bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System
Authors: Edrah Aisha , Ouda Abdelkader
Published: June 2024

With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold.
Comparing Ant Activity and Plants Bearing Extrafloral Nectaries in Rockland Habitats of the Florida Keys with Those of the Everglades and the Bahamas
Authors: Koptur Suzanne , Keeler Kathleen H.
Published: June 2024

Rocklands are present in South Florida, both on the peninsula and in the Keys. Previous work has shown that extrafloral nectaries on pine rockland plants in the Florida Everglades and surrounding areas play a role in attracting ant protectors. Species with nectaries are more abundant and ant activity greater in more recently burned pine rockland habitats compared with longer undisturbed rockland hammock forest. The floristic composition and prevailing climate of Keys rocklands differs substantially from those on the mainland, so we sought to investigate ant activity and the abundance of plants with extrafloral nectaries in Big Pine Key rockland habitats. Standard baiting methods were used to assess ant abundance and potential predation activity in hardwood hammock, shrubby pine rockland, and open pine rockland sites. Ant activity was the lowest in open pine rockland, greatest in hardwood hammock, and intermediate in shrubby pine rockland, the opposite of what has been observed in previous studies in southern peninsular Florida and on Andros, Bahamas. Sampling vegetation at these same sites revealed a larger proportion of plant species with extrafloral nectaries in the pine rocklands than the hammock, as observed previously in Everglades habitats. Possible reasons for differences in ant activity in Keys rockland habitats are discussed.
Clinical Utility of the aMAP Score for Predicting Hepatocellular Carcinoma Development in Patients with Chronic Hepatitis B
Authors: Chaiwiriyawong Supakorn , Assawasuwannakit Suraphon , Feuangwattana Poorikorn , Sripongpun Pimsiri , Chamroonkul Naichaya , Piratvisuth Teerha , Kaewdech Apichat
Published: June 2024

This study aimed to evaluate the efficacy of the aMAP score and compare it with other risk scores for predicting hepatocellular carcinoma (HCC) development in Thai patients with chronic hepatitis B (CHB). We retrospectively analyzed patients with CHB between 1 January 2008 and 31 December 2019. Data on demographics, clinical parameters, cirrhosis status, HCC imaging, and alpha fetoprotein surveillance were collected to calculate the aMAP score (0–100) based on age, sex, albumin–bilirubin level, and platelet count. Of the 1060 patients analyzed, 789 were eligible, of whom 51 developed HCC. The cumulative HCC incidences in the low-, moderate-, and high-risk groups at 3, 5, and 10 years were significantly different (log-rank, p < 0.0001). The area under the receiver operating characteristic curves (AUROCs) of the aMAP scores for predicting HCC at 3, 5, and 10 years were 0.748, 0.777, and 0.784, respectively. Among the risk scores, the CU-HCC score had the highest AUROCs (0.823) for predicting 5-year HCC development. The aMAP score is a valuable tool for predicting HCC risk in Thai patients with CHB and can enhance surveillance strategies. However, its performance is inferior to that of the CU-HCC score, suggesting the need for new predictive tools for HCC surveillance.
Recovery from Severe COVID-19 Is an Independent Predictor of Electrocardiographic Abnormal P-Wave Axis
Authors: Yılmaz Mücahid , Mirzaoğlu Çetin
Published: June 2024

Aim: Abnormal P-wave axis (aPwa) have been correlated with an increased risk of supraventricular arrhythmias. The aim of this study was to analyze whether infection with COVID-19 may cause a predisposition for supraventricular arrhythmia in the long term, following recovery. Materials and Methods: In this study, a total of 252 subjects with a confirmed history of COVID-19 (recovered COVID-19) and 251 healthy subjects without a history of COVID-19 were included. The recovered COVID-19 group was divided into three subgroups designated as mild, moderate, and severe according to the severity score of their chest CT. The aPwa data were obtained using 12-lead electrocardiography (ECG) and compared between the healthy subjects and the recovered COVID-19 subgroups. Results: This study showed that in the recovered severe COVID-19 subgroup the prevalence of aPwa was significantly increased compared to the controls and the other COVID-19 subgroups. No correlation could be detected in Spearman’s Rho correlation between the existence of aPwa and the number of positive PCR tests for COVID-19 and the time elapsed after infection with COVID-19. The binary logistic regression analysis showed that recovery from severe COVID-19, the severity score of the chest CT in the recovered from COVID-19 subjects, and the existence of hypertension (HT) were all independent predictors of aPwa (hazard ratio: 3.542, 95% confidence interval: 1.398–8.969, p: 0.01; hazard ratio: 0.896, 95% confidence interval: 0.840–0.955, p < 0.001; hazard ratio: 2.710, 95% confidence interval: 1.079–6.804, p: 0.03, respectively). Conclusions: Individuals who have recovered from severe COVID-19 have shown an increased prevalence of aPwa. The existence of aPwa was not associated with the number of positive PCR tests for COVID-19 or the time elapsed after infection with COVID-19. Therefore, recovery from severe COVID-19 is an independent predictor of electrocardiographic abnormal P-wave axis.
Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm
Authors: Subramani Jothimani , Kumar G. Sathish , Gadekallu Thippa Reddy
Published: June 2024

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC’s ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.
Impact of Pre-Transplant Left Ventricular Diastolic Pressure on Primary Graft Dysfunction after Lung Transplantation: A Narrative Review
Authors: Henry Jean Philippe , Carlier François , Higny Julien , Benoit Martin , Xhaët Olivier , Blommaert Dominique , Telbis Alin-Mihail , Robaye Benoit , Gabriel Laurence , Guedes Antoine , Michaux Isabelle , Demeure Fabian , Luchian Maria-Luiza
Published: June 2024

Lung transplantation (LT) constitutes the last therapeutic option for selected patients with end-stage respiratory disease. Primary graft dysfunction (PGD) is a form of severe lung injury, occurring in the first 72 h following LT and constitutes the most common cause of early death after LT. The presence of pulmonary hypertension (PH) has been reported to favor PGD development, with a negative impact on patients’ outcomes while complicating medical management. Although several studies have suggested a potential association between pre-LT left ventricular diastolic dysfunction (LVDD) and PGD occurrence, the underlying mechanisms of such an association remain elusive. Importantly, the heterogeneity of the study protocols and the various inclusion criteria used to define the diastolic dysfunction in those patients prevents solid conclusions from being drawn. In this review, we aim at summarizing PGD mechanisms, risk factors, and diagnostic criteria, with a further focus on the interplay between LVDD and PGD development. Finally, we explore the predictive value of several diastolic dysfunction diagnostic parameters to predict PGD occurrence and severity.
Investigating the Relationship between Oral Health and Severe Mental Illness: Analysis of NHANES 1999–2016
Authors: Kang Jing , Wu Jianhua , Aggarwal Vishal. R. , Shiers David , Doran Tim , Palmier-Claus Jasper
Published: June 2024

Objectives: To explore whether: (i) people with severe mental illness (SMI) experience worse oral health than the general population, and (ii) the risk factors for poor oral health in people with SMI. Methods: Cross-sectional data were extracted from the National Health and Nutrition Examination Survey (1999–2016), including on self-rated oral health, oral pain, tooth loss, periodontitis stage, and number of decayed, missing, and filled teeth. Candidate risk factors for poor oral health included demographic characteristics, lifestyle factors, physical health comorbidities, and dental hygiene behaviours. Ordinal logistic regression and zero-inflated negative binomial models were used to explore predictors of oral health outcomes. Results: There were 53,348 cases included in the analysis, including 718 people with SMI. In the fully adjusted model, people with SMI were more likely to suffer from tooth loss (OR 1.60, 95% CI: 1.34–1.92). In people with SMI, risk factors identified for poor oral health outcomes were older age, white ethnicity, lower income, smoking history, and diabetes. Engaging in physical activity and daily use of dental floss were associated with better oral health outcomes. Conclusions: People with SMI experience higher rates of tooth loss than the general population, and certain subgroups are particularly at risk. Performing regular physical exercise and flossing may lower the risk of poor oral health, while smoking and diabetes may increase the risk. These findings suggest opportunities for targeted prevention and early intervention strategies to mitigate adverse oral health outcomes in people with SMI.
Parameterized Multipartite Entanglement and Genuine Entanglement Measures Based on q-Concurrence
Authors: Ma Pan-Wen , Zhao Hui , Fei Shao-Ming , Zhang Mei-Ming , Wang Zhi-Xi
Published: June 2024

We study genuine multipartite entanglement (GME) and multipartite k-entanglement based on q-concurrence. Well-defined parameterized GME measures and measures of multipartite k-entanglement are presented for arbitrary dimensional n-partite quantum systems. Our GME measures show that the GHZ state is more entangled than the W state. Moreover, our measures are shown to be inequivalent to the existing measures according to entanglement ordering. Detailed examples show that our measures characterize the multipartite entanglement finer than some existing measures, in the sense that our measures identify the difference of two different states while the latter fail.
Utilizing TabNet Deep Learning for Elephant Flow Detection by Analyzing Information in First Packet Headers
Authors: Kądziołka Bartosz , Jurkiewicz Piotr , Wójcik Robert , Domżał Jerzy
Published: June 2024

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments.
Learning Causes of Functional Dynamic Targets: Screening and Local Methods
Authors: Zhao Ruiqi , Yang Xiaoxia , He Yangbo
Published: June 2024

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.
Fostering Educator Buy-in of Language and Literacy in the Science Classroom
Authors: Renn Jennifer , Duguay Annie Laurie , Wright Laura J.
Published: June 2024

This paper describes a project to build linguistic awareness among science educators at multiple levels: pre-service K–12 teachers, in-service K–12 teachers, and university faculty in both education and STEM fields. Educational linguists from a language-focused non-profit research organization created modules on teaching culturally and linguistically diverse students for a Master of Arts in Teaching (MAT) program for middle school science educators at a large urban U.S. university. The module content, which was integrated into three science education methods courses, was designed around three key principles: linguistic understanding, critical language awareness, and pedagogical modeling. A critical component to the project’s success was fostering “buy-in” from the various groups of educators through multiple means, including piloting materials with in-service science teachers, training university faculty on the language and literacy materials, and revising content based on feedback from the MAT program faculty and students. The findings demonstrate that over time, the educators re-examined their roles as teachers of language and literacy and reassessed their thinking related to science instruction, highlighting the efficacy of incorporating language development content into training and professional development for STEM educators at multiple levels.
Transformative Pathways: Implementing Intercultural Competence Development in Higher Education Using Kotter’s Change Model
Authors: Kuffuor Owura , Aggrawal Sakhi , Jaiswal Aparajita , Smith Ronald J. , Morris Pamala V.
Published: June 2024

This study explores the efficacy of Kotter’s eight-step change model in fostering intercultural competence among faculty and staff within a large Midwestern public university. Recognizing the need for intercultural development in higher education, this research applies a structured change framework coupled with the Intercultural Development Inventory (IDI) assessment tool to guide the change process. The development of intercultural competence among faculty and staff is crucial for creating inclusive learning environments, as they play a pivotal role in modeling intercultural competence and fostering students’ skills necessary to navigate an increasingly diverse and interconnected world. This study employs an instrumental case study design, utilizing interviews, document analysis, and Intercultural Development Inventory (IDI) assessments to examine the change process over a three-year period. The findings demonstrate how each step of Kotter’s model was successfully adapted to drive organizational change and enhance intercultural competence among 639 faculty and staff members across 11 departments in the College of Agriculture. This study highlights the importance of creating a shared vision, empowering stakeholders, and anchoring changes in the institution’s culture and emphasizes the pivotal role of structured change management in achieving substantive improvements in intercultural learning environments. The insights and strategies presented can serve as a foundation for other higher education institutions seeking to implement similar initiatives and foster inclusive learning environments.
A Knowledge Framework for Teachers of Physics and Physics Teacher Educators: The Genesis of a Knowledge Framework Based on the Knowledge Quartet
Authors: Farmer Stuart
Published: June 2024

To teach a subject successfully it is necessary to have knowledge of that subject and of pedagogy as it relates to that subject. Based on the observation of teachers teaching mathematics, Rowland and colleagues developed the Knowledge Quartet. Whilst some aspects of the Knowledge Quartet are set in the context of teaching mathematics, much is more generic and applicable to other subjects. In many jurisdictions, the professional standards for teachers do not have any subject-specific content. This paper describes the development of a knowledge framework, primarily based on the Knowledge Quartet, for teachers of physics, which is designed to sit alongside any teacher professional standards and school curriculum documents. The framework provides subject-specific guidance on the knowledge-base necessary for the teaching of physics as well as providing a common language to help facilitate collaboration between colleagues in relation to their professional learning. The framework was also extended to include the knowledge required by physics teacher educators supporting the professional learning of teachers of physics. The development process included gathering data from a range of physics teachers and physics teacher educators on the value and content of the framework and it being piloted with several physics teacher educators, resulting in an amendment and refinement of the framework. The resulting framework is offered as a useful tool for those involved in the education of physics teachers and as an exemplar of a knowledge framework that could be extended to other subjects.
Switching-on Delay Jitter Caused by Lateral Distribution of Current Channel of Avalanche Transistor
Authors: Cheng Zhenbo , Ning Hui , Tang Chuanxiang , Yan Youjie , Zhao Wei
Published: June 2024

The stability of the avalanche transistor’s (AT’s) switching-on process is essential for its extensive application in power semiconductors. The switching-on process was typically described in one-dimensional terms, overlooking the effects of multi-dimensional structural variations on stability. This paper investigated the influence of the lateral distribution of current channels on the switching-on delay jitter in the AT. The lateral size of the current channel affects the transit time by changing the electron path in the base region, resulting in the switching-on delay jitter of the AT. An analytical formula for the lateral size of the current channel and the switching-on delay jitter has been proposed. The two-dimensional simulation model of the AT gave the distribution of current channels. The model’s accuracy was verified by comparing experimental and simulation data. The experimental data proved that the base transit time was the main component of the switching-on delay. The results show that the switching-on delay jitter can be significantly reduced by adjusting the current channel’s lateral size. In addition, the trigger signal’s characteristics also change the current channel’s lateral distribution and then affect the stability of the switching-on delay, which provides a new perspective for the design and application of ATs.
Hybrid Time Series Model for Advanced Predictive Analysis in COVID-19 Vaccination
Authors: Khalil Amna , Awan Mazhar Javed , Yasin Awais , Kousar Tanzeela , Rahman Abdur , Youssef Mohamed Sebaie
Published: June 2024

This study aims to enhance the prediction of COVID-19 vaccination trends using a novel integrated forecasting model, facilitating better public health decision-making and resource allocation during the pandemic. As the COVID-19 pandemic continues to impact global health, accurately forecasting vaccination trends is critical for effective public health response and strategy development. Traditional forecasting models often fail to capture the complex dynamics of pandemic-driven vaccination rates. The analysis utilizes a comprehensive dataset comprising over 68,487 entries, detailing daily vaccination statistics across various demographics and geographic locations. This dataset provides a robust foundation for modeling and forecasting efforts. It utilizes advanced time series analysis techniques and machine learning algorithms to accurately predict future vaccination patterns based on the Hybrid Harvest model, which combines the strengths of ARIMA and Prophet models. Hybrid Harvest exhibits superior performance, with mean-square errors (MSEs) of 0.1323, and root-mean-square errors (RMSEs) of 0.0305. Based on these results, the model is significantly more accurate than traditional forecasting methods when predicting vaccination trends. It offers significant advances in forecasting COVID-19 vaccination trends through integration of ARIMA and Prophet models. The model serves as a powerful tool for policymakers to plan vaccination campaigns efficiently and effectively.
Emission of Harmful Substances from the Combustion of Wood Pellets in a Low-Temperature Burner with Air Gradation: Research and Analysis of a Technical Problem
Authors: Ciupek Bartosz , Nadolny Zbigniew
Published: June 2024

This paper includes a discussion of the results of tests concerning changes in the thermal and emission parameters of a boiler fuelled with wood biomass under the influence of air gradation in the combustion process. The test results ensure insight into the combustion process of wood biomass with air gradation, which significantly affected the operation of the device, increasing the mass concentration of the emitted nitrogen oxide (NOx) by combustion temperature lowering, especially in the afterburning zone. The authors observed an increase in the emission of particulate matter (PM) and carbon monoxide (CO) related to the change in the combustion process stoichiometry. The tests were carried out with the use of a heating boiler equipped with an automatic pellet burner. Apart from the mass concentration measurement of the pollution emitted, the tests focused on the measurements of temperature and oxygen levels in the flue gas. The objective of the tests was to confirm the applicability of the air gradation techniques in biomass combustion in order to reduce the emission of harmful substances from heating boilers, which is a technique that has recently been used in this group of devices. The test results obtained confirm the necessity for reorganising the technical systems of the currently used pellet burners and implementing further empirical tests.
Simulation and Modelling as Catalysts for Renewable Energy: A Bibliometric Analysis of Global Research Trends
Authors: Nica Ionuț , Georgescu Irina , Chiriță Nora
Published: June 2024

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like “renewable energy sources”, “optimization”, and “simulation” are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future.
A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions
Authors: Caban Jacek , Małek Arkadiusz , Kroczyński Dariusz
Published: June 2024

The aim of the research presented in the article is to use the Metalog family of probability distributions to assess the technical condition of traction battery packs from electric and hybrid vehicles. The description of the research object, which is a battery pack from a hybrid vehicle, will be provided. Then, a system for controlled charging and discharging of individual cells in a battery pack will be reviewed. It is an essential diagnostic and research device used to determine the capacity of individual cells. The capacity values of all battery cells will then be analyzed using the Metalog probability distribution family. The use of this tool allows us to determine the Probability Density Function for the entire battery pack. Based on this, the diagnostician is able to assess the technical condition of the tested package and decide on its further fate. It can be intended for repair, employed as a stationary energy storage facility, or used for disposal. The algorithm for assessing the technical condition of traction batteries proposed by the authors can be used in all battery packs regardless of the type of cells used and their energy capacity.
Global Review on Environmental Impacts of Onshore Wind Energy in the Field of Tension between Human Societies and Natural Systems
Authors: Sander Leon , Jung Christopher , Schindler Dirk
Published: June 2024

Deploying onshore wind energy as a cornerstone of future global energy systems challenges societies and decision-makers worldwide. Expanding wind energy should contribute to a more sustainable electricity generation without harnessing humans and their environment. Opponents often highlight the negative environmental impacts of wind energy to impede its expansion. This study reviews 152 studies to synthesize, summarize, and discuss critically the current knowledge, research gaps, and mitigation strategies on the environmental impacts of onshore wind energy. The investigated effects comprise impacts on the abiotic and biotic environment, with birds and bats in particular, noise and visual impacts. Effects are discussed in the context of social acceptance, other energy technologies, and wind energy expansion in forests. This review illustrates that many effects are highly case-specific and must be more generalizable. Studies are biased regarding the research focus and areas, needing more standardized research methods and long-term measurements. Most studies focus on the direct mortality of birds and bats at wind farms and are concentrated in Europe and North America. Knowledge gaps persist for many impact categories, and the efficacy of mitigation strategies has yet to be proven. More targeted, unbiased research is required that allows for an objective evaluation of the environmental impacts of wind energy and strategies to mitigate them. Impacts, such as those on biodiversity, need to be addressed in the context of other anthropogenic influences and the benefits of wind energy. This forms the basis for a socially acceptable, efficient, and sustainable expansion of wind energy.
Economic and Exergy Analysis of TiO2 + SiO2 Ethylene-Glycol-Based Hybrid Nanofluid in Plate Heat Exchange System of Solar Installation
Authors: Wciślik Sylwia , Taler Dawid
Published: June 2024

This paper concerns an economic and exergetic efficiency analysis of a plate heat exchanger placed in a solar installation with TiO2:SiO2/DI:EG nanofluid. This device separates the primary circuit—with the solar fluid—and the secondary circuit—in which domestic hot water flows (DHW). The solar fluid is TiO2:SiO2 nanofluid with a concentration in the range of 0.5–1.5%vol. and T = 60 °C. Its flow is maintained at a constant level of 3 dm3/min. The heat-receiving medium is domestic water with an initial temperature of 30 °C. This work records a DHW flow of V˙DHW,in = 3–6(12) dm3/min. In order to calculate the exergy efficiency of the system, first, the total exergy destruction, the entropy generation number Ns, and the Bejan number Be are determined. Only for a comparable solar fluid flow, DHW V˙nf=V˙DHW 3 dm3/min, and concentrations of 0 and 0.5%vol. is there no significant improvement in the exergy efficiency. In other cases, the presence of nanoparticles significantly improves the heat transfer. The TiO2:SiO2/DI:EG nanofluid is even a 13 to 26% more effective working fluid than the traditional solar fluid; at Re = 329, the exergy efficiency is ηexergy = 37.29%, with a nanoparticle concentration of 0% and ηexergy(1.5%vol.) = 50.56%; with Re = 430, ηexergy(0%) = 57.03% and ηexergy(1.5%) = 65.9%.
Study on the Influences of an Outer-Coreless-Rotor Permanent Magnet Synchronous Machine Using Halbach Magnet Array
Authors: Xu Cong , Wu Hao , Shi Yuchao , Wang Ning , Song Liwei
Published: June 2024

In order to evaluate the influences of the topology design of a Halbach Magnet Array (HA) on the performance of a motor, a PMSM with an outer coreless rotor using a Halbach Magnet Array (HAORPMSM) is proposed in this article. The design parameters of the HA could be separated into dividing methods per pole, magnet thickness, and initial magnetization direction angle. The phase Back-EMF under constant mechanical speed is chosen as the index to measure the performance of the motor. To start with, different dividing methods of the HA are evaluated. After that, the influence of thickness considering the utilization of the magnet is studied. Lastly, the relationship between initial magnetization direction and motor manufacturing is represented. The results show that the HA design meets the optimized performance considering the balance of the amount of magnet usage and manufacturing when using specific HA parameters.
Comparative Understory Development in Fenced and Unfenced Beech Forest Stands in the Southern Alps
Authors: Bovio Ernesto Renato , Richard Jacopo , Iacopino Simone , Campagnaro Thomas , Sitzia Tommaso
Published: June 2024

This study examines the impact of ungulate browsing on the understory dynamics of the Cansiglio Forest, the only European beech high forest in the Southern Limestone Alps. Utilizing seven pairs of fenced versus unfenced forest stands established over two decades, we assessed how fencing affects understory structure and tree regeneration. Significant differences were found in cover, height and vascular species composition in both tree regeneration and shrub layers. Fenced areas showed a significantly higher number and greater average height of young trees, supporting the hypothesis that ungulate browsing negatively impacts tree regeneration. Despite previous studies attributing the lack of silver fir (Abies alba Mill.) regeneration primarily to red deer (Cervus elaphus Linnaeus, 1758) browsing, our findings did not show a significant association of this species with fenced conditions. While Rubus abundance was higher inside fenced areas, overall vascular species richness did not differ. Our results highlight the evident effects of ungulate browsing and suggest the need to consider other factors influencing forest regeneration and understory development. Future research should establish additional study replicates, balanced across diverse site and stand structure conditions, to further explore these dynamics.
The Effects of Soil Compaction on the Growth and Architecture of the Seedlings of Species Commonly Used for Afforestation in Iran
Authors: Jourgholami Meghdad , Hosseiniala Elahe Alsadat , Latterini Francesco , Venanzi Rachele , Picchio Rodolfo
Published: June 2024

The aim of the present study was to elucidate the effects of soil compaction on the seedlings of two species of deciduous (Acer velutinum and Alnus subcordata) and evergreen trees (Pinus eldarica and Pinus nigra) in terms of above- and below-ground morphology in a greenhouse. Six soil compaction levels were applied: the lowest intensity (control), very low, low, moderate, heavy, and very heavy. The results showed that there were different effects according to the species. These effects were on lateral root length, stem diameter, leaf dry biomass, SSL (specific stem length), SRL (specific root length), LMR (leaf mass ratio), RMR (root mass ratio), SMR (stem mass ratio), and R/S (root-to-shoot ratio). The results showed that soil penetration resistance (SPR) had a significant effect on seedling variables such as lateral root length, stem diameter, leaf dry biomass, and SRL (p < 0.05). A. velutinum seedlings have the highest values of growth variables compared to three other species, followed by A. subcordata seedlings. The two evergreen species, Pinus eldarica and Pinus nigra, have the lowest values of these growth variables. It is worth noting that we found that deciduous species had enhanced growth up to a moderate compaction level (1.3 MPa), while the growth decreased at an SPR that was higher than this value.
A Systematic Review and Comprehensive Analysis of Pioneering AI Chatbot Models from Education to Healthcare: ChatGPT, Bard, Llama, Ernie and Grok
Authors: Wangsa Ketmanto , Karim Shakir , Gide Ergun , Elkhodr Mahmoud
Published: June 2024

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies.
Differential Private Federated Learning in Geographically Distributed Public Administration Processes
Authors: Ahmadzai Mirwais , Nguyen Giang
Published: June 2024

Public administration frequently deals with geographically scattered personal data between multiple government locations and organizations. As digital technologies advance, public administration is increasingly relying on collaborative intelligence while protecting individual privacy. In this context, federated learning has become known as a potential technique to train machine learning models on private and distributed data while maintaining data privacy. This work looks at the trade-off between privacy assurances and vulnerability to membership inference attacks in differential private federated learning in the context of public administration applications. Real-world data from collaborating organizations, concretely, the payroll data from the Ministry of Education and the public opinion survey data from Asia Foundation in Afghanistan, were used to evaluate the effectiveness of noise injection, a typical defense strategy against membership inference attacks, at different noise levels. The investigation focused on the impact of noise on model performance and selected privacy metrics applicable to public administration data. The findings highlight the importance of a balanced compromise between data privacy and model utility because excessive noise can reduce the accuracy of the model. They also highlight the need for careful consideration of noise levels in differential private federated learning for public administration tasks to provide a well-calibrated balance between data privacy and model utility, contributing toward transparent government practices.
Chemical Characterisation of New Oils Extracted from Cañihua and Tarwi Seeds with Different Organic Solvents
Authors: Ortiz-Sempértegui Jimena , Ibieta Gabriela , Tullberg Cecilia , Peñarrieta J. Mauricio , Linares-Pastén Javier A.
Published: June 2024

Vegetable oils are rich in health-beneficial compounds, including fatty acids, phenolic compounds, natural antioxidants, and fat-soluble vitamins. However, oil extraction methods can influence their composition. This study aims to understand the chemical basis for developing a green process to extract oils from two Andean seeds, cañihua (Chenopodium pallidicaule) and tarwi (Lupinus mutabilis). Ethanol, considered a green solvent, is compared to petroleum ether used at the laboratory level and hexane used at the industrial scale for extracting oils. The extraction efficiency is assessed in terms of yield, fatty acids profile, polar and neutral lipids, tocopherols, phenolic compounds, and antioxidant capacity. The chemical composition of edible commercial oils, such as sunflower, rapeseed, and olive oils, was used as a reference. Hexane had the highest extraction yield, followed by petroleum ether and ethanol. However, the oils extracted with ethanol having yields of tarwi 15.5% and cañihua 5.8%, w/w showed the significatively superior content of tocopherols (α, γ, and δ); phenolic compounds; and antioxidant capacity. In addition, ethanol-extracted (EE) oils have higher levels of polar lipids, such as phosphatidylcholine and phosphatidylinositol, than those extracted with the other solvents. Remarkably, EE oils presented comparable or slightly higher levels of monounsaturated fatty acids than those extracted with hexane. Finally, compared to the commercial oils, tarwi and cañihua EE oils showed lower but acceptable levels of oleic, linoleic and palmitic acids and a wider variety of fatty acids (10 and 13, respectively). The composition of tarwi and cañahua oils extracted with ethanol includes compounds associated with nutritional and health benefits, providing a sustainable alternative for oil production.
Dietary Supplementation of Haskap Berry (Lonicera caerulea L.) Anthocyanins and Probiotics Attenuate Dextran Sulfate Sodium-Induced Colitis: Evidence from an Experimental Animal Model
Authors: Dharmawansa K. V. Surangi , Stadnyk Andrew W. , Rupasinghe H. P. Vasantha
Published: June 2024

Haskap berry (Lonicera caerulea L.) is a rich dietary source of anthocyanins with potent anti-inflammatory properties. In this study, isolated haskap berry anthocyanins were encapsulated in maltodextrin and inulin (3:1) by freeze-drying to improve stability and bioavailability. The structural properties of microcapsules, encapsulation yield, efficiency, recovery, and powder retention were evaluated. The microcapsules that exhibited the highest encapsulation efficiency (60%) and anthocyanin recovery (89%) were used in the dextran sulfate sodium (DSS)-induced acute colitis in mice. Thirty-five BALB/c male mice of seven weeks old were divided into seven dietary supplementation groups (n = 5) to receive either free anthocyanins, encapsulated anthocyanins (6.2 mg/day), or probiotics (1 × 109 CFU/day) alone or as combinations of anthocyanin and probiotics. As observed by clinical data, free anthocyanin and probiotic supplementation significantly reduced the severity of colitis. The supplementary diets suppressed the DSS-induced elevation of serum inflammatory (interleukin (IL)-6 and tumor necrosis factor) and apoptosis markers (B-cell lymphoma 2 and Bcl-2-associated X protein) in mice colon tissues. The free anthocyanins and probiotics significantly reduced the serum IL-6 levels. In conclusion, the dietary supplementation of haskap berry anthocyanins and probiotics protects against DSS-induced colitis possibly by attenuating mucosal inflammation, and this combination has the potential as a health-promoting dietary supplement and nutraceutical.
The Relationship between Chocolate Consumption and the Severity of Acne Lesions−A Crossover Study
Authors: Daszkiewicz Magdalena , Różańska Dorota , Regulska-Ilow Bożena
Published: June 2024

The aim of this study was to assess the relationship between the daily consumption of 50 g of chocolate with 85% cocoa content and the severity of acne lesions. Methods: The study involved 92 participants with acne who were divided into two groups, A (n = 51) and B (n = 41). In the first week, both groups had to follow an anti-inflammatory diet (AID), then for the next 4 weeks, group A continued on with the AID, and group B followed an AID with chocolate. After this time, group B started a 4-week AID without chocolate, and group A started a 4-week AID with chocolate. The severity of acne lesions was assessed using the Investigator’s Static Global Assessment scale, where zero points indicated no lesions and five points indicated severe acne. Results: As a result of the consumption of 50 g of chocolate, a statistically significant intensification of acne lesions was observed in both groups. After 4 weeks of following the chocolate diet, the severity of acne lesions increased from 2.5 ± 0.7 to 3.4 ± 0.8 points (p < 0.0001) in group A, and from 2.4 ± 0.7 to 3.5 ± 0.6 points (p < 0.0001) in group B. Overall, chocolate intake contributed to the exacerbation of acne lesions by one point in 65 participants, by two points in 13 participants and by three points in one participant. Conclusions: The obtained results suggest that daily consumption of 50 g of chocolate with 85% cocoa content, even with an anti-inflammatory diet, may intensify acne lesions in this study group. However, it remains unclear which chocolate components may lead to the exacerbation of acne.
The Anti-Biofilm Properties of Phloretin and Its Analogs against Porphyromonas gingivalis and Its Complex Flora
Authors: Wu Desheng , Hao Lisha , Liu Xiaohan , Li Xiaofeng , Zhao Guanglei
Published: June 2024

Porphyromonas gingivalis is crucial for the pathogenesis of periodontitis. This research investigated the effects of the fruit-derived flavonoid phloretin and its analogs on the growth of pure P. gingivalis and the flora of P. gingivalis mixed with the symbiotic oral pathogens Fusobacterium nucleatum and Streptococcus mitis. The results showed that the tested flavonoids had little effect on the biofilm amount of pure P. gingivalis, but significantly reduced the biofilm amount of mixed flora to 83.6~89.1%. Biofilm viability decreased to 86.7~92.8% in both the pure- and mixed-bacterial groups after naringenin and phloretin treatments. SEM showed that phloretin and phlorizin displayed a similar and remarkable destructive effect on P. gingivalis and the mixed biofilms. Transcriptome analysis confirmed that biofilm formation was inhibited by these flavonoids, and phloretin significantly regulated the transcription of quorum sensing. Phlorizin and phloretin reduced AI-2 activity to 45.9% and 55.4%, respectively, independent of the regulation of related gene transcription. This research marks the first finding that these flavonoids possess anti-biofilm properties against P. gingivalis and its intricate bacterial community, and the observed performance variations, driven by structural differences, underscore the existence of intriguing structure–activity relationships.
Constraints on Prospective Deviations from the Cold Dark Matter Model Using a Gaussian Process
Authors: Khurshudyan Martiros , Elizalde Emilio
Published: June 2024

Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the H0 tension problem. The idea behind the learning procedure relies on a generated expansion rate, while the real expansion rate is just used to validate the learned results. In the present work, however, the emphasis is put on a Gaussian Process (GP), with the available H(z) data confirming the possible existence of the already learned deviation. Three cosmological scenarios are considered: a simple one, with an equation-of-state parameter for dark matter ωdm=ω0≠0, and two other models, with corresponding parameters ωdm=ω0+ω1z and ωdm=ω0+ω1z/(1+z). The constraints obtained on the free parameters ω0 and ω1 hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the H0 tension problem. It is concluded, however, that improved tools and more data are needed, to reach a better understanding of the reported deviation.