bluuebunny/crossref_metadata_2025_split huggingface dataset:
Repetitorium Sonographie der Nieren Teil 2: klinische Anwendung und Krankheitsbilder
Authors: Tsarov Dimitar , Furrer Marc A. 
Published: July 2024

ZusammenfassungDie transabdominale Sonographie spielt bei der Beurteilung von benignen und malignen Befunden des oberen Harntraktes eine wichtige Rolle, nicht nur in der elektiven ambulanten Sprechstunde, sondern auch in der Notfallsituation (z. B. zur Beurteilung einer Nierenstauung bei Flankenkoliken und Verdacht auf Urosepsis oder als E‑FAST [„extended focused assessment with sonography in trauma“] als bettseitiges Ultraschallprotokoll zum Nachweis von freier Flüssigkeit als Indiz für ein Urinom oder Hämatom beim Nierentraumapatienten).Die konventionelle Sonographie ist nicht nur essenziell für die Erstbeurteilung von benignen und pathologischen Befunden, sondern auch für die Indikationsstellung von weiteren diagnostischen und therapeutischen Massnahmen.Zu den benignen Befunden gehören u. a. anatomische Anomalien (z. B. Hufeisenniere), entzündliche Veränderungen (Nephritis, Abszess), vaskuläre Pathologien (Nierenarterienstenose), Steinleiden (Urolithiasis), Traumafolgen oder postoperative Komplikationen (Urinom, Blutung), Sekundärmanifestationen einer anderen Pathologie (Harnstauung oder vesikoureterorenaler Reflux) sowie Nierenzysten oder -divertikel sowie benigne Tumoren (Angiomyolipom, Onkozytom).Die häufigsten malignen Nierentumoren sind das Nierenzellkarzinom, bei den pädiatrischen Patienten das Nephroblastom (Wilms-Tumor) sowie weniger häufig Nierenmetastasen und Nierenlymphome.Erschwerend kommt bei der Unterscheidung zwischen benignen und malignen Befunden der Nieren dazu, dass gewisse sonomorphologische Eigenschaften sowohl bei benignen als auch malignen Veränderungen vorkommen. Zur besseren Differenzierung und Charakterisierung der Befunde wird der kontrastmittelverstärkte Ultraschall (CEUS) wird bei der Abklärung von pathologischen Befunden der Nieren immer häufiger durchgeführt. Als weitere diagnostische Mittel werden die Computertomographie des Abdomens (CT) sowie die Magnetresonanztomographie (MRT) eingesetzt.Diese Übersichtsarbeit soll als praxisorientiertes Repetitorium der Nierensonographie dienen und stellt einen Überblick über benigne und pathologische Befunde des oberen Harntrakts mit Fokus auf die Nieren bereit.
Damage detection of frame structure using a novel time-domain regression method
Authors: Ji Xingle , Xu Xueyong , Huang Kun 
Published: July 2024

AbstractShear structure model is the most frequently used to model for the damage detection of frame building structures. However, due to the existence of modelling error, using a shear structure model to perform damage detection of a complex frame structure often results in inaccurate detection results. In this paper, a novel reduced model for the frame is proposed, which converts a multi-story multi-bay plane frame into a beam-like model, having one translational and two rotational degrees-of-freedom for each floor. Based on the new model, a novel time-domain regression method (TDRM) was established using the spectral density function between the horizontal acceleration of the frame floor and the reference response to identify the equivalent layer stiffness and damping parameters. Finally, a five-story two-bay frame structure is used to demonstrate the efficacy of the proposed time-domain regression method of estimating structural parameters and identifying structural damage.The results show that this method can identify, locate, and quantify the structural stiffness changes accurately.
Prediction of Helicopter Rotor Loads and Fatigue Damage Evaluation with Neural Networks
Authors: Graziani Alberto , Prederi Davide , Trezzini Alberto Angelo , Favale Marco , Masarati Pierangelo 
Published: July 2024

AbstractIn recent years, machine learning algorithms have experienced rapid advancement, driven by the exponential growth of data availability and computational capabilities. Among these algorithms, artificial neural networks stand out as one of the most renowned and effective classes, possessing the ability to discern relationships within data. In this study, we harness neural networks to deduce the relationship between flight mechanics parameters and resulting loads in an articulated rotor configuration. The accuracy of these algorithms hinges closely on the quality of the dataset used for training. Given that rotor loads manifest as time-periodic signals with precise harmonic content, we train dedicated neural networks to predict each harmonic individually. Subsequently, the load time history is reconstructed post hoc by amalgamating predictions from each individual network. Various network architectures are explored, and a sensitivity analysis is conducted on hyper-parameters to determine the optimal configuration for this specific application. Moreover, these predictions serve as input for a fatigue damage calculation algorithm.
Seasonal Dynamic of NO3− and K+ in a Citrus Crop Irrigated by Different Water Qualities
Authors: Mira-García Ana Belén , Romero-Trigueros Cristina , Bayona José María , Nicolás-Nicolás Emilio 
Published: July 2024

AbstractThis study evaluated the effect of (i) irrigation water source: transfer (TW) and reclaimed water (RW), and (ii) crop phenological stage: winter rest (WR), flowering-sprouting (FS), and fruit growth (FG), on NO3− and K+ dynamics in soil and leaf of a citrus crop. The experiment was carried out during the 2018 and 2019 growing seasons on adult ‘Star Ruby’ grapefruit trees (Citrus paradisi Macf.). The concentration of both nutrients was periodically measured in soil and leaf samples and continuously monitored in the soil soluble fraction with nutrient sensors. Moreover, soil NO3− leaching was indirectly estimated by the periodic measurement of the leaf enrichment in 15N isotope (15Nleaf). The two water sources showed a different nutrient loading. Thus, NO3− and K+, were approximately 5 and 7 times higher, respectively, in the RW. Furthermore, the average contents of NO3− and K+ in the soil samples from the RW treatment were 10.1 and 19.7%, respectively, higher than in TW, with the highest soil NO3− leaching observed in RW treatment. In line with this, the mean contents of NO3− and K+ in the leaves from the RW treatment were 106.9 and 30.4% higher than the TW ones. As for the different phenological stages, in the FG stage, the lowest concentrations of NO3− in the soil samples and the highest in the leaf tissue were observed after a high soil leaching event. In this study the nutrient sensors measurements varied according to the dynamic of NO3− and K+ in the soil samples. The use of RW promoted an accumulation of NO3− and K+ in the soil and leaves of grapefruit trees, but also enhanced soil NO3− leaching, indicating that the proper management of this water source is necessary to avoid soil contamination. The mobilization of NO3− and K+ from soil to leaf was the highest in the FG stage, to ensure fruit development and vegetative growth.
Discovering the determinants of energy intensity of Indian manufacturing firms: a panel data approach
Authors: Sahu Udit Kumar , Pradhan Ashis Kumar 
Published: July 2024

AbstractOur study pores on evaluating the impact of firm specific and macro level determinants of energy intensity of Indian firms belonging to manufacturing and power sector. We collect a panel dataset of 3316 firms for 10 years ranging from 2010 to 2020. We employ the advanced biased corrected method of moments estimator and the quantile regression estimator for panel data models for our empirical analysis. We find that poor labor efficiency is largely responsible for increasing energy intensity of the sample firms. Energy intensity can be optimized by investing in better plant and machinery. Moreover, machineries that require frequent repairs and maintenance need to be replaced with latest tools. While larger firms are found to be energy intensive, profitable firms are found to be in a better position to enhance their energy efficiency. Under macroeconomic scenario, GDP growth and trade openness lead to boosting up the energy consumption at the firm level whereas, the global energy prices act as a constraint and necessitate careful use of energy. Based on our findings we provide certain policy recommendations and suggestions in order to limit energy intensity and enhance energy efficiency of the Indian manufacturing firms.
Design and experimental analysis of a closed-loop autonomous rotary hydroponics system for revolutionizing fenugreek yield and enhancing food security
Authors: Kadam Sudhir A. , Kadam Payal S. , Mohite Dadaso D. 
Published: July 2024

AbstractThe rotary hydroponics system offers a space-saving and potentially high-yielding solution for soilless cultivation, promoting efficient resource use and automation for sustainable food production. This research investigates a closed-loop rotary hydroponics system designed to revolutionize fenugreek yield and enhance food security through resource-efficient and automated cultivation. Building upon the established benefits of aeroponics, particularly its efficient use of space and resources, rotary hydroponics introduces a groundbreaking, cylindrical design. This innovative approach not only maximizes space efficiency but also paves the way for further automation within the cultivation process. The proposed study delves into the design, development, and experimental analysis of a closed-loop, self-sustaining, and low-maintenance rotary hydroponics system specifically designed for fenugreek cultivation. The system prioritizes minimal human intervention through the integration of software-controlled monitoring and parameter adjustments. The research investigates the effectiveness of the system in promoting plant growth and analyzes the growth stages of fenugreek seedlings transplanted into the system. The experiment yielded promising results, with fenugreek plants reaching full maturity within 30 days and achieving an average height of 15–20 cm. These findings highlight the potential of the rotary hydroponics system to revolutionize fenugreek yield and bolster food security through its resource-efficient and sustainable cultivation approach.
Potential of ChatGPT as a digital language learning assistant: EFL teachers’ and students’ perceptions
Authors: Slamet Joko 
Published: July 2024

AbstractChatGPT is one example of an artificial intelligence (AI) tool that is gaining attention in the field of language education due to its potential as a digital language learning assistant. ChatGPT allows for customized interactions and real-time feedback to improve language learning. Yet, there is a study void on English as a Foreign Language (EFL) teachers and students’ use of ChatGPT in language learning. To fill this void, this a cross-sectional survey study investigates the perceptions and preferences of the 80 EFL teachers and the 46 EFL students in East Java, Indonesia. The study’s primary objective is to gain insight into how they perceive about using ChatGPT to improve their language learning. The results revealed possible benefits, such as better language competency and individualized learning experiences, as well as problems, such as language accuracy and technological dependence. The study also highlights the importance of pedagogical support, curricular alignment, user-friendly interfaces, and compelling interactive activities for successful integration. In light of these results, this study suggests ways in which teachers might better include ChatGPT as a digital language learning assistant. This study contributes to the body of knowledge of ChatGPT’s function in EFL instruction and will guide the improvement of other digital tools for language learning.
Graphical Abstract
Optimizing Patient Stratification in Healthcare: A Comparative Analysis of Clustering Algorithms for EHR Data
Authors: Aljohani Abeer 
Published: July 2024

AbstractAdvanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.
Triglyceride-Glucose Index as Predictor for Hypertension, CHD and STROKE Risk among Non-Diabetic Patients: A NHANES Cross-Sectional Study 2001–2020
Authors: Sawaf Bisher , Swed Sarya , Alibrahim Hidar , Bohsas Haidara , Dave Tirth , Nasif Mohamad Nour , Hafez Wael , Tashrifwala Fatema Ali Asgar , Jabban Yazan Khair Eldien , Al-Rassas Safwan , Saleh Heba haj , Zaidi Abdul Rehman Zia , Alghalyini Baraa , Mohamed Shaymaa Abdelmaboud , Mohamed Waleed Farouk , Farwati Amr , Seijari Mohammed Najdat , Battikh Naim , Elnagar Basma , Iqbal Seema , Robles-Velasco Karla , Cherrez-Ojeda Ivan 
Published: July 2024

Abstract
Background
Cardiovascular disease (CVD) is a leading cause of global mortality. Early intervention and prevention of CVD depend on accurately predicting the risk of CVD. This study aimed to investigate the association between the TyG index and the risk of coronary heart disease (CHD), congestive heart failure (CHF), heart attack (HA), stroke, and hypertension (HTN) among patients without diabetes in the United States.

Methods
In this retrospective, cross-sectional study, we used data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2020. We conducted several regression analysis models and calculated the sensitivity and specificity of (TyG) index for predicting the onset of CHD, CHF, HA, stroke, and HTN.

Results
A total of 10,937 individuals without diabetes participated in our study. Individuals with a TyG index greater than 8.96 displayed significant increasing in various parameters, including BMI, systolic/diastolic blood pressure, total cholesterol, LDL, and Apo-B levels (p < 0.001). Almost all regression models ensured that a higher TyGI value was associated with higher odds of having CHD, CHF, HA, stroke, and HTN, which patients with a TyGI value higher than 8.96 have odds ratios of 2.24–5.58 for CHD, 1.68–4.42 for stroke, 2.45–3.77 for HA and 1.75–3.93 for HTN comparing than patients with a TyGI value lower than 8.11 (p-value < 0.05).We evaluated the predictive value of the TyG index for each endpoint, obtaining the following area under the curve (AUC) values: 54.75% for CHF (95% CI: 0.542–0.614), 52.32% for stroke (95% CI: 0.529–0.584), 55.67% for HA (95% CI: 0.595–0.646), 55.59% for HTN (95% CI: 0.574–0.597), and 50.31% for CHD (95% CI: 0.592–0.646).

Conclusion
The TyG index showed a strong correlation with cardiovascular risk factors in individuals without diabetes, however it was a poor predictor of almost studied cardiovascular diseases.
Design of a power processing unit with integrated telemetry for a vacuum arc thruster as part of the SeRANIS mission
Authors: Forster Roman , Szulc Michal , Schein Jochen 
Published: July 2024

AbstractIn this work the design and development of a power processing unit for a vacuum arc thruster is presented. The thruster is part of the Seamless Radio Access Networks for Internet of Space (SeRANIS) mission of the University of the Bundeswehr Munich, which will work as first multifunctional laboratory in orbit with public access. In addition to the basic functionality of generating a voltage peak for igniting the thruster, the power processing unit is equipped with techniques for controlling the ignition sequence and monitoring desired key values. The ignition procedure starts with generating the first trigger signal up to the point where a full-blown plasma is established. The PPU guarantees reliable performance by blocking every additional incoming signal while the ignition sequence is under way and the separation of the satellite’s power bus before the thruster discharges. The status of the power processing unit is constantly controlled and information is provided whether ignition was successful or not. The functionality of this circuit is based on simulation before assembly and testing. In addition, the presented system was designed to pass a test cycle of mechanical, thermal and electrical tests before being declared ready for the space mission.
Nitrogen-doped biochar derived from corn straw for CO2 adsorption: a new vision on nitrogen sources comparison
Authors: Sun Yuxuan , Jia Jixiu , Liu Zhidan , Liu Ziyun , Huo Lili , Zhao Lixin , Zhao Yanan , Yao Zonglu 
Published: July 2024

AbstractBiochar as a highly promising CO2 adsorbent is of great significance in addressing global warming and promoting human health. Research has shown that nitrogen doping improves the CO2 adsorption performance of biochar, but selecting chemical nitrogen sources such as urea and melamine to prepare nitrogen-doped biochar is not conducive to green production and environmental protection. Therefore, it is necessary to identify a new nitrogen source to enhance the emission reduction characteristics of this process. This study selected corn straw as the raw material and cow manure as a representative protein-based nitrogen source to explore its potential as a urea substitute and reveal the hydrothermal carbonization doping mechanism of different nitrogen sources. The results indicated that in raw materials with the same C/N ratio, biochar prepared from cow manure as the nitrogen source had a better doping effect and CO2 adsorption performance. Moreover, a moderate amount of cow manure was beneficial for efficient nitrogen doping and the adsorption of CO2 by biochar, with a maximum CO2 adsorption performance improvement of 32.7%. Due to the different carbon-nitrogen bonds of the different nitrogen sources, urea was more likely to retain amino groups, while macromolecular protein nitrogen sources tended to retain structural nitrogen. The results of this study provide new ideas and theoretical support for preparing other nitrogen-doped carbon materials derived from biomass.
Graphical Abstract
Pulmonary functions test in asymptomatic rheumatoid lung disease patients: a hospital-based study
Authors: Hassan Asmaa Mohammed , Osman Asim Alaaeldin , Ali Ibrahim Abdelrhim 
Published: July 2024

Abstract
Background
As a common extra-articular symptom of rheumatoid arthritis (RA), pulmonary involvement affects 20–67% of patients and accounts for 10–20% of RA-related deaths. This study aimed to assess the impact of RA on pulmonary function tests (PFTs) in Sudanese RA patients attending the rheumatology clinics of the Omdurman Military Hospital.

Methods
This was an analytical, cross-sectional, hospital-based study of 32 RA patients who met the 2010 Rheumatoid Arthritis Classification Criteria of the American College of Rheumatology (RA ACR/EULAR) and who were nonsmokers and free of known respiratory or chronic diseases. Pulmonary function parameters (PFTs), including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), the FEV1/FVC ratio, and the peak expiratory flow rate (PEFR), were measured for each participant using a digital spirometer. A control group of Sudanese subjects of similar age, sex, and height was matched, and the results were compared with normal reference values.

Results
A high prevalence of lung function abnormalities (93.8%) was observed in RA patients, and 6.2% of them had normal PFT parameters. Restrictive lung disease was recorded in 78.1% of the patients, and obstructive lung disease was observed in 15.7% of the patients. All measured PFT parameters in the RA patients were significantly lower than the published reference normal values for Sudanese subjects, except for the FEV1/FVC ratio. The mean values of FEV1, FVC, the FEV1/FVC ratio, and the PRFR in RA patients were 1.8 ± 0.3 L/min, 2.1 ± 0.4 L/min, 87.1 ± 12.2%, and 223.6 ± 86.2 L/min, respectively, compared to 2.4 ± 0.1 L/min, 2.7 ± 0.1 L/min, 90.81 ± 2.6%, and 345.4 ± 16.2 L/min, respectively, in the control group (P values of 0.001, 0.001, 0.299, and 0.001, respectively). There was no statistically significant correlation between PFT results and disease duration, age, or BMI.

Conclusions
Regardless of the duration of RA, restrictive lung abnormalities are frequent in asymptomatic RA patients. The PFT can serve as an indicator of RA. Spirometry has been advised as a baseline examination and for follow-up and to promote the early detection and management of pulmonary involvement in RA patients.
Characterizing the Spatial Distribution of Mixing and Transport in the Northern Middle Atmosphere During Winter
Authors: Curbelo J. , Mechoso C. R. 
Published: July 2024

AbstractA three‐dimensional winter (DJF) climatology of Lagrangian diffusivity characterizing eddy mixing and transport in the northern middle atmosphere is presented. To emphasize aspects other than zonal averages, we use the theory of Lagrangian diffusivity (κyy) hitherto not applied in stratospheric contexts to our knowledge. Our formulation of Lagrangian diffusivity requires the calculation of parcel trajectories, which is made on isentropic surfaces. A Lagrangian descriptor is used to estimate the boundary of the stratospheric polar vortex (SPV). To characterize quasi‐geostrophic motions and their influence on the SPV we apply the wave activity flux (W) and Local Wave Activity . Our data set is the ERA5 reanalysis for the period 1979–2013. Results for κyy show important zonal asymmetries. In the lower and middle stratosphere, κyy is highest at midlatitudes, particularly around the prime meridian. This location is surrounded by manifolds associated with hyperbolic trajectories emanating from the outer SPV boundary. κyy is also high within the SPV, and near the locations where the SPV boundary is open. Zonal asymmetries are also clear in W at midlatitudes. The larger values of are at high latitudes and upstream of the opening of the vortex boundary. The role of quasi‐geostrophic waves on the south‐north shift of the midlatitude westerlies is highlighted. In particular, the waves contribute to open the SPV boundary at around 90W. The interannual variability of κyy is explored by contrasting winters with positive‐negative Northern Annular Mode index, and Sudden Stratospheric Warmings of displacement‐split type.