bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Manual Massage, Foam Rolling, and Strength Training on Cardiovascular Responses in Adults: A Scoping Review
Authors: Monteiro Estêvão Rios , Aguilera Lavínia Martins , Ruá-Alonso María , Araújo Gleison da Silva , Corrêa Neto Victor Gonçalves , Bentes Claudio Melibeu , Vilaça-Alves José , Reis Victor Machado , Ferreira Arthur de Sá , Marchetti Paulo H. , da Silva Novaes Jefferson
Published: February 2025

Objectives: The purpose of this investigation was to review the cardiovascular responses after experimental conditions of massage – manual (MM) or with foam rolling (FR) – combined or not with strength training in healthy adults. Methods: A search was performed in CINAHL, Cochrane Library, PubMed®, and SciELO databases on January 29, 2025. Results: Among the 214 studies retrieved in the database search, 6 were selected for the present review. The included studies pointed to an improvement in cardiovascular responses characterized reduced in arterial stiffness and blood pressure and an increase in nitric oxide concentration and blood flow. These findings suggest that physical exercise prescribers should consider the cardiovascular effects promoted by massage (MM or FR). Conclusions: A change in arterial compliance, followed by a hypotensive effect on systolic blood pressure, reinforces the role of physical activity as a non-pharmacological agent and highlights the need for inclusion in the different groups that need adjuvant help for blood pressure control.
A Field-Scale Framework for Assessing the Influence of Measure-While-Drilling Variables on Geotechnical Characterization Using a Boruta-Shap Approach
Authors: Goldstein Daniel , Aldrich Chris , Shao Quanxi , O'Connor Louisa
Published: February 2025

This study presents an application of Boruta-SHapley Additive ExPlanations (Boruta-SHAP) for geotechnical characterization using Measure-While-Drilling (MWD) data, enabling a more interpretable and statistically rigorous assessment of feature importance. Pit-scale Measure-While-Drilling (MWD) data was used to characterize geotechnical properties via regression-based algorithms. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, Boruta-SHAP quantifies the individual contribution of each MWD parameter. This method ensures interpretable and reliable geotechnical characterization as well as robust feature selection by comparing predictors against randomized ‘shadow’ features. The Boruta-SHAP analysis revealed that bit air pressure and torque-to-penetration ratio were the most significant predictors of rock strength, contradicting previous assumptions that rate of penetration was the dominant factor. Moreover, feature importance was conducted for fracture frequency and Geological Strength Index (GSI), a rock mass classification system. Boruta-SHAP A comparative analysis of prediction performance was also performed using a range of different machine learning algorithms that resulted in strong coefficient of determinations of actual field or laboratory results versus predicted values. The results are plausible, confirming that MWD data could provide a high-resolution description of geotechnical conditions prior to mining leading to a more confident prediction of subsurface geotechnical properties. Therefore, the fragmentation from blasting as well as downstream operational phases, such as digging, hauling, and crushing, could be improved effectively.
Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke within the UK Biobank
Authors: MacCarthy Gideon , Pazoki Raha
Published: February 2025

Background and objective: Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally and its increasing incidence has become a global public health concern. Accurate stroke prediction is highly valuable for early intervention and treatment. Previous studies have utilized statistical and machine learning techniques to develop stroke prediction models. Only a few have included genome-wide stroke genetic liability and evaluated its predictive values. This study aimed to assess the added predictive value of genetic liability in the prediction of the risk of stroke. Materials and methods: The study included 243,339 participants of European ancestry. Stroke genetic liability was constructed using previously identified genetic variants associated with stroke by the MEGASTROKE project through genome-wide association studies (GWAS). In our study, we built four predictive models with and without stroke genetic liability in the training set: Cox proportional hazard (Coxph), Gradient boosting model (GBM), Decision tree (DT), and Random Forest (RF) to estimate time-to-event risk for stroke. We then assessed their performances in the testing set. Results: Each unit (standard deviation) increase in genetic liability increases the risk of incident stroke by 7% (HR = 1.07, 95% CI = 1.02, 1.12, P-value = 0.0030). The risk of stroke was greater in the higher genetic liability group, demonstrated by a 14 % increased risk (HR = 1.14, 95% CI = 1.02, 1.27, P-value = 0.02) compared with the low genetic liability group. The Coxph model including genetic liability was the best-performing model for stroke prediction achieving an AUC of 69.54 (95% CI = 67.40, 71.68), NRI of 0.202 (95% CI = 0.12, 0.28; P-value = 0.000) and IDI of 1.0×10-04 (95% CI = 0.000, 3.0×10-04; P-value = 0.13) compared with the Cox model without genetic liability. Conclusion: Incorporating genetic factors in the model may provide a slight incremental value for stroke prediction beyond conventional risk factors.
Inertial and Linear Re-Absorption Effects on a Synovial Fluid Flow Through a Lubricated Knee Joint
Authors: Maqbool Khadija , Ahmed Afifa , Siddiqui Abdul Majeed , Mann amer bilal
Published: February 2025

This study examines the flow dynamics of synovial fluid within a lubricated knee joint during movement, incorporating a linear re-absorption rate of water and nutrients at the synovium. The fluid behavior is modeled using a couple-stress fluid framework which accounts for inertial forces and employing a slip boundary condition, which plays a crucial role in reducing drag and enhancing joint lubrication for the formation of a uniform lubrication layer over the cartilage surfaces. Mathematically, the nonlinear governing equations are transformed into a system of linear partial differential equations using a recursive approach and inverse method is applied to further reduce these equations to a system of ordinary differential equations, which are solved using software Mathematica. The results indicate that synovial fluid flow generates high pressure and shear stress at the synovium due to the combined effects of inertial forces, linear re-absorption, and micro-rotation within the couple-stress fluid. Axial flow intensifies at the center of the joint capsule during activity driven by linear re-absorption and molecular rotation, while transverse flow increases near the synovium due to its permeability. These findings provide critical insights for biomedical engineers to quantify re-absorption rates and stress distributions in synovial fluid under normal physiological conditions.
Optimizing Binary Classification: An Alternative New Loss Function with Statistical Validation
Authors: Rabbi Mahmudul  Hasan
Published: February 2025

Binary classification is a fundamental task in machine learning with applications in financial fraud detection, robotics, sentiment analysis, health care, and autonomous systems. The binary cross-entropy loss function is more widely used for optimizing classification models. However, sometimes it demonstrates numerical instability and high variance during the training leading to inconsistent convergence. To solve this issue, We propose an alternative loss function designed for binary classification to improve numerical stability and reduce training fluctuations. Experimental results show that the proposed loss function achieves smoother training dynamics and lower variance in loss curves compared to conventional approaches. These findings suggest that it serves as a robust alternative for binary classification tasks, improving model reliability in real-world applications.
Advanced Silicon Modelling of Native Mitral Valve Physiology: A New Standard for Device and Procedure Testing
Authors: Delanoë Katell , Salaun Erwan , Rieu Régis , Côté Nancy , Pibarot Philippe , Stanová Viktória
Published: February 2025

Mitral valve regurgitation is among the most prevalent valvular heart diseases and increases with age. Percutaneous therapy has emerged for the management of mitral regurgitation in high surgical risk patients. However, long-term consequences of these interventions are still not fully understood due to their novelty and the difficulty to develop a strategy specific to the patient’s anatomy and/or pathology. To optimize these outcomes, an in vitro patient-specific approach could provide important insights for the most suitable strategy to use according to the patient profile. To ensure reliability of this in vitro approach, aim of this study was to reproduce the physiological behavior of the healthy native mitral valve for future applications. To do so, different silicon combinations reproducing physiological anatomy of healthy mitral valve were developed and tested under physiological hemodynamic conditions in a cardiac simulator. The hemodynamic and biomechanical behaviors of each mitral valve model were analyzed and compared to the physiological values provided in the literature. This study identified EcoFlex 00-50 and DragonSkin 10 (Smooth-On Inc, USA) as the optimal silicon combination resulting in physiological strain values and hemodynamic parameters. These findings could be useful for future patient-specific applications, helping in the optimization of percutaneous mitral valve therapy.
Assessing Climate Change Impacts on Combined Sewer Overflows: A Modelling Perspective
Authors: Galiatsatou Panagiota , Nikoletos Iraklis , Malamataris Dimitrios , Zafirakou Antigoni , Ganoulis Philippos , Gkatzioura Argyro , Kapouniari Maria , Katsoulea Anastasia
Published: February 2025

The study examines impacts of climate change on operation and capacity of the combined sewer network in the historic center of Thessaloniki, Greece. Rainfall data from three high-resolution Regional Climate Models (RCMs) from MED-CORDEX initiative with future estimations based on Representative Concentration Pathway (RCP) 4.5, are first corrected for bias based on existing measurements in the study area. Intensity-duration-frequency (IDF) curves are then constructed for the future data, using a temporal downscaling approach based on the scaling of the Generalized Extreme Value (GEV) distribution to derive the relationships between daily and sub-daily precipitation. Projected rainfall events associated with various return periods are subsequently developed and utilized as input parameters for the hydrologic-hydraulic model. The simulation results for each return period are compared with those of the current climate, and the projections from various RCMs are ranked according to their impact on the combined sewer network and overflow volumes.
Dealing with High-Risk Police Activities and Enhancing Safety and Resilience: Qualitative Insights into Austrian Police Operations from a Risk Perception and Group Dynamic Perspective
Authors: Renner Renate , Cvetković Vladimir , Lieftenegger Nicola
Published: February 2025

Special police units, such as Austria’s EKO Cobra, are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study delves into the operational risks and the critical role of group dynamics in fostering safety and enhancing resilience, emphasizing the interconnectedness of risk perception, training, and operational practices. Based on problem-centred interviews with current and former EKO Cobra membersthe research identifies key risk factors such as overconfidence, insufficient training, inadequate equipment, and the challenges posed by high-stakes scenarios. Using a structured yet flexible approach, the study integrates a group dynamics model as a theoretical framework and analyzes the data semi-inductively semi-deductivelyby applying a a qualitative research approach. The study examines risk categorization in ad-hoc operations, the interplay between risk perception and training, and actionable strategies to enhance safety and preparedness through tailored training programs. The findings underscore the transformative impact of intensive scenario-based and high-stress training exercises, which significantly improve situational awareness, automate critical actions, and reinforce teamwork. Group dynamics, including cohesion and effective communication, emerge as pivotal factors in mitigating risks and ensuring operational success. Crucially, this research highlights the broader sustainability dimensions in law enforcement, advocating for continuous, specialized training that is adaptive to emerging challenges. By linking theoretical frameworks with practical, actionable insights, the study proposes a holistic training approach that promotes resilience and long-term sustainability in police operations. These findings are relevant to elite units like EKO Cobra and provide valuable guidance for broader police frameworks, contributing to become safer, more effective and resilient.
Towards Circular Buildings in Hong Kong: A New Integrated Technology-Material-Design (TMD) Circularity Assessment Framework
Authors: Lau Ericson K.S. , Chan Daniel W.M. , I. Oluleye Benjamin , Olawumi Timothy O.
Published: February 2025

As Hong Kong faces increasing pressure on resources and environmental sustainability, there is a growing need to shift towards circular building practices. The growing demand for sustainable urban development necessitates innovative approaches to greener and more sustainable building design and construction. This paper introduces a new integrated Technology-Material-Design (TMD) Circularity Assessment Framework, a three-dimensional and comprehensive tool designed to evaluate and enhance the circularity level of buildings in Hong Kong. Through an extensive literature review, the research study identifies a new perspective with key metrics and best practices that inform the new assessment framework, enabling various key stakeholders to pinpoint effective strategies for overcoming profound challenges and seizing timely opportunities to foster a more sustainable and resilient built environment. This paper successfully categorizes all circularity assessment frameworks into three perspectives, i.e. material-based, technology-oriented, and design-supported. Future research could apply BIM technology to automate and circularize the new assessment framework. Another significant contribution of this paper is the derivation of a new formula of the Building Circularity Index (BCI) for Hong Kong, which quantifies building circularity level using a set of defined measurement metrics. By providing a robust assessment method, the TMD Circularity Assessment Framework facilitates informed decision-making for architects, engineers, governments, developers, policymakers, and other stakeholders in a new horizon. The review findings underscore the potential of the TMD Framework to guide the transition towards more circular buildings, ultimately contributing to the broader goals of environmental sustainability and resource efficiency in Hong Kong's construction and real estate sector.
Acoustic Noise of Induction Motor Drive with Voltage-Source Inverter by Random Space Vector PWM: Simulation and Experimentation Analysis
Authors: Henda Bouyahi , Khedher Adel
Published: February 2025

A The discrete tonal bands generated by an electric machine stator current spectrum for the fixed Space Vector Pulse Width Modulation (SVPWM) control, have side effects on the acoustic noise and vibration. Thus, reducing the magnitude of dominant harmonics and spreading the harmonic spectrum provide a remedy to this problem. A well-established concept has involved the application of the randomized Pulse Width Modulation (PWM) strategy in power converters to spread the power spectrum of the stator current to minimize its amplitude. Therefore, the Random SVPWM (RSVPWM) is a new switching method applied for power converters. For the control of the three-phase inverter, three different approaches RSVPWM are suggested: Random Switching Frequency (RSF), Random Zero Vector (RZV) and Random Pulse Position (RPP). This paper investigates the effect of the fixed and random SVPWM strategies on acoustic noise emitted by an Induction Machine (IM) fed by a three-phase inverter. Simulation results exhibit validity and efficiency of the random RSFSVPWM strategy to reduce sideband harmonics at different modulation indexes. The proposed PWM strategies evaluate the results of the equivalent experiments on an IM fed by a two-level VSI. The experimental results of the harmonic current and acoustic noise spectra demonstrate also that the acoustic noise is attenuated and dispersed totally for the proposed RSFSVPWM compared to the RZVSVPWM, RPPSVPWM strategies and the RPWM modulation.
Ludis, Lex Sportiva, Lex Mercatoria: A Comparative Analysis of Informal Legal Orders in a Globalized World
Authors: Alessi Longa Francesco
Published: February 2025

This article looks at the development and interaction of three types of law: ludis (playful culture), lex sportiva (the sporting law), and lex mercatoria (commercial law). Though each arose to meet particular social and economic purposes – from sociability to sporting control to trade – these legal systems exhibit autonomic regulation, dynamic creation of norms, and arbitration of disputes. Based on historical analysis, theoretical review, and current practices, this study establishes how non-state legal systems have contributed to legal pluralism and still affect the global legal systems. It concludes by identifying the potential and difficulties of combining these systems within the formal state structures in a increasingly interconnected world.
The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students
Authors: Rodriguez Joel Mark , Austria Genesis , Millar Glen
Published: February 2025

This study investigates how ABCD technologies can improve learning assessments in higher education. The objective is to research how students perceive things, plan their behavior, and how ABCD technologies affect individual learning, academic integrity, co-learning, and trust in the assessment. Through a quantitative research design, survey responses were gathered from university students, and statistical tests, such as correlation and regression, were used to establish relationships between Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Behavioral Intention (BI) towards ABCD adoption. The results showed that there was no significant relationship between PU, PEU, and BI, which suggests that students' attitudes, institutional policies, faculty support, and infrastructure matter more in adoption than institutional policies, faculty support, and infrastructure. While students recognize ABCD's efficiency and security benefits, fairness, ease of use, and engagement issues limit their adoption of these technologies. The research adds to Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT) by emphasizing external drivers of technology adoption. The limitations are based on self-reported data and one institutional sample. It is suggested that universities invest in faculty development, infrastructure, and policy-making to facilitate effective and ethical use of ABCD technologies in higher education.
Multiobjective Particle Swarm Optimization: A Survey of the State-of-the-Art
Authors: Li Zheng , Han Yibin , Yang Yanxia
Published: February 2025

In the last decade, multiobjective particle swarm optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving multiobjective optimization problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle with complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review on MOPSO from the basic principles to hybrid evolutionary strategies. To offer the readers insights on the prominent developments of MOPSO, the key parameters on the convergence and diversity performance of MOPSO were analyzed to reflect the influence on the searching performance of particles. Then, the main advanced MOPSO methods were discussed, as well as the theoretical analysis of multiobjective optimization performance metrics. Even though some hybrid MOPSO methods show promising multiobjective optimization performance, there is much room left for researchers to improve further, in particular in terms of engineering applications. As a result, further in-depth studies are required. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.
AI-Powered Data Vault 2.0 Modeling for Business Intelligence and Automation
Authors: Vines Andreea , Bologa Ana-Ramona , Bostan Andreea-Izabela
Published: February 2025

This study explores the innovative application of Artificial Intelligence (AI) in revolutionizing data engineering practices, specifically focusing on the enhancement of the Data Vault modeling process in the context of big data environments. By leveraging the TPC-DS data set, a widely recognized industry benchmark that simulates complex, large-scale data scenarios, the research investigates the capabilities of ChatGPT in automating, accelerating, and refining the creation of Data Vault models. The methodology includes an iterative approach where ChatGPT generates models using various prompt engineering techniques. Comparative analysis is conducted against traditional modeling methods, emphasizing critical factors such as scalability to massive data sets, the speed and efficiency of model creation, precision in handling diverse data formats, and the AI’s adaptability to dynamic schema changes. The study also examines ChatGPT’s ability to seamlessly integrate new, high-volume data sources into existing models while maintaining performance in big data processing contexts. The findings aim to uncover insights into the practical viability of ChatGPT as a transformation tool for data practitioners, highlighting its potential to ensure higher accuracy and streamline complexities inherent in large-scale Data Vault modeling. This exploration serves as a foundational step toward understanding the broader implications of AI in advancing the state of modern big data warehousing and analytics.
A Study on Factors Affecting Intention to Continuous Use Chatbot Services in Financial Institutions
Authors: Choi Yeun-su , Lee Seung-zoon , Choi Jeongil
Published: February 2025

The South Korean AI market expanded significantly from 2019 to 2024, with AI technologies be-coming increasingly vital in financial services. The rise of non-contact transactions and branch downsizing has shifted customer service towards non-contact interactions, highlighting chatbot services' importance. AI solutions like KB Kookmin Bank's chatbot 'Bibi' enhance customer satis-faction and institutional competitiveness. This study surveyed 250 financial institution chatbot users nationwide in February 2024, using SPSS and R to analyze factors affecting continuous usage in-tention. Results showed social presence, response accuracy, and assurance positively influenced expectation confirmation, while responsiveness had no significant effect. Security and interactivity showed positive effects, but reliability and ease of use were not significant. Usage frequency and education level variations were noted. Repeated use strengthened the relationship between social presence and expectation confirmation, though security concerns increased with usage. This study provides empirical evidence for improving chatbot service quality and user experience, offering strategic insights for non-contact financial services.
A Phantom-Based Study of X-Ray Fluorescence Detectability of Iron, Copper, Zinc, and Selenium in the Human Blood of Superficial and Cutaneous Vasculature
Authors: Gherase Mihai Raul , Mahajan Vega
Published: February 2025

Blood concentrations of essential trace elements can be used to diagnose conditions and diseases associated with excess or deficiency of these elements. Inductively coupled plasma mass spectrometry (ICP-MS) has been employed for such measurements, but maintenance and operation costs are high. XRF detectability in cutaneous blood of iron (Fe), copper (Cu), zinc (Zn), and selenium (Se) was assessed as alternative to ICP-MS. Three phantoms were made up of two polyoxymethylene (POM) plastic cylindrical cups of 0.6-mm and 1.0-mm thick walls and a 5.3-mm diameter POM cylindrical insert. Six water solutions of Fe in 0 to 500 mg/L and Cu, Zn, and Se in 0 to 50 mg/L concentrations, were poured into the phantoms to simulate x-ray attenuation of skin. Measurements using an integrated x-ray tube and polycapillary x-ray lens unit generated 24 calibration lines. Detection limit intervals in mg/L were: (36, 100), (14, 40), (3.7, 10), and (2.1, 3.4) for Fe, Cu, Zn, and Se, respectively. Fe was the only element with detection limits lower than its 480 mg/L median human blood concentration. Estimated radiation dose and equivalent dose to skin were below those of common radiological procedures. Applications will require further instrumental development, and finding a calibration method.
Analytical Approximation of the Stress Function for Conical Flywheels
Authors: Garcia Miguel , Orozco-López Onofre , Uribe-Chavira Jesús , Blanco-Ortega Andrés
Published: February 2025

The equilibrium equation for a variable thickness flywheel is approximated analytically, assuming that the flywheel’s thickness varies linearly and as smoothly as to neglect it. The solution shows an accurate approximation of maximum radial and tangential stresses, with respect to finite element method, for a wide range of outer/inner thickness relation. The performance of the results was quantified with the Pearson correlation factor, the mean absolute percentage error, and the relation between the maximum stresses calculated with FEA and the results obtained with the obtained solution.
“Bora” and Beyond: Implications on Women’s Access to Land in Peri‐Urban Settlement in Sierra Leone
Authors: Turay Bankolay Theodore , Omirin Modupe Moronke
Published: February 2025

In the North-west of Sierra Leone, customary land access hinges on "Bora" – a system tied to respect and social obligations, not formal ownership. ’’Bora’’ payment is a small token strangers and women from non-land-owning families give to the community or landowner related to accessing land for farming, building a house, using natural resources, or other purposes, usually a basket of rice or a few chickens, to allow them to use a piece of land to grow food for their families. This study is the first to utilize the "Bora" practice to examine land governance issues in rural areas of Sierra Leone.This study uses a mixed-methods approach, including biographic surveys and interviews with key stakeholders, to understand the social, economic, cultural, and political factors influencing women's land rights in the context of a case study.The findings from this study contributes to ongoing debates about gender, land rights, and the evolving nature of customary practices in the face of economic and social transformations. It recommends that policymakers and development practitioners consider the nuances of the "Bora" practices beyond the lens of mere monetization and advocate for solutions that truly empower rural women in land ownership and management.
The CMaDE-SFDM Model
Authors: Pérez-Ochoa Edwin L. , Matos Tonatiuh
Published: February 2025

Some recent observations of the universe seem to indicate that Dark Energy (DE) is not a cosmological constant, but must be dynamical. On the other hand, Cold Dark Matter (CDM) has faced several criticisms because there are observations whose explanation using CDM is not completely satisfactory. In a previous work we found that if we take into account the energy of the Gravitational Wave Background (GWB), we have to add a new term $\mathcal{M}=2\pi^2/\lambda^2$ to Einstein's equations, where $\lambda$ is the Compton wavelength of primordial gravitons. Using the actual size of the present universe $10^{26}$m, it implies that $\mathcal{M}\sim10^{-52}$1/m$^2$, just the size of the cosmological constant. We call it the Compton Mass Dark Energy (CMaDE) model. We use $\mathcal{M}$ as a DE model and find that this model fits cosmological observations better than the LCDM model. In the current work we use this model together with Scalar Field Dark Matter (SFDM) model as the DM of the universe and analyze its consequences.
Role of Toll-Like Receptors Nine and Ten Polymorphisms in Childhood Bronchial Asthma Control and Their Relation to Cardiac Function
Authors: Rabie Rehab Ahmed , Hussien Asmaa Elsharkawy , Abdelhameed Hesham Samy , Shedeed Soad Abdelsalam , Almadani Noura , Nofal Hanaa A , Elrafey Dina Sameh , Ali Hossam T. , Naguib Mohammed Sanad
Published: February 2025

Background: Asthma designates the most widely spread chronic disease in children. Toll-like receptors (TLRs) are linked to several human diseases including allergic diseases. We aimed to verify the linkage of TLR9 and TLR10 gene polymorphisms with bronchial asthma control and cardiac function. Methods: this a case-control study comprised 80 participants aged between 5 and 12 years old, divided into 20 healthy non-asthmatic participants and 60 asthmatic ones. The asthmatic group members were diagnosed clinically and subdivided by spirometry into three subgroups (well-controlled, partially-controlled, and uncontrolled). Diagnosis of bronchial asthma based on The Global Initiative for Asthma (GINA) guidelines, 2022. Genetic polymorphisms in TLR9 and TLR10 were detected using real-time PCR. Results: We discovered a significant statistical relation between TLR9 gene polymorphisms and PFTs with better results in CC genotype also a significant statistical relation with both RVFWSL (right ventricle free wall longitudinal strain) and GLS A2C (left ventricle global longitudinal strain apical 2-champer view) with better values were associated with CC genotype. Regarding TLR10, there was a significant statistical relation between gene polymorphisms and PFTs with better function in GG genotype and also there was a significant statistical relation between TLR10 gene polymorphisms and GLS AVG (left ventricle global longitudinal strain average) with GG type having significantly better cardiac function. Conclusion: Subclinical cardiac dysfunction of Left and Right ventricles was detected in asthmatic children. CC genotype of TLR9 and GG genotype of TLR10 are associated with better asthma control and better cardiac function. So TLR9 and TLR10 have role in asthma control and cardiac dysfunction.
New Mcconnellite Ceramic Pigment as a Selective Solar Absorber: Effects of Microwave Firing and Rare Earth Doping
Authors: Monrós Guillermo , Badenes José Antonio , Delgado Carolina , Monrós-Andreu Guillem , Llusar Mario
Published: February 2025

CuCrO₂ (mcconnellite) was synthesized using both the solid-state method and microwave dielectric firing. It was characterized as a novel black ceramic pigment for use in various industrial glazes. For the first time, the application of mcconnellite (CuCrO₂) and its coloured glazes as selective solar absorbers (SSA) for integral ceramic solar collectors has been reported. The addition of quartz or anatase was investigated as colour modifiers to prevent the bluing of the pigment in Zn-containing glazes, a phenomenon associated with the exsolution of copper. Furthermore, doping with lanthanide oxides was explored to address two key challenges: controlling the formation of pinhole defects in porcelain glazes, which are linked to the destabilization of Cu⁺, and adjusting the IR cut-off wavelength to improve its performance as SSA.
On Quaternionic Analysis and a Certain Generalized Fractal-Fractional ψ-Fueter Operator
Authors: González Cervantes José Oscar , Ramírez-Belman Juan Adrián , Bory-Reyes Juan
Published: February 2025

This paper introduce a fractional-fractal ψ-Fueter operator in the quaternionic context inspired in the concepts of proportional fractional derivative and Hausdorff derivative of a function with respect to a fractal measure. Moreover, we establish the corresponding Stokes and Borel-Pompeiu formulas associated to this generalized fractional-fractal ψ-Fueter operator.
Phosphorus Sources in Management Zones and Their Impact on Soybean Crops
Authors: Guesser Vagner Portes , Bottega Eduardo Leonel , Ferreira Paulo Ademar Avelar , Knies Alberto Eduardo , Oliveira Zanandra Boff de , Pes Luciano Zucuni
Published: February 2025

Phosphorus (P) is an essential element for plant growth and development. Studies show that up to 90% of phosphorus applied via inorganic fertilizer is adsorbed or precipitated into colloids and not made available to plants. In this context, the use of microorganisms as promoters of P availability in the soil solution is essential. The objectives of this work were to delimit management zones (MZ) based on the spatial variability of P content in the soil and to investigate the effects of P supply, from different sources (mineral and biological) applied in each ZM, on the soybean crop and on the soil after applications. The results indicate that P management, both via mineral sources and the use of solubilizers, is effective in increasing soybean productivity, grain weight, and P content in soil and grains, especially in management zones with lower initial P content. The use of management zones based on the spatial variability of P content in the soil proved to be an efficient tool for optimizing phosphorus application, contributing to greater productivity and better use of resources, especially in areas with low fertility. This reinforces the importance of precision management strategies in the use of fertilizers, adapted to specific soil conditions, to maximize yield and improve nutrient use efficiency.
The Informational Coherence Index A Framework for the Integration of Networks of Artificial Intelligence Models
Authors: Matuchaki Henry
Published: February 2025

The Informational Coherence Index (Icoer), developed within the framework of the Unified Theory of Information (TGU), offers a transformative approach to the autonomous integration of artificial intelligence (AI) networks. This work demonstrates how AI models can evolve independently, guided by informational coherence without human intervention. By leveraging dynamic parameters such as capacity (Ci), informational distance (ri), entropy (Si), and harmonic resonance (Γi), the Icoer ensures that networks maintain alignment with informational truth. Simulations with up to 100 interconnected models confirmed that the system achieves stable coherence through continuous optimization cycles. This approach not only enhances AI efficiency and resilience but also establishes a self-regulating mechanism for future AI evolution. The Icoer thus emerges as a foundational metric for the development of truly autonomous AI systems, where coherence becomes the guiding principle of intelligent adaptation and collaboration.
Comparison Of Backprojection Techniques For Rupture Propagation Modelling Of The Mw=7,8 Earthquake Near Kahramanmaras And Its Mw=7,5 Aftershock Near Elbistan Turkey, 2023
Authors: Nikolopoulos Dimitrios , Sultan Mahmood , Alam Aftab , Cantzos Demetrios , Priniotakis Georgios , Papoutsidakis Michail , Javed Farhan , Prezerakos George , Siddique Jamil , Ali Shah Muhammad , Rafique Muhammad , Yannakopoulos Panayiotis H.
Published: February 2025

This paper utilises teleseismic Z-component data to investigate the rupture\textquotesingle{s} propagation, extent and velocity field for two destructive earthquakes of the East Anatolian Fault Zone (EAFZ); the M_w=7,8 earthquake occurrence near Kahramanmaras on 2023/Feb/06 and its M_w=7,5 major aftershock at Elbistan (Cardak Fault). Both earthquakes have been the most devastating in that tectonic region during the last decades and have caused property damage and human casualties. The extent of the rupture was modelled with high-resolution beamforming and multichannel classification.The teleseismic data were derived from agencies in the US and Canada. The rupture of the M_w=7,8 earthquake was found to be bi-directional towards north-east and south-west. A distance of 299 km was covered in 185 s with varying velocity field values along the main fault, while its splay and the major aftershock, ruptured, also, bi-directionally, to an extent of 150 km within 46 s. The findings provide new insights of the evolution of the spatio-temporal rupture of the EAFZ and may serve as a basis for long-term earthquake hazard planning of the area.
Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
Authors: Ilieva Galina , Yankova Tania , Ruseva Margarita , Dzhabarova Yulia , Klisarova-Belcheva Stanislava , Dimitrov Angel
Published: February 2025

The consumption of ultra-processed foods (UPFs) has become a central topic in discussions surrounding public health, nutrition, and consumer behaviour. This study aims to investigate the key factors shaping customer perceptions and attitudes towards UPFs and explore their impact on purchase decisions. A total of 290 completed questionnaires from an online survey were analysed to identify the drivers influencing consumer actions and habits. Users’ opinions were systematized based on their attitudes towards UPFs, considering factors such as health consciousness, knowledge, subjective norms, and environmental concerns. Participants were then categorized using both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning (ML), and multi-criteria decision-making (MCDM) techniques were applied to identify hidden dependencies between variables from the perspective of UPF consumers. The developed models reveal the underlying relationships that influence acceptance or rejection mechanisms for UPFs. The results provide specific recommendations for stakeholders across the food production and marketing value chain. Public health authorities can use these insights the findings to design targeted interventions that promote healthier food choices. Manufacturers and marketers can leverage the findings to optimize product offerings and communication strategies with a focus on less harmful options, aligning more closely with consumer expectations and health considerations. Consumers benefit from enhanced product transparency and tailored information that reflects their preferences and concerns, fostering informed and balanced decision-making. As attitudes toward UPFs evolve alongside changing nutrition and consumption patterns, stakeholders should regularly assess consumer feedback to mitigate the impact of these harmful foods on public health.
Epidemiology of Tuberculosis in Oman: A Tertiary Care Hospital Perspective from 01/2015 to 12/2020
Authors: Al Kamzari Maryam , Al Busaidi Nasser , Al Rawahi Abdel-Hakeem , Al Lawati Redha
Published: February 2025

Background: Tuberculosis (TB) remains a significant global health concern despite advancements in screening and treatment. This study examines the clinical profile, risk factors, and diagnostic methods of TB patients in Oman. Methods: A retrospective cross-sectional study was conducted at the Royal Hospital, Oman (2015–2020). Data on demographics, symptoms, risk factors, comorbidities, diagnostic methods, drug resistance, and complications were collected from electronic medical records. Statistical analysis was performed using SPSS v23, with ethical approval from the Royal Hospital Ethical Committee. Results: Among 158 patients, 62.7% had pulmonary TB, with a male predominance (63.9%) and an average age of 50 years. Common comorbidities were hypertension, diabetes, and smoking. Key risk factors included travel (31%), TB contact (7%), and immunosuppression (13.3%). Symptoms included cough (60.5%), fever (58%), and weight loss (39.9%). Diagnosis was confirmed using Ziehl-Neelsen (ZN) staining (70 cases) and TB culture (105 cases). Complications included pleural effusion (23.7%) and death (10.1%). Conclusion: Findings align with global TB trends. Pulmonary TB was predominant, with hypertension, diabetes, and smoking as major comorbidities. Common symptoms were cough, fever, and weight loss. ZN staining and sputum culture were primary diagnostic tools. Pleural effusion and death were the main complications.
Epidemiological Data, Clinical Signs, Therapy and Outcome Evaluation in Dogs with Syringomyelia of Different Etiology
Authors: Baka Rania D. , Savvas Ioannis , Sarpekidou Eirini , Kazakos George , Polizopoulou Zoe
Published: February 2025

Syringomyelia detected in both animals and humans may cause variable degree of dis-comfort and its etiology is commonly unidentified. The aim of the study was to compare the outcome in dogs with syringomyelia of different etiology. Dogs with syringomyelia were subdivided into two groups: A: Syringomyelia associated with Chiari-like malfor-mation (S-CLM) (15 dogs) and B: syringomyelia of other etiology (SOA) (15 dogs). Age on-set of S-CLM clinical signs was earlier compared to SOA (mean S-CLM and SOA values: 50.53 and 97.6 months, respectively, p=0.021). Two neurological dysfunction scoring sys-tems alongside with nociception values were lower in SOA compared to S-CLM (mean values for neurological dysfunction scoring system SOA and S-CLM: 5.87 and 4.2, respec-tively, p=0.032) (mean values for nociception SOA and S-CLM: 20.97 and 10.03, respec-tively, p=0.03). Symptomatic therapy included combinations of corticosteroids, gabapen-tin (10/15, 66.6%) in S-CLM and NSAID +/- gabapentin (8/15, 53.3% and 9/15, 60%, respec-tively) in SOA dogs. Eight S-CLM dogs (53.4%) improved with symptomatic therapy and 11 were still alive however most SOA dogs (9/15, 73.4%) died/were euthanized by the end of the study. SOA dogs demonstrated more severe neurological signs compared to S-CLM, although outcome between the two groups was not associated (p=0.211).
Chemistry-Inspired Algorithm for Multi-Cloud Service Composition
Authors: Aldakheel Mona , Kurdi Heba
Published: February 2025

The increasing complexity of cloud service composition demands innovative approaches that can efficiently optimize both functional requirements and quality of service (QoS) parameters. While several methods exist, they struggle to simultaneously minimize the number of combined clouds, examined services, and execution time while maintaining high QoS. This paper introduces a novel Chemistry-based Approach (CA) that draws inspiration from the periodic table's organizational principles and electron shell theory. Just as chemical elements are organized in the periodic table and electrons organize themselves in atomic shells based on energy levels, our approach organizes cloud services in hierarchical structures based on their cloud number, composition frequencies, cloud quality, and QoS levels. By mapping chemical principles to cloud service attributes—where service quality levels correspond to electron shells and service combinations mirror molecular bonds—we create an efficient framework for service composition. Our experimental results demonstrate significant improvements over existing methods, such as Genetic Algorithms (GA), Simulated Annealing SA, and Tabu Search (TS) across multiple performance metrics: reductions of 2-33% in combined clouds, 18-96% in examined clouds, 74-99% in examined services, and 3-99% in execution time, while enhancing fitness levels by 0.1-14% compared to benchmarks. These results validate our approach's effectiveness in optimizing service composition while minimizing computational overhead in multi-cloud environments.
Token Pruning for Efficient NLP, Vision, and Speech Models
Authors: Jianhong Yong
Published: February 2025

The rapid growth of Transformer-based architectures has led to significant advancements in natural language processing (NLP), computer vision, and speech processing. However, their increasing computational demands pose challenges for real-time inference, edge deployment, and energy efficiency. Token pruning has emerged as a promising solution to mitigate these issues by dynamically reducing sequence lengths during model execution while preserving task performance. This survey provides a comprehensive review of token pruning techniques, categorizing them based on their methodologies, such as static vs. dynamic pruning, early exit strategies, and adaptive token selection. We explore their effectiveness across various domains, including text classification, machine translation, object detection, and speech recognition. Additionally, we discuss the trade-offs between efficiency and accuracy, challenges in generalization, and the integration of token pruning with other model compression techniques. Finally, we outline future research directions, emphasizing self-supervised token selection, multimodal pruning, and hardware-aware optimization. By consolidating recent advancements, this survey aims to serve as a foundational reference for researchers and practitioners seeking to enhance the efficiency of deep learning models through token pruning.
Automated Detection of the Main Pests in Cocoa Crops (Theobroma cacao L.) and Potential Natural Enemies Through Computerized Monitoring in the Dominican Republic
Authors: Iñiguez Freites Ender Antonio
Published: February 2025

The project successfully developed an automated system for pest detection and moni-toring in cacao crops in the Dominican Republic, using high-resolution cameras and artificial intelligence. Results showed a significant reduction in pest incidence, with 92% accuracy in pest identification and 85% in detecting natural enemies.
The system enabled a 40% reduction in pesticide use, optimizing application only in affected areas and promoting the conservation of beneficial species. Validation by ex-pert entomologists confirmed its reliability.
Additionally, continuous monitoring facilitated the implementation of Integrated Pest Management (IPM) strategies, improving crop health and environmental sus-tainability. The involvement of local farmers ensured its adaptability. In conclusion, the system provides an innovative solution for precision agriculture, optimizing productivity and reducing costs.
Results of Petrographic Investigations of Core Samples from the Basement of the Siljan Ring Structure
Authors: Kutcherov Vladimir , Sivalneva Olga , Buzilov Alexandr , Postnikov Alexandr
Published: February 2025

Among the large ring structures considered meteorite (impact) craters, the Siljan structure is one of the best-known in central Sweden. Samples of basement rocks were collected and studied from four sections penetrated by exploration boreholes along the periphery of the Siljan crater. Based on the results of laboratory studies, detailed petrographic characteristics of the rocks were obtained, which included a description of their structure and texture, mineral composition, and secondary transformations. Significant differences in the composition of the rocks in the sections located in the eastern and western parts of the structure were revealed. In the western part, boreholes exposed volcanic and metamorphosed volcanic-sedimentary rocks, while the section in the eastern part of the structure is composed of igneous rocks of predominantly acidic composition. This is probably due to structural shifts as a result of the influence of intense tectonic processes.