bluuebunny/crossref_metadata_2025_split huggingface dataset:
Positive Association between Dispositional Mindfulness and Knee OsteoarthritisCommon Health Problems
Authors: chu su feng , Chiu Aih-Fung , Wang Hsiu-Hung 
Published: April 2023

Abstract
Background Knee Osteoarthritis is one of the most common chronic illnesses that often cause health problems. The objective of our study is to explore whether dispositional mindfulness is associated with knee OA patient’s common health problems of physical, psychological, and activity behavior.Methods A cross-sectional study and purposive sampling were performed by orthopedic clinic patients in hospitals in Southern Taiwan. A total has 250 adults completed the study questionnaires. Multiple linear regression was used to explore potentially related factors for dispositional mindfulness.Results We found that higher dispositional mindfulness is associated with married or spouse, better-perceived health status, lower depression, and greater physical activity in the knee OA population. Dispositional mindfulness was not related to joint pain, joint stiffness, physical function, fear of falling, and exercise self-efficacy. However, this study demonstrated that dispositional mindfulness is positively associated with Knee OA common health problems. These relationships help distinguish knee OA’s at a high risk of lower dispositional mindfulness.Conclusions We found that persons without close partners (single, widowed, or divorced) may with lower dispositional mindfulness and need to pay attention to them. Also, more studies are desired to clarify and explain the role-playing of dispositional mindfulness on pain and physical activity. We suggest further study to assess whether the cultivation of mindfulness (e.g., mindfulness-based interventions) may lead to improved physical, psychological, and activity behavior in the knee OA population are needed.
Using ChatGPT to Predict the Future of Personalized Medicine
Authors: Hasanzad Mandana , Patrinos George , Sarhangi Negar , Sarrami Behnaz , Larijani Bagher 
Published: April 2023

Abstract
Personalized medicine is a novel frontier in health care that is based on each person’s unique genetic makeup. It represents an exciting opportunity to improve the future of individualized health care for all individuals. Pharmacogenomics, as the main part of personalized medicine, aims to optimize and create a more targeted treatment approach based on genetic variations in drug response. It is predicted that future treatments will be algorithm-based instead of evidence-based that will consider a patient’s genetic, transcriptomic, proteomic, epigenetic, and lifestyle factors resulting in individualized medication. A generative pretrained transformer (GPT) is an artificial intelligence (AI) tool that generates language resembling human-like writing enabling users to engage in a manner that is practically identical to speaking with a human being. GPT's predictive algorithms can respond to questions that have never been addressed. Chat Generative Pretrained Transformer (ChatGPT) is an AI chatbot's advanced with conversational capabilities. In the present study, Questions were asked from ChatGPT about the future of personalized medicine and pharmacogenomics. ChatGPT predicted both to be a promising approach with a bright future that holds great promises in improving patient outcomes and transforming the field of medicine.
Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm
Authors: Mohammadjafar Mohammadzadeh , Majid Mahboubiaghdam , Moharram Jahangiri , Aynur Nasseri 
Published: April 2023

Abstract
Most machine learning monitored algorithms used in the preparation of mineral potential prediction maps, require noise-free data to achieve high-performance and reliable results. To discover the hidden structures of a data set, unsupervised clustering methods are highly efficient. Therefore, in this paper, a combination of supervised and unsupervised methods was attempted using training and testing data to prepare a potential map with high accuracy in the Sonajil copper-gold deposit, located in NW of Iran. Here a semi-supervised Bayesian algorithm utilized to map the mineral landscape. At First, raster layers of 10 exploratory features were prepared. Then, 27 exploratory boreholes drilled were divided into 4 classes C1 to C4 based on the copper concentration, and from each class, two boreholes were selected and 100-meter buffering was performed around these boreholes to extract 1113 training data. Then, the existing data were clustered by the FCM method and the whole data and clustering data were entered into the Bayesian algorithm to evaluate the accuracy of Bayesian classifier method in 5 different clusters. The results show an increase in the average accuracy while using the clustered data instead of whole data for MPM mapping. Also, in case of not using cluster 5 data, it has the highest accuracy of 96%. To validate the Bayesian semi-supervised method, boreholes that were not used in data training were employed. Overall results based on the classification of boreholes and prepared potential map indicate accuracy of the Bayesian semi-supervised method as an optimal result for further exploration and delimiting more mineralization targets.
The pelagic food web of the Western Adriatic Sea: a focus on the role of small pelagics
Authors: FANELLI EMANUELA , Ros Zaira Da , Menicucci Samuele , Malavolti Sara , Biagiotti Ilaria , Canduci Giovanni , De Felice Andrea , Leonori Iole 
Published: April 2023

Abstract
Small pelagic fishes play a crucial role in pelagic food webs as both top-down and bottom-up controllers. Their role as predators could also exert a wasp-waist flow control. The Adriatic basin is one of the largest areas of occurrence of small pelagic shared stocks and the most exploited basin of the Mediterranean Sea. Here, we analyzed the ontogenetic and spatial variations in the stable isotope composition of three small pelagic fishes (i.e., Engraulis encrasicolus, Sardina pilchardus, and Sprattus sprattus) collected throughout the western side of the basin and relate such differences to both resource availability and environmental variables. Medium-sized specimens of the three species mostly fed on small zooplankton, while adult sprats relied on large copepods and those of sardine and anchovy seemed to shift on phytoplankton, confirming the high trophic plasticity of these two dominants small pelagic species. Resource partitioning occurred at each size and in each sub-area, also favored by the high productivity and in turn high resource availability of the basin, especially in the northern part. These small pelagic fish were located at different, but very close, trophic position with other small pelagic species, and thus competition might occur in the future driven by modified feeding conditions as consequence of increasing sea temperatures.
Extraction and Characterization of Bast Fiber from Xanthium Oriental plant
Authors: Azanaw Aklilu , Haile Adane , Birlie Bantamlak 
Published: April 2023

Abstract

The textile fiber market is dominated by synthetic fibers and costly natural fibers. The dual perspective with regards to finding substitutes for costly natural fibers and disadvantages due to environmental burden cum health problems by synthetic fibers need to be addressed as both entail sustainable improvement of the textile industry. The potential route for tackling such problems is utilizing unlimited plant resources in developing countries like Ethiopia. The objective of this study is to extract, characterize, and chemically treated xanthium oriental bast fiber extracted from the plant using the conventional water retting method. In the invitation, the experimental design was based on Central Composite Design (CCD) method, and treatment parameters were selected, specifically NaOH concentration, and treatment temperature. Analysis of variance (ANOVA) was used to analyze the significance of the results. The chemical composition analysis revealed 72% cellulose, 18% hemicellulose, and 10% lignin. Xanthium oriental fiber has a diameter of 26.85 μm, a fineness of 1.76tex, and a length of 110–145 cm with reduced values for fiber treated at different alkali concentrations. The moisture content of the extracted fiber was 12.74%. The raw fiber sample showed lower crimp (1.04%) as compared with those treated at different caustic soda concentrations. Moreover, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) was used to analyze the changes in surface, weight loss, and composition of xanthium oriental bast fiber treated with alkali under optimum conditions compared with untreated to obtain a deeper insight into the influential mechanisms of alkali treatment.
Delivery room intubation and severe intraventricular hemorrhage in extremely preterm infants without low Apgar scores: A Japanese retrospective cohort study
Authors: Tamai Kei , Matsumoto Naomi , Yorifuji Takashi , Takeuchi Akihito , Nakamura Makoto , Nakamura Kazue , Kageyama Misao 
Published: April 2023

Abstract
The purpose of this study was to assess the associations between delivery room intubation (DRI) and severe intraventricular hemorrhage (IVH), as well as other neonatal outcomes, among extremely preterm infants without low Apgar scores using data from a large-scale neonatal registry data in Japan. We analyzed data for infants born at 24–27 gestational weeks between 2003 and 2019 in Japan using robust Poisson regression. Infants with low Apgar scores (≤ 1 at 1 minute or ≤ 3 at 5 minutes) were excluded. The primary outcome was severe IVH. Secondary outcomes were other neonatal morbidities and mortality. The full cohort included 16081 infants (intubation cohort, 13367; no intubation cohort, 2714). The rate of DRI increased over time (78.6%, 2003–2008; 83.4%, 2009–2014; 87.8%, 2015–2019), while the rate of severe IVH decreased (7.1%, 2003–2008; 5.7%, 2009–2014; 5.3%, 2015–2019). Infants with DRI had a higher risk of severe IVH than those without DRI (6.8% vs. 2.3%; adjusted risk ratio, 1.86; 95% confidence interval, 1.33–2.58). The results did not change substantially when stratified by gestational age. Despite conflicting changes over time in DRI and severe IVH, DRI was associated with an increased risk of severe IVH among extremely preterm infants in Japan.
Newt-derived extracellular vesicles promote mammalian nerve growth
Authors: Middleton Ryan , Liao Ke , Liu Weixin , de Couto Geoff , Garcia Nahuel , Antes Travis , Wang Yizhou , Wu Di , Li Xinling , Tourtellotte Warren , Marbán Eduardo 
Published: April 2023

Abstract
Newts have the extraordinary ability for regeneration, including the ability to regrow nerve and retinal tissue, and even amputated limbs. In contrast, mammals lack broad regenerative capabilities. While the molecular basis of newts’ regenerative ability is the subject of active study, the underlying paracrine signaling factors remain largely uncharacterized. Extracellular vesicles (EVs) play an important role in cell-to-cell communication via EV cargo-mediated regulation of gene expression patterns within the recipient cells. Here, we report that newt myogenic precursor cells (A1 cells) secrete EVs (A1EVs) that contain messenger RNAs associated with early embryonic development, neuronal differentiation, and cell survival. Exposure of rat primary superior cervical ganglion (SCG) neurons to A1EVs increased neurite outgrowth, facilitated by increases in mitochondrial respiration. Canonical pathway analysis pinpointed activation of NGF/ERK5 signaling in SCG neurons exposed to A1EV, which we validated experimentally. Thus, newt EVs drive mammalian neurite growth and complexity.
Design of a Wankel pump constant pressure grouting system based on Fuzzy PID Method
Authors: Li Mengtian , Wang Chuan , Zhang Xiao , Wang Hanpeng , Zhang Bing , Yang Lei 
Published: April 2023

Abstract

In underground engineering disaster management, existing grouting devices are mainly plunger pumps with large output pulses, which easily cause secondary damage to the surrounding rock of tunnels. Using a rotary piston Wankel pump, an intelligent dual-liquid grouting automation control system (SDUZJ) was developed based on the Fuzzy-PID composite control method to achieve steady output pressure control. Fuzzy control was used when the target and current pressure difference was large to improve self-adaptability and response time. Conversely, PID controller was used when the difference was small to eliminate static differences for steady-state regulation. Combined with the human–computer interaction interface of the computer, automatic control of dual-liquid grouting, alarm recording, real-time monitoring curve, system data setting, and historical data query were realized. A constant-pressure fracturing grouting model experiment with three-axis stress was performed using the proposed system. The Fuzzy control mode was selected to rapidly increase the grouting pressure and flow (pressurization stage) and adjust the grouting pressure to the target value (fracture initiation stage) through rapid rotation of the stepper motor. In the pressure stabilization and fracturing expansion stage, the PID control mode was used for grouting pressure and flow stabilization. Therefore, the Fuzzy-PID pressure stabilization grouting control was realized.
Multi-scale deep residual shrinkage networks with a hybrid attention mechanism for rolling bearing fault diagnosis
Authors: Zhang Xinliang , Wei Shengqiang , Huang Jianhang , Jia Lijie 
Published: April 2023

Abstract
The fault diagnosis of rolling bearings based on deep networks is hindered by the unexpected noise involved with accessible vibration signals and global information abatement in deepened networks. To combat the degradation, a multi-scale deep residual shrinkage network with a hybrid-attention-mechanism (MH-DRSN) is proposed in this paper. First, a spatial domain attention mechanism is introduced into the residual shrinkage module to represent the distance dependence of the feature maps. Then, a hybrid attention mechanism considering both the inner-channeled and cross-channeled characteristics is constructed. Through the comprehensive evaluation of the feature map, it provides a soft threshold for the activation function and realizes the feature-map selection adaptively. Second, the dilated convolution with different dilation rates is implemented for multi-scale context information extraction. Through the feature combination of the DRSN and the dilated convolution, the global information of the rolling bearing fault is strengthened and preserved as the fault diagnosis network is deepened. Finally, the performance of the proposed fault-diagnosis model is validated on datasets from Case Western Reserve University (CWRU), Xi’an Jiaotong University and Zhejiang Changxing Sumyoung Technology Co. Ltd (XJTU-SY). Also, the influence of the number of residual shrinkage layers, model optimizers, and different learning rates on the accuracy of the diagnostic model has been discussed. The experimental results show that, compared with common convolution neural networks, the proposed neural diagnosis model provides a higher identification accuracy and better robustness under noise interference.
Economic and Environmental Analysis of a Grid-Connected Hybrid Power System for a University Campus
Authors: Akindeji Kayode Timothy , Ewim Daniel Raphael Ejike 
Published: April 2023

Abstract
Background: The generation of clean and affordable energy by 2030 is a challenging task, necessitating the integration of renewable energy sources to reduce greenhouse gas emissions associated with coal, crude oil, and natural gas. This study examines the optimization and performance analysis of a hybrid microgrid for a university campus as a potential solution to achieve this goal. The primary objective is to decrease the cost of energy and reduce CO2 emissions on the campus using a hybrid approach.
Results: The Howard college campus of the University of KwaZulu Natal (UKZN) was used as a case study, with meteorological data obtained from NASA and real hourly electrical load data for 2019 from the university smart meters. HOMER, an optimization software, was employed to model and simulate the case study. The results demonstrated significant savings of R15.7 million (approximately $1 million) in annual utility bills, a 51% reduction in CO2 emissions, a return on investment of 20%, and a payback period of 4 years.
Conclusions: The study's findings suggest that universities can become self-sustaining during load shedding periods, as recently experienced in South Africa. The implementation of a hybrid microgrid system on a university campus offers considerable economic and environmental benefits, providing a potential blueprint for other large institutions seeking to achieve similar sustainability goals.
Channel and Spatial Attention Fusion Module for Detection
Authors: Yan Jun , Zhu Rong , Chen Bin , Xu Huiying , Zhu Xinzhong 
Published: April 2023

Abstract

The importance of the attention mechanism in CV is growing, as it allows a neural network to focus more on what it should pay attention to. Channel attention and spatial attention are the two basic attention strategies now in use. Using one of them alone can enhance to some level, while combining the two is more beneficial, but it adds to the computational burden. We propose a Channel and Spatial Attention Fusion Module(CSAFM), besides the use of channel and spatial information, GroupNorm and reorganization operation are applied to enhance the ability of feature extraction and feature representation of feature maps, which can capture more details as the feature deepens. Only a few additional parameters are added while enhancing the network. It is a plug-and-play module. We validate our CSAFM through extensive experiments on PASCAL VOC2007 and MS COCO2017 datasets, which shows that our module is effective yet efficient.e.g., it has risen by 1.47%, 2.1%, and 1.59%, respectively, under the frameworks of VGG16, ResNet50, and ResNet101 compared to baseline. Code is available at https://github.com/zero-yan/FasterRCNN.git.
Is Cyberloafing an Indicator of Innovativeness?
Authors: Aciksoz Semra , Sendir Merdiye , Atar Nurdan Yalcin , Simsekoglu Nesibe , Baydili Kursad Nuri 
Published: April 2023

Abstract
Background The aim of this study was to evaluate the relationship between nurses’ cyberloafing behaviors and individual innovativeness characteristics.Methods This analytical study consisted of 230 nurses. Data were collected using a nurse information form, Cyberloafing Scale and Individual Innovativeness Scale. Data analysis was carried out using descriptive, comparative, correlational statistics.Results Nurses had low levels of minor and serious cyberloafing scores and an sceptics level of innovativeness. There was no correlation between cyberloafing and individual innovativeness.Conclusions Institutional regulations and initiatives should foster more conscious cyberloafing that will give nurses the opportunity for individual and professional self-improvement in the context of innovativeness.
A Multi-model Distributed Controller for Swarm Navigation: Obstacle Clearance Strategy
Authors: Nhu Truong , Nguyen Duy Anh , Hung Pham Duy , Ngo Trung Dung 
Published: April 2023

Abstract
This paper considers a multi-model Distributed Controller for swarm Navigation using the obstacle Clearance strategy (DCNC). If there is an obstacle blocking the swarm’s path, some agents are collectively assigned to push the obstacle away from its original position creating free space for the rest of the swarm navigating towards the destination. To clear the obstacle, we developed a distributed obstacle-pushing strategy consisting of a self-identified push section on an obstacle’s boundary and a collective pushing force control. Our pushing strategy ensures the uniform distribution of robots on the self-identified push section, resulting in the maximum pushing force over the pushing direction.
Single-cell sequencing and transcriptome analysis identified a comprehensive risk score model based on immune-related metabolic genes for improving the prediction of sensitivity in breast cancer immunotherapy
Authors: Qu Fei , Bao Shengnan , Liu Qian , Wu Xuefang , Jiang Mengping , Wang Xi , Lu Rongrong , Huang Xiang , Yin Yongmei , Li Wei 
Published: April 2023

Abstract
Background The metabolic reprogramming of breast cancer (BC) has gained great attention in recent years. Malignant and infiltrating immune cells compete for nutrients and metabolites; still, the impact of metabolism on them remains to be further elucidated. The specific objective of this analysis was to anatomy the action of immune-related metabolic genes in breast cancer and develop a combined model to predict susceptibility to immunotherapy, thus helping guide patient management and establish personalized risk assessment with superior accuracy and clinical applicability.Methods This study was based on data of 1048 BC patients from The Cancer Genome Atlas (TCGA). 46 immune-related metabolic genes were identified by differential expression analysis between different tissue states. Applying unsupervised clustering and other bioinformatics techniques, we illustrated how the divergent groups' immunometabolism and survival conditions varied. A comprehensive risk-sharing index model was developed using LASSO regression and multivariable Cox analysis method, and BC patients were categorized into two risk groups based on their levels of risk score. Another three independent GEO database sets [GSE20685, GSE42568, GSE124647] were selected for external validation. Finally, the single-cell sequencing data mining and analysis aimed to explore the immunometabolic heterogeneity of human breast cancers.Results Fourteen immune-related metabolic signatures (FABP6, LPA, RBP4, CETP, STAB2, PPARG, TYMP, CGA, GCGR, SDC1, BGN, ABCA1, PLA2G4A, PLK1) were identified for use in constructing a comprehensive prognostic model for BC. The high-risk group was characterized by poorer diagnosis, fewer activated immune cell infiltration and better treatment response to immune checkpoint inhibitors. Moreover, the index was combined with clinical parameters, weighted, and created a nomogram. It is imperative to point out that our model and corresponding nomogram are optimal and independent prognosis factors compared to other traditional clinical variables. They also have satisfactory predictive capacity validated by ROC curve, calibration plot and DCA analysis.Conclusions Our 14-MRDEGs and their multiple integrations reflected genetic-level and immunometabolic profile alterations in BC, allowing accurate prediction of survival risk and the efficacy of immunotherapy. The research conclusions may provide a reference for further analysis and drug development in target discovery.