bluuebunny/crossref_metadata_2025 huggingface dataset:
FlameViT: Wildfire Detection through Vision Transformers for Enhanced Satellite Imagery Analysis
Authors: Makhija Aria
Published: February 2025

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.
Asymptotic Stabilization of the Oilwell Drillstring Torsional and Axial Vibrations
Authors: Danciu Daniela , Răsvan Vladimir
Published: February 2025

The paper starts from the distributed parameter models for both torsional and axial vibrations of the oilwell drillstring. While integrating several accepted features, the considered models are deduced following the Hamilton variational principle in the distributed parameter case. At their turn, these models are now completed in order to take into account the elastic strain in driving signal transmission to the drillstring motions - rotational and axial (vertical). Stability and stabilization are tackled within the framework of the energy type Lyapunov functionals. Since from such “weak” Lyapunov functionals only non-asymptotic Lyapunov stability can be obtained, asymptotic stability follows from the application of the Barbashin-Krasovskii-LaSalle invariance principle. This use of the invariance principle is done by associating a system of coupled delay differential and difference equations, recognized to be of neutral type. For this system of neutral type the corresponding difference operator is strongly stable hence the Barbashin-Krasovskii-LaSalle principle can be applied. Note that this strong stability of the difference operator has been ensured by the aforementioned model completion with the elastic strain induced by the driving signals.
Non-Local Conformable Differential Inclusions Generated by Semigroups of Linear Bounded Operators or by Sectorial Operators with Impulses in Banach Spaces
Authors: AlAdsani Feryal , Ibrahim Ahmed Gamal
Published: February 2025

This paper aims to explore the sufficient conditions for assuring that, the set of mild solutions to two types of non-local semilinear fractional differential inclusions involving the conformable derivative, in the existence of non-instantaneous impulses, is not empty and compact. We will consider the case when the linear part in the studied problem is the infinitesimal generator of a C₀ - semigroup or a sectorial operator. We give the definition of mild solutions, and then, by using appropriate fixed point theorems for multi-valued functions and the properties of both the conformable derivative, and the measure of noncompactness, we achieve to our findings. Since the most of the known fractional derivatives do not satisfy many basic properties that usual derivatives have, the conformable derivative is introduced in a previous paper, and it is shows that it is the most natural definition. Therefore, many works have been done on differential equation with the conformable. But, works on semilinear differential inclusions are not reported until now. We will do not assume that the semigroup generated by the linear term is not compact,also, we will examine the case when the values of the multi-valued function are convex, also nonconvex. So, our work is novel, and interested. We give examples of the application of our theoretical resultsThis paper aims to explore the sufficient conditions for assuring that, the set of mild solutions to two types of non-local semilinear fractional differential inclusions involving the conformable derivative, in the existence of non-instantaneous impulses, is not empty and compact. We will consider the case when the linear part in the studied problem is the infinitesimal generator of a C₀ - semigroup or a sectorial operator. We give the definition of mild solutions, and then, by using appropriate fixed point theorems for multi-valued functions and the properties of both the conformable derivative, and the measure of noncompactness, we achieve to our findings. Since the most of the known fractional derivatives do not satisfy many basic properties that usual derivatives have, the conformable derivative is introduced in a previous paper, and it is shows that it is the most natural definition. Therefore, many works have been done on differential equation with the conformable. But, works on semilinear differential inclusions are not reported until now. We will do not assume that the semigroup generated by the linear term is not compact,also, we will examine the case when the values of the multi-valued function are convex, also nonconvex. So, our work is novel, and interested. We give examples of the application of our theoretical results.
A Review of the Application of Transfer Learning in Fault Diagnosis and its Potential in Aerospace Condition Based Maintenance
Authors: Jia Lilin , Ezhilarasu Cordelia , Jennions Ian
Published: February 2025

Condition based maintenance (CBM), maintenance that is triggered by knowledge of component degradation, relies heavily on fault diagnosis to pinpoint the component or system that requires maintenance. While there have been many advances in applying machine learning in fault diagnosis in recent years, there remains a problem of insufficient data with which to train machine learning algorithms. One approach to this problem is to reuse lessons learnt on one system on another system by transfer learning (TL). Previous reviews about the application of TL in fault diagnosis have concluded that TL is effective to cross-domain fault diagnosis problems through leveraging data from other working conditions or similar machines, and they systematically covered how various types of TL methods apply to different fault diagnosis problems. However, they did not consider what TL algorithms have never been applied to fault diagnosis that can benefit fault diagnosis research. Therefore, there is the necessity to comprehensively study TL in general and identify, from the whole scope of TL, any novel methods that may further facilitate fault diagnosis and aerospace CBM. By investigating into the history of TL, one such novel TL method found is high-level TL methods that enables knowledge transfer between both dissimilar source and target domains. Developing high-level transfer learning solutions in fault diagnosis would improve the current lack of diversity in the specific applications and domains of transfer in this field. Regarding the potential in aerospace CBM, high-level transfer learning is expected to significantly improve the efficiency of data usage.
The Effect of Telomerase Activator ASTCOQ02 on Cognitive Factors in Middle-Aged Adults: A randomized Double-Bling Study
Authors: de Jaeger Christophe , Baramki Hani , Beaudeux Jean Louis , Cherin Patrick
Published: February 2025

Background/Objectives: Telomere shortening is a hallmark of aging and is implicated in cognitive decline. This study aimed to evaluate the effects of ASTCOQ02, a natural telomerase activator derived from Astragalus, on cognitive performance in middle-aged adults. Secondary objectives included assessing telomerase activation and antioxidant capacity. Methods: A randomized double-blind, placebo-controlled trial was conducted with 40 healthy volunteers (mean age 56.1 ± 6.0 years) receiving ASTCOQ02 or placebo for six months. Cognitive performance was assessed using P300 potentials and Rey’s 15-word test. Telomerase activation was evaluated through telomere length dynamics measured by HT Q-FISH, and plasma antioxidant capacity was assessed as a secondary outcome. Results: The ASTCOQ02 group showed significant improvements in cognitive metrics, including reduced P300 latency (274.1 ± 17.5 ms at baseline vs. 262.4 ± 19.2 ms at six months; p = 0.017) and in-creased Rey’s test scores (58.5 ± 8.7 at baseline vs. 61.6 ± 8.4 at six months; p = 0.02). Telomerase activation was evidenced by an increase in median telomere length (9.81 ± 0.82 kb to 10.28 ± 0.88 kb; p = 0.008) and a reduction in critically short telomeres (6.65% to 4.87%; p = 0.04). Antioxidant capacity remained unchanged. Conclusions: ASTCOQ02 enhanced cognitive performance in middle-aged adults, likely via telomerase activation. These results support its potential as an intervention for mitigating age-related cognitive de-cline while maintaining telomere integrity.
Assessing Coastal Vulnerability Index in Mediterranean Areas: A Case Study of Casablanca
Authors: Muzirafuti Anselme
Published: February 2025

The on-going changing climate and its impacts are affecting our environment and most of the people who live in coastal areas. Seventy per cent of our home planet Earth is covered with oceans and they are accumulating more than 90% of the additional heat due to greenhouse gas emissions. That heat increases ocean volume through thermal expansion, and results in sea-level rise. To determine the consequences of climate change on the coastline of Casablanca, we used remote sensing technical approach combining the analysis processes of radar satellite altimetry images, Landsat satellite images, Google earth image, DEM image and geological and topographic maps of Casablanca. The creation of a database and its analysis show that the major parts of the coastline, especially on the sand beaches, situated at elevation less than 12 m are threatened by the sea rising at the rate of 2.6 mm/year and are under pressure of erosion possesses at a rate of -3 m/year. Fifty per cent of the study regions have a high coastal vulnerability index and are vulnerable to natural phenomena such as inundation, high tides, and coastal erosion.
Impact of Weather Conditions on Energy Consumption Modeling for Electric Vehicles: A Data-Driven Analysis
Authors: Mądziel Maksymilian
Published: February 2025

This study presents a methodology to develop an energy consumption model for electric vehicles based on dynamic vehicle and environmental data. Particular attention is given to analyzing the impact of ambient temperature on energy consumption modeling. To improve model accuracy, temperature and vehicle acceleration data were clustered using the K-Means method. As a result, four energy consumption models were created, each corresponding to a specific data cluster. This approach achieved strong validation results, with an R² value of 0.84 and a MAE ranging from 0.75 to 1.23 Wh, which is a satisfactory outcome given the large volume of input data. These models are designed for microscale energy consumption prediction, using vehicle speed and acceleration as input parameters.
The Unified Theory of Informational Spin A New Approach to Coherence, Gravitation, and Cosmological Structures
Authors: Matuchaki Henry
Published: February 2025

The Unified Theory of Informational Spin (TGU) proposes an alternative model for the description of physical systems, eliminating the need for traditional concepts such as dark matter and dark energy. Based on informational coherence, the TGU unifies quantum mechanics and gravitation through the interaction of informational spin, which acts as a central node of universal stability. The model has been tested on atomic, planetary, and cosmological scales, revealing predictable harmonic patterns in complex systems. In addition to serving as a bridge between cosmology and quantum physics, the TGU offers innovative explanations for various phenomena, from the stability of superclusters of galaxies to the organization of biological systems and genetic structure. By redefining the interaction between informational coherence and entropy, the theory clarifies the formation of neural networks, genetic regulation, and even the emergence of consciousness as a manifestation of informational coherence within complex systems. The application of TGU in quantum computing, astrophysical modeling, and bioinformatics demonstrates its potential as a unifying physics model, suggesting that reality is governed by interconnected informational patterns. The results obtained demonstrate the validity of the approach and its ability to predict and interpret natural phenomena coherently.
Radiomic Profiling of GBM Reveals Histopathological Correlations Associated with Tumour Response to Ionizing Radiation
Authors: Baxan Nicoleta , Perryman Richard , Chatziathanasiadou Maria V. , Syed Nelofer
Published: February 2025

Glioblastoma (GBM) is a particularly malignant brain tumour which carries a poor prognosis and presents limited treatment options. MRI is standard practice for differential diagnosis at initial presentation of GBM and can assist in both treatment planning and responses. MRI radiomics can generate mineable databases from radiological images providing sub-visual feature analysis capable to augment the morphological and functional tumour characteristics beyond traditional imaging techniques. Given that radiotherapy is part of the standard of care for GBM patients, establishing a platform for phenotyping radiation treatment responses using non-invasive methods is of high relevance. In this study, we modelled the responses to ionizing radiation across four orthotopic models of GBM using diffusion and perfusion radiomics. We have identified the optimal set of radiomic features that demonstrate significant correlations with tumour cellularity, microvascular organisation and blood flow alterations when directly compared with endpoint histopathological analysis. We showed that the selected radiomic features can quantify textural information and pixel interrelationships of tumour response to radiation therapy, revealing subtle image patterns that may reflect intra-tumoural spatial heterogeneity. When compared to GBM patients, similarities of selected radiomic features were noted between orthotopic tumours and non-enhancing central tumour areas of patients regarding tumour homogeneity, along with several differences in gray level nonuniformity and image complexity metrics. As the field evolves, radiomic profiling of GBM may enhance the evaluation of targeted therapeutic strategies, accelerate the development of new therapies and could be acting as a potential virtual biopsy tool.
SimulatorOrchestrator: a 6G-Ready Simulator for the Cell-Free/Osmotic Infrastructure
Authors: Gillgallon Rohin , Almutairi Reham , Bergami Giacomo , Morgan Graham
Published: February 2025

To the best of our knowledge, we define a new IoT-Osmotic simulator supporting 6G infrastructures, leveraging the similarities in Software Defined Wide Area Network (SD-WAN) architectures when used in Osmotic architectures and User-Centric Cell-Free mMIMO architectures - one of the proposed solutions for 6G infrastructures. As part of this work, we showcase the possibility of integrating SimulatorOrchestrator with a patient digital twin generating patient healthcare data, such as vital signs and emergency alerts, in smart ambulances, thus generating additional IoT streams of data, thus contextualising the approach within the healthcare domain, while showcasing the possibility of orchestrating different simulators at the same time. The combined provision of these two aspects joined with the addition of a ring network connecting all the first-mile edge nodes (i.e. Access Points) enables the definition of new packet routing algorithms streamlining previous solutions from SD-WAN architectures, thus remarking both the benefit of 6G architectures in achieving better network load balancing, as well as showcasing the limitations of previous approaches.
Diffusion Models in Generative AI: Principles, Applications, and Future Directions
Authors: Hadiyya Kholoud , Dina Rasim , Mokhtar Burhanuddin
Published: February 2025

Diffusion models have emerged as a powerful class of generative models, offering state-of-the-art performance across a wide range of applications in artificial intelligence. Rooted in probabilistic principles, these models generate data by iteratively refining random noise through a reverse diffusion process, enabling the synthesis of high-quality outputs in domains such as image generation, text-to-image translation, audio synthesis, and molecular design. Compared to earlier generative frameworks like GANs and VAEs, diffusion models excel in stability, diversity, and fidelity, while also supporting flexible conditioning mechanisms for multimodal and domain-specific tasks. Despite their success, diffusion models face several challenges, including high computational costs, scalability to high-dimensional data, and limited interpretability. Additionally, their deployment raises ethical concerns regarding potential misuse, bias, and societal impact. Recent advancements in efficient sampling techniques, hierarchical architectures, and theoretical insights aim to address these limitations, paving the way for broader adoption and impact. This paper provides a comprehensive overview of diffusion models, exploring their underlying principles, key applications, and current limitations. We also highlight future research directions, including the development of scalable and efficient frameworks, integration with emerging technologies, and ethical considerations for responsible deployment. By addressing these challenges, diffusion models have the potential to revolutionize generative AI, driving innovation across diverse fields and contributing to the advancement of artificial intelligence.
Feasibility of Motion Capture-Based Gait Analysis for a Customized Ankle-Foot Orthosis: A Case Report
Authors: Wang Hongwu
Published: February 2025

This study evaluates the feasibility of employing motion capture technology to assess the biomechanical effects of an ankle-foot orthosis (AFO) with a customized varus control strap on lower-limb kinematics and kinetics during gait. While the biomechanical impacts of hindfoot-posted orthoses are well-documented, the influence of varus corrective strapping on proximal joint mechanics remains underexplored. To address this gap, a single-subject pilot study involving a healthy participant was conducted, with kinematic and kinetic data collected across five walking trials under three conditions: barefoot, shod, and shod+AFO. Results demonstrated that the AFO increased double-limb support time from 15% (barefoot) to 20% (shod+AFO) of the gait cycle and reduced peak ankle plantarflexion and dorsiflexion during the swing phase. Ipsilateral knee extension decreased, while heightened variability in joint moments was observed at the contralateral hip and knee. These findings suggest that varus corrective strapping induces sagittal plane kinematic alterations, with compensatory effects extending to the contralateral limb. This pilot study highlights the utility of motion capture systems in evaluating AFO interventions and underscores the need to assess bilateral biomechanical adaptations. Future research should expand to pathological populations, such as individuals with neuromuscular disorders, and investigate frontal plane mechanics to inform AFO customization for clinical populations.
Towards Precision Forensic Psychiatry: An Advanced Machine Learning EEG Model for High-Accuracy Borderline Personality Disorder Diagnosis
Authors: Montgomery Richard Murdoch
Published: February 2025

AbstractBorderline Personality Disorder (BPD) is characterized by emotional instability, impulsivity, and turbulent interpersonal relationships. Despite its profound clinical and forensic implications, diagnosis largely relies on subjective assessments. Recent studies suggest that electroencephalography (EEG) can reveal neurophysiological biomarkers associated with BPD, such as altered spectral power, abnormal event-related potentials (ERPs), disrupted functional connectivity, and modified signal complexity. This article presents a comprehensive machine learning framework that integrates a wide range of EEG features to classify individuals with BPD versus healthy controls. Our approach employs traditional classifiers (e.g., Support Vector Machines, Random Forests) and deep learning models (e.g., Convolutional Neural Networks, Long Short-Term Memory networks, and transformer-based architectures) as well as ensemble strategies. Six graphs illustrate key findings: (1) power spectrum differences, (2) ERP differences (focusing on an emotional Late Positive Potential), (3) connectivity alterations, (4) complexity analysis via sample entropy, (5) performance comparison across models, and (6) a confusion matrix for the best model. Our results underscore the potential of EEG-based machine learning to contribute to a more objective and precise diagnosis of BPD.
Effect of Blue Finance and Marine Environmental Quality on the Marine Fishery Economy
Authors: Jiang Yiying , Huang Lei , Zhu Xiya , Song Weirong , Liu Yang
Published: February 2025

This study investigates the effects of blue finance and marine environmental quality of the marine fishery economy using panel data for marine fisheries in China’s coastal provinces spanning 2011–2023. We use entropy, moderating effects, and threshold effects to calculate the level of blue finance and marine environmental quality, the moderating role of marine environmental quality in blue finance’s effect on the marine fishery economy, and the threshold effect of blue finance and marine environmental quality on the marine fishery economy. We find that (1) blue finance can improve the marine fishery economy, with an influence coefficient of 0.245. (2) Marine environmental quality plays a significant positive moderating role in blue finance’s effect on the marine fishery economy, with an influence coefficient of 0.325. (3) Blue finance and marine environmental quality have a first-order threshold effect on the marine fishery economy, showing a U-shaped nonlinear relationship.
Intelligent methods of operational response to accidents in urban water supply systems based on LSTM neural network models
Authors: Kapanski Aliaksey A. , Hruntovich Nadezeya V. , Klyuev Roman V. , Boltrushevich Aleksandr E. , Sorokova Svetlana N. , Efremenkov Egor A. , Demin Anton Y. , Martyushev Nikita V.
Published: February 2025

This paper investigates the application of recurrent neural networks, specifically Long Short-Term Memory (LSTM) models, for pressure forecasting in urban water supply systems. The objective of this study was to evaluate the effectiveness of LSTM models for pressure prediction tasks. To acquire real-time pressure data, an information system based on Internet of Things (IoT) technology using the MQTT protocol was proposed. The paper presents a data preprocessing algorithm for model training, as well as an analysis of the influence of various architectural parameters, such as the number of LSTM layers, the utilization of Dropout layers for regularization, and the number of neurons in Dense (fully connected) layers. The impact of seasonal factors, including month, day of the week, and time of day, on the pressure forecast quality was also investigated. The results obtained demonstrate that the optimal model consists of two LSTM layers, one Dropout layer, and one Dense layer. The incorporation of seasonal parameters improved prediction accuracy. The model training time increased significantly with the number of layers and neurons, but this did not always result in improved forecast accuracy. The results showed that the optimally tuned LSTM model can achieve high accuracy and outperform traditional methods such as the Holt-Winters model. This study confirms the effectiveness of using LSTM for forecasting in the water supply field and highlights the importance of pre-optimizing the model parameters to achieve the best forecasting results.
Influence of Posture and Sleep Duration on Heart Rate Variability in Older Subjects
Authors: Yuda Emi , Yoshida Yutaka
Published: February 2025

Japan is facing challenges associated with its super-aging society, including increased social security burdens and a rise in the elderly workforce due to a declining younger labor force. Extending healthy life expectancy is one countermeasure, necessitating lifestyle improvements such as frailty prevention and ensuring adequate sleep duration. This study investigated the relationship between heart rate variability (HRV) and sleep duration among older adults (aged ≥65) using electrocardiogram (ECG) and three-axis accelerometer data from the Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) database, recorded between January 2019 and March 2021. Inclusion criteria required sinus rhythm and recording durations ≥80%. Continuous 24-hour ECG and accelerometer data were analyzed for 55,154 participants (mean age 76±6). Results consistently showed significant differences in HRV metrics, including MRRI, SDRR, ULF, LF, HF, and LF/HF, across sleep duration groups (G1–G4). Short-sleep groups (G4) exhibited decreased MRRI and SDRR and increased LF/HF, suggesting active lifestyles but reduced HRV. Conversely, long-sleep groups (G1) showed increased MRRI and reduced LF/HF but exhibited age-related declines in SDRR and ULF. These findings indicate that both insufficient and excessive sleep may contribute to HRV reduction in older adults. This study provides critical insights for improving elderly lifestyles through tailored interventions in exercise and sleep management.
Prevalence and Factors Associated with Repeat Mental Health Service Utilization During Rwanda's Genocide Commemoration Week
Authors: Bamukunde Anne Marie , Gishoma Darius , Tlhaloganyang Bakang Percy , Gordillo-Tobar Amparo Elena , Misago Nancy Claire , Muvunyi Claude Mambo
Published: February 2025

The genocide commemoration week in Rwanda often triggers heightened mental health (MH) needs, necessitating targeted support. Understanding factors influencing repeat MH service utilization is essential for effective interventions. This cross-sectional study analyzed data from individuals seeking MH services during the 2024 Rwandan genocide commemoration week, distinguishing between first-time and repeat users. Descriptive and logistic regression analyses examined factors associated with repeat utilization. Of the 825 individuals who accessed MH services during Rwanda’s 2024 genocide memorial week, 76 percent were repeat users. Bivariate analysis showed that age and insurance coverage were significantly associated with repeat service utilization, while gender and province were not. Logistic regression revealed that individuals ages 31–50 (AOR = 2·29, 95 percent CI: 1·13–4 ·64, p = 0·022) and those without insurance coverage (AOR = 3·31, 95 percent CI: 1·78–6·18, p < 0·001) were more likely to be repeat users compared to the reference groups (18–30 years old and those with insurance, respectively). Gender and province remained nonsignificant in the adjusted model. Improving MH access, particularly for middle-aged individuals and the uninsured, is crucial. Addressing barriers to care could enhance service delivery during the commemoration period.
Optimising Satellite-Derived Bathymetry using Optical Satellite Imagery over the Adelaide Metropolitan Coast
Authors: Downes Joram , Bruce David , Miot da Silva Graziela , Hesp Patrick A.
Published: February 2025

This study enhances the accuracy of optical satellite-derived bathymetric datasets in a shallow, mixed benthic, low wave energy coastal environment by identifying the optimal combination of input satellite imagery, spectral bands, and empirical derivation techniques. A total of 109 unique derivations were performed based on an exhaustive combination of these variables. These deri-vations were calibrated and validated using 1,064,536 ground truth observations. Results revealed that the multiband linear technique consistently outperformed the band ratio technique, achieving the best results with input bands from PlanetScope SuperDove imagery. The top-performing derivation attained an R² value of 0.94 and an RMSE of 0.41m when compared with ground truth data, surpassing published RMSE values in similar environments. Further validation beyond the calibration site confirmed its effectiveness within depths of 0.5m to 5m, demonstrating an RMSE of 0.51m, albeit with a gradual reduction in accuracy with increasing depth. This research not only identifies the optimal combination of variables but also provides valuable insights into how the number of input bands, their spatial resolution, and their specific spectral properties (central wavelength and bandwidth) influence the quality of satellite-derived bathymetry datasets. Challenges remain in accounting for mixed benthic types and their variable albedos.
On The Calculus of a Double Articulated Buckling Bar on Elastic Environment through Transfer-Matrix Method with Applications for Dental Implants
Authors: Gyorbiro Robert , Tripa Mihai-Sorin , Cojocariu-Oltean Otilia , Opritoiu Petre , Bărăian Iulia , Rotaru Doina-Iulia , Suciu Mihaela
Published: February 2025

Studies related to the methods and materials used for dental implants have been developed. This work presents an original idea, to assimilate the dental implant as a buckling double articulated bar on elastic environment, the mandibular bone being assimilated as an elastic environment. The buckling bar is treated through the Transfer-Matrix Method, that is very easy to program and gives results very quickly for problems of optimizing the form of some pieces must be solved, in occurrence, of dental implants. The most important material used for dental implants is titanium and its alloys, because it has good durability, it is resistant to corrosion and its most important characteristic is biocompatibility. Risks of buckling are higher for straight bars compressed with an axial force, that is very important to know the critical buckling force, that is important for the dental implant too. This study was done in two steps. In the first step it is considered the buckling double articulated bar on a rigid environment. The second step consisted in studying the same doubly articulated bar that the joint at the lower end of the bar is on an elastic environment, the bone in which the implant is placed being considered as an elastic environment. This work will serve to future research about dental implants.
A Low-Power CMOS Receiver with Quadrature Bandpass Continuous-Time Delta-Sigma ADC for IoT Applications
Authors: Kim Nam-Seog
Published: February 2025

This paper presents a low-power CMOS receiver with complex continuous-time delta-sigma ADC designed for IoT applications in the 2.4 GHz band. The architecture employs a quadrature bandpass continuous-time delta-sigma ADC optimized for Bluetooth Low Energy (BLE) standards, achieving an ENOB of 10.9 bits while consuming only 0.81 mW from a 1.0V supply. The receiver demonstrates impressive performance metrics, including a sensitivity of -95 dBm at 10⁻³ bit error rate, an image rejection ratio of 54.2 dBc, and a spurious-free dynamic range of 79.8 dBc. Operating at a 1.5 MHz intermediate frequency with 2 MHz bandwidth, the ADC achieves superior energy efficiency with a figure of merit (FOMW) of 103.2 fJ/conv. Implemented in 28nm CMOS technology, the complete receiver occupies 0.375 mm² for the RF front-end and 0.145 mm² for the ADC while consuming 4.08 mW total power, making it well-suited for battery-powered IoT sensor nodes requiring both power efficiency and reliable wireless connectivity.
Effectiveness of Artificial Intelligence Practices in Teaching of Social Sciences: A Multi-Complementary Approach Research on Pre-School Education
Authors: Doğan Yunus , Batdı Veli , Topkaya Yavuz , Özüpekçe Salman , Akşab Hatun Vera
Published: February 2025

The aim of this study is to evaluate artificial intelligence applications in the preschool education level within the framework of the Multi-complementary Approach (McA). McA is designed as a comprehensive approach that encompasses multiple analysis methods. In the first phase of the study, the pre-complementary knowledge process, meta-analysis and meta-thematic analysis methods were used; in the post-complementary knowledge process, an experimental design with a control group and pre-test-post-test was applied. Finally, in the complementary knowledge phase, the findings of the first two phases were combined, providing an opportunity to evaluate the effectiveness of artificial intelligence applications in preschool education from a more comprehensive and broader perspective. The study provides information about McA, and then the methodological process and findings of the research are presented in detail within this framework. After providing information about McA, the methodological process and results of the study are presented step by step within this framework. A literature review based on document analysis in the context of social sciences and teaching in preschool education using artificial intelligence applications has shown that the application of artificial intelligence has positive and significant effects on both student performance and various variables supporting teaching. The complementary results favoring artificial intelligence applications encourage the increased use of such technologies in preschool education, promoting their more widespread and systematic use in the teaching environment.
The Incorporation of Recycled Aggregate Concrete as a Strategy to Enhance the Circular Performance of Residential Building Structures in Spain
Authors: Vásquez-Cabrera Alicia , Montes Maria Victoria , Llatas Carmen
Published: February 2025

The construction industry increasingly relies on concrete to meet growing urban population demands. However, concrete has a high carbon footprint, which contradicts the Sustainable Development Goals and the Circular Economy policies promoted by the European Commission. The use of Recycled Aggregate Concrete (RAC) is a cost-effective circularity strategy to mitigate environmental impacts. Several countries have integrated RAC into their standards and have achieved promising circularity results. Spain is committed to enhancing resource productivity and using circular materials through practices established during the design phase. Although the residential sector plays a significant role within the construction industry, the potential for circularity of RAC in such residential building structures remains unexplored. The present study aims to fill this gap by assessing the circularity of four scenarios in a multi-family building using a circularity assessment method for residential building structures: the CARES Framework. The results reveal that RAC, following the Structural Code requirements, can enhance the circularity performance: at the material level by up to 42.82%; at the element level by 21.68%; and at the system level by 10.81%. These results demonstrate that circularity declines the higher the assessment levels is, which underscores the essential integration of circular materials with adaptability and disassembly criteria.
Obesity as a Risk Factor in Pediatric Sepsis: Α Retrospective Comparative Study Under the Phoenix Definition
Authors: Yuki Koichi , Koutsogiannaki Sophia
Published: February 2025

Objectives: The relationship between sepsis outcome and obesity has attracted a significant interest in medical community. However, this association has not been tested under a new pediatric sepsis definition- Phoenix criteria.
DESIGN: A single-center, observational, retrospective study of pediatric sepsis patients from January 2014 to December 2019
SETTING: The PICU located within a tertiary pediatric center in the United States.
PATIENTS: Children less than 18 years old with the diagnosis of sepsis.
INTERVENTIONS: Not applicable.
MEASUREMENT AND MAIN RESULTS: 640 patients with the diagnosis of sepsis based on the sepsis-2 definition were identified. Within the cohort, 631 patients met the definition of sepsis under Phoenix criteria. Patients were classified based on the body habitus as underweight, normal, overweight, and obese. Obese patients had significantly higher mortality compared to normal weight group (p=0.029). More respiratory and renal dysfunction was also seen in obesity group (p=0.008, and p= 0.0008, respectively). No difference in the frequency of identified gram positive, gram negative bacterial, viral, or fungal sepsis was observed based on the body habitus.
CONLUSIONS: In our cohort of pediatric sepsis, obesity was significantly associated with higher degree of organ dysfunction and mortality. However, no difference in the incidence of identified bacteria, fungal, or viral sepsis was observed.
Impact of Uncontrolled Urbanisation on Potential Aquifer Zones
Authors: Tamto Have Linda Well , Ariane Gweth Marthe Mbond , Kayo Pokam Euloge Felix , Gounou Pokam Blaise Pascal , Mbouombouo Ngapouth Ibrahim , Aben Ekok Nicolas Anicet , Pouth Nkoma Andre Michel , Oyoa Valentin , Njandjock Nouck; Philippe
Published: February 2025

With the increase in population in certain urban areas, resulting in the sometimes uncontrolled expansion of cities, the increase in water requirements and the multiplication of sources of groundwater pollution, it is very important to know the boundaries of even the most complex aquifers in the world in order not only to guarantee their sustainable use, but also to keep them at a safe distance from sources of pollution, especially anthropogenic. Many of the world's cities draw their water from great distances because the aquifers they contain were not identified and preserved at the time they were built and developed. Just as it can be difficult to know the geometry of pollution sources, it is equally complex to know the geometry of aquifers, especially in granitic or gneissic bedrock. This paper examines the impact of uncontrolled urbanisation on potential aquifers. To do this, it uses an approach that integrates field and remote sensing data in the Analytic Hierarchy Process (AHP) through Geographic Information Systems (GIS), combined with piezometry data used in a clusterisassion to delimit potentially aquiferous zones in an environment characterised by poorly controlled urban growth. This approach uses spatial representation to delimit potentially aquiferous zones in urban and peri-urban environments. Given the anarchic nature of certain urban developments, this work shows that several aquifers in many urban areas may still be threatened by anthropogenic pollution and deserve to be preserved. Furthermore, the preservation of these aquifers in urban and peri-urban areas seems to require more urgent and robust policies from both governments and development partners if they are to be preserved and used sustainably.
Socially Responsible Behaviors of Nursing Students in Private Universities in Santiago, Chile: A Study on the Alignment with Sustainable Development Goals.
Authors: Vera-Ruiz Sandra , Vega-Muñoz Alejandro , Contreras-Barraza Nicolás , Silva-Jiménez Diego , Iturra-González José A. , Martín-Romera Ana
Published: February 2025

This study investigates the socially responsible behaviors of nursing students at private universities in Santiago, Chile, focusing on their alignment with sustainable development values. Social responsibility, viewed as an ethical obligation toward society, is essential in preparing healthcare professionals to tackle global health and social challenges. The research utilized the Inventory of Socially Responsible Behaviors in University Students (ICOSORE-U) to assess nursing students’ engagement in social, academic, and civic responsibilities. A sample of 400 nursing students from four private universities participated, with an equal gender distribution. The data were analyzed through exploratory and confirmatory factor analysis (EFA and CFA) to assess the validity and reliability of the scale. The findings confirm that the ICOSORE-U scale effectively measures socially responsible behaviors, identifying key factors such as social assistance, respect, cultural citizenship, and academic responsibility. Notably, no significant demographic differences were found in students' social responsibility scores, indicating that nursing education programs universally foster these behaviors. This study underscores the importance of integrating social responsibility into nursing curricula to ensure that future nurses are not only clinically competent but also committed to ethical, sustainable practices in healthcare. These findings provide valuable insights into how nursing education can support the development of socially responsible behaviors, contributing to the enhancement of nursing programs and the promotion of sustainable healthcare practices aligned with the Sustainable Development Goals (SDGs).
Current Teaching of Endodontics in Dental Schools Around the World: A Systematic Review
Authors: Segura-Egea Juan J. , León-López María , Cabanillas-Balsera Daniel , Saúco-Márquez Juan J. , Crespo-Gallardo Isabel , Montero-Miralles Paloma , Martín-González Jenifer , Alonso-Ezpeleta Oscar
Published: February 2025

Background. Knowing how Endodontics is currently being taught in undergraduate dentistry studies is of great interest to determine whether new teaching methodologies, new materials and contemporary techniques, as well as new diagnostic and therapeutic criteria, are being incorporated in the teaching of dental students. The aim of this systematic review was to analyze current undergraduate endodontic teaching worldwide. Methods. A literature search was conducted to identify articles about undergraduate endodontic teaching, using the following combinations of keywords: (Endodontics OR endodontology) AND (teaching OR training OR education OR curriculum) AND (undergraduate OR pre-graduate) AND (evaluation OR assessment) AND (dental schools OR dental faculty). Considering that the aim of the review was to analyse the current teaching of Endodontics, the inclusion criteria established were: 1) studies published in the last 10 years, 2) in which those responsible for teaching Endodontics reported some data about the undergraduate training of Endodontics, 3) in dental schools around the world. Results. The search yielded 124 articles related to the teaching of Endodontics. After applying the inclusion criteria, fourteen articles were includes in the systematic review. The data provided were extracted and organized into five sections: 1) General characteristics of teaching Endodontics in the dental curriculum, 2) Teaching methodology in endodontic training, 3) Root canal treatment protocol used in undergraduate endodontic teaching, 4) Use of contemporary materials and technologies in endodontic training, and 5) Assessment methodology in endodontic training. Conclusions. The data found in the published studies show great consistency and, compared with the data found in studies carried out decades ago, allow us to conclude that the quality of current endodontic teaching in dental schools around the world has increased substantially. However, there is stillroom for improvement in some aspects, especially the incorporation of new technologies and materials into the teaching of Endodontics at the undergraduate level. This is the case of magnification, ultrasound and CBCT. The publication by the European Society of Endodontology of the Undergraduate Curriculum Guidelines for Endodontology, with numerous recommendations on the scope of endodontic education, may be the appropriate instrument to continue improving and homogenizing undergraduate teaching of Endodontics throughout the world.
Functional Characterization of the Hephaestin Variant D568H Provides Novel Mechanistic Insights on Iron‐Dependent Asbestos‐Induced Carcinogenesis
Authors: Zacchi Paola , Longo Francesco , Marconato Alice , Amadei Matteo , Bonaccorsi di Patti Maria Carmela , Avolio Elisa , Li Pengfei , Fan Hongkuan , Tetley Teresa D , Zabucchi Giuliano , Borelli Violetta
Published: February 2025

Local disruption of iron homeostasis leading to oxidative stress is considered one of the main mechanisms of asbestos-dependent cyto-/genotoxicity. Another aspect contributing to the risk of developing pathological consequences upon asbestos exposure is due to individual genetic fac-tors. In a previous study we identified a coding SNP in the hephaestin gene (HEPH) exerting a protective activity against the development of asbestos related thoracic cancers. Heph is a fer-roxidase which promotes iron export in concert with the permease ferroportin (Fpn1). Here, we performed an in-depth functional characterization of the HephD568H variant to gain insights on the molecular basis of its protective activity in asbestos-related carcinogenesis. We demonstrat-ed that HephD568H complexes with Fpn1 and possesses a full ferroxidase activity. Despite being more efficiently recruited at the plasma membrane, HephD568H is severely hampered in (iron-depleted) apo-Tfn binding, whose interaction with the WT ferroxidase emerged as a novel mechanism to perceive brain iron needs. Heph in human lung is expressed by pericytes and fi-broblasts and lung pericytes were shown to respond to iron request by up-regulating the iron exporter pair. These findings extend to the pulmonary vascular unit the paradigm of local iron regulation uncovered at the blood-brain barrier. Moreover, they mechanistically correlate alter-ations in iron sensing with the risk of developing asbestos-dependent malignancies.
Synthesis and Characterization of a Biopolymeric Membrane Derived from Mature Banana Peel Biomass
Authors: Sánchez Aramis A. , Guamán Ana , Castillo Darwin , Carrión Javier , Riofrío Grettel , Padilla-Martínez J.P. , Lakshminarayanan Vasudevan
Published: February 2025

Biopolymeric films derived from starch are gaining attention due to their potential applications, which are primarily attributed to their availability and biodegradability. Here, we report developing and characterizing a biopolymeric film utilizing banana peel waste (BM2). Analytical techniques were employed, including water absorption analysis, determination of soluble matter, UV-visible absorption spectrophotometry, tensile strength assessment, morphological examination using Scanning Electron Microscopy (SEM), and thermal analysis through Thermogravimetric Analysis (TGA). The water absorption analysis revealed a noteworthy absorption percentage of 115.23% and 61.75% of soluble matter. The UV-visible absorption spectrophotometry results demonstrated a light absorbance degree ranging from 0.9 to 720 nm, particularly between 400 and 1,000 nm. However, the mechanical strength tests indicated relatively low resistance at 0.8 MPa, attributed to the irregular surface observed in the film's morphology as evidenced by scanning electron microscopy (SEM). Thermal analysis conducted via TGA offered valuable insights into the degradation behavior of the film. The findings revealed a degradation temperature range spanning from 160 to 300 °C, thereby elucidating the thermal stability of the film and its potential applications. While mechanical limitations were evident, the biopolymeric film derived from banana peel waste demonstrated noteworthy water absorption properties, presenting potential in specific applications, particularly those that do not necessitate elevated mechanical strength. Continued efforts in optimizing and refining the film's structure promise to bolster its mechanical properties, making it suitable for a wider array of applications.