Time Usage Research Articles

Retrospective observational study on real world use of the Minimed™ 780G automated insulin delivery system: Impact of the settings on autocorrection and omitted meal boluses

IntroductionThe Medtronic MiniMed™ 780G (MM780G) system uses an algorithm that includes autocorrection bolus (AB) delivery. This study evaluates the impact of omitted meal boluses and the system settings, glucose target and active insulin time (AIT), on the AB. MethodRetrospective observational study on data uploaded by all MiniMed 780G users in our healthcare area, obtained through the remote monitoring platform Care Connect, from April to August 2023. Downloads with a sensor usage time <95% were excluded. Results235 downloads belonging to 235 users were analysed. AB delivery was significantly higher at 2 h AIT (36.08 ± 13.17%) compared to the rest of settings (2.25–4 h) (26.43 ± 13.2%) (p < 0.001). AB differences based on the glucose target were not found.Patients with <3 meal boluses per day had higher AB delivery (46.91 ± 19.00% vs 27.53 ± 11.54%) (p < 0.001) and had more unfavourable glucometric parameters (GMI 7.12 ± 0.45%, TIR 67.46 ± 12.89% vs GMI 6.78 ± 0.3%, TIR 76.51 ± 8.37%) (p < 0.001). However, the 2-h AIT group presented similar TAR, TIR and GMI regardless of the number of meal boluses. ConclusionThe fewer user-initiated boluses, the greater the autocorrection received. The active insulin time of 2 h entails a more active autocorrection pattern that makes it possible to more effectively compensate for the omission of meal boluses without increasing hypoglycaemias.

Development of a high-speed and ultrasensitive UV/Vis-CM for detecting total triterpenes in traditional Chinese medicine and its application

This study proposes a novel colorimetric method based on the ultraviolet/visible spectrophotometry-colorimetric method (UV/Vis-CM) for detecting and quantifying total triterpenoids in traditional Chinese medicine. By incorporating the colourants 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid, triterpenoid compounds colour development became more sensitive, and the detection accuracy was significantly improved. 2-hydroxy-5-methylbenzaldehyde and concentrated sulfuric acid were incorporated in a 1:3 vol ratio at room temperature to react with the total triterpenes for 25 min, incorporated to an ice bath for 5 min, and then detected at the optimal absorption wavelength. The accuracy and reliability of this method were verified by comparison with high-performance liquid chromatography and four other colorimetric methods. Additionally, this approach has the advantages of not requiring heating during operation, high sensitivity, short usage time, low solvent usage, and low equipment costs. This study not only offers a reliable method for detecting total triterpenes in traditional Chinese medicine but also offers a rapid detection tool for on-site testing and large-scale screening, laying a foundation for the modernization of traditional Chinese medicine research, quality control, and drug development.

Pengaruh Penggunaan Gadget terhadap Bahasa yang Digunakan oleh Anak Usia 6-10 Tahun dalam Berkomunikasi

In the increasingly advanced digital era, children are increasingly exposed to gadgets such as smartphones, tablets and computers which provide easy access to various online content. Almost all levels of society have gadgets. Excessive use of gadgets or without adequate supervision can have a negative impact on children's language development and use of Indonesian that is not appropriate for their age. Parents give gadgets to their children for several reasons, such as calming the child when they are fussy, making it easier for the child to sleep or eat, and giving parents free time so they can do household work or relax. The aim of this research is to analyze the influence of technological developments on the use of Indonesian by children in elementary schools in communicating and to identify forms of use of Indonesian that are not appropriate for the age of children in elementary schools due to technological developments. The research method used is analytical descriptive, namely research that attempts to describe phenomena that occur realistically, real and contemporary, because this research consists of making systematic, factual and precise descriptions, drawings or paintings regarding the facts, characteristics and relationships between the phenomena being studied. . The research results show that uncontrolled use of gadgets and lack of supervision can contribute to children using inappropriate language in their communication. Children who spend too much time on gadgets tend to be exposed to content that is inappropriate or not appropriate for their age. The influence of a less controlled environment can also influence the use of inappropriate language. It is important for parents and the environment around them to supervise children's use of gadgets by providing appropriate restrictions and managing usage times and also providing a clear understanding of the appropriate and ethical use of language in communication. Parents and the surrounding environment can act as positive examples in the use of appropriate language and provide continuous guidance regarding the importance of using appropriate language in communication.

Data Visualization &amp; Analysis of Disease X [Nipah Virus

Abstract: This article is about performing mathematical analysis and visualization of data that was researched and collected about Nipah Virus; which is treated as Disease X[deadly disease] by World Health Organization. It includes information about the field of Data Analysis and Visualization techniques which associate closely with current era and the project itself. Furthermore, there is description which provides information about the main application developed and implemented. The variety of research papers that were made use of, along-with significantly valuable data are cited too. Data Analyses has very close connection with data science, however, the key difference lies in its actual working, as Data Analysis makes extensive use of historically available data &amp; generates important insights from them, which are very considerable for future operations. Thus, due to such complex level usage, Data Analysis has gained recognition in the recent years, which makes it important for professionals to add it as a part of daily life tasks. When it comes to real time usage, Data Analyses is used for many purposes which includes but is not limited to customer preferences, finance sectors, research in markets and also in assessment of variety of risks. Data Analysis thus, has introduced a massive number of futuristic achievable tasks by taking important notes from the ones that were conducted in the past

The use of standing powered wheelchairs from the perspective of individuals with spinal cord injury

Purpose The aim of this study was to investigate the experiences of standing powered wheelchair (SPWC) users with spinal cord injury (SCI). Materials and methods This cross-sectional study included 20 participants with SCI. SPWC daily usage time (total, in sitting position and standing position) and the frequency of using the SPWC’s standing feature were recorded. Participants’ level of satisfaction and comfort associated with the SPWC were assessed on a five-point Likert scale. If there was a dissatisfaction reason with the SPWC, the issues related to it were recorded. Participants were asked about their positive and adverse experiences with the use of SPWC. Results Mean age of the participants was 41.65 (± 9.35) years. Eighty percent were tetraplegic. Ninety percent of the participants stood for at least 30 minutes at least one day a week, and 55% stood for at least 30 minutes at least four days a week. The most commonly reported perceived benefits were improvement in activities of daily living, bowel movements, and blood pressure regulation, better perception of body image, and feeling better. The most frequently identified reasons for dissatisfaction with the SPWC were its big dimensions, heavy weight, and difficulty of use in indoor environment and on uneven surfaces. Conclusions The findings show positive perceived experiences for SPWC users with SCI in terms of physical, social, and psychological aspects. Prospective controlled studies are needed to explore the benefits of SPWC use regarding secondary complications of SCI.

Czech medical coding assistant based on transformer networks

The International Classification of Diseases (ICD) hierarchical taxonomy is used for so-called clinical coding of medical reports, typically presented in unstructured text. In the Czech Republic, it is currently carried out manually by a so-called clinical coder. However, due to the human factor, this process is error-prone and expensive. The coder needs to be properly trained and spends significant effort on each report, leading to occasional mistakes.The main goal of this paper is to propose and implement a system that serves as an assistant to the coder and automatically predicts diagnosis codes. These predictions are then presented to the coder for approval or correction, aiming to enhance efficiency and accuracy. We consider two classification tasks: main (principal) diagnosis; and all diagnoses. Crucial requirements for the implementation include minimal memory consumption, generality, ease of portability, and sustainability.The main contribution lies in the proposal and evaluation of ICD classification models for the Czech language with relatively few training parameters, allowing swift utilisation on the prevalent computer systems within Czech hospitals and enabling easy retraining or fine-tuning with newly available data. First, we introduce a small transformer-based model for each task followed by the design of a transformer-based “Four-headed” model incorporating four distinct classification heads.This model achieves comparable, sometimes even better results, against four individual models. Moreover this novel model significantly economises memory usage and learning time. We also show that our models achieve comparable results against state-of-the-art English models on the Mimic IV dataset even though our models are significantly smaller.

Single versus double transseptal puncture for pulmonary vein isolation with radiofrequency catheter ablation for atrial fibrillation - insights from "Go for Zero Fluoroscopy" registry

Abstract Background Transseptal puncture is one of the most challenging steps in catheter ablation of atrial fibrillation (AF) because of the potential risk of complication. While single TSP reduces the puncture-related risk, double TSP simplifies the procedure (immediate signal visualization in pulmonary veins, avoidance of multiple changes of catheters through the single sheath). The purpose of this study was to analyze the impact of single versus double TSP during pulmonary vein isolation (PVI) with radiofrequency (RF) catheter ablation for AF on the fluoroscopy usage and procedure time. Methods Data reported from the international, multicenter prospective „Go For Zero Fluoroscopy Registry" performed in years 2018–2019 were analyzed. Overall 25 European electrophysiology centers from 14 countries and up to 5 operators from each center were enrolled. Only isolated PVI procedures were analyzed. Results A total data of 393 PVI RF procedures were collected from 25 centers. The median dose-area-product (DAP), fluoroscopy time and overall procedure time were: 810 (325 – 1839) cGy*cm2, 8 (4 – 14) minutes and 124 (92 – 168) minutes, respectively. Single TSP was associated with significantly higher DAP: 984 (459 – 1939) vs 667 (227 – 1469) cGy*cm2 (p=0.002), longer fluoroscopy time: 11 (6 – 19) vs 6 (3 – 11) minutes (p&amp;lt;0.001) and longer overall procedure time: 133 (103 – 168) vs 110 (90 – 168) minutes (p=0.003) as compared with double TSP. Single and double TSP group did not differ significantly in regards of general anesthesia usage, circular and multipolar mapping catheter use (p&amp;gt;0.05). However, single TSP was performed by operators with longer EP lab experience: 10 (4 – 15) vs 3 (0 – 10) years (p&amp;lt;0,001). Conclusions Double TSP is related with lower fluoroscopy usage and shorter overall procedure time of pulmonary vein isolation with radiofrequency catheter ablation for atrial fibrillation.

A Novel Algorithm Application in Vocal Signals Processing.

This study proposes an innovative application of the Goertzel Algorithm (GA) for the processing of vocal signals in dysphonia evaluation. Compared to the Fast Fourier Transform (FFT) representing the gold standard analysis technique in this context, GA demonstrates higher efficiency in terms of processing time and memory usage, also showing an improved discrimination between healthy and pathological conditions. This suggests that GA-based approaches could enhance the reliability and efficiency of vocal signal analysis, thus supporting physicians in dysphonia research and clinical monitoring.

Is Too Much Time on the Internet Making us Less Satisfied with Life?

The Internet’s profound impact on society, communication, and the global economy is undeniable. Despite the studies on Internet adoption and frequency of use, little attention has been given to the intensity of usage as measured by the time spent online. In highly developed countries like the European ones, bridging the access gap is nowadays less relevant, as almost everyone has Internet access. Instead, the focus should be on analysing usage intensity to uncover new digital disparities among different groups and understand potential impacts on individuals' subjective well-being (SWB).This study aims to deeply examine Internet usage time, its socioeconomic determinants, and its effects on SWB using data from the European Social Survey (ESS) spanning from 2016 to 2020–22 in 21 European countries. We seek to answer two research questions: (1) How do individuals' characteristics influence Internet usage intensity? (2) What is the impact of Internet usage intensity on individuals' SWB?Our findings show inequalities in Internet usage time driven by individuals' socioeconomic and sociodemographic characteristics. Traditionally disadvantaged groups, both offline and online, exhibit lower Internet usage time, consistent with the existing literature on the digital divide. As for the effect of intensity of Internet use on SWB, after accounting for individuals' characteristics and addressing Internet's endogeneity, we found a negative and significant relationship between Internet usage intensity and life satisfaction, especially for the most intensive internet users.

Association of Body Image Distortion with Smartphone Dependency and Usage Time in Adolescents: A Cross-Sectional Korean Youth Study.

Adolescent dependency on smartphones is the highest among all age groups. Adolescents can be influenced to evaluate their body image by popular ideals about beautiful bodies via smartphone content, which can cause body image distortion. This study aimed to examine the association between body image distortion and smartphone dependency and the duration of smartphone usage among Korean adolescents. This study used data from the 16th Korean Youth Risk Behavior Web-Based Survey (2020), and included 42,981 participants, who were grouped according to self-reported duration of smartphone usage and smartphone dependency, as measured by a questionnaire. Body image distortion is defined as an exaggerated subjective body image compared to the actual body image. Multivariable-adjusted logistic regression was used to determine the odds ratios (ORs) with 95% confidence intervals (CIs) of body image distortion on smartphone dependency and usage time after adjusting for various factors related to body image distortion. Among the 42,981 participants, both moderate and high levels of smartphone dependency were associated with body image distortion in boys (moderate: adjusted OR [aOR], 1.11; 95% CI, 1.01-1.22; high: aOR, 1.18; 95% CI, 1.05-1.32) and girls (moderate: aOR, 1.14; 95% CI, 1.05-1.23; high: aOR, 1.30; 95% CI, 1.18-1.42) compared to the low-level dependency group. However, no significant association was found between smartphone usage duration and body image distortion. Our study demonstrated that moderate to high levels of smartphone dependency are associated with body image distortion in adolescents. Therefore, it is necessary to establish strategies for checking and managing adolescents' smartphone dependence.

Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base

Call graphs facilitate various tasks in software engineering. However, for the dynamic language Python, the complex language features and external library dependencies pose enormous challenges for building the call graphs of real projects. Some program analysis techniques used for call graph construction in other languages are impractical for Python. In this paper, we present STAR, a practical technique for the construction of Python static call graphs. We reformulate call graph construction as an entity identification task. STAR leverages inter-module summary and cross-project dependencies to construct a fine-grained entity knowledge base to identify the possible nodes and edges of the call graph in the code, and then construct the call graph. Our evaluation of three benchmarks shows that (1) STAR improves recall in three benchmarks compared to three baseline tools. Especially, STAR improves the recall of reachable nodes and reachable edges compared with the state-of-the-art tool by 11.3% and 9.8%, respectively; (2) STAR achieves comparable performance as three baseline tools in execution time and memory usage and is more efficient in large projects; (3) STAR can be effectively used for the task of detecting vulnerability propagation with real-world cases. We expect our results will attract more exploration of practical methods and improve the application of Python call graphs.

Predicting the use of chatbot systems in education: a comparative approach using PLS-SEM and machine learning algorithms

Adopting innovations in educational practice is a challenging task. In order to promote the use of technological innovations, acceptance of the technology by potential users is a prerequisite. Indeed, understanding the various factors that influence technology acceptance is critical for technology acceptance research. The use and acceptance of chatbots in education as a technological innovation is a topic that needs to be investigated. Chatbots, which offer close to human interaction between the user and technology through text and voice, can provide significant benefits in educational environments. The UTAUT2 model (extending UTAUT), which is widely used to evaluate technology acceptance, can serve as a framework for evaluating the acceptance and use of chatbots. This study aims to predict factors influencing students' use of chatbots in education within the UTAUT2 framework. PLS-SEM and machine learning tested the model, involving 926 students. According to the findings of the study, behavioral intentions were influenced by various factors including performance expectations and attitudes. Facilitating conditions and intentions significantly impacted chatbot usage time. Moderator effects were observed with age, gender, and usage experience affecting behavioral intentions. Support vector machine and logistic regression showed high prediction accuracies for behavioral intentions and usage time, respectively. These results provide insights for chatbot designers to meet user needs in educational settings.

NeighboAR: Efficient Object Retrieval using Proximity- and Gaze-based Object Grouping with an AR System

Humans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.

Factors Influencing Public Building Energy Consumption: A Case Study of Changjiang River Administration of Navigation Affairs in China

Based on the energy consumption data of the Changjiang River Administration of Navigation Affairs public building located in Wuhan, China, this paper analyzes the energy consumption characteristics of public buildings. Using the DesignBuilder energy simulation software (v7.0.0.096), various factors influencing building energy consumption are investigated. Furthermore, SPSS (v26.0.0.0) is employed to conduct correlation analyses between measured and simulated energy consumption data. The results indicate that the most significant factors affecting energy consumption are outdoor temperature, office equipment usage time, anticipated room temperature settings, boiler usage time, and per capita daily water consumption. By exploring the factors influencing energy consumption in public buildings, this study contributes novel insights into setting targets, adjusting schemes, and achieving energy-saving effects.

Effects of computer use on upper limb musculoskeletal disorders and function in academicians

Objectives. This study aimed to investigate the effects of academician’s demographic characteristics and computer usage habits on upper limb musculoskeletal disorders (MSDs) and function. Methods. A cross-sectional observational study was conducted with 100 academicians. Data were collected using questionnaires, which included the patient-rated wrist evaluation questionnaire – Turkish version (PRWE-T), the Cornell musculoskeletal discomfort questionnaire – Turkish version (CMDQ-T), the upper extremity functional index – Turkish version (UEFI-T), demographic characteristics and average daily computer usage time. Results. A low-level significant correlation was found between the age of the individuals and the CMDQ-T forearm (p = 0.044; r = 0.202) and CMDQ-T wrist (p = 0.001; r = 0.337) scores. Women had higher CMDQ-T neck scores and lower UEFI-T scores than men (p < 0.05). Academicians who used computers for 6 h a day or more had higher PRWE-T and CMDQ-T neck, shoulder, upper arm and forearm scores, and had a lower UEFI-T score (p < 0.05). Conclusion. Neck, shoulder, upper arm and forearm symptoms were higher and upper extremity function was impaired in academicians who used computers for 6 h a day or more. Besides, gender and age were associated with upper limb MSDs and function, but occupation duration did not affect those outcomes in academicians.

A Study of Relationship Between Television Watching Habit and School Aggressive Behavior in Early Adolescent Students

If children are witnessing and experiencing excessive levels of violence, we cannot deny that these factors have a bad effect on our society and children. This study aimed to determine the relationship between early adolescent students’ aggressive behavior with their television watching habits. The descriptive survey research design had been adopted to answer the guiding two research questions. 100 students were selected for the administration of the standardized Television Watching Habit Questionnaire and Student's Aggressive Behavior Rating Scale (SABRS) developed by Chakraborty. The responses were then analyzed through frequency, percentage, and t-test to verify the hypotheses. The findings demonstrated that social media, including exposure to violent television shows, violent movies, videos, and video games, as well as evaluating various social media platforms, had a detrimental effect on teenagers by encouraging aggressive behavior. Restructuring secondary schools' academic curriculum to prioritize problem-solving, fact-finding, and practical learning over theory would allow teenagers to focus on academic pursuits at home, reducing screen time and social media usage.

Monitoring Pemakaian Daya Listrik Rumah Berbasis IoT

The current prepaid and postpaid electrical payment systems still have limitation, in monitoring electrical powerconsumption. In postpaid electricity, recording through officers is still done manuallywhen monitoring electrical power usage, whereas for the prepaid system users cannot know electrical power usage in real time. Based on these conditions, a toolwas designed that capable of calculatingthe electrical power used by using home appliancesin real time and can be monitored remotely. This system is designed using the N. meanwhile, it can beode MCU ESP8266as the main controller and the PZEM-004T sensor to measure voltage (V), current(A), plectrical Powerower (W), and energy (kWh), as well as fees that must be paid (Rp) accordingly. Usage, whre the data will bedisplayed on a 20x4 LCD for on-site monitoring. Meanwhile, it canbe monitored remotely using Blynk IoT application whichis connected to the internet network. Generally this system can work with in error rate of &lt;5%, where testing is carried out for 20 minutes for indescent lamp loads.

Biased versus unbiased numerical methods for stochastic simulations

Approximate numerical methods are one of the most used strategies to extract information from many-interacting-agents systems. In particular, numerical approximations are of extended use to deal with epidemic, ecological and biological models, since unbiased methods like the Gillespie algorithm can become unpractical due to high CPU time usage required. However, the use of approximations has been debated and there is no clear consensus about whether unbiased methods or biased approach is the best option. In this work, we derive scaling relations for the errors in approximations based on binomial extractions. This finding allows us to build rules to compute the optimal values of both the discretization time and number of realizations needed to compute averages with the biased method with a target precision and minimum CPU-time usage. Furthermore, we also present another rule to discern whether the unbiased method or biased approach is more efficient. Ultimately, we will show that the choice of the method should depend on the desired precision for the estimation of averages.

Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL

In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.

Analysis and mining of user behavior on e-commerce platforms based on decision trees and RFM models

User transaction behavior has driven the development of e-commerce. This article reveals the key role of the development of the e-commerce industry and user behavior in driving it through in-depth research on e-commerce sales data. Firstly, by grouping and summarizing user IDs through Pandas, a comprehensive user consumption situation was obtained, including consumption frequency, amount, etc., and user consumption behavior was visualized through visual means. On the basis of constructing effective features, Matplotlib was used to depict the number of users who receive coupons every week and day, providing a basis for the establishment of subsequent coupon distribution prediction models. By splitting the payment dates, an RFM model was constructed to analyze the purchase time period, frequency, consumption amount, and the proportion of each user's time behavior, forming a detailed user profile. Finally, the decision tree model is applied to predict the probability of coupon usage.