Temporal Factors Research Articles

Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting.

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention (THA) mechanism represents temporal factors that subsequently fed into feedforward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism (GAM). Moreover, we introduce conditional continuous normalization flow (CCNF) to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.

Bridging the Gap: Communicating with Emergency Management Professionals in the Midst of Climate-Related Disasters

Abstract Objective The objective of this research is to assess how New York City Health + Hospitals emergency management professionals (EMPs) perceive climate change-related events, to a.) improve communication for increased receptivity and b.) identify gaps in perceived risks. Methods The study surveys EMPs from 13 geographically different healthcare facilities in NYC about their perceptions on climate change and its related emergency scenarios, preferred information sources on climate change, and the best modes of communication during climate-related extreme weather events. Results The results of this study support a.) the importance of official hospital-system guidance regarding climate risks b.) the use of a diverse array of terminology, including messaging that refrains from the phrase ‘climate change’ and instead refers to the ‘future’ c.) the incorporation of clear actionable deliverables and d.) the inclusion of expert voices into messaging. Overall, recent events and upcoming seasonal hazards appear to affect perception of risk more than objective measures. Knowledge of city and temporal factors that impact perceived risk can be mobilized to harness engagement. Conclusions The results of this study inform best practices for climate communications to EMPs, critical in preparing our health systems for future climate disasters.

Intellectual analysis of foreign students’

To optimize the learning process and improve the quality of professional training of foreign students, the study considers the process of forming the readiness of foreign students to study at a Russian university. The formation of readiness was analyzed within the framework of the pedagogical system of the preparatory faculty, which foreign students study. The study of the main components of the pedagogical system was carried out using Orange Data Mining, an intellectual analysis tool applied to the results of a survey of foreign students. For the majority of respondents, the main goal of studying at the preparatory faculty is the study of Russian language and education in Russia. After receiving their education, most students plan to work at home, and some want to work in Russia, the United Arab Emirates, Canada, the USA, Iraq, Germany, and Australia. The teaching process is characterized by the following features: a decrease in the level of basic training of foreign students, the difficult development of the Russian language, absenteeism and failure to complete homework. In the process of studying, foreign students had psychological and personal problems, problems associated with living in a hostel, met with methodological and organizational difficulties. 19% of respondents did not mention any difficulties. Despite the existing difficulties, 93% of students are satisfied with the format of training and the content of disciplines; 96% of students are satisfied with communication with teachers and the quality of teaching. The readiness of foreigners to learn a specialty in Russian is expressed in the knowledge of the Russian language, in the formation of motivation, activity and methods of activity and communication, which constitute the means of learning in the new educational and pedagogical environment. The formation of readiness is determined by psychological, social, pedagogical and temporal factors. The success of the readiness formation process depends on how active the students are and how the pedagogical system helps to overcome difficulties. An analysis of the respondents’ opinions made it possible to build a model of the pedagogical system, which identifies the factors that influence the formation of the readiness of foreign citizens to study a specialty at a Russian university.

Oral Nutritional Supplement Prescription and Patient-Reported Symptom Burden Among Patients With Late-Stage Non-Dialysis Chronic Kidney Disease.

Malnutrition and protein-energy wasting (PEW) are nutritional complications of advanced chronic kidney disease (CKD) that contribute to morbidity, mortality, and decreased quality of life. No previous studies have assessed the effect of oral nutritional supplements (ONSs) on patient-reported symptom burden among patients with non-dialysis CKD (CKD-ND) who have or are at risk of malnutrition/PEW. The objective of this study was (1) to quantify the associations between baseline nutritional parameters and patient-reported symptom scores for wellbeing, tiredness, nausea, and appetite and (2) to compare the change in symptom scores among patients prescribed ONS with patients who did not receive ONS in a propensity-score-matched analysis. This study conducted observational cohort analysis using provincial registry data. This study was done in multidisciplinary CKD clinics in British Columbia. Adult patients >18 years of age with CKD-ND entering multidisciplinary CKD clinics between January 1, 2010-July 31, 2019 who had at least 2 Edmonton Symptom Assessment System Revised: Renal (ESASr:Renal) assessments. The measurements include nutrition-related parameters such as body mass index (BMI), serum albumin, serum phosphate, serum bicarbonate, neutrophil-to-lymphocyte ratio (NLR), and ESASr:Renal scores (overall and subscores for wellbeing, tiredness, nausea, and appetite). Multivariable linear regression was applied to assess associations between nutritional parameters and ESASr:Renal scores. Propensity-score matching using the greedy method was used to match patients prescribed ONS with those not prescribed ONS using multiple demographic, comorbidity, health care utilization, and temporal factors. Linear regression was used to assess the association between first ONS prescription and change in ESASr:Renal overall score and subscores for wellbeing, tiredness, nausea, and appetite. Of total, 2076 patients were included. Higher baseline serum albumin was associated with lower overall ESASr:Renal score (-0.20, 95% confidence interval [CI] = -0.40 to -0.01 per 1 g/L increase in albumin), lower subscores for tiredness (-0.04, 95% CI = -0.07 to -0.01), nausea (-0.03, 95% CI = -0.04 to -0.01), and appetite (-0.03, 95% CI = -0.06 to -0.01). Higher BMI was associated with higher overall ESASr:Renal score (0.32, 95% CI = 0.16 to 0.48 per 1 kg/m2 increase in BMI), higher symptom subscores for wellbeing (0.02, 95% CI = 0.00 to 0.04) and tiredness (0.05, 95% CI = 0.02 to 0.07). Higher baseline NLR was associated with higher overall score (0.21, 95% CI = 0.03 to 0.39 per 1 unit increase in NLR), higher symptom subscores for wellbeing (0.03, 95% CI = 0.01 to 0.05) and nausea (0.03, 95% CI = 0.02 to 0.05). In the propensity-score-matched analysis, there were no statistically significant associations between ONS prescription and change in overall ESASr:Renal (beta coefficient for change in ESASr:Renal = 0.17, 95% CI = -2.64 to 2.99) or for subscores for appetite, tiredness, nausea, and wellbeing. Possible residual confounding. The ESASr:Renal assessments were obtained routinely only in patients with G5 CKD-ND and/or experiencing significant CKD-related symptoms. This exploratory observational analysis of patients with advanced non-dialysis CKD demonstrated BMI, serum albumin, and NLR were modestly associated with patient-reported symptoms, but we did not observe an association between ONS use and change in ESASr:Renal scores.

Factors Associated with Prolonged Length of Stay in the Ambulatory Care Unit of a Tertiary Government Hospital.

Length of stay is one of the metrics of crowding in the emergency department. Identification of the factors associated with prolonged length of stay is vital for staffing and policy making to prevent overcrowding at the ambulatory care unit. This study aimed to determine the association of sociodemographic, clinical, and temporal factors with length of stay among patients seen at the ambulatory care unit of a tertiary government training hospital. A retrospective case-control study was conducted between January to December 2019 at the ambulatory care unit of a tertiary government hospital. Charts of patients who stayed for more than six (6) hours were classified as cases, while those who stayed for more than two (2) hours up to six (6) hours were classified as controls. Charts were reviewed to obtain the clinicodemographic profile of patients who satisfied the inclusion criteria. The case group consisted of 86 patients, while the control group consisted of 172 patients. Eight factors had an effect on the probability of prolonged length of stay at the ambulatory care unit: age 40-59 years old (OR = 2.29, 95% CI: 1.16-4.49), ESI 3 at triage level (OR = 3.35, 95% CI: 1.50-8.38), psychiatric complaint (OR = 6.97, 95% CI: 2.53-19.21), medications given and diagnostics done (OR = 2.16, 95% CI: 1.16-3.99), medications given/diagnostics/referral to other services done (OR = 7.67, 95% CI: 2.70-21.80), psychiatric/substance-related case (OR = 6.97, 95% CI: 2.63-18.49), transferred to other services (OR = 3.25, 95% CI: 1.33-7.94), and endorsed to next shift (OR = 6.94, 95% CI = 3.90-12.35). The factors associated with prolonged length of stay were middle-aged adults, conditions with severe presentation, psychiatric/substance-use-related cases, need for more diagnostic test and treatment intervention, and decision to transfer care to other services.

Enhancing System Reliability and Battery Longevity through Integrated Energy Sources and Algorithmic Optimization

This research examines the enhancement of low power electronic systems, such as low power wide area networks (LPWANs), by incorporating a mix of renewable energy sources like solar and wind in China. It addresses the challenges posed by the variability of these sources due to climatic and temporal factors by merging diverse energy harvesting (EH) methods to bolster power stability and availability. The study assesses the most effective combination of solar, wind, and rain EH for steady power supply. A novel algorithm is introduced for energy input management based on real‐time resource availability, with the goal of prolonging battery life by avoiding complete charge or discharge cycles. The feasibility of this multisource approach in possibly reducing the need for extra energy storage is evaluated. The investigation includes simulations using Chinese weather data to explore different combinations of energy sources. A real‐life system utilizing solar and wind energy, guided by the developed algorithm, was also implemented for empirical comparison. Findings suggest that solar and wind energies have a higher power yield than rain harvesting, with actual wind energy collection often falling short of simulated forecasts. The solar–wind energy mix achieves a 99% system availability and facilitates a more compact design owing to their high power density. Although the input‐switching algorithm lessens the frequency of complete battery drainage, a small degree of energy storage is still essential for maintaining system reliability. This integrated EH method, compared to single‐source options, allows for a smaller energy storage requirement. Moreover, the algorithm limits battery charging to 80%, significantly enhancing battery lifespan.

Predictive modeling of Alzheimer's disease progression: Integrating temporal clinical factors and outcomes in time series forecasting

Alzheimer's disease is a complex neurodegenerative disorder that profoundly impacts millions of individuals worldwide, presenting significant challenges in both diagnosis and treatment. Recent advances in deep learning-based methods have shown promising potential for predicting disease progression using multimodal data. However, the majority of studies in this domain have predominantly focused on cross-sectional data, neglecting the crucial temporal dimension of the disease's progression. In this study, we propose a novel approach to predict the progression of Alzheimer's disease by leveraging a multimodal time-series forecasting system based on graph representation learning. Our approach incorporates a Temporal Graph Network encoder, employing k-nearest neighbors and Cumulative Bayesian Ridge with high correlation imputation to generate graph node embeddings at each time step. Furthermore, we employ an Encoder-Decoder architecture, where a Graph Attention Network translates a dynamic graph into node embeddings, and a decoder estimates future edge probabilities. When utilizing all available patient features in the ADNI dataset, our proposed method achieved an Area Under the Curve (AUC) of 0.8090 for dynamic edge prediction. Furthermore, for neuroimaging data, the AUC improved significantly to 0.8807.

IS3CQL : Intelligent sleep scheduling of small cells in high density 5G MIMO networks via incremental Deep Q learning optimizations

Effective management of small cell networks is crucial as the demand for fast, lowlatency communication grows as a result of the development of 5G and Massive Multiple Input Multiple Output (M-MIMO) technology. Small cell sleep cycle scheduling in highdensity networks frequently encounter issues as it tries to strike a compromise between energy efficiency, quality of service (QoS), and maintaining a high throughput. Additionally, these traditional methods ignore the complex spatial and temporal communication characteristics, resulting in subpar network performance. In order to overcome these difficulties, we suggest a brand-new intelligent sleep scheduling approach that makes use of both fundamental green computing ideas and incremental deep learning. In order to determine the best sleep schedules for tiny cells, our model takes into account both temporal and spatial communication factors, such as end-to-end delay, packet delivery ratio, throughput, and energy consumption of the nodes. The model adjusts the small cells’ sleep scheduling based on the ongoing spatial-temporal fluctuations in the network, providing the best possible use of resources. Additionally, its focus on green computing helps to lower energy consumption during extensive connections, making it an environmentally beneficial alternative for different scenarios. Under enormous MIMO situations, the results from our model show a significant improvement over the current approaches. These improvements include a notable improvement in communication consistency, a notable increase in throughput, a noticeable increase in packet delivery ratio, and a noticeable decrease in energy consumption and delay. In summary, our concept is a major step forward in the continuous development of intelligent, resilient, and effective 5G MIMO networks.

A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review.

Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning-a neuroimaging technique that simultaneously measures the activity of multiple brain regions-has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.

295 Adolescent pregnancies between 2006-2019: Temporal trends, risk factors and adverse pregnancy outcomes

295 Adolescent pregnancies between 2006-2019: Temporal trends, risk factors and adverse pregnancy outcomes

The effects of person-centred active rehabilitation on symptoms of suspected Chronic Traumatic Encephalopathy: A mixed-methods single case design.

The objective was to investigate the effectiveness of a person-centred active rehabilitation programme on symptoms associated with suspected Chronic Traumatic Encephalopathy (CTE). This was accomplished by (1) assessing the effect that a person-centred active rehabilitation programme had on participant symptoms, and (2) exploring how temporal contextual factors affected the participants' experience with, and perceived effectiveness of, the active rehabilitation programme. A twelve-month mixed-methods single case experimental research design was used with six cases (participants). Individual cases were involved in a 51-week study period including an initial interview and three-week baseline phase. Cases were then randomly allocated to one of two n-of-1 study designs (i.e., A-B, B-A, B-A, A-B or B-A, A-B, A-B, B-A) where A and B represent a non-intervention and intervention phase, respectively. Interviews were conducted regularly throughout the study whilst outcome measures were assessed at each follow-up. Analysis of the data included visual, statistical, and qualitative analysis. Visual and statistical analysis of cognitive and executive function, and mindful attention, demonstrated trivial-to-large effects with the summary reflecting positive or unclear results. A mixed picture was observed for mood and behaviour with effects considered trivial-to-large, and the summary demonstrating positive, unclear and negative effects. Qualitative analysis indicated a perceived improvement in outcome measures such as memory, attention, anxiety, and emotional control despite mixed quantitative findings whilst a clear impact of contextual factors, such as COVID-19, the political atmosphere, exercise tolerance, programme progression, and motivation were evident during the intervention. This study has provided primary-level evidence to suggest active rehabilitation as a potential intervention for the management of suspected CTE symptoms. This study has also demonstrated the benefit of a person-centred approach to both clinical research and practice, particularly by considering contextual factors for a better understanding of an intervention effect.

Association between short-term exposure to ambient air pollutants and biomarkers indicative of inflammation and oxidative stress: a cross-sectional study using KoGES-HEXA data.

Air pollution-induced systemic inflammation and oxidative stress are hypothesized to be the major biological mechanisms underlying pathological outcomes. We examined the association between short-term exposure to ambient air pollutants and biomarkers of inflammation and oxidative stress in 2199 general middle-aged Korean population residing in metropolitan areas. Serum levels of inflammatory cytokines (interleukin [IL]-1β, IL-6, IL-8, IL-10, and tumor necrosis factor [TNF]-α) and urinary levels of 8-hydroxy-2'-deoxyguanosine (8-OHdG) were measured. Daily concentrations of a series of air pollutants (particulate matter [PM]10, PM2.5, SO2, NO2, CO, and O3) were predicted using the Community Multiscale Air Quality modeling system, and participant-level pollutant exposure was determined using geocoded residential addresses. Short-term exposure was defined as the 1- to 7-day moving averages. The multivariable-adjusted linear models controlling for the sociodemographic, lifestyle, temporal, and meteorological factors identified positive associations of PM with IL-1β, IL-8, IL-10, TNF-α, and 8-OHdG levels; SO2 with IL-10 levels, CO with IL-1β, IL-10, and TNF-α levels; and O3 with IL-1β, IL-8, and 8-OHdG levels. O3 levels were inversely associated with IL-10 levels. For each pollutant, the strongest associations were observed for the 7-day average PM and CO with IL-1β (per 10-µg/m3 increase in PM10: 2.7%, 95% confidence interval [CI] = 0.6-4.8; per 10-µg/m3 increase in PM2.5: 6.4%, 95% CI = 2.4-10.5; per 0.1-ppm increase in CO: 3.3%, 95% CI = 0.3-6.5); the 2-day average SO2 with IL-10 levels (per 1-ppb increase in SO2: 1.1%, 95% CI = 0.1-2.1); and the 7-day average O3 with IL-8 levels (per 1-ppb increase in O3: 1.3%, 95% CI = 0.7-1.9). Short-term exposure to ambient air pollutants may induce oxidative damage and pro-inflammatory roles, together with counter-regulatory anti-inflammatory response.

The 2022-2023 Event Management Journal State of Play Review

This paper provides the state of play of Event Management since the current editorial team took over in November 2021 until papers accepted in September 2023. Our bibliometric analysis indicates 234 distinct authors from Africa, the Americas, Asia, Europe, the Middle East and Oceania contributed to the journal. Together with the varied theories and perspectives used to ground the research, the international nature of our authors demonstrates the growth, maturity, and robustness of event studies published in Event Management. Though sport events dominated, festivals, conferences/private events, and mixed or industry-wide studies were also published. Five meta-themes emerged: the event industry, social impacts and sustainability, destination image, temporal and contextual factors, and performance. The focus on the human and social elements is striking and welcome in this post-pandemic era. Finally, we identify submission gaps, offer future research directions (e.g., artificial intelligence (AI)/technology and sustainability), and suggestions to strengthen the journal and field.

"You know that we travel a lot": Mobility narratives among female sex workers living with HIV in Tanzania and the Dominican Republic.

Female sex workers (FSW) are highly mobile, which may result in reduced access to and use of health services and increased risk for poor health outcomes, particularly for those living with HIV. Mobility includes spatial, temporal, and social elements that are not fully captured by quantitative measures. We conducted two rounds of in-depth interviews with FSW living with HIV in Iringa, Tanzania (n = 20), and Santo Domingo, Dominican Republic (n = 20), to describe mobility experiences and compare mobility narratives across settings. We integrated a thematic analysis of all interviews with a narrative analysis of a subset of 10 information-rich interviews (five in each country) with women who had recently traveled, for sex work or another reason, outside of their hometown. Across narratives, FSW living with HIV traveled locally or to seasonal destinations, for short and long periods. Social factors influencing mobility included economic drivers; risk of arrest, harassment, or violence; anonymity and/or familiarity; social relationships; and clients' mobility. Spatial, temporal, and social factors intersected in unique ways in FSW's mobility experiences, yet distinct mobility typologies were evident across settings and destinations. Together, mobility narratives of FSW living with HIV can inform quantitative research on mobility typologies in Tanzania, the Dominican Republic, and elsewhere. With the potential for economic circumstances, climate change, and other emergencies to increase people's mobility around the world, researchers and practitioners can learn from the lived experiences of FSW to inform whether and how to tailor and improve the accessibility of HIV care and treatment interventions based on spatial, temporal, and social characteristics of mobility.

Влияние пространственно-временных факторов на ценностное сознание студенческой молодежи

The spatial and temporal factors influencing the value consciousness of students are considered. Theoretical and methodological approaches to the temporal characteristics of values and meanings are analyzed. The author comes to the conclusion that the time perspective affects the formation of life goals and plans. As a social manifestation of temporality, uncertainty is considered as a socio-cultural phenomenon and its impact on the value consciousness of students. Uncertainty reflects the temporal aspects of the sociocultural sphere of society. It is proved that in conditions of uncertainty, a strategy of «social surfing» is formed, which is implemented in frequent changes of statuses and roles without deep identification with social structures. In this context, the time perspective is highlighted as a characteristic of the value consciousness of students, which also reflects the reaction of a social subject to a state of uncertainty. Digital communications are considered as a spatial factor in which virtual identity and virtual roles and related role dispositions are formed and constituted. The latter include a system of attitudes and values. The risks for the value consciousness of young people in the space of digital communications are highlighted.

Research on anthropogenic influence on satellite positioning accuracy

This paper presents the results of research into the accuracy of satellite-based positioning methods in the vicinity of operating industrial and energy facilities. Modern dual-frequency multi-system satellite receivers allow for high accuracy and productivity in compliance with the basic principles of work related to ensuring the criterion of unity of measurements and the mandatory implementation of the rules of localisation of the object and the receiver(s), as well as the study of geometric and temporal factors of satellite passage, with their subsequent application to perform quality measurements. This technique is widely and effectively used on large area and linear objects by qualified specialists in the field of geodesy. The accuracy of determinations is guaranteed by the openness of the sky and the absence of all kinds of man-made factors that potentially interfere with the reception of radio signals. This paper does not consider the extreme operation of receivers in areas of special signal suppression. It is of great interest to research the accuracy of satellite positioning methods in the vicinity of operating industrial and energy objects.

Considering geographical spatiotemporal attributes for seamless air temperature data fusion with high accuracy

High-resolution, high spatiotemporal continuity, and high-precision temperature data (3H Ta) are essential for understanding local to global climate change and for studying urban heat conditions. Our prior research demonstrated the effectiveness of combining deep learning and point-to-surface scaling to generate 3H Ta. However, the accuracy of the 3H Ta data fusion methods is influenced by the geospatial attributes of temperature itself. In this study, we investigated the effects of geographical spatio-temporal factors on the three aspects of temperature fusion, i.e., data input, deep learning-based temperature fusion, and fused temperature error calibration. The results showed that temperature data had strong geospatial attributes, exhibiting spatiotemporal autocorrelation at the microscale and different clustering characteristics in different macro spatial regions. After incorporating geospatial autocorrelation factors into the temperature fusion model, the R2 was 0.995, the RMSE was 0.697 °C, and the MAE was 0.527 °C after 10-fold cross-validation. Compared with the model that does not consider spatio-temporal factors, RMSE and MAE are reduced by 68% and 73%, respectively. The use of geographical spatio-temporal difference analysis (GSTDA) error correction combining spatio-temporal factors compensated for temperature underestimation or overestimation at specific times or locations. After calibrating for fusion temperatures at the four validation sites, the RMSE and MAE decreased from 0.75 °C to 0.64 °C–0.69 °C and 0.58 °C, with RMAE and MAE decreasing by 9.37% and 10.15%, respectively. Finally, we generated 500 m daily 3H Ta data for Wuhan Metropolitan Area in 2019. The result were expended to Austin, Texas and Los Angeles, in USA. Our research results and comparative analysis confirm the necessity of considering geospatial and temporal factors in temperature fusion models, which helps generate 3H Ta.

Effect of Different Mycorrhiza Treatments on Spore Counts in Oil Palm Topsoil

Mycorrhiza fungi are pivotal in oil palm cultivation, offering potential for enhanced growth and a reduced need for chemical fertilizers. However, the effectiveness of varying volumes and application timings of mycorrhiza remains an underexplored area. This gap, especially in the context of nursery-stage oil palm topsoil, challenges the advancement of sustainable cultivation practices. Addressing this, our study sought to evaluate the impact of different mycorrhiza volumes on spore counts in oil palm topsoil at nursery. Over a period of four months, we administered three levels of mycorrhiza treatments (0.5g, 1.0g, and 1.5g per plant) and conducted a thorough analysis using a 2-way ANOVA. The results revealed no statistically significant difference in spore counts due to varying mycorrhiza weights, as indicated by an F-value of 1.67 and a p-value of 0.209. However, the time factor, represented by different months, showed a significant impact on spore counts, with an F-value of 4.36 and a p-value of 0.014. Additionally, the interaction between mycorrhiza weight and month did not significantly influence spore counts, evidenced by an F-value of 0.31 and a p-value of 0.928. This study underscores the importance of temporal factors over mycorrhiza volume in oil palm nurseries and contributes valuable insights for optimizing sustainable cultivation practices.

Multi-dimensional unobserved heterogeneities: Modeling likelihood of speeding behaviors in different patterns for taxi speeders with mixed distributions, multivariate errors, and jointly correlated random parameters

Speeding behaviors can be classified into different patterns according to both speeding-range and speeding-distance. Among the speeding patterns, some are more frequently observed in specific traffic scenarios, implying that the likelihood of speeding behaviors may vary across the speeding patterns due to the inconsistent impact of temporal, road, environmental, and other traffic factors. Additionally, the trigger of speeding is a complex process so the researchers may not have access to all the critical information associated with the speeding behaviors. This issue may bring about not only independent heterogeneity but also multi-dimensional heterogeneities that are mutually correlated when modeling speeding behaviors by patterns. However, the joint solution to the above challenges is rarely seen in past studies. To fill the knowledge gaps, this study uses taxi GPS trajectories to extract speeding behaviors and classify them into four patterns. The speeder’s percent of speeding distance for each speeding pattern is respectively measured to represent the likelihood of speeding behaviors by patterns. Afterwards, we compare the data-fitting between the models combined with different beta-gamma mixture distributions and a multivariate Gaussian error in modeling the four patterns of speeding likelihood. The combination with the best fitness is used to incorporate jointly correlated random parameters. The results show that the model with beta-gamma-gamma-gamma mixed distributions performs better than other combinations. The model with jointly correlated random parameters outperforms models with other random parameters. The factor analysis reveals that percent of driving at night, percent of driving on roads with a low-speed limit (≤30 km/h), average delays in junctions along the trips, and hourly income have consistent effects on the likelihood of speeding behaviors in all patterns, while the effects of the remaining factors are inconsistent across the speeding patterns. Furthermore, the heterogeneity unveiled by the model parameters is discussed. The study highlights the necessity of considering mixed distributions and multi-dimensional heterogeneities in modeling speeding likelihood by different patterns.

Parenthood, spatial temporal environmental exposure, and leisure-time physical activity participation: Evidence from a micro-timescale retrospective longitudinal study

Parents with dependent children are at a high risk of physical inactivity. While previous studies have mostly focused on how parents' time constraints and changing social network may inhibit leisure time physical activity (LTPA) over the long-term, less is known about the integrated effects of parenting and spatial-temporal environmental exposure on the execution of LTPA during certain episodes of a day. By adopting an integrated social-spatiotemporal-environmental model (ST-ISEM) based on micro-timescale retrospective longitudinal analysis, we examine the association between LTPA participation and spatial-temporal environmental exposure at a micro-timescale, i.e., at the episode-level in working adults' workday, and specifically how parenting integrated with spatial-temporal environmental exposure can jointly influence episode-level LTPA participation. Using data from the day reconstruction method from 701 individuals in Shenzhen, China, we find that parenting may affect the participation of LTPA on workdays not only by shaping temporal environmental factors (time constraint path and social network path), but also by interacting with built environmental exposures (spatial path), both at the episode-level. This study contributes to the theorizing of an integrated social-environmental model for health and wellbeing by extending the ISEM from the life span to the micro-timescale and also by highlighting the importance of temporality in environmental exposure and health studies. It also contributes to the spatial temporal behavioral perspective of time geography literature by clarifying multiple pathways through which social and spatiotemporal environmental factors could interact and jointly affect health behaviors at a micro-timescale. This study contributes to the literature on parenting and LTPA decline by enriching and deepening the understanding of the time constraint and social network pathways through which parenting leads to LTPA change at the micro-timescale. While time constraints may decrease parents’ LTPA at long-term, increasing physical activities related to childcare after work may strongly obstruct moderate-to-vigorous LTPA at a micro-timescale. This study also identifies a spatial pathway by which parenting hinders LTPA due to changing understanding and usage of urban spaces. This pathway warrants attention from social epidemiologists, health geographers, and urban planners since existing interventions promoting physical activity in urban spaces may be ineffective for parents.