bluuebunny/crossref_metadata_2025_split huggingface dataset:
To evaluate the performance of machine learning algorithms in predicting student dropout on MOOC platforms
Authors: Kumar Gaurav , Singh Amar , Sharma Ashok 
Published: September 2022

Abstract
Online learning using Massive Open Online courses(MOOCs) has gained a lot of hype in recent years due to its great potential in having the widest reach in delivering the state-of-the-art resources to the unlimited number of online learners without limiting itself to any geographical boundary. Along with gaining popularity, MOOCs have been facing challenges like high attrition or dropout rate since its birth. The main motivating factor behind the study is to fill the gap which has been there because of very limited literature available there to find the real cause behind these challenges. The current study tries to find the solution of the said challenges by finding the significant contributing factors which highly affect the target variable in the study which is number of certified students in this case. The dataset used in this paper is publicly available in dataverse repository of Harvard university. The dataset is a compilation of student clickstream log data consisting of 641138 instances of enrolled students in various MOOC courses of Harvard and MIT. The study evaluates machine learning models like logistic regression, decision tree, random forest, K-Nearest Neighbor to determine their efficiency in predicting the student dropout. The results of this study can be used to create a framework for recommending necessary actions to the at-risk students to reduce the dropout rate.
Autonomous Assistance System for Visually Impaired using Tesseract OCR & gTTS
Authors: Khete Tushar , Bakshi Aditya 
Published: September 2022

Abstract
OCR makes machines to recognize text automatically. It not only saves retrieval times but also helps in digitalization of important documents. In this paper different techniques are used for converting textual matter from an paper document or an image into machine readable form (OCR text output) and then into audio output using gTTS (Google Text-to Speech, a Python library and CLI tool to interface with Google Translates text-to-speech API). In proposed model, the content writes spoken mp3 data to a file, a file-like object (byte string) for further audio manipulation with flexible pre-processing and tokenizing. The approach results into a graspable and comprehensible text output & then fed into audio output for inference with 99 % accuracy. This paper outlines the stages of development, the major challenges with some interesting result comparison with different state of approaches.
Co-Resident Attack and its impact on Virtual Environment
Authors: Jena Sudibyajyoti , Sahu Likhet Kashori , Mishra Debahuti , Rao Madhuri , Kumar Kulamala Vinod 
Published: September 2022

Abstract
Cloud computing (CC) is one of the emerging computing models that potentially transform the IT industry. The cloud computing is defined as a computing paradigm that enables suitable, universal, on-demand network access of various computing resources like data storage, OS (Operating System), computer applications and software as well. A Cloud model essentially is characterized with features like on-demand self-service, better network access, resource distribution, elasticity and measured services. Cloud computing is also known as Internet Computing and is indeed reshaping the way industries and organisations function. It facilitates the service taker or the client to use various IT resources like OS (Operating System), middleware and various applications without the need of downloading it. In this paper a brief view of this computing paradigm and the underlying cloud infrastructure is presented. We essentially present an analysis of various security challenges that are inherent in the Infrastructure as a Service(IaaS) cloud model and explain terms like virtualization, VM (virtual machine), SLA (Service level agreement), etc. Here, CloudSim simulator is explored to study the impact of variation of cache size of the Virtual Machine on the availability of data centres, that occurs due to co-resident attack. Further, a brief view of the co-resident attack is further presented and some of the feasible countermeasures are discussed further.
An LoRa based WSN for monitoring targeted Audience with Heart Diseases
Authors: Narenthira Prasath D , Singh Dushyant Kumar 
Published: September 2022

Abstract
Health 5.0, health digitization is growing rapidly with the help of Internet of Things (IoT) and Internet of Medical Things (IoMT). The growth in this sector motivated us in learning the loopholes in the advancements. The various communication technologies and strategies are discussed based on the power consumption, speed and range. The LoRa is seen to work on low power, high range with less data transmission. Investigating the fields of Wireless Sensor Network, LoRa is seen as a reliable protocol that can best fit for Wireless sensor Network. The applications based on the capability of the system is reviewed considering the wide scale properties of each component. The best fit processing unit based on cost with the transmission protocols is discussed to give a clear vision on the technologies. Various transmission protocols are reviewed and LoRa is taken ahead in exploring and enhancing the features considering the knowledge gap. The speed of LoRa is simulated using Pothos, and the range is tested on the real ground. The data distribution based on heavy data transmission is discussed. The role of IoT and IoMT in the growth of the sector is visualized as the major contribution is given by the technologies. The Future scope of research in healthcare is concluded motivating the researchers giving a wide range opportunity to work on.
Historical transboundary ozone health impact linked to affluence
Authors: Chen Lulu , Lin Jintai , Ni Ruijing , Kong Hao , Du Mingxi , Yan Yingying , Liu Mengyao , Wang Jingxu , Weng Hongjian , Zhao Yuanhong , Li Chunjin , Martin Randall V 
Published: September 2022

Abstract
Ozone pollution is a major transboundary threat to global health. Systematic improvement of mitigation strategy for transboundary ozone requires a socioeconomic understanding of historical lessons in countries at different affluence levels. Here, we explore the changes in transboundary ozone related premature deaths over 1951–2019 driven by anthropogenic emissions of four country groups categorized by income level. By integrating global emission datasets, a chemical transport model (GEOS-Chem), in situ ozone measurements worldwide and an ozone exposure-response model, we find that mortality caused by transboundary anthropogenic ozone increases by 27 times from 1951 to 2019, and on average contributes about 27% of global anthropogenic ozone related deaths. All groups exert and suffer from substantial transboundary ozone related mortality. The high-income and upper middle groups have each experienced an inverted U-shaped relationship between its affluence and per-million-people contribution to mortality caused by transboundary ozone, with the turning point around 23 000 USD and 6300 USD, respectively. The lower middle group has gradually matched the growth pathway of the upper middle group with a turning point less clear. Concerted efforts to ensure early turning points in less affluent countries will have considerable global health benefits.
Integrable boundaries for the q-Hahn process
Authors: Frassek Rouven 
Published: September 2022

Abstract
Taking inspiration from the harmonic process with reservoirs introduced by Frassek, Giardinà and Kurchan in (2020 J. Stat. Phys.
180 135–71), we propose integrable boundary conditions for its trigonometric deformation, which is known as the q-Hahn process. Following the formalism established by Mangazeev and Lu in (2019 Nucl. Phys. B 945 114665) using the stochastic R-matrix, we argue that the proposed boundary conditions can be derived from a transfer matrix constructed in the framework of Sklyanin’s extension of the quantum inverse scattering method and consequently preserve the integrable structure of the model. The approach avoids the explicit construction of the K-matrix.
Controlled release of gallium maltolate complex from injectable phosphocalcic cements
Authors: Dupleichs Manon , Limelette Maxence , Mellier Charlotte , Montouillout Valérie , Lefevre François-Xavier , Quillard Sophie , Mevellec Jean-Yves , Janvier Pascal 
Published: September 2022

Abstract
Some cancers have tropism for bone: breast, prostate, lung, kidney, and thyroid cancers are the most common. Bone metastases can be treated with surgical resection and the resulting bone defects can be filled with injectable biomaterials. Among these, calcium phosphates may be the biomaterials of choice because of their ability to locally release anticancer active ingredients. Herein, we propose the synthesis of injectable calcium phosphate cement (CPC) loaded with gallium maltolate (GaM). It is an extremely promising anticancer drug with also antibiotic and anti-inflammatory properties. This synthesis was based on commercial cement whose main component was α-tri-calcium phosphate (α-TCP), and the final product obtained after hardening was calcium-deficient apatite (CDA). Two formulations were prepared, containing 3.5% and 7% by mass of GaM (CPC-3.5G and CPC-7G respectively). Powder x-ray diffraction (pXRD), Fourier transform infrared (FTIR) spectroscopy, and magic-angle spinning nuclear magnetic resonance (NMR MAS) 31P analyses showed that the direct incorporation of GaM did not modify the final cement composition. Textural properties, such as setting time, injectability, workability, and cohesiveness, were well preserved or even improved. Additionally, the mechanical strength, although slightly reduced, remained perfectly compatible with surgical use. In vitro kinetics studies of GaM-loaded CPCs showed a controlled release of GaM (49% at 60 days for CPC-3.5G and 58% at 116 days for CPC-7G) following Fick’s law. Raman imaging was used to visualize its diffusion within the cement during in vitro release experiments. Finally, the structural integrity of the gallium complex in the CPC was confirmed using NMR MAS 71Ga.
Square grid pattern with direction-selective surface discharges in dielectric barrier discharge
Authors: ZHANG Jianhua , PAN Yuyang , FENG Jianyu , HE Yunan , CHU Jiahui , DONG Lifang 
Published: September 2022

Abstract
A new phenomenon that a filament discharged only once instead of twice in a cycle of the applied voltage is observed in a square grid pattern in a dielectric barrier discharge (DBD) with a larger gas gap, which is named intermittent discharge. Its spatiotemporal dynamics and the formation mechanism are studied by the multiple photomultiplier tubes and an intensified charge-coupled device. Corresponding to the positions of spots in the picture with an exposure time of 40 ms, there are some bright spots (discharge spots) and black spots (non-discharged spots) in the instantaneous image with an exposure time of 10 μs (a half cycle of the applied voltage). There are at least two bright spots around one black spot and vice versa. The surface discharges (SDS) can be observed between any two adjacent spots. The intensity of the SDS between the bright spot and the black spot is 2.5 times greater than that between two adjacent bright spots, which indicates that the SDS are directional-selective. The intermittent discharge with positive (negative) current polarity changes to that with negative (positive) current polarity, after it sustains up to 14 voltage cycles at the longest. The spatial distribution of the electric field component is calculated through COMSOL software to solve the Poisson equation numerically. It is found that the inhomogeneous distribution of surface electric field is caused by the inhomogeneous distribution of wall charges, which leads to direction-selective SDS. The intermittent discharge is formed by the competition between the direction-selective SDS and volume discharges (VDS) in DBD. This is the reason why the intermittent discharge is generated.
Expectation values from the single-layer quantum approximate optimization algorithm on Ising problems
Authors: Ozaeta Asier , van Dam Wim , McMahon Peter L 
Published: September 2022

Abstract
We report on the energy-expectation-value landscapes produced by the single-layer (p = 1) quantum approximate optimization algorithm (QAOA) when being used to solve Ising problems. The landscapes are obtained using an analytical formula that we derive. The formula allows us to predict the landscape for any given Ising problem instance and consequently predict the optimal QAOA parameters for heuristically solving that instance using the single-layer QAOA. We have validated our analytical formula by showing that it accurately reproduces the landscapes published in recent experimental reports. We then applied our methods to address the question: how well is the single-layer
QAOA
able to solve large benchmark problem instances? We used our analytical formula to calculate the optimal energy-expectation values for benchmark MAX-CUT problems containing up to 7000 vertices and 41 459 edges. We also calculated the optimal energy expectations for general Ising problems with up to 100 000 vertices and 150 000 edges. Our results provide an estimate for how well the single-layer QAOA may work when run on a quantum computer with thousands of qubits. In addition to providing performance estimates when optimal angles are used, we are able to use our analytical results to investigate the difficulties one may encounter when running the QAOA in practice for different classes of Ising instances. We find that depending on the parameters of the Ising Hamiltonian, the expectation-value landscapes can be rather complex, with sharp features that necessitate highly accurate rotation gates in order for the QAOA to be run optimally on quantum hardware. We also present analytical results that explain some of the qualitative landscape features that are observed numerically.
Association Between ABO Blood Group System and COVID-19 Severity
Authors: Bshaena Amina M , Almajdoub Osama H , Alshwesh Rajaa A , Omran Entesar A , Haq Soghra , Ismail Faisal 
Published: September 2022

Abstract

Objectives
A possible association between blood group systems (ABO and Rh) and coronavirus disease 2019 (COVID-19) severity has recently been investigated by various studies with conflicting results. However, due to variations in the prevalence of the ABO and Rh blood groups in different populations, their association with COVID-19 might be varied as well. Therefore, we conducted this study on Libyan participants to further investigate this association and make population-based data available to the worldwide scientific community.

Methods
In this case-control study, ABO and Rh blood groups in 419 confirmed COVID-19 cases in Zawia, Libya, and 271 healthy controls were compared using descriptive statistics and χ 2 tests.

Results
Blood group A was significantly more prevalent in patients with severe COVID-19 (64/125; 51.2%) than in patients with nonsevere COVID-19 (108/294, 36.7%) (P &lt; .034), whereas the O blood group prevalence was higher in nonsevere COVID-19 cases (131/294, 44.5%) compared with severe cases (43/125, 34.4%) (P &lt; .001).

Conclusions
The results showed a significant association between blood group A and the severity of COVID-19, whereas patients with blood group O showed a low risk of developing severe COVID-19 infection. No significant association was found between Rh and susceptibility/severity of the disease.
The Reemergence of Measles
Authors: Klassen-Fischer Mary K , Nelson Ann M , Neafie Ronald C , Neafie Fides A , Auerbach Aaron , Baker Thomas P , Burke Allen P , Datta Anandita A , Franks Teri J , Horkayne-Szakaly Iren , Lack Ernest E , Lewin-Smith Michael R , Luiña Contreras Alejandro , Mattu Rubina H , Rush Walter L , Shick Paul C , Zhang Yang , Rentas Francisco J , Moncur Joel T 
Published: September 2022

Abstract

Objectives
Present-day pathologists may be unfamiliar with the histopathologic features of measles, which is a reemerging disease. Awareness of these features may enable early diagnosis of measles in unsuspected cases, including those with an atypical presentation. Using archived tissue samples from historic patients, a unique source of histopathologic information about measles and other reemerging infectious diseases, we performed a comprehensive analysis of the histopathologic features of measles seen in commonly infected tissues during prodrome, active, and late phases of the disease.

Methods
Subspecialty pathologists analyzed H&E-stained slides of specimens from 89 patients accessioned from 1919 to 1998 and correlated the histopathologic findings with clinical data.

Results
Measles caused acute and chronic histopathologic changes, especially in the respiratory, lymphoid (including appendix and tonsils), and central nervous systems. Bacterial infections in lung and other organs contributed significantly to adverse outcomes, especially in immunocompromised patients.

Conclusions
Certain histopathologic features, especially Warthin-Finkeldey cells and multinucleated giant cells without inclusions, allow pathologists to diagnose or suggest the diagnosis of measles in unsuspected cases.
Exploring the effects of a smartphone-based meditation app on stress, mindfulness, well-being, and resilience in pharmacy students
Authors: Truhlar Laura M , Durand Cheryl , Cooper Maryann R , Goldsmith Carroll-Ann W 
Published: September 2022

Abstract

Purpose
The purpose of this study was to investigate the effects of the smartphone-based meditation app Ten Percent Happier on stress, mindfulness, well-being, and resilience in pharmacy students.

Methods
Pharmacy students in a professional year of study were recruited to participate. Students were instructed to meditate using the Ten Percent Happier app for at least 5 days a week for 4 weeks. Students could use the app at their discretion for weeks 5 to 12. Baseline, week 4, and week 12 responses were collected from the following instruments: the Perceived Stress Scale, the Five-Facet Mindfulness Questionnaire-15, the Flourishing Scale, and the Brief Resilience Scale.

Results
Eighty-nine pharmacy students volunteered for the study. Sixty (67%) enrolled by completing the baseline survey. Of these, 28 (47%) completed the week 4 survey and 22 (37%) completed the week 12 survey. Participants experienced a reduction in perceived stress (P = 0.0005) and increases in resilience (P &lt; 0.0001) and well-being (P = 0.0006). Increases in mindfulness were seen in 4 of the 5 subscales of the Five-Facet Mindfulness Questionnaire-15 (P ≤ 0.05). These benefits were noted at week 4 and maintained at week 12.

Conclusion
Pharmacy students who practiced mindful meditation through the Ten Percent Happier app for an average of 5 days a week for 4 weeks experienced reduced stress and improved mindfulness, well-being, and resilience. Benefits experienced during the intervention were maintained at the 8-week follow-up, despite app usage decreasing to an average of 4 days a week.
A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
Authors: Ren Zhong-Hao , You Zhu-Hong , Yu Chang-Qing , Li Li-Ping , Guan Yong-Jian , Guo Lu-Xiang , Pan Jie 
Published: September 2022

Abstract
Drug–drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local–global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local–global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF’s superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.
GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs
Authors: Dai Qiguo , Liu Ziqiang , Wang Zhaowei , Duan Xiaodong , Guo Maozu 
Published: September 2022

AbstractMotivation: CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. Results: We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA–disease associations. Firstly, the circRNA–circRNA similarity network and disease–disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA–disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA–disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.