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The need for subnational demographic, social, economic, and fiscal data in designing effective intergovernmental fiscal systems is becoming increasingly evident. In Central and Eastern European countries, the legacy of the region’s communist past are information systems rooted in the centralized economy. Such an approach becomes less acceptable as economic issues become more complex and subnational governments in these transition economies become responsible for the delivery of local services. As political imperatives support increasingly democratic forms of governance in which people’s needs must be taken into account in the design of policy options, there is a need for information systems that provide data to allow policymakers and citizens to assess the outcomes of policy choices. Subnational Data Requirements for Fiscal Decentralization summarizes the findings of needs assessment activities in five demonstration countries that are at different stages of fiscal decentralization: Bulgaria, Romania, the Slovak Republic, Slovenia, and Ukraine. These assessments are part of a program on subnational statistical capacity building, launched by the World Bank Institute, the Organisation for Economic Co-operation and Development, and the Economic Development Center of the Soros Foundation.

This chapter contains sections titled: Windows performance objects data Description of attacks and normal use activities Computer network setup for data collection Procedure of data collection Summary References

The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates.

Background Healthy childhood development is fostered through sufficient physical activity (PA; including time outdoors), limiting sedentary behaviours (SB), and adequate sleep; collectively known as movement behaviours. Though the COVID-19 virus outbreak has changed the daily lives of children and youth, it is unknown to what extent related restrictions may compromise the ability to play and meet movement behaviour recommendations. This secondary data analysis examined the immediate impacts of COVID-19 restrictions on movement and play behaviours in children and youth. Methods A national sample of Canadian parents ( n  = 1472) of children (5–11 years) or youth (12–17 years) (54% girls) completed an online survey that assessed immediate changes in child movement and play behaviours during the COVID-19 outbreak. Behaviours included PA and play, SB, and sleep. Family demographics and parental factors that may influence movement behaviours were assessed. Correlations between behaviours and demographic and parental factors were determined. For open-ended questions, word frequency distributions were reported. Results Only 4.8% (2.8% girls, 6.5% boys) of children and 0.6% (0.8% girls, 0.5% boys) of youth were meeting combined movement behaviour guidelines during COVID-19 restrictions. Children and youth had lower PA levels, less outside time, higher SB (including leisure screen time), and more sleep during the outbreak. Parental encouragement and support, parental engagement in PA, and family dog ownership were positively associated with healthy movement behaviours. Although families spent less time in PA and more time in SB, several parents reported adopting new hobbies or accessing new resources. Conclusions This study provides evidence of immediate collateral consequences of the COVID-19 outbreak, demonstrating an adverse impact on the movement and play behaviours of Canadian children and youth. These findings can guide efforts to preserve and promote child health during the COVID-19 outbreak and crisis recovery period, and to inform strategies to mitigate potential harm during future pandemics.

Background Dementia often eventually leads to dependency on others and finally to residential care. However, in Norway about half of the dementia population lives at home, due to individual and political wishes. There is scarce and inconclusive knowledge of how living in a nursing home differs from living at home for persons with dementia (PWDs) with regard to their quality of life (QoL). The first aim of the study was therefore to compare QoL, cognitive and physical functions, social contacts, sleep patterns, physical activity levels, exposure to light, and medication of PWDs in nursing homes and home-dwelling PWDs, and whether living in nursing homes was associated with a lower QoL than living at home for PWDs. A second aim was to examine if possible differences between residencies in QoL were consistent over time. Methods The cross-sectional study was based on baseline data from two RCT studies of PWDs. A total of 15 nursing homes with adapted units for PWDs and 23 adapted day care centres for home-dwelling PWDs recruited 78 and 115 participants respectively. Trained nurses scored sociodemographic data, level of dementia (on the Clinical Dementia Rating scale), amount of medication, and QoL (QUALID). Sleep patterns, physical activity levels, and light exposure were measured by actigraphy. A multiple regression analysis was used to test the association between residency and QoL. The association between residency and change in QoL over time was investigated by linear regression analysis of a subsample with follow-up data. Results Home-dwelling PWDs showed significantly higher QoL than PWDs in nursing homes. This difference was maintained even after stratifying on the severity of dementia. Home-dwelling PWDs with moderate dementia showed significantly less use of walking aids, more social contact, higher levels of activity and exposure to daylight, and less use of psychotropic medications. The regression model explained 28 % of the variance in QoL in persons with moderate dementia. However, only residency contributed significantly in the model. Residency also significantly predicted negative change over time in QoL. Conclusion The study indicated that living at home as long as possible is not only desirable for economic or health political reasons but also is associated with higher QoL for persons with moderate dementia. More studies are needed to investigate how QoL could be increased for PWDs in nursing homes.

The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments.

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

In light of rapid shifts in biodiversity associated with human impacts, there is an urgent need to understand how changing patterns in biodiversity impact ecosystem function. Functional redundancy is hypothesized to promote ecological resilience and stability, as ecosystem function of communities with more redundant species (those that perform similar functions) should be buffered against the loss of individual species. While functional redundancy is being increasingly quantified, few studies have linked differences in redundancy across communities to ecological outcomes. We conducted a review and meta‐analysis to determine whether empirical evidence supports the asserted link between functional redundancy and ecosystem stability and resilience. We reviewed 423 research articles and assembled a data set of 32 studies from 15 articles across aquatic and terrestrial ecosystems. Overall, the mean correlation between functional redundancy and ecological stability/resilience was positive. The mean positive effect of functional redundancy was greater for studies in which redundancy was measured as species richness within functional groups (vs. metrics independent of species richness), but species richness itself was not correlated with effect size. The results of this meta‐analysis indicate that functional redundancy may positively affect community stability and resilience to disturbance, but more empirical work is needed including more experimental studies, partitioning of richness and redundancy effects, and links to ecosystem functions.