bluuebunny/crossref_metadata_2025 huggingface dataset:
What do Researchers Need when Implementing Novel Interaction Techniques?
Authors: Raffaillac Thibault , Huot Stéphane
Published: June 2022

Interaction frameworks are the tools of choice for researchers and UI designers when prototyping new and original interaction techniques. But with little knowledge about actual needs, these frameworks provide incomplete support that restricts, slows down or even prevents the exploration of new ideas. In this context, researchers resort to hacking methods, creating code that lacks robustness beyond experiments, combining libraries of different levels and paradigms, and eventually limiting the dissemination and reproducibility of their work. To better understand this problem, we interviewed 9 HCI researchers and conducted an online survey. From the results we give an overview of the criteria for choosing frameworks, the problems often met with them, and the "tricks" used as solutions. Then we propose three design principles to better support prototyping for research in UI frameworks: (i) duplicate singular elements (e.g. mouse, caret) to foster opportunities for extensions, (ii) accumulate rather than replace to keep a history of changes, and (iii) defer the execution of predefined behaviors to enable their monitoring and replacement.
Solids on Soli
Authors: Čopič Pucihar Klen , Attygalle Nuwan T. , Kljun Matjaz , Sandor Christian , Leiva Luis A.
Published: June 2022

Gesture recognition with miniaturised radar sensors has received increasing attention as a novel interaction medium. The practical use of radar technology, however, often requires sensing through materials. Yet, it is still not well understood how the internal structure of materials impacts recognition performance. To tackle this challenge, we collected a large dataset of 14,090 radar recordings for 6 paradigmatic gesture classes sensed through a variety of everyday materials, performed by humans (6 materials) and a robot system (75 materials). Next, we developed a hybrid CNN+LSTM deep learning model and derived a robust indirect method to measure signal distortions, which we used to compile a comprehensive catalogue of materials for radar-based interaction. Among other findings, our experiments show that it is possible to estimate how different materials would affect gesture recognition performance of arbitrary classifiers by selecting just 3 reference materials. Our catalogue, software, models, data collection platform, and labeled datasets are publicly available.
Facilitating Asynchronous Collaboration in Scientific Workflow Composition Using Provenance
Authors: AbediniAla Mostafa , Roy Banani
Published: June 2022

Advances in scientific domains are led to an increase in the complexity of the experiments. To address this growing complexity, scientists from different domains require to work collaboratively. Scientific Workflow Management Systems (SWfMSs) are popular tools for data-intensive experiments. To the best of our knowledge, very few of the existing SWfMSs support collaboration, and it is not efficient in many cases. Researchers share a single version of the workflow in existing collaborative data analysis systems, which increases the chance of interference as the number of collaborators grows. Moreover, for effective collaboration, contributors require a clear view of the project's status, the information that existing SWfMSs do not provide. Another significant problem is most scientists are not capable of adding collaborative tools to existing SWfMSs, and they need software engineers to take on this responsibility. Even for software engineers such tasks could be challenging and time consuming. In this paper, we attempted to address this crucial issue in scientific workflow composition and doing so in a collaborative setting. Hence, we propose a tool to facilitate collaborative workflow composition. This tool provides branching and versioning, which are standard version control system features to allow multiple researchers to contribute to the project asynchronously. We also suggest some visualizations and a variety of reports to increase group awareness and help the scientists to realize the project's status and issues. As a proof of concept, we developed an API to capture the provenance data and provide collaborative tools. This API is developed as an example for software engineers to help them understand how to integrate collaborative tools into any SWfMS. We collect provenance information during workflow composition and then employ it to track workflow versions using the proposed collaborative tool. Prior to implementing the visualizations, we surveyed to discover how much the proposed visualizations could contribute to group awareness. Moreover, in the survey we investigated to what extent the proposed version control system could help address shortcomings in collaborative experiments. The survey participants provided us with valuable feedback. In future, we will use the survey responses to enhance the proposed version control system and visualizations.
SnappView, a Software Development Kit for Supporting End-user Mobile Interface Review
Authors: de Ryckel Xavier , Sluÿters Arthur , Vanderdonckt Jean
Published: June 2022

This paper presents SnappView, an open-source software development kit that facilitates end-user review of graphical user interfaces for mobile applications and streamlines their input into a continuous design life cycle. SnappView structures this user interface review process into four cumulative stages: (1) a developer creates a mobile application project with user interface code instrumented by only a few instructions governing SnappView and deploys the resulting application on an application store; (2) any tester, such as an end-user, a designer, a reviewer, while interacting with the instrumented user interface, shakes the mobile device to freeze and capture its screen and to provide insightful multimodal feedback such as textual comments, critics, suggestions, drawings by stroke gestures, voice or video records, with a level of importance; (3) the screenshot is captured with the application, browser, and status data and sent with the feedback to SnappView server; and (4) a designer then reviews collected and aggregated feedback data and passes them to the developer to address raised usability problems. Another cycle then initiates an iterative design. This paper presents the motivations and process for performing mobile application review based on SnappView. Based on this process, we deployed on the AppStore "WeTwo", a real-world mobile application to find various personal activities over a one-month period with 420 active users. This application served for a user experience evaluation conducted with N1=14 developers to reveal the advantages and shortcomings of the toolkit from a development point of view. The same application was also used in a usability evaluation conducted with N2=22 participants to reveal the advantages and shortcomings from an end-user viewpoint.
Memetic Algorithms for Spatial Partitioning Problems
Authors: Biswas Subhodip , Chen Fanglan , Chen Zhiqian , Lu Chang-Tien , Ramakrishnan Naren
Published: June 2022

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm calledswarm-based spatial memeticalgorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.
Modeling Thermal Behavior and Safety of Large Format All-Solid-State Lithium Metal Batteries under Thermal Ramp and Short Circuit Conditions
Authors: Johnson Nathan , Albertus Paul
Published: June 2022

Solid-state batteries are often considered to have superior safety compared to their liquid electrolyte counterparts, but further analysis is needed, especially because the higher specific energy of a solid-state lithium metal battery results in a higher potential temperature rise from the electrical energy in the cell. We construct a model of the temperature rise during a thermal ramp test and short circuit in a large-format solid-state LCO∣LLZO∣Li battery based on measurements of thermal runaway reaction thermochemistry upon heating. O2 released from the metal oxide cathode starting at ∼250 °C reacts with molten Li metal to form Li2O in an exothermic reaction that may drive the cell temperature to ∼1000 °C in our model, comparable to temperature rise from high-energy Li-ion cells. Transport of O2 or Li through the solid-state separator (e.g., through cracks), and the passivation of Li metal by solid products such as Li2O, are key determinants of the peak temperature. Our work demonstrates the critical importance of the management of molten Li and O2 gas within the cell, and the importance of future modeling and experimental work to quantify the rate of the 2Li+1/2O2→Li2O reaction, and others, within a large format solid-state battery.
Modeling of Porous Lithium Metal Electrodes: Turning the Li-Dendrite Problem Around
Authors: Bucci Giovanna , Swamy Tushar , Craig Carter W. , Behandish Morad
Published: June 2022

The properties of rechargeable lithium-ion batteries are determined by the electrochemical and kinetic properties of their constituent materials as well as by their underlying microstructure. Microstructural design can be leveraged to achieve a leap in performance and durability. Here we investigate a porous electrode structure, as a strategy to increase the surface area, and provide structural stability for Li-metal anodes. The porous architecture consists of a mixed electron/ion conductor that function as a scaffold for lithium metal deposition. A new finite element model was developed to simulate the large topological changes associated with Li plating/stripping. This model is used to predict the current density distribution as a function of material and structural properties. A dimensionless quantity that combines Li-ion conductivity, surface impedance and average pore size is shown to be a good indicator to predict the peak current density. Preventing current localization at the separator reduces the risk of cell shorting. The analyses show that the peak current scales as (hG)1/2, where h is the ratio between surface and bulk conductivity and G is the average pore size. Stability analyses suggest that the growth is morphologically stable, and that confining Li-plating into pores can enable high-energy density solid-state batteries.
Effect of Polyacrylonitrile Surface Coating on Electrochemical Performance of LiNi0.8Mn0.1Co0.1O2 in All Solid-State Batteries
Authors: Shrestha Sushovan , Carpenter Cole , Kim Jongbeon , Jeong Jejun , Lee Hye Jin , Kim Seul Cham , Hah Hoe Jin , Song Min-Sang , Oh Kyuhwan , Lee Se-Hee
Published: June 2022

Ni rich LiNi0.8Mn0.1Co0.1O2 (NMC811) is one of the most promising cathode materials for high energy all solid-state batteries. However, its poor ionic and electronic conductivity along with high interfacial instability with thiophosphate-based solid electrolytes result in severe capacity loss with cycling. In this paper, a polyacrylonitrile (PAN) coating is applied on the surface of NMC811 followed by a simple heat treatment in air. The PAN coating layer not only helps to improve the electronic conductivity of the cathode composite but also prevents the direct contact of NMC811 active material with the solid electrolyte, reducing the decomposition of the electrolyte and formation of passivating layers with low ionic conductivity. Compared to pristine NMC811, the electrochemical properties of PAN-NMC composites are significantly improved. When cycled at 0.1 C rate, PAN-NMC composite shows an initial capacity of 192.16 mAh g−1 and capacity retention of 68.5% over 100 cycles compared to the lower capacity of 173.06 mAh g−1 and retention 58.68% for the pristine NMC811. These results indicate that the PAN coating greatly improves the conductivity and cycling performance of NMC811.
A Survey on Deep Learning for Building Load Forecasting
Authors: Patsakos Ioannis , Vrochidou Eleni , Papakostas George A.
Published: June 2022

Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.
Application of Key Technologies of College English Online Teaching Platform in Deep Learning
Authors: Du Minghui
Published: June 2022

With the rapid development of information technology, the network has become a part of the daily life of current college students. The use of network tools to develop college English teaching platform is necessary and beneficial. Various versions of online English teaching platforms have been developed in China and used by many Chinese universities. Through research and analysis, according to the theory of communicative learning, this paper puts forward the design plan of English university teaching platform for Chinese colleges and universities. The article first summarizes the research origin and importance of the college English online teaching platform. It then outlines the current situation of college English online teaching platforms and provides a demonstration of a specific platform for a specific teaching network. This article discusses the research on deep learning using advanced technology on college English teaching platforms. Through questionnaires, group discussions, in-depth interviews, classroom observations, and other research methods, we can understand the current situation of school computer networks from a macro perspective. It integrates foreign language courses and the problems teachers and students encounter in the process. The results show that 56% of the students said that this method can promote their own learning, and 12% of the students think that there will be no good learning effect. For this new test method, 86% of the teachers think that it can achieve a certain learning effect, which is a good similar to an adaptive test method.
The Success of Ovulation Induction with Letrozole and Gonadotropins in Obese and Nonobese Women: A Study from a Tertiary Center
Authors: Gowri Vaidyanathan , Al-Amri Arwa , Almamari Thikra Mohammed Abdulrahman , Al Khaduri Maha , Jaju Sanjay
Published: June 2022

Background. Letrozole, an aromatase inhibitor, is suggested as a first-line drug for ovulation induction in women with polycystic ovary syndrome (PCOS) especially in obese women. Letrozole has also been used in women with unexplained infertility with similar rates of success to clomiphene. However, literature on letrozole and gonadotropins in obese and nonobese women is sparse. Hence, this study was done to assess the success of ovulation induction (OI) with letrozole plus follicle stimulating hormone (FSH) in obese (

BMI
≥
30

 kg/m2) and nonobese women (

BMI
<
30

 kg/m2). Methods. A retrospective descriptive cohort study was conducted involving 135 women who underwent OI with letrozole plus follicle stimulating hormone therapy and either timed intercourse or intrauterine insemination. The data was collected from the hospital information system, including the age, body mass index, the type of infertility, number of induction cycles with letrozole, number of gonadotropin injections, and the pregnancy occurrence following treatment. SPSS was used to analyze the data. Results. There were 135 women who used FSH injections along with letrozole. Of this, 28.5% obese women got pregnant compared to 29.2% nonobese women, but this did not attain statistical significance (

P
=
0.75

). About 70% of obese women and 57% on nonobese women had polycystic ovarian syndrome. The median number of FSH injections was six, and the interquartile range was 3 to 11. Conclusion. Of the 135 women undergoing letrozole and FSH, there was almost an equal probability of pregnancy in the obese group (

BMI
≥
30

 kg/m2) and nonobese women.
Reineckia carnea Alleviates the Production of Inflammatory Cytokines and MUC5AC in Rats with Chronic Obstructive Pulmonary Disease
Authors: Liu Wei , Li Jiang , Li Tongyao , Xie Youliang , Luo Caihong
Published: June 2022

Background. Reineckia carnea (RC), a perennial evergreen herb which belongs to Reineckia Kunth (Liliaceae), can be used for clearing the lungs and relieving cough, reducing phlegm and anti-inflammatory effects. Moreover, chronic obstructive pulmonary disease (COPD) is characterized by airway and lung inflammation and increased secretion of airway mucus. Therefore, RC has the potential to treat COPD. Methods. NR8383 cells were cultured and treated with various concentrations of RC (100 mg/mL, 10 mg/mL, 1 mg/mL, 100 μg/mL, 10 μg/mL, 1 μg/mL, 100 ng/mL, and 10 ng/mL). Cell viability and levels of interleukin (IL)-1β, cyclooxygenase-2 (COX-2), and prostaglandin E2 (PGE2) in the cell culture supernatant or rat serum were analyzed using CCK-8 and enzyme-linked immunosorbent assay (ELISA), respectively. Sprague Dawley rats were assigned to mock, COPD model, RC (0.67 g/kg, 1.35 g/kg, and 2.7 g/kg), and ambroxol (5.4 mg/kg) groups. Western blot and quantitative polymerase chain reaction (qPCR) analyses were used to evaluate the protein and mRNA expression levels of mucin 5AC (MUC5AC) and Toll-like receptor 4 (TLR4). Results. The results showed that Reineckia carnea (RC) extract (RCE) inhibited the proliferation of NR8383 cells and suppressed the production of IL-1β, PGE2, and COX-2 in NR8383 cells. Moreover, RCE decreased the levels of IL-1β, PGE2, and COX-2 in the serum of rats with COPD and alleviated the expression of TLR4 and MUC5AC induced by COPD in rat lung tissue. Conclusion. RCE alleviated COPD by inhibiting the expression of COPD-induced inflammatory cytokines and MUC5AC in rats.
Histopathological, Immunohistochemical and Biochemical Studies of Murine Hepatosplenic Tissues Affected by Chronic Toxoplasmosis
Authors: Yahia Samah Hassan , Etewa Samia Elsayed , Saleh Nesreen Saeed , Mohammad Samira Metwally , Aboulfotouh Nora Ibrahim , Kandil Ahmad Mansour , Sarhan Mohamed Hassan
Published: June 2022

Toxoplasmosis is a serious health problem in humans and animals resulting from obligatory intracellular invasion of reticuloendothelial tissue by Toxoplasma gondii. The profound pathologic effect of toxoplasmosis is confined to nervous tissue, but many other organs, including the liver and spleen, are insulted. Many molecules like caspase-3, CD3, and CD138 are implicated in the tissue immune response in a trial to alleviate hazardous toxoplasmosis impact. This study aimed to investigate the effect of chronic toxoplasmosis on the liver and spleen tissues of mice using biochemical and histopathological techniques and to detect the activity and level of expression of caspase-3, CD3, and CD138 in these tissues using immunohistochemical labeling. Compared with normal control, altered normal histological features accompanied by inflammatory reaction were recorded in hepatosplenic reticuloendothelial tissues in chronically infected mice. The biochemical profile of the liver has been changed in the form of increased liver enzymes, and oxidative stress has been evidenced by elevated nitric oxide (NO) concentration in liver homogenate. The levels of caspase3, CD3, and CD138 were markedly expressed in the liver and spleen of infected mice. Our findings revealed the persistent effect of latent toxoplasmosis on the host’s histological architecture, metabolic, and immunological profile, creating a continued challenging host-parasite relationship.
Evaluation of the Effects of Tunnel Lighting Environment on Energy Consumption and Drivers’ Reaction Time
Authors: Zhao Long , Hu Song , Wang Dongwei , Guo Yujin , Fu Chaoliang
Published: June 2022

The 24-hour artificial lighting of the tunnels consumes a large amount of electricity, which means increased environmental damage and carbon footprint for China, which is dominated by thermal power. In order to alleviate the above problems, the effects of the tunnel lighting environment on energy consumption and drivers’ reaction time were evaluated by the finite element method and laboratory test to optimize the lighting environments. In this study, based on the Wanxichong extra-long tunnel in Yunnan Province, a 3D tunnel model using DIALux software is established first. Then, the effects of climate conditions, layout method of the lamps, and layout height and reflection of the sidewall material on the average luminance and uniformity on the pavement, the efficiency ratio per unit length, and the actual energy efficiency value were investigated. Finally, the indoor lighting environment simulation experiments were conducted to examine the effect of the type of sidewall materials, contrast, background luminance, and eccentricity on drivers’ reaction time. Simulation results show that, under the same climate condition and layout method of lamps, the average luminance and luminance uniformity on the road surface are positively correlated with the reflectivity of the sidewall material. On the clear days, the efficiency ratio per unit length corresponding to the 2 m high sidewall material is the largest, while the efficiency ratio per unit length on cloudy days and mixed days corresponding to the 2.5 m high sidewall material is the largest. In addition, the staggered layout of the lamps at the entrance of the tunnel consumes less power, which is more conducive to energy saving. Experimental results show that, with the increase of background luminance, the reaction time of drivers decreases. Meanwhile, applying energy-storage reflective coating on the sidewalls of the tunnel has a better visual performance than using light yellow ceramic tiles and cement mortar on the sidewalls. This conclusion would provide a lower energy consumption lighting design method and higher drive comfortable for highway tunnels.
Music Score Recognition and Composition Application Based on Deep Learning
Authors: Liang Mingheng
Published: June 2022

Optical score recognition is a critical technology for retrieving music information, and note recognition is a critical component of score recognition. This article evaluates and discusses the current state of research on important technologies for score recognition. To address the issues of low note recognition accuracy and intricate steps in the present music score image, a deep learning-based music score recognition model is proposed. The model employs a deep network, accepts the entire score image as input, and outputs the note's time value and pitch directly. Experiments on music scores demonstrate that the method described in this study has a high note identification accuracy of 0.95 for time values and 0.97 for pitch, which is suitable for composition.
Gaussian Naïve Bayes Algorithm: A Reliable Technique Involved in the Assortment of the Segregation in Cancer
Authors: Anand M. Vijay , KiranBala B. , Srividhya S. R. , C. Kavitha , Younus Mohammed , Rahman Md Habibur
Published: June 2022

Cancer is a disease caused by uncontrollable cell growth. The disease is a constant subject of concern due to unavailability of treatment at a severe level. Patients who have suffered from the disease have the chance of getting saved if this fatal illness is identified in the beginning stage. The survival chance will be very low if it is detected in the final stage of cancer. As the patients could not survive in their last stage, to cure their disease, an early diagnosis is a key issue and is vital. For the classification of cancer, Gaussian Naïve Bayes is implemented in this work. By exerting it on two datasets, the algorithm is tested, in which the Wisconsin Breast Cancer Dataset (WBCD) is considered as earliest one and the next one is the Lung Cancer Dataset. The assessment result of the suggested algorithm attained 90% accuracy in the prediction of lung cancer, and in predicting breast cancer, the accuracy is 98%.
Occupational Dysfunction as a Mediator between Recovery Process and Difficulties in Daily Life in Severe and Persistent Mental Illness: A Bayesian Structural Equation Modeling Approach
Authors: Watanabe Aki , Kawaguchi Takayuki , Sakimoto Mai , Oikawa Yuya , Furuya Keiichiro , Matsuoka Taichi
Published: June 2022

Background. This study is aimed at verifying a hypothetical model of the structural relationship between the recovery process and difficulties in daily life mediated by occupational dysfunction in severe and persistent mental illness (SPMI). Methods. Community-dwelling participants with SPMI were enrolled in this multicenter cross-sectional study. The Recovery Assessment Scale (RAS), the World Health Organization Disability Assessment Schedule second edition (WHODAS 2.0), and the Classification and Assessment of Occupational Dysfunction (CAOD) were used for assessment. Confirmatory factor analysis, multiple regression analysis, and Bayesian structural equation modelling (BSEM) were determined to analyze the hypothesized model. If the mediation model was significant, the path coefficient from difficulty in daily life to recovery and the multiplication of the path coefficients mediated by occupational dysfunction were considered as each the direct effect and the indirect effect. The goodness of fit in the model was determined by the posterior predictive

P

value (PPP). Each path coefficient was validated with median and 95% confidence interval (CI). Results. The participants comprised 98 individuals with SPMI. The factor structures of RAS, WHODAS 2.0, and CAOD were confirmed by confirmatory factor analysis to be similar to those of their original studies. Multiple regression analysis showed that the independent variables of RAS were WHODAS 2.0 and CAOD, and that of CAOD was WHODAS 2.0. The goodness of fit of the model in the BSEM was satisfactory with a

PPP
=
0.27

. The standardized path coefficients were, respectively, significant at −0.372 (95% CI: −0.586, −0.141) from “difficulty in daily life” to “recovery” as the direct effect and at −0.322 (95% CI: −0.477, −0.171) mediated by “occupational dysfunction” as the indirect effect. Conclusions. An approach for reducing not only difficulty in daily life but also occupational dysfunction may be an additional strategy of person-centered, recovery-oriented practice in SPMI.
Theoretical Research on the Movement Law of Water Cone Behavior in Heavy Oil Reservoirs with Bottom Water
Authors: Wang Kai , Tang Chenyang , Zhang Tianyou , Gao Yufei , Zou Jingyun , Jiang Yan , Sun Kuo , Chen Yan
Published: June 2022

Aiming at the unclear reorganization of a water cone shape, its sweep range, and water saturation distribution of horizontal well in heavy oil reservoirs with bottom water, a mathematical method was proposed in this paper to establish a microtube model and physical model with the starting pressure gradient (SPG) to study the water cone behavior. The results showed that the different water-cut stage, mobility, depth from oil-water interface (DOWI), and liquid production strength have an obvious impact on water cone behavior. Moreover, we found an interesting phenomenon on an extreme point in water saturation derivation among water cone, which could be a reason for further adjustment. Finally, we confirmed the calculation results with physical simulation, which is highly consistent. The new method proposed in this paper was significant in the water cone behavior study and has a broad application in heavy oil reservoir development in the future.
Promotion Strategy of Ideological and Political Education Management in Colleges and Universities Using Clustering Techniques
Authors: Cao Fei , Huang Zhigang , Weng Yunxuan
Published: June 2022

Computer-based technologies have changed our daily lives, such as the way we live, interact, learn, and play. Data collected through these technologies are now being used to support the second round of revolution in all of these areas. Data mining (DM) research has progressed rapidly in recent years, and the information design of educational management systems has also progressed significantly. Using big data technologies, several educational management systems have collected a great deal of information. However, the DM implementation in educational management systems is still in its initial stages. The use of DM techniques in educational management systems is theoretically important and practically useful. With the advancement of information technology (IT) in the field of college education, the present ideological and political theory courses in colleges and universities have gradually developed into information-based teaching, which has increased teaching efficiency and quality. This paper investigates the “work assessment quantitative table” of ideological and political education (IAPE) administration in universities using data mining and clustering technology techniques. In addition, this study utilizes the k-means clustering algorithms to analyze the data from the “work assessment quantitative table” of IAPE of university administration, which successfully overcomes the limitations and inadequacies of standard analysis approaches.
Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network
Authors: Mishra Suchi , Dubey Rahul , Swami Preety D. , Jain Alok
Published: June 2022

A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets.
Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews
Authors: Gondhi Naveen Kumar , Chaahat  , Sharma Eishita , Alharbi Amal H. , Verma Rohit , Shah Mohd Asif
Published: June 2022

In today’s modern era, e-commerce is making headway through the process of bringing goods within everyone’s grasp. Consumers are not even required to step out of the comfort of their homes for buying things, which makes it very convenient for them. Moreover, there is a wide variety of brands to choose from. Since more customers depend on online shopping platforms these days, the value of ratings is also growing. To buy these products, people rely solely on the reviews that are being provided about the products. To analyze these reviews, sentiment analysis needs to be performed, which can prove useful for both the buyers and the manufacturer. This paper describes the process of sentiment analysis and its requirements. In this paper, Amazon Review dataset 2018 has been used for carrying out our research and Long Short-Term Memory (LSTM) has been combined with word2vec representation, resulting in improving the overall performance. A gating mechanism was used by LSTM during the training process. The proposed LSTM model was evaluated on four performance measures: accuracy, precision, recall, and F1 score, and achieved overall higher results when compared with other baseline models.
Design of a Smart Elbow Brace as a Home-Based Rehabilitation Device
Authors: Said Ramadhan Rashid , Yong Wang Quan , Bin Heyat Md Belal , Ali Liaqat , Qiang Song , Ali Arif , Rauf Hafiz Tayyab , Wu Zhe
Published: June 2022

Poststroke and traumatic elbow injuries are the most common cause of elbow stiffness, which results in loss of functional range of motion (ROM). Various studies support early mobilization of the elbow joint after injury or after surgery to reduce risks of elbow stiffness development. After hospitalization, patients are required to follow a long-term rehabilitation program during home recovery. Still, most patients do not adhere to their clinical therapy schedule due to either rehabilitation cost, social obligations, negligence, or lack of inspiration. Moreover, the numbers of therapists and assessment equipment are insufficient. This study introduces a smart elbow brace (SEB) as a home-based rehabilitation device that reduces regular in-patient rehabilitation costs and therapist workload and motivates patients to comply with the rehabilitation program that enhances the achievement of rehabilitation goals. Our device has two active degrees of freedom (2-DoF) that allow extension, flexion, pronation, and supination elbow motions. An extra sliding joint between forearm and wrist is added, which helps dump forces concentration at the elbow joint during extension-flexion movement. Mechanical design requirements, motion-tracking systems, and serious game development are described. The feasibility of a proposed SEB device is tested with five healthy subjects playing developed serious games with the device. The results show that subjects can attain maximum and minimum angles of flexion-extension and pronation-supination motion designed for elbow stiffness rehabilitation. The SEB device will be beneficial and be used at home as a complementary tool to support elbow stiffness rehabilitation during long-term home recovery.
Tai Chi for Overweight/Obese Adolescents and Young Women with Polycystic Ovary Syndrome: A Randomized Controlled Pilot Trial
Authors: Li Yan , Peng Changle , Zhang Meiwei , Xie Liangzhen , Gao Jinjin , Wang Yingji , Gao Yuanhe , Hou Lihui
Published: June 2022

Background. Exercise is one of the recommended interventions for polycystic ovary syndrome (PCOS), and current evidence has shown that Tai chi may have favorable effects. The objective of this randomized controlled pilot trial was to study the feasibility and potential effects of Tai chi for overweight/obese adolescents and young women with PCOS, so a future definitive randomized controlled trial (RCT) can be well designed and implemented. Materials and Methods. This study recruited 50 patients who were randomly assigned to two groups (Tai chi and self-monitored exercise) at a ratio of 3 : 2. The intervention lasted for 3 months, and the feasibility and effectiveness outcomes were measured. Results. A total of 42 patients completed the study, including 24 in the Tai chi group and 18 in the control group. Compared with the self-monitored exercise group, there was a significantly decreased body mass index (BMI) in the Tai chi group adjusted for baseline BMI. The testosterone level and lipid profile were also decreased compared to controls; the same tendency was also observed for the homeostasis model assessment of insulin resistance (HOMA-IR), but the difference did not achieve statistical significance. Twenty-four (out of 30, 80%) patients in the Tai chi group and 18 (out of 20, 90%) patients in the self-monitored exercise group completed the data collection. A total of 36 exercise sessions were held in both groups. Patients in the Tai chi group took a mean of 34.0 ± 2.21 classes (93.06%), and those in the self-monitored exercise group engaged in 32 ± 3.06 exercise sessions (88.27%) out of the 36 required exercise sessions. Conclusions. The present pilot study was feasible to deliver; there was a decrease in BMI, testosterone level, and lipid profile for PCOS patients in the Tai chi group at 3 months. In a future definitive trial, lower recruitment rate and outcome measurements lead to poor patient acceptance such as the 5-time point oral glucose tolerance test need to be considered and one fixed type of aerobic exercise and supervision from the investigator for the control group are also needed. Trial registration: ClinicalTrials.gov, NCT02608554.
Relationship between Activities of Daily Living of Home-Based Rehabilitation Users and Caregiver Burden-Induced Depression: A Retrospective Study
Authors: Ohashi Takuro , Fujita Takaaki , Togashi Yui , Ohashi Yuji , Kisara Yuta , Jinbo Ryohei , Kasahara Ryuichi , Yamamoto Yuichi
Published: June 2022

This study was aimed at determining the cutoff values of activities of daily living (ADL) and the combination of related factors associated with high caregiver burden that induces depression among caregivers. The study participants included 50 pairs of home-based rehabilitation users and their primary caregivers. They were classified into two groups: high-burden and low-burden groups according to the short version of the Japanese version of the Zarit Caregiver Burden Interview score of ≥13 or ≤12, respectively. The cutoff values of ADL and the combination of related factors associated with high caregiver burden were examined using the receiver operating characteristic curve and decision tree analyses. The cutoff value associated with high caregiver burden was 5 points for the controlling bladder item of the Barthel index (BI) (sensitivity: 90%, specificity: 70%). Regarding the decision tree, the controlling bladder item of BI (≤5 or 10 points) was selected as the first layer and the recipient’s age (≤78 or ≥79 years) as the second layer. High caregiver burden was identified in 85.7% of the caregivers in whom the score of controlling bladder of BI was ≤5 points and the patient was aged ≤78 years. A score of ≤5 points for the controlling bladder item of BI along with young recipient age was associated with high caregiver burden that induces depression among caregivers. This approach is useful to identify caregivers with high caregiver burden who are at risk for depression.
The Impact of Catchment Land Use Land Cover Changes on Lake Dandi, Ethiopia
Authors: Dega Mulugeta B. , Emana Alemayehu N. , Feda Habtamu A.
Published: June 2022

Highland freshwater lakes are currently threatened with catchment land use land cover changes particularly in developing countries like Ethiopia despite their wide range of valuable ecosystem services. This study was conducted to assess trends of catchment land use land cover change and associated impacts on a highland Lake, Dandi over three decades from 1990 to 2020 years period based on GIS (ArcMap 10.4.1) and remote sensing (ERDAS Imagine 14) software as well as questioner survey, key informant interviews, and field observation. The study covered 3,794 ha with five major land use land covers, namely mixed agriculture/settlement, bush‐/shrubland, grassland, bare land, and water body (Lake Dandi). The assessment revealed that farmland/residential area increased by 593 ha (35.34%), while bare land, grassland, shrubland, and Lake Dandi decreased by 290 ha (26.12%), 218 ha (12.15%), 57 ha (6.85%), and 28 ha (19.53%), respectively. Responses also indicated increased farmland/settlement area (66.66%) and declined areas of bare land (84.63%), bush‐/shrubland (84.86%), grassland (96.96%), and Lake Dandi (61.05%). Furthermore, responses indicated expanding agricultural land use (48%), population growth (38%), climate change (32%), overgrazing (30%), and poverty (28%) as major factors currently threatening the lake and its ecosystem services. Field observation also revealed expansion of agricultural land use in the catchment of Lake Dandi including in steeper slopes and hillsides that might exacerbate on‐site soil erosion as well as lake sedimentation and toxic contamination. Thus, results indicated deterioration in the storage capacity and water quality of Lake Dandi due to catchment land use land cover change that might in turn adversely affect its ecosystem services and the resident biota suggesting urgent intervention.
Dynamics of Unsteady Flow of Chemically Reactive Upper-Convected Maxwell Fluid with Temperature-Dependent Viscosity: Keller Box Analysis
Authors: Ullah Zia , Yasmin Samina , Younis Jihad , Abdullah Ameer , Abbas Shahid , Shah Asif
Published: June 2022

The heat and mass transfer characteristics of unsteady flow of incompressible chemically reactive upper-convected Maxwell fluid along a stretching surface in the presence of temperature-dependent viscosity has been studied. The theoretical analysis on heat and mass transfer over a stretching sheet is investigated for numerical analysis. With the use of stream function formulation, the governing boundary layer equations of momentum, energy, and concentration are reduced to a set of linked ordinary differential equations. The nonlinear ordinary differential equations are then solved numerically by using the Keller Box method. The physical behavior of governing parameters on velocity, temperature, and concentration profiles and the local skin friction coefficient and heat and mass transfer rates are graphed and tabulated. The physical impact of Maxwell parameter β, unsteadiness parameter M, Schmidt number Sc, Prandtl number Pr, reaction rate parameter γ, and variable viscosity parameter

ε

on the heat and mass transfer has been examined along the stretching surface numerically. The novelty of the present work is to examine the importance of destructive reaction and contractive reaction on the dynamics of upper-convected Maxwell fluid flow in the presence of temperature-dependent viscosity effects. It is observed an interesting behavior of temperature distribution and concentration profile is noted for lower value of viscosity parameter

ε

in the presence of chemical reaction. It is also found that skin friction and the rate of heat transfer are decreased by increasing the variable viscosity parameter

ε

.
An Insight into Anticancer Effect of Propolis and Its Constituents: A Review of Molecular Mechanisms
Authors: Elumalai Perumal , Muninathan Natarajan , Megalatha Sadhasivan T. , Suresh Arumugam , Kumar Kalimuthu Senthil , Jhansi Nathan , Kalaivani Kuppuswamy , Krishnamoorthy Gunasekaran
Published: June 2022

Propolis is a natural compound collected by honeybees from different parts of plants. Honeybees produce a sticky component besides honey by mixing the tree resin and other botanical sources with saliva called propolis or bee glue. Propolis was traditionally used as a wound healing substance, cosmetic, medicine, and many other conditions. Till now, there is no definite curable treatment for most cancers and chemotherapeutic drugs and drugs used for targeted therapies have serious side effects. According to a recent research, natural products are becoming increasingly essential in cancer prevention. Natural products are a great source of potential therapeutic agents, especially in the treatment of cancer. Previous studies have reported that the presence of caffeic acid phenethyl ester (CAPE), artepillin C, and chrysin is responsible for the anticancer potential of propolis. Most of the previous studies suggested that propolis and its active compounds inhibit cancer progression by targeting multiple signaling pathways including phosphoinositide 3-kinases (PI3K)/Akt and mitogen-activated protein kinase (MAPK) signaling molecules, and induce cell cycle arrest. Induction of apoptosis by propolis is mediated through extrinsic and intrinsic apoptotic pathways. The aim of this review is to highlight and summarize the molecular targets and anticancer potential of propolis and its active compounds on cell survival, proliferation, metastasis, and apoptosis in cancer cells.
Influences of Magnetic Resonance Imaging Superresolution Algorithm-Based Transition Care on Prognosis of Children with Severe Viral Encephalitis
Authors: Wang Yan , Zhang Yan , Su Ling
Published: June 2022

Objective. Its goal was to see how convolutional neural network- (CNN-) based superresolution (SR) technology magnetic resonance imaging- (MRI-) assisted transition care (TC) affected the prognosis of children with severe viral encephalitis (SVE) and how effective it was. Methods. 90 SVE children were selected as the research objects and divided into control group (39 cases receiving conventional nursing intervention) and observation group (51 cases performed with conventional nursing intervention and TC intervention) according to their nursing purpose. Based on SR-CNN-optimized MRI images, diagnosis was implemented. Life treatment and sequelae in two groups were compared. Results. After the processing by CNN algorithm-based SR, peak signal to noise ratio (PSNR) (40.08 dB) and structural similarity (SSIM) (0.98) of MRI images were both higher than those of fully connected neural network (FNN) (38.01 dB, 0.93) and recurrent neural network (RNN) (37.21 dB, 0.93) algorithms. Diagnostic sensitivity (95.34%), specificity (75%), and accuracy (94.44%) of MRI images were obviously superior to those of conventional MRI (81.40%, 50%, and 80%). PedsQLTM 4.0 scores of the observation group 1 to 3 months after discharge were all higher than those of the control group (

54.55
±
5.76

vs.

52.32
±
5.12

and

66.32
±
8.89

vs.

55.02
±
5.87

). Sequela incidence in the observation group (13.73%) was apparently lower than that in the control group (43.59%) (

P
<
0.05

). Conclusion. (1) SR-CNN algorithm could increase the definition and diagnostic ability of MRI images. (2) TC could reduce sequelae incidence among SVE children and improve their quality of life (QOL).