bluuebunny/crossref_metadata_2025 huggingface dataset:
Ischemic Postconditioning Regulates New Cell Death Mechanisms in Stroke: Disulfidptosis
Authors: Liu Shanpeng , Wu Qike , Xu Can , Wang Liping , Wang Jialing , Liu Cuiying , Zhao Heng
Published: October 2024

Background and Objective: Stroke poses a critical health issue without effective neuroprotection. We explore ischemic postconditioning’s (IPostC) potential to mitigate stroke-induced brain injury, focusing on its interaction with disulfidptosis, a novel cell death pathway marked by protein disulfide accumulation. We aim to clarify IPostC’s protective mechanisms against stroke through gene sequencing and experimental analysis in mice. Methods: Through our initial investigation, we identified 27 disulfidptosis-related genes (DRGs) and uncovered their interactions. Additionally, differential gene analysis revealed 11 potential candidate genes that are linked to disulfidptosis, stroke, and IPostC. Our comprehensive study employed various analytical approaches, including machine learning, functional enrichment analysis, immune analysis, drug sensitivity analysis, and qPCR experiments, to gain insights into the molecular mechanisms underlying these processes. Results: Our study identified and expanded the list of disulfidptosis-related genes (DRGs) critical to stroke, revealing key genes and their interactions. Through bioinformatics analyses, including PCA, UMAP, and differential gene expression, we were able to differentiate the effects of stroke from those of postconditioning, identifying Peroxiredoxin 1 (PRDX1) as a key gene of interest. GSEA highlighted PRDX1’s involvement in protective pathways against ischemic damage, while its correlations with various proteins suggest a broad impact on stroke pathology. Constructing a ceRNA network and analyzing drug sensitivities, we explored PRDX1’s regulatory mechanisms, proposing novel therapeutic avenues. Additionally, our immune infiltration analysis linked PRDX1 to key immune cells, underscoring its dual role in stroke progression and recovery. PRDX1 is identified as a key target in ischemic stroke based on colocalization analysis, which revealed that PRDX1 and ischemic stroke share the causal variant rs17522918. The causal relationship between PRDX1-related methylation sites (cg02631906 and cg08483560) and the risk of ischemic stroke further validates PRDX1 as a crucial target. Conclusions: These results suggest that the DRGs are interconnected with various cell death pathways and immune processes, potentially contributing to IPostC regulating cell death mechanisms in stroke.
The Dual Burden: Exploring Cardiovascular Complications in Chronic Kidney Disease
Authors: Caturano Alfredo , Galiero Raffaele , Rocco Maria , Tagliaferri Giuseppina , Piacevole Alessia , Nilo Davide , Di Lorenzo Giovanni , Sardu Celestino , Russo Vincenzo , Vetrano Erica , Monda Marcellino , Marfella Raffaele , Rinaldi Luca , Sasso Ferdinando Carlo
Published: October 2024

Chronic kidney disease (CKD) represents a significant global health challenge, affecting millions of individuals and leading to substantial morbidity and mortality. This review aims to explore the epidemiology, cardiovascular complications, and management strategies associated with CKD, emphasizing the importance of preventing cardiovascular disease and early intervention. CKD is primarily driven by conditions such as diabetes mellitus, hypertension, and cardiovascular diseases, which often coexist and exacerbate renal impairment. Effective management requires a multifaceted approach, including lifestyle modifications, pharmacological interventions, and regular monitoring. Dietary changes, such as sodium restriction and a controlled intake of phosphorus and potassium, play a vital role in preserving renal function. Pharmacological therapies, particularly angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), and emerging agents like SGLT2 inhibitors, have shown efficacy in slowing disease progression and improving patient outcomes. Furthermore, patients undergoing dialysis face increased cardiovascular risk, necessitating comprehensive management strategies to address both renal and cardiac health. As the landscape of CKD treatment evolves, ongoing research into novel therapeutic options and personalized medical approaches are essential. This review underscores the urgent need for awareness, education, and effective preventive measures to mitigate the burden of CKD and enhance the quality of life for affected individuals.
Bridging Employees’ Perceptions of Corporate Social Responsibility, Sense-Making for Meaningfulness, and Work Engagement for Successful Self-Regulation
Authors: Wang Zheni , Carroll Steve , Wang Eric H.
Published: October 2024

In response to calls for research on the psychological mechanisms, such as perceptions and attitudes toward corporate citizenship, in promoting positive outcomes at work, this research presents a novel approach by empirically testing a calling conditioned path model from P perception of corporate CSR (P-CSR) to work engagement via meaningfulness under the theoretical framework of self-determination theory. Survey data collected from 224 corporate employees in the US were tested using the PROCESS plugin (version 4.3) in SPSS. The regression results supported the positive direct and indirect paths from employees’ P-CSR to meaningfulness and work engagement but not the conditioning effect of calling work orientation. This study’s unique findings, limitations, future research, and implications are discussed, expanding micro-CSR research and unboxing the management assumptions of employees as purposeful autonomous agents seeking consistent interpretations and authentic perceptions of organizational CSR activities during their sense-making processes. Non-confirming of the calling conditioning the path model shed light on it being a dynamic multi-dimensional and multi-level construct to be further researched.
Optimized bp Neural Network Based on Improved Dung Beetle Optimization Algorithm to Predict High-Performance Concrete Compressive Strength
Authors: Wang Zhipeng , Cai Jie , Liu Xiaoxiao , Zou Zikang
Published: October 2024

In modern architecture, the structural safety of buildings largely depends on the compressive strength of high-performance concrete (HPC), which is determined by the complex nonlinear relationships between its components. In order to more accurately forecast HPC’s compressive strength, this paper proposes a prediction model based on an improved dung beetle optimization algorithm (OTDBO)-optimized backpropagation neural network (BPNN). Extreme Gradient Boosting (XGBoost) is employed to determine the inputs for the BPNN, enhancing the computational efficiency under high-dimensional data feature conditions. To address the issues of local optima entrapment and slow convergence in the dung beetle optimization algorithm (DBO), four improvements were made to enhance its performance. In the initial population generation stage, the optimal Latin hypercube method was used to increase the population diversity. In the rolling stage, the osprey optimization algorithm’s global exploration strategy was introduced to improve the global search capability. The variable spiral search strategy was employed in the reproduction stage, and an adaptive t-distribution perturbation strategy was combined in the foraging stage to enhance the algorithm’s adaptability and search efficiency. The improved dung beetle optimization algorithm (OTDBO) outperformed other algorithms in performance tests on the CEC2017 benchmark functions. In terms of predicting the compressive strength of HPC, the XG-OTDBO-BP model developed in this study outperformed models optimized by other algorithms in terms of fitting outcomes and prediction accuracy. These findings support the XG-OTDBO-BP model’s superiority in the compressive strength of HPC prediction.
Seismic Performance of Precast Concrete Bridge Piers with Built-In Steel Tube Connection Key
Authors: Zhang Renwei , Zheng Liqun , Zhong Hai , Qin Shuangshuang
Published: October 2024

Investigating the seismic behavior of precast concrete bridge piers is crucial in the design process due to the complex stress distribution in the connecting components. To demonstrate the seismic behavior of precast concrete bridge piers with hybrid joint connections, three bridge piers were designed with a scaling ratio of 1:8 and then tested under low cyclic loading conditions. The tests involved varying shapes of steel tube connection keys as parameters. This study involved examining failure modes and crack development, as well as analyzing the hysteretic performance, deformation capacity, energy dissipation, and stiffness degradation of the specimens. Furthermore, a finite element model was developed using ABAQUS, and the validity of the modeling approach suggested in this study was confirmed through tests. The results indicate that the precast piers exhibit reduced concrete damage at the joints. The enhanced strength of the joints is attributed to the incorporation of steel tube connection keys. The circular steel tube connection key integrated into the precast bridge pier offers a superior bearing capacity, energy dissipation, and stiffness degradation compared to the cross-shaped steel tube connection key. The presence of the built-in circular steel tube connection key in the precast bridge pier suggests that it complies with the seismic structural measures and is consistent with the design principle of “strong joint and weak member”.
Multi-Source Monitoring and Numerical Simulation Deformation on Highway Steep Slopes Under Rainfall Effects
Authors: Li Peijun , Li Qing , Feng Qingshan , Huang Zhendong , Gan Xun , Ding Haibin , Xu Changjie
Published: October 2024

Rainfall is one of the most important factors affecting slope stability. This study employed multi-source monitoring devices to observe the slope displacements in real time under rainfall infiltration and performed numerical simulations to investigate the effects of different rainfall conditions and anti-slip pile configurations on slope stability. Specifically, multi-source monitoring operations were conducted on the high and steep slopes along the Yunmao Expressway. Real-time data on slope deformation, rainfall, and displacement at the tops of anti-slip piles were collected and analyzed, and numerical simulations were conducted using Geo Studio finite-element software. The findings indicated that abrupt deformation of slopes occurs once a threshold rainfall amount is surpassed and sustained over a specific duration. Slope displacement decreased with increasing slope depth above the potential slip fracture surface, with a more rapid reduction in deformation rates observed in slopes reinforced with anti-slip piles. For equivalent rainfall amounts, short-duration, intense rainfalls led to a rapid decrease in the slope safety factor, which also recovered rapidly once the rainfall ceased, in contrast to long-duration, mild rainfalls. The presence and location of anti-slip piles significantly influenced slope stability; therefore, project implementation should carefully consider factors such as cost and duration for optimal decision making.
A Systematic Review of the Digital Twin Technology in Buildings, Landscape and Urban Environment from 2018 to 2024
Authors: Liu Wenhui , Lv Yihan , Wang Qian , Sun Bo , Han Dongchen
Published: October 2024

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry has been slow to undergo digital transformation. The paper utilizes the Systematic Literature Review (SLR) approach to study a total of 842 papers on the application of DT in buildings, landscapes, and urban environments (BLU) from 2018 to 2024. Based on the research results, suggestions have been made for future research and practical directions. Meanwhile, it provides assistance to BLU’s designers, constructors, managers, and policymakers in establishing their understanding of the digital transformation of the AEC industry. The existing relevant research can be mainly divided into three categories: case study, framework study, and technology study. Compared with the buildings and urban environment industries, the number and depth of research in the landscape industry are relatively low. Through in-depth analysis of BLU projects, three research trends in the future are determined: (1) research and application of DT framework in the design and planning stage; (2) development of design tools and basic theory based on DT model; (3) application and exploration of DT technology in the landscape industry.
Study of the Structure and Properties of Concrete Modified with Nanofibrils and Nanospheres
Authors: Beskopylny Alexey N. , Stel’makh Sergey A. , Shcherban’ Evgenii M. , Varavka Valery , Meskhi Besarion , Mailyan Levon R. , Kovtun Maksim , Kurlovich Sergei , El’shaeva Diana , Chernil’nik Andrei
Published: October 2024

The application of modifying nanoadditives in the technology of cement composites is currently a relevant and widely researched topic in global materials science. The purpose of this study was to investigate new nanoadditives—nanofibrils made from synthesized wollastonite (NF) and nanospheres from corundum (NS)—produced by LLC NPK Nanosystems (Rostov-on-Don, Russia) as a modifying additive. During the experimental investigations, the mechanical properties of cement pastes and concrete were examined. This included an analysis of the density, compressive and bending strength, as well as water absorption of concrete that had been modified with NF and NS additives. X-ray phase and microstructural analyses of concrete were performed. It was established that modification of cement composites with NF and NS additives had a beneficial effect on their properties, and the optimal amount for both types of additives was 0.3% by binder weight. The highest recorded enhancements in compressive and flexural strength of concrete with 0.3% NF were 7.22% and 7.04%, respectively, accompanied by a decrease in water absorption by 4.70%. When modifying concrete with 0.3% NS, the increases in compressive and flexural strength were 2.71% and 2.48%, and water absorption decreased by 1.96%. Modification of concrete with NF and NS additives did not have a significant effect on the change in concrete density, which was no more than 1%. Based on the results of phase analysis, it was established that concrete with NF and NS additives were characterized by the presence of five main phases: quartz, portlandite, calcite, larnite, and olivine-Ca. It was found that compositions with 0.3% NF and NS differed from the control composition by the presence of such a phase as olivine-Ca. Microstructural analysis confirmed the effectiveness of NF and NS additives. The microstructure of the modified concretes was distinguished by the extensive occurrence of clusters composed of calcium silicate hydrate zones. The conducted studies prove the possibility of using NF and NS as modifying nanoadditives in the technology of cement composites. The addition of nanofibrils from synthesized wollastonite is the most effective and promising and is recommended for use in real construction practice.
Mechanical Response of Hot-Mixed Epoxy Asphalt Concrete Steel Deck Pavement Under Thermal and Load
Authors: Xu Xuan , Zhang Hui , Cui Lei , Luo Ruilin , Li Guoqing , Li Min , Gao Peiwei
Published: October 2024

In recent years, fatigue cracking in orthotropic steel bridge deck pavements has become a significant concern, so the investigation of the mechanical response of the pavement layer has become a central focus in pavement structure design. This experiment subjected a full-scale specimen to a constant amplitude dynamic load of 60 kN to 300 kN over 2 million cycles. Throughout the testing, a circulating water bath elevated the temperature of the pavement layer from 15 °C to 50 °C. Key locations were monitored for strain and deflection data, facilitating an investigation into the mechanical response of the epoxy asphalt pavement system under the effects of temperature and load. The results indicate that the maximum transverse strain at the bottom of the steel deck occurs at the U-rib weld aligned with the load center, reaching 190% of the initial loading strain. Meanwhile, the maximum transverse strain on the pavement surface is observed at the U-rib weld adjacent to the loaded area, measuring 167% of the initial strain. The maximum longitudinal strain is lower than the maximum transverse strain. In the load zone, the longitudinal strain between the U-ribs exceeds that at the U-rib weld. Both transverse strain and relative deflection increase as the load intensifies. The relationship between transverse strain and applied load is characterized by an exponential function, while deflection exhibits a cubic relationship with the applied load. Elevated temperatures also contribute to increased transverse strains at both the bottom of the steel deck and the pavement surface, following an exponential trend. Relative deflection is primarily influenced by the applied load and remains relatively unaffected by temperature variations. When accounting for the coupling of load and temperature, the maximum transverse strains at both the bottom of the steel deck and the pavement surface can be modeled as an exponential function of the independent variables: load and temperature.
Epigenetics of Conjunctival Melanoma: Current Knowledge and Future Directions
Authors: Flick Kaylea M. , Demirci Hakan , Demirci F. Yesim
Published: October 2024

The purpose of this article is to provide a literature review of the epigenetic understanding of conjunctival melanoma (CM), with a primary focus on current gaps in knowledge and future directions in research. CM is a rare aggressive cancer that predominantly affects older adults. Local recurrences and distant metastases commonly occur in CM patients; however, their prediction and management remain challenging. Hence, there is currently an unmet need for useful biomarkers and more effective treatments to improve the clinical outcomes of these patients. Like other cancers, CM occurrence and prognosis are believed to be influenced by multiple genetic and epigenetic factors that contribute to tumor development/progression/recurrence/spread, immune evasion, and primary/acquired resistance to therapies. Epigenetic alterations may involve changes in chromatin conformation/accessibility, post-translational histone modifications or the use of histone variants, changes in DNA methylation, alterations in levels/functions of short (small) or long non-coding RNAs (ncRNAs), or RNA modifications. While recent years have witnessed a rapid increase in available epigenetic technologies and epigenetic modulation-based treatment options, which has enabled the development/implementation of various epi-drugs in the cancer field, the epigenetic understanding of CM remains limited due to a relatively small number of epigenetic studies published to date. These studies primarily investigated DNA methylation, ncRNA (e.g., miRNA or circRNA) expression, or RNA methylation. While these initial epigenetic investigations have revealed some potential biomarkers and/or therapeutic targets, they had various limitations, and their findings warrant replication in independent and larger studies/samples. In summary, an in-depth understanding of CM epigenetics remains largely incomplete but essential for advancing our molecular knowledge and improving clinical management/outcomes of this aggressive disease.
Expression Profiling Identified TRPM7 and HER2 as Potential Targets for the Combined Treatment of Cancer Cells
Authors: Egawa Miyuki , Schmücker Eva , Grimm Christian , Gudermann Thomas , Chubanov Vladimir
Published: October 2024

TRPM7 is a divalent cation-permeable channel that is highly active in cancer cells. The pharmacological inhibitors of TRPM7 have been shown to suppress the proliferation of tumor cells, highlighting TRPM7 as a new anticancer drug target. However, the potential benefit of combining TRPM7 inhibitors with conventional anticancer therapies remains unexplored. Here, we used genome-wide transcriptome profiling of human leukemia HAP1 cells to examine cellular responses caused by the application of NS8593, the potent inhibitor of the TRPM7 channel, in comparison with two independent knockout mutations in the TRPM7 gene introduced by the CRISPR/Cas9 approach. This analysis revealed that TRPM7 regulates the expression levels of several transcripts, including HER2 (ERBB2). Consequently, we examined the TRPM7/HER2 axis in several non-hematopoietic cells to show that TRPM7 affects the expression of HER2 protein in a Zn2+-dependent fashion. Moreover, we found that co-administration of pharmacological inhibitors of HER2 and TRPM7 elicited a synergistic antiproliferative effect on HER2-overexpressing SKBR3 cells but not on HER2-deficient MDA-MB-231 breast cancer cells. Hence, our study proposes a new combinatorial strategy for treating HER2-positive breast cancer cells.
Cerebellar Structural and N-Acetylaspartate, Choline, and Creatine Metabolic Profiles in Parkinson’s Disease and Essential Tremor
Authors: Hong Chien-Tai , Yang Cheng-Chang , Chen David Yen-Ting , Chao Shu-Ping , Chan Lung
Published: October 2024

Background: The role of the cerebellum in Parkinson’s disease (PD), particularly in tremor-dominant subtypes, is increasingly recognized. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) provide anatomical and metabolic insights, suggesting compensatory hyperactivity or degenerative changes in the cerebellum in PD. Volumetric analysis of cerebellar structures in MRI images, combined with metabolic profiles from MRS, offers possibilities for differentiating PD from essential tremor (ET). The cerebellum may be a potential therapeutic target due to its role in neurocircuitry of PD and ET. Methods: Brain structural data were obtained using MRI, and cerebellar metabolic profiles, focusing on the quantification of N-acetylaspartate (NAA), choline, and creatine peaks were obtained using MRS. This study enrolled patients with ET and PD, both with and without tremor, as well as disease controls with cerebellar atrophy (including spinocerebellar ataxia and multiple system atrophy). Volumetric analysis of cerebellar structures was performed. Differences in MRI and MRS parameters were analyzed using one-way analysis of covariance with a significance threshold of p < 0.05. Results: From November 2018 to March 2023, 111 patients were enrolled, including 29 ET, 29 cerebellar atrophy, 12 PD without tremor, and 41 PD with tremor. No significant differences in cerebellar volume and N-acetylaspartate/creatine and choline/creatine ratios were found between ET and PD with tremor. Conclusions: This preliminary retrospective study suggests similarities in cerebellar structures and metabolic profiles between ET and PD, highlighting the need for advanced imaging techniques to better differentiate between these conditions. Future research should integrate clinical data, such as tremor severity and cognitive assessments, to explore the relationships with cerebellar MRI parameters.
Enhancing the Understanding of Abdominal Trauma During the COVID-19 Pandemic Through Co-Occurrence Analysis and Machine Learning
Authors: Radulescu Dumitru , Calafeteanu Dan Marian , Radulescu Patricia-Mihaela , Boldea Gheorghe-Jean , Mercut Razvan , Ciupeanu-Calugaru Eleonora Daniela , Georgescu Eugen-Florin , Boldea Ana Maria , Georgescu Ion , Caluianu Elena-Irina , Marinescu Georgiana-Andreea , Trasca Emil-Tiberius
Published: October 2024

Background: This study examines the impact of the COVID-19 pandemic on abdominal trauma management by comparing pre-pandemic (17 February 2018–26 February 2020) and pandemic periods (27 February 2020–7 March 2022). Methods: Analyzing data from 118 patients at the Emergency County Clinical Hospital of Craiova, we identified significant shifts in clinical practices affecting patient outcomes. Results: During the pandemic, a moderate increase in surgical interventions for specific abdominal traumas indicated the effective adaptation of the medical system. Prioritizing critical cases and deferring non-urgent procedures optimized limited resources. Demographic and clinical factors—including age, sex, body mass index (BMI), and red cell distribution width (RDW)—significantly influenced the hospitalization duration and recovery outcomes. Gender disparities in mortality lessened during the pandemic, possibly due to standardized interventions and the physiological effects of SARS-CoV-2. The link between occupation and obesity highlighted how work environments impact trauma severity, especially as lifestyle changes affect BMI. While age remained a major predictor of mortality, its influence slightly decreased, potentially due to improved protocols for elderly patients. RDW emerged as an important prognostic marker for disease severity and mortality risk. Conclusions: Employing advanced co-occurrence analysis enhanced with machine learning, we uncovered complex relationships between clinical and demographic variables often overlooked by traditional methods. This innovative approach provided deeper insights into the collective impact of various factors on patient outcomes. Our findings demonstrate the healthcare system’s rapid adaptations during the pandemic and offer critical insights for optimizing medical strategies and developing personalized interventions in global crises.
Real-Time Diagnostics on a QKD Link via QBER Time-Series Analysis
Authors: Maragkopoulos Georgios , Mandilara Aikaterini , Nikas Thomas , Syvridis Dimitris
Published: October 2024

The integration of QKD systems in metro optical networks raises challenges that cannot be fully resolved with current technological means. In this work, we devised a methodology for identifying different types of impairments for a QKD link embedded in a communication network. Identification occurs in real time using a supervised machine learning model designed for this purpose. The model takes only QBER and SKR time-series data as the input, making its applicability not restricted to any specific QKD protocol or system. The output of the model specifies the working conditions for the QKD link, which is information that can be valuable for users and key management systems.
Lifting the Gate: Evaluation of Supplemental Instruction Program in Chemistry
Authors: Pham Duyen , Ye Li
Published: October 2024

College-level introductory chemistry has a high impact for predicting students’ early success and long-term academic outcomes. Students from traditionally underserved communities are disproportionately held back in this course. To improve student success, the Supplemental Instruction (SI) program at a public four-year Hispanic-serving institution was revamped from a voluntary option to a co-requisite for the introductory chemistry course. The study evaluates the effectiveness of the new format of SI program and explores associated factors contributing to the impacts of the program. Students with or without SI were compared through multiple quantitative metrics, including course GPA, final exam score, DFW rate, and pass rate. Data analysis showed that students who completed SI with credit obtained 0.9 higher average course GPA than their counterparts and performed significantly better on all the other metrics. More importantly, the equity gaps between underserved and better-served students were narrowed down. Furthermore, students who took introductory chemistry with SI still obtained 0.3 higher average course GPA in their subsequent General Chemistry I course than those without it. The findings suggested that incorporating the SI into introductory chemistry as co-requisite is necessary and effective to improve students’ success and narrow down the equity gaps in gateway chemistry courses.
Global Path Optimal Planning Method for Autonomous Vehicles Based on Image Feature Extraction
Authors: Wu QingE , Wu Fan , Song Shaojing
Published: October 2024

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference.
Research on Sub-Module Fluctuating Power Decoupling Strategy for Modular Multilevel Converter-Based Medium-Voltage Motor Drive System with Wide Speed Range
Authors: You Yanfei , Shao Kaikai , Fan Xintao , Yu Minli , Lin Xinpo , Qi Lei , Teng Jiaxun
Published: October 2024

There is a problem of excessively large sub-module capacitance in modular multilevel converter (MMC) applications for medium-voltage motor drive systems, which is particularly noticeable during the low-speed operation of the motor. This paper proposes a low-capacitance MMC (LC-MMC) motor drive system based on lateral sub-module interconnection, achieving capacitance lightweighting across the wide speed range of the motor. The LC-MMC is based on the three-phase symmetrical fluctuating current input of the sub-modules in the interconnection channel, achieving their mutual cancellation, thus significantly reducing the sub-module capacitance size. This paper starts from the traditional MMC motor drive system and analyzes its specific requirements for sub-module capacitance. Then, it provides a detailed description of the LC-MMC scheme for achieving capacitance lightweighting across the wide speed range of the motor, and the two MMC motor drive systems are compared and evaluated. The evaluation results show that the volume of the LC-MMC is reduced by nearly 70% compared to a traditional MMC. Finally, the LC-MMC scheme is simulated and experimentally verified.
A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms
Authors: Bai Ruxue , Chen Rongshang , Lei Xiao , Wu Keshou
Published: October 2024

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.
Container-Based Electronic Control Unit Virtualisation: A Paradigm Shift Towards a Centralised Automotive E/E Architecture
Authors: Ayres Nicholas , Deka Lipika , Paluszczyszyn Daniel
Published: October 2024

The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle weight, greater demands placed on power, more complex hardware and software, ad hoc methods for updating software, and subsequent increases in costs for both vehicle manufacturers and consumers. To address these issues, the research presented in this paper proposes that virtualisation technologies be applied within automotive electrical/electronic (E/E) architecture. The proposed approach is evaluated by comprehensively studying the CPU and memory resource requirements to support container-based ECU automotive functions. This comprehensive performance evaluation reveals that lightweight container virtualisation has the potential to welcome a paradigm shift in E/E architecture, promoting consolidation and enhancing the architecture by facilitating power, weight, and cost savings. Container-based virtualisation will also enable efficient and robust online dynamic software updates throughout a vehicle’s lifetime.
PII-GCNet: Lightweight Multi-Modal CNN Network for Efficient Crowd Counting and Localization in UAV RGB-T Images
Authors: Niu Zuodong , Pi Huilong , Jing Donglin , Liu Dazheng
Published: October 2024

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment.
Editorial: Special Issue “From Waste to Energy: Anaerobic Digestion Technologies”
Authors: Aragón-Briceño Christian , Boutikos Panagiotis , Manga Musa
Published: October 2024

Anaerobic digestion (AD) has become a cornerstone in the global pursuit of sustainable energy solutions and effective waste management [...]
Modeling the Impact of Traffic Parameters on Electric Vehicle Energy Consumption
Authors: Skuza Adriana , Szumska Emilia M. , Jurecki Rafał , Pawelec Artur
Published: October 2024

The aim of this paper is to examine the relationship between the operating parameters of electric vehicles (EVs) and parameters related to road conditions. The data for analysis came from urban driving trips conducted at different times of day. The average energy consumption was the dependent variable in the model. The following parameters were used as independent variables: the stopping time;; the journey time; the average speed; the quartiles of speed, acceleration, and deceleration, and their maximum values; the number of braking; stops; and the acceleration time and regenerative braking. A multiple linear regression model was developed to predict the average energy demand of an EV based on its kinematic parameters. The proposed model enables the analysis of the influence of various factors related to the route and driving style of the driver on the average energy consumption in the vehicle.
Hydrothermal Liquefaction of Sugarcane Bagasse and Straw: Effect of Operational Conditions on Product Fractionation and Bio-Oil Composition
Authors: Silva Raquel Santos , da Silva Reinaldo Alves , de Andrade Flávio Montenegro , Acácio Neto Pedro Nunes , do Nascimento Rayane Maria , Santos Jandyson Machado , Stragevitch Luiz , Pimentel Maria Fernanda , Simoes Diogo Ardaillon , Danielski Leandro
Published: October 2024

In the energy transition process, aiming for zero disposal and clean production in the elimination of waste is crucial; consequently, agricultural residues have significant potential for reduction in the use of fossil fuels. This study investigates the hydrothermal liquefaction (HTL) of sugarcane bagasse (BSC) and straw (SSC), examining the products’ distribution and bio-oil composition relative to the reaction conditions. The experiments used a 23 factorial design, evaluating the temperature (300–350 °C), constant heating time (0–30 min), and the use of the K2CO3 concentration as the catalyst (0–0.5 mol/L−1). The main factor affecting the biocrude yield from BSC and SSC was the use of K2CO3. Statistically significant interaction effects were also observed. Milder conditions resulted in the highest bio-oil yields, 36% for BSC and 31% for SSC. The catalyst had no impact on the biocrude production. The bio-oils were analyzed by GC/MS and FTIR; a principal component analysis (PCA) was performed to evaluate the samples’ variability. The FTIR highlighted bands associated with common oxygenated compounds in lignocellulosic biomass-derived bio-oils. The GC-MS results indicated a predominance of oxygenated compounds, and these were highest in the presence of the catalyst for both the BSC (90.6%) and SSC (91.7%) bio-oils. The SSC bio-oils presented higher oxygenated compound contents than the BSC bio-oils. Statistical analysis provided valuable insights for optimizing biomass conversion processes, such as determining the optimal conditions for maximizing product yields.
Quantitative Characterization of Surfactant Displacement Efficiency by NMR—Take the Tight Oil of Chang 8 Member of Yanchang Formation in Fuxian Area, Ordos Basin, as an Example
Authors: Yin Hu , Zhong Gaorun , Liu Jiangbin , Wu Yanjun
Published: October 2024

Surfactant flooding is a pivotal technique for enhancing oil recovery efficiency. The Chang 8 member of the Yanchang Formation in the Ordos Basin exemplifies a quintessential tight oil reservoir. Specifically, 47.9% of wells yield less than 0.1 t/d, 27.0% produce between 0.1 and 0.2 t/d, and 18.8% generate outputs ranging from 0.2 to 0.3 t/d, while only a mere 6.3% exceed production rates of over 0.3 t/d, indicating minimal efficacy of water flooding development in this context. In this study, we conducted an extensive investigation into the geological characteristics of the Yanchang 8 reservoir within the Ordos Basin, leading to the identification and evaluation of three surfactants based on their interfacial tension properties. The optimal injection concentration was determined through on-line displacement nuclear magnetic resonance imaging analysis that refined surface activity conducive to developing the Chang 8 member, ultimately resulting in increased spread volume and enhanced crude oil production from individual wells. The results indicate the following: (1) The interfacial tension of NP-10, FSD-952, and GPHQ-1 at concentrations of 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% exhibited a pattern of initial decrease followed by an increase. The mass concentration corresponding to the minimum interfacial tension for NP-10 is identified as 0.1%, which is also the case for GPHQ-1; however, for FSD-952, this occurs at a concentration of 0.4%. Among the surface-active agents NP-10, GPHQ-1, and FSD-952, GPHQ-1 demonstrated the lowest interfacial tension value at an impressive measurement of 0.0762 mN/m. (2) When the displacement of the 0.1% GPHQ-1 surfactant reaches 10 PV, the displacement efficiency improves from 69.69% to 76.36%, representing an increase of 6.67%. The minimum pore size observed during GPHQ-1 surfactant displacement is 0.01 μm. In contrast, when the displacement of the NP-10 surfactant at a concentration of 0.1% reaches 10 PV, the efficiency rises from 68.32% to 72.02%, indicating an enhancement of 3.7%. The corresponding minimum pore size for NP-10 surfactant displacement is recorded at 0.02 μm. Furthermore, when the displacement of the FSD-952 surfactant at a concentration of 0.4% achieves 10 PV, its efficiency increases from 69.93% to 74.77%, reflecting an improvement of 4.81%. The minimum pore size associated with the activated portion of FSD-952 is noted as being approximately 0.03 μm.
Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks—Harmonic State Estimation
Authors: Mack Patrick , de Koster Markus , Lehnen Patrick , Waffenschmidt Eberhard , Stadler Ingo
Published: October 2024

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.
Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method
Authors: Zhang Haijie , Pu Junwei , Zhang Li , Deng Hengjian , Yu Jihao , Xie Yingming , Tong Xiaochang , Man Xiangjie , Liu Zhonghua
Published: October 2024

Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.
Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity
Authors: Chen Yue , Lu Jian
Published: October 2024

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.