bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Pain Neuroscience Education and Physiotherapy on Chronic Low Back Pain, Fear of Movement and Functional Status: A Randomised Pilot Study
Authors: Lendraitienė Eglė , Styraitė Barbora , Šakalienė Rasa , Misytė Gabija , Bileviciute-Ljungar Indre
Published: April 2024

Background: Chronic non-specific low back pain is a non-harmous condition often found in the general population. It is one of the most significant disabilities and needs different treatment modalities. This study investigates the effects of pain neuroscience education and physiotherapy on pain intensity, fear of movement and functional status in a Lithuanian cohort with non-specific low back pain. Methods: The study was performed at the primary health care unit in Kaunas, Lithuania. The key inclusion criterion was persistent non-specific low back pain longer than three months and which affects daily life functions. Thirty participants (mean 33.47, SD 4.38 years age, 70% women) were randomised into two training groups with and without pain neuroscience education (for a total of 60 min of teaching). Physiotherapy was performed twice per week during 45 min/session for a period of 10 weeks with exercises which strengthen, stabilize, and stretch the spinal cord muscles. Outcomes included pain intensity, kinesiophobia and disability and these were measured by self-scored questionnaires (numeric rating scale, Tampa scale for kinesiophobia-11, Oswestry disability index and the Roland–Morris questionnaire, respectively). Results: The results indicate that both groups improved in the measured outcomes, with the only difference between them being a better improvement in kinesiophobia in the group receiving physiotherapy and pain neuroscience education. Conclusions: The results of this study confirm that a relatively short intervention of pain neuroscience education enhances the effects of physiotherapy and should be implemented in clinical practice.
Thoughts on the Etiology of Cherubism
Authors: Hyckel Peter , Liehr Thomas
Published: April 2024

Cherubism is nowadays classified as an autoimmune disease and was first described in 1933. Although suspected at that time to be the result of defective tooth development, it was primarily classified as a bone disease caused by a mutation in the SH3BP2 gene. Despite a knock-in mouse model, phenotypic signs in the jaw area were not reproducible in this model. The features of classical cherubism can be attributed to a disturbed formation of the dental placode of the second molar. Since 2019, it has become clear that inhibition of the WNT pathway leads to the accumulation of SH3BP2 via tankyrase inhibition. As the dental placode is triggered via WNT (in epithelia) and MSX1 (in mesenchyme), aplasia of the second and third molars occurs due to a block in the WNT pathway. The mesenchymal part, which occurs prior to the body plan regulation of the WNT/MSX1 pathway, remains unaffected and provides the substrate for the giant cell granuloma. Considering macrophage polarization and the role of the extracellular matrix in general, cherubism is situated in the field of tension between autoimmune diseases and cancer. In this sense, we see the cause of cherubism in a WNT-related dysregulation, which can be proven postnatally in the neural crest-related tooth development of the replacement tooth ridge, both genotypically and phenotypically.
Consensus Statements among European Sleep Surgery Experts on Snoring and Obstructive Sleep Apnea: Part 2 Decision-Making in Surgical Management and Peri-Operative Considerations
Authors: Olszewska Ewa , De Vito Andrea , O’Connor-Reina Carlos , Heiser Clemens , Baptista Peter , Kotecha Bhik , Vanderveken Olivier , Vicini Claudio
Published: April 2024

Background: Reaching consensus on decision-making in surgical management and peri-operative considerations regarding snoring and obstructive sleep apnea (OSA) among sleep surgeons is critical in the management of patients with such conditions, where there is a large degree of variability. Methods: A set of statements was developed based on the literature and circulated among eight panel members of European experts, utilizing the Delphi method. Responses were provided as agree and disagree on each statement, and the comments were used to assess the level of consensus and develop a revised version. The new version, with the level of consensus and anonymized comments, was sent to each panel member as the second round. This was repeated for a total of five rounds. Results: The final set included a total of 71 statements: 29 stand-alone and 11 with 42 sub-statements. On the 33 statements regarding decision-making in surgical management, there was 60.6%, 27.3%, and 6.1% consensus among all eight, seven, and six panelists, respectively. On the 38 statements regarding the peri-operative considerations, there was 55.3%, 18.4%, and 15.8% consensus among all eight, seven, and six panelists, respectively. Conclusions: These results indicate the need for an expanded review of the literature and discussion to enhance consensus among the sleep surgeons that consider surgical management in patients with snoring and OSA.
BMI-Stratified Exploration of the ‘Obesity Paradox’: Heart Failure Perspectives from a Large German Insurance Database
Authors: Hobbach Anastasia J. , Feld Jannik , Linke Wolfgang A. , Sindermann Jürgen R. , Dröge Patrik , Ruhnke Thomas , Günster Christian , Reinecke Holger
Published: April 2024

Background: The global rise of obesity and its association with cardiovascular risk factors (CVRF) have highlighted its connection to chronic heart failure (CHF). Paradoxically, obese CHF patients often experience better outcomes, a phenomenon known as the ‘obesity paradox’. This study evaluated the ‘obesity paradox’ within a large cohort in Germany and explored how varying degrees of obesity affect HF outcome. Methods: Anonymized health claims data from the largest German insurer (AOK) for the years 2014–2015 were utilized to analyze 88,247 patients hospitalized for myocardial infarction. This analysis encompassed baseline characteristics, comorbidities, interventions, complications, and long-term outcomes, including overall survival, freedom from CHF, and CHF-related rehospitalization. Patients were categorized based on body mass index. Results: Obese patients encompassed 21.3% of our cohort (median age 68.69 years); they exhibited a higher prevalence of CVRF (p < 0.001) and comorbidities than non-obese patients (median age 70.69 years). Short-term outcomes revealed lower complication rates and mortality (p < 0.001) in obese compared to non-obese patients. Kaplan–Meier estimations for long-term analysis illustrated increased incidences of CHF and rehospitalization rates among the obese, yet with lower overall mortality. Multivariable Cox regression analysis indicated that obese individuals faced a higher risk of developing CHF and being rehospitalized due to CHF but demonstrated better overall survival for those classified as having low-level obesity (p < 0.001). Conclusions: This study underscores favorable short-term outcomes among obese individuals. The ‘obesity paradox’ was confirmed, with more frequent CHF cases and rehospitalizations in the long term, alongside better overall survival for certain degrees of obesity.
Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants
Authors: Park Seoyeon , Moon Junhyung , Eun Hoseon , Hong Jin-Hyuk , Lee Kyoungwoo
Published: April 2024

Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children’s Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system’s performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.
Diagnostic Methods for the Prenatal Detection of Cleft Lip and Palate: A Systematic Review
Authors: Baeza-Pagador Ana , Tejero-Martínez Ana , Salom-Alonso Lucas , Camañes-Gonzalvo Sara , García-Sanz Verónica , Paredes-Gallardo Vanessa
Published: April 2024

Background: Accurate prenatal diagnosis of cleft lip and palate is essential to discuss severity prediction, perform appropriate parental counseling, and, at last, establish long-term treatment planning. The aim of this systematic review was to analyze the accuracy of various imaging techniques for the prenatal diagnosis of cleft lip and palate, assess the pregnancy phase for orofacial clefts diagnosis, and study the different cleft types in terms of diagnostic methods, timing, and predictability. Methods: A search of the PubMed, EMBASE, Scopus, and Web of Science databases was conducted to identify potentially relevant studies published until January 2024. The quality of the selected articles was assessed using the Newcastle–Ottawa scale for methodological quality assessment of cohort studies and the QUADAS-2 scale for diagnostic test studies. Results: A total of 18 studies met the eligibility criteria and were included in the review. The findings of this review indicate that the majority of studies showed improved diagnostic accuracy when supplementary techniques, such as 3D ultrasound or magnetic resonance imaging, were added to 2D ultrasound. Conclusions: The implementation of magnetic resonance imaging as a standard procedure could significantly improve the precision of diagnosing cleft lip and palate. Therefore, the diagnostic technique used will play a crucial role in the accuracy of the diagnosis.
Nitric Oxide Photorelease from Silicone Films Doped with N-Nitroso BODIPY
Authors: Virts Natalia A. , Karogodina Tatyana Yu. , Panfilov Mikhail A. , Vorob’ev Alexey Yu. , Moskalensky Alexander E.
Published: April 2024

Nitric oxide (NO) is a unique biochemical mediator involved in the regulation of vital processes. Light-controllable NO releasers show promise in the development of smart therapies. Here, we present a novel biocompatible material based on polydimethylsiloxane (PDMS) doped with BODIPY derivatives containing an N-nitroso moiety that is capable of the photoinduced generation of NO. We study the green-light-induced NO-release properties with the following three methods: electrochemical gas-phase sensor, liquid-phase sensor, and the Griess assay. Prolonged release of NO from the polymer films after short irradiation by narrow-band LED light sources and a laser beam is demonstrated. Importantly, this was accompanied by no or little release of the parent compound (BODIPY-based photodonor). Silicone films with the capability of controllable and clean NO release can potentially be used as a highly portable NO delivery system for different therapeutic applications.
A Comprehensive Study on the Optimization of Drilling Performance in Hybrid Nano-Composites and Neat CFRP Composites Using Statistical and Machine Learning Approaches
Authors: Nargis Tanzila , Shahabaz S. M. , Acharya Subash , Shetty Nagaraja , Malghan Rashmi Laxmikant , Shetty S. Divakara
Published: April 2024

Carbon fiber-reinforced polymer (CFRP) composites have gradually replaced metals due to their exceptional strength-to-weight ratio compared to metallic materials. However, the drilling process often reveals various defects, such as surface roughness, influenced by different drilling parameters. This study explores the drilling quality of uni-directional CFRP composites, as well as hybrid Al2O3 alumina and hybrid SiC silicon carbide nano-composites, through experimental exploration using step, core, and twist drills. Response surface methodology (RSM) and statistical tools, including main effect plots, ANOVA, contour plots, and optimization techniques, were used to analyze the surface roughness of the hole. Optimization plots were drawn for optimal conditions, suggesting a spindle speed of 1500 rpm, feed of 0.01 mm/rev, and a 4 mm drill diameter for achieving minimum surface roughness. Furthermore, two machine learning models, artificial neural network (ANN) and random forest (RF), were used for predictive analysis. The findings revealed the robust predictive capabilities of both models, with RF demonstrating superior performance over ANN and RSM. Through visual comparisons and error analyses, more insights were gained into model accuracy and potential avenues for improvement.
An Anti-Windup Method Based on an LADRC for Miniaturized Inertial Stabilized Platforms on Unmanned Vehicles in Marine Applications
Authors: Fu Tianlei , Guan Lianwu , Gao Yanbin , Qin Chao
Published: April 2024

This paper investigates an anticipatory activation anti-windup approach based on Linear Active Disturbance Rejection Control (LADRC) to address the influences of accelerated saturation on the actuators in a Miniaturized Inertial Stabilized Platform (MISP) with extreme external disturbance. The proposed method aims to eliminate the high-frequency vibrations on the Line of Sight (LOS) of electro-optical devices during actuator saturation. To achieve this, the Linear Extended State Observer (LESO) is modified by adding saturation feedback to the total disturbance observed state variable, which is operated as an anticipatory activation anti-windup compensator. The stability of the proposed controller is discussed, and the gains are optimized by the Linear Matrix Inequality (LMI) constraints though quadratic programming and an H-infinite performance indicator. Additionally, as the multiple activated scheme for anti-windup, the effectiveness of immediate activation in dealing with accelerated saturation is compared and analyzed. These comparisons and verification are implemented through simulations, where the external disturbance is introduced using recorded attitude data from USV sailing. Finally, experiments are conducted on an MISP for a visual tracking system, demonstrating that the anticipatory activation mothed effectively suppresses high-frequency vibrations on the LOS during instances of accelerated saturation.
The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment
Authors: Badr Yara , Abdul Kader Lamis , Shamayleh Abdulrahim
Published: April 2024

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.
Short and Extra Short Dental Implants in Osseous Microvascular Free Flaps: A Retrospective Case Series
Authors: Hocková Barbora , Slávik Rastislav , Azar Basel , Stebel Jakub , Poruban Dušan , Bonfante Estevam A. , Ewers Rolf , Cheng Yu-Chi , Stebel Adam
Published: April 2024

There is limited information regarding implant and prosthetic survival after osseous microvascular free flap (OMFF). This case series aims to describe the placement of short and extra short implants in osseous microvascular free flaps to support prostheses, and present an up to 40-month retrospective follow-up. Short and extra short dental implants were placed in six fibula free flaps (FFF) and in two microvascular deep circumflex iliac artery (DCIA) flaps. In total, 27 short and extra short dental implants have been placed into two different types of free flaps. Kaplan–Meyer (K-M) survival analyses were performed to evaluate the survival and success outcomes of implants and prostheses. Out of the eight patients reconstructed with free flap, five were rehabilitated with prostheses, one patient has a temporary prosthesis, and two patients are in the process of prosthetic rehabilitation. Twenty-seven implants were followed up for up to 40 months, and K-M analyses showed 100% implant survival probability (95% confidence interval: 100%), while the implant success probability was 91.0% (95% confidence interval: 68.6–97.7%). Short and extra short dental implants placed in OMFF presented high survival and success rates in a retrospective case series after up to 40 months.
Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment
Authors: Mahmood Salman , Hasan Raza , Yahaya Nor Adnan , Hussain Saqib , Hussain Muzammil
Published: April 2024

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.
Soil Carbon Storage, Enzymatic Stoichiometry, and Ecosystem Functions in Indian Himalayan Legume-Diversified Pastures
Authors: Ghosh Avijit , Ahmad Suheel , Singh Amit K. , Jha Pramod , Kumar Yadav Rajendra , Jiménez Ballesta Raimundo , Bhatt Sheeraz Saleem , Biradar Nagaratna , Hamid Mir Nazim
Published: April 2024

The influences of legume diversification on soil carbon (C) pools and sequestration, particularly in Himalayan pastureland, remain poorly understood. Moreover, the impact of legume diversification activities and the stoichiometry of soil enzymes in C biogeochemistry at the ecosystem level remains largely overlooked. The purpose of this study is to investigate the influences of legume diversification on activities and the stoichiometry of soil enzymes and their control of C sequestration in pasturelands. Four experimental fertilized species combinations, namely, SG (50% Festuca arundinacea + 50% Dactylis glomerata), SGL1 (25% Festuca arundinacea + 25% Dactylis glomerata + 50% Onobrychis viciifolia), SGL2 (25% Festuca arundinacea + 25% Dactylis glomerata + 50% Trifolium pratense), SGL12 (25% Festuca arundinacea + 25% Dactylis glomerata + 25% Onobrychis viciifolia + 25% Trifolium pratense), and natural pasture (NG) were compared. Soils under SGL1, SGL2, and SG12 had ~18, 36, and 22% greater soil C than SG, respectively. Among the pastures with fertilization, the C mineralization was suppressed by legume diversification. C sequestration under SGL1, SGL2, and SG12 was ~27, 22, and 38% higher than SG, respectively, at the 0–30 cm soil layer. The ratios of DHA are as follows: for PhOX and DHA, PerOX significantly decreased with an increasing grass–legume mixture, suggesting greater C sequestration. PCA analysis revealed that C sequestration under legume diversification and enzymatic stoichiometry had an indirect but substantial impact on C sequestration. The increasing C sequestration under SGL12 was complemented by higher productivity. Data suggested that increasing legumes in pastureland might greatly enhance ecosystem functions such as soil C storage, productivity, ecorestoration efficiency, and biological activity in Indian Himalayan pastureland.
Cultural Heritage Recognition through Protection of Historical Value and Urban Regeneration: CSOA Forte Prenestino
Authors: Ricci Laura , Mariano Carmela , Perrone Francesca
Published: April 2024

The conformation and dynamics of metropolitanisation act as propulsive elements of territorial transformations. The deficiency of infrastructural equipment, the heterogeneity of urban fabric and the lack of services and public spaces contribute to severing the identity ties between settled communities and territories. In light of this, within the more general reflection concerning urban regeneration, we recall the role that cultural heritage plays in the physical and functional organisation of the city, as a reflection of the interaction between community and context. The contribution is contextualized in the research work on the activities related to Thematic Line 4 of the Extended Partnership 5—CHANGES (NRRP). The thematic line activities follow three phases: 1. contextualization; 2. operational phase; 3. experimentation. The research work presented here is part of the ‘operational phase’, to identify strategies and projects for heritage-led regeneration. The article analyses the pilot case of CSOA Forte Prenestino as a starting point for thinking about expanding the research activity to other similar cases. It is a self-managed community centre in Rome (Italy) located in the nineteenth-century Forte, which has become a symbol of collective identity. The case study was identified following three levels of investigation: 1. identification of the municipality, first-level administrative subdivision of the city of Rome (Italy); 2. identification of the main historical, archaeological and architectural emergencies of the municipality; 3. identification of an asset to be analysed as “Heritage by designation” (involvement of experts) and “Heritage by appropriation” (involvement of communities). The research results show the “Recognition Path” of Forte Prenestina: according to what has been ‘designated’ by urban planning instruments, project instruments, legislative instruments and authors of scientific publications and conferences; and on the basis of the bottom-up ‘appropriation’ process of the asset that has allowed its management, assessment of its cultural and social potential and its development. The research results allow us to reflect on heritage-led urban regeneration as a strategy capable of capturing and promoting the links between social integration and cultural–historical identity.
Landscapes at Risk: Social Capital Assets in the COVID-Scape Climate
Authors: Trovato Maria Rosa , Giuffrida Salvatore
Published: April 2024

Up until the current pandemic, the terms “urban-scape” and “human-scape” have been meant, assumed, and practised as parallel dimensions of in territorial analysis, marginalising our knowledge about and evaluation of landscape risk [...]
Analysing the Influence of Speed and Jumping Performance Metrics on the Percentage Change of Direction Deficit in Adolescent Female Soccer Players
Authors: Roso-Moliner Alberto , Gonzalo-Skok Oliver , Villavicencio Álvarez Víctor Emilio , Calero-Morales Santiago , Mainer-Pardos Elena
Published: April 2024

Studies show that although female soccer players often have shorter change of direction (COD) deficits than males, indicating different biomechanical profiles, there is a lack of research on the impact of physical metrics on COD performance in females. The purpose of this work was to analyse whether performance metrics based on speed and jumping could explain the variation in %CODD in young female soccer players. Thirty-three highly trained adolescent female soccer players with an age of 16 ± 0.95 years, a body mass of 55.7 ± 7.22 kg, and a height of 160.4 ± 5.22 cm performed COD180 tests, 10 m and 30 m sprint tests, single-leg countermovement, and horizontal jumps. Acceleration in the first 10 m of a sprint was identified as a significant predictor of COD180 performance (R2 = 28%), (R2 = 50%), (p < 0.01), indicating that early sprint performance may largely determine an individual’s ability to change direction. However, no predictors were found for %CODD. Significant correlations were observed between COD180 performance and %CODD, acceleration, linear speed, and horizontal jump performance (r = −0.59 to 0.70; p < 0.05). The study suggests that specific physical performance metrics, particularly early acceleration, are crucial for enhancing COD skills in female soccer players, emphasizing the need for targeted training interventions.
1H Nuclear Magnetic Resonance, Infrared, and Chemometrics in Lipid Analysis of Brazilian Edible-Oil-Based Nutraceuticals
Authors: Flores Igor S. , Annunciação Daniel L. R. , Pinto Vinícius S. , Lião Luciano M.
Published: April 2024

Edible oils have commercial and nutritional value due to the presence of essential fatty acids. They can be consumed fresh in the form of capsules known as nutraceuticals. The quality of such products is of interest to the consumer. In this context, this study describes a method based on high-resolution nuclear magnetic resonance (NMR) and Fourier-transform mid-infrared spectroscopic analysis (FTIR), combined with statistical analyses, to differentiate different edible oils used as nutraceuticals in Brazil by fatty acid content. Through the analysis of 1H NMR spectra, the levels of saturated and unsaturated fatty acids in edible oils were characterized and quantified. Statistical analysis of the data confirmed the real distinctions between nutraceutical raw materials, with emphasis on ω-9, ω-6, and ω-3 fatty acids. The analytical approach presented also demonstrates the potential to identify the origin (animal or vegetable) of edible oils used as nutraceuticals.
Research Advances of Non-Noble Metal Catalysts for Oxygen Evolution Reaction in Acid
Authors: Yan Zhenwei , Guo Shuaihui , Tan Zhaojun , Wang Lijun , Li Gang , Tang Mingqi , Feng Zaiqiang , Yuan Xianjie , Wang Yingjia , Cao Bin
Published: April 2024

Water splitting is an important way to obtain hydrogen applied in clean energy, which mainly consists of two half-reactions: hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). However, the kinetics of the OER of water splitting, which occurs at the anode, is slow and inefficient, especially in acid. Currently, the main OER catalysts are still based on noble metals, such as Ir and Ru, which are the main active components. Hence, the exploration of new OER catalysts with low cost, high activity, and stability has become a key issue in the research of electrolytic water hydrogen production technology. In this paper, the reaction mechanism of OER in acid was discussed and summarized, and the main methods to improve the activity and stability of non-noble metal OER catalysts were summarized and categorized. Finally, the future prospects of OER catalysts in acid were made to provide a little reference idea for the development of advanced OER catalysts in acid in the future.
Influence of Materials of Moulds and Geometry of Specimens on Mechanical Properties of Grouts Based on Ultrafine Hydraulic Binder
Authors: Hortigon Beatriz , Rodriguez-Mayorga Esperanza , Santiago-Espinal Jose Antonio , Ancio Fernando , Gallardo Jose Maria
Published: April 2024

Ultrafine hydraulic binder grout injection is a technique utilised for repairing masonry, either to connect sections, seal joints, or fill voids due to its great capacity for penetration and higher mechanical strength than lime grout. In this research, the mechanical properties of ultrafine hydraulic cement grout are analysed considering the influence of the mould material for preparing the specimens and their geometry characteristics in the context of the specifications set out in several international standards. The test campaign to ascertain compressive and flexural strength in different circumstances is supplemented with a physical and chemical characterisation of both binder and fresh and hardened grout. Significant differences in mechanical properties between specimens prepared with absorbent or non-absorbent-water material are found due to the influence of drying shrinkage and decanting binder during the curing process. Furthermore, the slenderness of specimens is presented as an important factor in determining the compressive strength of mixtures.
Effect of Multi-Path Asynchronous Rolling Process on Microstructure and Mechanical Properties of ZK60 Magnesium Alloy
Authors: Jiang Peng , Liu Dawen , Zou Haixin , Liu Jianfu , Wu Wangping , Pan Haijun , Wang Zhizhi , Zhang Yi , Dai Guohong
Published: April 2024

At the initial rolling temperature of 400 °C, ZK60 magnesium alloy was hot rolled by three different rolling paths with different roll speed ratios (RSR) of 1:1.15, 1:1.2, and 1:1.5, respectively. The effects of different rolling processes on the microstructure and mechanical properties of the alloy were studied. The microstructure, plasticity, strength, hardness, and texture intensity of rolled samples were analyzed in this work. The results show that the microstructure uniformity of the alloy under multi-path asynchronous rolling (MAR) is significantly improved, which improves the mechanical properties of the material to a certain extent, and effectively weakens the texture intensity of the basal plane and reduces the anisotropy. The amount of randomly oriented grains of ZK60 magnesium alloy rolled by the C-1.5 (path C combined with the RSR of 1:1.5) process are significantly increased, which significantly weakens the basal texture and improves the ductility of the alloy, greatly enhancing the processing and formability of ZK60 magnesium alloy.
Effect of Copper Surface Roughness on the High-Temperature Structural Stability of Single-Layer-Graphene
Authors: Yao Songsong , Zhong Boan , Guo Chongxiao , Ni Jiamiao , Yang Kunming , Hu Siqi , Gong Zheng , Liu Yue , Song Jian , Fan Tongxiang
Published: April 2024

Graphene (Gr) has shown great potential in the field of oxidation protection for metals. However, numerous studies have shown that Gr will suffer structural degradation on metal surface during high-temperature oxidation, which significantly limited the effectiveness of their oxidation protection. Therefore, understanding the degradation mechanism of Gr is of great interest to enhance their structural stability. Here, the effect of copper (Cu) surface roughness on the high-temperature structural stability of single-layer graphene (SLG) was examined using Cu covered with SLG as a model material. SLG/Cu with different roughness values was obtained via high-temperature annealing of the model material. After high-temperature oxidation at 500 °C, Raman spectra analysis showed that the defect density of the oxidized SLG increased from 41% to 81% when the surface roughness varied from 37 nm to 81 nm. Combined with density functional theory calculations, it was found that the lower formation energy of the C-O bond on rough Cu surfaces (0.19 eV) promoted the formation of defects in SLG. This study may provide guidance for improving the effectiveness of SLG for the oxidation protection of metallic materials.
Excess Conductivity Analysis of an YBCO Foam Strut and Its Microstructure
Authors: Slimani Yassine , Hannachi Essia , Koblischka-Veneva Anjela , Koblischka Michael Rudolf
Published: April 2024

Struts of a superconducting YBa2Cu3Oy (YBCO) foam prepared by the infiltration growth method on the base of commercial polyurethane foams were extracted from the bulk, and thoroughly characterized concerning the microstructure and the magnetoresistance, measured by the four-point technique. Optical microscopy, electron microscopy, electron backscatter diffraction and atomic force microscopy observations indicate a unique microstructure of the foam struts which shows a large amount of tiny Y2BaCuO5 (Y-211) particles (with diameters between 50 and 100 nm) being enclosed in channel-like grain boundaries between the YBCO grains and a one-of-a-kind surface of the struts covered with Ba3Cu5Oy-particles. The resistance data obtained at temperatures in the range 4.2 K ≤T≤ 150 K (applied magnetic fields ranging from 0 to 7 T) were analyzed in the framework of the fluctuation-induced conductivity (FIC) approach using the models of Aslamazov–Larkin (AL) and Lawrence–Doniach (LD). The resulting FIC curves reveal the presence of five distinct fluctuation regimes, namely, the short-wave (SWF), one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuation domains. The analysis of the FIC data enable the coherence length in the direction of the c-axis at zero-temperature (ξc(0)), the irreversibility field (Birr), the upper critical magnetic field (Bc2), the critical current density at T= 0 K (Jc(0)) and several other parameters describing the the material’s superconducting properties to be determined. The present data reveal that the minuscule Y-211 particles found along the YBCO grain boundaries alter the excess conductivity and the fluctuation behavior as compared to conventional YBCO samples, leading to a quite high value for Jc(0) for a sample with a non-optimized pinning landscape.
Effect of Weave and Weft Type on Mechanical and Comfort Properties of Hemp–Linen Fabrics
Authors: Vasile Simona , Vermeire Sofie , Vandepitte Katrien , Troch Veronique , De Raeve Alexandra
Published: April 2024

In this study the influence of fabric weave (plain, twill, and panama) and weft type (flax and hemp yarns) on selected mechanical and comfort properties of six fabrics was analyzed. The results showed that tear and abrasion properties were most affected by the weave. The tensile properties of the linen fabrics were not significantly different when weft hemp yarns were used instead of flax. Fabrics with the same weave seemed to be equally resilient to abrasion regardless of the type of weft. By contrast, the hemp weft yarns favorized the physical and comfort properties of the investigated fabrics. For the same weave, the hemp–linen fabrics were slightly lighter and exhibited lower bulk density, significantly larger air permeability, and improved moisture management properties. Although the results of maximum thermal flux (Qmax) suggested a cooler sensation of the linen fabrics with panama and twill, the hemp–linen fabric with a plain weave seemed to be the optimal choice when a cool sensation was desired. Higher thermal conductivity values also suggested slightly better heat transfer properties of the hemp–linen fabrics, and these were significantly influenced by the weave type. The results clearly indicated the advantages of using hemp for improving physical and specific comfort properties of linen fabrics.
Underwater Low-Frequency Acoustic Wave Detection Based on a High-Q CaF2 Resonator
Authors: Yuan Guangzheng , Rong Jiamin , Zhang Dewei , Xing Enbo , Liu Wenyao , Li Li , Tang Jun , Liu Jun
Published: April 2024

Whispering gallery mode (WGM) resonators with an ultra-high quality (Q) factor provide a new idea for high-precision underwater acoustic sensing. However, acoustic energy loss due to watertight encapsulation has become an urgent problem for its underwater application. In order to solve this problem, this paper proposes a hollowed-out array structure. The finite element simulation shows that the acoustic wave transmission loss is improved by 30 dB compared with that of the flat plate encapsulation structure. Using a calcium fluoride (CaF2) resonator with a Q factor of 1.2 × 108 as an acoustic sensitive unit, the amplitude and frequency of the loaded acoustic wave are retrieved by means of the dispersion coupling response mechanism. The resonator’s underwater experimental test range is 100 Hz–1 kHz, its acoustic sensing sensitivity level reaches −176.3 dB re 1 V/µPa @ 300 Hz, and its minimum detectable pressure can be up to 0.87 mPa/Hz1/2, which corresponds to a noise-equivalent pressure (NEP) of up to 58 dB re 1 µPa/Hz1/2.
Various Feature-Based Series Direct Current Arc Fault Detection Methods Using Intelligence Learning Models and Diverse Domain Exclusion Techniques
Authors: Dang Hoang-Long , Kwak Sangshin , Choi Seungdeog
Published: April 2024

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.
Effective Data Reduction Using Discriminative Feature Selection Based on Principal Component Analysis
Authors: Nwokoma Faith , Foreman Justin , Akujuobi Cajetan M.
Published: April 2024

Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used unsupervised learning to discover the top-k discriminative features present in the large multivariate IoT dataset used. We used the statistics of principal component analysis to filter the relevant features based on the ranks of the features along the principal directions while also considering the coefficients of the components. The selected number of principal components was used to decide the number of features to be selected in the SVD process. A number of experiments were conducted using different benchmark datasets, and the effectiveness of the proposed method was evaluated based on the reconstruction error. The potency of the results was verified by subjecting the algorithm to a large IoT dataset, and we compared the performance based on accuracy and reconstruction error to the results of the benchmark datasets. The performance evaluation showed consistency with the results obtained with the benchmark datasets, which were of high accuracy and low reconstruction error.
Adaptive Fuzzy Backstepping Control for Itô-Type Nonlinear Switched Systems Subject to Unknown Hysteresis Input
Authors: Wan Xiaohe , Li Yan
Published: April 2024

The adaptive fuzzy backstepping control problem is studied for Itô-type nonlinear switched systems subject to unknown hysteresis input. Compared with existing works, the unknown hysteresis and stochastic disturbances are considered in the pure-feedback switched systems. The mean value theorem tackles the non-affine functions. The backstepping technique introduces an auxiliary virtual controller. In addition, the Nussbaum function is employed to solve the difficulty caused by the unknown hysteresis under arbitrary switching. Based on a fuzzy logic system and backstepping technique, a new adaptive control proposal is obtained, which ensures that the system states satisfy semiglobally uniformly ultimately bounded (SGUUB) in probability and that the tracking error converges to a region of the origin. Finally, we provide two examples to show the validity of the presented scheme.
A Quantum Formalism for Abstract Dynamical Systems
Authors: Micó Joan C.
Published: April 2024

This paper presents a quantum formulation for classical abstract dynamical systems (ADS), defined by coupled sets of first-order differential equations. They are referred to as “abstract” because their dynamical variables can be of different interrelated natures, not necessarily corresponding to physics, such as populations, socioeconomic variables, behavioral variables, etc. A classical linear Hamiltonian can be derived for ADS by using Dirac’s dynamics for singular Hamiltonian systems. Also, a corresponding first-order Schrödinger equation (which involves the existence of a system Planck constant particular of each system) can be derived from this Hamiltonian. However, Madelung’s reinterpretation of quantum mechanics, followed by the Bohm and Hiley work, produces no further information about the mathematical formulation of ADS. However, a classical quadratic Hamiltonian can also be derived for ADS, as well as a corresponding second-order Schrödinger equation. In this case, the Madelung reinterpretation of quantum mechanics provides a quantum Hamiltonian that does provide the quantum formulation for ADS, which provides new quantum variables interrelated dynamically with the classical variables. An application case is presented: the one-dimensional autonomous system given by the logistic dynamics. The differences between the classical and the quantum trajectories are highlighted in the context of this application case.
Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection
Authors: Mandrikova Oksana , Mandrikova Bogdana
Published: April 2024

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure.
Hybrid Neural Networks for Solving Fully Coupled, High-Dimensional Forward–Backward Stochastic Differential Equations
Authors: Wang Mingcan , Wang Xiangjun
Published: April 2024

The theory of forward–backward stochastic differential equations occupies an important position in stochastic analysis and practical applications. However, the numerical solution of forward–backward stochastic differential equations, especially for high-dimensional cases, has stagnated. The development of deep learning provides ideas for its high-dimensional solution. In this paper, our focus lies on the fully coupled forward–backward stochastic differential equation. We design a neural network structure tailored to the characteristics of the equation and develop a hybrid BiGRU model for solving it. We introduce the time dimension based on the sequence nature after discretizing the FBSDE. By considering the interactions between preceding and succeeding time steps, we construct the BiGRU hybrid model. This enables us to effectively capture both long- and short-term dependencies, thus mitigating issues such as gradient vanishing and explosion. Residual learning is introduced within the neural network at each time step; the structure of the loss function is adjusted according to the properties of the equation. The model established above can effectively solve fully coupled forward–backward stochastic differential equations, effectively avoiding the effects of dimensional catastrophe, gradient vanishing, and gradient explosion problems, with higher accuracy, stronger stability, and stronger model interpretability.