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The burnout, stress, and work-life balance challenges faced by teachers have received renewed interest due to the myriad disruptions and changes to K-12 schooling brought about by the COVID-19 pandemic. Even prior to the pandemic, relatively little was known about teachers' time use outside of the classroom, the blurring of work and home boundaries, and how teachers compare to similar professionals in these regards. We use daily time-diary data from the American Time Use Survey (ATUS) for 3,168 teachers and 1,886 professionals in similarly prosocial occupations from 2003 to 2019 to examine occupational differences in time use. Compared to observationally similar non-teachers, teachers spend significantly more time volunteering at their workplace and completing work outside the workplace during the school year. On average, teachers spend 19 more minutes working outside of the workplace on weekdays than observably similar non-teachers and 38 more minutes on weekends. The weekend disparity is particularly large among secondary school teachers. This suggests that before the widespread switch to online and hybrid learning necessitated by the COVID-19 pandemic, teachers were already navigating blurrier work-life boundaries than their counterparts in similar professions. This has important implications for teacher turnover and for the effectiveness and wellness of teachers who remain in the profession.

Relationships have changed dramatically in the last 50 years. Fewer couples are marrying, more are cohabiting. Reasons for this shift include more attractive labor market opportunities for women and changing social norms, but the shift may have consequences of its own. A number of models predict that those cohabiting will specialize less than those marrying. Panel data on time use – particularly housework time – as well as on the degree of specialization in more narrowly defined household tasks from the 2001–2019 waves of the Household, Income and Labour Dynamics in Australia survey are used to test this prediction.

The time use data for men provides only limited supporting evidence for specialization. The results for women are much stronger. Women who marry without first cohabiting increase their reported housework time more than those who enter cohabitations (by 3.7 hours versus 1.2 hours). The latter generally make up a third of the difference if they do marry. Expanding the analysis to other uses of time yields some further evidence of specialization.

Survey responses on the degree of specialization are more informative. The raw data show substantial intrahousehold specialization and further analysis reveals that on average married couples specialize more than cohabiting couples. Adding couple-specific fixed effects reveals that specialization increases when cohabiting couples marry. Interestingly, there does not appear to be a substantial tradeoff between tasks; partners who report specializing more on one task are more likely to report specializing on other tasks as well. Given the important roles couples have in family formation and the labor market, it is important to understand this intrahousehold behavior.

The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.

First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.

According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.

This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

Using the American and the French time-use surveys, we examine whether people have a preference for a more diversified mix of activities, in the sense that they experience greater well-being when their time schedule contains many different activities rather than is concentrated on a very small number. This could be due to decreasing marginal utility, as is assumed for goods consumption, if each episode of time is conceived as yielding a certain level of utility per se. With returns to specialization, people would then face a trade-off between efficiency and diversity in choosing how to allocate time. We examine these issues and investigate potential gender differences, considering both instantaneous feelings and life satisfaction.

Using the two waves of the India Time Use Survey, 1998–1999 and 2019, we document a 110-minute (30%) increase in average daily learning time. The largest offsetting decrease was in work time: 61 minutes. The composition of leisure changed, with television rising by 19 minutes, while talking fell by 10 minutes and games by 17 minutes. We then implement a Gelbach decomposition, showing that 68 minutes of the unconditional learning increase are predicted by demographic covariates. Of these predictors the most important are a child's state of residence and usual principal activity, which captures extensive-margin transitions into schooling.

We make use of rich microdata from the Belgian MEqIn survey, which contains detailed information on individual consumption, public consumption inside households, and time use. We explain the observed household behavior by means of a collective model that integrates marriage market restrictions on intrahousehold allocation patterns. We adopt a revealed preference approach that abstains from any functional form assumptions on individual utility functions or intrahousehold decision processes. This allows us to (set) identify the sharing rule, which governs the intrahousehold sharing of time and money, and to quantify economies of scale within households. We use these results to conduct a robust individual welfare and inequality analysis, hereby highlighting the important role of detailed consumption and time use data.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

This study aims to explore how brands use brand personification techniques in real-time marketing on social media, particularly Twitter, and examine how these techniques impact consumer engagement, moderated by brand-event congruence levels.

Data included 464 tweets posted by 95 brands around three large events in 2019. The types of brand personification techniques and the level of brand-event congruence applied by the tweets were content-analyzed, and regression analyses were conducted to examine their linkages to consumer engagement metrics.

Results confirmed the use of diverse personification techniques in brands’ real-time marketing tweets as in the previous literature. The study also revealed a new personification technique, tacit expression, not reported in previous literature. The study also showed that the overall effectiveness of multimedia-based (vs caption-based) personification techniques in increasing consumer engagement on social media was greater, but their relative effectiveness varied depending on whether or not the event was functionally congruent with the brand.

The findings offer valuable suggestions to brand managers regarding prioritizing brand personification techniques and aligning brands’ social media marketing with real-time events to maximize the effectiveness of real-time marketing in boosting consumer engagement.

This research offers insights into the dynamic effects of different brand personification techniques in the new context of real-time marketing, extending the scope of literature on brand personification and anthropomorphism. The revelation of a new type of brand personification not captured in the extant literature is also a significant contribution.

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

The paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.

Both partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.

The simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.

The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

This paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.