Time-related Characteristics Research Articles

The Relationship Between Time-Related Characteristics of Visual Impairment and Psychological Symptoms in Adults Who Are Blind

Introduction The prevalence of depression among blind people is higher than in sighted populations. The purpose of this study was to investigate the associations between time-related characteristics of visual impairment (i.e., onset and duration), as well as depressive symptoms, fatigue, sleepiness, and stress, in the context of self-reported total blindness and the inability to perceive light exposure. Methods This study employed a cross-sectional design utilizing the combination of online survey and telephone survey. The Centre for Epidemiological Studies–Depression Inventory, the Fatigue Syndrome Inventory, the Epworth Sleepiness Scale, and the Perceived Stress Scale were selected for the self-reported questionnaires. For statistical analysis, independent t-tests, analysis of variance tests, Wilcoxon–Mann–Whitney tests, chi-square tests, multiple linear regression models, and multivariable ordinal logistic regressions were conducted using the R software program and its required packages. Results A total of 154 respondents completed the online and telephone surveys. Although the number of years of experiencing blindness showed negative correlations with the scores of fatigue ( r = −.16, p < .05), stress ( r = −.18, p < .05), and depressive feelings ( r = −.21, p < .05) in Model 1, the impacts of the onset of blindness on the depressive symptoms (β = −.02, p = .570) and the presence of severe depressive symptoms, OR = 1.42, 95% CI [0.49, 4.19], p = .518, were insignificant in Model 2. Discussion Blindness seems to be regarded as a more severe chronic condition which affects individuals’ psychological status. Given the effects of the limited range of social activities and daily tasks and the dramatically reduced accessible visual information inputs, the severe depressive symptoms in this group can be understood. Implications for Practitioners Regular counseling and psychological services monitoring depressive symptoms, chronic fatigue, and severe distress should be continued even after the completion of rehabilitation programs.

A systematic data characteristic understanding framework towards physical-sensor big data challenges

Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data.

Prediction of energy production in a building-integrated photovoltaic system using machine learning algorithms

Globally, the building sector is a significant consumer of energy. Implementing effective energy management system (EMS) strategies is crucial for reducing energy consumption and optimizing energy usage across all building elements. Moreover, solar energy is increasingly recognized as essential for sustainable building and urban development. Thus, the energy production forecast from photovoltaic systems installed on buildings is pivotal for a smart EMS's realization and optimal functioning. By incorporating accurate energy production forecasts, we can optimize energy consumption by fitting consumption patterns with production peaks and valleys. This study presents a comparative analysis of five widely used machine learning regressing models, including Random Forest, Decision Trees, XGBoost, LightGBM, and CatBoost for predicting the hourly energy production from a PV system mounted on a building. In this investigation, weather data and time-related characteristics were utilized as predictors. The results showed that LightGBM outperformed the other models, proving its suitability for PV energy forecasting tasks.

Towards Sustainable Buildings: Predictive Modeling of Energy Consumption with Machine Learning

The building sector's energy consumption remains a significant problem, requiring the development of powerful energy management systems to improve energy efficiency. The main aim of this research is to forecast energy use through indoor temperature, relative humidity, time, and time lag as crucial factors. The research project aims to enhance power consumption forecasting throughout the operating phase of buildings by using advanced machine learning (ML) regression models such as XGBoost, LightGBM, Extra Trees, and Random Forest. The dataset includes 10 days during which the building's operation testing took place, illustrating the complexities of real-world conditions. The inclusion of time-related characteristics and time delay arises as a critical element in enhancing prediction accuracy, emphasizing the temporal sensitivity of power use. A hyperparameter tuning method was used to improve the performance of the implemented ML algorithms. The results emphasize the effectiveness of Extra Trees as the most suitable model for forecasting power consumption in building operations with an accuracy of 88.41%.

Spatial Distribution and Enrichment Dynamics of Foodborne Norovirus in Oyster Tissues.

The prevalence of norovirus in oysters poses a significant threat to food safety, necessitating a comprehensive understanding of contamination patterns. This study explores the temporal dynamics of norovirus distribution in various oyster tissues over a contamination period ranging from 6 to 96 h. Four tissues-the gill, palp, digestive gland, and stomach-were subjected to systematic monitoring using RT-qPCR for absolute quantification. Results revealed rapid norovirus detection in all tissues six hours post-contamination, with subsequent variations in detection rates. Gill and digestive gland tissues exhibited a peak in detection at 12-24 h, aligning with the oyster's gastrointestinal circulatory system. The digestive gland, distinguished by specific enrichment and adsorption capabilities, demonstrated the highest virus concentration at 48 h. In contrast, the stomach displayed a reemergence of norovirus. Beyond 72 h, detection remained exclusive to the digestive gland, with Ct values comparable to earlier time points. At 96 h, a limited amount of norovirus was detected in the digestive gland, emphasizing the importance for timely monitoring. In addition to providing critical insights into optimal detection strategies, these findings highlight the time-related characteristics of norovirus contamination in oysters. The study identifies the digestive gland as a key target for reliable monitoring, providing valuable data to improve protocols for reducing hazards associated with oyster consumption and foodborne norovirus infections. This research contributes to the understanding of norovirus dynamics in oyster tissues and reinforces current efforts aimed at ensuring food safety and public health.

Time-Related Characteristics of Medical and Psychosocial Decisions in Japanese Women with Breast Cancer: Results of a Web-Based Open-Ended Response.

This study aimed to articulate chronological characteristics of decisions made by Japanese women with breast cancer (BC) since their diagnosis. Open-ended questions were asked using an Internet-based cross-sectional survey method. Qualitative content analysis was performed on 1,158 free descriptive responses obtained from 549 participants, which were categorized according to the content of decisions. Furthermore, 994 text data from 433 respondents to the quantitative questions were categorized according to the decisions' timing and examined in relation to medical and sociodemographic factors. Whereas more than 60% of medical decisions, except chemotherapy, were made before initial treatment, approximately more than one-third of sociopsychological decisions were made only after the initial treatment. In decisions regarding medical care, only surgical decisions showed an association between timing and the participants' decision-making style. Meanwhile, in decisions regarding sociopsychological matters, socioeconomic status at the time of diagnosis, such as marital and employment status, along with the perceived importance of what was to be determined, were associated with the timing of decision-making related to employment, attitudes toward life with BC, family matters and financial affairs. Women make various decisions depending on the amount of time since the diagnosis of BC. Generally, medical decisions are made prior to initial treatment, while these temporal characteristics are not observed for decisions relating to sociopsychological matters. Furthermore, socioeconomic status influences the timing of decision-making regarding sociopsychological matters. This finding can illustrate the manner in which to go through life with BC, and thus, help women who are unexpectedly diagnosed with BC to be more prepared.

Timelines of adverse event journeys of LVAD patients.

The INTERMACS Events data set contains an expansive collection of temporal evidence of the course of adverse events (AEs) of >15 000 patients that have received a left ventricular assist device (LVAD). The chronology of AEs may contain insightful information of the "AE journeys" of LVAD patients. The purpose of this study is to investigate the timelines of AEs within the INTERMACS database. Descriptive statistics were applied to 86 912 recorded AEs of 15 820 patients with a continuous flow-LVAD between 2008 to 2016, extracted from INTERMACS registry. The characteristics of the timelines of AE journeys were investigated by posing six descriptive research questions. The analysis revealed several time-related characteristics and patterns of the AE journey after LVAD including the most common time of occurrences of AEs after surgery, duration of AEs journeys, the time of first and last AEs, and the time gaps between AEs. The INTERMACS Event dataset is a valuable resource for research about the timeline of AE journeys of patients who received an LVAD. It is necessary for future studies to first explore and consider the time-related characteristics of the data set such as diversity and sparsity to effectively choose an appropriate scope of time and time granularity and to acknowledge potential challenges.

Time-resolved encryption via photochromic and mechanochromic system based on silane-substituted spiropyran

With the continuous progress of forgery and decryption, the traditional static encryption technology is becoming more and more inadequate, which also strongly demands the development of multilevel anti-counterfeiting materials and advanced multi-dimensional encryption strategies and technologies. Here a new strategy to realize time-resolved encryption based on a silane-substituted spiropyran is presented, which exhibits tunable dynamic photochromic features caused by the reversible photochromic and mechanochromic properties with exceptional reversible absorption/luminescence modulation ability both in powder and crystal state. This compound shows excellent photochromic properties, including extremely rapid response time, high contrast color change, high reversibility etc. In addition, the growth of large-size needle-like single crystal by solution evaporation is helpful to further explore the mechanism of grinding discoloration of the compound. What's more, it is possible to modulate the luminescence emission and color change by simply changing the ultraviolet light stimulation time or grinding time, providing fine and adjustable time-related characteristics for the material, which can not only be used for data processing with more extended information, but also be used to construct confidential materials by time-resolved multidimensional encryption and dynamic anti-counterfeiting.

The Construction and Exploration of University English Translation Teaching Mode Based on the Integration of Multimedia Network Technology

The teaching mode of English translation in universities has been changed greatly, so the design of teaching mode is needed to be optimized. Besides, the network teaching mode should be studied. Specifically, in this article, the advantages of English translation teaching in network environment are analyzed. Finally, we focus on the effective countermeasures to solve the problems of translation teaching mode and provide the reference values for teaching staff. This paper summarizes the integration of information technology and interpretation teaching according to the evolutionary route of technology application from three aspects: (1) general field, (2) interpretation professional field, and (3) education field. Furthermore, we propose a correlation analysis method of English translation teaching based on data fusion technology. Considering the time-related characteristics of English translation teaching, a perturbation time period division method based on moving time window is proposed based on the actual data and the development results of cutting-edge technologies. Finally, interpretation teaching is foreseen in four aspects: intelligence, simulation, precision, and collaboration.

Identifying the Causal Relationship between Travel and Activity Times: A Structural Equation Modeling Approach

This study aims to identify the causal relationship between travel and activity times using the dataset collected from the 2019 Time Use Survey in Korea. As a statistical solution, a structural equation model (SEM) was developed. A total number of 31,177 and 20,817 cases were used in estimating the weekday and weekend models, respectively. Three types of activities (subsistence, maintenance, and leisure), 13 socio-demographic variables, and a newly proposed latent variable (vitality) were incorporated in the final model. Results showed that (1) the magnitude of indirect effects were mostly greater than that of direct effects, (2) all types of activities affected travel time regardless of what the travel purpose was, (3) travel can be treated as both a utility and disutility, and (4) personal status could affect the travel time ratio. It indicates the significance of indirect effects on travel time, thereby suggesting a broad perspective of activities when establishing a transportation policy in practical areas. It also implies that unobserved latent elements could play a meaningful role in identifying travel time-related characteristics. Lastly, we believe that this study contributes to literature by clarifying a new perspective on the lively debated issue discussing whether travel time is wasted or productive.

It’s About Time: Understanding Job Crafting Through the Lens of Individuals’ Temporal Characteristics

Job crafting refers to the myriad ways employees customize their jobs, such as by altering their tasks and social interaction at work. Numerous scholars over the past 20 years have remarked on the overall need to better understand the role of time in job crafting. However, the literature has not considered how employees think about time, or, relatedly, how they use and manage it—and why this might matter for job crafting. To address these unresolved issues, the current paper develops a conceptual model of individual-level, time-related characteristics that shape employees’ engagement in job crafting and the effects of job crafting efforts on their well-being. We first review the prevailing understanding of time in job crafting research: merely operating as a medium for change, in the background. We then introduce our new conceptualization of time as central to job crafting—as temporal characteristics of the job crafter—and develop a conceptual model in which time-related constructs play a key role in influencing job crafting and its effects. Our model proposes that employees’ career stage, as well as their polychronicity preference and temporal focus, predicts engagement in job crafting, whereas employees’ time management and time urgency act as key moderators that shape the implications of job crafting for employees’ well-being. By theorizing on time in job crafting, our model thus contributes to understanding relevant antecedents and outcomes of job crafting. We conclude our paper by offering an agenda for future research to further incorporate the role of time in job crafting.

Retrospective identification of latent subgroups of emergency department patients: A machine learning approach.

This research aims to (i) identify latent subgroups of ED presentations in Australian public EDs using a data-driven approach and (ii) compare clinical, socio-demographic and time-related characteristics of ED presentations broadly using the subgroups. We examined presentations to four public hospital EDs in Queensland from 2009 to 2014. An unsupervised machine learning algorithm, Clustering Large Applications, was used to cluster ED presentations. There were six subgroups common across the EDs, primarily distinguishable by age, and subsequently by triage category, ED length of stay, arrival mode, departure status and several time-related attributes. Around 10% to 30% of the total presentations had high resource utilisation, with half of these from older patients (55+ years). ED resource utilisation per population was highest among the oldest cohort (75+ years). Children and young adults more frequently presented to the ED outside general-practitioner hours, mostly on Sundays. Older persons were more likely to present at any time, rather than specific hours, days or seasons. ED service performance measured against commonly used access-target indicators were rarely satisfied for older people and frequently satisfied for children. Clustering Large Applications is effective in finding latent groups in large-scale mixed-type data, as demonstrated in the present study. Six types of ED presentations were identified and described using clinically relevant characteristics. The present study provides evidence for policy makers in Australia to develop alternative ED models of care tailored around the care needs of the differing groups of patients and thereby supports the sustainable delivery of acute healthcare.

Clinical Practice Guideline for Physical Therapist Management of People With Rheumatoid Arthritis.

This guideline revises the 2008 Royal Dutch Society for Physical Therapy guideline for physical therapy for patients with rheumatoid arthritis (RA). This revised guideline was developed according to the Appraisal of Guidelines for Research and Evaluation tool and the Guidelines International Network standards. A multidisciplinary guideline panel formulated clinical questions based on perceived barriers in current care. For every clinical question, a narrative or systematic literature review was undertaken, where appropriate. The guideline panel formulated recommendations based on the results of the literature reviews, the values and preferences of patients and clinicians, and the acceptability, feasibility, and costs, as described in the Grading of Recommendations Assessment, Development and Evaluation evidence-to-decision framework. The eventual guideline describes a comprehensive assessment based on the International Classification of Functioning, Disability and Health Core Set for RA. It also includes a description of yellow and red flags to support direct access. Based on the assessment, 3 treatment profiles are distinguished: (1) education and exercise instructions with limited supervision, (2) education and short-term supervised exercise therapy, and (3) education and intensified supervised exercise therapy. Education includes RA-related information, advice, and self-management support. Exercises are based on recommendations concerning the desired frequency, intensity, type, and time-related characteristics of the exercises (FITT factors). Their interpretation is compliant with the individual patient's situation and with public health recommendations for health-enhancing physical activity. Recommended measurement instruments for monitoring and evaluation include the Patient-Specific Complaint instrument, Numeric Rating Scales for pain and fatigue, the Health Assessment Questionnaire Disability Index, and the 6-minute walk test. An evidence-based physical therapy guideline was delivered, providing ready-to-use recommendations on the assessment and treatment of patients with RA. An active implementation strategy to enhance its use in daily practice is advised. This evidence-based practice guideline guides the physical therapist in the treatment of patients with RA. The cornerstones of physical therapist treatment for patients with RA are active exercise therapy in combination with education. Passive interventions such as massage, electrotherapy, thermotherapy, low-level laser therapy, ultrasound, and medical taping play a subordinate role.

Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming

Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of students’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.

Telemetry Data-Based Spacecraft Anomaly Detection With Spatial–Temporal Generative Adversarial Networks

The telemetry data obtained from an on-orbit spacecraft contain important information to indicate anomaly of the spacecraft. However, the large number of monitoring variables and the large amount of data points, as well as the lack of prior knowledge about anomaly due to complicated structure of spacecraft and its working conditions, pose great challenge to the anomaly detection. This article proposes an anomaly detection algorithm based on a spatial–temporal generative adversarial network (GAN) for the anomaly detection in telemetry data. This algorithm establishes a GAN-based model combining convolutional neural network (CNN) and long short-term memory (LSTM) to extract spatial and temporal features of the telemetry data, which facilitates the automatic and simultaneous representation of nonnegligible time-related characteristics of a monitoring variable and complex correlation between variables. Using these features, many kinds of anomalies including multivariate anomalies and contextual anomalies can be detected. Moreover, an anomaly score specifically designed to fit the GAN-based algorithm is also proposed to evaluate the possibility of anomaly by weighted fusion of the generator metric and the discriminator metric, which is proved to be significantly helpful to the accuracy of anomaly detection. Finally, experiments on one real telemetry data set and two public telemetry data sets are conducted, by which the proposed anomaly algorithm is demonstrated to be effective and accurate in detecting outliers in telemetry data. Comparison with three other state-of-the-art methods also reveals the advantages of our proposed algorithm.

The Use of Cross-Sectional and Longitudinal Weights in Korean Health Panel Survey

In household panel surveys, the same households and individuals are interviewed at multiple time points and then information is obtained from them. Panel studies have considerable analytical advantages over one-time surveys such as analysis of gross change, analysis of unit-level change, analysis of time related characteristics of events and identifying causality [1,2]. However, panel surveys often suffer from a high degree of panel attrition between waves, where panel attrition means the continued loss of respondents from the original sample due to mortality, migration between waves and survey non-response [2,3].

Perception of atomic speech

Natural speech signals are continuous and have great redundancy in time, which is learned by human beings and guarantees speech comprehension even in challenging circumstances. This work develops a sound synthesis method to study some time-related characteristics of the human auditory system. The temporal information within the individual frequency band of a speech is down-sampled using Gaussian-shaped pulses and then recombined into a new sound which may have some remaining intelligibility or feasible phonetic features. The “atomic” sound is coined to the sound with an extremely spectral-temporally sparse spectrogram generated using the method. A battery of speech perception tests was administered in normal-hearing listeners. Results show that (1) atomic sounds from clear speech can be understood as speech, although the listeners often reported a feeling of water sound textures; (2) the temporal and spectral resolution could be traded off in the atomic speech comprehension; and (3) only one maximum envelope value preserved among a 32-channel filter-bank at a rate of 400 Hz was surprisingly adequate for speech understanding, which indicates that the brain can organize the only spectral peak within each short duration no longer than 2.5-ms into sentence understanding without either explicit or implicit encoding of the first three formants.

Beyond Calendars and Maps: Rethinking Time and Space for Effective Knowledge Governance in Protected Areas

Protected area managers rely on relevant, credible, and legitimate knowledge. However, an increase in the rate, extent, severity, and magnitude of the impacts of drivers of change (e.g., climate change, altered land use, and demand for natural resources) is affecting the response capacity of managers and their agencies. We address temporal aspects of knowledge governance by exploring time-related characteristics of information and decision-making processes in protected areas. These areas represent artefacts where the past (e.g., geological periods and evolutionary processes), the present (e.g., biodiversity richness), and the future (e.g., protection of ecosystem services for future generations) are intimately connected and integrated. However, temporal horizons linked with spatial scales are often neglected or misinterpreted in environmental management plans and monitoring programs. In this paper, we present a framework to address multi-dimensional understandings of knowledge-based processes for managing protected areas to guide researchers, managers, and practitioners to consider temporal horizons, spatial scales, different knowledge systems, and future decisions. We propose that dealing with uncertain futures starts with understanding the knowledge governance context that shapes decision-making processes, explicitly embracing temporal dimensions of information in decision-making at different scales. We present examples from South Africa and Colombia to illustrate the concepts. This framework can help to enable a reflexive practice, identify pathways or transitions to enable actions and connect knowledge for effective conservation of protected areas.

Comprehensive analysis of miRNA profiles reveals the role of Schistosoma japonicum miRNAs at different developmental stages

Schistosomiasis is an important zoonotic disease affecting up to 40 kinds of animals and 250 million people. It has been reported that the miRNAs play a role in the metabolism, differentiation, development and reproduction in many organisms. However, the roles of miRNAs regulating the development, maturation and production in schistosome in both females and males remains unclear. Here we present the dynamic transcriptome analysis of all 79 known Schistosoma japonicum miRNAs from pairing to production, including 14 days post-infection (dpi), 16, 18, 20, 22, 24, 26, 28 dpi female and male, by small RNA sequencing. The miRNA expression profiles showed time-related characteristics in male and female from paring to production, which could be clustered into three patterns, characterized by pairing stage highly expressed (cluster 1), maturating stage highly expressed (cluster 2), and egg producing stage highly expressed (cluster 3). The enrichment of miRNA cluster targeted genes in female and male were distinctly different. Network analysis of miRNAs and their target regulation showed that cluster 1 had 15 miRNAs involved in the regulation of interaction, communication, immune response in female–male and parasite–host. The other 11 miRNAs were involved in gender differentiation and the meiotic cell cycle process. In cluster 2, 11 miRNAs were involved in development and sexual maturation. In cluster 3, 45 miRNAs possibly regulate metabolism and synthesis of the substance for egg production. Analysis of the miRNA regulation network would contribute to understanding the molecular mechanism in S. japonicum development and egg production.

Towards a Methodological Framework for Sequence Analysis in the Field of Self-Regulated Learning

In recent decades, conceptualizations and operationalizations of self-regulated learning (SRL) have shifted from SRL as an aptitude to SRL as an event. Alongside this shift, increased technological capability has introduced computer log files to the investigation of SRL, uncovering new research avenues. One such avenue investigates the time-related characteristics of SRL through learners’ behavioural sequences. Although sequence analysis is still relatively new in SRL research, other fields have fruitful traditions in its application and may serve as a basis for applications in the field of SRL. Ten years of investigating SRL through sequence analysis have produced a wide range of methodological approaches. While this variety of methods illustrates the diversity of opportunities, it also indicates the lack of consensus regarding the most appropriate approach. Since the introduction of sequences analysis in the field of SRL, researchers have emphasized the need for a methodological framework to guide its application. Yet, to date, no such framework has been proposed, hindering our progress through (1) transparent methods and (2) comparative studies to (3) empirical and ecological applications. To help overcome this issue, this manuscript aims to foster discussions of a methodological framework for the use of sequence analysis in SRL research. We first make a case for why such a framework is necessary; secondly, we propose a set of considerations which could serve as a starting point for the construction of a framework.