bluuebunny/crossref_metadata_2025 huggingface dataset:
Circulating Autoantibodies in Adults with Hashimoto’s Thyroiditis: New Insights from a Single-Center, Cross-Sectional Study
Authors: Tripolino Omar , Mirabelli Maria , Misiti Roberta , Torchia Antonio , Casella Denise , Dragone Francesco , Chiefari Eusebio , Greco Marta , Brunetti Antonio , Foti Daniela P.
Published: November 2024

Background: Hashimoto’s thyroiditis (HT) is a common autoimmune thyroid disorder characterized by elevated anti-thyroid peroxidase (A-TPO) antibodies. HT frequently coexists with other autoimmune conditions, which are marked by organ-specific and non-organ-specific autoantibodies, reflecting a deregulated immune response. However, the burden and clinical significance of these circulating autoantibodies in adult patients with HT remains unclear. Methods: A cross-sectional study was conducted at the University Hospital “R. Dulbecco” in Catanzaro, Italy, from November 2023 to May 2024, involving 200 euthyroid adults. The study population comprised 100 A-TPO-positive HT patients and 100 A-TPO-negative controls, matched for age and sex. Laboratory assessments included thyroid function tests and detection of autoantibodies [e.g., antinuclear antibodies (ANA), anti-parietal cell antibodies (APCA), and anti-neutrophil cytoplasmic antibodies (ANCA)]. Cytokine profiles were also measured using sensitive chemiluminescent multi-array technology. Results: HT patients were predominantly female (77.0%) with a median age of 56 years. Compared to controls, HT patients had higher median thyroid stimulating hormone (TSH) levels (2.215 vs. 1.705 μIU/mL, p = 0.025). Circulating autoantibodies were more prevalent in the HT group, with higher rates of APCA positivity (16.3% vs. 4.1%, p = 0.008) and atypical ANCA positivity (27.3% vs. 10.2%, p = 0.003). This suggests an increased risk for autoimmune gastritis and systemic inflammation. Additionally, HT patients with positive atypical ANCA showed elevated inflammatory cytokines, particularly interleukin-1 alpha (IL-1α), in female patients (p = 0.035). Conclusions: HT is significantly associated with a higher prevalence of circulating autoantibodies, such as APCA and atypical ANCA, which may indicate a heightened risk for autoimmune gastritis and broader autoimmune involvement. Detecting these autoantibodies in HT patients could serve as markers for more severe autoimmune dysfunction. These findings emphasize the need for proactive screening, especially in older patients and those with elevated A-TPO levels. Further research is essential to better understand the clinical implications and develop targeted management strategies for these patients.
Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis
Authors: Lee Kyu-Hong , Lee Ro-Woon , Yun Jae-Sung , Kim Myung-Sub , Choi Hyun-Seok
Published: November 2024

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.
Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?
Authors: Aloufi Areej Saud , Alomrani Mona , Mohtasib Rafat , Altassan Bayan , Bin Rakhis Afaf , Malik Mehreen Anees
Published: November 2024

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM’s potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40–65 who underwent DM with DBT at King Khaled University Hospital from 2020–2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.
Myeloid-Derived Suppressor Cells (MDSCs) and Obesity-Induced Inflammation in Type 2 Diabetes
Authors: Ghemiș Larisa , Goriuc Ancuța , Minea Bogdan , Botnariu Gina Eosefina , Mârțu Maria-Alexandra , Ențuc Melissa , Cioloca Daniel , Foia Liliana Georgeta
Published: November 2024

Type 2 diabetes mellitus is a complex metabolic disorder characterized by insulin resistance and, subsequently, decreased insulin secretion. This condition is closely linked to obesity, a major risk factor that boosts the development of chronic systemic inflammation, which, in turn, is recognized for its crucial role in the onset of insulin resistance. Under conditions of obesity, adipose tissue, particularly visceral fat, becomes an active endocrine organ that releases a wide range of pro-inflammatory mediators, including cytokines, chemokines, and adipokines. These mediators, along with cluster of differentiation (CD) markers, contribute to the maintenance of systemic low-grade inflammation, promote cellular signaling and facilitate the infiltration of inflammatory cells into tissues. Emerging studies have indicated the accumulation of a new cell population in the adipose tissue in these conditions, known as myeloid-derived suppressor cells (MDSCs). These cells possess the ability to suppress the immune system, impacting obesity-related chronic inflammation. Given the limited literature addressing the role of MDSCs in the context of type 2 diabetes, this article aims to explore the complex interaction between inflammation, obesity, and MDSC activity. Identifying and understanding the role of these immature cells is essential not only for improving the management of type 2 diabetes but also for the potential development of targeted therapeutic strategies aimed at both glycemic control and the reduction in associated inflammation.
Energy Consumption Performance of a VTOL UAV In and Out of Ground Effect by Flight Test
Authors: Su Shanfei , Shan Xiaowen , Yu Peng , Wang Hao
Published: November 2024

Current research on ground-effect unmanned aerial vehicles (UAVs) predominantly centers on numerical aerodynamic optimization and stability analysis in the ground effect, leaving a significant gap in the thorough examination of flight performance through flight tests. This study presents the design of a vertical takeoff and landing (VTOL) ground-effect UAV, featuring a vector motor configuration. The control system utilizes a decoupled strategy based on position and attitude, enabling stable altitude control in the low-altitude ground-effect region. Comprehensive flight tests were conducted to evaluate the UAV’s flight stability and energy consumption in the ground-effect region. The results reveal that the ground-effect UAV successfully performed rapid takeoff maneuvers and maintained stable forward flight in the designated ground-effect region. In the span-dominated ground-effect region, a significant 33% reduction in flight current was observed, leading to a corresponding 33% decrease in total power consumption compared to flight conditions outside the ground effect. These findings highlight a substantial improvement in flight performance under the influence of ground effect. The real-time flight data produced by this system provides valuable insights for optimizing the design of VTOL ground-effect UAVs.
A Unified Collision Avoidance Trajectory Planning with Dual Variables for Collaborative Aerial Transportation Systems
Authors: Chai Yi , Liang Xiao , Han Jianda
Published: November 2024

As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs.
Towards a Field-Based Bayesian Evidence Inference from Nested Sampling Data
Authors: Westerkamp Margret , Roth Jakob , Frank Philipp , Handley Will , Enßlin Torsten
Published: November 2024

Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propose to transform the prior volume estimation into a Bayesian inference problem, which allows us to incorporate a smoothness assumption for likelihood–prior–volume relations. As a result, we aim to increase the accuracy of the volume estimates and thus improve the overall log-evidence calculation using NS. The method presented works as a post-processing step for NS and provides posterior samples of the likelihood–prior–volume relation, from which the log-evidence can be calculated. We demonstrate an implementation of the algorithm and compare its results with plain NS on two synthetic datasets for which the underlying evidence is known. We find a significant improvement in accuracy for runs with less than one hundred active samples in NS but a proneness for numerical problems beyond this point.
Advancing Mathematical Epidemiology and Chemical Reaction Network Theory via Synergies Between Them
Authors: Avram Florin , Adenane Rim , Neagu Mircea
Published: November 2024

Our paper reviews some key concepts in chemical reaction network theory and mathematical epidemiology, and examines their intersection, with three goals. The first is to make the case that mathematical epidemiology (ME), and also related sciences like population dynamics, virology, ecology, etc., could benefit by adopting the universal language of essentially non-negative kinetic systems as developed by chemical reaction network (CRN) researchers. In this direction, our investigation of the relations between CRN and ME lead us to propose for the first time a definition of ME models, stated in Open Problem 1. Our second goal is to inform researchers outside ME of the convenient next generation matrix (NGM) approach for studying the stability of boundary points, which do not seem sufficiently well known. Last but not least, we want to help students and researchers who know nothing about either ME or CRN to learn them quickly, by offering them a Mathematica package “bootcamp”, including illustrating notebooks (and certain sections below will contain associated suggested notebooks; however, readers with experience may safely skip the bootcamp). We hope that the files indicated in the titles of various sections will be helpful, though of course improvement is always possible, and we ask the help of the readers for that.
Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers
Authors: Favier Gérard , Rocha Danilo Sousa
Published: November 2024

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work.
PANDA: A Polarized Attention Network for Enhanced Unsupervised Domain Adaptation in Semantic Segmentation
Authors: Kao Chiao-Wen , Chang Wei-Ling , Lee Chun-Chieh , Fan Kuo-Chin
Published: November 2024

Unsupervised domain adaptation (UDA) focuses on transferring knowledge from the labeled source domain to the unlabeled target domain, reducing the costs of manual data labeling. The main challenge in UDA is bridging the substantial feature distribution gap between the source and target domains. To address this, we propose Polarized Attention Network Domain Adaptation (PANDA), a novel approach that leverages Polarized Self-Attention (PSA) to capture the intricate relationships between the source and target domains, effectively mitigating domain discrepancies. PANDA integrates both channel and spatial information, allowing it to capture detailed features and overall structures simultaneously. Our proposed method significantly outperforms current state-of-the-art unsupervised domain adaptation (UDA) techniques for semantic segmentation tasks. Specifically, it achieves a notable improvement in mean intersection over union (mIoU), with a 0.2% increase for the GTA→Cityscapes benchmark and a substantial 1.4% gain for the SYNTHIA→Cityscapes benchmark. As a result, our method attains mIoU scores of 76.1% and 68.7%, respectively, which reflect meaningful advancements in model accuracy and domain adaptation performance.
Double Counterfactual Regret Minimization for Generating Safety-Critical Scenario of Autonomous Driving
Authors: Wang Yong , Sun Pengchao , Shuai Liguo , Zhang Daifeng
Published: November 2024

Developing a high-quality scenario library is crucial for evaluating more reliable autonomous driving systems. A fundamental prerequisite for constructing such a library is the generation of safety-critical scenarios. In this paper, we propose a nested game algorithm to assign trajectories and their time series to multiple background vehicles. This method aimed to generate safety-critical scenarios with varying degrees of interference for the tested vehicle. To ensure the realism of the generated scenarios, we extracted a series of natural trajectories from an existing dataset as the input for the algorithm. We then analyzed multiple types of scenarios generated by this method and evaluated their danger using generalized metrics, such as the Time-to-Collision (TTC) and Minimum Safe Distance Factor (MSDF), which demonstrated the effectiveness of our approach. The experimental results demonstrate that the nested game-based approach could efficiently construct safety-critical scenarios, contributing to the development of high-quality test scenario libraries.
A Multidimensional Financial Data Model for User Interface with Process Mining Systems
Authors: Lopata Audrius , Gudonienė Daina , Butleris Rimantas , Veitaitė Ilona , Rudžionis Vytautas , Gudas Saulius
Published: November 2024

Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their attributes. There are data mining (DM) and process mining (PM) methods for analyzing enterprise operational data and identifying deficiencies in business process management. There is a need to find new user experience (UX)-driven methods for user interface with the specification of DM and PM tools on the level of business process management concepts. The paper presents the UX design-based approach to designing the user interface (UI) of process mining and data mining systems and is based on a conceptual semantic model named financial data space (FDS). The peculiarity of FDS is that it can include the characteristics of financial data and other UX-related characteristics (events, environmental and internal changes, business location) that may have an impact on changes in the values of financial objects (FO). The presented multidimensional financial data model helps increase the possibility of uncovering management weaknesses by identifying anomalies in large amounts of financial data. The prototypes of components of the financial data analysis system are described and developed using the process mining tool. The presented method of a multidimensional representation of financial data and transformation into a PM project is a user-friendly solution that allows to increase the analytical capabilities of the auditor’s work with large amounts of data, providing a more flexible view of the financial indicators of the company’s activity.
Reducing Interface Resistance in Semiconductor System Through the Integration of Graphene
Authors: Hong Tae Yeong , Park Jong Kyung , Hong Seul Ki
Published: November 2024

In the quest to improve overall semiconductor system performance as scaling down continues, reducing resistance in interconnects and bonding interfaces has become a critical focus. This study explores the use of graphene, a highly conductive 2D material, as an interfacial layer between metal and dielectric layers to enhance adhesion and stability while reducing contact resistance. Graphene’s excellent adhesion properties make it a promising candidate for improving bonding strength at metal–dielectric interfaces. We investigated the following two approaches: direct growth of graphene via chemical vapor deposition and the transfer of pre-grown graphene onto the metal surface. The contact resistance characteristics of both methods were analyzed, with results indicating that graphene effectively enhances the bonding interface while significantly lowering contact resistance. These findings suggest that incorporating graphene as an interfacial material could lead to improved performance in advanced semiconductor devices, particularly in applications like hybrid bonding and interconnect technology.
SafeMD: Ownership-Based Safe Memory Deallocation for C Programs
Authors: Yin Xiaohua , Huang Zhiqiu , Kan Shuanglong , Shen Guohua
Published: November 2024

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system that enforces the automatic deallocation of unused resources without using a garbage collector. In light of Rust’s promise of safety, a natural question arises about the possible benefits of exploiting ownership to ensure the memory safety of C programs. In our previous work, we developed a formal ownership checker to verify whether a C program satisfies exclusive ownership constraints. In this paper, we further propose an ownership-based safe memory deallocation approach, named SafeMD, to fix memory leaks in the C programs that satisfy exclusive ownership defined in the prior formal ownership checker. Benefiting from the C programs satisfying exclusive ownership, SafeMD obviates alias and inter-procedural analysis. Also, the patches generated by SafeMD make the input C programs still satisfy exclusive ownership. Usually, a C program that satisfies the exclusive ownership constraints is safer than its normal version. Our evaluation shows that SafeMD is effective in fixing memory leaks of C programs that satisfy exclusive ownership.
The Sharing Energy Storage Mechanism for Demand Side Energy Communities
Authors: Bala Uda , Li Wei , Wang Wenguo , Gong Yuying , Su Yaheng , Liu Yingshu , Zhang Yi , Wang Wei
Published: November 2024

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side.
Coordinated Optimal Operation Method for Snow-Shaped Distribution Networks Based on Game Theory
Authors: Wang Zhe , Zhang Zhang , Luo Fengzhang , Mu Ranfeng , Wu Xuan , Zhang Xuefei , Duan Jiali
Published: November 2024

The snow-shaped distribution network is developed and upgraded from the traditional single-loop network and double-loop network structure. It is composed of three or four groups of single-loop networks in pairs that form a feeder cluster in the form of a ring network. Based on this topology, the coordinated optimization operation problem is studied. First, the game elements are analyzed, and then a cooperative game-based, coordinated optimization operation model is established by taking the minimum network loss and voltage deviation of the system as the utility function. Then, the alternating direction multiplier method (ADMM) is employed in Cplex solver to solve the established optimization model. Finally, a numerical case is given to verify the effectiveness of the proposed model and method. The results show that, by using the complementary difference relationship of source–charge characteristics between different regions in the snow-shaped distribution networks, the cooperative game model can realize the energy interconnection among different regions to improve the operation level.
Simulation Study of Single-Adsorber Heat Pump Cycle with Heat Recovery Through Stratified Storage in Both Adsorber and Evaporator/Condenser Loops
Authors: Sadeghlu Alireza , Schmidt Ferdinand P.
Published: November 2024

A modified single-adsorber cycle for a gas-fired adsorption heat pump (GAHP) is described and analyzed through dynamic simulation with Modelica. The adsorption modules are based on the SAPO-34 / water working pair with parameters taken from a recent R&D project. A stratified storage integrated into the primary heat pump loop is employed for heat recovery of the adsorber as well as the EC heat exchanger (functioning both as an evaporator and condenser). A heating system of a stock multifamily residential building in Germany is assumed as the load to be covered by the heat pump. Representative load points according to VDI 4650-2 and DIN EN 12309-6 rules are considered. The hydraulic system design and implications of the storage integration on degrees of freedom during operation are discussed. The results show that, for a well-stratified storage, similar COP values to a dual-adsorber heat pump cycle based on the same adsorption module type are achievable. Possible additional benefits of the storage are discussed.
Advanced Technologies in HVAC Equipment and Thermal Environment for Building
Authors: Yang Li
Published: November 2024

With the development of new technologies, HVAC technology research is no longer limited to temperature regulation [...]
Stability Analysis and Controller Optimization of MMC in Standalone Mode
Authors: Liu Xingyu , Song Shuguang , Ma Wenzhong , Wang Yusheng
Published: November 2024

The modular multilevel converter (MMC) plays an important role in large-scale renewable energy integration and transmission, and it can also operate in standalone mode, powering AC passive loads. This paper focuses on the impact of load variation on the stability of the MMC. First, the impact of load variation on the MMC transfer function is analyzed in detail using the harmonic state-space (HSS) modeling method. Then, by means of the impedance-based stability analysis method, it is found that the MMC tends to become unstable with the increase in inductive loads. If the controller is not well-designed, the system may fail when loads change. Therefore, the worst-case design is used to guarantee the overall system’s stability under all load conditions. The impact of traditional proportional resonant (PR) controller parameters on the system’s stability is analyzed, revealing that the stability margin and control performance of the controller are limited. Thus, an improved controller structure with an additional series of compensators is proposed. Extensive simulation results in MATLAB/Simulink R2024a verify the analysis of this work and the effectiveness of the proposed controller.
Energy Poverty Alleviation in the Era of Energy Transition—Case Study of Poland and Sweden
Authors: Janikowska Olga , Generowicz-Caba Natalia , Kulczycka Joanna
Published: November 2024

The energy transition, aimed at significantly reducing greenhouse gas emissions to combat climate change, presents both opportunities and challenges in addressing energy poverty. This article explores the differing approaches of Poland and Sweden in energy poverty alleviation within the context of this transition. Poland, with its historical dependence on coal, faces considerable obstacles as it seeks to shift towards cleaner energy sources while minimizing the impact on vulnerable populations. Conversely, Sweden, supported by its advanced energy infrastructure and strong welfare systems, has implemented effective strategies that have largely mitigated energy poverty, though challenges persist for low-income households, especially in rural areas. The article delves into the definitions, drivers, and alleviation strategies of energy poverty in both countries. Through a comprehensive literature review and the analysis of key initiatives such as Poland’s Clean Air Program and Sweden’s Warm Rent scheme, the study underscores the potential for policy interventions to address energy poverty. It concludes with recommendations on how both countries can further reduce energy poverty, highlighting the critical role of energy efficiency, social support systems, and the integration of renewable energy in achieving a fair and equitable energy transition.
Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering
Authors: Liu Yan , Chen Jun , Yong Jun , Yang Cheng , Yan Liqin , Zheng Yanping
Published: November 2024

To address the limitations in the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, stemming from model accuracy, particle degradation, and insufficient diversity in the particle filter (PF) algorithm, this paper proposes a battery RUL prediction method utilizing a randomly perturbed unscented particle filter (RP-UPF) algorithm, based on the constructed battery capacity degradation model. The method utilizes evaluation metrics adjusted R-squared (Radj2) and the Akaike Information Criterion (AIC) to select the battery capacity decline model C5 with a higher goodness of fit. The initial values for constructing the C5 model are obtained using the relevance vector machine (RVM) and nonlinear least squares methods. Based on the constructed battery capacity decline model C5, the RP-UPF algorithm is employed to estimate the posterior parameters and iteratively approach the true battery capacity decline curve, thereby predicting the battery’s RUL. The research results indicate that, using battery B0005 as an example and starting the prediction from the 50th cycle, the RUL prediction results obtained with the RP-UPF algorithm demonstrate reductions in absolute error, relative error, and probability density function (PDF) width of 2%, 2.71%, and 10%, respectively, compared to the PF algorithm. Similar conclusions were drawn for batteries B0006 and B0018. Under the constructed battery capacity degradation model C5, the RP-UPF algorithm shows higher prediction accuracy for battery RUL and a narrower PDF range compared to the PF algorithm. This approach effectively addresses the issue of particle weight degradation in the PF algorithm, providing a more valuable reference for battery RUL prediction.
The Effect of Channel Surface Roughness on Two–Phase Flow Patterns: A Review
Authors: Sikora Małgorzata , Bohdal Tadeusz , Tandecka Katarzyna , Kacalak Wojciech , Mathia Thomas G.
Published: November 2024

This review article highlights the critical impact of surface roughness in modifying the structure of two-phase flow within mini- and microchannels, particularly in processes such as boiling and condensation. Channel surface roughness enhances flow resistance, affects the distribution of vapor bubbles, and enhances heat transfer by providing additional nucleation sites. Several experiments have shown that while increased surface roughness enhances the efficiency of heat transfer, increased flow resistance may hurt system performance. This is so because too high a surface roughness negatively impacts flow resistance, a factor of importance in the optimization for a balance between heat transfer and flow resistance, especially in high-performance compact heat exchangers. Furthermore, the review identifies that higher-degree measurement and characterization techniques of the surface roughness are increasingly required, as traditional 2D parameters may not fully represent the actual physics of complex surface interactions in two-phase flow systems. Consequently, the article calls for further research that can examine the exact relationship between roughness, flow structure, and thermal performance with the aim of improving design strategies for future heat exchanger technologies.
Enhancing Thermal–Hydraulic Performance in Nuclear Reactor Subchannels with Al2O3 Nanofluids: A CFD Analysis
Authors: Sardar Mohammad A. I. , Rahman Mushfiqur , Rubini Philip
Published: November 2024

In this paper, the performance of aluminum-based nanofluids with a possible application in pressurized water reactors is numerically investigated. A 605 mm long 4-rod array square (2 × 2) subchannel geometry with a uniform heat flux of 50 kW/m2 has been used in CFD simulation. This analysis has been carried out using the RNG k-epsilon turbulence model with standard wall function in ANSYS FLUENT 2022R1. The impact of various flow conditions and nanofluid concentrations has been examined. The effects of variable velocities on nanofluid performance have been studied using different Reynolds numbers of 20,000, 40,000, 60,000, and 80,000. The analysis was conducted with Al2O3/water nanofluid concentrations of 1%, 2%, 3%, and 4%. A comparison of the Nusselt number based on five different correlations was conducted, and deviations from each correlation were then presented. The homogeneous single-phase mixer approach has been adopted to model nanofluid characteristics. The result shows a gradual enhancement in the heat transfer coefficient with increasing volume concentrations and Reynolds numbers. A maximum heat transfer coefficient has been calculated for nanofluid at maximum volume concentrations (ϕ = 4%) and highest velocities (Re = 80,000). Compared to the base fluid, heat transfer was enhanced by a factor of 1.09 using 4% Al2O3. The Nusselt number was calculated with a minimal error of 3.62% when compared to the Presser correlation and the maximum deviation has been found from the Dittus–Boelter correlation (13.77%). Overall, the findings suggest that aluminum-based nanofluids could offer enhanced heat transfer capabilities in pressurized water reactors.
Resilience and Resistance of Vegetation in Response to Droughts in a Subtropical Humid Region Dominated by Karst
Authors: Sun Qijia , Zhou Qiuwen , Luo Yingzhong , Shi Chunmao , Hu Yundi
Published: November 2024

The resilience and resistance of vegetation are important indicators of the vegetation’s response to droughts. Owing to the uniqueness of the environment in humid karst areas, results from studies on other climatic zones may not necessarily present the status of vegetation resilience and resistance in humid karst areas. Herein, We calculated vegetation resilience and resistance by autoregressive modeling using Enhanced Vegetation Index (EVI), Total Water Storage Anomaly (TWSA), temperature (TA), precipitation (PRE) data, An analysis of variance (ANOVA) was then conducted to compare the differences in resilience and resistance of different vegetation types in the study area, as well as the differences in resilience and resistance of vegetation in different sub-geomorphic zones. Finally, natural factors affecting vegetation resilience and resistance were quantified using partial least squares structural equation modeling (PLS-SEM). The results demonstrate the following points. First, vegetation resilience, total-water-storage anomaly resistance, and vegetation resistance against precipitation anomalies were lower in karst areas of the study area than in non-karst areas of the study area (except for vegetation resistance against temperature anomalies). Second, vegetation resilience was the lowest in some sub-geomorphic zones within karst areas, and it was still comparable to that in semiarid areas. Third, precipitation and temperature were important factors that affected the resilience and resistance of vegetation in karst areas, and the geochemical indicators (CaO, MgO, and SiO2) of soil parent material were major factors that affected the resistance and resilience of vegetation in non-karst areas. In summary, this study was undertaken to reveal the natural characteristics of vegetation resilience and resistance in humid karst regions. Our findings complement and expand the existing body of knowledge on vegetation resilience and resistance in other ecologically fragile zones limited by moisture.
Effect of Inoculation with Rhizosphere Phosphate-Solubilizing Bacteria on the Growth and Physiological Characteristics of Parashorea chinensis
Authors: Hu Zhongheng , Wei Shuang , Li Wannian , Wu Tong , Ullah Saif , Yang Mei
Published: November 2024

Given the challenges of slow growth and low survival rates in the early stages of Parashorea chinensis cultivation, identifying sustainable methods to enhance seedling performance is critical for successful reforestation and conservation efforts. This study aimed to address these by investigating the growth-promoting effects of phosphate-solubilizing bacteria (PSB). One-year-old seedlings of P. chinesis were inoculated with PSB strains isolated from the rhizosphere soil of Parashorea chinensis H. Wang plantations Y3, W5, H8, and a mixed strain (Mix), with inoculated seedling as a control (CK). The effects of inoculation on seedling growth, photosynthetic physiology, plant nutrient status, and physiological indicators were comprehensively evaluated. Results showed that PSB inoculation increased seedling height and basal diameter growth of P. chinensis, with an increase of 1.56 cm and 0.53 mm compared to CK, respectively, though the differences were not significant. The Mix treatment significantly improved photosynthesis, with increases in net photosynthetic rate (106.3%), transpiration rate (93.89%), and intercellular CO2 concentration (75.51%) compared to CK. Nutrient levels including total nitrogen, total phosphorus, and total potassium were significantly increased by 15.98%, 25.54%, and 32.12%, respectively, in the Mix treatments compared to CK. Moreover, stress resistance also improved, with higher proline content, soluble sugar, and soluble protein levels. Antioxidant enzyme activities (SOD, CAT, and POD) were increased by 9.83%, 23.66%, and 292.32%, respectively, while MDA content was significantly reduced by 69.01%. The mixed strain treatment also significantly increased acid phosphatase activity by 111.88%. In conclusion, PSB inoculation, particularly with the mixed strain, promoted growth and nutrient uptake photosynthetic efficiency and stress resistance in P. chinensis seedlings, offering a promising biotechnological solution for improving seedling performance.
The Properties of Pectin Extracted from the Residues of Vinegar-Fermented Apple and Apple Pomace
Authors: Muslu Can Asli , Metin Yildirim Rusen , Karadag Ayse
Published: November 2024

In this study, both apple slices and apple pomace, the by-product of apple juice processing, were subjected to vinegar fermentation. The pectins extracted from the solid residue of vinegar-fermented apple slices (FAP) and apple pomaces (FAPP) were compared to the pectin extracted from non-fermented apple pomace (AP). All samples were classified as high-methoxyl pectins, and vinegar fermentation increased pectin extraction yield. FAP, which has a lower degree of methylation, also exhibited lower thermal stability. The changes in the pectin structure were dependent on both fermentation and the state of the raw material used to produce the vinegar. Compared to AP, the proportion of homogalacturonans (HGs) increased, and the proportion of rhamnogalacturonan I (RG-I) decreased in FAP, providing higher linearity, whereas in FAPP, the RG-I region became more dominant with reduced linearity. The molecular weight distribution of samples showed that pectin from vinegar-fermented sources changed the average molecular weights and mass fraction ratios of two peaks (1255 and 340 kDa) eluted from AP. In FAP, the mass fraction of the first peak (1294 kDa) increased from 35 to 89%, whereas in FAPP, the mass fraction of the second peak (478 kDa) increased to 91%. FAPP showed greater viscosity and a more noticeable shear-thinning behavior. G′ and G″ in FAPP were also higher than those of AP and FAP at the same concentrations (5%, 7%, and 10% w/v). This study found that applying vinegar fermentation to apple slices and apple pomaces altered the structural and rheological properties of the extracted pectins. FAP and FAPP could be suitable food additives when certain properties are required.
6G Use Cases and Scenarios: A Comparison Analysis Between ITU and Other Initiatives
Authors: Vizzarri Alessandro , Mazzenga Franco
Published: November 2024

In the next decade, the amount of network traffic is estimated to reach Zettabytes. The future International Mobile Telecommunications-2030 (IMT-2030) standard of mobile networks, known as 6G, introduces an important paradigm shift in the context of wireless communication systems thanks to capabilities such as low latency and high data rates. Official documents on 6G standardization have been released by the International Telecommunication Union (ITU). However, other visions and use cases of 6G have been proposed by industrial stakeholders and research institutions, thus generating a multitude of use cases and usage scenarios that are only apparently different from each other. This paper would contribute to providing a holistic vision of the 6G-enabled use cases and potentially impacted vertical market sectors. The differences and similarities between what has been proposed by ITU and other initiatives are identified through a comparison based on the technological characterization of use cases and of the considered vertical market sectors. The main findings presented in this paper demonstrate that many of the use cases proposed by ITU and by the other initiatives are almost identical in many cases.
Experimental and Statistical Investigations for Tensile Properties of Hemp Fibers
Authors: Sadeghi Peyman , Cao Quang , Abouzeid Ragab , Shayan Mohammad , Koo Meensung , Wu Qinglin
Published: November 2024

This study investigated the tensile behaviors of hemp fiber bundles and examined how properties including tensile strength and Young’s modulus vary with the bundle diameter. Hemp fibers were extracted, degummed, and separated into bundles of different diameters ranging from less than 50 μm to over 150 μm. Tensile tests were conducted on these fiber bundles using a rheometer-based tensile testing machine. The results showed that hemp fibers exhibited a tensile strength of 97.33 MPa and a Young’s modulus of 3.77 GPa at a 50% survival probability. However, the scale parameters for breaking stress and Young’s modulus were determined to be 620.57 MPa and 29.88 GPa, respectively. As the fiber bundle diameter increased, the tensile strength decreased significantly. This was attributed to the higher probability of defects and irregularities acting as weakness points in larger fiber bundles. In contrast, Young’s modulus (stiffness) increased with increasing bundle diameter, likely due to improved fiber–fiber interactions. To further understand the variability and reliability of the tensile properties, statistical models were developed. The Weibull distribution analysis was applied, revealing critical insights into the variability of diameter, stress at break, Young’s modulus, and strain at break. The Weibull parameters provided a comprehensive understanding of the fibers’ mechanical reliability. Additionally, the Griffith model was employed to predict the strength and Young’s modulus based on fiber diameters, supporting the observation that thinner fibers generally exhibited higher tensile strength due to fewer defects. Overall, this work highlights the importance of understanding structure–property relationships in natural fibers like hemp for optimizing their performance in composites.