Variable Usage Research Articles

EXAMINATION OF DIGITAL LITERACY SKILLS OF UNDERGRADUATE STUDENTS ACCORDING TO VARIOUS VARIABLES

The purpose of this research was aimed to determine the digital literacy skills of undergraduate students studying in different departments of the university according to the variables of gender, type of faculty, and daily internet use. In order to achieve this goal, 388 undergraduate students studying at the Faculty of Education, Faculty of Health Sciences, and Faculty of Economics and Administrative Sciences at İzmir Democracy University were determined as participants. The “Personal Information Form” prepared by the researchers and the “Digital Literacy Scale (DLS)” developed by Bayrakcı & Narmanlıoğlu (2021) and consisting of six sub-dimensions were used as data collection tools. According to the findings of the research, it was determined that the digital literacy levels of the students were moderate. When examined according to the gender variable, a significant difference was found between female and male students in the sub-dimensions of digital literacy levels. When examined according to the faculty variable, it was determined that there was a significant difference between the sub-dimensions of digital literacy levels, Daily Use, Professional Production, and Privacy and Security. As a result of the research, according to the daily internet usage variable, a significant difference was found between the General Knowledge and Functional Skills sub-dimensions of the digital literacy levels of the students according to the daily internet usage time. It is expected that the results obtained will contribute to the literature and draw attention to the development of digital literacy skills of university students.

Adaptation of the Phubbing Scale and of the Generic Scale of Being Phubbed for the Portuguese population

Excessive, abusive, or inappropriate use of mobile phones can have a negative effect on interpersonal relationships. This study aims to adapt the Phubbing Scale (PS) and the Generic Scale of Being Phubbed (GSBP) for the Portuguese population, establishing the convergent validity of the instruments with others that assess approximate constructs, such as the Partner Phubbing Scale (PPS)and the Nomophobia Questionnaire (NMP-Q), and investigate the differences and/or relationships between sociodemographic variables, mobile phone usage variables and the PS and the GSBP. This is a cross sectional study, including 641 participants, aged between 18-71 (M=27.91; SD=10.60). The Portuguese version of the PS kept the number of items and factors, however, distributed differently from the original version. The Portuguese version of the GSBP kept the structure proposed by the authors of the original version. The correlations obtained by both scales with other instruments ensured convergent validity. Differences were found in the values of the scales according to some sociodemographic variables and some variables regarding mobile phone use. The findings provide culturally adapted and validated two instruments and are helpful to researchers to assess this phenomenon and intervene in a timely manner.

Strategies to identify and dissect trade-offs in plants.

Trade-offs between traits arise and reflect constraints imposed by the environment and physicochemical laws. Trade-off situations are expected to be highly relevant for sessile plants, which have to respond to changes in the environment to ensure survival. Despite increasing interest in determining the genetic and molecular basis of plant trade-offs, there are still gaps and differences with respect to how trade-offs are defined, how they are measured, and how their genetic architecture is dissected. The first step to fill these gaps is to establish what is meant by trade-offs. In this review we provide a classification of the existing definitions of trade-offs according to: (1) the measures used for their quantification, (2) the dependence of trade-offs on environment, and (3) experimental designed used (i.e. a single individual across different environments or a population of individuals in single or multiple environments). We then compare the approaches for quantification of trade-offs based on phenotypic, between-individual, and genetic correlations, and stress the need for developing further quantification indices particularly for trade-offs between multiple traits. Lastly, we highlight the genetic mechanisms underpinning trade-offs and experimental designs that facilitate their discovery in plants, with focus on usage of natural variability. This review also offers a perspective for future research aimed at identification of plant trade-offs, dissection of their genetic architecture, and development of strategies to overcome trade-offs, with applications in crop breeding.

A User-Driven Method for Using Research Products to Empirically Assess Item Importance in National Surveys.

Large-scale, nationally representative surveys serve many vital functions, but these surveys are often long and burdensome for respondents. Cutting survey length can help to reduce respondent burden and may improve data quality but removing items from these surveys is not a trivial matter. We propose a method to empirically assess item importance and associated burden in national surveys and guide this decision-making process using different research products produced from such surveys. This method is demonstrated using the Survey of Doctorate Recipients (SDR), a biennial survey administered to individuals with a Science, Engineering, and Health doctorate. We used three main sources of information on the SDR variables: 1) a bibliography of documents using the SDR data, 2) the SDR website that allows users to download summary data, and 3) web timing paradata and break-off rates. The bibliography was coded for SDR variable usage and citation counts. Putting this information together, we identified 35 unused items (17% of the survey) by any of these sources and found that the most burdensome items are highly important. We conclude with general recommendations for those hoping to employ similar methodologies in the future.

A Hybrid Exact–Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity

Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the “due times rule”, the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.

Gender Differences in Usage and Subjective Appreciation of an Online Cognitive Behavioral Therapy for Wildfire Evacuees: Descriptive Study.

Based on the most common psychological difficulties of the evacuees from the 2016 Fort McMurray wildfires in Alberta, Canada, a therapist-guided cognitive behavioral self-treatment was developed. This study aimed to explore how gender influences the usage and subjective appreciation of the RESILIENT online treatment. Our study included 81 English-speaking evacuees with significant posttraumatic symptoms, or with some posttraumatic symptoms accompanied by at least mild depression symptoms or subclinical insomnia, and who logged into the platform at least once. Various usage and subjective appreciation variables were analyzed, including number of completed sessions, number of logins, number of words per session, perceived efforts, perception of usefulness and intention to continue using the different strategies. No difference was detected in most objective usage indicators. The number of words written in sessions 7 and 10 was significantly greater for women than for men. Regarding subjective appreciation, men had a greater perception of having put strong efforts in the cognitive restructuring strategy, while women reported in a greater proportion that they wanted to continue using physical exercise as a behavioral activation strategy. Our study offers a first look into how women and men use online treatments, and what their preferences are.

A methodology for refined evaluation of neural code completion approaches

Code completion has become an indispensable feature of modern Integrated Development Environments. In recent years, many approaches have been proposed to tackle this task. However, it is hard to compare between the models without explicitly re-evaluating them due to the differences of used benchmarks (e.g. datasets and evaluation metrics). Besides, almost all of these works report the accuracy of the code completion models as aggregated metrics averaged over all types of code tokens. Such evaluations make it difficult to assess the potential improvements for particularly relevant types of tokens (i.e. method or variable names), and blur the differences between the performance of the methods. In this paper, we propose a methodology called Code Token Type Taxonomy (CT3) to address the issue of using aggregated metrics. We identify multiple dimensions relevant for code prediction (e.g. syntax type, context, length), partition the tokens into meaningful types along each dimension, and compute individual accuracies by type. We illustrate the utility of this methodology by comparing the code completion accuracy of a Transformer-based model in two variants: with closed, and with open vocabulary. Our results show that the refined evaluation provides a more detailed view of the differences and indicates where further work is needed. We also survey the state-of-the-art of Machine Learning-based code completion models to illustrate that there is a demand for a set of standardized benchmarks for code completion approaches. Furthermore, we find that the open vocabulary model is significantly more accurate for relevant code token types such as usage of (defined) variables and literals.

The effect of torrefaction temperature and catalyst loading in Microwave-Assisted in-situ catalytic Co-Pyrolysis of torrefied biomass and plastic wastes

In the current study, the effect of torrefaction temperatures (125–175 °C) and catalyst quantity (5–15 g) on co-pyrolysis of torrefied sawdust (TSD) and polystyrene (PS) are investigated to obtain value-added products. The role of torrefaction in co-pyrolysis of TSD: PS was analyzed to understand the product yields, synergy, and energy consumption . As the torrefaction temperature increases, oil yield (48.3–59.6 wt%) and char yield (24.3–29 wt%) increase while gas yield (27.4–11.4 wt%) decreases. Catalytic co-pyrolysis showed a significant level of synergy when compared to non-catalytic co-pyrolysis. For the conversion (%), a positive synergy maximum (-2.6) exists at a torrefaction temperature of 175 °C and 15 g of KOH catalyst. To develop the model, polynomial regression-based machine learning was used to predict pyrolysisproduct yields and energy usage variables. The developed models showed significant prediction accuracy (R2 > 0.98), suggesting the experimental values and the predicted values matched well.

A multidisciplinary safety briefing for acutely ill and deteriorating patients: A quality improvement project

BackgroundSafety briefings can help promoting situational awareness, interprofessional communication and improve patient safety. Local problemA clinical survey highlighted that 90% of the participants including the medical team and the critical care outreach team nurses perceived the meeting for escalating acutely ill and deteriorating patients during the out-of-hours period (20.00 to 08.00) to have unconstructive and unwelcoming atmosphere with belittling, hostility and unhelpful criticisms. The participants reported that the communication across teams lacked in structure and clear information given; but staff also self-reported lacking confidence in communicating key issues. MethodA quality improvement project with Plan-Do-Study-Act was adopted to design and implement a dedicated multidisciplinary safety briefing with a structured format. ResultsThe multidisciplinary safety briefing was to 90% of clinicians, and it took a median of 10 min to complete. Delayed referrals to the critical care outreach team were reduced by 46%. Positive changes included increased situational awareness and clearer communication across teams. Barriers identified were variable usage and need for face-to-face presence. Considering all the findings and the time constraint during the SARS-CoV-2 pandemic, we changed to a telephonic safety briefing directly to the team leaders. ConclusionA structured multidisciplinary safety briefing can improve patient safety and support management of deteriorating and acutely ill patients on the wards during the out-of-hours period.

Variables Contributing to the Awareness of Online Gendered Violence: Focus on Observers

The study aimed to examine variables that could contribute to the awareness of online gendered violence (OGV). For this, an online survey was used. First, we assessed whether sexism and violence justification affect awareness of gender-based cyberviolence. Second, this effect was evaluated considering the smartphone usage. Third, we studied differences in awareness of OGV according to gender, digital generation, and academic field of study. Fourth, we tested for multiple associations of attitudinal variables, smartphone usage, and sociodemographic variables with OGV. The study involved 453 young adults from the Andalucia region, Spain ( Mage = 21.9, SD = 2.15; 64% women). The results showed that, with more sexist attitudes and normalization of violence, the limits regarding what constitute gender-based violence in digital media are more permissive (less awareness). Regarding smartphone use, when it became problematic, desensitization to cyberviolence against women and sexual and gender minorities increased. Multiple correspondence analysis revealed an observer profile, that is, a woman studying for a humanistic or healthcare career who does not believe in psychosocial sex differences, or that violence as a reaction is justified, and also does not support biological fatalism of sexism and violence. This observer also has high awareness of the cyber-behaviors that constitute OGV. In conclusion, this study provides key data for psychoeducational strategies aiming to increase awareness of gender-based cyberviolence.

Thermal and solutal energy transport analysis in entropy generation of hybrid nanofluid flow over a vertically rotating cylinder

An investigation of an axisymmetric mixed convective boundary layer flow of silver-titanium dioxide/water (Ag−TiO2/H2O) hybrid nanofluid towards vertically and rotating stretching cylinder with entropy generation is conducted. The Cattaneo-Christov theory and joule heating effect are used to analyze the features of thermal energy. Moreover, the magnetic impact and convective boundary conditions on the vertical surface also considered in the current investigation. The developing equations for momentum, energy and entropy generation are modelled and by the usage of similarity variables to transform into the system of nonlinear ordinary differential equations (ODEs). The solutions of nonlinear ODEs are obtained numerically with the assistance of BVP4C MATLAB built-in scheme. The graphical consequences and relevant physical reasoning regarding the velocity, temperature, and concentration profiles are discussed. It is noteworthy that strong estimation of buoyancy ratio and mixed convection parameter enhances axial velocity, but the swirl velocity is diminished. The fluid temperature and concentration both are diminished due to thermal and solutal stratification effects. It is also seen that thermal Biot and Eckert numbers enhance the temperature distribution. Further, the Reynold number improves entropy generation.

Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials.

BackgroundDepression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings.ObjectiveThis study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment.MethodsData were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants’ baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants’ baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression.ResultsFrom participants’ baseline characteristics, lower level of education (odds ratio [OR] 3.33, 95% CI 1.51-7.32) and both lower and higher age (a quadratic effect; age: OR 0.62, 95% CI 0.47-0.82, and age2: OR 1.55, 95% CI 1.18-2.04) were significantly associated with a higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR 0.60, 95% CI 0.42-0.85; age2: OR 1.59, 95% CI 1.13-2.23), medium versus high social support (OR 3.03, 95% CI 1.25-7.33), and a higher number of days to module completion (OR 1.05, 95% CI 1.02-1.08) predicted a higher risk of dropout, whereas a self-reported negative event in the previous week was associated with a lower risk of dropout (OR 0.24, 95% CI 0.08-0.69). A model that combined baseline characteristics and intervention usage data generated the most accurate predictions (area under the receiver operating curve [AUC]=0.72) and was significantly more accurate than models based on baseline characteristics only (AUC=0.70) or intervention usage data only (AUC=0.61). We found no significant influence of pain, disability, or depression severity on dropout.ConclusionsDropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.

Urban household water usage in adaptation to climate change: Evidence from China

While it has been concluded that climate change poses a significant threat to worldwide supply of freshwater resources, it is unclear if and how demand for water would also be affected. To fill this knowledge gap, we leverage on ‘big data’ collected using smart water meters from over 40,000 Chinese urban households, spanning nine years and ten provinces to examine the relationship between daily household water usage and climate variability. At the baseline, we find that municipal water is not only a coping mechanism for heat, but its usage is accelerated during heatwave events. Heterogeneity analyses reveal that households from lower-valued properties are more likely to substitute water for electricity to counter heat. Importantly, we find evidence of adaptation behaviors where over time, households are using increasingly more water to cope with high-temperature days. In all, after feeding our results into climate projection models, it is estimated that household water usage will increase by around 7–44% in the long-term (2080–2099) under emissions scenarios of SSP245 and SSP370. Our findings are especially relevant for water-scarce countries such as China as well as developing countries where water is a cheaper and more accessible resource to cope with heat.

MOTIVES, NEEDS AND THE RELATIONS OF USE BEHAVIOR OF INDUSTRIAL PRODUCT

The research discussed the subject of motives and needs and their relationship to the use behavior of the industrial product by the user. Proceeding from the statement of the nature of the motives and how they represent the main incentive for the acquisition. As well as defining what the needs are and how they differ from the desires, which today represent the basic need of the user of the current age. The relationship between arousal, attention, motives and behavior is an emerging relationship from the type of effect that the product has, and these elements are what excite the user and generate the motive for the acquisition, and by virtue of the motive, the user goes towards the product with specific behaviors that affect the overall experience of the user’s interaction with the industrial product with its physical, intellectual and emotional effects.Motivation, behavior and perceived value are a conjugative triad, one linked to the other according to the premises of actual need - or perceived - and all of them are affected by the user experience, which may increase the perceived value and then the motivation and certainly the use behavior or quite the opposite, depending on the type of use experience. The research aims to discuss the variables of usage, user experience, and how usage behavior is affected by the type of user experience, whether it is good or bad.These cognitive paradoxes were discussed according to a methodological foundation based on a problem represented by the research question: What are the frameworks of motives and needs that the user works on to determine his desires toward the industrial product design from the features and characteristics that the product contains? What is the relationship between usage behavior and user motives and needs? This problem was a logical manifestation that must be delved into in light of the importance of the research, which was to shed light on the concepts of motives, needs and user behavior from within the field of industrial product design. And to show the links and overlaps between these concepts and the overall interactive process of the user's experience with the industrial product. In order to achieve the two objectives of the research: Determining the nature of the relationship between motives and needs within the interaction of the user with the industrial product. And the impact of motives and needs on the user's use behavior. The researcher was able to achieve the goals in a way that addresses the research problem in light of a set of conclusions that were discussed in a way that fulfilled the research requirements.

인공지능을 활용한 빈집 실태조사 효율화 방안에 관한 연구 : 군산시를 사례로

Due to the recent centralization of the metropolitan area, the number of vacant houses in local cities continues to increase. Accordingly, the government is conducting a survey on vacant houses, but the cost of on-site surveys is high due to the low accuracy of the preliminary survey. Therefore, in order to efficiently conduct an empty house survey, it is necessary to accurately find buildings suspected of empty houses in the preliminary survey stage and take follow-up measures accordingly.BR This study aims to expand the efficiency of the survey of vacant houses by attempting to estimate vacant houses through artificial intelligence by using data on electricity, water usage, buildings, and socioeconomic variables to help early detection of vacant houses.BR As a result, Decision Tree Ensemble Models showed the best performance based on Accuracy, F1-score, and AUC scores, and building data in addition to electricity usage were also identified as important variables in estimating empty houses.

Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities

IntroductionThe integration of a web-based computer-adaptive patient-reported outcome test (CAT) platform with persuasive design optimised features including recommendations for action into routine healthcare could provide a promising way to translate...

Exploring the Impact of Slang Usage Among Students on WhatsApp: A Dig-ital Linguistic Analysis

This research delves into the dynamic world of informal language usage among students on the popular messaging platform, WhatsApp. As digital communication becomes an integral part of daily life, the study examines the frequency, variability, motivations, and social dynamics of slang usage among students. Through surveys, interviews, and, where possible, data analysis of WhatsApp conversations, the research uncovers the complex interplay between language, technology, and human connection in the digital realm. The findings reveal that slang is not merely a linguistic phenomenon but a reflection of the adaptability of language in the digital age. It serves as a linguistic bridge that enables informal communication in digital interactions. The lexicon of slang is diverse and ever-evolving, reflecting the cultural and social context in which it thrives. Motivations for slang usage go beyond humor and informality, extending to self-expression, emotional connection, and the formation of digital identities. Slang enhances social bonds and fosters a sense of belonging among peers, shaping the quality of digital interactions. Demographic variations in slang usage demonstrate its context-dependent nature, influenced by factors such as age, gender, and geographical location. Slang's impact on digital communication is significant, enhancing informal exchanges while presenting challenges, particularly in cross-cultural interactions. This research underscores the importance of digital literacy and cross-cultural understanding in online interactions and has implications for education, linguistic research, and cross-cultural communication. As the digital landscape continues to evolve, this research offers a deeper understanding of the role of language in shaping human connections in the digital age. It calls for ongoing exploration into the ever-changing linguistic dynamics of digital communication and its profound impact on contemporary society.

Is There Risk Compensation among HIV Infected Youth and Adults 15 Years and Older on Antiretroviral Treatment in South Africa? Findings from the 2017 National HIV Prevalence, Incidence, Behaviour and Communication Survey

In this paper, risk compensation among individuals on antiretroviral therapy (ART), using the 2017 South African national survey on HIV, is explored. A multi-stage stratified cluster random sampling approach was used to realize 11,130 participants 15 years and older. Logistic regression analysis assessed the association between multiple sexual partners, condom use at last sexual encounter, consistency of condom usage and potential explanatory variables using HIV status and ART exposure as a mediator variable. HIV positive participants who were aware and on ART were less likely to have multiple sexual partners, and less likely not to use a condom at last sex compared to HIV positive participants who were aware but not on ART. The odds of reporting multiple sexual partners were significantly lower among older age groups, females, non-Black Africans, and rural settings, and higher among those with tertiary level education, and risky alcohol users. The odds of no condom use at last sexual encounter were more likely among older age groups, females, other race groups, and less likely among those with secondary level education. The odds of inconsistent condom use were more likely among older age groups, females, and other race groups, and less likely among those with tertiary level education, high risk and hazardous alcohol users. Risk compensation is not apparent among HIV infected adults who are on ART. Risk groups that should receive tailored interventions to reduced risky sexual behaviours were identified.

Implementing night light data as auxiliary variable of small area estimation

Along with the growing popularity of the small area estimation method, the need to utilize good auxiliary variables also increases. Remote sensing data, such as night light imagery, offers advantages such as time-cost efficiency and global coverage but is easily accessible. This research aims to implement night light intensity as an auxiliary variable for the EBLUP model to estimate per capita consumption expenditure at West Java in 2018. This research employs three scenarios of auxiliary variables usage in EBLUP model construction: official data, night light intensity, and the combination between both data. The results show that night light intensity is an efficient auxiliary variable for estimating per capita consumption expenditure. Furthermore, the EBLUP model with a combination of official data and night light as auxiliary variables gives the best accuracy with coefficient of variation (CV) as evaluation.

Intrathecal Drug Delivery Systems Survey: Trends in Utilization in Pain Practice.

BackgroundThe use of intrathecal drug delivery for chronic and cancer pain medicine has been established for decades. However, optimization and utilization of this technique still lag behind other modalities for pain control. Some of this may be due to variability of surgical technique, medication usage and education. It is currently unclear on whether or not practitioners follow available algorithms for the use of intrathecal drug delivery systems.MethodsA survey developed by the American Society of Pain and Neuroscience (ASPN) was sent to its members via email using the cloud-based SurveyMonkey. After 30 days of being available, 159 different providers responded to the survey that consisted of 31 various multiple choice and free response questions. Each question was not required and the number of responses to each varied from 128 to 159.ResultsApproximately 9% of those who successfully received and opened the email containing the survey responded, likely due to a small number of providers working with intrathecal drug delivery systems. Eighty-six of respondents practice medicine in the United States, and 87% of the respondents were attending physicians. A majority of respondents, approximately 74%, were board certified in pain medicine with 69% of respondents being train in anesthesiology. The first and second most used medications for intrathecal pump trial were morphine and fentanyl, respectively. Most respondents, approximately 96%, provide pre-operative/intra-operative antibiotics. The most common first-choice medication for implanted intrathecal pumps was also morphine with the most common implanted location being the abdomen.ConclusionInterestingly, there is currently fairly substantial variation in the way providers utilize intrathecal pump delivery for both chronic and cancer pain. There is variation from the training background of the providers providing care, to the pre-implantation trial medications, to where the pump is implanted for each patient, to if the patient has the option to give themselves boluses once implanted. Further research is needed to elucidate current and best practices for intrathecal drug delivery system trials, implantations, and utilization.