bluuebunny/crossref_metadata_2025 huggingface dataset:
Full-Thickness Perfused Skin-on-a-Chip with In Vivo-Like Drug Response for Drug and Cosmetics Testing
Authors: Rhee Stephen , Xia Chunguang , Chandra Aditya , Hamon Morgan , Lee Geonhui , Yang Chen , Guo Zaixun , Sun Bingjie
Published: October 2024

In this study, we present a novel 3D perfused skin-on-a-chip model fabricated using micro-precision 3D printing, which offers a streamlined and reproducible approach for incorporating perfusion. Perfused skin models are well-regarded for their advantages, such as improved nutrient supply, enhanced barrier function, and prolonged tissue viability. However, current models often require complex setups, such as self-assembled endothelial cells or sacrificial rods, which are prone to variability and time-consuming. Our model uses projection micro-stereolithography 3D printing to create precise microcapillary-like channels using a biocompatible resin, overcoming the drug-absorbing properties of PDMS. A customized chip holder allows for the simultaneous culture of six perfused chips, enabling high-throughput testing. The engineered skin-on-a-chip features distinct dermis and epidermis layers, confirmed via H&E staining and immunostaining. To evaluate drug screening capabilities, inflammation was induced using TNF-α and treated with dexamethasone, with cytokine levels compared to 2D cultures and human skin biopsies. Our 3D model exhibited drug response trends similar to human skin, while showing reduced cytotoxicity over time compared to biopsies. This perfused skin-on-a-chip provides a reliable, physiologically relevant alternative for drug and cosmetics screening, simplifying perfusion setup while preserving key benefits.
Genetic Adaptations of Soybean to Cold Stress Reveal Key Insights Through Transcriptomic Analysis
Authors: Liu Xiulin , Zhang Chunlei , Lamlom Sobhi F. , Zhao Kezhen , Abdelghany Ahmed M. , Wang Xueyang , Zhang Fengyi , Yuan Rongqiang , Han Dezhi , Zha Bire , Lu Wencheng , Ren Honglei , Zhang Bixian
Published: October 2024

Low temperatures greatly restrict the development, growth, and productivity of soybeans, with their effects differing across various cultivars. The present work investigated the transcriptome and physiological reactions of two soybean cultivars, namely “KD52” exhibiting cold tolerance and “DS17” displaying cold sensitivity, to cold stress across a precisely defined period. The soybean plants were subjected to cold treatment at 6 °C for durations of 0, 2, 4, and 8 h. A comparative physiological marker study revealed distinct reactions to cold stress in the two cultivars. The findings showed that increased malondialdehyde levels provided evidence of DS17’s heightened vulnerability to lipid peroxidation and membrane degradation. In contrast, the KD52 cultivar exhibited increased activities of antioxidant enzymes, including peroxidase and superoxide dismutase, in response to cold exposure, suggesting a strong antioxidant defense system against oxidative stress. The transcriptomic analysis revealed dynamic responses, mapping 54,532 genes. Within this group, a total of 234 differentially expressed genes (DEGs) were found to be consistently changed at several time intervals, showing unique expression patterns across the two cultivars. Analysis of the association between these important DEGs and the physiological indicators revealed candidate genes that may be involved in controlling oxidative damage and antioxidant defenses. Some key genes showed a progressive rise in expression over time in both cultivars, with a more significant acceleration in KD52, and are probably involved in promoting adaptation processes during extended periods of cold exposure. The identification of improved defense mechanisms in KD52, together with the identification of crucial genes, offers great prospects for enhancing the cold stress resilience of soybean.
Gene Expression Analysis for Drought Tolerance in Early Stage of Potato Plant Development
Authors: Kanat Rakhim , Shamekova Malika , Sapakhova Zagipa , Toishimanov Maxat , Daurov Dias , Raissova Nurgul , Abilda Zhanar , Daurova Ainash , Zhambakin Kabyl
Published: October 2024

Drought has increasingly affected the yield of Solanum tuberosum L. (potato) every year over the last decade, posing serious economic problems for the global agricultural industry. Therefore, it is important to research drought tolerance in plants and obtain more robust varieties of crops. The aim of the present work was to study the expression of drought-upregulated genes in drought-tolerant and drought-sensitive varieties of potato. Bioreactors were used to identify whether each variety was drought-tolerant or drought-sensitive; then, expression analysis was performed according to the morphological characteristics of the plantlets in two different media: Murashige and Skoog (MS) medium and MS medium with 20% PEG-6000 to simulate osmotic stress. Based on the quantitative parameters of six initial varieties, two varieties were selected (Gala and Aksor) for further gene expression analysis. The expression of genes commonly upregulated in drought (ER24, TAS14, DREB147315, PP2C, 102605413 and NF-YC4) was higher in the drought-tolerant variety than in the sensitive one. Therefore, the expression of these genes can be used to determine the drought tolerance of a potato variety in vitro in the early plant development stage. Moreover, comparative analysis showed that some of the targeted genes used to identify drought tolerance in this study are conserved across different plant species.
Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)
Authors: Bhattarai Alok , Meyer Jan , Petersilie Laura , Shah Syed I. , Neu Louis A. , Rose Christine R. , Ullah Ghanim
Published: October 2024

With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program’s ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.
Approachability and Sensory Changes Following Mild Traumatic Brain Injury in Pigs
Authors: Pavlichenko Mark , Lilova Radina L. , Logan-Wesley Amanda , Gorse Karen M. , Lafrenaye Audrey D.
Published: October 2024

Background/Objectives: Traumatic brain injury (TBI) is a global healthcare concern affecting millions, with wide-ranging symptoms including sensory and behavioral changes that can persist long-term. Due to similarities with human brain cytoarchitecture and inflammation, minipig models are advantageous for translational TBI research. However, gaps in knowledge exist regarding their behavioral and sensory sequelae following injury. Methods: Therefore, in this study, we assessed changes in approachability using a forced human approach task (FHAT) and mechanical nociception using the von Frey test in adult male and female Yucatan minipigs for up to one week following a sham or central fluid percussion injury (cFPI). Specifically, the FHAT assessed each animal’s response to a forced interaction with either a known or unknown experimenter. To evaluate changes in nociceptive sensory sensitivity, von Frey monofilaments ranging from 0.008 to 300 g of force were applied to the pinna of the ear or base of the tail. Results: We found that forced approachability was affected by experimenter familiarity as well as cFPI in a sex-specific manner at subacute timepoints. We also found reductions in sensitivity following cFPI on the ear in male minipigs and on the tail in female minipigs. Conclusion: Overall, the current study demonstrates that cFPI produces both behavioral and sensory changes in minipigs up to one-week post-injury.
Reversal of Atherosclerotic Plaque Growth and Vulnerability: Effects of Lipid-Modifying and Anti-Inflammatory Therapeutic Agents
Authors: Papafaklis Michail I. , Koros Rafail , Tsigkas Grigorios , Karanasos Antonios , Moulias Athanasios , Davlouros Periklis
Published: October 2024

Atherosclerotic plaque development constitutes the primary substrate of coronary artery disease (CAD) and is the outcome of an intricate process involving endothelial damage, inflammation, and lipid retention. The clinical efficacy of many lipid-lowering therapies in patients with CAD has been well established. Over the past few decades, a substantial and significant advance regarding the use of invasive and non-invasive imaging modalities has been observed. Numerous studies have been conducted using these imaging techniques and have investigated the changes in morphology (e.g., atheroma volume) and composition (e.g., lipid burden, fibrous cap thickness, macrophage accumulation) at the plaque level that explain the improved clinical outcomes by various pharmacological interventions. Lipid-lowering agents, such as statins and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, demonstrate direct effects on plaque volume and composition that enhance plaque stabilization and/or regression beyond the reduction of low-density lipoproteins. An increasing amount of clinical research is also focused on the role of inflammation in plaque vulnerability and future adverse cardiac events. Consequently, there is a pressing need to explore therapeutic strategies that are capable of disrupting the inflammatory response as well as reducing atheroma burden and modifying high-risk plaque characteristics. This review provides a comprehensive analysis of the current evidence regarding the effects of traditional and novel therapeutic strategies targeting modification of the lipid profile and inflammatory processes on reversing plaque growth and attenuating vulnerable features, thereby promoting plaque stabilization and passivation.
Comparative Analysis of Space Efficiency in Skyscrapers with Prismatic, Tapered, and Free Forms
Authors: Ilgın Hüseyin Emre , Aslantamer Özlem Nur
Published: October 2024

This study offers a thorough comparative analysis of space efficiency in skyscrapers across three distinct forms: prismatic, tapered, and free. By examining case studies from each form category, this research investigates how architectural and structural design features impact space utilization in supertall towers. The findings reveal form-based differences in space efficiency and design element usage. In prismatic skyscrapers, which are primarily residential and utilize concrete outrigger frames, the average space efficiency was around 72%, with the core occupying 24% of the gross floor area (GFA). Tapered skyscrapers, commonly mixed-use with composite outrigger frames, showed an average space efficiency of over 70%, with a core-to-GFA ratio of 26%. Freeform towers, often mixed-use and using composite outrigger frames, demonstrated a space efficiency of 71%, with an average core-to-GFA ratio of 26%. Despite these variations, a consistent trend emerged: as the height of a building increases, there is a general decline in space efficiency, highlighting the challenges in optimizing space in taller structures. This analysis adds to the understanding of skyscraper design and space utilization, providing important insights for architects and urban planners aiming to improve the efficiency of future high-rise developments.
Analysis of Spatial Vitality Characteristics and Influencing Factors of Old Neighborhoods: A Case Study of Ya’an Xicheng Neighborhood
Authors: Chen Lan , Jiang Xinqi , Tan Lin , Chen Chunhua , Yang Shuiying , You Wenjuan
Published: October 2024

Amidst the pursuit of sustainable urban development, bolstering the spatial vitality of historic neighborhoods emerges as a pivotal endeavor. This study, centering on the Xicheng neighborhood in Ya’an City, employs a multifaceted data analysis approach to unravel the intricate spatial vitality patterns. By adopting a dual lens focusing on both “people” and “space”, we delve into the intricate interplay between spatial vitality, spatial perception, and various influencing factors, such as the spatial environment, business format layout, and place atmosphere. Leveraging spatial correlation analysis, we meticulously examine the extent to which these multifarious factors shape spatial vitality and perception. Our findings reveal a profound connection between the emotional dimension of perception and the distribution of spatial vitality. Notably, aspects of spatial character—green rating, accessibility, and interface richness—exert the most pronounced influence on vitality. Furthermore, the creation of a distinctive atmosphere in a place is more likely to resonate with a person’s spatial perception than the physical environment itself.
Crime in the Architecture, Engineering and Construction (AEC) Industry—The Role of Subcontractors
Authors: Lohne Jardar , Drevland Frode
Published: October 2024

This article examines the challenges posed by criminal subcontractors in the construction industry. It aims to delineate the specific crimes committed by these subcontractors and assess their impact against the broader backdrop of industry-related criminality. Employing a scoping literature review, the study explores the existing research, summarizes key findings, and highlights gaps in the current knowledge. The construction industry’s inherent complexities and reliance on extensive subcontracting create an environment ripe for criminal activities. The research questions addressed are the following: (1) What crimes are carried out by subcontractors? (2) What are the consequences of crimes carried out by subcontractors? This study identifies several major concerns: (1) adverse impacts on project management in terms of timelines, quality, and budgetary control; (2) widespread exploitation within supply chains, ranging from wage disparities to modern slavery; (3) prevalent fraudulent practices, such as bribery, collusion, and embezzlement; and (4) the detrimental effect on Health, Environment, and Safety (HES) standards. The article underscores the diversity of legal frameworks across jurisdictions and signals the need for concerted efforts to enhance crime prevention measures, foster industry-wide collaboration, and establish robust control systems. There is an urgent need for a profound understanding of the contractor–subcontractor dynamic and procurement of substandard materials. The findings suggest that the construction industry faces formidable challenges due to criminal elements, profoundly affecting project efficiency, legal compliance, and worker welfare.
Automatic Indoor Thermal Comfort Monitoring Based on BIM and IoT Technology
Authors: Liang Wenli , Qiang Guofeng , Fan Lei , Zhang Haoyu , Ye Zihao , Tang Shu
Published: October 2024

Building Information Modeling (BIM) and Internet of Thing (IoT) integration technologies can improve operational efficiency in the operational phase of construction projects. Currently, research on the integration of BIM and IoT has yet to ensure secure data transmission and lacks real-time data processing capabilities. This study builds a framework to collect and analyze BIM and IoT data in real time. The framework is verified to be effective through a case study in an office building. The monitoring system can automatically calculate the Predicted Mean Vote (PMV) value, upload and update real-time temperature and humidity data, and visualize thermal comfort through heat maps. The proposed integration approach offers building management strategies to enhance thermal comfort in office environments, fostering a more inclusive and accommodating workspace that acknowledges the diverse cultural backgrounds of occupants.
Utility of the TG/HDL-C Ratio in Estimating Pediatric Cardiometabolic Risk in a Community Hospital
Authors: Awujoola Adeola O. , Torga Ana P. , Valencia Jose E. , Alemneh Hermella , Prince Olaseni P. , Kant Sandipagu P.
Published: October 2024

Background: The evaluation of metabolic syndrome (MetS) is critical among children and adolescents as it can predict cardiovascular disease in adulthood. The TG/HDL-C ratio has the best predictive accuracy among the different lipid ratios. This study aims to evaluate the prevalence and factors associated with a high TG/HDL-C ratio and the predictive accuracy for pediatric MetS. Methods: This is a cross-sectional study of adolescents aged 9–21 to determine the prevalence of and associations between an elevated TG/HDL-C ratio and MetS. A ROC curve analysis was performed to determine the predictive accuracy of TG/HDL-C with MetS. Results: Of the 604 subjects, 29.8% had elevated TG/HDL-C, MetS was identified in 28.2%, and 54.5% were obese. Among patients with MetS, those with obesity were more likely to have an elevated TG/HDL-C ratio compared to those with a normal BMI (64% vs. 14%, p < 0.05). Gender, obesity, ethnicity, and METS are significantly associated with a high TG/HDL-C ratio. TG/HDL-C has a good discriminatory ability to distinguish patients with MetS from those without (AUC 0.65, p < 0.05). Conclusions: The TG/HDL-C ratio was able to distinguish between children and adolescents with MetS. Hence, the TG/HDL-C ratio may be used in the rapid risk assessment of pediatric MetS, especially in those with obesity.
4D Printing: Research Focuses and Prospects
Authors: Jin Yuran , Liu Jiahui
Published: October 2024

As an emerging technology in the field of additive manufacturing, 4D printing is highly disruptive to traditional manufacturing processes. Therefore, it is necessary to systematically summarize the research on 4D printing to promote the development of related industries and academic research. However, there is still an obvious gap in the visual connection between 4D printing theory and application research. We collected 2070 studies from 2013 on 4D printing from the core collection of Web of Science. We used VOSviewer 1.6.20 and CiteSpace software 6.3.3 to visualize the references and keywords to explore focuses and trends in 4D printing using scientometrics. In addition, real-world applications of 4D printing were analyzed based on the literature. The results showed that “tissue engineering applications” is the most prominent focus. In addition, “shape recovery”, “liquid crystal elastomer”, “future trends”, “bone tissue engineering”, “laser powder bed fusion”, “cervical spine”, “4D food printing”, “aesthetic planning” are also major focuses. From 2013 to 2015, focuses such as “shape memory polymers” and “composites” evolved into “fabrication”. From 2015 to 2018, the focus was on “technology” and “tissue engineering”. After 2018, “polylactic acid”, “cellulose”, and “regenerative medicine” became emerging focuses. Second, emerging focuses, such as polylactic acid and smart polymers, have begun to erupt and have become key research trends since 2022. “5D printing”, “stability” and “implants” may become emerging trends in the future. “4D + Food”, “4D + Cultural and Creative”, “4D + Life” and “4D + Clothing” may become future research trends. Third, 4D printing has been widely used in engineering manufacturing, biomedicine, food printing, cultural and creative life, and other fields. Strengthening basic research will greatly expand its applications in these fields and continuously increase the number of applicable fields.
Comparative Analysis of Glandular and Extraglandular Manifestations in Primary and Secondary Sjögren’s Syndrome: A Study in Two Academic Centers in North-East Romania
Authors: Lodba Alexandru , Ancuta Codrina , Tatarciuc Diana , Ghiorghe Angela , Lodba Luciana-Oana , Iordache Cristina
Published: October 2024

Background: This study investigates the clinical characteristics and differences between primary Sjögren’s Syndrome (pSS) and secondary Sjögren’s Syndrome (sSS) in a cohort of 50 patients. Methods: Conducted across two academic facilities in North-East Romania, the study emphasizes the importance of glandular and extraglandular manifestations, focusing on salivary flow rates, pH levels, and buffer capacity. Patients were diagnosed using the 2016 ACR-EULAR classification criteria, with a detailed examination including salivary tests, biopsies, and antibody presence. Results: The findings highlight significant differences between pSS and sSS, particularly in salivary function, with pSS patients exhibiting more severe glandular dysfunction. The study also notes a higher prevalence of inflammatory joint involvement in sSS patients, often associated with rheumatoid arthritis. Statistical analysis revealed correlations between salivary parameters and disease progression, underscoring the necessity of tailored treatment strategies. The research suggests that lower salivary flow rates and altered pH levels in pSS patients contribute to compromised oral health, including increased dental cavities and periodontal disease. Conclusions: The study’s results contribute to a deeper understanding of Sjögren’s Syndrome and reinforce the need for multidisciplinary management to address both systemic and oral health complications in these patients.
Exploring the Relationship Between Hypertension and Cerebral Microvascular Disease
Authors: Katsi Vasiliki , Mavroudis Andreas , Liatakis Ioannis , Konstantinos Manousiadis , Tsioufis Konstantinos
Published: October 2024

Background/Objectives: Hypertension exerts negative effects on the vasculature representing a key risk factor for cardiovascular disorders, cerebral and Cerebral Small Vessel Disease (CSVD). Methods: An extensive research in the literature was implemented in order to elucidate the role of hypertension in the pathogenesis of CSVD. Results: Hypertension-mediated vascular dysfunction and chronic cerebral hypoperfusion are closely linked to CSVD. CSVD encompasses a wide range of lesions depicted on brain Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. The presenting symptoms and clinical course are highly variable, as a significant proportion of patients remain asymptomatic. Nevertheless, CSVD is associated with an increased risk of stroke, dementia and mobility disorders. Various randomised controlled trials have been implemented trying to shed light on the effect of vascular risk-modifying agents and lifestyle interventions on the prevention and treatment of small vessel disease. Conclusions: Hypertension has a pivotal role in the pathogenesis of CSVD. However, further research is required for a better understanding of the relationship between blood pressure levels and CSVD progression.
Not All Fluctuations Are Created Equal: Spontaneous Variations in Thermodynamic Function
Authors: Crutchfield James P. , Aghamohammadi Cina
Published: October 2024

We identify macroscopic functioning arising during a thermodynamic system’s typical and atypical behaviors, thereby describing system operations over the entire set of fluctuations. We show how to use the information processing second law to determine functionality for atypical realizations and how to calculate the probability of distinct modalities occurring via the large-deviation rate function, extended to include highly correlated, memoryful environments and systems. Altogether, the results complete a theory of functional fluctuations for complex thermodynamic nanoscale systems operating over finite periods. In addition to constructing the distribution of functional modalities, one immediate consequence is a cautionary lesson: ascribing a single, unique functional modality to a thermodynamic system, especially one on the nanoscale, can be misleading, likely masking an array of simultaneous, parallel thermodynamic transformations that together may also be functional. In this way, functional fluctuation theory alters how we conceive of the operation of biological cellular processes, the goals of engineering design, and the robustness of evolutionary adaptation.
Global Traction Battery Cathode Material Industrial Chain Trade Analysis: A Multilayer Modeling Approach
Authors: Peng Peng , Xu Yang , Yu Li , Xie Xiaowei
Published: October 2024

The fast expansion of the electric vehicle market has led to a significant increase in the demand for traction batteries, an essential element in these vehicles that provide the opportunity to achieve low-carbon and environmentally friendly growth and carbon neutrality. By analyzing the network structure and key trading countries from 2000 to 2021, this research uses multilayer network theory to explore the trade patterns and the evolution of the global cathode material industrial chain of traction batteries. Our findings indicate the following: (1) The industrial networks display multi-core trading country characteristics. Trade connections among the top 20 countries, which account for more than 80% of the global trade volume, have strengthened. (2) Over time, the geographic center of trade has shifted from being primarily focused in Europe, North America, and East Asia to embracing the entire world, including regions such as Africa, South America, and Oceania. (3) In 2021, Australia overtook Japan as the main exporter, which held the top position in 2000. Similarly, China surpassed the United States, which was the top importer in 2000. (4) Changes in global trade relationships have affected the trading habits of the top-ranked countries.
Critical Points in the Noiseberg Achievable Region of the Gaussian Z-Interference Channel
Authors: Costa Max H. M. , Nair Chandra , Ng David
Published: October 2024

The Gaussian signaling strategy with power control for the Gaussian Z-interference channel with weak interference is reviewed in this paper. In particular, we study the various communication strategies that may arise at various points of the capacity region and identify the locations of the phase transitions between the various strategies. The Gaussian Z-interference channel with weak interference is known to have two critical points in its capacity region, where the slope of the region shows a sudden change. They occur at the points of the unconditional maximum rate for one of the users and the maximum rate that can be accommodated by the other user. In this paper, we discuss additional critical points (locations of phase transitions) in the achievable region of this channel. These turn out to be second-order phase transitions, i.e., we do not observe a discontinuous slope in the achievable rate region, but there is a discontinuity in the second derivative of the rate contour of the achievable region. This review paper is mainly based on some of our ITA (Information Theory and Applications Workshop, UCSD, San Diego, CA, USA) papers since 2011.
Interval-Valued Random Matrices
Authors: Sadeghkhani Abdolnasser , Sadeghkhani Ali
Published: October 2024

This paper introduces a novel approach that combines symbolic data analysis with matrix theory through the concept of interval-valued random matrices. This framework is designed to address the complexities of real-world data, offering enhanced statistical modeling techniques particularly suited for large and complex datasets where traditional methods may be inadequate. We develop both frequentist and Bayesian methods for the statistical inference of interval-valued random matrices, providing a comprehensive analytical framework. We conduct extensive simulations to compare the performance of these methods, demonstrating that Bayesian estimators outperform maximum likelihood estimators under the Frobenius norm loss function. The practical utility of our approach is further illustrated through an application to climatology and temperature data, highlighting the advantages of interval-valued random matrices in real-world scenarios.
Marketization of Shadow Education in Switzerland: How “Edupreneurs” Promote Preparation Programmes for a Selective School Transition
Authors: Landolt Sara , Bauer Itta
Published: October 2024

Private supplementary tutoring (PST) is a flourishing market for edupreneurs whose services relate closely to mainstream education. While international research elaborates on geographical variations in PST and edupreneurs’ marketing strategies, the PST market in Switzerland has been largely understudied. This paper contributes to fill this research gap by presenting a thematic analysis of the websites of edupreneurs offering preparation programmes for the highly selective central entrance examination (CEE) to the academically focused public school track of Gymnasium in Zurich. Conceptually, we draw on “problematization” and “commodificiation” as key terms elaborated by “geographies of marketization”. With this performative conceptualisation of the education market, we examine the marketing strategies of the edupreneurs offering CEE preparation courses. We offer two contributions to the existing research. First, the analysis elaborates that private CEE preparation courses are advertised as a market solution that compensates for an omission caused by public education. Second, we show how edupreneurs use rational and emotional arguments to convince families of the need of booking CEE preparation courses for their children. In the discussion, we engage with the findings of our local case study and argue that selective educational transitions have much wider relevance for policy and society elsewhere.
A 23–29 GHz GaN Low-Noise Amplifier with Drain-to-Source Coupling Feedback
Authors: Mao Fengyuan , Chen Zhijian , Li Bin , Wu Zhaohui , Chen Xinhuang , Guan Quansheng
Published: October 2024

In this paper, a four-stage gallium nitride (GaN) low noise amplifier (LNA) using coupled-line (CL) feedback in a 0.15-μm GaN-on-SiC process is proposed. The electromagnetic coupling feedback between the drain and source of each transistor is employed to generate an additional signal path for neutralization, which enhances gain and improves stability performance. A series transmission line-capacitor-transmission line (TL-C-TL) network is introduced between stages of the LNA for wider band interstage matching. The measured results show that the designed LNA achieves a 3-dB bandwidth of 23.6 to 29.8 GHz, a peak gain of 23.7 dB at 25.8 GHz, and a minimum noise figure (NF) of 2.2 dB at 27.8 GHz. The output-referred 1-dB compression point (OP1dB) is 13 dBm. The total power consumption of the LNA is 200 mW.
Optimization Study of Coal Gangue Detection in Intelligent Coal Selection Systems Based on the Improved Yolov8n Model
Authors: Zong Guilin , Yue Yurong , Shan Wei
Published: October 2024

To address the low recognition accuracy of models for coal gangue images in intelligent coal preparation systems—especially in identifying small target coal gangue due to factors such as camera angle changes, low illumination, and motion blur—we propose an improved coal gangue separation model, Yolov8n-improvedGD(GD—Gangue Detection), based on Yolov8n. The optimization strategy includes integrating the GCBlock(Global Context Block) from GCNet(Global Context Network) into the backbone network to enhance the model’s ability to capture long-range dependencies in images and improve recognition performance. The CGFPN (Contextual Guidance Feature Pyramid Network) module is designed to optimize the feature fusion strategy and enhance the model’s feature expression capabilities. The GSConv-SlimNeck architecture is employed to optimize computational efficiency and enhance feature map fusion capabilities, thereby improving the model’s robustness. A 160 × 160 scale detection head is incorporated to enhance the sensitivity and accuracy of small coal and gangue detection, mitigate the effects of low-quality data, and improve target localization accuracy.
Design Considerations for Power-Efficient Fully Integrated 3:1 Switched Capacitor DC-DC Converter for PV Modules
Authors: Saini Sunita , Saini Davinder Singh , Balyan Vipin
Published: October 2024

This article presents a power-efficient DC-DC converter based on a switched-capacitor (SC) cell in power management systems supplied for fully integrated photovoltaic (PV) modules. These modules shall provide high-performance point-of-load voltage regulation. The primary objective of this study is to better utilize capacitance and switches by selecting a proper SC topology in order to improve the power efficiency of SC converters. A general steady-state performance model is investigated to optimize and compare a variety of SC DC-DC topologies. The investigation method relies on a charge-multiplier approach and considers the impact of area constraint on capacitors. To identify the most suitable topology for a given conversion ratio, the performance-limit metrics of SC converters are calculated. The analysis provides framework to determine optimum switch size and switching frequency for a two-phase 3:1 series–parallel converter for a target load current of 10 mA implemented on a 22 nm process technology. The results shows that a minimum of 250 MHz switching frequency is desirable for achieving a target efficiency greater than 85% while maintaining the minimum output voltage of 0.34 V. The analysis results are verified through MATLAB and PSpice-based simulations.
Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range
Authors: Nguyen Xuan Luong , Dang Thi Thanh Thuy , Nguyen Phung Bao , Tran Viet Hung
Published: October 2024

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.
Biofuel Dynamics in Brazil: Ethanol–Gasoline Price Threshold Analysis for Consumer Preference
Authors: Mosquéra Letícia Rezende , de Oliveira Matheus Noschang , Martins Patricia Helena dos Santos , Bispo Guilherme Dantas , Borges Raquel Valadares , Serrano André Luiz Marques , Pompermayer Fabiano Mezadre , Neumann Clovis , Gonçalves Vinícius Pereira , Bork Carlos Alberto Schuch
Published: October 2024

The global transition towards environmentally friendly energy sources plays a major role in addressing both energy security and climate change. Brazil is at the forefront of this transition due to its rich natural resources and increasing investments in biofuels. Therefore, this investigation examines the consumption patterns and interactions between ethanol, primarily sourced from sugarcane, and gasoline within Brazil’s energy framework. Ethanol’s renewability, reduced environmental impact, and superior combustion characteristics position it as a feasible substitute for traditional fossil fuels. Nonetheless, obstacles like competition for land use and inadequate distribution infrastructure impede its widespread acceptance. This study explores the economic interaction between ethanol and gasoline, focusing on pricing dynamics and regional influences. Using consumer preferences and the accessibility of ethanol, this research identifies a range of price ratios within which consumer preferences shift from gasoline to ethanol in various Brazilian regions. The study also classifies Brazilian states into three distinct ranges based on the ethanol-to-gasoline price ratio in 2023 for a granular analysis of the economic dynamics influencing fuel choice. The research identifies states with competitive and dominant ethanol markets by examining the interplay between ethanol market share, fuel prices, and the adoption of flex-fuel vehicles (FFVs) in the country. Lastly, the findings support the importance of regional economic conditions and the influence of price ratios on consumer behavior, highlighting that ethanol’s market share does not always correlate with favorable pricing.
Exploratory Analysis of a Novel Modular Green Wall’s Impact on Indoor Temperature and Energy Consumption in Residential Buildings: A Case Study from Belgium
Authors: Radujković Milana , Versele Alexis , Breesch Hilde
Published: October 2024

One possible solution that mitigates the effects of climate change is the implementation of vertical greenery systems, which have the potential to reduce the need for cooling and provide energy savings for heating. This paper evaluates the effects of an innovative modular green wall on indoor temperature and energy use in a residential case study building. This research was carried out on a residential house in the city of Ghent, Belgium, whose southwest facade is covered with a specific type of modular green wall (a structure with a specific substrate and plants that have the ability to purify water so that it can be reused in the house). The monitoring process included four different temperatures (in front of and behind the green wall, in the substrate, and on the wall without greenery) during winter and summer periods. To analyze the effect on the internal temperature and energy use, a DesignBuilder simulation model was built and validated against these experimental results. This green wall has proven to have the greatest effect during the hottest summer days by reducing the indoor temperature by up to 3.5 °C. It also effectively increases the indoor temperature by up to 1.4 °C on a cold winter day, leading to energy savings of 6% on an annual basis.
Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad
Authors: Ghaedi Peyman , Eskandari Aref , Nedaei Amir , Habibi Morteza , Parvin Parviz , Aghaei Mohammadreza
Published: October 2024

Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%.
Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement
Authors: Huang Yongcan , Liu Senyi , Pang Rui , Liu Xingbang , Rao Xi
Published: October 2024

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.
Green Technology Innovation and Carbon Emission Performance of the Middle Reaches of the Yangtze River Urban Agglomeration: Mechanism and Spatio-Temporal Evolution
Authors: Guo Yijun , Li Xifan , Li Sheyun
Published: October 2024

Amid the increasingly severe global climate change situation, green technology innovation has become an important means to promote carbon reduction and achieve the transition to a low-carbon economy. This study aims to systematically analyze the relationship between green technology innovation and carbon emission performance in the urban agglomeration of the middle reaches of the Yangtze River, exploring the degree of coupling and coordination between different cities. Utilizing data from 2011 to 2021, we employ methods such as the Coupling Coordination Degree Model and fixed effects model to achieve our objectives. Our findings reveal that both green technology innovation and carbon emission performance in this region are on an upward trend; however, the growth rate of green technology innovation showed a slowdown in 2021. Notably, there are disparities in the coupling coordination degree among cities, with economically developed areas exhibiting a faster growth rate. Moreover, green technology innovation significantly enhances carbon emission performance, and heterogeneity tests indicate that this impact is even more pronounced in cities with weaker environmental regulations. Despite regional differences, the overall trend remains positive.
Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System
Authors: Ukoima Kelvin Nkalo , Okoro Ogbonnaya Inya , Obi Patrick Ifeanyi , Akuru Udochukwu Bola , Davidson Innocent Ewean
Published: October 2024

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.
Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method
Authors: Syllignakis John E. , Kanellos Fotis D.
Published: October 2024

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.