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Presentation on usage data for HathiTrust's Emergency Temporary Access Service (ETAS).

For managing the supply-demand imbalance in free-floating bike sharing systems, we propose dynamic hubbing (i.e., geofencing areas varying from one day to another) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, the basic concept is that dynamic hubs are determined to encourage users to return bikes to desired areas towards the end of the day through a user incentive program. Then, for the remaining unbalanced bikes, an operator-based rebalancing operation will be scheduled. The proposed modeling and optimization solution algorithm determines the number of hubs, their locations, the start time for initiating the user incentive program, and the amount of incentive by considering two conflicting objectives, i.e., level of service and rebalancing cost. In this study, for free-floating bike sharing, the level of service is represented by the walking distance of users for locating a usable bike, which is different from level of service metrics commonly used by station-based bike sharing, and the rebalancing cost is weighted incentive credits plus operator-based rebalancing cost. We implemented the proposed method on the Share-A-Bull free-floating bike sharing system at the University of South Florida. Results show that a hybrid rebalancing and dynamic hubbing strategy can significantly reduce the total rebalancing cost and improve the level of service. Moreover, taking the advantage of crowd-sourcing (or job-sharing) reduces negative impacts—energy consumption and greenhouse gas emissions—of the operation of rebalancing vehicles and makes bike sharing a more promising environmentally friendly sharing transportation mode.

Problem definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, composed mainly of proprietary lockers and collection points in commercial areas set up by large delivery companies. How do we determine the density and coverage of the LA network to promote adoption of locker pickup in Singapore? What will be the impact on the delivery profile in the central business district, far from the residential areas? Academic/practical relevance: We discuss the operational challenges associated with the problem of public locker installation in a city, following a new smart nation initiative in Singapore. We used data analytics to address the following questions: What are the chances that a customer will choose to pick up the parcel from a locker, over home or office deliveries, based on walking distance (to lockers) and a variety of other features? Without knowing the transit routes of the customers, how do we design the LA network to ensure that the lockers will be well utilized? Methodology: We use a set of locker usage data from a commercial courier company to calibrate a locker choice model to determine the impact of walking distance on locker pickup intentions. We use the current (observed) parcel delivery profile to develop a facility location model for the LA network. We use this model to extrapolate and approximate the true adoption and new delivery profiles when the LA network is built. Results: Contrary to conventional wisdom, our model does not always place lockers near areas with peak parcel volume (in preexisting data) because the LA lockers provide another option for customers to pick up from lockers near residential areas. Furthermore, the model suggests that a coverage of 250 meters is appropriate for the LA network in Singapore. Managerial implications: Commercial parcel locker installation has traditionally focused on hot spots in the transit routes of the citizens in the city. The LA network is the first attempt in Singapore to allow public lockers in residential areas. This paper develops an analytical method to determine network density and coverage based on a locker choice model and argues how useful insights can be gleaned from the model, despite not having the full transit route information of all citizens in the city.

This presentation was delivered at Open Repositories 2023 Conference as part ofPanel 5: Supporting Trusted OA Usage Data Reporting and Analytics through Shared Infrastructure.

This presentation was delivered at Open Repositories 2023 Conference as part ofPanel 5: Supporting Trusted OA Usage Data Reporting and Analytics through Shared Infrastructure.

The OA Book Usage Data Trust is exploring how an International Data Space (IDS) can address issues of trust, data quality, and resource-burdens for OA usage data reporting and analytics. Repositories must normalize, curate, and aggregate open usage and impact metrics to articulate the relative impact of repository participation for a given scholar or institution. While well-resourced organizations may have the time and expertise to put their own usage statistics in an aggregated cross-platform context, spanning open and privileged usage data from COUNTER and non-COUNTER compliant reports, smaller repositories often do not have this capacity. To address this disparity and improve the quality and interoperability of OA book usage data, OA book publishing stakeholders are exploring how to adopt the international data space (IDS) models and standards. Awarded 1.2Million USD by the Mellon Foundation, a team led by OPERAS, OpenAIRE, and the University of North Texas are hosting community consultations to determine what data stewardship and security practices are needed to achieve trusted direct usage data exchange at scale. This poster will provide background to the OAEBU Data Trust effort and a preview of emerging participation and ethical data exchange and use guidelines for the data trust.

In this study, a metaheuristic-based method, called Balanced Water Strider Optimization Algorithm (BWSOA), is presented to predict the thermal variables of a building. The measurement of these variables is not easy and the determination of them by features of materials is difficult. Also, it depends on the building type and the structure of the building The variable prediction problem is calculated as a single-objective optimization problem and the DesignBuilder™ is utilized to design the thermal model, which is a black-box here. The simulated and practical energy usage data of the building is utilized to show the operation of the suggested model by employing the model to two various geographical conditions placed in New Zealand. The algorithm performance is more showed by assuming simulated energy usage data; made after subjecting the thermal model of the building to a simulated geographical environment of another city (Tauranga); with the considerable various weather condition in comparison to that of Wellington. The obtained results stated that the suggested method can estimate these variables efficiently. Moreover, this approach can be efficient to develop energy management systems for the building.

In everyday life, electricity is necessary, and proper use is critical. To strengthen home electricity control, the existing systems have been examined over the years. However, the existing PMAS method’s error ratio is higher and does not allow for a remote monitoring system. Therefore, this study proposes a smart monitoring and control system (SMACS) for household appliances. The application’s significance is to monitor household appliances’ electricity usage using hardware and the Internet of Things (IoT) methods. The prototype of the proposed system is designed and developed considering Arduino UNO, a liquid crystal display (LCD), an ACS712 current sensor module, relays, and AC sources. The components are selected from the software library, and the simulation results are found the same as the prototype. WiFi module ESP8266 is not included in the design because it is not provided in the system. The data is recorded in cloud storage using Thing-speak. A mobile application (Virtuino) also accesses the data to visualize it through the graphical and numerical display. This study provides users with an easy system to monitor and control household appliances’ power consumption using mobile applications. Results show that the proposed system provides 0.6% current errors for the hairdryer appliance, whereas the existing Power Monitoring and Switching (PMAS) system provides 7.8% current errors.

Dictionary usage research views dictionaries primarily as tools for solving linguistic problems. A large proportion of dictionary use now takes place online and can thus be easily monitored using tracking technologies. Using the data gathered through tracking usage data, we hope to optimize user experiences of dictionaries and other linguistic resources. Usage statistics are also used for external evaluation of linguistic resources. In this paper, we pursue the following three questions from a quantitative perspective: (1) What new insights can we gain from collecting and analysing usage data? (2) What limitations of the data and/or the collection process do we need to be aware of? (3) How can these insights and limitations inform the development and evaluation of linguistic resources?

Background: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-to- use, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. Objective: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. Methods: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggre- gated, based on log ins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. Conclusion: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice. Keywords: UpToDate clinical decision support tool; Makerere University College of Health Sciences; Uganda.

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

The amount of retrievable smartphone data is escalating; while some apps on the smartphone are evidently exploiting and leaking users’ data. These phenomena potentially violate privacy and personal data protection laws as various studies have showed that technologies such as artificial intelligence could transform smartphone data into personal data by generating user identification and user profiling. User identification identifies specific users among the data based upon the users’ characteristics and users profiling generates users’ traits (e.g. age and personality) by exploring how data is correlated with personal information. Nevertheless, the comprehensive review papers discussing both of the topics are limited. This paper thus aims to provide a comprehensive review of user identification and user profiling using smartphone data. Compared to the existing review papers, this paper has a broader lens by reviewing the general applications of smartphone data before focusing on smartphone usage data. This paper also discusses some possible data sources that can be used in this research topic.

Mobile health technologies are increasingly used by cardiovascular nurses and allied health clinicians to support behaviour change and self-care in patients. Randomized controlled trials provide rigorous methodology for evaluation but progress slowly, so the technology is often outdated by completion. A helpful modification to the trial design includes identification of the behaviour intervention principles involved and concurrently track in-app usage data, allowing updates to be made. These data collected alongside user experiences enables analysis of effects on outcomes and determination of the relative influence of component parts. Cardiovascular and mental health mHealth strategies are used to exemplify these recommendations.

The purpose of this study is to analyze and visualize internet usage data in the Jabodetabek area for the last 3 years, namely 2017, 2018, 2019 using quantitative methods which will display data per city and per year which is processed into the percentage of internet usage in the area. In addition, we also used the K-means clustering method as a method of grouping data and we chose to use the age variable for analysis. Along with the development of information technology, governments in various regions have also begun to expand the circulation of the internet to broaden people's insights and disseminate information more easily. However, in the current situation and conditions, it is felt that the distribution of internet use, especially in the Jabodetabek area, is still not optimal. Even though the area is a metropolitan city as the center of urban community activity. Therefore, through data on the percentage of Jabodetabek areas that access the internet, it can be seen as one of the factors for the government to be able to increase the performance of the internet evenly. The data will be visualized using the Tableau application in graphical form to facilitate information delivery to users in reading the results of data analysis.

Providence College is a Catholic, Dominican, liberal arts institution of higher education and a community committed to academic excellence in pursuit of the truth, growth in virtue, and service of ...

Objective This study evaluated the validity of self-reported smartphone usage data against objectively-measured smartphone usage data by directly tracking the activities in the participants’ smartphone among Chinese adolescents and young adults in Hong Kong.Methods A total of 187 participants were recruited (mean age 19.4, 71.7% female) between 2017 and 2018. A smartphone usage tracking app was installed on all participants’ smartphone for 7 consecutive days. After the 7-day monitoring period, they completed a selfadministered questionnaire on smartphone usage habits.Results Although the correlation between self-reported and objectively-measured total smartphone usage time was insignificant (ρ=-0.10, p=0.18), in three out of the four usage domains were positively and significantly correlated, namely social network (ρ=0.21, p=0.005), instant messaging (ρ=0.27, p

The COVID-19 pandemic has had an impact on education and re-search in universities throughout the world. Many academic libraries have been closed, and users have had to use e-books instead of printed books in libraries. As e-books are not yet commonly used in Japan, this paper examines the impact of the pandemic on the use of e-books in academic libraries in Japan. As a case study, the usage data for each e-book platform in Kyushu University in Japan were analyzed, and the overall trends in each period before and during the pan-demic were revealed. The access counts of e-books from January to June 2019 and those for 2020 were examined. The number of total access counts in 2020 was higher than that in 2019 on all nine platforms. The platform that saw the highest access count growth rate was JSTOR, with an 846% increase. The us-age of e-books in the Maruzen eBook Library, which holds many Japanese textbooks, was examined in detail. E-books on mathematics, medical science, and programming languages were in constant use both before and during the pandemic. E-books in law and economics started to be used during the pandem-ic. These results indicate that the pandemic has evoked new needs for e-books in certain fields.

Despite large investments in smartwatch development, the market growth remains smaller than forecasted. The purpose of smartwatch use remains unclear, indicated by the lack of large-scale adoption. Thus, we aim to better understand the early adoption and everyday smartwatch use. We investigate a diverse usage data of smartwatches logged over a period of up to 14 months from 79 individuals between December 2015 and March 2017, one of the largest wearable datasets collected. First, we identify both explorative and accepted behaviours that users exhibit and further investigate how the individual usage traits and features differ between the two categories. Our analysis offers an insightful perspective on how smartwatch use evolves organically. Our results improve our shared understanding of smartwatch use and users adapting their use of smartwatch over time to match the capabilities of the technology by validating numerous findings from previous literature.

Designing and implementing online or digital learning material is a demanding task for teachers. This is even more the case when this material is used for more engaged forms of learning, such as inquiry learning. In this article, we give an informed account of Go-Lab, an ecosystem that supports teachers in creating Inquiry Learning Spaces (ILSs). These ILSs are built around STEM–related online laboratories. Within the Go-Lab ecosystem, teachers can combine these online laboratories with multimedia material and learning apps, which are small applications that support learners in their inquiry learning process. The Go-Lab ecosystem offers teachers ready–made structures, such as a standard inquiry cycle, alternative scenarios or complete ILSs that can be used as they are, but it also allows teachers to configure these structures to create personalized ILSs. For this article, we analyzed data on the design process and structure of 2414 ILSs that were (co)created by teachers and that our usage data suggest have been used in classrooms. Our data show that teachers prefer to start their design from empty templates instead of more domain–related elements, that the makeup of the design team (a single teacher, a group of collaborating teachers, or a mix of teachers and project members) influences key design process characteristics such as time spent designing the ILS and number of actions involved, that the characteristics of the resulting ILSs also depend on the type of design team and that ILSs that are openly shared (i.e., published in a public repository) have different characteristics than those that are kept private.

The automotive industry is transitioning towards a more digital future - not just the digitalization of the product itself but also the process of customer interaction, such as during the product selection process. In the product selection process, companies are enabled to collect and analyze large amounts of customer and usage data but are also challenged to understand the gathered digital data. In designing physical components, analyzing, and understanding such data can provide a competitive advantage. In gaining deeper insight into the digital selection process of cars and for understanding customers preferences related to perceived comfort of car seats, we applied a visual appearance study by presenting digital information of physical components.

Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer’s data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

Smartphones account for a large and growing portion of global electronic waste, and the life extension of electronic systems—particularly by refurbishment—is a viable solution for reducing this waste stream. In some secondary markets, sales of refurbished smartphones have begun to outpace sales of new smartphones. However, there are concerns over the resulting reliability and, in some cases, the safety of such products. This paper explores the environmental, reliability, and safety impact of extending the life of electronic systems, with a focus on smartphones. We find that smartphones undergo varying levels of refurbishment that consumers are often not aware of, the environmental advantages of refurbished devices are often outweighed by the disadvantages, and that refurbished electronics will never be as reliable as new products. It is thus necessary to create standards for smartphone refurbishment and to capture and analyze lifetime environmental and usage data to enable the assessment and prediction of the reliability of refurbished devices and their components.

Introduction Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. Methods Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. Results Three qualitatively different sequence clusters were identified. “Disengagers” (576/1,313) almost exclusively used the goal-setting feature. “Tryers” (375/1,313) used goal setting and two of the values- and acceptance-based components (“Be Aware,” “Be Willing”). “Committers” (362/1,313) primarily used two of the values- and acceptance-based components (“Be Willing,” “Be Inspired”), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). Discussion WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. Implications This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

We conduct an independent cluster validation study on published clustering solutions of a research testbed corpus, the Astro dataset of publication records from astronomy and astrophysics. We extend the dataset by collecting external validation data serving as proxies for the latent structure of the corpus. Specifically, we collect (1) grant funding information related to the publications, (2) data on topical special issues, (3) on specific journals’ internal topic classifications and (4) usage data from the main online bibliographic database of the discipline. The latter three types of data are newly introduced for the purpose of clustering validation and the rationale for using them for this task is set out. We find that one solution based on the global citation network achieves better results than the competitors across three validation data sources but that another solution based on bibliographic coupling performs best on the special issues data.

A tight connection between competence and performance is a central tenet of the usage-based model. Methodologically, however, corpus frequency is a poor predictor of acceptability – a phenomenon known as the “frequency/acceptability mismatch”. This article argues that the mismatch arises from a “methodological mismatch”, when simple frequency measures are mapped onto complex grammatical units. To illustrate, we discuss the results of acceptability judgments of go/come- v. The construction is subject to a formal constraint (Go see the doctor! vs. *He goes sees the doctor), which results from its mandative semantics (directives, commissives). While a formal model makes no prediction with regard to gradient acceptability of bare (“grammatical”) go/come-v, the usage-based view assumes that acceptability is a function of compatibility with an abstract schema. The experimental ratings are compared with a number of corpus-derived measures: while acceptability is largely independent of (raw) frequency, it is not independent of frequency-related usage distribution. The results add to recent suggestions that the frequency/acceptability mismatch is substantially reduced if the syntactic complexity of a unit is appropriately captured in usage data.

Aims: We have set forth three main objectives in the work presented in this paper, they are namely, to study how social networking media usage is surging over the time for three social media networks viz., Facebook, Twitter and LinkedIn, ii.to develop best fitting time series predictive models for predicting future usage of three network media and, iii. to make a comparative analysis to herald the ups and downs noticed in the usage across three network media considered. Study Design: Application of time series techniques for the analysis of social network user’s data. The main research question addressed by this work is to see how time series models augurs for time dependent data such as the one chosen in this research. Place and Duration of Study: Research Center, Department of Master of Computer Applications, Siddaganga Institute of Technology, Tumakuru, Karnataka, India, between January 2020- April 2020. Methodology: The work delved on collection three social network users (Facebook, LinkedIn, and Twitter) data for a span of nine years i.e., for the tenure 2011-2019. One dimensional, two dimensional and three dimensional visual analytics is made prior to time series analysis. Time series predictive analytics involved development of best fits for prediction. To select the best fits among linear, polynomial, exponential, power function and logarithmic models, mean absolute error and root mean square error metrics were used. Results: Linear, polynomial function trend lines proved to be the best for Facebook, LinkedIn and Twitter respectively with low values of MAE and RMSE and high values of regression coefficients as compared with other kinds of models. Apart from the error metrics, the Theil’s U-statistic values of 0.928, 1.008 and 1.21 for Facebook, Twitter and LinkedIn also heralded the fact that these functions are superior models when compared with other naïve models. It is also projected that by 2025, Facebook will see 10,000 billion, followed by LinkedIn at 1500 billion while Twitter would see 750 billion people if same kind of surge trend prevails in user numbers across three networks considered in this research. Conclusion: This paper presented a unique work which is supposedly deemed to be the first of its kind to the best of the knowledge of authors. The models come with a limitation that, they can provide accurate projection if the same trend prevails in the pattern of upheavals in usage.

In Europe, small electricity consumers are not using the full potential offered by smart meters. Although the European Union requires Member States to provide consumers with their energy usage data, small consumers are not using this data for improving their energy efficiency. This paper proposes: first, the standardization of the electrical load curve offered to small energy consumers at European level. Second, the use of open innovation challenges as a policy instrument in the European Union to improve the energy efficiency of the small electricity consumers and to encourage the development of new market niches. Finally, the paper quantifies how ICT energy efficiency solutions for small electricity consumers can contribute to the achievement of the 2020 European Union energy efficiency targets.

BACKGROUND: Physiologic neonatal hyperbilirubinemia (jaundice) is common and affects most newborn infants. However, there is a risk for permanent neurological damage if the bilirubin levels rise above a certain threshold. The management of neonatal jaundice includes the assessment of bilirubin laboratory values, consideration of patient-specific risk factors, and plotting on a bilirubin nomogram reference to determine risk and guide therapy. When performed manually, the process can be time consuming and error-prone. Therefore, web-based calculators such as BiliTool™ have been developed to assist in risk assessment. METHODS: To streamline the risk assessment calculation process further within our electronic health record (EHR), we created a “BiliReport” to display patient bilirubin-related data and automate transmission of deidentified patient data to the BiliTool™ website (https://bilitool.org). After implementation, we evaluated usage data, provider satisfaction, and accuracy of documentation. RESULTS: We demonstrated high provider use of the BiliReport and satisfaction with the workflow. We found a significant improvement in the accuracy of bilirubin risk level documentation, with a reduction in erroneous risk stratification from 4% (15/232) to 0.4% (1/243), p

The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.

In this article, daily electricity usage data for 34,040 enterprises in Suzhou (China) were examined for economic activity associated with the response to COVID-19. Employing a difference-in-differ...

Currently, many remote communities rely on costly diesel engine (DE) generators as their only energy resource. Therefore, to reduce cost and CO2 emissions, renewable energy such as photovoltaic (PV) power is widely used. However, such integration may have reliability issues due to intermittency and uncertainty of PV power. Hence, energy storage is further implemented to compensate for the variability of PV output. As a result, the critical problem is to find an optimized PV-storage control strategy and capacity considering the techno-economic aspects, cost, and reliability. Previous research has not provided convincing results due to the little attention paid to the practical operational constraints of existing DEs (time delay management, spinning reserve strategies, ramp rate, etc.), and rough hourly usage data in a time-series model. Besides, no applicable systematic evaluation method has been established. This paper develops a new model which fully considers the DE generator's operational characteristics. Furthermore, based on the recorded 1-min load and PV generation data in a real microgrid, a systematic evaluation method is proposed, which searches for the best results for both control strategies and capacities of all components. This method can be utilized for the integration of high renewable (PV) penetration into existing DE powered remote networks.

International audience; Gamification, defined as the use of game elements in non game situations, is a widely used method to foster learner engagement and motivation. It is generally accepted that in order to be effective, gamification should be tailored to users. Currently, most systems adapt by assigning different game elements based on a single learner profile (e.g. dominant player type, personality or gender). However, there is no study yet that analyse the effect of combining several profiles. In this paper, we study the usage data from 258 students who used a gamified learning environment as a part of their mathematics class. By simulating different adaptation techniques, we show that the learner model chosen to tailor gamification has significant effects on learners' motivation and engaged behaviours depending on the profile(s) used in this context. We also show that tailoring to initial motivation to learn mathematics can improve intrinsic motivation. Finally, we show that tailoring to both player type and motivation profiles can improve intrinsic motivation, and decrease amotivation, compared to a single adaptation only based on learner motivation. We discuss the implications of our findings regarding the choice of a learner model for tailoring gamification in educational environments.

Aim This paper explores the real practices of users of a smart carpooling application that learns their mobility habits and predicts their future trips to propose relevant matches. Method A combination of usage data and interviews analysis allows us to explore the commuter experience from registration to the first and the next shared rides. Findings The results highlight the shortcomings associated with human factors in carpooling and with human-smart system interactions. They show that perceptions of practical constraints and poor counterparts are the major reasons for difficulty in incorporating carpooling into daily mobility. Psychosocial barriers take different forms at different steps of the carpooling experience (search for information or guarantees about other users, the necessity of conversing with others, much uncertainty about how to behave). The fact that the service is smart amplifies these problems and reduces the desire to carpool again because it creates new misunderstandings (i.e., the user does not understand what the system vs. the other users do) and discomfort in relation to other riders (no answer, too many refusals, necessity of refusing, negative carpool experience, or concern over proposing a bad carpool). Despite these difficulties, the users perceive carpooling as a good solution and a positive human experience when the matching is accurate. We propose some recommendations to overcome the identified difficulties.

We do not always think of the impact we are making on the climate every time we turn on a light, use a computer or watch T.V. For this research-informed action research project we studied how electricity usage can impact the climate. We conducted a mini correlational study at Erindale Secondary School to learn more about our peers’ electricity consumption. We asked our peers how many fluorescent light bulbs they use in their homes, and how often they turn off their lights when they leave the room. Our mini study reveals that the majority of boys and girls do not know how many light bulbs in their homes are energy efficient. In addition, equal proportion of girls and boys always or sometimes turn off the lights when they are not in use. For the action portion of our project, we organized the Earth Hour at our school to lower the energy consumption and to raise awareness about the importance of energy conservation. The Principal of our school also agreed to turn off the lights in the cafeteria every night to save more energy. We also challenged David Suzuki Secondary School in Mississauga to a friendly competition to see which school will save more energy. We still await the electricity usage data from the board to see who won the challenge.

There is no doubt that customer retention is vital for the service sector as companies’ revenue is significantly based on their customers’ financial returns. The prediction of customers who are at the risk of leaving a company’s services is not possible without using their connection details, support tickets and network traffic usage data. This paper demonstrates the importance of data mining and its outcome in the telecommunication area. The data in this paper are collected from different sources like Net Flow logs, call records and DNS query logs. These different types of data are aggregated together to decrease the missing information. Finally, machine learning algorithms are evaluated based on the customer dataset. The results of this study indicate that the gradient boosting algorithm performs better than other machine learning algorithms for this dataset. Keywords: Data analysis, customer satisfaction, subscriber churn, machine learning, telecommunication.