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This paper brings focus and attention to the aspect of time within health information behaviour. The purpose of this paper is to critically assess and present strengths and weaknesses of utilising the infodemiology approach and metrics as a novel way to examine temporal variations and patterns of online health information behaviour. The approach is shortly exemplified by presenting empirical evidence for temporal patterns of health information behaviour on different time-scales.

A short review of online health information behaviour is presented and methodological barriers to studying the temporal nature of this behaviour are emphasised. To exemplify how the infodemiology approach and metrics can be utilised to examine temporal patterns, and to test the hypothesis of existing rhythmicity of health information behaviour, a brief analysis of longitudinal data from a large discussion forum is analysed.

Clear evidence of robust temporal patterns and variations of online health information behaviour are shown. The paper highlights that focussing on time and the question of when people engage in health information behaviour can have significant consequences.

Studying temporal patterns and trends for health information behaviour can help in creating optimal interventions and health promotion campaigns at optimal times. This can be highly beneficial for positive health outcomes.

A new methodological approach to study online health information behaviour from a temporal perspective, a phenomenon that has previously been neglected, is presented. Providing evidence for rhythmicity can complement existing epidemiological data for a more holistic picture of health and diseases, and their behavioural aspects.

Examines the effect of weather and temporal factors on the total calls for service (CFS) at the Minneapolis Police Department. Aims at the eventual development of an instrument to predict short‐term variations in CFS over time ‐ a goal which at present is unique. Finds that CFS increase at times when individuals have less habitual routine activities, e.g. after dark; during vacation months. Suggests that temporal factors have much more impact than the weather. Points out some practical implications for the criminal justice system, e.g., future schedules can be adjusted for temporal variations, thus helping in the deployment of officers. Shows that accurate prediction of the overall number of CFS is possible when temporal and weather factors are used.

The purpose of this paper is to present the temporal reflection of electromagnetic waves (EMWs) in simple polar-molecule reactions whose polarization changes with the proceeding of the reactions.

At a temporal boundary, based on the continuity of the electric displacement and magnetic induction, the reflected condition of EMWs is obtained, and the expression of the transmission and reflection coefficients in the reactions is derived. Subsequently, a one-dimensional model is used to validate the reflected condition and expression.

If the time scale of the component concentration variation is greater than the wave period, the polarization of the reactions at a temporal boundary is continuous. The reflection does not happen. On the other hand, when the time scale of the component concentration variation is smaller than the wave period, the polarization is not continuous at a temporal boundary. The impedance of the reactions at the temporal boundary changes and the reflection occurs.

The results may be helpful in disclosing the non-uniform distribution of EMWs in chemical reactions.

The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices.

This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models.

The empirical results show that the ML algorithms achieve good accuracy (R² of 0.873 after cross-validation), and the temporal regression produces competitive results (R² of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type.

Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

How does state repression influence levels of mobilization in authoritarian regimes? This study argues that the relationship between repression and protest is temporally dynamic. Specifically, the short- and long-term effects of autocrats' coercive actions differ conditionally on each phase of the contentious cycle. This argument is tested taking advantage of an original database of protest events in Pinochet's Chile between 1982 and 1989. Using an Interrupted Time Series design, the results show that the State of Siege declarations issued in 1984 and again in 1986 had divergent short- and long-term influence. When the cycle was on an expansive stage, the State of Siege shows no immediate influence on the protests, followed by an increase in long-term mobilization. However, when the mobilization was declining, the State of Siege was associated with an immediate and prominent drop in mobilization, followed by a progressive decrease in the number of protests over the long term. This chapter contributes to the literature on the protest–repression nexus by providing new evidence on the dynamics shaping the relationship between state repression and civil disobedience in authoritarian regimes.

Documents a case study in the use of Shainin Design of Experiments in an industrial honing operation. Provides an overview of the process which attempts to reduce process variability by isolating the most influential factor (Red X). Shows the details of the week‐long experiment and gives some analysis of the data. Uses multiple statistical techniques to identify Red X and discusses corrective action. Suggests that this methodology is very practical and easily executable in many settings, making it one of the most approachable quality techniques available.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlet of a single strand bare tundish. The numerical prediction of the tracer concentration has been made with three different turbulence models; (a standard k‐ε, a k‐ε RNG and a Low Re number Lam‐Bremhorst model) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of k‐ε model with that of the experiment is better than the other two turbulence models as far as gross quantities like mean residence time and ratio of mixed to dead volume are concerned. However, it has been found that the initial transient development of the tracer concentration is best predicted by the Lam‐Bremhorst model and then by the RNG model. The k‐ε model predicts the tracer concentration much better than the other two models after the initial transience (t>40 per cent of mean residence time) and the RNG model lies in between the k‐ε and the Lam‐Bremhorst one. The numerical study has been extended to a multi strand tundish (having 6 outlets) where the effect of outlet positions on the ratio of mix to dead volume has been studied with the help of the above three turbulence models. It has been found that all the three turbulence models show a peak value for the ratio of mix to dead volume (a mixing parameter) when the outlets are placed 200 mm away from the wall (position‐2) thus signifying an optimum location for the outlets to get highest mixing in a given multi strand tundish.

This study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.

The research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.

EPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km² of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.

This research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlets of a six strand billet caster tundish. The numerical prediction of the tracer concentration has been made with six different turbulence models (the standard k‐ε, the k‐ε RNG, the Low Re number Lam‐Bremhorst model, the Chen‐Kim high Re number model (CK), the Chen‐Kim low Re number model (CKL) and the simplest constant effective viscosity model (CEV)) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of the k‐ε model, the RNG, the Lam‐Bremhorst and the CK model is much better than the CKL model and the CEV model as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. However, the k‐ε model predicts the closest value to the experimental observation compared to all other models. The prediction of the transient behavior of the tracer is best done by the Lam‐Bremhorst model and then by the RNG model, but these models do not predict the gross quantities that accurately like the k‐ε model for a single strand bare tundish. With the help of the above six turbulence models mixing parameters such as the ratio of mix to dead volume and the mean residence time were computed for the six strand tundish for different outlet positions, height of advanced pouring box (APB) and shroud immersion depth. It was found that three turbulence models show a peak value in the ratio of mix to dead volume when the outlets were placed at 200 mm away from the wall. An APB was put on the bottom of the tundish surrounding the inlet jet when the outlets were kept at 200 mm away from the wall. It was also found that there exists an optimum height of the APB where the ratio of mix to dead volume and the mean residence time attain further peak values signifying better mixing in the tundish. At this optimum height of the APB, the shroud immersion depth was made to change from 0 to 400 mm. It was also observed that there exists an optimum immersion depth of the shroud where the ratio of mix to dead volume still attains another peak signifying still better mixing. However, all the turbulence models do not predict the same optimum height of the APB and the same shroud immersion depth as the optimum depth. The optimum height of the APB and the shroud immersion depth were decided when two or more turbulence models predict the same values.