bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlation between Bronchopulmonary Dysplasia and Cerebral Palsy in Children: A Comprehensive Analysis Using the National Inpatient Sample Dataset
Authors: Al-Matary Abdulrahman , Abozaid Sameh , Al Suliman Mustafa , Alsubaie Mohammed , Aldandan Faisal K , Alzehairi Faisal Mohammed , Alyahyawi Huda Yahya , Alsharief Abrar Nayel , Alahmadi Ghadeer Ghazi , Althubaiti Faris , Alyahyawi Naseem , Mazi Ahlam , Abu-Zaid Ahmed , Alnajashi Hind , Alyoubi Reem Abdullah
Published: September 2024

Background: The existing literature lacks conclusive evidence regarding the relationship between bronchopulmonary dysplasia (BPD) and cerebral palsy (CP). This large epidemiological study aimed to explore the co-occurrence of BPD and CP among children. Methods: This retrospective cohort analysis utilized the National Inpatient Sample (NIS) dataset from 2016 to 2019, investigating pediatric patients with BPD and CP diagnoses. Descriptive and inferential statistics, including univariate and multivariate regression analyses, were conducted to explore the association between BPD and CP. Results: Overall, 3,951,039 patients were analyzed. Among them, 28,880 patients had CP (n = 796 with BPD and n = 28,084 without BPD). The rates of intraventricular hemorrhage grade 3 and 4, central nervous system anomalies, chromosomal disorders, retinopathy of prematurity (≥grade 3), periventricular leukomalacia, prematurity, and low birth weight were significantly higher in the CP-with-BPD arm contrasted to the CP-without-BPD arm. Univariate regression demonstrated a significant BPD–CP association (odds ratio [OR] = 7.78, 95% confidence interval [CI]: 7.24–8.37, p < 0.0001). Multivariate analysis, adjusting for various confounders, reinforced this association (OR = 5.70, 95% CI: 5.17–6.28, p < 0.0001). We observed a significant association between increasing prematurity in neonates with BPD and an elevated risk of CP. Conclusions: This nationwide study identified a strong correlation between the co-occurrence of BPD and CP, though it does not establish causality. Rigorous adjustments revealed that patients with BPD appear to have a six-fold increased likelihood of being diagnosed with CP later on, compared to those without BPD. While aligned with the existing literature, this study represents the largest sample size with recommendations for targeted preventive strategies to mitigate the burden of CP.
Parental Knowledge, Attitudes, and Practices Regarding Early Childhood Caries in Bihor, Romania: A Cross-Sectional Study
Authors: Moca Abel Emanuel , Juncar Raluca Iulia , Moca Rahela Tabita , Juncar Mihai , Marton Rebeca Daniela , Vaida Luminița Ligia
Published: September 2024

Background/Objectives: Early childhood caries (ECC) is a significant global health issue, particularly affecting deciduous teeth in young children. ECC is prevalent in Romania, where nearly half of children experience dental caries. This study aimed to assess the influence of gender, age, and living environment on parents’ knowledge, attitudes, and practices (KAP) regarding ECC in Bihor County, Romania, using a KAP-type questionnaire. Methods: A cross-sectional study was conducted between March and September 2024, utilizing an online KAP questionnaire distributed via social networks. The sample comprised 419 parents of children under six years old. Respondents provided socio-demographic data and answered questions regarding their knowledge, attitudes, and practices concerning ECC prevention, treatment, and oral health. Statistical analysis was conducted using Chi-square tests to assess associations between demographic factors and parental KAP. A p-value of less than 0.05 was considered statistically significant. Results: Of the 419 respondents, 83.1% were female, and 62.5% were between the ages of 31 and 40. Significant gender-based differences were found in knowledge about pain from caries (93.7% of female participants vs. 81.7% of male participants, p = 0.004) and the importance of brushing twice daily (93.7% of female respondents vs. 80.3% of male respondents, p = 0.010). Younger parents (aged 18–30) were more likely to affirm the effectiveness of fluoride (65.4%) compared to those aged 31–40 (53.1%, p = 0.02). Urban parents were more likely to correctly identify the timing of tooth eruption (59.1% vs. 52.6% of rural parents, p = 0.021). Conclusions: The study highlights gender, age, and urban–rural disparities in parental knowledge and attitudes towards ECC. These findings underscore the need for targeted educational interventions to improve oral health outcomes and reduce ECC prevalence in the Bihor region. Tailored public health strategies addressing demographic factors could enhance preventive oral health behaviors and reduce the healthcare burden associated with untreated dental caries.
Indulgent Parenting and Adolescents’ Maladjustments: The Roles of Cultural Context and Parental Gender
Authors: Feng Qinglan , Yang Yanyun , Cui Ming
Published: September 2024

Background/Objectives. Adolescence is a critical developmental stage marked by the exploration of independence and self-identity. In this study, we aimed to examine the association between indulgent parenting (characterized by high responsiveness and low demandingness) and adolescents’ maladjustments across emotional, behavioral, and social domains. Methods. Using a cross-cultural sample of high school students from the U.S. (n = 268) and China (n = 189), we tested the hypotheses that indulgent parenting was associated with adolescents’ maladjustments, and that such association varied by cultural context (U.S. vs. China) and parental gender. Results. The results from Bayesian structural equation modeling supported the hypotheses, showing significant associations between indulgent parenting and adolescents’ maladjustments and differences in the associations across cultures and parental gender. Conclusions. The findings highlighted the need for culturally informed parenting programs to foster healthy adolescent development.
Preliminary Investigation into the Association between Scoliosis and Hypoxia: A Retrospective Cohort Study on the Impact of Eliminating Hypoxic Factors on Scoliosis Outcomes
Authors: Ugur Fatih , Topal Kubra , Albayrak Mehmet , Taskin Recep , Topal Murat
Published: September 2024

Objective: This study delves into the implications of adenoidectomy for scoliosis progression, investigating the intricate nexus of hypoxia, spinal curvature, and surgical intervention. With adenoidectomy being a common procedure for addressing pediatric sleep-disordered breathing, this research study explores its potential impact on spinal health. Patients and Methods: Employing a retrospective cohort design, this study gathered data from patients who underwent adenoidectomy, including those with scoliosis, between January 2017 and March 2023. Initial and follow-up evaluations involved clinical and radiological assessments, notably measuring the Cobb angle to quantify spinal curvature. Results: This study enrolled 218 patients under 10 years old. Among them, 18 exhibited Cobb angles of 10° or more, with a mean Cobb angle of 12.8°. In the follow-up evaluation, 83% of patients with initial Cobb angles of 10° or more were reached out to, along with 84.6% of those with Cobb angles below 10°. The postoperative follow-up revealed a notable decrease in Cobb angles for most patients, particularly those with an initial Cobb angle exceeding 10°. Conclusions: This study underscores the potential connection between adenoidectomy, hypoxia, and scoliosis regression, highlighting the importance of early intervention for scoliosis management. Despite certain limitations, this investigation lays the foundation for future research involving larger patient cohorts and multifaceted analyses. The observed interactions between airway function, hypoxia, and spinal health open avenues for refining clinical strategies in scoliosis treatment.
Corrosion and Protection of Chinese Bronze Relics: A Review
Authors: Zhang Lingling , Yang Chao , Guo Yingzhi
Published: September 2024

The corrosion problem affecting ancient Chinese bronze relics and the protective measures required post-excavation are crucial for the study of historical cultural heritage and for ensuring heritage revitalization and sustainable development. This work includes a statistical analysis, clusters information, and thoroughly examines international research on bronze relic corrosion and protection. It delves into the timeline and trends of research, the main countries leading the research efforts, the research content, and the relationships between these factors. A comprehensive review is provided on the corrosion principles, materials, detection methods, and protection techniques for bronze. The study explores the corrosion principles and processes of bronze from a materials science perspective both before and after excavation. It summarizes non-destructive detection methods and examines specific factors that influence corrosion. Furthermore, the article reviews current corrosion protection methods for bronze and related protection materials, including commonly used strategies such as surface corrosion inhibitors and organic resin coatings for protection. It also discusses the potential application of advanced corrosion protection methods in the realm of metal materials in recent years to safeguard bronze. Proposing innovative solutions, the study suggests the possibility of constructing biomimetic superhydrophobic surfaces to create a barrier isolating humid air from contacting bronze materials, thereby reducing the adhesion of corrosive media to the substrate and significantly diminishing the likelihood of corrosion. In conclusion, the article looks towards the future, considering the challenges and potential development directions for the corrosion protection of bronze and related protection materials.
The Construction of a Small-Caliber Barrel Wear Model and a Study of the Barrel Wear Rule
Authors: Zou Libo , Fan Jiangtao , Huang Jianwen , Chen Jiahao
Published: September 2024

The wear of small-caliber barrels is one of the key factors affecting barrel life. Based on the Archard wear model, a high-temperature pin plate wear experiment was carried out, and wear models of chrome-plated layers and gun barrel materials were established. In addition, a finite element model of the interaction between the bullet and the barrel was established. The movement of the projectile along the barrel was simulated and analyzed, and the force distribution of the spatial geometry structure of the rifling was mastered through simulation. The wear law of the gun barrel along the axial direction was obtained based on the wear model of the chrome-plated layer and gun barrel material. A position 100 mm away from the barrel breech wears very fast; this position is where the cone of the bullet is engraved in the barrel. At the position 150–350 mm away from the barrel breech, the barrel bore wears even faster. The barrel chrome layer is mainly affected by the gunpowder impact and projectile engraving, which is consistent with the actual failure of the coating. When the distance to the barrel breech is 350 m, the wear becomes stable. Through an analysis of the diameter of the barrel, it was found that, when the diameter of the barrel exceeded 12.85 mm, the barrel reached the end of its life.
YOLOv8-Based Drone Detection: Performance Analysis and Optimization
Authors: Yilmaz Betul , Kutbay Ugurhan
Published: September 2024

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.
The Enamelled Tiles of Olite’s Castle (Spain): Characterization, Provenance, and Manufacture Technology
Authors: Ruiz-Ardanaz Iván , Lasheras Esther , Durán Adrián
Published: September 2024

The objective of this study was to determine the authorship, provenance, and technology of the mudejar enamelled tiles from the Olite Castle (northern Spain, 14th century). According to previous knowledge, Olite’s enamelled tiles had been manufactured in Manises (Valencia, Spain). The analysis of ceramic pastes revealed the existence of two different chemical compositions, suggesting the use of two different clay sources, probably one from the Tudela area, and another from the Tafalla–Olite area. Those probably made in the Tudela area stood out with a higher diopside (CaMgSi2O6) content. Those probably made in the Tafalla–Olite area stood out for their calcium-bearing minerals, such as calcite (CaCO3) or gehlenite (Ca2Al(AlSi)O7). On this basis, production in Manises has been ruled out. However, it is highly probable that the artisans of Manises would have led the production from Tudela. The study of the firing temperatures and composition of the enamels indicated that the production methods and materials used in Tafalla–Olite (800–850 °C) and Tudela (higher than 900 °C) were different, reflecting the influence of local and Manises artisans, respectively. In Olite tiles, enamel was applied following recipes from the 14th and 15th centuries.
Impact of Mg on the Feeding Ability of Cast Al–Si7–Mg(0_0.2_0.4_0.6) Alloys
Authors: Djurdjevic Mile , Manasijevic Srecko , Patarić Aleksandra , Stopic Srecko , Mihailović Marija
Published: September 2024

The demand for high-performance Al–Si casting alloys is driven by their mechanical properties, making them popular in automotive, aerospace, and engineering industries. These alloys, especially hypoeutectic Al–Si–Mg, offer benefits like high fluidity, low thermal expansion, and good corrosion resistance. Silicon and magnesium primarily define their microstructure and mechanical properties. Silicon enhances fluidity, while magnesium improves strength and fatigue resistance. However, challenges like shrinkage porosity persist during solidification. Understanding solidification feeding regions is crucial, influenced by factors such as chemical composition, solidification characteristics, and casting design. This study investigates magnesium’s influence on feeding ability in hypoeutectic Al–Si7–Mg alloys through experimental tests. Increasing magnesium content from 0% to 0.6% affects the interdendritic and burst feeding regions. This could impact shrinkage porosity formation. The “Sand Hourglass” test results indicate a rise in porosity levels with higher magnesium content, which is linked to the narrowing of interdendritic channels and the formation of magnesium-rich intermetallic compounds. These changes hinder the liquid metal flow, worsening shrinkage porosity. Therefore, magnesium’s role in expanding the interdendritic region is a key factor in developing porosity in cast hypoeutectic Al–Si7–Mg alloys. This study highlights that porosity levels increase from 0% in magnesium-free Al–Si7 to 0.84% in Al–Si7–Mg0.6, underscoring magnesium’s significant impact on the occurrence of porosity in these alloys.
Analytical Validation of Cxbladder® Detect, Triage, and Monitor: Assays for Detection and Management of Urothelial Carcinoma
Authors: Harvey Justin C. , Cambridge Lisa M. , Ellen Charles W. , Colonval Megan , Hazlett Jody A. , Newell Jordan , Zhou Xin , Guilford Parry J.
Published: September 2024

Background: Cxbladder® assays are reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tests incorporating five genetic biomarkers (CDK1, MDK, IGFBP5, HOXA13, and CXCR2) to provide risk stratification for urothelial carcinoma (UC) in patients with hematuria or undergoing surveillance for recurrent disease. This study evaluated the analytical validity of the Cxbladder Detect, Triage, and Monitor assays. Methods: Pre-specified acceptance criteria, including the assays’ fundamental aspects (sample and reagent stability, RNA extraction quality, RT-qPCR linearity, and analytical sensitivity and specificity), accuracy and precision, and reproducibility between laboratories. Results: Cxbladder had an analytical sensitivity of 12.5–31.1 RNA copies/mL urine for the CDK1, MDK, IGFBP5, and HOXA13 UC biomarkers and 68.9 RNA copies/mL for the inflammatory biomarker CXCR2. All the pre-specified analytical criteria were met. Cxbladder had diagnostic sensitivity, specificity, positive predictive value, and negative predictive values of 77%, 94%, 68%, and 96%, respectively, for Detect; 95%, 46%, 20%, and 98% for Triage; and 91%, 39%, 21%, and 96% for Monitor. Cxbladder had high analytical accuracy (≤10.63% inaccuracy across all biomarkers) and good reproducibility (>85% concordance between laboratories). Conclusions: Cxbladder accurately and reproducibly detects UC biomarker expression and can aid clinicians in risk stratification of hematuria patients or those undergoing surveillance for recurrent UC.
Detection of Circulating Tumor Cells and EGFR Mutation in Pulmonary Vein and Arterial Blood of Lung Cancer Patients Using a Newly Developed Immunocytology-Based Platform
Authors: Dejima Hitoshi , Nakanishi Hayao , Takeyama Ryo , Nishida Tomoki , Yamauchi Yoshikane , Saito Yuichi , Sakao Yukinori
Published: September 2024

Background: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors are powerful molecular targeted therapeutic agents for lung cancer. We recently developed an original immunocytology and glass slide-based circulating tumor cell (CTC) detection platform for both CTC enumeration and EGFR mutation analysis with DNA extracted from CTCs. Methods: Using this platform, we conducted a pilot clinical study for CTC enumeration in peripheral blood (PB), pulmonary arterial blood (PA), and pulmonary venous blood (PV) from 33 patients with lung cancer (Stage I–III) who underwent surgery, followed by digital PCR-based EGFR mutation analysis of CTCs in PV from 12 patients. Results: The results showed that CTC levels were significantly higher in PV and PA than in PB (p < 0.05, p < 0.01. respectively), with a notably greater number of small and large CTC clusters (p < 0.01). Genetic analysis of EGFR mutations of CTCs from PV (n = 12) revealed six mutations, including three Exon19del and three L856R, in CTCs and eight EGFR mutations, including five Exon19del and three L856R, in lung tumor tissue. CTC mutation status matched that of tissue samples in nine patients, was unmatched in two patients, and controversial in one patient, indicating a sensitivity of 0.75 (6/8) and specificity of 1.0 (4/4) with some false-negative results for the mutation analysis of CTCs. Conclusions: This immunocytology-based CTC detection platform is a convenient method for detecting both CTC number and EGFR mutation status under microscopy, suggesting its potential as a liquid biopsy tool in the hospital for patients with lung cancer in some clinical settings.
Neurobiological Implications of Chronic Stress and Metabolic Dysregulation in Inflammatory Bowel Diseases
Authors: Sic Aleksandar , Cvetkovic Kiana , Manchanda Eshanika , Knezevic Nebojsa Nick
Published: September 2024

Chronic stress is a significant factor affecting modern society, with profound implications for both physical and mental health. Central to the stress response is cortisol, a glucocorticoid hormone produced by the adrenal glands. While cortisol release is adaptive in acute stress, prolonged exposure to elevated levels can result in adverse effects. This manuscript explores the neurobiological implications of chronic stress and its impact on metabolic dysregulation, particularly in the context of inflammatory bowel diseases (IBDs). The hypothalamic–pituitary–adrenal (HPA) axis regulates cortisol production, which influences metabolism, immune response, and neurobiology. Elevated cortisol levels are associated with the development and exacerbation of metabolic disorders like IBD and contribute to neurodegenerative processes, including cognitive impairments and increased susceptibility to psychiatric conditions. The interaction between cortisol and its receptors, particularly glucocorticoid receptors, underscores the complexity of these effects. This review aims to elucidate the mechanisms through which chronic stress and cortisol dysregulation impact metabolic health and neurobiological function, providing insights into potential therapeutic strategies for mitigating these effects.
Effectiveness of Defocus Incorporated Multiple Segments in Slowing Myopia Progression in Pediatric Patients as a Function of Age: Three-Year Follow-Up
Authors: Buzzonetti Luca , Petroni Sergio , Federici Matteo , Valente Paola , Iarossi Giancarlo
Published: September 2024

Background: The purpose of this study is to evaluate the effectiveness of Defocus Incorporated Multiple Segments (DIMSs) in slowing myopia progression in pediatric patients as a function of age. Methods: This was a non-randomized experimenter-masked retrospective controlled observational study of European individuals aged 6–16 years with progressive myopia but no ocular pathology. We retrospectively reviewed the charts of the participants allocated to receive DIMS spectacles (Hoya® MiyoSmart®) or single-vision spectacle lenses (control group). Cycloplegic spherical equivalent (SE) and axial length (AL) were measured at baseline and at 12-, 24-, and 36-month follow-ups. The results were stratified by age into four groups: patients wearing DIMS spectacles older or younger than 10 years of age (group A, 20 patients mean age 13.6 ± 2.2, and group C, 20 patients mean age 9.0 ± 1.2) and age-matched control groups (group B, 18 patients mean age 13.2 ± 2.5, and group D, 22 patients mean age 8.5 ± 0.9). Results: At 36 months, SE and AL increase were significantly reduced in groups A and C, respectively, compared to groups B and D (p < 0.05). Linear regression analysis showed a significant correlation (p < 0.05) between patient age and myopia progression for SE in groups A and C, but only in group A for AL. Groups B and D did not show any significant correlation (p > 0.05). Conclusions: DIMS spectacles seem to slow myopia progression in pediatric patients; however, their effectiveness shows the greatest results in children older than 10 years of age. Moreover, our findings suggest that AL may be the more reliable parameter for evaluating myopia progression.
Autonomous Vehicles Traversability Mapping Fusing Semantic–Geometric in Off-Road Navigation
Authors: Zhang Bo , Chen Weili , Xu Chaoming , Qiu Jinshi , Chen Shiyu
Published: September 2024

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process.
Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering
Authors: Rodriges Zalipynis Ramon Antonio
Published: September 2024

Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many of the toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.
Investigating the Drivers of Firm Internationalisation: A Fuzzy Set Analysis Using Global Entrepreneurship Development Index Data
Authors: Ndofirepi Takawira Munyaradzi
Published: September 2024

Firm internationalisation, a key driver of global economic growth, is influenced by various entrepreneurial resources. This study explores the relationships between human capital, risk capital, risk acceptance, opportunity perception, and firm internationalisation using cross-national data from the Global Entrepreneurship Development Index (GEDI). Employing fuzzy set qualitative comparative analysis (fsQCA), this study analyzes data from 137 countries. The findings highlight two primary configurations driving internationalisation: (1) risk acceptance and opportunity perception, and (2) risk capital and opportunity perception. Opportunity perception emerges as a critical factor in both configurations, while human capital is not found to be a necessary condition. These results contribute to a better understanding of the factors that foster firm internationalisation and inform policies aimed at promoting global entrepreneurial ecosystems.
Investigating the Effects of Practice Time on Student Achievement Considering Variations in Demographics across Various Chemistry Topics
Authors: Suresh Sloka , Toy Stephanie , Gondra Neha , Anilao Auddy Guerrero , Vernoy Brandon , Eilks Ingo , Gulacar Ozcan
Published: September 2024

This study examined the relationship between the time students spend on practice problems and their performance on exams in various chemistry topics, considering their demographics. The researchers divided 91 general chemistry students into three groups based on the time allotted for solving intervention questions: Minimum, Average, and Maximum. The results showed that the Minimum and Average time groups benefited almost equally, but the performance of the Maximum time group declined. This suggests that, while additional practice is beneficial, there could be an optimal amount of time that students should spend on each question. Spending too much time on a single question can lead to mental and emotional fatigue, resulting in a decline in performance. Additionally, the researchers noted variations in performance across different chemistry topics and student groups, and they examined the relationship between student demographics and their problem-solving performances. The study provides recommendations for educators, testing services, and online homework systems to improve the effectiveness of chemistry instruction, highlighting the importance of finding the right balance between practice time and student engagement, and suggesting that a uniform approach to practice problems may not be ideal for every student.
Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction
Authors: Vargas-Solar Genoveva
Published: September 2024

The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from these texts requires a deep understanding of natural language nuances and accurately representing content while preserving essential information. This process enables effective knowledge extraction, inference, and discovery. This paper proposes a critical study of state-of-the-art contributions exploring the complexities and emerging trends in representing, querying, and analysing content extracted from textual data. This study’s hypothesis states that graph-based representations can be particularly effective when annotated with sophisticated querying and analytics techniques. This hypothesis is discussed through the lenses of contributions in linguistics, natural language processing, graph theory, databases, and artificial intelligence.
Denoising Diffusion Implicit Model for Camouflaged Object Detection
Authors: Cai Wei , Gao Weijie , Jiang Xinhao , Wang Xin , Di Xingyu
Published: September 2024

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.
Control Method for Ultra-Low Frequency Oscillation and Frequency Control Performance in Hydro–Wind Power Sending System
Authors: Wu Renjie , Jiang Qin , Li Baohong , Liu Tianqi , Zeng Xueyang
Published: September 2024

In a hydropower-dominated power grid, the primary frequency regulation (PFR) capability of hydropower units is typically compromised to suppress ultra-low frequency oscillations (ULFOs). However, as renewable wind power is further integrated, a practicable solution to damp ULFOs has emerged, which is to adjust the frequency control parameters of wind turbine (WT) units. Driven by the goals of overall damping enhancement and ULFO suppression, this paper first establishes an extended unified frequency model (EUFM) of a hydro–wind power sending system. Based on EUFM, the damping torque of the hydro–wind power sending system is derived, and the specific impact of WT control parameters on ULFOs and PFR characteristics is investigated. Then, a novel optimization objective function considering damping in the ultra-low frequency band and PFR is formulated and solved using an intelligence algorithm. By optimizing the parameters of the WT to suppress ULFOs, the PFR capability of hydropower units can be released. Finally, simulation results verify that the optimized WT parameters can simultaneously address the ULFO problem and guarantee PFR performance, thereby enhancing the frequency dynamic stability of the sending system.
A Light-Weight Self-Supervised Infrared Image Perception Enhancement Method
Authors: Xiao Yifan , Zhang Zhilong , Li Zhouli
Published: September 2024

Convolutional Neural Networks (CNNs) have achieved remarkable results in the field of infrared image enhancement. However, the research on the visual perception mechanism and the objective evaluation indicators for enhanced infrared images is still not in-depth enough. To make the subjective and objective evaluation more consistent, this paper uses a perceptual metric to evaluate the enhancement effect of infrared images. The perceptual metric mimics the early conversion process of the human visual system and uses the normalized Laplacian pyramid distance (NLPD) between the enhanced image and the original scene radiance to evaluate the image enhancement effect. Based on this, this paper designs an infrared image-enhancement algorithm that is more conducive to human visual perception. The algorithm uses a lightweight Fully Convolutional Network (FCN), with NLPD as the similarity measure, and trains the network in a self-supervised manner by minimizing the NLPD between the enhanced image and the original scene radiance to achieve infrared image enhancement. The experimental results show that the infrared image enhancement method in this paper outperforms existing methods in terms of visual perception quality, and due to the use of a lightweight network, it is also the fastest enhancement method currently.
Real-Time Semantic Segmentation Algorithm for Street Scenes Based on Attention Mechanism and Feature Fusion
Authors: Wu Bao , Xiong Xingzhong , Wang Yong
Published: September 2024

In computer vision, the task of semantic segmentation is crucial for applications such as autonomous driving and intelligent surveillance. However, achieving a balance between real-time performance and segmentation accuracy remains a significant challenge. Although Fast-SCNN is favored for its efficiency and low computational complexity, it still faces difficulties when handling complex street scene images. To address this issue, this paper presents an improved Fast-SCNN, aiming to enhance the accuracy and efficiency of semantic segmentation by incorporating a novel attention mechanism and an enhanced feature extraction module. Firstly, the integrated SimAM (Simple, Parameter-Free Attention Module) increases the network’s sensitivity to critical regions of the image and effectively adjusts the feature space weights across channels. Additionally, the refined pyramid pooling module in the global feature extraction module captures a broader range of contextual information through refined pooling levels. During the feature fusion stage, the introduction of an enhanced DAB (Depthwise Asymmetric Bottleneck) block and SE (Squeeze-and-Excitation) attention optimizes the network’s ability to process multi-scale information. Furthermore, the classifier module is extended by incorporating deeper convolutions and more complex convolutional structures, leading to a further improvement in model performance. These enhancements significantly improve the model’s ability to capture details and overall segmentation performance. Experimental results demonstrate that the proposed method excels in processing complex street scene images, achieving a mean Intersection over Union (mIoU) of 71.7% and 69.4% on the Cityscapes and CamVid datasets, respectively, while maintaining inference speeds of 81.4 fps and 113.6 fps. These results indicate that the proposed model effectively improves segmentation quality in complex street scenes while ensuring real-time processing capabilities.
Multi-Objective Optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO Algorithm
Authors: Cen Yufei , Shen Haoyu , Wang Xiaoyuan , Wu Yongming , Du Jingjuan
Published: September 2024

In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.
An Active Distribution Network Voltage Optimization Method Based on Source-Network-Load-Storage Coordination and Interaction
Authors: Liang Junyu , Zhou Jun , Yuan Xingyu , Huang Wei , Gong Xinyong , Zhang Guipeng
Published: September 2024

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system.
The Effect of Increasing Aggregation Levels of Electrical Consumption Data on Renewable Energy Community (REC) Analyses
Authors: Raugi Marco , Consolo Valentina , Rugani Roberto
Published: September 2024

The growing number of renewable energy communities (RECs) exemplifies the potential of citizen-driven actions towards a more sustainable future. However, obtaining hourly measured consumption data from REC members remains challenging, hindering accurate feasibility studies for the development of communities. This study examines the impact of estimating hourly consumption from aggregated data on REC analysis results. A case study with real consumption data from diverse users, representative of a typical community in Tuscany, Italy, was analysed to investigate various influencing factors. Multiple scenarios were simulated: two open-source tools estimated energy production from the community’s PV plants, and two REC configurations were considered—one with consumers and prosumers and another with consumers and a producer (with the same total installed power). Additionally, three locations were evaluated to consider the importance of geographical positioning. The study revealed that the impact of consumption data aggregation is more significant in scenarios with low energy sharing, such as the scenario where prosumers were replaced with a producer. Geographical positioning showed no major discrepancies in energy and economic outcomes, implying that using estimated hourly consumption data from aggregated data has a relevant impact regardless of location. Furthermore, different weather files did not affect the impact of aggregated consumption data.
Comparative Analysis of Magnetic Field Distribution Characteristics of Two Shapes of Air-Core Bridge Arm Reactors
Authors: Jiang Tao , Yang Zhe
Published: September 2024

The application of air-core reactors in power systems is extensive and primarily aimed at enhancing system stability, limiting short-circuit currents, and providing reactive power compensation. Currently, the type most commonly used in power systems is the cylindrical-shaped air-core reactor (CAR), known for its stable mechanical structure and mature manufacturing process. However, the external magnetic field generated by this reactor propagates over a considerable distance in the air, which can interfere with the normal operation of many power electronic devices. This paper presents a comparative analysis between a novel annular-shaped air-core bridge arm reactor (AABAR) and the widely used cylindrical-shaped air-core bridge arm reactor (CABAR) within a DC transformer system. The comparison focuses on the magnetic field distribution, including magnetic flux density, magnetic field radiation range, and magnetic field energy, as well as the attenuation characteristics of these physical quantities. The concept of magnetic clearance (MC) is introduced as a quantitative metric. Through finite element simulation software (AEDT 2021 R1), it is demonstrated that the annular-shaped air-core reactor design can significantly improve spatial utilization and reduce the actual usage space of the reactors in DC transformer systems.
Early Design Stage Evaluation of All Electric Aircraft Power Systems Focusing on Long-Term Behavior
Authors: Hoffmann Melanie , Inkermann David , Knieke Christoph , Zeng Fanke , Kopp Tobias , Terörde Michael , Kurrat Michael
Published: September 2024

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research.
Model-Based PID Tuning Method for a Reactor for Microwave-Assisted Chemistry
Authors: Kozłowski Sebastian , Korpas Przemysław , Wojtasiak Wojciech , Borowska Magdalena
Published: September 2024

This paper presents a model-based PID tuning method for a reactor used in microwave-assisted chemistry. The reactor is equipped with a solid-state source of microwaves and a PID controller capable of increasing or decreasing the delivered microwave power to maintain the reacting substances at the desired temperature. The model has the form of an algorithm applied in numerical simulations of two simultaneous processes: heating a substance by absorbing microwave radiation and cooling it by dissipating heat to the surroundings. It has proven its suitability for tuning the PID controller in a time-efficient manner. Despite some noticeable inaccuracies, the presented approach easily finds PID coefficients that result in stable and repeatable controller operation. In this way, significant time savings can be achieved, as well as minimizing the risks associated with, for example, boiling liquid spills. The article demonstrates that a carefully designed, but still relatively simple, model can yield significant benefits in tuning PID controllers.
Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems
Authors: Liu Zhenchang , Wu Aiguo , Wen Haitang
Published: September 2024

The automatic control device is a critical component of absorption refrigeration systems. Its functional enhancement can reduce operating costs, improve energy efficiency, and ensure long-term stable unit operation. Given that absorption refrigeration systems operate under various dynamic conditions, the rational design of control strategies is particularly important. This study analyzes the influence of changes in the cooling water and heat source water flow rates on the outlet temperature of chilled water in the unit based on the open-loop response characteristics of absorption refrigeration systems. It proposes a dual-loop energy-saving control strategy for single-effect hot water lithium bromide absorption refrigeration systems based on the setpoint comprehensive optimization algorithm. Considering the multiple variables, strong coupling, large inertia, long time delay, and nonlinear characteristics of absorption refrigeration systems, as well as the difficulties in modeling these systems, this study applies a model-free adaptive control algorithm to the system’s control. It derives both SISO and MIMO model-free control algorithms with time-delay components. Through simulations comparing MFAC, improved MFAC, and traditional PID control, the dual-loop energy-saving control strategy is demonstrated to effectively reduce system heat consumption by approximately 20%, decrease power consumption by about 10%, and enhance the system’s SCOP by approximately 19.3%.