bluuebunny/crossref_metadata_2025_split huggingface dataset:
Magnetic coupling resonant ultra‐high voltage electric vehicle wireless charging system based on parity‐time symmetry
Authors: Tang Jianlin , Liu Liang , Xiao Yong , Yang Siqi , Lin Weibin , Jun Pingxinjue , Lin Xiaoming , Li Kezhao 
Published: February 2024

SummaryIn recent years, the electric vehicle (EV) industry has developed rapidly, and research on wireless charging systems for the EV is very meaningful. This paper proposes a magnetic coupling resonant ultra‐high voltage EV wireless charging system based on parity‐time symmetry to meet the need of high transmission efficiency and high transmission power for the EV wireless charging system. Through the application of magnetic coupling resonance and parity‐time symmetry, the charging system can maintain constant transmission efficiency within a larger spatial range, which greatly reduces the high‐precision requirements for coil alignment. Through the proposed intelligent coil alignment system, the alignment degree of the power transmission coil, user experience, and energy transmission efficiency can be improved further, promoting the intelligence of the charging system. At the same time, an ultra‐high voltage booster module is added to the primary side of the system to provide high‐power input for the EV charging system. Simulation analysis and experiments verify the rationality and practicability of the charging system. Finally, the practical significance of the proposed system applied to power grid inspection work is discussed.
Association of quality and quantity of macronutrients intake with obesity, new anthropometric indices, lipid accumulation, and blood lipid risk index in Tehranian women
Authors: Salehi Zahra , Rahbarinejad Pegah , Ghosn Batoul , Azadbakht Leila 
Published: February 2024

AbstractBackgroundThis study examines the association between micronutrient intake, anthropometric indices, lipid accumulation, and blood lipid risk index among Tehranian women.MethodsA cross‐sectional study was conducted on 556 Tehranian women. Dietary intake was measured using a Food Frequency Questionnaire. Biochemical assessment and anthropometric indices were measured, and demographic information and physical activity were collected.ResultsParticipants with the highest intake of carbohydrates were more prone to obesity. Conversely, those in the top tertile for protein intake had a lower likelihood of obesity and higher levels of lipid accumulation product (LAP). The highest fat consumers had a 63% decreased chance of having a high Castelli's Risk Index 1 (CRI‐1). A higher glycemic index (GI) and glycemic load (GL) were linked to an increased probability of a high atherogenic coefficient (AC). Women in the top tertile of GL were significantly more likely to be obese and had lower odds for high LAP. Participants in the top tertile of aromatic amino acids/branched chain amino acids (AAA/BCAA) had significantly lower chances of high CRI‐1 and a high atherogenic index of plasma (AIP). Those in the highest tertile of monounsaturated fatty acids/polyunsaturated fatty acids (MUFA/PUFA) had lower odds of obesity and high AIP.ConclusionsThe amount of carbohydrate (g) and protein intake (%), dietary GL, and the ratio of MUFA to PUFA were associated with obesity. The amount of fat intake (g) and AAA/BCAA indices were associated with CRI‐1. LAP decreased with an increase in GL. AC increased with an increase in GI and GL. AAA/BCAA and MUFA/PUFA were associated with AIP.
Spatial and temporal groundwater dynamics in extreme arid basins
Authors: Wei Xuan , Guo Yuchuan , Zhou Mingtong , Wang Ning , Wang Huijing , Bai Yunbao 
Published: February 2024

AbstractTo accurately obtain the spatial distribution characteristics of groundwater level in an extremely arid zone and its dynamic change patterns under the influence of human activities, based on the data of 55 groundwater observation wells in the middle and lower reaches of the Kriya River, spatial interpolation of regional groundwater level data were performed using the inverse distance weight, spline function, trend surface, and the ordinary kriging methods. The optimal interpolation method was selected by its accuracy to spatially interpolate the groundwater level data in the study area from 2019 to 2021. The results show that: (1) the ordinary kriging method has the highest interpolation accuracy (MAE = 7.1393, MRE = 0.0058, RMSE = 9.4314) and reflects the spatial and temporal variability and distribution characteristics of groundwater levels with great accuracy. (2)The relationship between surface water–groundwater recharge and discharge in different areas of the river channel in the desert section varies depending on geological structure, surface water seepage, and other elements. (3) Groundwater in the Taklamakan Desert has little effect on groundwater recharge in the Dariyabui Oasis, and changes in groundwater dynamics in the oasis are predominantly influenced by surface runoff. (4) Monthly changes in groundwater levels in the Yutian Oasis are continuous, with ‘V’ shaped fluctuations, a declining trend in the southern part, no significant change in the central part, and a slight increase in the northern part. These results contribute to the sustainable management of water resources in the Kriya River Basin, provide a basis for groundwater prediction, and offer a reference for studies of other, similar extreme desert area basins.
Factors affecting the sediment concentration in runoff from a slope during the spring thawing period
Authors: Zhang Kai , Zhang Chengcheng , Wang Zhidan , Bai Yikui , Wang Xuan 
Published: February 2024

AbstractSediment concentration (S) is vital in soil erosion models. However, research on estimating S at different positions on slopes in the spring thaw period is difficult. Thus, in this research, four flow discharges, two slope gradients, and four initial thawing depths were used in combination with Rare Earth Elements (REEs) tracing techniques to investigate the effects of hydraulic parameters at different locations of freeze–thaw rill flows on S. The results show that the S on the freeze–thaw slope gradually increases from the first section to the third section of the slope, with an increase of 43.99% from the first slope section to the third slope section. The flow velocity is the most critical factor to affect the S (R2 > 0.7). The prediction equation of S on the entire slope under freeze–thaw conditions was established based on the flow turbulence intensity and relative gravity (R2 = 0.769). This study examined the S and hydraulic parameters of the freeze–thaw rill flow, which provided a reference for further research on the soil erosion model during the spring thawing period.
Humanized dopamine D4.7 receptor male mice display risk‐taking behavior and deficits of social recognition and working memory in light/dark‐dependent manner
Authors: Alachkar Amal , Phan Alvin , Dabbous Travis , Alhassen Sammy , Alhassen Wedad , Reynolds Bryan , Rubinstein Marcelo , Ferré Sergi , Civelli Olivier 
Published: February 2024

AbstractThe dopamine D4 receptor 7‐repeat allele (D4.7R) has been linked with psychiatric disorders such as attention‐deficit–hyperactivity disorder, autism, and schizophrenia. However, the highly diverse study populations and often contradictory findings make it difficult to draw reliable conclusions. The D4.7R has the potential to explain individual differences in behavior. However, there is still a great deal of ambiguity surrounding whether it is causally connected to the etiology of psychiatric disorders. Therefore, humanized D4.7R mice, with the long third intracellular domain of the human D4.7R, may provide a valuable tool to examine the relationship between the D4.7R variant and specific behavioral phenotypes. We report that D4.7R male mice carrying the humanized D4.7R variant exhibit distinct behavioral features that are dependent on the light–dark cycle. The behavioral phenotype was characterized by a working memory deficit, delayed decision execution in the light phase, decreased stress and anxiety, and increased risk behavior in the dark phase. Further, D4.7R mice displayed impaired social recognition memory in both the light and dark phases. These findings provide insight into the potential causal relationship between the human D4.7R variant and specific behaviors and encourage further consideration of dopamine D4 receptor (DRD4) ligands as novel treatments for psychiatric disorders in which D4.7R has been implicated.
The impact of personality traits and cultural values on coordination effectiveness: A study of software development teams effectiveness
Authors: Shameem Mohammad , Kumar Chiranjeev , Chandra Bibhas , Ali Khan Arif , Nadeem Ahmed Md. , Verner J. M. , Nadeem Mohammad , Azeem Akbar Muhammad 
Published: February 2024

AbstractSoftware development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.
Theoretische Perspektiven auf das Verhältnis von Hochschulweiterbildung und Weiterbildungsmarkt
Authors: Tokarski Bianca Maria 
Published: February 2024

ZusammenfassungDie Hochschulweiterbildung folgt einer mehrfachen Systembindung: Sie unterliegt sowohl den Funktionslogiken des Hochschul- und Wissenschaftssystems als auch den Wirkungsmechanismen des Weiterbildungsmarktes. Aus dem Versuch, beiden Systembindungen zu genügen, werden Handlungslogiken entwickelt, um hochschulinternen und -externen Anspruchsgruppen möglichst umfassend gerecht zu werden. Einerseits sollen Angebote geschaffen werden, die berufsfeldspezifischen und vornehmlich praxisorientierten Ansprüchen genügen. Andererseits sollen diese Angebote zugleich anschlussfähig an den aktuellen wissenschaftlichen Diskurs sein.
Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer
Authors: Zhang Yun-Feng , Zhou Chuan , Guo Sheng , Wang Chao , Yang Jin , Yang Zhi-Jun , Wang Rong , Zhang Xu , Zhou Feng-Hai 
Published: February 2024

Abstract
Purpose
Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer.

Methods and materials
Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors.

Results
The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799–0.989) and 0.85 (95% CI, 0.714–0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735–0.979) and 0.93 (95% CI, 0.854–1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit.

Conclusion
Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.
Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary
Authors: Guo Yuchen , Unger János , Khabibolla Almaskhan , Tian Guohang , He Ruizhen , Li Huawei , Gál Tamás 
Published: February 2024

AbstractUrban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.
Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems
Authors: Herkert Robin , Buchfink Patrick , Haasdonk Bernard 
Published: February 2024

AbstractClassical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems with slowly decaying Kolmogorov n-widths. Additionally, Hamiltonian structure of dynamical systems may be available and should be preserved during the reduction. In the current presentation, we address these two aspects by proposing a corresponding dictionary-based, online-adaptive MOR approach. The method requires dictionaries for the state-variable, non-linearities, and discrete empirical interpolation (DEIM) points. During the online simulation, local basis extensions/simplifications are performed in an online-efficient way, i.e., the runtime complexity of basis modifications and online simulation of the reduced models do not depend on the full state dimension. Experiments on a linear wave equation and a non-linear Sine-Gordon example demonstrate the efficiency of the approach.
Impact of cellulose nanofibers on cellulose acetate membrane performance
Authors: Bastida Gabriela A. , Aguado Roberto J. , Galván María V. , Zanuttini Miguel Á. , Delgado-Aguilar Marc , Tarrés Quim 
Published: February 2024

AbstractMembranes find applications across a wide spectrum of industries, including water treatment, energy production, and biomedicine. In this study, nonwoven membranes were fabricated using cellulose acetate (CA) as the primary component, with varying percentages of cellulose nanofibers (CNFs) embedded as reinforcement. These CNFs were prepared through an oxalic acid pretreatment (Oxalic-CNFs). Their incorporation into electrospun membranes represents an innovative approach, enhancing their mechanical properties for applications subjected to high loads and improving its functionalization capabilities. The impact of Oxalic-CNFs on membrane properties was investigated at nanofiber loadings ranging from 0 to 18 wt%. Membranes produced with 6 wt% Oxalic-CNF exhibited the superior physical and mechanical properties. This improvement can be attributed to the formation of threads with higher intrinsic strength, a reduction in pore size, and an increase in density. When higher percentages of CNFs were added, the membranes were not properly formed, because filaments were not continuous and the needle became clogged. The substructure of the membrane proved to be a critical factor for mechanical properties, with remarkable increases in tensile strength and elastic modulus (around 300%) when comparing 4–6 wt% Oxalic-CNF-loaded membranes to CA membranes.
The Zemiological Afterlife of Wrongful Conviction: Spoiled Identity, Repair and Survivorship
Authors: Hearty Kevin 
Published: February 2024

AbstractBuilding on the recent global interest in ‘innocence projects’, this article critically examines the various harms experienced by the wrongfully convicted after their release from prison. Locating itself within the zemiology literature, it uses the memoirs of a number of wrongfully convicted persons to conduct a narrative victimological critique of social harms that are often unacknowledged in policy and practice around the reintegration of the wrongfully convicted and in media and societal discussion of their experiences. Insights from these memoirs, it is argued, problematise the various forms of repair offered to the wrongly convicted because these often compound rather than alleviate particular post-release social harms. However, the first-hand accounts contained within their memoirs also illustrate how, far from being caught in a state of passive victimhood, the wrongfully convicted often regain agency through activism and telling their story.
Computational repurposing of drugs for viral diseases and current and future pandemics
Authors: Winkler David A. 
Published: February 2024

AbstractA large fraction of the world’s population is directly impacted by acute or chronic viral infections, many of which have high mortality. As was brought home to us in 2020, viruses also have great potential to generate global pandemics that have killed millions and caused massive damage to economies. Clearly, we need cost-effective and rapid methods for finding drug treatments for poorly met infectious diseases and for responding effectively to the current and future pandemics. Repurposing or off-label use of existing drugs, whose safety and pharmacokinetics are well understood, is one useful way to provide fast drug therapies for patients. Computational methods have an important role to play because of their increasing effectiveness, high speed, and relatively low cost. Here we review the application of the main types of computational drug repurposing methods to discovery of therapies for viral diseases and for future pandemics highly likely to be caused by viral pathogens.
Graphical abstract
QPCR-Guided Screening of Levansucrase: Levan Characterization and Genomic Insights
Authors: Aytar Çelik Pınar , Barut Dilan , Erdal Altintaş Özlem , Enuh Blaise Manga , Nural Yaman Belma , Mutlu Mehmet Burçin , Çabuk Ahmet , Toksoy Öner Ebru 
Published: February 2024

AbstractEighty-seven bacteria isolates were screened for the presence of the levansucrase encoding gene and those possessing the gene were used in gene expression studies and the production of levan. Based on both screening methods, isolate P19-1 was determined as the producer bacterium with the highest amounts of levan. Whole genome sequencing was done to characterize the isolate and determine its genomic potential. Both its 16 S rRNA and genome were closest to Chromohalobacter salexigens. The levansucrase gene and genes suggesting a versatile carbohydrate and nitrogen metabolism were annotated. A secondary metabolism suggestive of a possible new role in promoting plant growth in hypersaline environments was revealed. The chemical structure analysis of the produced polymer was confirmed by comparison with commercial pure Erwinia herbicola levan. Also, C. salexigens levan showed high biocompatibility. Overall, this is a practical molecular approach that will help researchers to effectively screen microbes with levan production potential.
Development of a Multimodal, Physiotherapist-Led, Vocational Intervention for People with Inflammatory Arthritis and Reduced Work Ability: A Mixed-Methods Design Study
Authors: Bakker N. F. , van Weely S. F. E. , Hutting N. , Heerkens Y. F. , Engels J. A. , Staal J. B. , van der Leeden M. , Boonen A. , Vliet Vlieland T. P. M. , Knoop J. 
Published: February 2024

Abstract
Purpose
Work ability of people with rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is reduced, but underexamined as a clinical treatment target. The evidence on vocational interventions indicates that delivery by a single healthcare professional (HCP) may be beneficial. Physiotherapist (PT)-led interventions have potential because PTs are most commonly consulted by RA/axSpA patients in the Netherlands. The aim was to develop a PT-led, vocational intervention for people with RA/axSpA and reduced work ability.

Methods
Mixed-methods design based on the Medical Research Council (MRC) framework for developing and evaluating complex interventions, combining a rapid literature review and six group meetings with: patient representatives (n = 6 and 10), PTs (n = 12), (occupational) HCPs (n = 9), researchers (n = 6) and a feasibility test in patients (n = 4) and PTs (n = 4).

Results
An intervention was developed and evaluated. Patient representatives emphasized the importance of PTs’ expertise in rheumatic diseases and work ability. The potential for PTs to support patients was confirmed by PTs and HCPs. The feasibility test confirmed adequate feasibility and underlined necessity of training PTs in delivery. The final intervention comprised work-focussed modalities integrated into conventional PT treatment (10–21 sessions over 12 months), including a personalized work-roadmap to guide patients to other professionals, exercise therapy, patient education and optional modalities.

Conclusion
A mixed-methods design with stakeholder involvement produced a PT-led, vocational intervention for people with RA/axSpA and reduced work ability, tested for feasibility and ready for effectiveness evaluation.