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Social annotation plays a crucial role in nurturing and sustaining a collaborative reading community, offering the potential to enhance students' motivation and performance within socially supportive learning environments. Nonetheless, research on the dynamic changes in student engagement in social annotation remains limited. This study aims to unveil the temporal changes in students' behavioral, cognitive, affective, and social engagement within the context of social annotation. In addition, it examines the disparities in social annotation behaviors between students with different engagement profiles. Using a multivariate time series clustering approach to analyze a dataset comprising 91 undergraduate students interacting with 29 reading materials, this study identified two distinct engagement profiles. Both profiles revealed a decline in behavioral engagement as the number of reading activities increased. However, students' cognitive, affective, and social engagement levels in social annotation remained relatively stable across these activities. Subsequent analyses showed that students exhibiting a declining engagement profile displayed higher levels of aggregated behavioral, cognitive, and social engagement. Furthermore, they posted a significantly greater number of annotations and responses to peers' annotations compared to students characterized by a low engagement profile. Potential explanations and pedagogical implications of these findings were discussed. [ABSTRACT FROM AUTHOR]

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16 days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies. [ABSTRACT FROM AUTHOR]

Although cryptocurrency trading can be highly profitable, it carries significant risks due to extreme price fluctuations and high degree of market noise. To increase profits and minimize risks, traders typically use various forecasting methods, such as technical analysis and Machine Learning (ML), but developing effective trading strategies in noisy markets still remains a challenging task. Recently, Deep Reinforcement Learning (DRL) agents have achieved high performance on challenging tasks, including algorithmic trading, however it requires significant amount of time and high-quality data to train effectively. Additionally, DRL agents lack explainability, making them a less popular option for traders. The purpose of this paper is to address these challenges by proposing a reliable trading framework. Our framework, named UNSURE, generates high-quality features from candlestick data using technical analysis along with a novel parameterization method, and then exploits high price fluctuations by combining three ML components: A) Unsupervised component, which further improves feature quality by clustering market data; B) DRL component, which is responsible for training agents that open Buy or Short positions; C) Supervised component, which estimates price fluctuations in order to open and close positions efficiently, while reducing trading uncertainty. We demonstrate the effectiveness of this approach on nine cryptocurrency markets using several risk-adjusted performance metrics. [ABSTRACT FROM AUTHOR]

Background : The nationwide epidemiology and clinical practice patterns for younger children hospitalized with urinary tract infections (UTIs) were unclear. Methods: We conducted a retrospective observational study consisting of 32,653 children aged < 36 months who were hospitalized with UTIs from 856 medical facilities during fiscal years 2011–2018 using a nationally representative inpatient database in Japan. We investigated the epidemiology of UTIs and changes in clinical practice patterns (e.g., antibiotic use) over 8 years. A machine learning algorithm of multivariate time-series clustering with dynamic time warping was used to classify the hospitals based on antibiotic use for UTIs. Results: We observed marked male predominance among children aged < 6 months, slight female predominance among children aged > 12 months, and summer seasonality among children hospitalized with UTIs. Most physicians selected intravenous second- or third-generation cephalosporins as the empiric therapy for treating UTIs, which was switched to oral antibiotics during hospitalizations for 80% of inpatients. Whereas total antibiotic use was constant over the 8 years, broad-spectrum antibiotic use decreased gradually from 5.4 in 2011 to 2.5 days of therapy per 100 patient-days in 2018. The time-series clustering distinctively classified 5 clusters of hospitals based on antibiotic use patterns and identified hospital clusters that preferred to use broad-spectrum antibiotics (e.g., antipseudomonal penicillin and carbapenem). Conclusions: Our study provided novel insight into the epidemiology and practice patterns for pediatric UTIs. Time-series clustering can be useful to identify the hospitals with aberrant practice patterns to further promote antimicrobial stewardship. [ABSTRACT FROM AUTHOR]

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems. [ABSTRACT FROM AUTHOR]

Temporal trends and spatial patterns of six warm season (May–September) hot temperature indicators (WSHTIs) were developed and explored for Saudi Arabia. The indicators focus on the frequency and intensity of hot days and nights, and heat waves. Systematic upward trends in maximum (Tmax) and minimum (Tmin) temperatures were found at most of the stations, suggesting ongoing change in the climatology of the upper-tail of the frequency distribution. Taking into the account the observed effects of climate change on the country's climate, hot temperature events were defined using a monthly and decadal, time-sensitive approach. Indicators of event frequency are count data; thus, different Poisson models were used for temporal analysis. Further, a novel method of time-series clustering was introduced to recognize spatiotemporal patterns of WSHTIs. Different patterns were observed over time and space not only across stations but also among WSHTIs. Generally, warming trends were detected in the upper limits for both Tmax and Tmin across the warm season months with a few surprising exceptions, mostly for Tmax. Results suggest that the impact of climate change on hot weather events was more pronounced at night at most of the stations in Saudi Arabia. The overall results suggest that not only local and regional factors, such as elevation, latitude, and distance from a large body of water, but also large-scale factors such as atmospheric circulation patterns are likely responsible for the observed temporal and spatial patterns. [ABSTRACT FROM AUTHOR]

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-h periods and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs. [ABSTRACT FROM AUTHOR]