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This study investigates the perceived level and sources of learning Online Information Searching (OIS) skills of the students of business studies. The study also explores the differences in the students' searching skill level of information resources, based on their selected personal and academic variables including gender, age, type of university, level of degree and major academic subjects. A cross-sectional survey research method was used to collect data on a self-reporting questionnaire. Business students of undergraduate, graduate and postgraduate levels from 24 public and private sector universities of Lahore, Pakistan were selected through convenient sampling technique. Total population of this study was 114,500. With the margin error of 5% and confidence level of 97%, the sample of this study was 470 students. The study found a satisfactory level of students' searching skills. There was no significant difference in the skills based on various variables like gender, age, type of university and level of degree. However, short courses and training workshops had a positive impact on the level of skills. There is a lack of research on the topic and this paper will fill the gap in existing literature. This study will be helpful for Library Information Service (LIS) academicians, librarians, professional associations and LIS trainers to design and implement training programs for university students in the area of OIS. This study will also be helpful for Higher Education Commission (HEC) national digital library for selection of appropriate databases for business students.

The present study aims to explore the behavioral patterns of the social knowledge construction process and the online searching behaviors in a collaborative problem solving learning activity for high school students, and further compares the different behavioral patterns of the high- and low-performing teams. A total of 78 high school students from two schools participated. This study applied sequential analysis to analyze the students’ social knowledge construction and online searching behavior from a large amount of screen-recording data. Interestingly, the results indicate that social interaction that is irrelevant to the discussion task is significantly correlated with academic-related discussion content. Reaching a higher cognitive level of social knowledge construction (e.g., reaching agreement or applying newly constructed meaning) contributes to a successful team project. For online searching behavior, the high-performing teams exhibited systematic online searching behavior and concentrated on the task, while the low-performing teams displayed chaotic searching behavior and were distracted from the task at hand, and seemed to rarely propose their searched results or ideas in their discussions. Based on the results, several possible explanations and suggestions are proposed including the need to promote more adaptive motivation and to provide scaffolding for collaborative skills.

Patellofemoral pain is considered one of the most common forms of knee pain, affecting adults, adolescents, and physically active populations. Inconsistencies in reported incidence and prevalence exist and in relation to the allocation of healthcare and research funding, there is a clear need to accurately understand the epidemiology of patellofemoral pain. An electronic database search was conducted, as well as grey literature databases, from inception to June 2017. Two authors independently selected studies, extracted data and appraised methodological quality. If heterogeneous, data were analysed descriptively. Where studies were homogeneous, data were pooled through a meta-analysis. 23 studies were included. Annual prevalence for patellofemoral pain in the general population was reported as 22.7%, and adolescents as 28.9%. Incidence rates in military recruits ranged from 9.7-571.4/1,000 person-years, amateur runners in the general population at 1080.5/1,000 person-years and adolescents amateur athletes 5.1%-14.9% over 1 season. One study reported point prevalence within military populations as 13.5%. The pooled estimate for point prevalence in adolescents was 7.2% (95% Confidence Interval: 6.3%-8.3%), and in female only adolescent athletes was 22.7% (95% Confidence Interval 17.4%-28.0%). This review demonstrates high incidence and prevalence levels for patellofemoral pain. Within the context of this, and poor long term prognosis and high disability levels, PFP should be an urgent research priority. CRD42016038870.

Firms typically use query-based search to help consumers find information/products on their websites. We consider the problem of optimally ranking a set of results shown in response to a query. We propose a personalized ranking mechanism based on a user’s search and click history. Our machine-learning framework consists of three modules: (a) feature generation, (b) normalized discounted cumulative gain–based LambdaMART algorithm, and (c) feature selection wrapper. We deploy our framework on large-scale data from a leading search engine using Amazon EC2 servers and present results from a series of counterfactual analyses. We find that personalization improves clicks to the top position by 3.5% and reduces the average error in rank of a click by 9.43% over the baseline. Personalization based on short-term history or within-session behavior is shown to be less valuable than long-term or across-session personalization. We find that there is significant heterogeneity in returns to personalization as a function of user history and query type. The quality of personalized results increases monotonically with the length of a user’s history. Queries can be classified based on user intent as transactional, informational, or navigational, and the former two benefit more from personalization. We also find that returns to personalization are negatively correlated with a query’s past average performance. Finally, we demonstrate the scalability of our framework and derive the set of optimal features that maximizes accuracy while minimizing computing time. This paper was accepted by Juanjuan Zhang, marketing.

PurposeRelaxation has significant restorative properties and implications for public health. However, modern, busy lives leave limiting time for relaxation. Virtual reality (VR) experiences of pleasant and calming virtual environments, accessed with a head-mounted display (HMD), appear to promote relaxation. This study aimed to provide a systematic review of feasibility, acceptability, and effectiveness of studies that use VR to promote relaxation in the general population (PROSPERO 195,804).MethodsWeb of Science, PsycINFO, Embase, and MEDLINE were searched until 29th June 2020. Studies were included in the review if they used HMD technology to present virtual environments that aimed to promote or measure relaxation, or relaxation-related variables. The Effective Public Health Practice Project (EPHPP) quality assessment tool was used to assess methodological quality of studies.Results6403 articles were identified through database searching. Nineteen studies published between 2007 and 2020, with 1278 participants, were included in the review. Of these, thirteen were controlled studies. Studies predominantly used natural audio-visual stimuli to promote relaxation. Findings indicate feasibility, acceptability, and short-term effectiveness of VR to increase relaxation and reduce stress. Six studies received an EPHPP rating of ‘strong’, seven were ‘moderate’, and six were ‘weak’.ConclusionsVR may be a useful tool to promote relaxation in the general population, especially during the COVID-19 pandemic, when stress is increasing worldwide. However, methodological limitations, such as limited randomised controlled trials and longer-term evidence, mean that these conclusions should be drawn with caution. More robust studies are needed to support this promising area of VR relaxation.

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license ( https://github.com/marbl/mash ).

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

As the quality of online health information remains questionable, there is a pressing need to understand how consumers evaluate this information. Past reviews identified content-, source-, and individual-related factors that influence consumer judgment in this area. However, systematic knowledge concerning the evaluation process, that is, why and how these factors influence the evaluation behavior, is lacking. This review aims (1) to identify criteria (rules that reflect notions of value and worth) that consumers use to evaluate the quality of online health information and the indicators (properties of information objects to which criteria are applied to form judgments) they use to support the evaluation in order to achieve a better understanding of the process of information quality evaluation and (2) to explicate the relationship between indicators and criteria to provide clear guidelines for designers of consumer health information systems. A systematic literature search was performed in seven digital reference databases including Medicine, Psychology, Communication, and Library and Information Science to identify empirical studies that report how consumers directly and explicitly describe their evaluation of online health information quality. Thirty-seven articles met the inclusion criteria. A qualitative content analysis was performed to identify quality evaluation criteria, indicators, and their relationships. We identified 25 criteria and 165 indicators. The most widely reported criteria used by consumers were trustworthiness, expertise, and objectivity. The indicators were related to source, content, and design. Among them, 114 were positive indicators (entailing positive quality judgments), 35 were negative indicators (entailing negative judgments), and 16 indicators had both positive and negative quality influence, depending on contextual factors (eg, source and individual differences) and criteria applied. The most widely reported indicators were site owners/sponsors; consensus among multiple sources; characteristics of writing and language; advertisements; content authorship; and interface design. Consumer evaluation of online health information is a complex cost-benefit analysis process that involves the use of a wide range of criteria and a much wider range of quality indicators. There are commonalities in the use of criteria across user groups and source types, but the differences are hard to ignore. Evidently, consumers' health information evaluation can be characterized as highly subjective and contextualized, and sometimes, misinformed. These findings invite more research into how different user groups evaluate different types of online sources and a personalized approach to educate users about evaluating online health information quality.

Over the past decade, topological materials—in which the topology of electron bands in the bulk material leads to robust, unconventional surface states and electromagnetism—have attracted much attention. Although several theoretically proposed topological materials have been experimentally confirmed, extensive experimental exploration of topological properties, as well as applications in realistic devices, has been restricted by the lack of topological materials in which interference from trivial Fermi surface states is minimized. Here we apply our method of symmetry indicators to all suitable nonmagnetic compounds in all 230 possible space groups. A database search reveals thousands of candidate topological materials, of which we highlight 241 topological insulators and 142 topological crystalline insulators that have either noticeable full bandgaps or a considerable direct gap together with small trivial Fermi pockets. Furthermore, we list 692 topological semimetals that have band crossing points located near the Fermi level. These candidate materials open up the possibility of using topological materials in next-generation electronic devices. An algorithm based on symmetry indicators is used to search a crystallographic database and finds thousands of candidate topological materials, which could be exploited in next-generation electronic devices.