bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Hybrid Multi-Attribute Group Decision-Making with Two Reference Points for Electricity Sales Package Recommendation
Authors: Chen Yanji , Ma Yuanqian
Published: February 2025

Given that existing electricity sales package recommendation methods mainly consider scenarios where customers are familiar with all the attributes of the packages, they tend to overlook the mixed nature of quantitative and qualitative attributes, psychological factors, and the determination of attribute weights in the decision-making process. To address these limitations, this paper proposes a dynamic hybrid multi-attribute group decision-making with two reference points for electricity sales package recommendation, combining prospect theory. The specific approach is as follows: First, the AAP clustering technique is used to divide the sample customer set and establish a customer load information database, from which customer characteristic profiles are extracted to identify sample customers similar to the target customer. Second, considering the mixed nature of quantitative and qualitative attributes of electricity sales packages and the different characteristics of these attributes, the three-parameter interval gray number and triangular fuzzy number are used to characterize them. A mixed target model is proposed as an external reference point. Next, time reference points are set by using the mean and average development speed to examine the dynamic development of the electricity sales packages. Then, the vector angle method is used to assess the similarity between individual decision-making and group decision-making, establishing an appropriate customer weight adjustment model. The comprehensive prospect value of each package is calculated under the two reference points by comparing the values, which are then used to rank all electricity sales packages. Finally, a case study of customers from a region in China is conducted to verify the accuracy and effectiveness of the proposed recommendation method.
Experimental Studies and Computational Fluid Dynamics Simulations to Evaluate the Characteristics of the Air Velocity Profile Generated by the Positive Pressure Ventilator
Authors: Kaczmarzyk Piotr , Ziegler Bartosz , Warguła Łukasz , Burdzy Tomasz , Popielarczyk Tomasz , Sowa Tomasz , Antosiewicz Piotr
Published: February 2025

Determining the appropriate position of a positive pressure ventilator, where it exhibits the highest efficiency (measured by the achieved volumetric flow rate), can influence the success of rescue operations conducted by fire protection units. The aim of this article is to evaluate the possibility of using LES (Large Eddy Simulation) analyses to verify the positioning parameters of positive pressure ventilators in numerical simulation conditions, without the need for time-consuming experiments. The article presents a comparative analysis of full-scale experimental studies (conducted on a test setup to assess the velocity profile of the air jet in open flow) and CFD numerical analyses. The analysis confirmed the convergence of the flow rate parameter entering the surface of the door opening model installed on the test setup. Depending on the distance of the ventilator position (1–7 m), a convergence degree ranging from 1.6% to 3.8% was achieved for the volumetric flow rate. This publication demonstrates that the LES model is a suitable tool for effectively determining the working positions of positive pressure ventilators, as defined in real working conditions (open flow). The analysis may serve as a helpful tool for manufacturers of mobile ventilators, who can use the method for the technological testing of their units without conducting time-consuming experiments.
A Novel Pseudo-Siamese Fusion Network for Enhancing Semantic Segmentation of Building Areas in Synthetic Aperture Radar Images
Authors: Liao Mengguang , Huang Longcheng , Li Shaoning
Published: February 2025

Segmenting building areas from synthetic aperture radar (SAR) images holds significant research value and practical application potential. However, the complexity of the environment, the diversity of building shapes, and the interference from speckle noise have made building area segmentation from SAR images a challenging research topic. Compared to traditional methods, deep learning-driven approaches exhibit superiority in the aspect of stability and efficiency. Currently, most segmentation methods use a single neural network to encode SAR images, then decode them through interpolation or transpose convolution operations, and finally obtain the segmented building area images using a loss function. Although effective, the methods result in the loss of detailed information and do not fully extract the deep-level features of building areas. Therefore, we propose an innovative network named PSANet. First, two sets of deep-level features of building areas were extracted using ResNet-18 and ResNet-34, with five encoded features of varying scales obtained through a fusion algorithm. Meanwhile, information on the deepest-level encoded features was enriched utilizing an atrous spatial pyramid pooling module. Next, the encoded features were reconstructed through skip connections and transposed convolution operations to obtain discriminative features of the building areas. Finally, the model was optimized using the combined CE-Dice loss function to achieve superior performance. The experimental results of the SAR images from regions with different geographical characteristics demonstrate that the proposed PSANet outperforms several recent State-of-the-Art methods.
Spatiotemporal Distribution Characteristics of PM2.5 Components in the Yeosu and Gwangyang Industrial Complexes
Authors: Jang Hyeok , Park Shin-Young , Kim Young-Hyun , Lee Cheol-Min
Published: February 2025

Particulate matter (PM) composition and distribution heavily rely on the spatiotemporal characteristics of its emission sources. This study analyzed the distribution characteristics and sources of fine PM (PM2.5) and its chemical components at five locations near the Yeosu and Gwangyang industrial complexes. Samples were chemically analyzed, considering their ionic, carbonaceous, and trace elements, from August 2020 to July 2023. Spatial characteristics were examined using the coefficient of divergence and Pearson correlation coefficient, and temporal characteristics were investigated using monthly, seasonal, and annual distribution patterns. The range of average PM2.5 concentrations at the five measurement sites was 17.2–20.3 μg/m3. The time-series analysis indicated that ionic species consistently comprised the largest proportion of PM2.5 across all seasons and years (51.4–60.3%), with the highest proportion observed in winter. Trace elements and carbonaceous species were present in higher proportions in spring (19.4%) and autumn (35.7%), respectively. High concentrations of PM2.5 and its chemical components were identified by temporal variations and emission sources. Principal component analysis showed that the chemical components of PM2.5 were mainly from soil dust, vehicular emissions, the steel industry, and other pollution sources. This study provides foundational data for future research aimed at air pollutant emissions management and PM2.5 source apportionment.
Polyautoimmunity Reflecting Immune Dysregulation in Common Variable Immunodeficiency
Authors: Danieli Maria Giovanna , Murdaca Giuseppe , Mezzanotte Cristina , Claudi Ilaria , Buti Elena , Martini Matteo , Bilò Maria Beatrice , Gangemi Sebastiano , Moroncini Gianluca
Published: February 2025

Background: Common variable immunodeficiency (CVID) is the most frequent symptomatic inborn error of immunity (IEI) in adulthood. Other than recurrent infections, CVID may present with non-infectious complications such as enteropathy, lymphoproliferation, malignancy, and autoimmune diseases. Patients could have a single autoimmune disease (monoautoimmunity) or two or more autoimmune diseases (overt polyautoimmunity). “Latent polyautoimmunity” corresponds to the presence of autoantibodies without a clinically evident autoimmune disease. Methods: The aim of this retrospective study was to describe autoimmunity and polyautoimmunity in a population of 81 CVID adult patients, enrolled from January 2008 to July 2022 (mean follow-up: 8.5 years). Results: We documented at least one autoimmune disorder in 40 patients (49.4%). Moreover, 15 subjects (37.5% of patients with autoimmunity and 18.5% of all CVID population) presented polyautoimmunity. Despite the humoral immune deficiency, we detected different autoantibodies in CVID patients with or without a concomitant autoimmune disease. In both groups with monoautoimmunity and polyautoimmunity, cytopenias were the most common manifestation. Conversely, enteropathy was recorded only in patients with polyautoimmunity (27%, p = 0.006). Patients with polyautoimmunity showed a significantly lower mean age at diagnosis (−12 years, p = 0.018) compared to those with monoautoimmunity. We documented a higher frequency of autoimmunity in CVID patients who had increased diagnostic (+5.6 years) and therapeutic (+7.2 years) delay (p = 0.093 and 0.054, respectively). Conclusions: Polyautoimmunity is a frequent condition in patients affected by CVID. An early start of Ig replacement therapy could help prevent autoimmune complications.
Sensing Microorganisms Using Rapid Detection Methods: Supramolecular Approaches
Authors: Lahiri Hiya , Basu Kingshuk
Published: February 2025

Supramolecular chemistry relies on the dynamic association/dissociation of molecules through non-covalent interactions. These interactions of a self-assembled system can be strategically exploited for sensing several microorganisms. Moreover, supramolecular systems can also be combined with other functional components like nanoparticles, self-assembled monolayers, and microarray systems to produce multicomponent sensors with higher sensitivity and lower detection time. In this review, we will discuss how cutting-edge supramolecular chemistry has enabled scientists to develop microbial biosensors with high reliability and rapid detection time. Moreover, they produce high-throughput operations, real-time monitoring, extensive operation platforms, and cost-effective production. This review can serve as a conceptual background for understanding state-of-the-art rapid detection methods of microbial biosensing.
Advances and Challenges in Zeolite-Based Catalysts for the Selective Catalytic Oxidation of Ammonia
Authors: Chen Xiaoxin , Huang Jun , Yang Guoju
Published: February 2025

Ammonia (NH3) emissions from mobile sources pose significant environmental challenges, contributing to air pollution, ecosystem degradation, and climate change. The selective catalytic oxidation of NH3 (NH3-SCO) offers a sustainable solution by converting NH3 into nitrogen and water, yet designing catalysts that balance high efficiency, selectivity, and stability under operational conditions remains a critical challenge. This review provides a comprehensive overview of zeolite-based catalysts, renowned for their high surface area, tunable pore structures, and exceptional hydrothermal stability, which make them ideal for NH3-SCO applications. The review synthesizes recent advancements in catalyst design, emphasizing innovative architecture, the role of zeolite frameworks in active site dispersion, and strategies for optimizing catalytic architectures. Key insights include an enhanced understanding of NH3-SCO reaction mechanisms, progress in mitigating catalyst deactivation caused by poisoning and sintering, and the development of bimetallic and core-shell catalysts to improve performance and durability. Current limitations, including the sensitivity of catalysts to operational environments and scalability issues, are critically analyzed, and potential strategies for overcoming these barriers are proposed. This review highlights the state-of-the-art in zeolite-based NH3-SCO catalysis, offering valuable insights into the fundamental and applied aspects of catalyst design. The findings presented here provide a roadmap for future innovations in environmental catalysis, paving the way for more efficient and robust solutions to ammonia emission control.
Advancements in Metal–Organic Framework Materials for Photocatalytic CO2 Reduction
Authors: Zheng Jilong , Yan Xueli , Guo Xiaojuan , Wang Xinyi , Tang Shanfa , Liu Maochang
Published: February 2025

In recent years, metal–organic frameworks (MOFs) have garnered significant attention as highly efficient catalysts for CO2 photoreduction, owing to their unique electronic configurations and exceptional CO2 adsorption properties. This review provides a comprehensive analysis of recent advancements in the design, synthesis, and application of MOF-based photocatalysts for CO2 reduction. Following a concise overview of the fundamental properties of MOF materials, the review focuses on pure MOFs, highlighting the structural and functional roles of metal clusters and organic ligands. Subsequently, it explores into MOF-based composites, analyzing their compositional design, CO2 uptake capabilities, and photocatalytic efficiency. This review concludes by discussing the current challenges, future opportunities, and potential research directions for MOF photocatalysts in the field of CO2 conversion, offering valuable insights to advance this rapidly progressing field.
The Roles of Distinct Transcriptional Factors in the Innate Immunity of C. elegans
Authors: Afridi Muhammad Irfan , Tu Haijun
Published: February 2025

Deleterious molecules or factors produced by pathogens can hinder the normal physiological functioning of organisms. In response to these survival challenges, organisms rely on innate immune signaling as their first line of defense, which regulates immune-responsive genes and antimicrobial peptides to protect against pathogenic infections. These genes are under the control of transcription factors, which are known to regulate the transcriptional activity of genes after binding to their regulatory sequences. Previous studies have employed Caenorhabditis elegans as a host–pathogen interaction model to demonstrate the essential role of different transcription factors in the innate immunity of worms. In this review, we summarize the advances made regarding the functioning of distinct transcription factors in the innate immune response upon pathogen infection. Finally, we discuss the open questions in the field, whose resolutions have the potential to expand our understanding of the mechanisms underlying the innate immunity of organisms.
Voices of Children with Cancer and Their Siblings in the Family Talk Intervention
Authors: Ayoub Maria , Lövgren Malin , Kreicbergs Ulrika , Udo Camilla
Published: February 2025

Background: Children in pediatric oncology report unmet needs related to communication and information about the illness, care involvement, and psychosocial support. Supporting the whole family involves challenges, with a risk that children’s voices are not heard above those of the adults. Article 12 of the UNCRC has been a catalyst in supporting children’s voices and their right to participate in processes that affect them. The aim of this study was to explore how children with cancer and their siblings experienced participation in a family-centered psychosocial support intervention, the Family Talk Intervention (FTI). Methods: Interviews were held with 35 children (ill and siblings) from 26 families in pediatric oncology after having completed the FTI. A combined deductive and inductive qualitative content analysis was undertaken, guided by the Lundy model of child participation. Results: Children’s experiences of being able to express their views, being listened to, and being involved during FTI were mainly positive. This was related to their participation in individual meetings where they could raise their concerns and views, undertake small activities while talking, and have their voices and needs mediated to relevant adults, such as parents and professionals. Conclusions: The findings of this study showed that the FTI for families in pediatric oncology created opportunities to promote child participation. These findings indicate that, by offering children an individual space where they can express themselves freely and supporting them in various ways to do so, the children’s voices and involvement are strengthened.
The Effect of Tranexamic Acid on Estimated Blood Loss and Transfusion Rates in Children with Cerebral Palsy Undergoing Single-Event Multi-Level Surgery, a Retrospective Study
Authors: Julien-Marsollier Florence , Simon Anne-Laure , Pardessus Pierre , Presedo Ana , Ilharreborde Brice , Dahmani Souhayl
Published: February 2025

Purpose: Multiple osteotomies are frequently associated with single-event multi-level surgery (SEMLS) in children with cerebral palsy (CP). However, appropriate management of perioperative blood loss is crucial for decreasing the length of hospital stay and early rehabilitation. Tranexamic acid (TxA) has been proven to significantly reduce perioperative bleeding in multiple major orthopedic surgeries. The aim of this study was to investigate the effectiveness of TxA in decreasing blood loss in children with CP undergoing SEMLS procedures. Materials and Methods: Between September 2016 and September 2022, 101 consecutive children with CP who underwent SEMLS were identified—50 patients did not receive TxA peri-operatively (Control Group, from September 2016 to September 2018), and 51 patients received TxA (TxA Group since September 2018). Bleeding, hemoglobin levels, transfusion rate, length of hospital stay and postoperative hematocrit were compared between the groups. The predictive factors of blood transfusion were determined. Results: The transfusion rate significantly decreased in the TxA Group (43.3% vs. 4%, p < 0.001). The use of TxA and epidural analgesia were the identified factors for a significant transfusion rate decrease. Bleeding (estimated red cell loss) was decreased in the TxA group in comparison to the control group 22.3 [17.3–27.3] versus 33.78 [27.4–40.2], p < 0.05). The hospital length of stay significantly decreased in the TxA group (7.3 vs. 6 days, p = 0.01). No TxA-related complications occurred in any of the patients. Conclusions: A blood loss prevention strategy based on a low dose of TxA in children scheduled for SEMLS significantly decreased bleeding and transfusion rates, allowing an earlier discharge from the hospital for patients.
An Artificial Intelligence Approach to the Craniofacial Recapitulation of Crisponi/Cold-Induced Sweating Syndrome 1 (CISS1/CISS) from Newborns to Adolescent Patients
Authors: Pascolini Giulia , Didona Dario , Tarani Luigi
Published: February 2025

Background/Objectives: Crisponi/cold-induced sweating syndrome 1 (CISS1/CISS, MIM#272430) is a genetic disorder due to biallelic variants in CRFL1 (MIM*604237). The related phenotype is mainly characterized by abnormal thermoregulation and sweating, facial muscle contractions in response to tactile and crying-inducing stimuli at an early age, skeletal anomalies (camptodactyly of the hands, scoliosis), and craniofacial dysmorphisms, comprising full cheeks, micrognathia, high and narrow palate, low-set ears, and a depressed nasal bridge. The condition is associated with high lethality during the neonatal period and can benefit from timely symptomatic therapy. Methods: We collected frontal images of all patients with CISS1/CISS published to date, which were analyzed with Face2Gene (F2G), a machine-learning technology for the facial diagnosis of syndromic phenotypes. In total, 75 portraits were subdivided into three cohorts, based on age (Cohort 1 and 2) and the presence of the typical facial trismus (Cohort 3). These portraits were uploaded to F2G to test their suitability for facial analysis and to verify the capacity of the AI tool to correctly recognize the syndrome based on the facial features only. The photos which passed this phase (62 images) were fed to three different AI algorithms—DeepGestalt, Facial D-Score, and GestaltMatcher. Results: The DeepGestalt algorithm results, including the correct diagnosis using a frontal portrait, suggested a similar facial phenotype in the first two cohorts. Cohort 3 seemed to be highly differentiable. The results were expressed in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and p Value. The Facial D-Score values indicated the presence of a consistent degree of dysmorphic signs in the three cohorts, which was also confirmed by the GestaltMatcher algorithm. Interestingly, the latter allowed us to identify overlapping genetic disorders. Conclusions: This is the first AI-powered image analysis in defining the craniofacial contour of CISS1/CISS and in determining the feasibility of training the tool used in its clinical recognition. The obtained results showed that the use of F2G can reveal valid support in the diagnostic process of CISS1/CISS, especially in more severe phenotypes, manifesting with facial contractions and potentially lethal consequences.
DNA Methylation Array Analysis Identifies Biological Subgroups of Cutaneous Melanoma and Reveals Extensive Differences with Benign Melanocytic Nevi
Authors: Schwendinger Simon , Jaschke Wolfram , Walder Theresa , Hench Jürgen , Vogi Verena , Frank Stephan , Hoffmann Per , Herms Stefan , Zschocke Johannes , Nguyen Van Anh , Schmuth Matthias , Jukic Emina
Published: February 2025

Background/Objectives: Genetics and epigenetics play an important role in the pathogenesis of cutaneous melanoma. The majority of cases harbor mutations in genes associated with the MAPK signaling pathway, i.e., BRAF, NRAS, or NF1. The remaining neoplasms, often located on acral sites, are condensed as the triple-wildtype subtype and are characterized by other molecular drivers. This study aimed to elucidate genetic and epigenetic differences within cutaneous melanoma and to compare it with melanocytic nevi. Methods: DNA was extracted from archived tissue samples of cutaneous melanoma (n = 19), melanocytic nevi (n = 11), and skin controls (n = 11) and subsequently analyzed by massive parallel (next generation) gene panel sequencing and genome-wide DNA methylation array analysis. The sample size was increased by including repository data from an external study. Results: There were major differences in the genomic landscape of MAPK-altered and triple-wildtype cutaneous melanoma, the latter presenting with a lower number of mutations, a different pattern of copy number variants, and a low frequency of TERT promoter mutations. Dimensional reduction of DNA methylation array analysis clearly separated cutaneous melanoma from melanocytic nevi but revealed no major differences between classical cutaneous melanoma and the triple-wildtype cases. However, it identified a possible biological subgroup characterized by intermediately methylated CpGs. Conclusions: Dimensional reduction of methylation array data is a useful tool for the analysis of melanocytic tumors to differentiate between malignant and benign lesions and may be able to identify biologically distinct subtypes of cutaneous melanoma.
Investigating Some Issues Relating to Regime Matching
Authors: Hall Anthony D. , Pagan Adrian R.
Published: February 2025

Markov switching models are a common tool used in many disciplines as well as in Economics, and estimation methods are available in many software packages. Estimated models are commonly used for allocating observations to regimes. This allocation is usually done using a rule based on the estimated smoothed probabilities, such as, in the two regime case, when it exceeds the threshold of 0.5. The accuracy of the regime matching is often measured by the concordance index. Can regime matching be improved by using other rules? By replicating a number of published two-and three- regime studies and the use of simulation methods, it demonstrates that other rules can improve on the performance of the rule based on the threshold of 0.5. Using simulated models we extend the analysis of a single series to investigate, and demonstrate the efficacy of Markov switching models identifying a common factor in multiple time series.
Development of a SPICE Model for Fabricated PLA/Al/Egg Albumin/Al Memristors Using Joglekar’s Approach
Authors: Choudhury Hirakjyoti , Gogoi Pallab Kr , Knaap Ramon van der , Goswami Rupam , Vanhamel Jurgen
Published: February 2025

Memristors have emerged as prospective two-terminal elements, having applications in memory, neuromorphic systems, and analog circuits. Biological materials such as egg albumin exhibit memristive behavior, displaying a distinctive pinched hysteresis signature in their current-voltage characteristics. However, such memristive behavior must be mathematically modeled to gain insights into the material’s operation and utilize it in various circuit applications. This article proposes a novel SPICE-level framework for fabricated egg albumin memristors using Joglekar’s memristor model. Experimental current-voltage characteristics are used to calibrate the SPICE model, ensuring accurate reproducibility of the experimental results. Additionally, the impact of variations in model-specific parameters on dynamic resistance and device performance is explored.
A Multi-Agent System for Parking Allocation: An Approach to Allocate Parking Spaces
Authors: Icarte-Ahumada Gabriel , He Zhangyuan , Godoy Victor , García Francisco , Oyarzún Mauricio
Published: February 2025

Traffic congestion and the search for parking spaces have become significant challenges in modern urban development. These issues lead to increased fuel consumption and contribute to stressful lifestyles. To address this, efficient parking allocation has emerged as a challenge in urban environments, requiring innovative solutions to optimize the use of available parking spaces. Various approaches have been employed to tackle this problem, including mathematical programming and heuristic procedures, which typically follow a centralized model. Another promising solution involves the use of multi-agent systems (MASs), which adopt a decentralized approach. However, there is limited research on the application of MASs in parking allocation. This paper presents a multi-agent system where intelligent agents represent vehicles and parking spaces to efficiently allocate parking spaces to cars. The paper evaluates four coordination mechanisms based on the Contract Net Protocol for parking allocation. The results show that the concurrent use of the Contract Net Protocol with decommitment capacities is the most effective approach, as it reduces the time required for parking space allocation while optimizing the utilization of available parking spaces.
Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management
Authors: Cavus Muhammed , Dissanayake Dilum , Bell Margaret
Published: February 2025

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs.
Reliability Challenges, Models, and Physics of Silicon Carbide and Gallium Nitride Power Devices
Authors: Avraham Tsuriel , Dhyani Mamta , Bernstein Joseph B.
Published: February 2025

Silicon Carbide (SiC) and Gallium Nitride (GaN) are revolutionizing power electronics with greater efficiency and durability than Silicon. Nevertheless, their widespread use is limited by reliability challenges, including thermal degradation, defect propagation, and charge trapping, affecting their stability and lifetime. This review explores these reliability issues, comparing empirical and physics-based models for predicting device performance and identifying practical limitations. We also examine strategies to enhance robustness, from material design improvements to advanced testing methods. We propose a demonstrative GaN power circuit topology specifically for demonstrating reliability in real-world conditions. This work highlights key challenges and opportunities in developing more reliable SiC and GaN technologies for future applications.
Power Transformers Cooling Design: A Comprehensive Review
Authors: Sorte Sandra , Monteiro André Ferreira , Ventura Diogo , Salgado Alexandre , Oliveira Mónica S. A. , Martins Nelson
Published: February 2025

Efficient cooling technologies for power transformers are critical to modern power systems, ensuring reliability, performance, and AN extended lifespan. This review systematically analyses advancements, challenges, and opportunities in cooling systems for power transformers. Oil-immersed transformers, widely used due to their superior insulation and effective cooling, require efficient thermal management to prevent overheating and ensure operational stability. This review evaluates key cooling strategies across oil-natural air-natural (ONAN), oil-natural air-forced (ONAF), oil-directed air-forced (ODAF), and oil-forced air-forced (OFAF) systems. It highlights innovations in radiator design, such as top-mounted radiators and chimney caps, and explores sustainable alternatives, including biodegradable esters, nanofluids, and hybrid ventilation methods. Advanced computational tools like Computational Fluid Dynamics (CFD) and artificial intelligence (AI), particularly neural networks, are identified as transformative for optimising cooling performance, predicting thermal behaviour, and enabling real-time monitoring. Despite progresses, challenges persist in radiator optimisation, airflow dynamics, and scalability of innovative cooling methods. By offering a comprehensive review and identifying critical areas for improvement, this study provides a foundation for developing cost-effective, reliable, and environmentally sustainable cooling systems, aligning with the growing demand for efficient energy infrastructure.
Power Dissipation Reduction System for Adjustable Power Supplies
Authors: Savu Valeriu , Rusu Madalin Ion , Savastru Dan , Manea Dragos
Published: February 2025

The design of high-power sources intended for domestic consumption in the case of renewable energies, research laboratories, the operation of diode-pumped lasers, electric vehicles, etc. requires reducing the price, increasing efficiency, reducing overall dimensions, and so on. Achieving these requirements applicable to the manufacture of high-power sources represents a challenge in research, which leads to new concepts for the realization of these sources. The present work aims to improve the parameters of high-power adjustable linear voltage sources, also presenting practical results and simulations. The combined source designed and realized by us combines two systems, one commutative and the other linear. Both the simulated and practical results showed that the new combined source that we propose dissipates up to 10 times less power than a linear source designed for the same output and power supply parameters under the same conditions.
Carbon Price Point and Interval-Valued Prediction Based on a Novel Hybrid Model
Authors: Chen Haoyu , Wu Qunli , Han Chonghao
Published: February 2025

Accurate carbon price forecasting enables the steady operation of the carbon trading market and optimal resource allocation while also empowering market participants to understand dynamics and make informed decisions, ultimately supporting sustainable development in the carbon market. While early research primarily focused on point forecasting of single-value carbon price, recent studies have shifted towards interval prediction, although there is still a lack of research dedicated to developing models for interval-valued predictions. The importance of interval-valued forecasting lies in its ability to better capture the upper and lower bounds of the carbon price range across different time dimensions, thereby revealing the intrinsic patterns and trends of price fluctuations and assisting in point forecasting to comprehensively capture carbon market volatility. This study offers a novel approach based on a CEEMDAN-CNN-BiLSTM-SENet hybrid model, providing a framework for both point and interval-valued carbon price predictions. The model makes a more comprehensive analysis of the carbon market possible by combining the predictions from these two approaches. In the case study using Hubei market’s data, the mean absolute percentage error for carbon pricing was 0.8125%, with the MAPE for the highest and lowest prices being 1.8898% and 1.7852%, respectively—both outperforming other comparative models. The results demonstrate that this model can measure trends of carbon pricing effectively.
Indirect Electrostatic Discharge (ESD) Effects on Shielded Components Installed in MV/LV Substations
Authors: Attolini Giuseppe , Celozzi Salvatore , Stracqualursi Erika
Published: February 2025

Standards describing the test procedures recommended to investigate the shielding effectiveness of enclosures have two major issues: they generally prescribe the assessment of the electromagnetic field of empty cavities, and they do not deal with very small enclosures. However, the dimensions of some very common shielded apparatus are smaller than those considered in the standards and the electromagnetic field distribution inside the shielded structure is strongly affected by the enclosure content. In this paper, both issues have been investigated for two components commonly used in medium voltage/low voltage (MV/LV) substations: a mini personal computer used to store, process, and transmit relevant data on the status of the electric network, with these aspects being essential in smart grids, and an electronic relay which is ubiquitous in MV/LV substations. Both components are partially contained in a metallic enclosure which provides a certain amount of electromagnetic shielding against external interferences. It is observed that an electrostatic discharge may cause a failure and/or a loss of data, requiring an improvement of shielding characteristics or a wise choice of the positions where the most sensitive devices are installed inside the enclosure. Since the dimensions of very small enclosures, fully occupied by their internal components, do not allow for the insertion of sensors inside the protected volume, numerical analysis is considered as the only way for the appraisal of the effects induced by a typical source of interference, such as an electrostatic discharge.
Higher-Order Markov Chain-Based Probabilistic Power Flow Calculation Method Considering Spatio-Temporal Correlations
Authors: Liu Muyang , Xiong Yinjun , Li Quan , Murad Mohammed Ahsan Adib , Zhong Weilin
Published: February 2025

The uncertainty caused by renewable energy (RES) and diverse load demands may cause power flow fluctuations in modern power systems, where the probabilistic power flow (PPF) is a reliable method for quantifying and analyzing such power flow fluctuations. This paper proposes a higher-order Markov chain-based modeling framework to represent the stochastic behaviors of the photovoltaic (PV) output and load profiles. The proposed method effectively captures nonlinear temporal autocorrelations across multiple time intervals. In addition, by constructing joint probability distributions, the proposed method can not only handle the situation of linear correlations among distinct PV outputs and similar load types but also reveal nonlinear correlations between co-located PV generation and load variations. In addition, an inverse transformation strategy is developed to generate spatially and temporally correlated PV–load scenarios, ensuring more realistic system representations. Finally, the Mehler formula is adopted to calculate equivalent correlation coefficients under high-linearity conditions, which enhances the computational tractability of the overall approach. Numerical case studies demonstrate that our method achieves both accuracy and efficiency in PPF computations while preserving critical spatio-temporal correlation characteristics.
Optimized Strategy for Energy Management in an EV Fast Charging Microgrid Considering Storage Degradation
Authors: Paixão Joelson Lopes da , Abaide Alzenira da Rosa , Danielsson Gabriel Henrique , Sausen Jordan Passinato , da Silva Leonardo Nogueira Fontoura , Neto Nelson Knak
Published: February 2025

Current environmental challenges demand immediate action, especially in the transport sector, which is one of the largest CO2 emitters. Vehicle electrification is considered an essential strategy for emission mitigation and combating global warming. This study presents methodologies for the modeling and energy management of microgrids (MGs) designed as charging stations for electric vehicles (EVs). Algorithms were developed to estimate daily energy generation and charging events in the MG. These data feed an energy management algorithm aimed at minimizing the costs associated with energy trading operations, as well as the charging and discharging cycles of the battery energy storage system (BESS). The problem constraints ensure the safe operation of the system, availability of backup energy for off-grid conditions, preference for reduced tariffs, and optimized management of the BESS charge and discharge rates, considering battery wear. The grid-connected MG used in our case study consists of a wind turbine (WT), photovoltaic system (PVS), BESS, and an electric vehicle fast charging station (EVFCS). Located on a highway, the MG was designed to provide fast charging, extending the range of EVs and reducing drivers’ range anxiety. The results of this study demonstrated the effectiveness of the proposed energy management approach, with the optimization algorithm efficiently managing energy flows within the MG while prioritizing lower operational costs. The inclusion of the battery wear model makes the optimizer more selective in terms of battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan.
A Class of Subcritical and Critical Schrödinger–Kirchhoff Equations with Variable Exponents
Authors: Li Shuai , An Tianqing , Wu Yue , Zhang Zhenfeng
Published: February 2025

In the present paper, we discuss a Schrödinger–Kirchhoff equation involving the p(x)-Laplacian in the entire space RN. The primary focus of this article is on subcritical and critical nonlinearities. We deduce the existence of solutions by employing the mountain pass theorem in two distinct scenarios. Firstly, we discuss the equation when the potential function satisfies a weaker condition in the subcritical case. Secondly, we address the lack of compactness in the critical case without utilizing the concentration compactness principle.
The Association Between Statin Drugs and Rhabdomyolysis: An Analysis of FDA Adverse Event Reporting System (FAERS) Data and Transcriptomic Profiles
Authors: Morris Robert , Bu Kun , Han Weiru , Wood Savanah , Hernandez Velez Paola M. , Ward Jacob , Crescitelli Ariana , Martin Madison , Cheng Feng
Published: February 2025

Background/Objectives: Rhabdomyolysis, a dangerous breakdown of skeletal muscle, has been reported as an adverse event in those prescribed a statin therapy for the treatment of hypercholesterolemia. Statin drugs are some of the most prescribed treatments for elevated cholesterol levels. The purpose of this comparative study was to determine the association between the statin drugs used and the risk of rhabdomyolysis using the FDA Adverse Event Reporting System (FAERS) and transcriptomic data. Methods: A disproportionality analysis was performed to compare the risk of rhabdomyolysis between the reference statin drug (simvastatin) and the treatment group, with patient age assessed as a possible confounder. In addition, association rule mining was utilized to both identify other adverse events that frequently presented with rhabdomyolysis and identify possible drug-drug interactions (DDIs). Finally, public transcriptomic data were explored to identify the possible genetic underpinnings highlighting these differences in rhabdomyolysis risk across statins. Results: Rhabdomyolysis is a commonly reported adverse event for patients treated with statins, particularly those prescribed simvastatin. Simvastatin was associated with a more than 2-fold increased likelihood of rhabdomyolysis compared to other statins. Men were twice as likely to report rhabdomyolysis than women regardless of statin treatment, with the highest risk observed for pravastatin (ROR = 2.30, p < 0.001) and atorvastatin (ROR = 2.03, p < 0.0001). Several possible DDIs were identified, including furosemide/Lasix, allopurinol clopidogrel/Plavix, and pantoprazole, which may elevate rhabdomyolysis risk through impaired muscle function and delayed statin metabolism. Finally, nine myopathic genes were identified as possible regulators of statin-induced rhabdomyolysis, including DYSF, DES, PLEC, CAPN3, SCN4A, TNNT1, SDHA, MYH7, and PYGM in primary human muscle cells. Conclusions: Simvastatin was associated with the highest risk of rhabdomyolysis. The risk of rhabdomyolysis was more pronounced in men than women. Several possible DDIs were identified including furosemide/Lasix, allopurinol clopidogrel/Plavix, and pantoprazole.
Science Communication Practices in UNESCO Global Geoparks: A Benchmark Analysis
Authors: Rodrigues Joana , Costa e Silva Elsa , Pereira Diamantino Ínsua
Published: February 2025

UNESCO Global Geoparks address global environmental challenges and advance Sustainable Development Goals. As renowned institutions in geoscience communication, they are instrumental in raising awareness and promoting sustainable practices. However, there has been a notable lack of systematic research in this area. This study aims to fill this gap by benchmarking science communication strategies employed by Geoparks, identifying best practices and critical factors and offering data-driven insights to enhance management and strategic planning. The key findings of this study underscore the necessity for improved engagement and the adoption of more interactive and inclusive communication approaches. The research also reveals a significant disparity between current practices and contemporary trends in science communication.
Arthroscopy for Femoroacetabular Impingement in Athletes Versus Non-Athletes: Systematic Review and Meta-Analysis
Authors: Migliorini Filippo , Maffulli Nicola , Bardazzi Tommaso , Ramasubramanian Swaminathan , Jeyaraman Naveen , Jeyaraman Madhan
Published: February 2025

Background: Femoroacetabular impingement (FAI) is a frequently observed hip condition among young, active individuals—especially athletes—that can result in pain, restricted mobility, and a heightened risk of osteoarthritis. Hip arthroscopy has increasingly become the preferred surgical approach for managing FAI due to its ability to alleviate symptoms and improve function. However, potential differences in outcomes between athletes and non-athletes have not been thoroughly investigated. This systematic review and meta-analysis compared arthroscopic management for FAI in athletes versus non-athletes. The outcomes of interest were patient-reported outcome measures (PROMs) and complications. Methods: PubMed, Web of Science, and Embase were systematically accessed until October 2024. The studies eligible were clinical investigations comparing athletes and non-athletes undergoing hip arthroscopy for FAI with a minimum follow-up of 24 months. The outcomes assessed included the Visual Analogue Scale (VAS), Hip Outcome Score for Activities of Daily Living (HOS-ADL), and the Hip Outcome Score–Sport-Specific Subscale (HOS-SSS). Data on reoperation rates and progression to total hip arthroplasty were also extracted. The ROBINS-I tool was used to assess the risk of bias, and meta-analyses were performed using Review Manager 5.3. Results: Three comparative investigations, comprising 808 patients (165 athletes and 643 non-athletes), met the inclusion criteria. Baseline characteristics were similar across both groups. The analyses demonstrated no statistically significant differences in the PROMs (VAS: p = 0.7; HOS-ADL: p = 0.5; HOS-SSS: p = 0.4), reoperation rates (p = 0.7), or the rate of progression to arthroplasty (p = 0.4) between athletes and non-athletes. Furthermore, meta-analyses of two studies reinforced the absence of significant differences in VAS and HOS-SSS outcomes. Conclusion: Hip arthroscopy for FAI appears to yield equivalent improvements in pain and functional outcomes for both athletes and non-athletes, with comparable complication rates at an approximate two-year follow-up. Despite the limited number of studies and a moderate risk of bias, the findings support the effectiveness of arthroscopic intervention across varying physical activity levels.
Geographic Named Entity Matching and Evaluation Recommendation Using Multi-Objective Tasks: A Study Integrating a Large Language Model (LLM) and Retrieval-Augmented Generation (RAG)
Authors: Zhang Jiajun , Fang Junjie , Zhang Chengkun , Zhang Wei , Ren Huanbing , Xu Liuchang
Published: February 2025

Geographical named entity matching, a crucial step in address encoding, aims to enhance address resolution accuracy through the precise identification and linkage of geographical named entity data. However, existing approaches tend to ignore the spatial information of entities, leading to misclassification. Drawing on the human process of searching for addresses, this study proposes a multi-objective learning model named GNEMM that integrates the semantic and spatial information of geographical named entities. To further mimic the human cognitive process during address search, it incorporates the Retrieval-Augmented Generation (RAG) technique. By integrating newly added external address data with an advanced large language model (LLM) like GPT-4, it achieves precise address evaluation and recommendation. The model was tested using a standard geographical named entity dataset from Shandong Province, focusing on three sub-tasks: element segmentation, matching, and spatial similarity score prediction. The experimental results indicate that the method achieves a geographical named entity matching accuracy of up to 99%, with improvements of 10% and 5% in the segmentation and prediction sub-tasks. GNEMM performs best in address-matching tasks of various scales, and the vectors extracted by GNEMM perform best in the downstream retrieval and matching of various address types, which verifies its applicability in geographical named entity recommendation applications.