bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Route Planning for Oil Sample Transportation Based on Improved A* Algorithm
Authors: sang yingjun , Chen Xianyan , Chen Quanyu , Tao Jinglei , Fan Yuanyuan 
Published: August 2023

Abstract
The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A* algorithm along with a path smoothing technique has been proposed. Firstly, the Directional Search A* algorithm introduces an angle constraint condition through the evaluation function. By converting sharp turns into obtuse angles, the path turning points become smoother. This approach reduces the occurrence of sharp turns in the path, resulting in improved path smoothness.Secondly, the algorithm enhances the distance function to strengthen the directional guidance during the path search. By optimizing the distance function, the algorithm tends to prefer directions that lead towards the target, which helps reduce the search space and shorten the overall path planning time. Additionally, the algorithm removes redundant nodes along the path, resulting in a more concise path representation. Lastly, the algorithm proposes an improved step size adjustment method to optimize the number of path nodes obtained. By appropriately adjusting the step size, the algorithm further reduces the number of nodes, leading to improved path planning efficiency. By applying these methods together, the Directional Search A* algorithm effectively addresses the limitations of the traditional A* algorithm and produces smoother and more efficient path planning results. Simulation experiments comparing the modified A* algorithm with the traditional A* algorithm were conducted using MATLAB. The experimental results demonstrate that the improved A* algorithm can generate shorter paths, with reduced planning time and smoother trajectories. This indicates that the Directional Search A* algorithm, incorporating the angle constraint condition in the evaluation function and the direction-guided strategy, outperforms the traditional A* algorithm in path planning and provides better solutions to the existing issues.
Screening for Severe Coronary Stenosis in Patientswith Apparently Normal Electrocardiograms Based Ondeep Learning
Authors: Xue Zhengkai , Geng Shijia , Guo Shaohua , Mu Guanyu , Yu Bo , Wang Peng , Hu Sutao , Xu Weilun , Liu Yanhong , Yang Lei , Tao Huayue , Hong Shenda , Chen Kangyin 
Published: August 2023

Abstract

Background
Patients with severe coronary artery stenosis may have apparently normal electrocardiograms (ECGs), making it difficult to detect the adverse health conditions during screening or physical examinations, resulting in them missing the optimal window of treatment.
Methods
The goal of this study was to develop an effective model which can distinguish severe coronary stenosis from no or mild coronary stenosis in patients with apparently normal ECGs. Deep learning (DL) models trained from scratch and with pre-trained parameters (transfer learning) were tested on ECG alone as well as on ECG along with clinical information (age, sex, hypertension, diabetes, dyslipidemia and smoking status).
Results
We found that DL models trained from scratch with ECG alone can achieve a specificity of 0.746; however, they have low sensitivity, which is comparable to the performance of logistic regression with clinical data. Although adding clinical information to the ECG DL model trained from scratch can improve the sensitivity, it reduces the specificity. Combining clinical information with the ECG transfer learning model provides the best performance, with a 0.847 AUC, 0.848 sensitivity, and 0.704 specificity.
Conclusion
The findings not only demonstrate the effectiveness of using DL models to identify severe coronary stenosis with apparently normal ECGs, but also validate an efficient approach using existing ECG models. With transfer learning techniques, we can extract “deep features” that summarize the ECG’s inherent information with inexpensive calculations.
Edge Assignment in Edge Federated Learning
Authors: Do Thuy , Tran Duc A. , Vo Anh 
Published: August 2023

Abstract
Federated Learning (FL) is a recent Machine Learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Because FL depends on a central server for repeated aggregation of local training models, this server is prone to become a performance bottleneck. Therefore, one can combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. We show that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. Specifically, we propose an assignment solution which is especially useful for the case of non-IID training data (well-known to hinder today's FL performance). Our findings are substantiated with an evaluation study using real-world datasets.
Do different weight initialization strategies have an impact on transfer learning for plant disease detection?
Authors: TERZI DUYGU SINANC 
Published: August 2023

Abstract
The concept of weight initialization technique for transfer learning refers to the practice of using pre-trained models that can be modified to solve new problems, instead of starting the training process from scratch. By using pre-trained models as a starting point, the network can learn from patterns and features present in the original data, improving overall accuracy and allowing for faster convergence during training. In this study, four different transfer learning weight initialization strategies are proposed for plant disease detection: random initialization, pre-trained model on different domain (ImageNet), model trained on related domain (ISIC 2019), and model trained on same domain (PlantVillage). Weights from each strategy are transferred to a target dataset, Plant Pathology 2021. These strategies were implemented using four state-of-the-art CNN-based architectures: AlexNet, DenseNet, MobileNetV2, and VGG. The best result was obtained when both the target and source datasets included images of plant diseases. In this case, VGG was used and resulted in an 85.9% weighted f-score, which is a 9% improvement from random initialization. The transfer of knowledge from small-sized, related domain data (skin cancer data) was almost as successful as the transfer from ImageNet. Transferring from ImageNet yielded an f-score of 85.7%, while transferring from skin cancer data resulted in an f-score of 85.2%. This indicates that ImageNet, which is widely favored in the literature, may not necessarily represent the most optimal transfer source for the given context. Finally, the classifications made by the proposed models were visualized using Grad-CAM to better understand the decision-making process.
Characterization and potential mechanism of resistance to double-stranded RNA in willow leaf beetle, Plagiodera versicolora
Authors: Liao Chongyu , Zhang Miao , Zhang Jiang 
Published: August 2023

Abstract

RNAi-based pesticides have emerged rapidly in recent decades and are believed to be the third generation of pesticides. Although two case studies of the resistance to RNA pesticides have been reported in western corn rootworm and Colorado potato beetle, whether RNAi-resistance is general phenomena in other coleopteran insects and the underlying mechanism of resistance to RNA pesticides are largely unknown. Here we report the development of a highly (> 4110-fold) dsRNA-resistant population (Pv-30R) of Plagiodera versicolora by feeding a laboratory-rearing susceptible population (Pv-S) with the leaves of willow plants after seven episodes of selection using foliar coating dsRNA targeting a signal recognition particle protein 54k gene. We showed that Pv-30R was cross-resistant to another dsRNAs (dsActin and dsSnap) but susceptible to the Cry3Bb protein from Bacillus thuringiensis, and the resistance was an autosomal and recessive trait. Although no significant differences of the dsRNA stability in the midgut of larvae between Pv-S and Pv-30R were observed, uptake of dsRNA in the midgut tissue of larvae from Pv-30R was impaired. Overall, these results demonstrate that high-levels of resistance to RNA pesticides can developed quickly in P. versicolora in laboratory condition. These findings highlight the requirements to counter the potential rapid evolution of insect resistance to dsRNA in the field.
Healthcare Waste Management Practices in Public Health Facilities in Nairobi City County, Kenya
Authors: Mogaka Brenda , Maru Shital , Karimi Peter , Nimpagaritse Manassé 
Published: August 2023

Abstract

Background
Proper healthcare waste management (HCWM) involves the accurate categorization and segregation of waste by healthcare waste (HCW) producers in correct color-coded bins.
According to the World Health Organization (WHO), hazardous waste affects 10-25% of HCWs and can have many health risks for healthcare providers, patients, and the general public. Many health facilities have poor waste-segregation techniques, causing up to 50% of garbage to be infectious.
Methodology
This study utilizeda descriptive cross-sectional research design. The study utilized self-administered questionnaires to obtain quantitative data from health care workers in four main departments, namely, pharmacy, laboratory, clinical and support staff, in the public health facilities in Nairobi County.
Results
The study conducted in Nairobi County found that the majority of staff in public health facilities were female (61.6%), primarily from the nursing cadre (28.1%), and most had worked for 1-10 years (58.3%). The quantity of general waste generated was high (46.0%), and the majority of facilities (93.8%) had all the necessary bins for waste segregation, including red, yellow, black bins, and sharp boxes (89.3%). The overall rating of healthcare waste management practices, including waste segregation, collection, transportation, storage, and treatment, was considered satisfactory (33%). The respondents demonstrated adequate knowledge of waste management practices (80.0%), but a significant number were unaware of the guidelines and policies governing healthcare waste management (74.6%). There was limited access to the guidelines (36.6%), and the guideline most accessed was the Kenya National Infectious Prevention Control (IPC) Policy and Guidelines 2021 (28.6%). HCWM was included in the annual work plan for the majority of facilities (56.3%), although awareness of budgetary allocation (41.2%) and waste disposal costs (40.6%) was relatively low. While there was a person responsible for waste handling (64.3%), most of them (80%) were not qualified waste handlers.
Conclusion
Health centers (Level 3) generate the highest quantities of general waste. The healthcare workers demonstrated adequate knowledge of waste management practices, and the practices were satisfactory.
Rumor spreading model with a focus on educational impact and optimal control
Authors: Li Deliang , Zhao Yi , Deng Yang 
Published: August 2023

Abstract
Rumor spreading brings great misconception and harm to society. To control the spread of rumors, it is essential to model rumor propagation and provide appropriate interference in inhibiting the propagation. In this paper, we establish an extended rumor-spreading model with focus on the influence of knowledge education and intervention strategies in reducing rumor propagation. The mathematical rationality of the proposed model is examined, which demonstrates the existence of equilibrium and local asymptotic stability. To simulate the dynamics of rumor spreading in the proposed model and calibrate its unknown variables to a real case, we employ a novel rumor-informed neural network (RINN), which is constructed based on the physics-informed neural network (PINN) and real rumor spreading. The numerical simulation experiments indicate that the reinforcement of education on rumor identification and timely refutation of false information is effective in controlling the propagation of rumors. Moreover, the optimal control strategies are further proposed to determine the efficient means of mitigating the risk associated with the rapid spread of rumors. Our findings present that proactive dissemination of publicity and educational content can effectively enhance individuals’ awareness of rumor information. Specifically, prompt dispelling of false information can result in a higher success rate of dispelling rumors, a shorter duration of rumor dissemination, and a lower peak in the number of rumor disseminators, thereby facilitating effective control of the spread of rumors.
Stress-strain characteristics of FRP-PVC confined spontaneous combustion gangue concrete
Authors: Zhang Jinyang , Liu Haiqing , Wang Jinli , Lei Ming , Chen Zimu 
Published: August 2023

Abstract
A total of 40 fiber reinforced polymer (FRP) and polyvinyl chloride (PVC) confined spontaneous combustion gangue coarse-aggregate concrete (SAC) specimens were subjected to axial compression tests and theoretical studies. The effects of the substitution proportion of spontaneous combustion gangue (SCG) were analyzed regarding the CFRP confinement form and the number of CFRP layers on the axial compression performance of CFRP-PVC confined SAC (CFRP-PVC-SAC). The results showed that the CFRP-PVC confinement form effectively improved the axial compressive load carrying capacity and axial deformation of the members. With increased SCG content, the load-bearing capacity of specimens decreased and the crack morphology and dilation properties of the confined SAC significantly different compared with those of ordinary concrete. In addition, based on the framework of the existing FRP-confined plain concrete model, a modified model was established to facilitate prediction of stress-strain relationships for short columns of CFRP-PVC-SAC, with the calculated results in good agreement with experimental values.
Care for the Cardiovascular Accident Survivor: Experiences of Family Caregivers
Authors: Atsu Fortune Selasi , Botha Nkosi Nkosi , Ansah Edward Wilson , Segbedzi Cynthia Esinam , Ogum Mary Aku , Apaak Daniel , Tsedze Ivy Selorm , Akoto Lucy Adjanor 
Published: August 2023

Abstract
Background
The role of family caregivers in the management of cardiovascular accident (CVA) survivors is invaluable. So far, there is strong evidence affirming the efficacy of effective family support for CVA survivors. Meanwhile, caring for CVA survivors can be labour and time intensive and pretty stressful for caregivers. The purpose was to examine the experiences of family caregivers providing support for CVA survivors.
Methods
Using a 15-item interview guide and descriptive phenomenology, we gathered and analysed data from 37 family caregivers in the Ho Municipality, Ghana.
Results
We found that caregivers had limited knowledge about cardiovascular disease-risk factors and were ill-prepared for their roles. Additionally, we found limited knowledge about coping strategies among the caregivers, and further report that some caregivers use fasting and prayer as coping strategies.
Conclusions
Caregivers suffered broken relationship, loss of jobs and incomes due to their caregiving role. Moreover, some engaged in fasting and prayers and alcohol use to cope with the stress of caregiving. We discuss the implications of our findings to achieving Sustainable Development Goal 3.4, which seeks to reduce by 75% mortality due to non-communicable diseases.
Efficient approach for Predicting Sales using Supervised Machine Learning Algorithms
Authors: Chintapanti Anusha , Maiti Sandipan 
Published: August 2023

Abstract
The significant impact in businesses is generally affected by manufacturing, planning, supply chain, marketing, warehousing, logistics, and resource management, usually managed by sales forecasting. Casual forecasting techniques and the correlations between factors are used to anticipate future sales behaviour without relying on historical data and trends. Despite the wide usage in research and application, there are severe drawbacks regarding the forecasting techniques related to classic time series. The sales related to supermarkets, along with association rules, regression techniques, time series algorithms, etc., are estimated by numerous available methods. This paper explains constructing a prediction model based on a supervised machine learning algorithm known as Ada Boost to estimate possible sales for 45 Walmart stores in various locations. It is a great opportunity for researchers to predict sales for Walmart, as it is the largest store existing in the world. The sales will be affected on a periodic basis during an event or holidays. This affect might also extend on a daily basis.
Natural Language Processing for Forecasting Mortality and Premature Death in Companion Animals
Authors: Farrell Sean , Noble Peter-John Mäntylä , Al-Moubayed Noura 
Published: August 2023

Abstract
Accurate tracking and monitoring of companion animal mortality rates are essential for promoting animal welfare and safeguarding public health. However, current methods suffer incompleteness and unreliability, with no frequent monitoring of companion animal mortality in the UK. Here we introduce PetBERT-mortality, a novel forecasting tool that comprises two key components. The first component demonstrates precision and recall rates of over 98\% and 97\%, respectively, in accurately identifying animals declared dead during consultations. Leveraging a dataset of over 8 million electronic health records (EHRs) from first-opinion veterinary practices, we identify 92,548 deceased cats and dogs. We present breed-specific life expectancies allowing us to identify 4,146 premature deaths and uncover their causes using the high-level syndromic ICD-11 disease coding framework and novel insight into risk factors associated with premature mortality. The second component focuses on characterising past consultation events and associated factors, such as age, sex, and geographical circumstances, to forecast expected mortality with up to 73\% predictive accuracy over 12 months. We investigate that end-of-life discussions during penultimate consultations occur in fewer than 24\% of cases. Overall, this study underscores the potential of PetBERT-mortality to enhance the health and well-being of companion animals while supporting veterinary clinicians in delivering optimal care.
Synergistic Impact of Arbutin and Kaempferol-7-O-α-L- rhamnopyranoside from Nephelium lappaceum L. on Whitening Efficacy and Stability of Cosmetics Formulations
Authors: Siridechakorn Ittipon , Pimpa Junsuda , Choodej Siwattra , Pudhom Khanitha 
Published: August 2023

Abstract
Four flavonoid glycosides, namely quercetin-3-O-rhamnoside (1), kaempferol-3-O-β-D-glucopyranosyl (2), kaempferol-7-O-α-L-rhamnopyranoside (3), and kaempferol-3-O-β-D-glucopyranosyl-7-O-α-L-rhamnopyranoside (4), from Nephelium lappaceum L. seeds were evaluated for their efficacy against melanin inhibition in B16F10 melanoma cells and tyrosinase inhibition. Among them, kaempferol-7-O-α-L-rhamnopyranoside (3) displayed the highest potency in both activities without any significant cytotoxicity. The combination of compound 3 and arbutin in specific proportions demonstrated a synergistic effect (CI < 1) in inhibiting melanin production in B16F10 cells and tyrosinase inhibition. Additionally, a cosmetic formulation containing compound 3 and arbutin as active ingredients exhibited favorable stability under accelerated storage conditions. Quantitative analysis indicated that compound 3 and arbutin levels in the formulation were above 90% after one month of storage.
Association between GNRI and Mortality Risk in the Elderly: An Analysis of NHANES Data
Authors: Chen Naifei , Liu Xiangliang , Li Yuguang , Chen Xinqiao , Zhang Rong , Xu Feiyang , Yin Hongmei 
Published: August 2023

Abstract
Geriatric Nutritional Risk Index (GNRI) is a simple tool to assess nutritional status in the elderly. Current research on the dose-response relationship between GNRI and mortality risk, especially with different causes of death, in the elderly is limited.10,617 participants aged ≥ 60 years were included from NHANES 2005–2016, with 7,063 excluded for inability to calculate GNRI, leaving 3,454 finally enrolled. They were divided into high nutritional risk (GNRI < 82), moderate nutritional risk (82 ≤ GNRI < 92), low nutritional risk (92 ≤ GNRI ≤ 98) and no nutritional risk (GNRI > 98) groups by GNRI levels. The primary outcome was all-cause mortality and secondary outcomes were cancer mortality and cardiovascular mortality. The correlations between GNRI and mortality risks were evaluated using Cox regression and restricted cubic spline models. Compared with the optimal GNRI group, the high nutritional risk group had 65% increased all-cause mortality, 4 times the cardiovascular mortality, and 2.5 times the cancer mortality. GNRI was linearly and negatively correlated with all-cause and cardiovascular mortality, while the correlation with cancer mortality was weaker. GNRI was negatively associated with mortality risk especially cardiovascular mortality in the elderly, and is an effective tool for assessing mortality risk in this population.
The relationship between School bullying and suicidal ideation among diploma medical students: the mediating effect of emotional response
Authors: Zhu Ping , Yin Chongzhi , Lei Wei , Xiang Bo , Fu Jing , Tan JinHua 
Published: August 2023

Abstract

Background ,y also seeks to analyze the impact of School bullying and emotional response on suicide ideation and explore the Mesomeric effect of emotional response between school bullying and suicide ideation.
Methods
A cross-sectional survey was conducted, with a questionnaire being administered to 3013 vocational school medical students. Statistical analysis was done by descriptive statistics, Pearson correlation analysis, multiple linear regression, and structural equation model.
Results
There was a positive correlation between School bullying and suicidal ideation, as well as between emotional response and suicidal ideation. There was an indirect Mesomeric effect between emotional response to School bullying and suicidal thoughts.
Conclusions
Suicide ideation is common amongdiploma medical students. School bullying and emotional reaction are significant causes. The emotional reaction has an indirect Mesomeric effect on school bullying and suicide ideation. Therefore, the results of the study explored measures to reduce suicidal ideation and prevent school bullying. In addition, the results emphasized the important role of emotion control in emotional response.