Ageing and Technology – Systematic Review of the Merger of Ageing & Technology in the New Normal Era

Technology is massive support in the current times in living our lives. Technology surrounds every day and every bit of our daily lives. The technological advancements and their acceptance also justify the need for a complete understanding across generations. We live in a society where older people are also an integral part of society. When we check them and their needs, especially during the COVID-19 pandemic and in these new normal times, we can see that their need and quick adaptation to the surrounding seem more obligatory. Therefore, the merger of technology with age is critical. Assessing their need and looking at their well-being and better living becomes the priority for every stakeholder living in our society. Technology comes as a powerful, liberating way of backing older adults. This research follows a systematic review of journal papers published between 2000–2022 and tries to check how other researchers looking for this merger of technology and ageing are reporting in this synthesis. A detailed scientific plan is followed in extraction and analysing the seminal work done in this area in the considered time period. The research identified and reported many diverse areas where researchers worked on understanding the merger between technology and ageing. The study reported the critical areas of the technology amalgamation with age and identified the gaps in the existing theories where the future direction of work can take place. The study also highlighted specific vital takeaways for the practitioner that can be considered the next big step in this advanced technology adoption in this new normal era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BACKGROUND: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. OBJECTIVE: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. METHODS: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. RESULTS: The final prediction model had an R² of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 µm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. CONCLUSIONS: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Measuring discord among multidimensional data sources

Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and record merging. To solve the latter, it is mostly assumed that ground truth can be determined, either as master data or from user feedback. However, in many cases, this is not the case because firstly the merging processes cannot be accurate enough, and also the data gathering processes in the different sources are simply imperfect and cannot provide high quality data. Instead of enforcing consistency, we propose to evaluate how concordant or discordant sources are as a measure of trustworthiness (the more discordant are the sources, the less we can trust their data). Thus, we define the discord measurement problem in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different data (for example, cases and deaths), we wish to assess whether the different sources are concordant, or if not, measure how discordant they are. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Merging Datasets for Emotion Analysis

Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.

Ensemble feature selection: Are stability metrics a proxy or a complement to predictive performance?

Proper identification of biomarkers, used in the development of drugs, is critical as has been shown with the race to find a vaccine for the Covid19. Gene-expression based marker discovery often entails that feature selection be performed. However, a plethora of feature selection methods exist and they do not result in the selection of the same feature subsets for the same dataset. Often, users are faced with having to select which subset to use. To help in this conundrum, several approaches have been proposed to guide feature subset selection, among which the use of ensemble methods (i.e., combining subsets from multiple methods) has gained attention recently. In an ensemble approach there are two issues that deserve attention: the stability of the feature subsets being combined and the classification performance of the combined feature subsets. Hence the interest in exploring how stability and performance relate, which is the central topic investigated in this paper. First 5/6 different feature selection methods are used to create feature subsets for 3 different transcriptomics datasets. Then, the stability and performance of these feature subsets under a given merging strategy are computed using 5 stability metrics and 3 performance metrics for 3 different classifiers. Our results suggest that performance and stability criteria are complementary and conflicting and that both must be considered to decide on the final selected feature subsets. We use two reference metrics to illustrate such selection. © 2021 ACM.