Reviewerly/exHarmony huggingface dataset:
EPH177 Assessing Student Pharmacists’ Knowledge and Perceptions of Electronic Nicotine Delivery Systems (ENDS) – A Cross Sectional Study at One College of Pharmacy

Use of electronic nicotine delivery systems (ENDS) has increased in recent years. However, healthcare professionals’ knowledge and perceptions of ENDS are not well known. This study assessed the knowledge and perceptions of ENDS among student pharmacists. A 37-item cross sectional electronic questionnaire was administered to all third- and fourth-year student pharmacists enrolled at one US college of pharmacy (N=256). The questionnaire consisted of 20 multiple-choice knowledge items, 15 Likert-type perception statements (response options: disagree, somewhat disagree, somewhat agree, agree), and two descriptive items. Data were collected over six weeks in March/April 2022. Data were analyzed by assigning one point for each correct knowledge item, and then summing to create a total knowledge score for each person. Differences in the proportion of students who correctly answered each knowledge item were compared between year groups using a chi-square test, while differences between year groups for total knowledge score were compared using a two-sample t-test. Perception statements were scored from zero to three (0=disagree, 1=somewhat disagree, 2=somewhat agree, 3=agree). Differences between groups for each perception statement were compared using a Mann-Whitney U test. The a priori alpha level was 0.05. Fifty students (third year=30, fourth year=20; female=60%) completed the survey (19.5% response rate). Student level of ENDS knowledge varied depending on the item. There was no statistically significant difference between third- and fourth-years students for each knowledge item (p>0.05), for total mean knowledge scores (third year=12.5±3.3, fourth year=11.2±3.1, p=0.1780), or for median perception scores (p>0.05). The findings from this survey of student pharmacists at one US college of pharmacy indicate a need for more education around ENDS for student pharmacists so that they are better able to counsel patients on their use. Further research among a larger sample of student pharmacists is required to improve the external validity of these findings.
Students’ Attitude Towards Ruanguji Mobile-Based Assessment: An Explanatory Case Study in Tangerang, Indonesia

This qualitative study aims to unveil the junior high school students’ attitude towards Ruanguji mobile-based assessment. It uses an explanatory case study design engaging 225 students in Tangerang, Indonesia as the respondents. To analyze the data got by the questionnaire, the study uses the multi-component model of attitude and one-dimensional perspective. The study reveals that their attitude towards Ruanguji comprises the cognitive, affective, and behavioral components with different degree. For the students, Ruanguji is a supplementary media offering various tests and assignments to help them face the national examination. With the application, they feel convenient to prepare themselves for the examination. If they have chances, they will use it to explore testing materials and assignments. Their attitude is identified as a positive one that impedes the existence of the negative one. Its positivity degree is determined by internal factors, like their perception and emotion; and external factors, like the application’s user-friendliness. In addition, their positive attitude towards the application also contributes to their success in their school’s summative tests and national examination. The result of the study leads to the conclusion that a positive attitude towards the mobile assessment can be a determinant factor of their success in the study.
Research on Automatic Replacement Tool of Linear Insulator for Live Working in distribution network

Abstract Aiming at the problems of high risk, high labor intensity, and complex operation processes in the existing manual replacement of insulators, an automatic replacement tool for linear insulators was developed. The device can be used for the quick replacement of linear insulators under live working conditions. Firstly, a mechanical structure with high positioning accuracy and strong stability was designed according to the shape of the linear insulator and the simulation of the artificial live working process. Secondly, the static simulation of the clamping mechanism of the device was carried out. The results show that the strength of the clamping mechanism is high, and the maximum elastic deformation is 4.952 mm, which meets the requirement of the structure design. Finally, the relevant simulation test of the automatic replacement tool was carried out at the operation site. The experimental results show that the designed mechanical structure is light and convenient, which can quickly complete the automatic disassembly, installation, and fastening of linear insulators. The designed mechanical structure also effectively improves the efficiency of maintenance operations and has high promotion and application value.
The nine-year <i>Get it Now</i> experiment: Using copyright clearance center’s article-delivery service long term

Since 2012, Cullom-Davis Library at Bradley University has used Get It Now, an article delivery service from Copyright Clearance Center. It was initially added to fill a hole in the collection created by a 20% reduction in the library budget. In the succeeding 9 years, the service continued to be offered. However, changes were made along the way to keep the costs down. This article discusses these changes, the usage of this service long-term, and its sustainability over time. This is the first study to look at Get It Now over such a long period of time.
Comparison of Histogram Features and Co-occurrence Matrix in Identification of Lung Cancer X-Ray Images with Naive-Bayes Method

Abstract Many studies have been carried out on the identification of objects in the image. One feature often used in object identification is the histogram feature of digital images. The next development is the use of a digital image co-occurrence matrix as an identifier. This study aims to compare the use of two object identifiers in images that are not derived from visible light but are images derived from X-rays. In this study, the X-ray image is an X-ray image of lung cancer, and the object identification method used is the Naive Bayes method. The number of lung X-ray images used was 100 images. The master image is divided into 60 images for training and 40 images for testing. This study's results indicate that using a digital image co-occurrence matrix as an identifier gives better results than the histogram feature. This study concludes that the comparison of histogram features and co-occurrence matrix in X-ray images shows the same results as in visible light images.
Spatial search by continuous-time quantum walks on renormalized Internet networks

We study spatial search with continuous-time quantum walks on real-world complex networks. We use smaller replicas of the Internet network obtained with a recent geometric renormalization method introduced by Garc\'{\i}a-P\'erez et al., Nat. Phys. 14, 583 (2018). This allows us to infer for the first time the behavior of a quantum spatial search algorithm on a real-world complex network. By simulating numerically the dynamics and optimizing the coupling parameter, we study the optimality of the algorithm and its scaling with the size of the network, showing that on average it is considerably better than the classical scaling $\mathcal{O}(N)$, but it does not reach the ideal quadratic speedup $\mathcal{O}(\sqrt{N})$ that can be achieved, e.g. in complete graphs. However, the performance of the search algorithm strongly depends on the degree of the nodes and, in fact, the scaling is found to be very close to optimal when we consider the nodes below the $99\mathrm{th}$ percentile ordered according to the degree.
Dynamic Load Regulation of Robots with Multi-sensor Fusion

Abstract Multi-sensor fusion technology has been extensively deployed for the autonomous navigation and control of robots. Theoretically, by increasing the number of sensors, more effective data on the surrounding environment and obstacle information can be obtained. However, in practical applications, a massive number of sensors will not only increase the cost of using sensors in the system but also add to the difficulty and cost of data processing, leading to unnecessary resource consumption. To solve this problem, first, the robot navigation area is divided according to the importance of the navigation area and the characteristics of sensor parameters. The navigation non-core area adopts the single lidar sensor, while the core area adopts the multi-sensor fusion of “lidar +” to reduce the number of sensors used in the fusion and improve the performance of multi-sensor fusion after the division. Then, in consideration of the higher requirement for computing capacity of the device imposed by deep learning, the multi-sensor fusion algorithm is introduced into the dynamic load regulation mechanism to solve the problem from the algorithm level so that the outperformance of deep learning in feature extraction and other aspects can be utilized to reduce the performance requirements of the device and the overall power consumption of the system. Finally, by building the ROS experimental platform, it is verified that the robot can use autonomous navigation in complex and diversified home scenes.
Security Issues in Cloud Computing

Cloud computing has become one of the most discussed and interesting topics in the IT industry today. It adds a lot of capabilities and provides the extra edge to INFORMATION TECHNOLOGY industry. Over the last few years cloud computing has grown considerably in information technology. Cloud model of computing as a resource has changed the landscape of computing as it promises of increased greater reliability massive scalability, and decreased costs have attracted businesses and individuals alike. As more and more information of individuals and companies are placed in the cloud, there is a growing concern about the safety of information stored on the same. There are lot of players around the globe in area of cloud computing, Amazon Web Services (AWS), Microsoft Azure are name of few. Despite the hype about the cloud, customers are reluctant to deploy their business in the cloud. Security issues is one of the biggest concerns that has been affecting the growth of cloud computing. It adds complications with data privacy and data protection continues to affect the market. Users need to understand the risk of data breaches in the cloud environment. The paper highlights issues related to cloud computing. Keywords – Cloud Computing, Security Issue, Information Technology
Efficient Transformer based Sentiment Classification Models

Recently, transformer models have gained significance as a state-of-the art technique for sentiment prediction based on text. Attention mechanism of transformer model speeds up the training process by allowing modelling of dependencies without regard to their distance in the input or output sequences. There are two types of transformer models – transformer base models and transformer large models. Since the implementation of large transformer models need better hardware and more training time, we propose new simpler models or weak learners with lower training time for sentiment classification in this work. These models enhance the speed of performance without compromising the classification accuracy. The proposed Efficient Transformer-based Sentiment Classification (ETSC) models are built by setting configuration of large models as minimum, shuffling dataset randomly and experimenting with various percentages of training data. Early stopping and smaller batch size in training techniques improve the accuracy of the proposed model. The proposed models exhibit promising performance in comparison with existing transformer-based sentiment classification models in terms of speed and accuracy.
SA81 Feasibility and Challenges of Virtual Time Trade-Off Interviews: The Case of the EQ-5D-3L Valuation Study in Jordan

Valuation studies are usually implemented through face-to-face interviews. However, the COVID-19 pandemic has its own impact on conducting valuation studies around the world. The Jordanian EQ-5D-3L valuation study team conducted Time Trade-Off (TTO) tasks virtually using video conferencing. This study aims to describe and assess the feasibility and challenges of conducting TTO valuation studies virtually in Jordan. This is a descriptive study of the implementation of the EQ-5D-3L valuation study in Jordan during the period: October 2021 to March 2022. Feasibility was tested using 4 indicators: interviewers’ compliance with protocol, availability of computers, internet accessibility, accessing elderly participants. Challenges were described based on reasons for dropout and interviewers’ feedback throughout the study implementation. Data were collected from 301 participants covering the 12 governorates of Jordan. The sample was representative to the Jordanian national population in term of region, age, and gender. Interviewers were compliant with the protocol with only one flagged interview out of 301 conducted interviews. 39% of the approached population could not participate due to not having computers, 11% could not participate due to not having access to internet, while 12% could not participate because they do not have either internet access or a computer. The quota for elderly (age above 65) participants was achieved with only 2% difference from general population percentage. The main study challenges were no response by participants after providing initial consenting and no show after scheduling the interview. Virtual Time Trade-Off interviews for valuation studies can be a useful mode of administration option in upper middle-income countries with access to internet and availability of computers. However, it is recommended to test the feasibility of using a hybrid method that includes both virtual and face to face interviews.
Exploring Monological and Dialogical Phases in Naturally Occurring Explanations

Abstract Recent approaches to Explainable AI (XAI) promise to satisfy diverse user expectations by allowing them to steer the interaction in order to elicit content relevant to them. However, little is known about how and to what extent the explainee takes part actively in the process of explaining. To tackle this empirical gap, we exploratively examined naturally occurring everyday explanations in doctor–patient interactions ( N = 11). Following the social design of XAI, we view explanations as emerging in interactions: first, we identified the verbal behavior of both the explainer and the explainee in the sequential context, which we could assign to phases that were either monological or dialogical; second, we investigated in particular who was responsible for the initiation of the different phases. Finally, we took a closer look at the global conversational structure of explanations by applying a context-sensitive model of organizational jobs, thus adding a third layer of analysis. Results show that in our small sample of conversational explanations, both monological and dialogical phases varied in their length, timing of occurrence (at the early or later stages of the interaction) and their initiation (by the explainer or the explainee). They alternated several times in the course of the interaction. However, we also found some patterns suggesting that all interactions started with a monological phase initiated by the explainer. Both conversational partners contributed to the core organizational job that constitutes an explanation. We interpret the results as an indication for naturally occurring everyday explanations in doctor–patient interactions to be co-constructed on three levels of linguistic description: (1) by switching back and forth between monological to dialogical phases that (2) can be initiated by both partners and (3) by the mutual accomplishment and thus responsibility for an explanation’s core job that is crucial for the success of the explanation. Because of the explorative nature of our study, these results need to be investigated (a) with a larger sample and (b) in other contexts. However, our results suggest that future designs of artificial explainable systems should design the explanatory dialogue in such a way that it includes monological and dialogical phases that can be initiated not only by the explainer but also by the explainee, as both contribute to the core job of explicating procedural, clausal, or conceptual relations in explanations.
DESAIN E-LEARNING PADA TAYANGAN PROGRAM BELAJAR DARI RUMAH MATA PELAJARAN BAHASA INDONESIA UNTUK SD DI TVRI

This research is to describe the e-learning design of the Home Learning Program in Indonesian for elementary schools aired on TVRI. The main focus into five sub-focuses, namely on learning indicators, a series of lessons, learning methods, delivery strategies, and evaluation strategies. This research is descriptive qualitative research. Data were collected using observation sheets. Learning indicators are composed of C1 and C2 competencies adjusted to the level of student intellectual development. The learning series was arranged based on a prerequisite method or prerequisite and sequentially connected. The learning method used is an expositive method that emphasizes the aspects of absorbing new information. The delivery strategy through TVRI is the right choice because it can be accessed by all students in Indonesia, both signal and operational. Evaluation cannot be carried out optimally, especially for competencies that require practice because students cannot interact with students directly
BLOOD GROUP DETECTION USING DEEP LEARNING

Division and counting of platelets are considered as a critical development that helps with isolating features to dissect a few specific diseases. The manual counting of RBCs in tiny pictures is an incredibly grim, dreary, and misguided system. Modified examination will allow hematologist experts to perform faster and all the more definitively. Assessment of blood characterization plays a basic get-together in the helpful field for any treatment. Misleading bonding of blood will incite various issues. This structure gives straightforward and fast techniques for recognizing verification of blood characterizations and Rhesus factor none prominently. Our construction is taken a stab at a couple of veritable instructive assortments of various individual pictures of human fingertip pictures. Blood characterization is gathered ward on the closeness and nonappearance of specific natural substances called antibodies and moreover reliant upon the proximity or nonattendance of obtained antigenic protein substances on the surfaces of the erythrocytes in the body. Thusly by using the optical properties of the antigens and the rhesus ascertain present the blood, the blood social events can be requested.
PEMBELAJARAN BAHASA INGGRIS BERBASIS BLENDED LEARNING DI ERA NEW NORMAL DENGAN MENGGUNAKAN “MISSING PIECES ACTIVITIES”

Since Covid-19 attacked sevral parts of the world, students are suggested to do distance learning. However, since New Normal era, students are allowed to do offline learning by implementing health protocol. This research is aimed to describe teaching method with blended learning model namely “Missing Pieces Activities” in English Subject for Twelfth Graders of Senior High School. This teaching method involves various activities, they are: Missing Words for listening, Missing Small Thing speaking, and Missing Sentences for Reading. With this blended learning model, the activities are done by combining the online learning and offline learning. Source of data in this research is activities done by the students during the teaching and learning process. The data was collected through observation and inerview. The finding of the research shows that students are more actively involved in the teaching and learning process. Further, they have higher motivation in learning due to the various activities they did in learning process. Therefore, this Missing Pieces Activities can be one of learning methods in New Normal Era to increase students’ activeness and motivation both in distance and offline learning.
IMPLEMENTASI STRATEGI PEMBELAJARAN DENGAN PENDEKATAN INDEX CARD MATCH DALAM MENINGKATKAN MOTIVASI BELAJAR SISWA DI MTS NEGERI 2 SLEMAN

In the some units of educational institutions, there are still use monotonous learning strategies, the teacher lecturing and students listening, it has an impact on students who are not enthusiastic to participating in the class. The index card match strategy is not implemented in all educational units, due to the lack of teachers in applying this method in classroom learning. In fact, with this method, it can overcome the problems that exist in the field related to the learning process.&#x0D; The purpose of this study is to interpret the implementation of learning strategies with the index card match to increasing student motivation at MTS N 2 Sleman. This research is qualitative. Data collection techniques used are observation methods, documentation methods and interview methods. The data analysis technique used is Miles, Huberman and Saldana with the stages of data collection, data condensation, data presentation and drawing conclusions.&#x0D; The results showed that the implementation of the learning strategy with the index card match approach was successful and successful in increasing students learning motivation at MTS N 2 Sleman. Therefore, with the index card match strategy, it will provide a new nuance for education, especially in the learning process. Learning with this strategy will make the learning atmosphere more fun and not boring.
HOW LISTENING DEEMED AS THE MOST DIFFICULT ENGLISH SKILLS DURING ONLINE LEARNING AMONG STUDENTS?

Despite the growing interest in online learning over the past years, Indonesian students had to adjust to online interactions. Thus, Indonesian students, in general, still needed to be thoroughly instilled in this learning mode due to various challenges. This qualitative research aimed to discover the most difficult English skills during online learning from Indonesian students’ perspectives. This research utilized a survey design to rank the complex English skills; Writing, Speaking, Listening, and Reading. There were 55 students from different majors as participants from various universities in Indonesia who already partake in English courses by undergoing online mode. The instruments in this research are questionnaires regarding how one of the English skills is regarded as the most difficult during online learning, including the cause and methods to lessen the difficulty of obtaining thorough explanations. Critical Discourse Analysis was utilized as a data analysis technique to comprehend multiple perspectives better; in educational and non-educational contexts. The findings discovered that the majority of the participants deemed listening as the most difficult one. This research also discovered the factors that made listening difficult among students and the methods devised to lessen the difficulties in listening during online learning.