Usage Patterns Research Articles

Assessment of Pattern of using personal audio devices among adults in Karnataka: A Cross sectional study

Introduction: Exposure to personal audio devices for longer and loud noise contributes to hearing loss. Recent advances in technology have led to evolution of PAD and changes of usage pattern. This study highlights advancement in technology impact the choices, habits, behavioural trends of adults on the pattern of usage. This study is undertaken to determine the prevalence and pattern of using of personal audio devices (PAD) in Karnataka as the research is scarce. Methods and materials: This cross-sectional study was conducted during July 2022-December 2022 using Google forms in an online platform in adults. A convenience method was used and 200 participants who gave consent were recruited. Socio-demographic profile, the pattern of using personal audio devices were included in the proforma. Data analysis was done in SPSS 26. Descriptive statistics and inferential statistics was used. Results: The most commonly used PAD was Bluetooth 55%, majority used for a duration of 1-5yrs 54.5%, most of them used routinely 33.5%, in a medium volume 76.5%. Around 83.5% used during travelling, 54% while walking. Pearsons Correlation between no. of days of usage of PAD and hours showed significant results with moderate positive correlation with r=0.5 and p=.000. Conclusion: Highly risky behaviour in the pattern of using PAD is found in adults. Hence there is need to create awareness to practice healthy using patterns of PAD.

Exploring the relation between media usage frequency and anxiety among Chinese residents: a latent profile analysis

ObjectiveThis study investigates the relationship between media usage patterns and anxiety levels, specifically examining how different media usage profiles influence anxiety across various demographic groups.MethodologyA total of 11,031 respondents from 120 cities across China were classified into three media usage profiles—Traditional Media-Dominant Users, New Media-Dominant Users, and Omni-Media Users—using Latent Profile Analysis (LPA) based on their media usage frequency. Demographic covariates were excluded during the initial profiling to ensure the analysis focused solely on media usage patterns. Multiple linear regression analyses were then conducted to examine the relationship between media usage types and anxiety levels. Finally, factors influencing anxiety across the different media usage profiles were explored separately.ResultsThe analysis revealed that both Traditional Media-Dominant and Omni-Media Users exhibited significantly higher levels of anxiety compared to New Media-Dominant Users. Factors such as geographic region, health literacy, income, debt, employment stability, and property ownership showed varying effects on anxiety across the profiles. Additionally, perceived stress and depression were identified as consistent, positive predictors of anxiety in all media usage groups.ConclusionsCompared to New Media-Dominant Users, both Traditional Media-Dominant and Omni-Media Users exhibited stronger associations with anxiety. These findings suggest that anxiety is influenced by multiple intersecting factors across media usage profiles, highlighting the need for tailored interventions that consider individuals’ specific media engagement patterns.

Penerapan Artificial Neural Network untuk Memprediksi Persediaan Obat Esensial

The availability of essential medicines is a fundamental factor in ensuring high-quality healthcare services, especially in primary healthcare facilities such as Puskesmas. Inefficient drug inventory management can lead to various issues, including drug shortages that disrupt medical services and overstocking that may result in waste due to expiration. An accurate prediction system is essential to support more effective and efficient drug inventory planning. This study aims to analyze historical drug usage patterns to generate more accurate predictions. The research methodology includes problem identification, data collection, preprocessing, ANN architecture design, implementation, and system evaluation. Historical drug usage data from previous years is used for training and testing, with a division of 70% for training and 30% for testing. The backpropagation algorithm is applied to optimize the model by adjusting parameters such as the number of neurons in the hidden layer, learning rate, and activation function. The study results show that the ANN model with a 12-12-1 architecture achieves a high prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.13% for paracetamol stock. The developed MATLAB application provides an interactive platform for users to input historical data and obtain dynamic stock predictions. This system implementation is expected to help Puskesmas manage drug inventory more effectively, reduce the risks of shortages and overstocking, and improve efficiency in essential drug distribution. This study contributes to the field of health informatics by demonstrating the effectiveness of ANN in drug inventory prediction. Future research may explore hybrid machine learning models or integrate external factors, such as seasonal disease patterns and community demand levels, to enhance predictive accuracy and adaptability.

Electronic device usage pattern is associated with sleep disturbances in adolescents: a latent class analysis.

Four electronic device usage patterns were found among adolescents. Compared with the pattern of minimal screen exposure and a learning purpose, other patterns were more likely to be associated with sleep disturbances. These findings carry significant implications for healthy electronic device usage and potential strategies to improve adolescents' sleep health. • As screen use among adolescents increases, promoting healthy digital habits is crucial for reducing sleep disruptions and enhancing well - being. • Existing recommendations on adolescents' electronic device use mainly focus on usage duration and lack systematic advice. • Latent class analysis (LCA) identified four electronic device usage patterns among Chinese adolescents, based on screen time, usage timing, purpose, pre-sleep usage, and device presence in bedrooms. • Reducing usage, especially pre-bedtime, using devices for education, and keeping them out of the bedroom were less linked to sleep issues.

Embracing AI advisors for making (complex) financial decisions: an experimental investigation of the role of a maximizing decision-making style

PurposeThis study investigates the impact of experimentally priming a maximizing decision-making style on individuals’ likelihood of using artificial intelligence (AI) advisors for making complex financial decisions, such as building an investment portfolio for their retirement. It examines whether individuals with stronger maximizing tendencies are more likely to perceive algorithms as effective, thereby reducing their algorithm aversion, and ultimately increasing the likelihood of using AI advisors in their financial decision-making.Design/methodology/approachA qualitative pre-study amongst individuals differing in their maximizing tendencies to learn more about the existing usage patterns of AI advisors for financial decisions was combined with a quantitative study to experimentally test our hypotheses. For both studies, US participants were recruited through Prolific. The data were analyzed using thematic analysis in NVivo and regression analysis in the SPSS Process macro.FindingsThe results show that individuals primed with a maximizing mindset demonstrated a higher likelihood of using AI advisors for their financial decisions. This effect was serially mediated by the perception of enhanced algorithm effectiveness and reduced algorithm aversion.Practical implicationsThis study provides actionable insights for financial service providers such as banks, pension funds and insurance companies into strategies on how to reduce algorithm aversion and encourage greater AI usage in decision-making amongst their (potential) clients. In particular, to increase the likelihood that consumers will rely on AI advisors for financial decisions, financial service providers can induce a maximizing mindset in these individuals by adjusting the wording of their marketing communications material.Originality/valueThis study extends our understanding of how maximizing tendencies influence the likelihood of using AI advisors. It contributes to the literature by highlighting the role of perceived effectiveness and algorithm aversion and by demonstrating that experimentally inducing a maximizing mindset can increase AI usage for financial decisions; doing so is important as AI can help provide consumers with personalized advice in a cost-effective way.

Pornography and problematic pornography use: occurrence, patterns, and associated factors in a national gender-based controlled cross-sectional study.

Pornography and problematic pornography use: occurrence, patterns, and associated factors in a national gender-based controlled cross-sectional study.

Menstrual Hygiene Nonwoven Products: Comprehending Sustainability and Sanitary Product Usage Patterns

Nonwoven fabrics play a significant role in menstrual hygiene products, offering advantages such as superior absorbency, comfort, and cost-effectiveness. The reliance on synthetic materials in menstrual hygiene products raises concerns about environmental sustainability and health impacts. This study examines the usage patterns, type of material used in sanitary products and the potential of eco-friendly alternatives to mitigate the environmental footprint of menstrual hygiene products. Here, a survey was conducted among 200 female undergraduate students. A structured questionnaire was administered using Google Forms, encompassing sections on knowledge, attitudes, and usage patterns. The questionnaire consisted of questions based on the Likert scale which took readings on a five-point scale. The results showed that the majority of respondents (66%) were from semi-urban regions, followed by 24% from urban areas and 10% from rural areas. Social media emerged as the leading source of information about the environmental impact of conventional menstrual products, followed by schools/colleges (21.8%), peers (16.8%), and news/media (8.69%). Moreover, the high willingness to switch to eco-friendly options (74.2%) suggests a positive outlook for sustainability initiatives. Moreover, the survey identifies key barriers to adopting sustainable menstrual products, such as high costs, limited availability, and social stigma. It also highlights strategies for promoting eco-friendly practices, including raising awareness, providing subsidies, and addressing cultural taboos, giving readers a comprehensive preview of the findings. The study concluded that addressing the challenges of cost, accessibility, and awareness through targeted education, policy support, and affordable pricing can promote the adoption of eco-friendly menstrual products. By shifting towards sustainable nonwoven materials, environmental waste can be reduced and a more sustainable future in menstrual hygiene can be fostered.

Study on Operation Control Strategy for Campus Public Building Heating Systems in Severe Cold Areas

This study addresses the optimization of heating systems for university building clusters in severe cold regions, focusing on their functional complexity, temporal usage patterns, and spatial heterogeneity. The actual university heating project in Harbin was chosen as a case study, breaking through the limitations of previous studies focusing on residential or commercial buildings. The research systematically investigates heating load variations during operational periods. It proposes three regulation strategies: constant supply water temperature with constant temperature difference regulation, variable supply water temperature with constant temperature difference regulation, and variable supply water temperature with constant temperature difference regulation combined with time-division and zone-based heating for partial buildings. The energy-saving potential of the three schemes is analyzed in depth by comparison. The results demonstrate that, compared to the constant temperature water supply and constant temperature difference regulation scheme, the other two schemes achieved energy-saving rates of 17.1% and 34.8%, respectively. In the time dimension, these schemes match the time period of energy use in universities, and in the spatial dimension, they can realize the differentiated heat supply in the functional zoning of the building clusters. This study provides a reference for optimizing the regulation of heating systems in severe cold areas.

Lost in translation: conserved amino acid usage despite extreme codon bias in foraminifera.

Analyses of codon usage in eukaryotes suggest that amino acid usage responds to GC pressure so AT-biased substitutions drive higher usage of amino acids with AT-ending codons. Here, we combine single-cell transcriptomics and phylogenomics to explore codon usage patterns in foraminifera, a diverse and ancient clade of predominantly uncultivable microeukaryotes. We curate data from 1,044 gene families in 49 individuals representing 28 genera, generating perhaps the largest existing dataset of data from a predominantly uncultivable clade of protists, to analyze compositional bias and codon usage. We find extreme variation in composition, with a median GC content at fourfold degenerate silent sites below 3% in some species and above 75% in others. The most AT-biased species are distributed among diverse non-monophyletic lineages. Surprisingly, despite the extreme variation in compositional bias, amino acid usage is highly conserved across all foraminifera. By analyzing nucleotide, codon, and amino acid composition within this diverse clade of amoeboid eukaryotes, we expand our knowledge of patterns of genome evolution across the eukaryotic tree of life.IMPORTANCEPatterns of molecular evolution in protein-coding genes reflect trade-offs between substitution biases and selection on both codon and amino acid usage. Most analyses of these factors in microbial eukaryotes focus on model species such as Acanthamoeba, Plasmodium, and yeast, where substitution bias is a primary contributor to patterns of amino acid usage. Foraminifera, an ancient clade of single-celled eukaryotes, present a conundrum, as we find highly conserved amino acid usage underlain by divergent nucleotide composition, including extreme AT-bias at silent sites among multiple non-sister lineages. We speculate that these paradoxical patterns are enabled by the dynamic genome structure of foraminifera, whose life cycles can include genome endoreplication and chromatin extrusion.

Large-scale empirical study of electric vehicle usage patterns and charging infrastructure needs

Large-scale empirical study of electric vehicle usage patterns and charging infrastructure needs

Antibiotic Usage Patterns of Community-Dwelling Adults in Thailand: A Cross-Sectional Study.

To investigate factors predictive of antibiotic use behaviour in the community. A cross-sectional study was conducted from May to November 2023. Standardised instruments were administered to patients who had received services from nurses in sub-district health-promoting hospitals in Thailand for at least one of the three diseases: (1) upper respiratory infection, (2) acute diarrhoea or (3) fresh traumatic wounds. Antibiotic use behaviour was modelled using the generalised estimating equation with an independent error component to account for the clustering of the hospitals. Five hundred and eighty-five participants (556 patients and 29 nurses) met the inclusion criteria in this study. Of the 556 adult patients who were surveyed, the majority had upper respiratory infections, reported an appropriate level of antibiotic use, a moderate level of awareness of appropriate antibiotic use and antibiotic resistance, and an inadequate literacy level of Rational Drug Use. The generalised estimating equation analysis revealed that factors predictive of antibiotic use behaviour were rational drug use literacy, awareness, process of care, nurses' experience and rate of rational antibiotic prescribing for acute diarrhoea. The nurses and healthcare providers should focus on enhancing the quality of care by educating and collaborating with the community to ensure appropriate antibiotic use behaviour. This study indicated that healthcare policymakers should prioritise patient education on antibiotic use behaviour while also ensuring that healthcare workers adhere to strict caregiving protocols. Providing services by monitoring symptoms and home visits can help patients gain confidence in the treatment approach and lead to a rational change in antibiotic use. We adhere to the STROBE checklist. No patient or public involvement.

Mobile Phone Usage in Higher Education Among Pre-Service Teachers in Nigeria: A Qualitative Study

Technology integration has transformed education globally, making learning more accessible and seamless. Mobile education plays a crucial role in this transformation, relying heavily on mobile technology devices. Among these devices, mobile phones are particularly prevalent among students in higher education institutions worldwide. However, concerns have emerged regarding how students utilize these devices, prompting discussions among educational stakeholders. This study employed a qualitative research design, selecting 40 pre-service teachers through random sampling and conducting interviews to explore their experiences with mobile phone usage in natural contexts. Guided by the Uses and Gratifications Theory, the research addressed five key research questions. The findings indicate that students use mobile phones extensively, engaging actively on six social media platforms, with Facebook and WhatsApp being the most frequently used. Mobile phones are also widely utilized for academic purposes, with the majority of students using them for reading. Notably, the study identified gender differences in mobile phone usage patterns. Challenges associated with mobile phone usage were also highlighted, including a lack of teacher support and limited access to browsing data. The study concludes by offering recommendations to enhance the effective use of mobile phones for learning, including the implementation of policies to regulate and guide their use in educational settings.

Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping

ABSTRACT Bike-sharing systems (BSSs) have recently become important in urban transportation due to several factors, such as their cost-effectiveness and environmental considerations. The BSS provides an enormous amount of data that is recorded regarding trips. This huge volume of bike sharing data raises various challenges and opportunities. Many research studies have used bike sharing datasets to understand the geographical, social, financial, and behavioral aspects of bike user behaviors. While existing literature primarily focuses on predicting the number of rentals and returns per station, this study addresses the complementary aspect of predicting the trip duration and distance of the trip. Accurate prediction of ride duration allows a better estimate of bike availability at stations, while distance predictions assist in maintenance planning based on bike usage patterns. The contribution of this work is twofold. First, the proposed work clusters the BSS dataset into k sub-datasets based on similarity of dataset instances. Then, the predictive model is trained to predict the data of each sub-dataset separately. Thus, there will be k models for the k sub-dataset. Next, the performance of the proposed method, the average score of the k models, will be compared to the performance of a model trained on the complete dataset on predicting BSSs ride duration and distance of the trip. The rationale for splitting the dataset into k sub-datasets is to separate similar patterns in one sub-dataset. Second, the utilization of the dynamic time warping (DTW) algorithm on the BSSs data was proposed for the clustering purpose, as the DTW usage is very limited in the current literature of BSSs. The dataset clustering is based on the similarity of the curves representing the number of trips between each pair of bike stations throughout the day hours. Then, the DTW algorithm is used to measure the curve similarity between these bike station pairs’ curves. These two contributions of the proposed approach complement existing prediction models for rentals and returns, providing a comprehensive solution for BSS optimization. The proposed method was thoroughly evaluated on two real datasets of different sizes. For the two datasets, the obtained results show that the best improvements of the predictive model’s accuracy are 30% and 42% on average for predicting trip duration and distance of the trip, respectively.

Decoding codon usage in human papillomavirus type 59.

Human Papillomavirus Type 59 (HPV-59) is a high-risk subtype linked to cervical and other cancers. However, its codon usage patterns remain underexplored despite their importance in understanding viral behavior and vaccine optimization. This study reveals a mild codon usage bias in HPV-59, with a notable preference for A/T-ending codons and 29 favored codons, primarily ending in A or T. Additionally, CpG dinucleotides were significantly underrepresented, potentially aiding immune evasion. Analyses using the Parity Rule 2, Effective Number of Codons plot, and neutrality plot indicate that both mutational pressure and natural selection shape codon usage, with natural selection playing a dominant role. The virus's codon usage moderately aligns with human translational machinery, as shown by the Isoacceptor tRNA pool, Codon Adaptation Index, and Relative Codon Deoptimization Index, reflecting an evolutionary balance between protein synthesis efficiency and host compatibility. These findings provide valuable insights into HPV-59 biology, offering guidance for developing optimized vaccines.

Development of web-based resource usage patterns among English-to-Chinese trainee translators

The aim of this article was to examine changes in trainee translators’ web-based resource use patterns during a four-month English-to-Chinese translation practice course. An analytical framework was employed to statistically examine resource use development, including new and emerging resources. The research design incorporated four repeated measures with a cohort of 19 participants, and data were collected via the simultaneous use of a key logger and a screen recorder in a remote setting. A quality assessment was conducted to investigate the correlation between resource use and the overall quality of the final translation products. Findings indicate a shift in resource preference from dictionaries to knowledge-based resources, with trainee translators focusing on larger contextual issues rather than lexical problems. The quality assessment revealed a slight negative correlation between the translation error scores and the percentage of time allocated to knowledge-based resources. These findings highlight the importance of understanding trainee translators’ shifting resource use and evaluating their resource competence. The implications of the findings for translator training are discussed, and the need to adapt training programs to meet changing resource landscapes emphasized.

School-Family Communication in Early Childhood Education Through Digital Tools

Abstract This study addresses the communication between schools and families in the early childhood education stage (0–6 years) using digital tools. Using a descriptive-exploratory cross-sectional design, it aims to explore teachers’ knowledge and attitudes regarding the use of these technologies. To gather information, a questionnaire consisting of 28 items was distributed to teaching teams in Mallorca (Balearic Islands, Spain), yielding a total of 273 responses. The results reveal that, while email and instant messaging are widely recognised and used, specific applications remain underutilised, with significant variations in usage patterns depending on age group and level of experience. Teachers generally perceive digital tools as effective, but identify certain gaps in families’ technological competence. This study underscores the urgent need for targeted professional development to enhance teachers’ competence and confidence in using digital tools for communication. Recommendations include promoting training initiatives tailored to experience levels and integrating user-friendly, secure platforms to foster inclusive, two-way communication, ultimately strengthening collaboration between families and schools.

The use of adverbs in Ngor-Okpala dialect of Igbo

Adverbs play a crucial role in language by providing additional information about various elements of a sentence such as verbs, adjectives, other adverbs, prepositional phrases, subordinate clauses, and even complete sentences. Very little attention has been given to usage-based approach to parts of speech, particularly the use of adverbs in different dialects, this study investigates the use of adverbs in Ngor-okpala dialect of Igbo. Adverbs in Igbo language is known as Nkwuwa or Enyemaka Ngwaa, Adverbs in the dialect are used to answer questions such as "Olee ?- where, Olee nke- which, Olee otu- how, Olee onye-who, Olee mgbe-when, Olee ihe-what, etc. It aims to understand their role, their varied usage, the implications they hold for linguistic expression and impact in the dialect. The study adopts a descriptive approach, identifying and analyzing adverbs and their usage in the Ngor-Okpala dialect, to reveal patterns of usage, syntactic structures, and semantic implications. This work adopts the Universal grammar theory. The study reveals that the use of various adverbs in the Ngor-okpala dialect help to convey details about how, when, where, how often, or to what extent an action occurs or a quality is expressed in the dialect, intensifying the meaning of the words they modify, making them a significant component of linguistic expression in Ngor-okpala dialect. The study concludes that adverb is an essential component of the dialect’s linguistic expression, providing various details about actions, qualities, and relationships within sentences. An understanding of this is crucial for effective communication in the dialect where precision and clarity are paramount.

Current trends in e-mental health interventions: Health care providers' attitudes and usage patterns

Current trends in e-mental health interventions: Health care providers' attitudes and usage patterns

Analysis of synonymous codon usage bias of Lassa virus.

Analysis of synonymous codon usage bias of Lassa virus.

Exploring the Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Using the Technology Acceptance Model

The current research investigated the factors associated with the intensity of AI chatbot usage among university students, applying the Technology Acceptance Model (TAM) and its extended version, TAM3. A daily diary study over five days was conducted among univserity students, distinguishing between inter-individual (between-person) and intra-individual (within-person) variations. Multilevel structural equation modeling (SEM) was used to analyze the data. In Study 1 (N = 72), results indicated that AI chatbot anxiety was associated with perceived ease of use (PEOU) and perceived usefulness (PU), which serially mediated the link with AI chatbot usage intensity. Study 2 (N = 153), supported these findings and further explored the roles of facilitating conditions and subjective norm as additional predictors of PEOU and PU. Results from both studies demonstrated that, at the between-person level, students with higher average levels of PEOU and PU reported more intensive AI chatbot usage. In Study 1, the relationship between PEOU and usage intensity was mediated through PU at the within-person level, while the mediation model was not supported in Study 2. Post-hoc comparisons highlighted much higher variability in PEOU and PU in Study 1 compared to Study 2. The results have practical implications for enhancing AI chatbot adoption in educational settings. Emphasizing user-friendly interfaces, reducing AI-related anxiety, providing robust technical support, and leveraging peer influence may enhance the usage intensity of AI chatbots. This study underscores the necessity of considering both stable individual differences and dynamic daily influences to better understand AI chatbot usage patterns among students.