-UAV-Assisted Wireless Powered IoT Networks
Authors: Gu Yu , Qiu Hongbing , Chen Baoqing
Published: January 2025

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.
Local Predictors of Explosive Synchronization with Ordinal Methods
Authors: Leyva I. , Almendral Juan A. , Letellier Christophe , Sendiña-Nadal Irene
Published: January 2025

We propose using the ordinal pattern transition (OPT) entropy measured at sentinel central nodes as a potential predictor of explosive transitions to synchronization in networks of various dynamical systems with increasing complexity. Our results demonstrate that the OPT entropic measure surpasses traditional early warning signal (EWS) measures and could be valuable to the tools available for predicting critical transitions. In particular, we investigate networks of diffusively coupled phase oscillators and chaotic Rössler systems. As maps, we consider a neural network of Chialvo maps coupled in star and scale-free configurations. Furthermore, we apply this measure to time series data obtained from a network of electronic circuits operating in the chaotic regime.
Numerical Analyses of Entropy Production and Thermodynamics Exergy on a Hydrogen-Fueled Micro Combustor Featuring a Diamond-Shaped Bifurcated Inner-Tube Structure for Thermophotovoltaic Applications
Authors: Almutairi Faisal
Published: January 2025

To improve the heat transfer mechanisms from the thermal energy to the walls, the current work presents a new structure for a micro combustor fueled by hydrogen featuring a diamond-shaped bifurcated inner-tube configuration. For this purpose, a series of three-dimensional (3D) numerical analyses are conducted to investigate the effects of the length of the diamond-shaped structure, width of inner flame channels, inlet equivalence ratio, and hydrogen volume flow rate on the key performance and thermodynamic parameters. In comparison to the conventional design, the outcomes reveal that the proposed configuration exhibits remarkable improvements in energy conversion efficiency, as it reduces the mean exhaust gas temperature by 585.98 K and boosts the exergy and radiation efficiencies by 7.78% and 14.08%, respectively. The parametric study of the design parameters indicates that elongating the diamond-shaped structure and widening the inner flame channels enhance the thermal dynamics and consequently improve the rates of heat absorption by the walls. The increase in the hydrogen volume flow rates feeds the system with additional energy and, therefore, advances the average wall temperature and its uniformity across the external surface. Nevertheless, it also reduces system efficiency due to the limited capacity of the micro combustor to utilize a large energy input along with the high magnitude of entropy production resulting particularly from the mechanism of chemical entropy generation. Operating under a stoichiometric condition balances hydrogen and oxygen in the premixed charge, achieving optimal thermal performance for the micro combustor.
A Complex Structure for Two-Typed Tangent Spaces
Authors: Naudts Jan
Published: January 2025

This study concerns Riemannian manifolds with two types of tangent vectors. Let it be given that there are two subspaces of a tangent space with the property that each tangent vector has a unique decomposition as the sum of a vector in one subspace and a vector in the other subspace. Then, these tangent spaces can be complexified in such a way that the theory of the modular operator applies and that the complexified subspaces are invariant for the modular automorphism group. Notions coming from Kubo–Mori theory are introduced. In particular, the admittance function and the inner product of the Kubo–Mori theory can be generalized to the present context. The parallel transport operators are complexified as well. Suitable basis vectors are introduced. The real and imaginary contributions to the connection coefficients are identified. A version of the fluctuation–dissipation theorem links the admittance function to the path dependence of the eigenvalues and eigenvectors of the Hamiltonian generator of the modular automorphism group.
Analysis of the Protocols for Action Against Strandings of Sea Turtles and Their Evolution in Rehabilitation on Tenerife Island (Canary Islands, Spain)
Authors: López Vega Beatriz , Hurtado-Pampín Claudia , de la Cruz-Modino Raquel
Published: January 2025

Numerous interactions between human activities and wildlife currently impact various species. In recent decades, these activities have contributed to declining sea turtle populations. The waters around Spain serve as important staging areas for sea turtles, particularly Caretta caretta, which spend part of their life cycle there. However, these same waters threaten turtles because of various causes of stranding. This study aims to comprehensively understand the rehabilitation process for these animals at the Wildlife Rehabilitation Center (WRC) La Tahonilla in Tenerife, Canary Islands. It also seeks to explore rehabilitation efforts at different centers focused on recovering sea turtles in both the mainland and the Canary Islands. Additionally, we have examined the procedures for addressing stranded sea turtles where a recovery center does not exist. Our findings reveal the existence of varying protocols among different centers in Spain’s mainland and islands. Improving and unifying these protocols is essential for enhancing the rehabilitation of sea turtles in Spain.
What Worked for the U.S. Students’ Learning During the Pandemic? Cross-State Comparisons of Remote Learning Policies, Practices, and Outcomes
Authors: Lee Jaekyung , Seo Young Sik
Published: January 2025

This study provides critical policy insights into the U.S. students’ academic achievement trends and the impacts of remote learning policies and practices during the COVID-19 pandemic. Linking cross-state education assessment and survey datasets, it applies multivariate regression and case study methods to examine the relationship between remote learning policies and student achievement in reading and math. The results reveal large cross-state variations in outcomes along with regional patterns of in-person vs. remote learning policy divides. The states that adopted top-down, stringent school closure/reopening policies and relied more on remote instruction experienced relatively larger achievement declines. The government’s funding support, teacher help, and home learning resources such as technology did not work. In contrast, the states that adopted flexible school closure/reopening policies with more in-person instruction reported smaller achievement losses. Further, students’ digital literacy and remote learning self-efficacy such as online searching and help-seeking worked. The policy and research implications are discussed.
An Effectiveness Study of Generative Artificial Intelligence Tools Used to Develop Multiple-Choice Test Items
Authors: May Toni A. , Fan Yiyun Kate , Stone Gregory E. , Koskey Kristin L. K. , Sondergeld Connor J. , Folger Timothy D. , Archer James N. , Provinzano Kathleen , Johnson Carla C.
Published: January 2025

Generative artificial intelligence (GenAI) tools developed to support teaching and learning are widely available. Trustworthiness concerns, however, have prompted calls for researchers to study their effectiveness and for educators and educational researchers to be involved in their creation and piloting processes. This study investigated one type of GenAI created to support educators: multiple-choice question generators (MCQ GenAI). Among the nine MCQ GenAI tools investigated, a variety of useful options were available, but only one indicated teacher involvement and none mentioned testing experts in development processes. MCQ GenAI-created items (n = 270) were coded based on MCQ quality item-writing guidelines. Results showed 80.00% of items (n = 216) violated at least one guideline, with 73.70% (n = 199) likely to produce major measurement error (should not use without revision), 6.30% (n = 17) likely to elicit minor measurement error (consider modifying), and 20.00% (n = 54) acceptable (usable as created). Implications suggest multidisciplinary teams are needed in educational GenAI tool development.
Enhanced Hydrogen Evolution Reaction of a Zn+2-Stabilized Tungstate Electrocatalyst
Authors: Paudel Dasu Ram , Kaphle Gopi Chandra , Poudel Bhoj Raj , KC Mukunda , Singh Manjinder , Ojha Gunendra Prasad
Published: January 2025

Due to their diverse properties and functionalities, cost-effective transition metal-based nanomaterials have been rigorously studied for electrochemical applications. Ultrathin nanosheets have been identified as the most effective electrodes for catalyzing water-splitting reactions in both acidic and alkaline environments. Here, we reported ZnWO4, a member of the tungstate family, as an effective electrocatalyst for promoting the electrochemical hydrogen evolution reaction. The Zn+2-stabilized tungstate showed a remarkable cathodic reaction during the water-splitting reaction with low overpotential (136 mV at 10 mA cm−2) and small HER kinetics (Tafel Slope = 75.3 mV dec−1) and long-term cyclic durability. The high-valence tungsten stabilized with divalent Zn+2 promotes electron transfer during the reaction, making it an advanced electrocatalyst for green hydrogen production.
Meta-YOLOv8: Meta-Learning-Enhanced YOLOv8 for Precise Traffic Light Color Detection in ADAS
Authors: Tammisetti Vasu , Stettinger Georg , Cuellar Manuel Pegalajar , Molina-Solana Miguel
Published: January 2025

The ability to accurately detect traffic light color is critical for the functioning of Advanced Driver Assistance Systems (ADAS), as it directly impacts a vehicle’s safety and operational efficiency. This paper introduces Meta-YOLOv8, an improvement over YOLOv8 based on meta-learning, designed explicitly for traffic light color detection focusing on color recognition. In contrast to conventional models, Meta-YOLOv8 focuses on the illuminated portion of traffic signals, enhancing accuracy and extending the detection range in challenging conditions. Furthermore, this approach reduces the computational load by filtering out irrelevant data. An innovative labeling technique has been implemented to address real-time weather-related detection issues, although other bright objects may occasionally confound it. Our model employs meta-learning principles to mitigate confusion and boost confidence in detections. Leveraging task similarity and prior knowledge enhances detection performance across diverse lighting and weather conditions. Meta-learning also reduces the necessity for extensive datasets while maintaining consistent performance and adaptability to novel categories. The optimized feature weighting for precise color differentiation, coupled with reduced latency and computational demands, enables a faster response from the driver and reduces the risk of accidents. This represents a significant advancement for resource-constrained ADAS. A comparative assessment of Meta-YOLOv8 with traditional models, including SSD, Faster R-CNN, and Detection Transformers (DETR), reveals that it outperforms these models, achieving an F1 score, accuracy of 93% and a precision rate of 97%.
A Methodology for Online Situational Awareness Provision in a Business Entity
Authors: Amanowicz Marek
Published: January 2025

This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business objectives. Among other things, situational awareness requires precise identification and understanding of multilateral dependencies of infrastructure, services, and processes executed by the entity and services provided by external businesses, resulting from the strong interrelationship of the critical infrastructure sectors and the sharing of resources. Accordingly, this paper presents a cyberspace modeling methodology that supports an in-depth analysis of the causes and consequences of threat proliferation in a complex infrastructure–business environment and its implementation in a Situational Awareness Management System (SAMS). An emphasis is placed on threat propagation analysis and dynamic risk assessment mechanisms and how they are used to identify and take preemptive actions protecting or limiting the scope of the threats’ propagation. The paper concludes with insights from a pilot implementation of the system prototype and directions for further work.
Cybersecurity of Automotive Wired Networking Systems: Evolution, Challenges, and Countermeasures
Authors: Canino Nicasio , Dini Pierpaolo , Mazzetti Stefano , Rossi Daniele , Saponara Sergio , Soldaini Ettore
Published: January 2025

The evolution of Electrical and Electronic (E/E) architectures in the automotive industry has been a significant factor in the transformation of vehicles from traditional mechanical systems to sophisticated, software-defined machines. With increasing vehicle connectivity and the growing threats from cyberattacks that could compromise safety and violate user privacy, the incorporation of cybersecurity into the automotive development process is becoming imperative. As vehicles evolve into sophisticated interconnected systems, understanding their vulnerabilities becomes essential to improve cybersecurity. This paper also discusses the role of evolving standards and regulations, such as ISO 26262 and ISO/SAE 21434, in ensuring both the safety and cybersecurity of modern vehicles. This paper offers a comprehensive review of the current challenges in automotive cybersecurity, with a focus on the vulnerabilities of the Controller Area Network (CAN) protocol. Additionally, we explore state-of-the-art countermeasures, focusing on Intrusion Detection Systems (IDSs), which are increasingly leveraging artificial intelligence and machine learning techniques to detect anomalies and prevent attacks in real time. Through an analysis of publicly available CAN datasets, we evaluate the effectiveness of IDS frameworks in mitigating these threats.
An H-Bridge Switched Tank Converter with Reduced Inductance
Authors: Yang Xinxin , Meng Runquan , Li Huajian , Zhang Jiahui , Bai Xiang , Li Ruishu
Published: January 2025

Due to the restrictions of the operating environment and on-site space conditions, the energy routing devices used in Antarctic research stations must have a compact structure and require the internal power converter to have a high enough power density to reduce its size, so the internal DC/DC conversion link of the energy router adopts a two-stage voltage regulation scheme. In this paper, a Switched Tank Converter (STC) is used to realize the coarse voltage adjustment of the first stage. In order to further improve the power density of the STC, this paper integrates the half bridge with the same switching action in the STC, and several resonant slots share one inductor to obtain an H-bridge STC with reduced inductance. At the same time, an improved control method is proposed to solve the influence of passive device parameter error and the parasitic parameter on the resonant frequency by adjusting the on-time value of the switch on the rectifier side. This control method can effectively solve the influence of the passive device parameter difference on the converter without adding new devices, ensure the power density advantage of the converter, and improve efficiency. Finally, the validity and rationality of the circuit and the improved control method are verified by simulation and experiment. The experimental result shows that the H-bridge STC with reduced inductance has a power density of 1041 W/in3 at 600 W, which greatly improves the overall operating efficiency of the energy router.
Magnetic Gear Wireless Power Transfer System: Prototype and Electric Vehicle Charging
Authors: Dunlap Caleb , Van Neste Charles W.
Published: January 2025

This paper investigates the potential of a magnetic gear wireless power transfer (WPT) system for electric vehicle (EV) charging, with the advantages of low-frequency operation, low foreign object interference, low electromagnetic emissions, and high misalignment tolerance. The study explores the novel impact of Halbach arrays that enhance the flux density in desirable locations while decreasing the flux in undesirable locations, which provides the benefit of decreased foreign object attraction. The initial prototype results demonstrate that the Halbach system can transmit approximately 34.65 W with a transfer efficiency of 64% across a gap of 104 mm. The Halbach system is experimentally compared to a conventional magnet arrangement, which achieved a maximum power transfer of 88 W over 104 mm. The Halbach system is applied to a personal mobility EV to enable wireless charging at low frequency. The axial design of this WPT system has the unique benefit of a 360° radial coupling angle that maintains constant, near-maximum levels of power transfer and efficiency. This full circle coupling angle allows the personal EV to park in any direct vicinity of the charger and achieve the same level of charging given a certain distance. This study delivers important contributions to advancing a low-frequency wireless EV charging technology based on magnetic gears, that sets the stage for future innovations focused on optimizing efficiency, increasing safety, and simplifying the charging process.
Fault Detection of Permanent Magnet Synchronous Machines: An Overview
Authors: Li Henghui , Zhu Zi-Qiang , Azar Ziad , Clark Richard , Wu Zhanyuan
Published: January 2025

These days, as the application of permanent magnet synchronous machines (PMSMs) and drive systems becomes popular, the reliability issue of PMSMs gains more and more attention. To improve the reliability of PMSMs, fault detection is one of the practical techniques that enables the early interference and mitigation of the faults and subsequently reduces the impact of the faults. In this paper, the state-of-the-art fault detection methods of PMSMs are systematically reviewed. Three typical faults, i.e., the inter-turn short-circuit fault, the PM partial demagnetization fault, and the eccentricity fault, are included. The existing methods are firstly classified into signal-, model-, and data-based methods, while the focus of this paper is laid on the signal sources and the signatures utilized in these methods. Based on this perspective, this paper intends to provide a new insight into the inherent commonalities and differences among these detection methods and thus inspire further innovation. Furthermore, comparison is conducted between methods based on different signatures. Finally, some issues in the existing methods are discussed, and future work is highlighted.
Discussion on AC Resistance and Temperature of ACSR Based on Finite Element Model Assistance
Authors: Yu Jianbo , Wu Changqing , Huang Hao , Xie Dexin , Qin Feixiang , Jiang Jian , He Gaohui
Published: January 2025

In overhead wire transmission systems, the presence of AC resistance results in increased energy dissipation, adversely affecting the lines’ capacity to conduct current. This paper employs a finite element aluminum conductor steel-reinforced (ACSR) model, combined with electrical measurement techniques, to investigate AC resistance. By applying varying levels of AC current, the model is employed to determine the AC resistance which closely aligns with theoretical values estimated using the Morgan algorithm. The trends observed in the parameters are consistent, thereby validating the accuracy of the model. Following simulations and analyses regarding both AC resistance and temperature variations within the conductors—and incorporating empirical measurement results—it is demonstrated that, when environmental factors are not considered, any increase in the conductor temperature can be integrated into a revised model. This updated model is subsequently compared against test results obtained from an experimental platform; the findings confirm that the estimation errors remain within an acceptable range. Overall, this simulation model serves as a valuable reference for assessing AC losses in existing conductors, as well as contributing to reduced experimental costs while mitigating the associated risks and challenges. In summary, this simulation model serves as an essential reference for assessing AC losses in current conductors and aids in reducing experimental costs while addressing the associated risks and challenges.
Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model
Authors: He Qian , Zhao Mingbin , Li Shujie , Li Xuefang , Wang Zuoxun
Published: January 2025

The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R2 of 0.968, due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data, significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production, demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.
A Novel Decision-Support Framework for Supporting Renewable Energy Technology Siting in the Early Design Stage of Microgrids: Considering Geographical Conditions and Focusing on Resilience and SDGs
Authors: Sugumar Bharath Kumar , Anglani Norma
Published: January 2025

This research is focused on microgrid design supporting tools and presents an innovative framework for renewable energy (RE) sources’ site selection, integrating multicriteria decision-making (MCDM) methods with resilience considerations and alignment to the Sustainable Development Goals (SDGs). It addresses present climatic challenges, identifies key causes of possible power failures, and develops strategies to mitigate their effects, while providing tools for energy managers and decision-makers to select suitable RE sources/technologies, based on geographical and sustainability criteria. The framework categorizes criteria into quantitative and qualitative types, adopting a cost (C)- and benefit (B)-based approach. The Analytic Hierarchy Process (AHP) calculates criteria weights to ensure accuracy and compatibility in decision-making, integrating SDG objectives into the evaluation process. This study focuses on five major RE options, photovoltaic (PV), wind, wave, tidal, and geothermal, analyzing more than 50 criteria for each energy type. This evaluation incorporates the expertise of over 50 experts and case studies, making it one of the most extensive research efforts in RE site selection. By systematically addressing resilience challenges and linking them with SDG priorities, this study provides a robust framework for evaluating and optimizing RE options. Its methodologies offer significant contributions to advancing sustainable energy development and enhancing energy systems’ resilience to climate and infrastructural challenges.
Global Value Chain Embedding and Total Factor Productivity in Carbon Emission Reduction: A Multi-Country Analysis of the Paper Industry
Authors: Xie Xiwei , Li Huijuan , Cheng Baodong , Li Fangfang , Mikkilä Mirja
Published: January 2025

Against the backdrop of carbon reduction and low-carbon economic development, this study takes the global paper industry chain as its research subject and employs the SBM-GML model and input–output model to measure the low-carbon TFP and value chain embedding levels of 42 major global economies from 2001 to 2021. Using fixed-effects and threshold effect models, the study examines both the overall and stage-specific impacts of value chain embedding on low-carbon TFP. The results reveal that between 2001 and 2021, while the low-carbon TFP of major countries in the global paper industry chain steadily increased, significant disparities persisted among them. Most countries experienced low-carbon TFP growth driven by technological progress, whereas only a few high low-carbon TFP nations also demonstrated upward trends in technical efficiency. The division of labour position in the value chain significantly promoted low-carbon TFP at a ratio of 1:0.26. In contrast, the low-carbon TFP effects of participation displayed a nonlinear impact with a threshold of 0.21: it exerted an insignificant inhibitory effect in the initial stage but, upon crossing the threshold, resulted in a significant positive effect with a ratio of 1:0.13. These findings highlight the importance of optimizing GVC strategies to achieve sustainable industrial transformation and growth.
Addition of Phospholipids Improved the Physical Stability and Fat Globule Structure of Processed Milk
Authors: Pan Yue , Zhang Lei , Fu Xuanfei , Li Xiaodong , Liu Lu , Wang Xuezhen , Zhang Jinfeng , Zhou Wenli
Published: January 2025

The manufacturing processes for infant formula disrupt the structure of phospholipid-coated milk fat globules, thereby impacting the physical stability of the system. In this study, either soybean phospholipids (SPs) or egg yolk phospholipids (EYPs) were incorporated into the milk system to reduce this damage, and their effects on the stability and fat globule structure of processed milk were examined. The findings revealed that the addition of phospholipids improved the physical stability and fat globule structure of different processed milk. In pasteurized milk, the stability constant of samples with phospholipids decreased from 0.42 to 0.37 compared to phospholipid-free milk, but no significant difference was found between the EYP and SP groups. In homogenized milk, adding EYPs resulted in a smaller particle size (870.35 nm versus 953.39 nm) and stability constant (0.28 versus 0.30) than the addition of SP. Moreover, homogenized milk added with EYPs exhibited a denser phospholipid interface film which led to a more intact fat globule structure. Consequently, the milk powder particles in the spray-dried milk supplemented with EYPs showed a more uniform distribution and smoother surface. These findings suggested that EYPs were superior to SPs in restructuring fat globules and enhancing newly formed fat globule stability during milk powder processing. This offers valuable insights for improving the physical and structural properties of dairy products, such as infant formula.
Gintonin-Enriched Panax ginseng Extract Induces Apoptosis in Human Melanoma Cells by Causing Cell Cycle Arrest and Activating Caspases
Authors: Lee Su-Hyun , Park Gyun-Seok , Lee Rami , Hong Seongwoo , Han Sumin , Lee Yoon-Mi , Nah Seung-Yeol , Han Sung-Gu , Oh Jae-Wook
Published: January 2025

Gintonin, a non-saponin glycolipoprotein from Panax ginseng, acts as a lysophosphatidic acid ligand. However, its anticancer effects, especially in melanoma, remain unclear. This study investigated the anti-proliferative effects and intracellular signaling mechanisms of a gintonin-enriched fraction (GEF) from Panax ginseng in human melanoma cell lines. In vitro, GEF treatment significantly inhibited cell proliferation, reduced clonogenic potential, and delayed wound healing in melanoma cells. Flow cytometry and terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining showed that GEF induced apoptosis, as evidenced by increased apoptotic cell populations and nuclear changes. GEF also caused cell cycle arrest in the G1 phase for A375 cells and the G2/M phase for A2058 cells. It triggered apoptotic signaling via activation of caspase-3, -9, poly (ADP-ribose) polymerase cleavage, and downregulation of B cell lymphoma-2 (Bcl-2). GEF treatment also raised intracellular reactive oxygen species (ROS) levels and mitochondrial stress, which were mitigated by N-acetyl cysteine (NAC), an ROS inhibitor. In vivo, GEF suppressed tumor growth in A375- and A2058-xenografted mice without toxicity. These findings suggest that GEF from Panax ginseng has potential antitumor effects in melanoma by inducing apoptosis and cell cycle arrest, presenting a promising therapeutic avenue.
Polysaccharides from Sea Cucumber (Stichopus japonicus) Synergize with Anti-PD1 Immunotherapy to Reduce MC-38 Tumor Burden in Mice Through Shaping the Gut Microbiome
Authors: Li Jiahui , Jia Jinhui , Teng Yue , Wang Xiaojuan , Xia Xiaojun , Song Shuang , Zhu Beiwei , Xia Xiaodong
Published: January 2025

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and significantly improved outcomes for patients with certain malignancies. However, immunotherapy with ICIs is only effective in a subset of patients and the gut microbiota have been identified as an important factor associated with response to ICI therapy. Polysaccharides from sea cucumber (Stichopus japonicus) (SCP) have been shown to modulate the gut microbiota and exhibit beneficial health functions, but whether SCP could synergize with anti-PD1 immunotherapy remains unexplored. In this study, mice with ICI-sensitive MC38 tumors were treated with anti-PD1 antibody after supplementation with or without SCP to examine the potential impact of SCP on the efficacy of immunotherapy. SCP strongly amplified the anti-tumor activity of anti-PD1 in MC38 tumor-bearing mice. Flow cytometry and immunohistological staining demonstrated that SCP treatment increased cytotoxic CD8+ T lymphocytes while decreasing regulatory Foxp3+ CD4+ T lymphocytes. Gut microbiota and metabolomic analysis revealed that SCP modulated the microbiota and increased the abundance of certain metabolites such as indole-3-carboxylic acid. Furthermore, fecal microbiota transplantation experiments justified that the synergistic effect of SCP with anti-PD1 was partially mediated through the gut microbiota. Mice receiving microbiota from SCP-treated mice showed a boosted response to anti-PD1, along with enhanced anti-tumor immunity. These findings indicate that SCP could be utilized as a dietary strategy combined with anti-PD1 therapy to achieve improved outcomes in patients.
Nonlinear Analysis of the U.S. Stock Market: From the Perspective of Multifractal Properties and Cross-Correlations with Comparisons
Authors: Han Chenyu , Xu Yingying
Published: January 2025

This study investigates the multifractal properties of daily returns of the Standard and Poor’s 500 Index (SPX), the Dow Jones Industrial Average (DJI), and the Nasdaq Composite Index (IXIC), the three main indices representing the U.S. stock market, from 1 January 2005 to 1 November 2024. The multifractal detrended fluctuation analysis (MF-DFA) method is applied in this study. The origins of the multifractal properties of these returns are both long-range correlation and fat-tail distribution properties. Our findings show that the SPX exhibits the highest multifractal degree, and the DJI exhibits the lowest for the whole sample. This study also examines the multifractal behaviors of cross-correlations among the three major indices through the multifractal detrended cross-correlation analysis (MF-DCCA) method. It is concluded that the indices are cross-correlated and the cross-correlations also exhibit multifractal properties. Meanwhile, these returns exhibit different multifractal properties in different stages of the market, which shows some asymmetrical dynamics of the multifractal properties. These empirical results may have some important managerial and academic implications for investors, policy makers, and other market participants.
Encapsulation of Fatty Acids Using Linear Dextrin from Waxy Potato Starch: Effect of Debranching Time and Degree of Unsaturation
Authors: Xie Huifang , Duan Qingfei , Hu Guohua , Dong Xinyi , Ma Litao , Fu Jun , Yang Yiwen , Zhang Huaran , Song Jiahui , Gao Qunyu , Yu Long
Published: January 2025

This study investigates the effects of the debranching time of waxy potato starch using pullulanase and recrystallization on particle morphology, debranching degree, and crystal structure. The results demonstrated that after gelatinization and debranching, the surface of the starch crystals became rough and uneven due to hydrolysis, with most particles showing a fragmented surface. The crystalline state was not significantly changed with debranching time. X-ray diffraction analysis revealed no significant differences in the patterns of recrystallized linear dextrin (LD) after various debranching times. Notably, the short-range ordered structure of LD after debranching and recrystallization was more organized than that of the original or gelatinized starch. Additionally, polarized light microscopy showed that the birefringent pattern disappeared as a result of debranching and recrystallization, indicating the breakdown of particle structure, although the overall particle morphology did not change significantly with varying debranching times. Furthermore, linear dextrin derived from starch debranched for 6 h (with pullulanase at 15 μg/g) successfully embedded stearic acid, oleic acid, and linoleic acid, forming a VI-type starch–fatty acid complex.
Landscape of Noncoding RNA in the Hypoxic Tumor Microenvironment
Authors: Gong Lianfeng , Zou Chuanxin , Zhang Haixia , Yang Fei , Qi Gui , Ma Zhaowu
Published: January 2025

Amidst the prevalent and notable characteristic of a hypoxic microenvironment present in the majority of solid tumors, a burgeoning number of studies have revealed the significance of noncoding RNAs (ncRNAs) in hypoxic tumor regions. The transcriptome of cancers is highly heterogeneous, with noncoding transcripts playing crucial roles. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are two distinctive classes of ncRNA that are garnering increasing attention. Biologically, they possess intriguing properties and possess significant regulatory functions. Clinically, they present as promising biomarkers and therapeutic targets. Additionally, recent research has evaluated the clinical applications of these ncRNAs in RNA-based treatments and noninvasive liquid biopsies. This review provides a comprehensive summary of recent studies on lncRNAs and circRNAs within the hypoxic tumor microenvironment. Furthermore, the clinical significance of lncRNAs and circRNAs in cancer diagnosis and treatment is emphasized, which could pave the way for the development of effective targeted therapies.