bluuebunny/crossref_metadata_2025_split huggingface dataset:
High Spatiotemporal Availability of Hydrogen by Electrolysis of Municipal Reclaimed Water in China
Authors: Lu Lu , Chao Weixiang , Yan Xiuling 
Published: November 2024

Abstract

The large-scale electrolytic H2 production requires a steady supply of both electricity and water as well as nearby H2 demands, which is usually challenged in China due to a spatiotemporal mismatch of these resources. This necessitates reconfiguring them or finding alternatives. Reclaimed water produced from municipal wastewater treatment plants shows widespread distribution with human activities and huge reserves. Here, we conducted a life-cycle assessment of H2 production from reclaimed water in terms of potential capacity, cost and carbon emissions based on available water and electricity and H2 market in each province of China, which was compared to that using pure water and seawater. Reclaimed water with easy access to nearby electricity and H2 demand shows a great H2 production potential of 95.7–213.1 million tons per year between 2021 and 2060, which can theoretically meet the national H2 demand (33.4–130.9 million tons) alone and allows H2 self-sufficiency in most provinces. In contrast, other electrolytic H2 routes only have a potential of 19.8–79.1 million tons. The current cost ($8.8 ± 0.6/kg H2) and carbon emissions (40.4 ± 0.7 kg CO2-eq/kg H2) of H2 produced from reclaimed water are comparable to other electrolytic approaches, but expect to reach $4.0 ± 0.5 kg H2 and 4.99 ± 0.05 kg CO2-eq/kg H2 by 2050 due to advances in electrolysis technology and electricity decarbonization, indicating a highly competitive performance with those of fossil H2 (~$4.3/kg H₂) and defined green H2 (~4.9 kg CO2-eq/kg H2). This study provides a new insight into sustainable electrolytic H₂ production by using unconventional water resources.
STSNN-DPC: spatio-temporal shared nearest neighbors and density peaks based clustering method
Authors: Zhang Fengling , Huang Shengqiang , Zhang Haiyan , Luo Yonglong 
Published: November 2024

Abstract

Density peaks clustering (DPC) algorithm is a novel clustering method, which offers advantages such as simple parameter adjustment and ease of implementation. DPC-based clustering algorithms can effectively analyze and mine data to discover hidden patterns, making it a significant research topic. Existing research primarily focuses on the neighborhood of data points at spatial latitude and clusters these points based on neighborhood density. However, we find that there is a correlation between the spatial and temporal neighborhoods of spatio-temporal data. Ignoring this correlation significantly reduces the accuracy of clustering results for spatio-temporal data. In this paper, we propose a spatio-temporal shared nearest neighbors and density peaks based clustering method (STSNN-DPC). STSNN-DPC represents spatio-temporal neighborhood correlation by constructing spatio-temporal shared nearest neighbors, which improves the accuracy of clustering results for spatio-temporal data. Specifically, we propose spatio-temporal shared nearest neighbors to capture the shared neighbors of spatio-temporal data in different dimensions. Based on this, we propose a multidimensional similarity metric to measure the local density and relative distance of spatio-temporal data points. Experimental results show that STSNN-DPC exhibits excellent clustering performance on multiple synthetic datasets and real pedestrian datasets, with an 8% improvement over state-of-the-art clustering algorithms.
Diversity of Endophytic Bacteria in Paris Polyphylla var. yunnanensis and Their Correlation with Polyphyllin Content
Authors: Shu Qing , Wu Yuying , RUAN Liping , Jin Lin , Wang Jing , Peng An zhong , Li Haifeng 
Published: November 2024

Abstract

Background
Paris polyphylla var. yunnanensis (PPY) is commonly used in traditional Chinese medicine formulas and folk families. Nearly more than 100 chemical substances with medicinal values have been reported in PPY, among which steroidal saponins are the main active components of PPY. Due to its long growth cycle, the resource of PPY becomes too scarce, and the current production capacity of PPY is still far from meeting the market demand. Numerous studies have shown that endophytic bacteria not only promote the production of secondary metabolites in the host plant, but also certain endophytes themselves can produce the same secondary metabolites as the host. However, little is known about the endophytic bacteria associated with PPY in different geographic conditions and tissues. In order to compare the endophytic bacterial communities associated with PPY in different geographic conditions and plant tissues, the endophytic bacteria from roots, stems, and leaves of PPY collected from five locations were isolated, and the diversity, richness, and homogeneity of bacterial communities were analyzed, and the dominant genera correlation with polyphyllin content was further investigated.
Results
A total of 268 endophytic bacterial strains were isolated and identified from PPY. The experimental results showed that the isolates belonged to 5 phyla, 7 classes, 14 orders and 39 genera of bacteria, of which the optimal order was Bacillariophyta and the optimal genera were Bacillus, Pseudomonas and Agrobacterium. In general, the differences in the distribution pattern and diversity of endophytic bacteria in PPY were characterized by the highest diversity and richness index of endophytic bacterial communities in QS and the highest evenness index in FY. The diversity, richness and evenness of bacterial communities in terms of tissue state showed a hierarchical pattern of root > stem > leaf. The three optimal genera were positively correlated with polyphyllin content.
Conclusion
The distribution pattern and diversity of endophytic bacteria in PPY were influenced by tissue type and habitat. In addition, three endophytic bacteria (Pseudomonas, Bacllius and Agrobacterium) were positively correlated with the content of polyphylins, and could be used as potential candidates for producing bioactive compounds.
Air-coupled piezoelectric micromachined ultrasonic transducer array based on low-cost and large remnant polarization PZT thin film
Authors: Lin Jia-Ling , Liu Ya-Han , Kao Chien-Lun , Wu Shao-Wei , Hsu Han-Jen , Lin Hsiao-Chi , Li Chih-Ying , Chen Chun-Yuan , Wang Yenog-Her , Huang Chih-Hsien 
Published: November 2024

Abstract

Due to the development of semiconductor technology, control and driving circuits for large phased arrays have become cheaper and more compact, enabling air-coupled ultrasonic transducer arrays to be applied in areas such as ultrasonic haptic feedback and acoustic levitation. However, current air-coupled ultrasonic transducers are quite bulky, which prevents the commercialization of these applications. Hence, piezoelectric micromachining ultrasonic transducers (pMUT) have been proposed to replace conventional ultrasound transducers. Currently, PZT, or lead zirconate titanate, thin film is the most reliable and, consequently, most popular piezoelectric layer for pMUT, but existing PZT fabrication methods are too expensive or require rare metals. To counter this problem, this study proposed a cost-effective and reliable PZT thin film fabrication method. By depositing a commercial sol-gel solution as a seed layer followed by radio frequency sputtering, a PZT thin film with remnant polarization of 113.35 μC/cm2 and a coercive field of 211.6 kV/cm was demonstrated. Moreover, high-throughput air-coupled pMUTs and pMUT arrays were made based on the proposed PZT thin film. Finite element method simulations were conducted to optimize the structure parameters for the 40 kHz pMUT. A standard microphone was used to measure the acoustic pressure at 3 cm above the pMUT and the pMUT array to evaluate their performance. The pMUT generated an acoustic pressure of 0.764 Pa/V with a resonance frequency of 52.2 kHz, and the pMUT array’s maximum acoustic pressure was 87.4 Pa. This is the highest known acoustic output pressure among sub-100 kHz air-coupled pMUT arrays, highlighting the potential of using the proposed PZT thin film deposition method for high-pressure mid-air ultrasonic applications such as ultrasonic haptic feedback and acoustic levitation.
Assessing and Enhancing Dietary Diversity among Women of Reproductive Age in Tanzania: Insights from the Tanzania National Panel Survey Wave 4 Data
Authors: Swai Aleswa , Msoga Japhet J , Kamuhabwa Jocelyn , Mukasa Oscar R 
Published: November 2024

Abstract

Studying the Minimum Dietary Diversity of Women of reproductive age (MDD-W) is of paramount public health importance, considering their vulnerability due to intensified nutritional necessities during pregnancy and lactation. This study seeks to create a proxy for MDD-W in Tanzania, which is general missing in national-level surveys. We analyzed data from Tanzania National Panel survey wave 4 (TNPS) from which sixty food items were considered and grouped into 10 food categories as per Food and Agriculture Organization guide (2021). We considered only female household members aged 15–49 years. Data of women of reproductive age (WRA) were linked, person’s unique identifiers, with other characteristics related to food consumption and household’s data files. Grain, roots and tuber were consumed by majority (56.62%) while meat, poultry and fish by 19.3% and the rest of food groups by less than 3%. Slightly more than half (57%) of the women consumed from at least two food groups and collective MDD-W of the group was 1.83%. For that reason, none of the women attained a minimum dietary diversity for women of reproductive age. The findings suggest a potential contributor to micronutrient deficiencies in Tanzania and the insights from the study highlight the need for diversified diets to improve nutrition and health outcomes for this specific group under the study.
RSEIU: A recommendation algorithm for shopping based on explicit and implicit feedback of user
Authors: Gao Li , Fan Haiping , Liu Yi , Zhao Jinyang 
Published: November 2024

Abstract

Business websites typically employ recommendation systems to process large amounts of information. The lack of behavioral data for new users leads to the system’s inability to make recommendations for them, which is known as the cold-start problem. Additionally, these algorithms heavily rely on sparse explicit feedback data, making it challenging to provide users with precise recommendations , leading to the data sparsity problem. To address these issues, a recommendation algorithm for shopping based on explicit and implicit feedback from the user is proposed. Firstly, the algorithm uses special filters to extract features and generate a candidate list. Secondly, the interaction learning model and the multi-implicit feedback learning model are used to extract implicit relationships from the rating data for each item in the candidate list. The multi-implicit feedback learning model analyzes the implicit relationship from three perspectives. The three types of implicit feedback are used as auxiliary data to solve the data sparsity problem. The effectiveness of the algorithm in shopping recommendation is validated using the Tafeng dataset and the BookCrossing dataset.
Evaluation of fetal exposure to environmental noise using a computer-generated model
Authors: Gélat Pierre , Wout Elwin van 't , Haqshenas Reza , Melbourne Andrew , David Anna , Mufti Anna , Henriques Julian , de Maisieres Aude Thibaut , Jauniaux Eric 
Published: November 2024

Abstract

Acoustic noise can have profound effects on wellbeing, impacting the health of the pregnant mother and the development of the fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels. Using anatomical data from MRI scans (N=3), we used a computational model to quantify the acoustic field inside the pregnant maternal abdomen. We obtained acoustic transfer characteristics across the human audio range and pressure maps in transverse planes passing through the uterus at 5 kHz, 10 kHz and 20 kHz, showcasing multiple scattering and modal patterns. Our calculations suggest that for all datasets, the sound transmitted in utero is attenuated by as little as 6 dB below 1 kHz, confirming results from animal studies that the maternal abdomen and pelvis do not shelter the fetus from external noise.
Two-stage dual-level dispatch optimization model of multi-virtual power plants based on Stackelberg game
Authors: Tang jincheng , Li Xiaolan 
Published: November 2024

Abstract

With the continuous increase in the number of electric vehicles (EVs) and the rapid development of demand response (DR) technology, the power grid faces unprecedented challenges. Two-stage dual-level dispatch optimization model of multi-virtual power plants based on Stackelberg game is proposed in this paper. In the day-ahead stage, a two-layer optimization scheduling model is established, where the EV layer optimizes for maximum comprehensive user satisfaction, and the VPP layer optimizes for minimal operating costs and interaction power, determining the scheduling arrangements for each distributed energy resource. In the intraday stage, a Stackelberg game model is constructed with the distribution network operators(DNO) as the leader, aiming to maximize proffts, and the VPP as the follower, aiming to minimize its own operational costs, with both parties engaging in a game based on electricity prices and energy consumption strategies. In the simulation case study, the effectiveness of the constructed model is veriffed, and the results show that the model can effectively reduce user costs and achieve full consumption of new energy.
Predicting Compressive Strength of Concrete Using Advanced Machine Learning Techniques: A Combined Dataset Approach
Authors: Mandal Abinash 
Published: November 2024

Abstract

Assessing the compressive strength of concrete is crucial to ensure safety in civil engineering projects. Conventional methods often rely on manual testing and empirical formulae, which can be time-consuming and error-prone, respectively. In this study, the advanced machine learning techniques are employed to predict the concrete strength. The paper explores multiple base models, such as linear regression (including polynomial features up to degree 3), decision trees, support vector machines, and k-nearest neighbors. Hyperparameter tuning is utilized to improve the models and cross-validation is carried out to check any overfitting issues. In addition, artificial neural networks and ensemble learning methods such as voting, stacking, random forest, gradient boosting, and XGBoost are implemented. Two datasets from different sources are utilized in this study. It is shown that models trained on one dataset do not perform satisfactorily on second dataset and vice-versa, due to covariant shift in the datasets. In fact, this approach implied that rather than relying on advanced machine learning models, linear regression gave approximate results. After combining these datasets, the models were successful in generalizing over wider range of features. The results show that gradient boosting achieved the highest accuracy with an R² of 0.93 and an RMSE of 3.54 for the combined datasets. The paper further delves into finding the lower and upper bound of the predictions with 95% confidence interval using bootstrapping technique. The author recognizes the necessity of diverse datasets to improve model generalization. However, if the models are trained on limited datasets, and inference is to be made on those with different distributions of features than training data, then the prediction interval can be the indication of the confidence of the models. Further for inference on new unseen data, Mahalanobis distance is measured to indicate whether the data is outlier, thus improving the reliability.
The Association Between Social Outsiderhood and School Absence is Mediated by Internalizing Symptoms
Authors: Alanko Katarina , Söderberg Patrik , Lagerström Martin , Laakso Mikko-Jussi , Junttila Niina 
Published: November 2024

Abstract

Internalizing psychiatric symptoms and school absenteeism often co-occur. This study explores their detailed associations and examines whether internalizing symptoms (depression and anxiety) mediate the relationship between social outsiderhood (loneliness and ostracism) and school absence. It also investigates the links between various types of school absence (truancy, emotion-basedabsence, school withdrawal, and school exclusion) and experiences of loneliness and/or ostracism. Data were collected from 1,866 Finnish middle school adolescents (M age = 14.95, SD = 0.86). Findings indicate that both loneliness and ostracism are associated with school absenteeism, with internalizing symptoms serving as a mediator, however, with a low effect size. Students experiencing school exclusion reported the highest levels of social outsiderhood. These results underscore the importance of addressing students' social situations when assessing school absence and developing preventive interventions within the school context.
Correlation Between Residual Dizziness After Repositioning of Benign Paroxysmal Positional Vertigo and Motion Sickness
Authors: Lin Bingtong , Huang Genquan , Zhou Xuyou , Meng Danyang , Qu Juan , Xu Junjie , Wang Baoxiang , Hu Jin 
Published: November 2024

Abstract

Background Motion sickness is dizziness, nausea, vomiting and other symptoms caused by exposure to uncomfortable stimuli in the exercise environment. During the onset of benign paroxysmal positional vertigo ( BPPV ), patients with motion sickness may experience more severe vestibular symptoms, which may affect the occurrence of residual dizziness ( RD ) after BPPV repositioning. Methods The clinical data of patients with BPPV were collected. According to the history of motion sickness, the patients were divided into motion sickness group and non-motion sickness group, and the clinical characteristics of BPPV combined with motion sickness were analyzed. According to whether there was RD after successful canalith repositioning procedure (CRP), the patients were divided into RD group and non-RD group. The differences between the two groups were analyzed first, and then the influencing factors of RD were analyzed by multivariate logistic regression. Results ①The proportion of women, dizziness handicap inventory (DHI) score, Hamilton Anxiety Scale(HAMA) score and incidence of RD in the motion sickness group were higher than those in the non-motion sickness group, and the differences were statistically significant (P < 0.05). ②The age, course of diseases, history of BPPV, history of motion sickness, DHI score and HAMA score in the RD group were higher than those in the non-RD group, and the differences were statistically significant (P < 0.05). ③Multivariate Logistic regression analysis showed that history of motion sickness, age ≥ 60 years, course of disease > 1week, history of BPPV, DHI > 30 points, and HAMA ≥ 7 points were the influencing factors of RD (P < 0.05). Conclusion Motion sickness is correlated with RD, and can be an effective predictor of RD.
Wound Depth Measurement System in Forensic Case using Image Processing and Machine Learning
Authors: Wahyuni Elvira Sukma , Ahnaf Kern Cesarean , Firdaus Firdaus , Kadir Nurul Ashikin Abdul , Zakaria Nor Aini , Wiraagni Idha Arfianti , Kadarmo Diwangkoro Aji 
Published: November 2024

Abstract

Accurate evaluation of wound depth is vital for forensic investigations, as it can greatly influence case assessments and outcomes. This research presents a new method for classifying wound depth using a Support Vector Machine (SVM) model, comparing its efficacy against Decision Tree and Logistic Regression models by utilizing color-based features derived from HSV and LAB color spaces. The dataset included 76 images, divided into three categories: stage 2 (36 images), stage 3 (12 images), and stage 4 (28 images). Performance was measured using confusion matrices, as well as F1-Score, precision, and recall. The SVM model attained an overall accuracy of 85%, showing high precision and recall across all stages, in contrast to the Decision Tree and Logistic Regression models, which achieved 50% and 70%, respectively. The results illustrated the strong performance of the SVM model, especially in distinguishing stage 2 wounds, while highlighting difficulties in differentiating between stages 3 and 4 compared to the other approaches. Furthermore, ROC curves and statistical tests, including paired t-tests, validated the significant performance improvements of the model over baseline methods. The results indicate that the proposed system could improve the accuracy and efficiency of forensic wound evaluations by offering a quick and objective classification tool. Future research should aim to broaden the dataset, integrate additional features, and investigate other machine learning models to enhance the system's robustness and applicability across various forensic scenarios.
FTO O-GlcNAcylation induces inactive form Pyrin expression, which suppresses NLRP3 inflammasome activation during LPS stimulation
Authors: Zhang Xiao-Lian , Zhang Lu , Liu Min , Xie Yan , Yuan Bi-Feng , Peng Zhiyong , Xiong Jun 
Published: November 2024

Abstract

The fat mass and obesity-associated protein (FTO), as a key RNA N6-methyladenosine (m6A) demethylase, has been recently highlighted in modulating inflammatory response. However, the role and underlying mechanisms of FTO, especially FTO O-GlcNAcylation, in LPS-induced inflammasome remain elusive. Here we uncover that FTO undergoes O-GlcNAcylation specifically at the Ser95 site. LPS-enhanced O-GlcNAcylation of FTO promotes TRIM21-mediated FTO ubiquitination degradation, subsequently increasing m6A-modified Mefv (encoding Pyrin) expression in macrophages. LPS stimulation or FTO O-GlcNAcylation increases inactive form Pyrin expression and can not cause Pyrin-ASC-Casp1 inflammasome activation. The Pyrin competitively blocks NLRP3-ASC-Casp1 inflammasome assembly and activation through the Pyrin-PYD domain, reduces gasdermin D (GSDMD) pyroptosis and restrains TNF-α/IL-1β/IL-6 production. FTO O-GlcNAcylation-deficiency or Pyrin knockdown aggravates the S. Typhimurium or LPS-induced sepsis and dextran sulfate sodium (DSS)-induced inflammatory bowel diseases (IBD). Our findings clarify that there is a regulatory network among protein O-GlcNAcylation, m6A modification, and inflammasome activation. FTO O-GlcNAcylation or Pyrin acts as a negative regulator of NLPR3-ASC-Casp1 inflammasome. The Pyrin plays an anti-inflammatory role in LPS-treated macrophages. Enhancement of FTO O-GlcNAcylation or inactive form Pyrin may offer a potential therapeutic strategy for combating endotoxin-induced NLRP3-mediated inflammatory responses and sepsis.