Daily Events Research Articles

Transitions and resilience in ecological momentary assessment: A multiple single-case study

Ecological momentary assessment (EMA) of affect, cognition and behavior aims to provide a ‘window into a person’s daily life’. But what should we look for through this window? In this paper, we compare a statistical perspective, grounded in probability theory, with a dynamic pattern perspective, grounded in complexity theory, on two common phenomena in EMA data: non-stationarity and outlying values. From a statistical perspective, these phenomena are considered nuisances that should be dealt with. From a dynamic pattern perspective, in contrast, non-stationarity may signal transitions from one dynamic pattern to another (e.g., a transition from a neutral to a persistent sad mood), whereas outlying values may signal recovery from perturbations (e.g., stressful life events). We evaluated the dynamic pattern view with a triangulation study of multiple single cases that took part in the Track your Mood EMA study, where participants reported on their emotions and daily events for 60 days. We found that non-stationarity was indeed related to a pattern transition, whereas outlying values were related to recovery after perturbations. These findings show that person-oriented EMA research would benefit from a dynamic pattern perspective that can identify highly meaningful and clinically relevant phenomena that are otherwise at risk of being missed. Complementing EMA time series with contextual information and qualitative data will be essential to genuinely understand these phenomena.

Aplikasi Pemesanan Papan Bunga Dan Spanduk Di Kabupaten Pidie Berbasis Android

The Application for Ordering Flower Boards and Banners is a tool to make it easier for sellers to market in the process of ordering and selling this product, the large number of residents in the Pidie Regency triggers requests to order quite a lot of flower boards and banners when there are daily events in Pidie district, so that making this Android-based application will help sellers and buyers because there is a system engaged in selling flower boards and banners. This system is an operating system designed for touchscreen mobile devices, such as smartphones and tablets. So there are lots of programmers who flock to make applications or modify this operating system in the community.

A probabilistic machine learning framework for daily extreme events forecasting

Forecasting daily extreme events is crucial, particularly amidst the escalating severity of tropical storms and hurricanes in the East and Gulf coasts of the United States. The intensity of these hydrometeorological events, exacerbated by climate variability and change, has resulted in catastrophic floodings, posing significant risks to public safety and infrastructure. In response to the shortcomings associated with the deterministic rainfall-runoff models, specifically to account for uncertainties, we develop a probabilistic framework to enhance the accuracy and reliability of streamflow forecasting during extreme events. The core of our framework is a Monte Carlo-based deep learning model based on Long Short-Term Memory (LSTM) to account for model uncertainty. We employed the wavelet transform in our probabilistic framework to decompose observed discharge data, breaking down the data into its constituent components to identify trends. Furthermore, to account for the aleatory uncertainty in model input, we perturb the forcing datasets. Therefore, the proposed probabilistic framework accounts for both types of uncertainty, aleatory and epistemic. The model efficacy is tested across twenty-four basins in Southeast Texas, with a particular focus on the extreme conditions during Hurricane Harvey event. The results show that the proposed model outperforms the basic LSTM model by 45%, 40%, 9%, and 40% in NSE, KGE, PCC, and RMSE, respectively. This validation demonstrates the model’s robustness and applicability in real-world scenarios and underscores its potential as an effective tool for decision-makers in planning and risk management during extreme hydrological events.

Purpose in Life and Stress Reactivity.

Individuals with a stronger sense of purpose in life experience better health across the lifespan. This chapter describes one mechanism linking purpose to health, namely stress reactivity. Across survey, experimental, and daily diary studies, research demonstrates that sense of purpose may yield a homeostatic function, insofar that it helps individuals stay the course to progress toward their purpose in life. Future research is needed to examine how self-regulatory capabilities explain why sense of purpose influences reactivity to stressors and daily events.

Boosting Campus Life with Real-Time Event Updates and Rewards

This project addresses the common issue of students being unaware of daily events happening on their college campus. To solve this problem, we have developed an innovative event management website designed to keep students informed and engaged. The platform provides realtime updates on upcoming events, allowing students to stay connected with campus activities easily.A unique feature of our website is the incorporation of a points-based incentive system. Students earn coins based on their level of engagement in events, fostering active participation. Event organizers, led by a designated head, assign points to participants: the head receives 15 points, while other team members earn between 5-10 points based on their involvement as determined by the organizing committee. These accumulated points can be redeemed for various rewards, including bonus marks, merchandise, or course subscriptions, providing tangible benefits for active participation. The implementation of this platform not only enhances awareness and participation in college events but also encourages a more vibrant and connected student community. By integrating gamification elements, we aim to motivate students to engage more actively in campus life, thereby enriching their overall educational experience. Future enhancements will focus on expanding the reward system and integrating feedback mechanisms to continually improve user engagement and satisfaction.

Impact of minimal self disorders on naturalistic episodic memory in first-episode psychosis and parallels in healthy individuals with schizotypal traits.

Self-disorders constitute a core feature of the schizophrenia spectrum, including early stages such as first-episode psychosis (FEP). These disorders impact the minimal Self, or bodily self-consciousness, which refers to the basic, pre-reflective sense of embodied experience. The minimal Self is intrinsically linked to episodic memory, which captures specific past experiences of the Self. However, research on this relationship in the schizophrenia spectrum remains scarce. This pilot study aimed to investigate how the minimal Self modulated episodic memory of naturalistic events in FEP, using immersive virtual reality. A secondary objective was to examine the relationships between sense of Self, embodiment, episodic memory, schizotypal personality traits in healthy participants (CTL), and psychopathology in FEP. A full-body illusion was induced in 10 FEP and 35 matched CTL, using a first-person avatar, with synchronous or asynchronous visuomotor stimulation (strong or weak embodiment conditions, respectively). Following embodiment induction, participants navigated a virtual city and encountered naturalistic daily life events, which were incidentally encoded. Episodic memory of these events was assessed through a comprehensive recognition task (factual and contextual information, retrieval phenomenology). Sense of Self, schizotypal personality traits, and psychopathology were assessed via self-reported questionnaires or clinical assessments. Synchronous visuomotor stimulation successfully induced a stronger sense of embodiment in both FEP and CTL. The strong embodiment condition was associated with reduced perceived virtual space occupation by the body in FEP. Under strong embodiment, FEP performed significantly worse than CTL in contextual information recognition, but their ratings for retrieval phenomenology were comparable to CTL. Conversely, under weak embodiment, FEP performed similarly to CTL in contextual information recognition, but they rated retrieval phenomenology significantly lower. For CTL, we observed a slight, though non-significant, enhancement in recognition memory under strong compared to weak embodiment. Additionally, higher schizotypy in CTL correlated with a diminished sense of Self and poorer episodic memory. Disturbances in the minimal Self in FEP are associated with episodic memory impairments. These findings emphasise the importance of targeting minimal Self-disorders in schizophrenia spectrum disorders, since episodic memory impairments may negatively affect patients' quality of life and psychosocial outcomes. Additionally, they support a fully dimensional model of schizotypy.

Intraurban spatial configuration, hydrologic similarity areas and their roles in urban drainage dynamics

ABSTRACT Hydrologic Similarity Areas (HSA) were delimited in the Belém catchment, and their responses on the water balance were evaluated. A cluster analysis of intraurban spatial configuration was conducted to delimit six HSA. The urban water balance was performed in the Aquacycle model, whose calibration resulted in satisfactory adherence to the measured streamflow. Indeed, the modeling results show that the intraurban spatial heterogeneity is reflected in the annual and daily water balance. The evapotranspiration rates can represent 25% to 50% precipitation in the different HSA. Moreover, infiltration and runoff can range five times among the HSA. In analyzing the daily events, we identified the areas that most contributed to runoff and those with higher infiltration capacity during flood events. Such results may support future studies on understanding flood dynamics and guide the implementation of sustainable urban drainage solutions that aim to reduce runoff and control flood risk.

Simulating Daily Large Fire Spread Events in the Northern Front Range, Colorado, USA

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.

Real-time biopsychosocial antecedents and correlates of functional neurological symptoms in daily life: A pilot remote monitoring technology study

Functional neurological symptom disorder (FNSD) is a neuropsychiatric diagnosis referring to symptoms resembling those of neurological disorders, occurring without causal neuropathology. FNSD has a complex biopsychosocial aetiology but its mechanisms are poorly understood. Remote monitoring technologies (RMT) could provide critical insights into functional neurological symptoms (FNS) in real-world contexts. We examined the feasibility and acceptability of a novel RMT protocol, to identify psychobiological correlates and antecedents of FNS in everyday life. Seventeen individuals with FNS (seizures/motor) and 17 healthy controls (HC) completed ecological momentary assessments (EMA) eight times daily for 1-week, reporting FNS severity, associated physical and psychological symptoms, and subjectively significant events. Sleep quality was reported daily. Physiological variables were measured using wearable Fitbit 5 devices. Multilevel modelling examined variables associated with FNS variability. Average EMA completion rates were good in both groups (≥80%). At week-level, the FNS group reported significantly greater subjective arousal, pain, fatigue, dissociation, negative affect, daily events, stressful events, and sleep duration, compared to HC. Objective sleep disturbance and duration, and resting heartrate, were also significantly greater in the FNS sample. FNS severity correlated significantly with daily events, affect, subjective arousal, pain, fatigue and sleep disturbance, at day- or within-day levels. Daily events and negative affect were the most prominent time-lagged predictors of within-day moment-to-moment FNS severity. RMTs are feasible and acceptable tools for investigation of FNS in real-world settings, revealing daily events and negative affect as possible triggers of FNS. Interventions targeting affective reactivity and regulation might be beneficial in this group. Larger-scale, longer-term RMT studies are needed in this population.

A randomized controlled clinical trial of a Wim Hof Method intervention in women with high depressive symptoms

ObjectiveStress is a driver of depression, and people with depression often struggle to cope with stress and anxiety. This study directly compares the mental health effects of a Wim Hof Method intervention to an active control condition (slow breathing) in women with high stress and high depressive symptoms. MethodsWe randomized 84 healthy midlife women with high stress and high depressive symptoms to either: 1) the hormetic stress condition based on the Wim Hof Method (WHM) involving a breathing technique designed to induce intermittent hypoxia and cold showers (n = 41) or 2) an active comparison condition involving slow-paced breathing and warm showers (n = 43). We provided participants with daily audio instructions (15 min) for three weeks during the COVID-19 pandemic (2020–2021). Our primary outcomes were depressive symptoms, anxiety symptoms, and perceived stress collected at pre-intervention, post-intervention, and 3 months later. We also assessed daily stress rumination and affect with daily diary during the intervention, and participants completed a laboratory stressor, the Trier Social Stress Test, before and after the intervention, and provided samples for salivary cortisol reactivity. ResultsParticipants in the active control condition perceived the intervention to be more credible and expected greater mental wellbeing benefits compared to those in the Wim Hof Method condition. Differential attrition was observed with six participants (7 %) dropping out -- all from WHM condition. Among the participants who completed the intervention, both groups improved on mental health outcomes immediately after the intervention with a 24 % reduction in depressive symptoms, a 27 % reduction in anxiety symptoms, and 20 % reduction in perceived stress. Improvements were maintained at the 3-month follow-up with 46 % of the sample reporting mild or no depressive symptoms. Participants in the WHM condition had significant reductions in rumination after daily stressful events compared to those in the active control group. Both conditions had reduced daily negative affect across the intervention and lower peak cortisol reactivity to the lab stressor post-intervention. ConclusionsCounter to the preregistered predictions, and despite participants’ differing expectations, the interventions led to equivalent reductions in depressive symptoms, anxiety symptoms, and perceived stress, which were sustained at three months. They also produced comparable reductions in cortisol reactivity and daily negative affect. However, the WHM condition was associated with greater reduction in reported rumination after daily stressful events than the active control, a finding that needs replication with larger and more diverse samples.

Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.

Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non-paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject-specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within-day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R-package.

Relationship between Tibetan Plateau Surface Heat Fluxes and Daily Heavy Precipitation in the Middle and Lower Yangtze River Basins (1980–2022)

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR.

Rainfall events and daily mortality across 645 global locations: two stage time series analysis

ObjectiveTo examine the associations between characteristics of daily rainfall (intensity, duration, and frequency) and all cause, cardiovascular, and respiratory mortality.DesignTwo stage time series analysis.Setting645 locations across 34 countries or regions.PopulationDaily...

Unpleasant emotions and task-unrelated thoughts and their associations with amygdala activity during emotional distraction: A functional magnetic resonance imaging study

Previous emotion regulation studies revealed that emotional distraction decreases unpleasant emotions. This study examined whether distraction tasks decrease unpleasant task-unrelated thoughts (TUTs) and unpleasant emotions when recalling stressful daily interpersonal events. Amygdala activity was examined to assess implicit emotional changes using functional magnetic resonance imaging (fMRI). The behavioral data of 20 university students (mean age: 20.35 ± 1.42 years; range: 18–24 years) and fMRI data of 18 students were examined. As an emotion induction procedure, participants initially freely recalled memories of daily stressful interpersonal events and then responded to a series of questions about their recalled memories presented on a monitor. In the distraction experiment, the questions were re-represented as an emotional stimulation; a distraction task (nonconstant or constant finger tapping) or rest condition was then performed, and ratings were given for attentional state, thought types conceived during the tasks, and emotional state. Decreases in unpleasant emotions and TUTs were defined as distraction effects. We found that unpleasant TUTs decreased in the nonconstant relative to rest condition (p < .05). Furthermore, increased right amygdala activation positively correlated with unpleasant emotions, and bilateral amygdala activation correlated with unpleasant TUTs only in the rest condition, indicating the existence of amygdala activation associated with unpleasant emotions and thoughts. However, such associations were not under nonconstant or constant conditions, indicating distraction effects. Notably, this study showed that emotional distraction can decrease the degree of unpleasant emotions and the occurrence of unpleasant thoughts regarding common daily emotions.

Verbal attacks on terrorist groups increase violence against civilians

Abstract Terrorists and other transnational extremist groups are responsible for thousands of civilian deaths. In confronting extremists, governments have relied heavily on threats, demands, denunciations, and other forms of verbal conflict. Do these efforts at verbal coercion have any effect on terrorist behavior? This analysis focuses on the Islamic State in Iraq and Syria (ISIS), which continues to be the world's deadliest terrorist group and was responsible for recent high-profile attacks in Baghdad, Vienna, Kabul, and Russia. We use Bayesian structural vector autoregression models to analyze daily event data on interactions between ISIS and foreign governments for the 2014–2020 period. We find that verbal conflict initiated by governments not only failed to deter ISIS but in fact increased the frequency of ISIS's attacks on civilians. Additional empirical analyses, combined with evidence from ISIS's publications and public statements, suggest that this effect resulted from a perceived credibility deficit. Extremists use terror attacks to signal that they have the capabilities and willingness to inflict pain and suffering on civilian targets. Government attempts to coerce extremist groups verbally, rather than militarily, reflect an underestimation of the group's capabilities and resolve. In an effort to solidify their reputations, extremists engage in further violence toward civilians, thus leading to worse humanitarian consequences. We extend the analysis to Al-Qaeda in Iraq and Boko Haram and find similar results.

Statistics and Meteorology of Cutoff Lows over South Africa 1970–2023

The meteorology of cutoff lows over South Africa is characterized by statistical analysis of daily field data in the period 1970–2023. An index is formulated by subtracting 500 hPa geopotential height in the mid-latitudes from the subtropics. Cutoff lows (COL) are identified by positive values, mostly in autumn and spring. Statistics indicate that climate forcing is seasonal: La Nina/El Nino favors COL in March–May/September–November. Hemispheric regressions reveal anomalous highs across the southern mid-latitudes when COL are frequent over South Africa. A 14-case composite was formed from the most intense daily COL events in autumn and spring. The composite shows a NW- tilted Rossby wave and jet stream loop around the COL. Maritime easterlies induce a warm east—cool west SST pattern, but composite moist inflows are shallow, so stormy weather hugs the coastal plains. Overturning circulations meet in an upper-level “saddle” over South Africa. 500 hPa sinking motions to the Southwest are of similar strength to rising motions to the Northeast. A COL case study exhibited hourly rain rates &gt;10 mm at Port Alfred 18–20 October 2012 fed by tropical inflow. New insights emerged from this study via composite height sections over South Africa.

A high-resolution dataset for future compound hot-dry events under climate change

Global climate change is leading to an increase in compound hot-dry events, significantly impacting human habitats. Analysing the causes and effects of these events requires precise data, yet most meteorological data focus on variables rather than extremes, which hinders relevant research. A daily compound hot-dry events (CHDEs) dataset was developed from 1980 to 2100 under various socioeconomic scenarios, using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to address this. The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres), including three indicators, namely D (the yearly sum of hot-dry extreme days), prI (the intensity of daily precipitation), and tasI (the intensity of daily temperature). To validate the accuracy of the dataset, we compared observational data from China (National Meteorological Information Center, NMIC), Europe (ERA5), and North America (ERA5). Results show close alignment with estimated values from the observational daily dataset, both temporally and spatially. The predictive interval (PI) pass rates for the CHDEs dataset exhibit notably high values. For a 90% PI, D has a pass rate exceeding 85%, whilst prI and tasI respectively show a pass rate above 70% and 95%. These results underscore its suitability for conducting global and regional studies about compound hot-dry events.

Effects and Remedies of Construction Claims in Hydropower Projects: Case Studies of Selected Medium-Scale Hydropower Projects in Nepal

This research aims to analyze the perspectives of various stakeholders involved in hydropower projects, including employers, engineers, and contractors, to identify common challenges and propose strategies for minimizing claims. The data analysis involved a questionnaire survey conducted among representatives of employers, engineers, and contractors across seven selected hydropower projects. Similar questionnaires were distributed to gather insights from all three parties involved in the projects. The responses were compiled and analyzed collectively using a general ranking method, allowing for a comprehensive assessment of the findings related to the thesis objectives. The analysis revealed that claims significantly impact construction projects, manifesting as both cost overruns and time delays. If not addressed promptly, these claims can escalate into disputes that typically require resolution through Alternate Dispute Resolution (ADR) procedures or may lead to complex legal proceedings. The study identified several key factors contributing to claims, including inadequate tender documentation, poor record-keeping of daily site events, and the occurrence of major design changes. To mitigate the risks associated with claims in hydropower projects, it is essential to implement several best practices: preparing clear and unambiguous tender documents, maintaining comprehensive daily records of site activities, minimizing design changes whenever possible, ensuring site possession prior to contractor mobilization, and conducting thorough field investigations before finalizing designs.

Variations and Trends in 115 Years of Graded Daily Precipitation Records at Three Hydrometeorological Stations in Finland

This study investigated the variability and trends in 115 years (1909–2023) of daily precipitation observed at three hydrometeorological stations in southern (Kaisaniemi), central (Kajaani), and northern (Sodankylä) Finland. We also identified the most significant climate teleconnections influencing daily precipitation variability at these three stations during the period 1951–2023. The daily precipitation records were primarily classified into six grades, including very light (≤1 mm), light (1–≤5 mm), moderate (5–≤10 mm), heavy (10–≤15 mm), very heavy (15–≤20 mm), and extreme (&gt;20 mm). On average, the most intense daily precipitation was determined at the Kaisaniemi station in southern Finland. At this station, however, very light and light precipitation showed the lowest frequency, but other graded daily precipitation events were the most frequent. At all three stations, the intensity of very light precipitation significantly declined during the past 115 years, while its frequency increased. The highest rates of such decreases and increases in the intensity and frequency of very light daily precipitation were found at the Sodankylä stations in northern Finland, respectively, but the lowest rates were at the Kaisaniemi station in the south. At the Kajaani station in central Finland, the intensity of light precipitation decreased, but very heavy precipitation intensified. At this station, however, the number of both moderate and heavy precipitation events increased over time. Finally, historical variations in both the intensity and frequency of graded daily precipitation events in Finland showed significant relationships with different climate teleconnections, particularly the Scandinavia (SCAND) and the North Atlantic Oscillation (NAO) patterns.

An advanced learning approach for detecting sarcasm in social media posts: Theory and solutions

AbstractObjectiveUsers of social media platforms such as Facebook, Instagram, and Twitter can view and share their daily life events through text, photographs, or videos. These platforms receive many sarcastic posts daily because there were fewer limits on what could be posted. The presence of multiple languages and media types in a single post makes it harder to identify sarcastic messages on the current platform than on posts written solely in English.MethodsThis study provides both the theory and solutions about sarcastic post detection on social platforms. Hindi–English code‐mixed data were used to train and test the automated models for sarcasm detection. The models in this study were constructed using traditional machine learning, deep neural networks, LSTM (long short‐term memory), CNN (convolutional neural network), and the combinations of BERT (Bidirectional Encoder Representations from Transformers) with LSTM.ResultsThe experimental results confirm that in the Hindi–English code‐mixed data set, the CNN, LSTM, and BERT‐LSTM ensemble perform best for sarcasm detection. The proposed model achieved an accuracy of 96.29 percent and outperformed by 2.29 percent compared to the existing models.ConclusionThe performance of the proposed system strengthens the code‐mixed sarcastic post detection on social platforms. The model will help filter not only English but also Hindi‐English code‐mixed sarcastic posts on social platforms.