bluuebunny/crossref_metadata_2025 huggingface dataset:
Descriptive Study of the Influence of Foot Type on Physical Characteristics, Laxity, Strength and Baropodometry in Children Aged 5 to 10 Years
Authors: Molina-García Cristina , Álvarez-Salvago Francisco , Pujol-Fuentes Clara , López-del-Amo-Lorente Andrés , Ramos-Petersen Laura , Martínez-Sebastián Carlos , Martínez-Amat Antonio , Jiménez-García José Daniel , De Diego-Moreno Manuel
Published: September 2024

Background: Foot morphology in children is a crucial factor influencing multiple aspects of their physical development. Between the ages of 5 and 10 years, the critical period of child development is when the movement and stability patterns are consolidated that can affect their long-term physical performance and quality of life. The aim of this study is to analyze how the type of foot influences different physical characteristics, laxity, strength, motor tests, and baropodometric variables in children aged 5 to 10 years. Methods: A cross-sectional study involving 196 children was conducted. Different physical characteristics, laxity, strength, motor tests, and baropodometric variables of the sample were analyzed for age and Foot Posture Index (FPI). Results: Differences in all variables were examined by age and FPI. Statistical analysis showed a moderate to high correlation (r > 0.6, p < 0.01) between FPI and the relaxed calcaneal stance position (RCSP) test. Some significant differences were also found in variables related to foot pronation and supination. These results provide valuable information for understanding differences in motor and functional development during childhood and pre-adolescence. Conclusions: The findings highlight the variability in physical and functional development between age and foot type groups, highlighting the importance of considering these differences in the assessment and management of foot-related conditions and biomechanics in childhood. Foot type significantly influences children’s growth and development.
A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples
Authors: Cheng Xiaohan , Lu Yuxin , Liang Zhihao , Zhao Lei , Gong Yuandong , Wang Meng
Published: September 2024

In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios.
Characterizing Temporal Patterns of Intra-Urban Human Mobility in Bike-Sharing through Trip Analysis: A Case Study of Shanghai, China
Authors: Zhang Pengdong , Liu Min , Xu Jinchao , Zhu Zhibin , Cao Ruihan
Published: September 2024

Human mobility, encompassing the movement of individuals and/or groups across space and time, significantly impacts various aspects of society, with intra-urban mobility being a major research focus of scholars in diverse disciplines. Bike-sharing systems have become an alternatives in cities for achieving more sustainable transportation. Hence, bike-sharing-related data are considered an important data source to study intra-urban human mobility. To better understand human mobility in cities, it is essential to characterize the typical patterns involved in intra-urban human mobility. This paper mainly focuses on characterizing the temporal patterns of intra-urban human mobility on bike-sharing based on the trip information of the acquired bike-sharing data. To achieve this, on the one hand, we adopted an exploratory data analysis (EDA) method to describe the temporal patterns by performing exploratory analyses of bike-sharing trips. On the other hand, we used the continuous triangular model (CTM) to conduct multi-temporal-scale analysis of bike-sharing trips for further explorations of the temporal patterns where necessary. The data of bike-sharing trips in Shanghai, China, were adopted as the dataset for the case study. Generally, the study was conducted at two different levels: the trip level and the bike level. Specifically, at each level, the explorations were conducted from varying perspectives. According to the analyses, numerous meaningful temporal patterns were discovered, and several distinctive findings were acquired. The results of this study show the effectiveness of the EDA and CTM methods in characterizing temporal patterns of intra-urban human mobility, based on which potentially insightful information and suggestions can be provided to assist related actions.
Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
Authors: Ortiz Ricardo , Olivares Douglas , Rojas Luis A. , Taquichiri Abel , Portillo Carlos , Lavín Paris , Valenzuela Diego , Galleguillos Madrid Felipe M. , Fuentealba Edward
Published: September 2024

Photovoltaic technology has proven to be a reliable, economical, and clean energy source that is capable of adapting to diverse geographical conditions. However, factors such as soiling overshadow these qualities, thus leading to production losses and affecting the profitability of this technology. For these reasons, soiling is a highly studied topic, which involves considering the physicochemical characterization of the deposited material, mitigation strategies, effect predictions, and cleaning mechanisms. However, there is a relatively unexplored area related to the microbiological contribution to soiling. The surface of photovoltaic modules, along with the deposited material and local atmospheric factors, fosters favorable conditions for the colonization of microorganisms. These microorganisms influence the soiling mechanisms and optical properties of photovoltaic modules. This work presents a detailed characterization of the microbial diversity present in the soiling deposited on photovoltaic modules installed in the Atacama Desert. Two study sites were defined: Antofagasta and the Solar Platform of the Atacama Desert, which have warm and cold desert climates, respectively. Mineralogical characterization tests, heavy metal analyses, TOC, and inorganic element analyses were conducted on the deposited material. Additionally, the culturable isolates and the metagenomic DNA of the soiling samples and biofilms grown on standard PV glass were characterized using next-generation sequencing. The results show that the deposited soiling contained a microbiological component that had adapted to extreme desert conditions. The presence of the genera Arthrobacter, Kocuria, and Dietzia were identified in the culturable isolates from Antofagasta, while Arthrobacter and Dietzia were obtained from the Solar Platform of the Atacama Desert. The metagenomic DNA was mainly represented by the genera Pontibacter, Noviherbaspirillum, Massilia, Arthrobacter, Hymenobacter, and Deinococcus at Antofagasta. However, at the Solar Platform of the Atacama Desert, the analyzed samples presented DNA concentrations below 0.5 ng/µL, which made their preparation unviable. At the PSDA, the biofilms formed by the genera Peribacillus and Kocuria were identified, whereas the UA showed a greater abundance of bacteria that favored biofilm formation, including those that belonged to the genera Bacillus, Sporosarcina, Bhargavaea, Mesaobacillus, Cytobacillus, Caldakalibacillus, and Planococcus. Based on these results, we propose a soiling mechanism that considers the microbiological contribution to material cementation.
Digital Infrastructure as a New Organizational Digital Climate Dimension
Authors: Avtalion Ziv , Aviv Itzhak , Hadar Irit , Luria Gil , Bar-Gil Oshri
Published: September 2024

This study investigates the influence of digital infrastructure on creating an organizational climate conducive to digital transformation. It highlights the critical role of data lakes, network connectivity, and a shared digital language in cultivating an environment that aligns managerial objectives with employee engagement in digital initiatives. Through grounded theory methodology, the research uncovers how robust digital infrastructure can bridge the gap between leadership’s digital aspirations and employees’ practical use of digital tools, promoting data-driven decision-making and improving organizational performance. The findings suggest that a well-developed digital infrastructure is essential for fostering a digital climate that supports strategic business goals and enhances competitive advantage.
Spatialization and Analysis of China’s GDP Based on NPP/VIIRS Data from 2013 to 2023
Authors: Li Weiyang , Wu Mingquan , Niu Zheng
Published: September 2024

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.
Antioxidant Protein Hydrolysates from Hemp Seed Oil Cake—Optimization of the Process Using Response Surface Methodology
Authors: Doneva Maria , Dyankova Svetla , Terziyska Margarita , Metodieva Petya , Nacheva Iliana
Published: September 2024

Hemp seed oil cake, a by-product of hemp seed oil extraction, is characterized by its high protein content and bioactive components, making it a valuable resource for the development of functional products through enzymatic hydrolysis. Hemp seed oil itself is renowned for its rich content of essential fatty acids, vitamins, and antioxidants, contributing to its widespread use in health and wellness products. Consequently, the residual cake presents significant potential for the food, pharmaceutical, and cosmetic industries as a source of high-quality protein ingredients. The optimization of enzymatic hydrolysis conditions is crucial for maximizing the efficiency and quality of the resulting protein hydrolysates. This study aims to optimize the hydrolysis process of hemp seed oil cake with bromelain, focusing on three key factors: enzyme concentration (E/S ratio), temperature, and time, to achieve hydrolysates with superior antioxidant activity. Response Surface Methodology (RSM) was applied using a Box–Behnken design to model and optimize the hydrolysis conditions. The experimental design involved three levels for each factor: 1%, 2%, and 3% for bromelain concentration; 20 °C, 30 °C, and 40 °C for temperature; and 60, 120, and 180 min for hydrolysis duration, resulting in 21 experimental runs. The antioxidant activity was assessed via DPPH and ABTS radical scavenging assays (%RSA), and the derived regression models were statistically analyzed and validated. The findings indicate that the optimal conditions for obtaining protein hydrolysates with the highest antioxidant activity are a bromelain concentration of 3.0%, a temperature of 40 °C, and a hydrolysis time of 60 min.
Robotic Cell Layout Optimization Using a Genetic Algorithm
Authors: Sánchez-Sosa Raúl-Alberto , Chavero-Navarrete Ernesto
Published: September 2024

The design of the work area of a robotic cell is currently an iterative process of trial and improvement, where, in the best cases, the user places the workstations and robotic manipulators in a 3D virtual environment to then semi-automatically verify variables such as the robot’s reach, cycle time, geometric interferences, and collisions. This article suggests using an evolutionary computation algorithm (genetic algorithm) as a tool to solve this optimization problem. Using information about the work areas and the robot’s reach, the algorithm generates an equipment configuration that minimizes the cell area without interference between the stations and, therefore, reduces the distances the robotic manipulator must travel. The objective is to obtain an optimized layout of the workstations and to validate this optimization by comparing the transfer times between stations with the actual times of an existing screwdriving cell. As a result, the transfer time was reduced by 9%. It is concluded that the algorithm can optimize the layout of a robotic cell, which can lead to significant improvements in efficiency, quality, and flexibility.
Industrial-Level Brewing Using Oenological Saccharomyces cerevisiae and Schizosaccharomyces pombe as Mixed-Inoculum
Authors: Baiano Antonietta , Fiore Anna , Maruccia Francesco , Gerardi Carmela , Povero Marco , Grieco Francesco , Tufariello Maria
Published: September 2024

The development of new food processes and formulations begins at the laboratory stage, progresses through pilot plant trials, and culminates in industrial production. Although the positive effects in terms of sensory characteristics and qualitative differentiation have been widely studied at laboratory level, fermentations conducted at the industrial level by oenological Saccharomyces cerevisiae and non-Saccharomyces strains have not been thoroughly investigated. Scaling up to the industrial level is a critical process that involves more than simply increasing the dimensions of the process itself. The purpose of our research was to compare laboratory and industrial-level brewing of a novel craft beer produced with the addition of common unmalted wheat and fermented by Schizosaccharomyces pombe and S. cerevisiae strains. Fermentation was carried out using a S. cerevisiae strain either of oenological origin alone or through sequential inoculations with S. pombe. Beers produced with the mixed starter showed greater reproducibility between the two production levels than those fermented by S. cerevisiae alone. According to the results, the main differences highlighted between laboratory and industrial-level trials with S. cerevisiae alone concerned the extent of starch degradation, fermentation efficiency, and alcohol production, which were higher in brewing at the laboratory level. In contrast, beers produced at industrial level using sequential inoculation received significantly higher scores for foam quantity and persistence, as well as overall olfactory intensity, while scoring significantly lower scores for saltiness and sourness. To our knowledge, this research is the first to explore the use of Sc. pombe for industrial beer production.
Comparing the Acute Effects of Diagonal Mobilization and Nordic Hamstring Curls on Knee Flexion and Extension Strength: A Randomized, Double-Blinded Parallel Study in Young Soccer Players
Authors: Studnicki Rafał , Tomaszewsk Urszula , Hansdorfer-Korzon Rita , Kawczyński Adam
Published: September 2024

Background: Understanding the diverse acute effects elicited by physiotherapists in soccer players may be pertinent for enhancing performance and aiding in the mitigation of injury risk. Methods: Fifty regional-level soccer players aged 13 to 15 took part in a randomized double-blind trial. They were allocated randomly to either receive diagonal manual mobilization (DM) treatment or undergo a Nordic hamstring curl (NHC) intervention, designated as the control group. Evaluations were carried out before and after the interventions to gauge standing knee extensors (KE) and knee flexors (KF) strength using the ForceFrame Strength Testing System. The recorded variables encompassed average, maximal force, and maximal impulse for both KE and KF. Results: Following the intervention, evaluations showed no significant differences between groups in KF maximal force (F1,48 = 1.238; p = 0.271; ηp2 = 0.025), KF average force (F1,48 = 0.957; p = 0.333; ηp2 = 0.020), KF maximal impulse (F1,48 = 0.246; p = 0.622; ηp2 = 0.005), KE maximal force (F1,48 = 1.514; p = 0.225; ηp2 = 0.031), KE average force (F1,48 = 0.118; p = 0.733; ηp2 = 0.002), and KE maximal impulse (F1,48 = 2.540; p = 0.118; ηp2 = 0.050). Analysis within the DM group showed significant differences in KF maximal force (24.2 N; p = 0.004), KF average force (23.4 N; p = 0.001), KE maximal force (25.8 N; p = 0.005), KE average force (20.0 N; p = 0.044), and KE maximal impulse (265.9 Ns; p = 0.027), although no significant differences were found in KF maximal impulse (150.1 Ns; p = 0.058). Conclusions: This study indicates that both DM and NHC elicit similar effects on acute responses in KE and KF strength following intervention, although DM shows more favorable adaptations within the group. Perhaps DM could serve as a suitable warm-up strategy in specific circumstances, immediately influencing readiness for strength activities.
A Transparent Pipeline for Identifying Sexism in Social Media: Combining Explainability with Model Prediction
Authors: Mohammadi Hadi , Giachanou Anastasia , Bagheri Ayoub
Published: September 2024

In this study, we present a new approach that combines multiple Bidirectional Encoder Representations from Transformers (BERT) architectures with a Convolutional Neural Network (CNN) framework designed for sexism detection in text at a granular level. Our method relies on the analysis and identification of the most important terms contributing to sexist content using Shapley Additive Explanations (SHAP) values. This approach involves defining a range of Sexism Scores based on both model predictions and explainability, moving beyond binary classification to provide a deeper understanding of the sexism-detection process. Additionally, it enables us to identify specific parts of a sentence and their respective contributions to this range, which can be valuable for decision makers and future research. In conclusion, this study introduces an innovative method for enhancing the clarity of large language models (LLMs), which is particularly relevant in sensitive domains such as sexism detection. The incorporation of explainability into the model represents a significant advancement in this field. The objective of our study is to bridge the gap between advanced technology and human comprehension by providing a framework for creating AI models that are both efficient and transparent. This approach could serve as a pipeline for future studies to incorporate explainability into language models.
Constraints on the Geometry of Peripheral Faults above Mafic Sills in the Tarim Basin, China: Kinematic and Mechanical Approaches
Authors: Yao Zewei
Published: September 2024

Host rock deformation associated with sill emplacement is used to constrain magma transfer and storage within the upper crust. In contrast to classic models suggesting that the host rock above mafic sills is dominated by elastic bending, recent studies show that bounding faults that limit the uplift area can occur at the peripheries of a mafic sill. However, the accurate dip of this type of fault, named peripheral faults here, is still not well constrained. Their origin is also controversial in some cases. In this study, kinematic modeling and limit analysis are performed to better constrain the structure and mechanical properties of the peripheral faults based on seismic interpretation of a mafic sill from the Tarim Basin, China. The trishear kinematic model successfully reproduces peripheral faulting and associated folding of the host rock by performing a displacement of 58 m on a vertical fault plane with a fault propagation (P) to fault slip (S) ratio of 2.5. The limit analysis also predicts vertical damage at the sill tip by sill inflation. These results suggest that the dip angle of the fault in the case study is 90°, which is more accurate than that from the seismic interpretation with an 88° inward dip. This value may vary in other cases as it depends on the sill geometry (such as diameter and inclination), thickness, depth, and mechanical properties of the host rock. The study supports that peripheral faulting and associated folding can occur at the tips of the mafic sill due to the vertical uplift of the host rock caused by sill inflation. It is also suggested that trishear kinematic modeling and limit analysis are effective methods for studying the geometry of peripheral faults.
Analytic Hierarchy Process (AHP)-Based Evaluation of Extremely-Low-Frequency Magnetic Field Contribution Rates
Authors: Fu Hongyu , Pan Mengchun , Zhang Qi , Hu Jiafei , Guan Feng , Xu Yujing , Huang Bo , Li Haibin , Chen Dixiang , Liu Zhongyan
Published: September 2024

The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine’s ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.
Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm
Authors: Zhang Daming , Wang Zijian , Sun Fangjin
Published: September 2024

To tackle the shortcomings of the Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization (SFEDBO) Algorithm is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the Manta Ray Foraging Optimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm’s ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFEDBO algorithm is utilized to optimize 23 benchmark test functions. The results show that the SFEDBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFEDBO algorithm was applied to the practical application problems of pressure vessel design, tension/extension spring design, and 3D unmanned aerial vehicle (UAV) path planning, and better optimization results were obtained. The research shows that the SFEDBO algorithm proposed in this paper is applicable to actual optimization problems and has better performance.
Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art
Authors: D’Urso Fabiana , Broccolo Francesco
Published: September 2024

The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiota’s role in human health and disease, particularly focusing on CIDs and probiotic interventions.
Adaptive MAC Scheme for Interference Management in Ad Hoc IoT Networks
Authors: Ali Ehsan , Fazil Adnan , Ryu Jihyoung , Ashraf Muhammad , Zakwan Muhammad
Published: September 2024

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness.
Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
Authors: Canbay Yavuz , Adsiz Seyda , Canbay Pelin
Published: September 2024

This paper introduces a new privacy-preserving transfer learning framework for the classification of kidney diseases. In the proposed framework, transfer learning is employed for feature extraction, and differential privacy is used to obtain noisy gradients. A variety of CNN architectures, including Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19 are utilized to evaluate the proposed framework. Analysis of a large dataset of 12,400 labeled kidney CT images shows that transfer learning architectures based on the proposed framework achieve excellent accuracy ratios in privacy-preserving classification. These results demonstrate the effectiveness of the proposed framework in enabling transfer learning models to classify kidney diseases while ensuring privacy. The MobileNet architecture stands out for its exceptional performance, with an impressive accuracy of 99.83% in privacy-preserving classification. Considering the findings of this study, it is evident that the proposed framework is appropriate for the early and private diagnosis of kidney diseases and promotes the achievement of promising results in this field.
How Did 19th-Century Alphorns Sound? A Reconstruction Based on Written Accounts of Its Musical Timbre
Authors: Wey Yannick
Published: September 2024

This paper reconstructs the sound of 19th-century alphorns based on contemporary written descriptions, which allows for a better understanding of literature and compositions that quoted and imitated the alphorn throughout the 19th century. In the absence of sound recordings, historical documents and literary sources provide valuable insights into the timbre of these traditional Alpine instruments. The research examines descriptions from 19th-century texts, comparing them with modern understandings of musical timbre. By analyzing the language used to describe the alphorn’s sound, the study identifies recurring descriptors and contextualizes them within the broader acoustic environment, including the influence of natural sounds like waterfalls and echoes. Historical sources reveal a complex perception of the alphorn’s timbre, described in terms of its resemblance to muted trumpets and a blend of brass and woodwind qualities. Authors such as Hermann Alexander von Berlepsch and François-Joseph Fétis provided detailed accounts, noting contrasting characteristics like “rough”, “soft”, “sharp”, and “melodious”, which varied with the listener’s distance from the instrument. These descriptions highlight the alphorn’s unique sound profile, distinct from modern perceptions that emphasize a warmer, fuller timbre. The findings underscore the importance of considering ecological and psychoacoustic contexts in the study of historical musical instruments.
Is It Possible to Breathe Fresh Air in Health Resorts? A Five-Year Seasonal Evaluation of Benzo(a)pyrene Levels and Health Risk Assessment of Polish Resorts
Authors: Majewski Grzegorz , Niezgoda Weronika , Klik Barbara
Published: September 2024

This study examines air quality in Polish health resorts (HR) and its potential health risks, challenging the belief that these environments always provide safe air for recovery. Over five years, air quality was evaluated by measuring concentrations of PM10 and benzo(a)pyrene (B(a)P) in seven resorts with varying environmental conditions. Using data from 3781 daily samples, both non-carcinogenic and carcinogenic risks were assessed for visitors (various age groups) and employees to determine health risks from prolonged exposure. The findings show frequent exceedances of national B(a)P limits, with some resorts, such as HR2 in Rabka-Zdrój and HR3 in Polanica-Zdrój, surpassing permissible levels by up to 320% and 373%, especially in winter. Non-carcinogenic risks exceeded safe limits by up to 40% for visitors in HR2 and 18% for employees in HR6 (Szczawno-Zdrój). Carcinogenic risks were up to 3.74 times higher than acceptable levels for visitors in HR2 and 3.15 times higher for employees in HR5 (Latoszyn), indicating a continuous risk from long-term exposure. These findings underscore the urgent need for measures to improve air quality in HRs. Given the global implications, similar risks could affect resorts worldwide. Therefore, future research should focus on cross-border studies and innovative strategies to manage pollution and protect health.
Developing a Chained Simulation Method for Quantifying Cooling Energy in Buildings Affected by the Microclimate of Avenue Trees
Authors: Flowers Bryon , Huang Kuo-Tsang
Published: September 2024

This paper introduces a methodology aimed at bridging the gap between building energy simulation and urban climate modeling. A coupling method was developed through the Building Control Virtual Test Bed (BCVTB) and applied to a case study in Taipei City, Taiwan, to address the microclimate factors of street trees crucial to cooling energy consumption. The use of the Urban Weather Generator for weather file modification revealed a 0.63 °C average air temperature disparity. The coupling method emphasized the importance of accurate wind speed and convective heat transfer coefficients (CHTCs) on building surfaces in determining cooling energy. The results indicated that elevated CHTC values amplify heat exchange, with higher wind velocities playing a crucial role in heat dissipation. The presence of street trees was found to significantly reduce heat flux penetration, leading to a reduction in building surface temperatures by as much as 9.5% during hot months. The cooling energy was lowered by 16.7% in the BCVTB simulations that included trees compared to those without trees. The EnergyPlus-only simulations underestimated the cooling energy needs by approximately 9.3% during summer months. This research offers valuable insights into the complex interactions between buildings and their environments. The results highlight the importance of trees and shading in mitigating the heat island effect and improving energy-efficient urban planning.
Characteristics and Source Profiles of Volatile Organic Compounds (VOCs) by Several Business Types in an Industrial Complex Using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS)
Authors: Kim Kyoung-Chan , Oh Byeong-Hun , Baek Jeong-Deok , Lee Chun-Sang , Lim Yong-Jae , Joo Hung-Soo , Han Jin-Seok
Published: September 2024

Volatile organic compounds (VOCs) are one of significant contributors to air pollution and have profound effects on human health and the environment. This study introduces a detailed analysis of VOC emissions from various industries within an industrial complex using a high-resolution measurement instrument. This study aimed to identify the VOC profiles and their concentrations across 12 industries. Sampling was conducted across 99 facilities in an industrial complex in South Korea, and VOC analysis was performed based on measurement data using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS). The results indicated that the emission of oxygenated VOCs (OVOCs) was dominant in most industries. Aromatic hydrocarbons were also dominant in most industries, except in screen printing (SP), lubricating oil and grease manufacturing (LOG), and industrial laundry services (ILS) industries. Chlorinated VOCs (Cl-VOCs) showed a relatively higher level in the metal plating (MP) industry than those in other industries and nitrogen-containing VOCs (N-VOCs) showed high levels in general paints and similar product manufacturing (PNT), MP, and ILS industries, respectively. The gravure printing industry was identified as the highest emitter of VOCs, with the highest daily emissions reaching 5934 mg day−1, primarily consisting of ethyl acetate, toluene, butyl acetate, and propene. The findings suggest that the VOC emissions from the gravure printing and plastic synthetic leather industries should be primarily reduced, and it would be the most cost-effective approach to improving air quality. This study can provide the fundamental data for developing effective reduction technologies and policies of VOC, ultimately contributing to enhanced atmospheric models and regulatory measures.
Measurement and Flexible Atomic Code (FAC) Computation of Extreme Ultraviolet (EUV) Spectra of Eu
Authors: Clementson Joel H. T. , Beiersdorfer Peter , Brown Gregory V. , Hell Natalie , Träbert Elmar
Published: September 2024

A group of EUV lines of H- and He-like ions of C provides excellent wavelength calibrations for a position-sensitive multichannel detector at a high-resolution spectrograph. We have exploited this setting for a series of spectra of highly charged Eu ions recorded at the Livermore SuperEBIT electron beam ion trap. A variation in the electron beam energy results in spectra with correspondingly staggered highest Eu ion charge states ranging from Na- through to Ni-like Eu ions. A number of spectral features can be identified from the literature, but the majority of line identifications need guidance from computations of simulated spectra on the basis of collisional-radiative models. For ions with more than two electrons in the valence shell, the typical computational results are of a markedly lower accuracy. We have applied the Flexible Atomic Code (FAC), which is capable of handling all our measured ions with reasonable accuracy. We look into the systematics of the deviation of the computed transition energies from the measured ones as a function of the electron number.
Linear Regression-Based Procedures for Extraction of Li-Ion Battery Equivalent Circuit Model Parameters
Authors: Savu Vicentiu-Iulian , Brace Chris , Engel Georg , Didcock Nico , Wilson Peter , Kural Emre , Zhang Nic
Published: September 2024

Equivalent circuit models represent one of the most efficient virtual representations of battery systems, with numerous applications supporting the design of electric vehicles, such as powertrain evaluation, power electronics development, and model-based state estimation. Due to their popularity, their parameter extraction and model parametrization procedures present high interest within the research community, with novel approaches at an elementary level still being identified. This article introduces and compares in detail two novel parameter extraction methods based on the distinct application of least squares linear regression in relation to the autoregressive exogenous as well as the state-space equations of the double polarization equivalent circuit model in an iterative optimization-type manner. Following their application using experimental data obtained from an NCA Sony VTC6 cell, the results are benchmarked against a method employing differential evolution. The results indicate the least squares linear regression applied to the state-space format of the model as the best overall solution, providing excellent accuracy similar to the results of differential evolution, but averaging only 1.32% of the computational cost. In contrast, the same linear solver applied to the autoregressive exogenous format proves complementary characteristics by being the fastest process but presenting a penalty over the accuracy of the results.
A Wearable Extracorporeal CO2 Removal System with a Closed-Loop Feedback
Authors: Zhang Andrew , Haimowitz Brian J. , Tharwani Kartik , Rojas-Peña Alvaro , Bartlett Robert H. , Potkay Joseph A.
Published: September 2024

Extracorporeal Carbon Dioxide Removal (ECCO2R) systems support patients with severe respiratory failure. Concurrent ambulation and physical therapy improve patient outcomes, but these procedures are limited by the complexity and size of the extracorporeal systems and rapid changes in patient metabolism and the acid–base balance. Here, we present the first prototype of a wearable ECCO2R system capable of adjusting to a patient’s changing metabolic needs. Exhaust gas CO2 (EGCO2) partial pressure is used as an analog for blood CO2 partial pressure (pCO2). Twin blowers modulate sweep gas through the AL to achieve a desired target EGCO2. The integrated system was tested in vitro for 24 h with water, under varying simulated metabolic conditions and target EGCO2 values, and in a single test with whole blood. When challenged with changing inlet water pCO2 levels in in vitro tests, the system adjusted the sweep gas to achieve target EGCO2 within 1 min. Control runs with a fixed sweep gas (without negative feedback) demonstrated higher EGCO2 levels when challenged with higher water flow rates. A single in vitro test with whole ovine blood confirmed functionality in blood. This is the first step toward wearable ECCO2R systems that automatically respond to changing metabolism. Such devices would facilitate physical therapy and grant greater autonomy to patients.
Interplay of Glucose Metabolism and Hippo Pathway in Chondrocytes: Pathophysiology and Therapeutic Targets
Authors: Jahn Jacob , Ehlen Quinn T. , Kaplan Lee , Best Thomas M. , Meng Zhipeng , Huang Chun-Yuh
Published: September 2024

In this review, we explore the intricate relationship between glucose metabolism and mechanotransduction pathways, with a specific focus on the role of the Hippo signaling pathway in chondrocyte pathophysiology. Glucose metabolism is a vital element in maintaining proper chondrocyte function, but it has also been implicated in the pathogenesis of osteoarthritis (OA) via the induction of pro-inflammatory signaling pathways and the establishment of an intracellular environment conducive to OA. Alternatively, mechanotransduction pathways such as the Hippo pathway possess the capacity to respond to mechanical stimuli and have an integral role in maintaining chondrocyte homeostasis. However, these mechanotransduction pathways can be dysregulated and potentially contribute to the progression of OA. We discussed how alterations in glucose levels may modulate the Hippo pathway components via a variety of mechanisms. Characterizing the interaction between glucose metabolism and the Hippo pathway highlights the necessity of balancing both metabolic and mechanical signaling to maintain chondrocyte health and optimal functionality. Furthermore, this review demonstrates the scarcity of the literature on the relationship between glucose metabolism and mechanotransduction and provides a summary of current research dedicated to this specific area of study. Ultimately, increased research into this topic may elucidate novel mechanisms and relationships integrating mechanotransduction and glucose metabolism. Through this review we hope to inspire future research into this topic to develop innovative treatments for addressing the clinical challenges of OA.
Myokines May Be the Answer to the Beneficial Immunomodulation of Tailored Exercise—A Narrative Review
Authors: Lu Zheng , Wang Zhuo , Zhang Xin-An , Ning Ke
Published: September 2024

Exercise can regulate the immune function, activate the activity of immune cells, and promote the health of the organism, but the mechanism is not clear. Skeletal muscle is a secretory organ that secretes bioactive substances known as myokines. Exercise promotes skeletal muscle contraction and the expression of myokines including irisin, IL-6, BDNF, etc. Here, we review nine myokines that are regulated by exercise. These myokines have been shown to be associated with immune responses and to regulate the proliferation, differentiation, and maturation of immune cells and enhance their function, thereby serving to improve the health of the organism. The aim of this article is to review the effects of myokines on intrinsic and adaptive immunity and the important role that exercise plays in them. It provides a theoretical basis for exercise to promote health and provides a potential mechanism for the correlation between muscle factor expression and immunity, as well as the involvement of exercise in body immunity. It also provides the possibility to find a suitable exercise training program for immune system diseases.
Determining the Effect of Non-Thermal Plasma on the Transmembrane Kinetics of Melittin through Molecular Explorations
Authors: Cui Yanxiu , Zhao Tong , Niu Yanxiong , Wang Xiaolong , Zhang Yuantao
Published: September 2024

Non-thermal plasma (NTP) synergistic anticancer strategies are a current hotspot of interest at the intersection of plasma biomedicine. Melittin (MEL) has been shown to inhibit cancer in many malignant tumors; however, its clinical application is controversial. Therefore, the transmembrane process and mechanism of MEL activity in different cell systems were studied and the combination of MEL and NTP was proposed in this paper. The results showed that the electrostatic attraction between MEL and the lipid bilayer contributes to the stable orientation of MEL on the membrane surface. In addition, sialic acid overexpression affects the degree to which MEL binds the membrane system and the stability of the membrane structure. The use of NTP to reduce the dosage of MEL and its related nonspecific cytolysis activity has certain clinical application value. The results of this study provide theoretical support for improving the clinical applicability of MEL and contribute to the further development of plasma biomedicine.