bluuebunny/crossref_metadata_2025 huggingface dataset:
Chronobiology in Paediatric Neurological and Neuropsychiatric Disorders: Harmonizing Care with Biological Clocks
Authors: Giannotta Gabriele , Ruggiero Marta , Trabacca Antonio
Published: December 2024

Background: Chronobiology has gained attention in the context of paediatric neurological and neuropsychiatric disorders, including migraine, epilepsy, autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD). Disruptions in circadian rhythms are associated with key symptoms such as sleep disturbances, mood dysregulation, and cognitive impairments, suggesting a potential for chronobiology-based therapeutic approaches. Methods: This narrative review employs a systematic approach to identify relevant studies through searches of three major scientific databases, NCBI/PubMed, ScienceDirect, and Scopus, up to July 2024. We used a combination of broad and condition-specific keywords, such as “chronobiology”, “biorhythm”, “pediatric”, “epilepsy”, “ADHD”, and “ASD”, among others. Articles in English that focused on clinical features, treatments, or outcomes related to circadian rhythms in paediatric populations were included, while non-peer-reviewed articles and studies lacking original data were excluded. Rayyan software was used for article screening, removing duplicates, and facilitating consensus among independent reviewers. Results: A total of 87 studies were included in the analysis. Findings reveal a consistent pattern of circadian rhythm disruptions across the disorders examined. Specifically, dysregulation of melatonin and cortisol secretion is observed in children with ASD, ADHD, and PTSD, with altered circadian timing contributing to sleep disturbances and mood swings. Alterations in core clock genes (CLOCK, BMAL1, PER, and CRY) were also noted in children with epilepsy, which was linked to seizure frequency and timing. Chronotherapy approaches showed promise in managing these disruptions: melatonin supplementation improved sleep quality and reduced ADHD symptoms in some children, while light therapy proved effective in stabilizing sleep–wake cycles in ASD and ADHD patients. Additionally, behaviour-based interventions, such as the Early Start Denver Model, showed success in improving circadian alignment in children with ASD. Conclusions: This review highlights the significant role of circadian rhythm disruptions in paediatric neurological and neuropsychiatric disorders, with direct implications for treatment. Chronobiology-based interventions, such as melatonin therapy, light exposure, and individualized behavioural therapies, offer potential for improving symptomatology and overall functioning. The integration of chronotherapy into clinical practice could provide a paradigm shift from symptom management to more targeted, rhythm-based treatments. Future research should focus on understanding the molecular mechanisms behind circadian disruptions in these disorders and exploring personalized chronotherapeutic approaches tailored to individual circadian patterns.
The Role of Inflammatory and Nutritional Indices in Postmenopausal Osteoporosis: A Retrospective Study
Authors: Demir Cendek Busra , Bayraktar Burak , Sapmaz Mehmet Alican , Yıldırım Ayse Ecenaz , Can Ibanoglu Mujde , Engin Ustun Yaprak
Published: December 2024

Background: Postmenopausal osteoporosis is characterized by impaired bone metabolism, inflammation, and nutritional deficiencies. This study aimed to evaluate the potential of inflammatory and nutritional markers in identifying decreased bone mineral density (BMD) in postmenopausal women. Methods: This cross-sectional study retrospectively analyzed postmenopausal women from January 2018 and December 2023. A total of 368 women were divided into three groups based on T-scores: 61 women with osteoporosis (T-score ≤ −2.5), 153 women with osteopenia (−1 > T-score > −2.5), and 154 women with normal BMD (T-score > −1). Inflammatory and nutritional biomarkers included the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), pan-immune inflammation value (PIV), geriatric nutritional risk index (GNRI), triglycerides, total cholesterol, and body weight index (TCBI), prognosis nutritional index (PNI), hemoglobin, albumin, lymphocyte, and platelet (HALP) score, 25-OH Vitamin D level, Na, K, Ca, Mg, and their ratios. Results: The GNRI was significantly lower in the osteoporosis group compared to the control group. The NLR, PLR, SII, SIRI, PIV, TCBI, PNI, and HALP were similar between the groups. The GNRI and TCBI showed a positive correlation with T-scores. The Mg level was lower in the osteoporosis group than in the control group and osteopenia group, and the Na/Mg ratio was higher. Additionally, the Ca/Mg ratio was lower in the osteoporosis group than in the control group. The T-score was positively correlated with Mg and Ca/Mg, while the Na/Mg ratio showed a significant negative correlation. Vitamin D, other minerals, and their ratios did not show significant differences between the groups. Conclusions: Our findings suggest that the GNRI could serve as a useful indicator for assessing bone health and the risk of osteoporosis. Furthermore, maintaining appropriate levels of Mg and balanced Na/Mg and Ca/Mg ratios appears crucial for BMD.
Treatment Advances in Perinatal Depression: Innovations and Promising Approaches
Authors: Kendall-Tackett Kathleen
Published: December 2024

Background/Objectives: Psychotherapy and antidepressants are the standard treatment for depression during pregnancy or postpartum. However, several new treatments for depression represent major advances and paradigm changes. This commentary highlights some innovative treatment options that are on the horizon. Most of these modalities are promising, and most are non-invasive. Many of these modalities have been used in the general population, where evidence supports their use. The methods have only recently been used for pregnant and postpartum women. Other modalities are specifically for perinatal women but do not have an established track record. Methods: This commentary describes some promising approaches to treatment, while acknowledging that the literature is preliminary. The goal is to highlight some interesting approaches drawn from a recent comprehensive review of the entire literature on treatment for perinatal mental illness. Results: Integrative treatments include vitamin D, infant massage, mindfulness-based cognitive therapy, acupuncture, and repetitive transcranial magnetic stimulation (rTMS). Many studies in the general population have found that these methods are effective, and they also show promise for perinatal women without side effects associated with medications. Some of these treatments can also be adjuncts to what is considered standard care. Two new medications, brexanolone and esketamine, quickly and effectively treat severe depression and work on GABA and glutamate receptors rather than serotonin or norepinephrine. These medications become less effective after 30 days but can be combined with selective serotonin reuptake inhibitors (SSRIs). Conclusions: Pregnant and postpartum women seeking care for depression and other mental health conditions have many options beyond psychotherapy and SSRI/SNRI antidepressants. These modalities can also be added to their care.
Factors Affecting Sport-Related Concussion Non-Disclosure in Women’s Rugby—A Multi-Country Qualitative Analysis
Authors: Ryan Lisa , Daly Ed , Hunzinger Katherine
Published: December 2024

Background and Objectives: Collision-sport athletes, such as rugby players, are at risk of sport-related concussion (SRC). Women are known to be at higher risk of SRC and may experience more severe and chronic symptomology than men. Knowledge of the factors that affect a player’s disclosure of their concussive symptoms could help to inform strategies to improve compliance with reporting and management of head injuries. The aim of this study was to investigate the factors that may impact women rugby players’ disclosure of a concussion. Methods: Twenty-eight adult (≥18 years of age) elite and semi-elite women rugby players from the UK and Ireland (n = 17) and the United States (n = 11) were interviewed on their playing background and SRC experience in women’s rugby via one-on-one interviews (UK and Ireland) or an online questionnaire (US). Results: SRC data were analysed inductively using a thematic analysis approach to determine the potential reasons for SRC non-disclosure in women’s rugby. Four main themes were identified which may influence a player’s SRC non-disclosure: 1. women rugby players are self-managing SRC; 2. work-related concerns impact on SRC disclosure; 3. players and support staff lack knowledge on SRC management; and 4. poor internal and external communication affect the support players receive when injured. Conclusions: The findings were consistent across players from different countries. This research highlighted several factors that may impact on women rugby players’ disclosure of SRC regardless of country of origin (UK, Ireland, or US) and access to concussion care. Coaches and management teams should be aware of these reasons, which may enhance how they discuss and manage concussion. There is a clear need for further education on concussion for players and support staff and for strategies to create environments where women can openly discuss their concussion concerns.
Experimental Investigation into the Influence of Filament Layer Arrangement on the Tensile Strength of FFF-Manufactured Components
Authors: Böhm Matthias , Buss Christoph
Published: December 2024

One major disadvantage of fused filament fabricated components (FFF) is its well-known anisotropy, which results from the layer-wise adding of material, and that it is not always possible to avoid loading in the layer build-up direction. In particular, components that are exposed to multi-axial load conditions must manage with reduced tensile strength in the build-up direction. This work is therefore concerned with improving the tensile strength transverse to the layering by changing the layer structure without directly changing the material itself. Therefore, the print-defining G-Code was modified to change the arrangement between the layers. The effectiveness of this method was investigated by means of tensile tests using thermoplastic samples made of Acrylonitrile Styrene Acrylate (ASA), Poly Cyclohexylenedimethylene Terephthalate Glycol (PCTG), Poly Ethylene Terephthalate Glycol (PETG) and Poly Lactic Acid (PLA) for layer thicknesses of 0.16 mm and 0.28 mm. The results show that the G-Code modification generally resulted in an increase in tensile strength. For PETG, an improvement of 25% was achieved.
Noise Reduction of Velocity Measured by Frequency-Supervised Combined Doppler Sonar Using an Adaptive Sliding Window and Kalman Filter
Authors: Liu Peng , Liu Bingxin , Zhu Xueyuan , Chen Peng , Li Ying
Published: December 2024

Velocity is fundamental information for ocean engineering. It is difficult for traditional Doppler sonar to provide accurate and wide-range velocity measurement information with a short time lag. Therefore, a frequency-supervised combined Doppler sonar system using an adaptive sliding window and Kalman filter is proposed. In this method, the initial value of the integer ambiguity is calculated based on the average value of the conventional Doppler sonar. The change value of the integer ambiguity is calculated by the difference of the adjacent velocities measured by coherent Doppler sonar. The velocity of combined Doppler sonar is calculated by the cumulative result of the initial and change values of integer ambiguities. Finally, the velocity bias due to the error of the integer ambiguity calculation is corrected by the frequency supervision using the Kalman filter in a sliding time window under different signal-to-noise ratios. The experimental results show that the proposed method is more accurate than the conventional Doppler sonar, has a wider measurement range compared with coherent Doppler sonar, and suppresses the impulsive noise well. The frequency-supervised combined Doppler sonar using an adaptive sliding window and Kalman filter can provide accurate and precise velocities with a short time lag over a wide range of signal-to-noise ratios.
Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm
Authors: Cui Bin , Chen Yuanming , Hong Xiaobin , Luo Hao , Chen Guanqiao
Published: December 2024

Path following is one of the key technologies for unmanned surface vehicles (USVs). This paper proposes a path-tracking control method for a single-outboard-motor USV based on a Deep Deterministic Policy Gradient (DDPG) algorithm and model predictive control (MPC) algorithm. Initially, the motion model and outboard motor model of the USV are analyzed. Subsequently, simulation and real ship experiments provide a comprehensive performance comparison between the proposed DDPG-MPC method and the traditional ALOS-PID method. The results indicate that for straight path tracking, the DDPG-MPC algorithm achieves 37% and 21% reductions in the average cross error and heading angle error, respectively, compared to the ALOS-PID algorithm. The real ship experiments further validate the DDPG-MPC algorithm’s advantages in real-world environments. Specifically, under disturbances like wind, waves, and currents, the maximum cross error of the DDPG-MPC algorithm is one-third of the ALOS-PID algorithm. Additionally, the DDPG-MPC algorithm sustains a higher and more stable longitudinal velocity over extended periods, while the ALOS-PID algorithm shows greater instability and variability. Overall, the findings confirm the feasibility and effectiveness of the proposed approach, highlighting its potential for enhancing path-tracking control performance in single-outboard-motor USVs.
Investigating Tidal Stream Turbine Array Performance Considering Effects of Number of Turbines, Array Layouts, and Yaw Angles
Authors: Zhang Can , Zhang Kai , Cheng Xiaoming , Lin Xiangfeng , Zhang Jisheng , Wu Chengsheng , Ren Zhihao
Published: December 2024

The performance of a tidal stream turbine array can be affected by numerous factors. Investigating the connection between array power production and these factors will be helpful in improving the development of tidal stream energy. This study investigates the impact of array layout, turbine number, and yaw angles on turbine array performance using an open-source coastal ocean modelling system. The results show that the total power output of the turbine array rises with the number of turbines. Under realistic conditions, there are not many differences in power output between aligned and staggered turbine array configurations. By extending the distance between the turbines, the array power output can be improved in both layouts. It appears that considering each turbine’s yaw angle can improve array power generation, since the downstream turbines will greatly benefit from the steering wake of the upstream turbines. Furthermore, using a gradient-based optimization algorithm to simultaneously adjust the yaw angles and turbine positions will boost the turbine array’s efficiency more than just optimizing the turbine position alone.
Evaluation of the Body Burden of Short- and Medium-Chain Chlorinated Paraffins in the Blood Serum of Residents of the Czech Republic
Authors: Parizkova Denisa , Sykorova Aneta , Tomasko Jakub , Parizek Ondrej , Pulkrabova Jana
Published: December 2024

Short- and medium-chain chlorinated paraffins (SCCPs and MCCPs) are environmental contaminants known for their persistence and bioaccumulation in fatty tissues. SCCPs are considered potential carcinogens and endocrine disruptors, with similar effects expected for MCCPs. This study investigated the body burden of SCCPs and MCCPs in residents of two regions of the Czech Republic with different levels of industrial pollution. Blood serum samples from 62 individuals in Ceske Budejovice (control area) and Ostrava (industrial area) were analysed. The results showed higher concentrations of SCCPs (<120–650 ng/g lipid weight (lw)) and MCCPs (<240–1530 ng/g lw) in Ostrava compared to Ceske Budejovice (SCCPs: <120–210 ng/g lw, MCCPs: <240–340 ng/g lw). The statistical analysis revealed no significant correlations between chemical concentrations and demographic variables such as age, BMI, or gender. The findings are consistent with European and Australian studies but significantly lower than levels reported in China. This is the first comprehensive survey of SCCPs and MCCPs in human blood serum in the Czech Republic and the second study in Europe. The data collected in this study are essential for assessing SCCPs and MCCPs. They will contribute to a better understanding the potential health risks associated with exposure to these chemicals.
Analysis of Financial Contagion and Prediction of Dynamic Correlations During the COVID-19 Pandemic: A Combined DCC-GARCH and Deep Learning Approach
Authors: Chung Victor , Espinoza Jenny , Mansilla Alan
Published: December 2024

This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 in the United States on the Latin American stock market using a dynamic conditional correlation approach. The study covers the period from 2014 to 2020, divided into the pre-COVID-19 period (January 2014–February 2020) and the COVID-19 period (March 2020–November 2020), to examine the sudden change in average conditional correlation from one period to the next and identify the contagion effect. The contagion test showed significant contagion between the S&P 500 and Latin American indices, except for Argentina’s MERVAL. Additionally, we applied deep learning models, specifically LSTM, to predict market dynamics and changes in volatility as an early warning system. The results indicate that incorporating LSTM improved the accuracy of predicting dynamic correlations and provided early risk signals during the crisis. This suggests that combining DCC-GARCH with deep learning techniques is a powerful tool for predicting and managing financial risk in highly uncertain markets.
Evaluating the Financial Performance of Colombian Companies: A Data Envelopment Analysis Without Explicit Inputs and Technique for Order Preference by Similarity to the Ideal Solution Approach
Authors: Mendoza-Mendoza Adel , Mendoza Casseres Daniel , De La Hoz-Domínguez Enrique
Published: December 2024

The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest companies in Colombia based on their financial results for the period 2022–2023. An analysis of 100 companies was conducted, utilizing four critical criteria: operating income, net profit, total assets, and equity. The evaluation followed a two-stage process. In the first stage, the weights or importance of each selected criterion were objectively established using data envelopment analysis without explicit inputs (DEA-WEIs). This approach reveals that operating income (35.23%) and total assets (28.57%) are the most influential criteria, while net profit is the least influential (13.51%). In the second stage, companies are ranked using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), with the results highlighting Refinería de Cartagena, Empresas Públicas de Medellín, and Terpel S.A. as the top-performing companies. The classification shows clear differentiation, forming two statistically distinct groups validated through discriminant analysis, achieving a 100% correct classification rate. These findings provide actionable insights for benchmarking and improving financial performance in the corporate sector.
Electrodermal Activity for Quantitative Assessment of Dental Anxiety
Authors: Bari Dindar S. , Rammoo Mohammed Noor S. , Youssif Ardawan A. , Najman Hoger M. , Aldosky Haval Y. Yacoob , Tronstad Christian , Hou Jie , Martinsen Ørjan G.
Published: December 2024

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether dental anxiety can be quantified objectively using the EDA parameters. EDA (skin conductance (SC), skin susceptance (SS), and skin potential (SP)) parameters and heart rate (HR) were recorded from 40 participants during two different sessions (baseline and anxiety). In addition, the Modified Dental Anxiety Scale (MDAS) scale was also used to record the level of anxiety. The physiological data from EDA and HR were compared with the subjective self-reports of anxiety provided on the MDAS to show whether higher EDA and HR readings correspond to higher scores on the MDAS. To elicit dental anxiety, participants were exposed to several film clips associated with dental treatment. EDA signals were compared between the two sessions for all clips and all EDA scores. SC and HR significantly (p < 0.05) increased during the anxiety session compared to the baseline session. The number of fluctuations per minute in the SC, SS, and SP notably increased during the anxiety session. The MDAS results revealed that the participants had dental anxiety when they were exposed to video clips of dental procedures. The study results imply that EDA parameters could be used as a useful tool to monitor dental anxiety, in particular in young children and non-verbal patients or those with intellectual disabilities, which may aid the dentist in the successful management of dental anxiety during treatment. The moment-to-moment EDA data on a patient’s anxiety levels throughout particular dental operations provides a clearer picture of anxiety variations than pre- or post-appointment surveys alone, in addition to offering unbiased tracking of dental anxiety levels over self-reports. This study seeks to encourage further research into the most effective EDA parameters for improving the management of dental anxiety.
The Impact of Built Environment on Mixed Land Use: Evidence from Xi’an
Authors: Li Jianwei , Chen Yun , Zhao Dan , Zhai Jiagang
Published: December 2024

Mixed land use is recognized as a crucial strategy for enhancing urban vitality and promoting urban renewal. While existing research has mainly focused on measuring mixed land use along single dimensions, there remains a notable gap in studies that explore multidimensional measurements and their impacts at the block scale. This study introduces a multidimensional mixed-degree index based on diverse data sources such as POI and land use status to comprehensively assess mixed land use levels at the block scale in Xi’an’s central urban area. Additionally, a multiple linear regression model is applied to analyze how the built environment influences mixed land use. Findings reveal that mixed land use at the block scale can be objectively evaluated across three dimensions: quantity, distance, and attribute. In Xi’an, mixed land use demonstrates a spatial distribution characterized by core agglomeration and concentric decline. The study highlights that block area and road network density significantly influence mixed land use, with block area negatively impacting it the most and road network density positively affecting it secondarily. These insights provide valuable guidance for optimizing land use practices and promoting high-quality urban development.
Google Earth as a Tool for Supporting Geospatial Thinking
Authors: Jaeger Allison J.
Published: December 2024

In landscape planning and design, geospatial technologies (GSTs) are used to aid in visualizing and interpreting geographic environments, identifying geospatial patterns, and making decisions around information based on maps and geospatial information. GSTs are related to the different tools and technologies used to represent the earth’s surface and have transformed the practice of landscape design and geospatial education. These technologies play an important role in promoting the development and application of STEM-relevant geospatial thinking. Curricula that incorporate GSTs have been used across educational levels, from elementary school through college, and have been shown to support the development of geospatial learning and understanding. The present work discusses the use of one type of GST, virtual globes, as a tool for developing geospatial thinking, with a specific focus on Google Earth. This review highlights outcomes of several studies using Google Earth in the context of disciplines related to landscape design, such as geography and earth science. Furthermore, the potential mechanisms underlying the effectiveness of this technology for supporting the development of geospatial knowledge, such as its role in facilitating data visualization and supporting student’s ability to think flexibly about spatial patterns and relations, are discussed. Finally, the limitations of the current research on Google Earth as a tool for supporting geospatial learning are discussed, and suggestions for future research are provided.
Breaking Spatial Constraints: A Dimensional Perspective-Based Analysis of the Eco-Efficiency of Cultivated Land Use and Its Spatial Association Network
Authors: Wang Xingjia , Wang Dongyan
Published: December 2024

Global urbanization has caused enormous challenges that seriously threaten ecological security and the food system. Thus, there is a need for finding an optimal solution for the eco-efficiency of cultivated land use (ECLU) that can promote the development of new-type urbanization, while ensuring the sustainable utilization of limited cultivated-land resources. The quantitative system of multi-scale ECLU used in existing studies is inadequate; it is necessary to establish a measurement system from the perspective of geographical spatial relationship that uses evaluation as a key basis for management. In this study, we considered the Changchun Metropolitan Area and a representative urban–rural transition area as the target regions and customized new ECLU evaluation systems for different scales. The super slack-based measure and gravity and social network analyses methods were applied to evaluate the ECLU and explore the structural characteristics of its spatial association network. The average ECLU value for the Changchun Metropolitan Area was 0.974; the results indicated that most of the study area was eco-efficient. The value of ECLU for the urban–rural transition area varied from 0.022 to 1.323; thus, the highly efficient cultivated land was mainly distributed around the urban built-up area. The spatial association network of ECLU revealed that the overall spatial correlations were relatively weak, with a significant “bipolar” division of ECLU; furthermore, the network hierarchy and stability needed improvement. Moreover, we noted distant attraction capacity and siphoning effects outside regional boundaries. In the Changchun Metropolitan Area, it manifested as a monocentric radiation, with Changchun City as the center. In the urban–rural transition area, the cultivated land in proximity to the newly built urban area was more likely to experience spatial spillover. These findings have important implications for strengthening land-use management and advancing sustainable agricultural development for new-type urbanization. Our study can be used by policymakers and stakeholders to design sustainable urban cities, while improving land-use management and optimizing resource use.
Comparing Long-Term Outcomes in Glomerular Disease Patients Presenting with Nephrotic Syndrome Versus Nephrotic Range Proteinuria
Authors: Ștefan Gabriel , Stancu Simona , Zugravu Adrian , Petre Nicoleta
Published: December 2024

Background: Despite extensive research on proteinuria’s impact on chronic kidney disease progression, there is no direct comparison of outcomes in biopsy-diagnosed glomerular disease (GD) patients with nephrotic syndrome (NS) or nephrotic range proteinuria (NRP). Our study addresses this gap, comparing long-term outcomes between NS and NRP. Methods: We conducted a retrospective study on 240 kidney biopsy-proven GD patients, tracked from 2010 to 2015 until end-stage kidney disease (ESKD), death, or the study end in January 2022. Results: The median follow-up was 8.8 years. Diagnoses were predominantly nonproliferative (53%), proliferative (25%) nephropathies, diabetic nephropathy (12%), and paraprotein diseases (10%). NS was observed in 141 (59%) patients, presenting more frequently with arterial hypertension, higher eGFR, increased proteinuria, and dyslipidemia than NRP patients. NRP patients often had proliferative GD and diabetic nephropathy; their renal chronicity score was higher. The ESKD endpoint occurred in 35% NS and 39% NRP patients (p 0.4). The cohort’s mean kidney survival time was 8.2 years. In a multivariate analysis, NS, lower eGFR, a higher renal chronicity score, and diabetic nephropathy were associated with ESKD. A total of 64 patients (27%) died, 73% post-kidney replacement therapy initiation, and mostly from cardiovascular disease (63%). Mortality between proteinuria forms showed no difference. The multivariate analysis found lower eGFR, a higher Charlson comorbidity score, and diabetic nephropathy associated with mortality. Conclusions: Our study found no difference in all-cause mortality between NS and NRP in glomerular diseases. However, an adjusted analysis revealed poorer kidney survival for NS patients, emphasizing the need for personalized management to improve renal prognoses.
Supply Chain Management Control in the Aerospace Sector: An Empirical Approach
Authors: Torralba-Carnerero Gonzalo , García-Nieto Manuel , Ramón-Jerónimo Juan Manuel , Flórez-López Raquel
Published: December 2024

Introduction: The aerospace industry has been significantly disrupted by recent economic downturns, underscoring the need for robust supply chain management. This is especially important given the complexity of aircraft manufacturing, the globalization of supply chains, and the requirement to meet stringent regulatory standards. While outsourcing is widely adopted to improve cost competitiveness, it also introduces risks, such as compromised product quality, inefficiency, and delays. Methods: This study explores how aerospace firms manage outsourcing relationships using control mechanisms. Data were gathered through seven semi-structured interviews with supply chain managers from contracting and supplier firms focusing on both formal and informal controls in supplier selection and relationship management. Results: Supplier selection is primarily guided by trust, past performance, and delivery reliability. Firms employ formal controls, such as KPIs and certifications, alongside informal practices, including embedding internal staff within supplier operations. This dual approach ensures quality, mitigates risks, and maintains compliance with regulatory standards. Conclusions: This study concludes that combining formal and informal controls is vital for balancing outsourcing efficiency with risk mitigation, offering valuable insights into supply chain management practices in regulated industries like aerospace.
Tribological Investigation of Polymer Composite Dynamic Shaft Seals in Extraterrestrial Applications
Authors: Kalácska Ádám , Coen Alexander , Poletto Jean Carlos , De Baets Patrick , Kalácska Gábor
Published: December 2024

Polymer seals are utilized in various engineering applications to prevent leakage and contamination. The study investigates the wear and friction behavior of PTFE-based dynamic rotary seals, targeting their usage in space applications. Pin-on-disc dry sliding wear tests were performed with 0.5 MPa contact pressure and 0.2 m/s sliding velocity combining different lip seal (PTFE, PTFE+GF+MoS2), packing (PTFE, PTFE+Aramid fiber+solid lubricant) and shaft materials (34CrNiMo6, PEEK) involving third-body lunar (LHS-1) and Martian regolith (MGS-1) simulants. To understand the different influences of extraterrestrial regolith simulants compared to commonly encountered abrasives on Earth, quartz sand was selected as a reference. Quartz soil resulted in lower wear rates but a similar coefficient of friction to other regoliths. In the case of lip seals, testing with LHS-1 on PEEK and testing with MGS-1 on steel resulted in the most severe wear. Post-mortem surface analysis revealed the effect of external abrasive particles on the wear process and the transfer layer formation. The surface analysis confirmed that both lunar and Martian regolith simulants resulted in significant embedded particles. Based on the wear performance results, the lip seals performed better, but installation with an external packing could further aid the tribosystem.
Sliding Wear Behavior of WP7V Tool Steel with Different Hardnesses Under Reciprocating Test Rig
Authors: Breganon Rogério , Arieta Francisco , Pintaude Giuseppe
Published: December 2024

This study involved the investigation of the mechanical and tribological behaviors of DIN 1.2344 and WP7V tool steels, quenched in a salt bath after austenitization at 1050 °C, followed by triple tempering for 2 h. The selection of tempering temperatures produced two hardness levels under four metallurgical conditions, with the hardest level found only for WP7V steel (54 and 57 HRC). The mechanical properties were evaluated using Rockwell C, Vickers, and nanoindentation methods, along with unnotched impact tests, according to the SEP 1314 guidelines. Wear tests were conducted in a tribometer configured for a reciprocating setup, with a frequency of 5 Hz, a load of 25 N, and a time of 60 min, at room temperature and at 200 °C. As counterbodies, alumina balls of 5 mm in diameter were used. Wear tracks were evaluated through scanning electron microscopy, EDS, interferometry, and Raman spectroscopy. Friction and wear behaviors were affected by the variation in temperature for softer steels (DIN 1.2344 and WP7V of 48.5 HRC): the higher the temperature, the better the tribological performance. The harder steels were not sensitive to temperature testing. These effects depend on maintaining iron oxide (hematite) at the point of contact. The wear rates determined for the hardest material (57 HRC), considering its impact resistance, make it unsuitable for severe conditions such as hot stamping.
Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network
Authors: Resendiz-Ochoa Emmanuel , Calderon-Uribe Salvador , Morales-Hernandez Luis A. , Perez-Ramirez Carlos A. , Cruz-Albarran Irving A.
Published: December 2024

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability.
AI-Assisted 3D Modeling Strategy for Microstructure-Based Functional Surfaces Using ChatGPT and Random Forest
Authors: Noh Younghun , Park Sucheong , Lee Sungho
Published: December 2024

With the development of computer programming, product design is being carried out through computer programming such as 3D CAD/CAM when developing products, and the cost of developing new products can be greatly reduced by using 3D modeling tools in which various engineering theories are programmed. With the recent rapid development of artificial intelligence (AI), various AI applications have been actively proposed in various fields. In this study, we report a ChatGPT-based 3D modeling program (CMP) which can easily design microstructures, and the theory of wettability that was programmed into the CMP. Since the theory related to wettability is programmed into the CMP, once the user enters basic information about the desired functional surface, such as superhydrophobic surfaces, at the CMP prompt, 3D structures with the performance of superhydrophobicity are immediately provided. Through microstructure fabrication techniques, the microstructure designed by the CMP was actually fabricated, wettability experiments were conducted, and it was confirmed that a superhydrophobic surface was successfully designed. As such, the AI-based CMP can easily design microstructure-based functional surfaces, and we expect this method not only to decrease the fabrication costs of designing micro/nano structures but also to be useful in various fields.
A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions
Authors: Huang Boyan , Song Kai , Jiang Shulin , Zhao Zhenqing , Zhang Zhiqiang , Li Cong , Sun Jiawen
Published: December 2024

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.
A Solution-Structure B-Spline-Based Framework for Hybrid Boundary Problems on Implicit Domains
Authors: Qarariyah Ammar , Yang Tianhui , Deng Fang
Published: December 2024

Solving partial differential equations (PDEs) on complex domains with hybrid boundary conditions presents significant challenges in numerical analysis. In this paper, we introduce a solution-structure-based framework that transforms non-homogeneous hybrid boundary problems into homogeneous ones, allowing exact conformity to the boundary conditions. By leveraging B-splines within the R-function method structure and adopting the stability principles of the WEB method, we construct a well-conditioned basis for numerical analysis. The framework is validated through a number of numerical examples of Poisson equations with hybrid boundary conditions on different implicit domains in two and three dimensions. The results reflect that the approach can achieve the optimal approximation order in solving hybrid problems.
Carbon Emissions and Sustainable Supply Chains: A Stackelberg Game Analysis of Multinational Firm Relationships
Authors: Tian Bo , Liu Meiqi , Pan Bin , Yuan Guanghui , Xie Fei
Published: December 2024

Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives.
Adaptive Centroid-Connected Structure Matching Network Based on Semi-Supervised Heterogeneous Domain
Authors: Sun Zhoubao , Tang Yanan , Zhang Xin , Zhang Xiaodong
Published: December 2024

Heterogeneous domain adaptation (HDA) utilizes the knowledge of the source domain to model the target domain. Although the two domains are semantically related, the problem of feature and distribution differences in heterogeneous data still needs to be solved. Most of the existing HDA methods only consider the feature or distribution problem but do not consider the geometric semantic information similarity between the domain structures, which leads to a weakened adaptive performance. In order to solve the problem, a centroid connected structure matching network (CCSMN) approach is proposed, which firstly maps the heterogeneous data into a shared public feature subspace to solve the problem of feature differences. Secondly, it promotes the overlap of domain centers and nodes of the same category between domains to reduce the positional distribution differences in the internal structure of data. Then, the supervised information is utilized to generate target domain nodes, and the geometric structural and semantic information are utilized to construct a centroid-connected structure with a reasonable inter-class distance. During the training process, a progressive and integrated pseudo-labeling is utilized to select samples with high-confidence labels and improve the classification accuracy for the target domain. Extensive experiments are conducted in text-to-image and image-to-image HDA tasks, and the results show that the CCSMN outperforms several state-of-the-art baseline methods. Compared with state-of-the-art HDA methods, in the text-to-image transfer task, the efficiency has increased by 8.05%; and in the image-to-image transfer task, the efficiency has increased by about 2%, which suggests that the CCSMN benefits more from domain geometric semantic information similarity.
The Role of Re-Entrant Microstructures in Modulating Droplet Evaporation Modes
Authors: Vu Hoang Huy , Nguyen Nam-Trung , Kashaninejad Navid
Published: December 2024

The evaporation dynamics of sessile droplets on re-entrant microstructures are critical for applications in microfluidics, thermal management, and self-cleaning surfaces. Re-entrant structures, such as mushroom-like shapes with overhanging features, trap air beneath droplets to enhance non-wettability. The present study examines the evaporation of a water droplet on silicon carbide (SiC) and silicon dioxide (SiO2) re-entrant structures, focusing on the effects of material composition and solid area fraction on volume reduction, contact angle, and evaporation modes. Using surface free energy (SFE) as an indicator of wettability, we find that the low SFE of SiC promotes quick depinning and contact line retraction, resulting in shorter CCL phases across different structures. For instance, the CCL phase accounts for 55–59% of the evaporation time on SiC surfaces, while on SiO2 it extends to 51–68%, reflecting a 7–23% increase in duration due to stronger pinning effects. Additionally, narrower pillar gaps, which increase the solid area fraction, further stabilize droplets by extending both CCL and constant contact angle (CCA) phases, while wider gaps enable faster depinning and evaporation. These findings illustrate how hydrophobicity (via SFE) and structural geometry (via solid area fraction) influence microscale interactions, offering insights for designing surfaces with optimized liquid management properties.
West Nile Virus: An Update Focusing on Southern Europe
Authors: Carrasco Lara , Utrilla Maria Jose , Fuentes-Romero Beatriz , Fernandez-Novo Aitor , Martin-Maldonado Barbara
Published: December 2024

West Nile Virus (WNV) is a zoonotic, vector-borne pathogen affecting humans and animals, particularly in Europe. The virus is primarily transmitted through mosquitoes that infect birds, which serve as the main reservoirs. Humans and horses are incidental hosts. This review focuses on the epidemiology of WNV in southern Europe, particularly its increasing prevalence. Methods included an extensive literature review and analysis of recent outbreaks. WNV is largely asymptomatic in humans, but a small percentage can develop West Nile neuroinvasive disease (WNND), leading to severe neurological symptoms and fatalities. Horses can also suffer from neurological complications, with high mortality rates. Climate change, migratory birds, and mosquito population dynamics contribute to the virus spread across Europe. Control efforts focus on vector management, and while vaccines are available for horses, none has been approved for humans. Surveillance, particularly of bird and mosquito populations, and further research into the virus molecular structure are crucial for understanding and mitigating future outbreaks.