Future Time Frame Research Articles

Rethinking real-time risk prediction from multi-step time series forecasting on highway car-following scenarios

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems.

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer)

In this research, the impact of the human factors and climate change on groundwater level fluctuations affected by uncertainty within 27-year upcoming period (2018–2045) in the Razan Plain is examined. To simulate the aquifer performance, the GMS model is calibrated and verified for two 18-month periods, respectively. To forecast climate variables changes in the future time-frame, six CMIP5 models with three scenarios Rcp 2.6, Rcp 4.5 and Rcp 8.5 are utilized. To study the prediction uncertainty of the climate change models, the method of probabilistic levels of precipitation and temperature changes were used. In this technique, by combining 6 climate change models and 3 mentioned scenarios for each month, 18 prediction values for ∆T and ∆P in upcoming years were approximated. After that, by implementing appropriate distribution for each month, next values of ∆T and ∆P in the probabilistic levels of 50% and 90% are estimated. Finally, in two probabilistic levels of 50% and 90% considering the uncertainty of general circulation models, the climate variables of precipitation and temperature were forecasted. Eventually, based on the probabilistic level technique and using the GMS model, the influence of the human factors and climate change on the groundwater level variations under these scenarios are determined. Results showed that climatic factors have a lesser contribution in reducing the groundwater level in the plain, and the largest contribution is related to human factors and excessive withdrawal from the aquifer. The contribution of climate change in the reduction of the groundwater level in probability scenarios of 0.9 and 0.5 and emission scenarios Rcp8.5, Rcp4.5 and Rcp2.6 is about 40.8, 24.3, 32.3, 27.6 and 22.2 percent respectively. Based on these results, the first priority for aquifer planning and management should be focused on human activities and controlling the amount of withdrawal from the aquifer. These results clearly show that the main cause of creating sinkholes and the sharp reduction of the groundwater level in the region is the excessive extraction of groundwater resources as a result of human activities, including agriculture and industrial demands, and not climate change.

The Future Effects of COVID-19 on the Health System: Applying the Futures Wheel Method.

Background: Although the evidence emphasizes that COVID-9 incurs considerable primary effects on public economics and health, it is not so clear what the future effects of this pandemic might be. This study aims to identify the primary and future effects of COVID-19 on the health system. Methods: Futures Wheel (FW) method was used to find the primary and future effects of COVID-19 on eight important dimensions of the health system, including the six building blocks. To gather relevant information, PubMed, SCOPUS, Web of Knowledge, and other sources were searched to find potential studies reporting the potential effects of COVID-19 on the health system. Following that, an expert panel with nine participants to depict the findings was held. Results: Fifty-four studies met the inclusion criteria. The participants reached a consensus on nineteen main primary effects of COVID-19 that could impose 26 main future effects with specific risk opportunities on different dimensions of the health system. Workforce, stewardship and health policy, and infrastructure and hospital capacity dimensions were the most affected by COVID-19 in both the primary and future timeframe. Most of the signals of COVID-19-related opportunities could stem from health technologies and research systems, and service delivery dimensions. Conclusion: COVID-19 comes with considerable risks, especially for the health system governance and workforce dimensions. There are some opportunities to improve the resilience of the health system by using digital health platforms, promoting health literacy of the population, and also adopting inclusive health policy-making processes.

Margaret Atwood's The Handmaid's Tale and An Untold Tale of Subjugation and Eschatological Reality

Margaret Atwood's The Handmaid's Tale (1986) is adystopian novel that opens us to the bizarre reality of women's custodianrape and violence. Things look quite strange and alarming due towomen's oppression which results in a traumatized experience. This isovertly a political novel and tries to spotlight the sense of ineffable lifethat is miserable and also self-revealing. The novel narrates the story ofOffred, a handmaid a sinister handmaid. She was forced to become onedue to the rise of fanatic power in the states of America. America is nowthe Republic of Gilead, where everything is controlled by dominance,oppression, and bigotry. It is, glamorized as a fantasy that impinges onour real life. The novel lends itself to solicit the feminist cause whichleads us to an eschatological reality. Briefly, it tells a tale in the mostpersonal sense about the complicity, fidelity, and betrayal, in the politicalsetup in the contemporary United States.As the novelist, Atwood builds up fine gossamer ofimaginative tale out of a deep love for nature, libertine feminist activismand inclination of science, etc., and perhaps an awful condition we arestruggling to tackle but all in the future time frame.

Medical resource allocation planning by integrating machine learning and optimization models

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.

Analisis Efisiensi Pelaksanaan Anggaran Belanja Daerah pada Badan Pengelola Keuangan dan Aset Daerah (BPKAD) Kota Medan

The budget is a financial plan for the future time frame which contains provincial incomes and consumptions, which can portray the public authority’s arrangement to understand and dispense reserves. Great asset the board is a significant component in understanding the use spending plan, for this situation the planning completed and arranged by the provincial government should be thought about suitably and precisely, so the proficiency level increase can be acknowledged true to form. The reason for this study was to decide the degree of efficiency in the execution of the Local Spending plan at the Medan City BPKAD. The strategy utilized in this examination is descriptive research, specifically by gathering all information connected with the acknowledgment of the Local Spending plan which is then ordered, explained, and dissected which can then be finished up. The aftereffects of the review show that the acknowledgment of the Territorial Financial plan at the Medan City BPKAD during the 2020-2021 monetary year has expanded and is classified as efficient with an average percentage of efficiency of 64.56%.
 Keywords: efficiency, realization of Regional Budget

Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer.

Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research.

Ecosystem services at risk in Italy from coastal inundation under extreme sea level scenarios up to 2050: A spatially resolved approach supporting climate change adaptation.

According to the latest projections of the Intergovernmental Panel on Climate Change, at the end of the century, coastal zones and low-lying ecosystems will be increasingly threatened by rising global mean sea levels. In order to support integrated coastal zone management and advance the basic "source-pathway-receptor-consequence" approach focused on traditional receptors (e.g., population, infrastructure, and economy), a novel risk framework is proposed able to evaluate potential risks of loss or degradation of ecosystem services (ESs) due to projected extreme sea level scenarios in the Italian coast. Three risk scenarios for the reference period (1969-2010) and future time frame up to 2050 under RCP4.5 and RCP8.5 are developed by integrating extreme water-level projections related to changing climate conditions, with vulnerability information about the topography, distance from coastlines, and presence of artificial protections. A risk assessment is then performed considering the potential effects of the spatial-temporal variability of inundations and land use on the supply level and spatial distribution of ESs. The results of the analysis are summarized into a spatially explicit risk index, useful to rank coastal areas more prone to ESs losses or degradation due to coastal inundation at the national scale. Overall, the Northern Adriatic coast is scored at high risk of ESs loss or degradation in the future scenario. Other small coastal strips with medium risk scores are the Eastern Puglia coast, Western Sardinia, and Tuscany's coast. The ESs Coastal Risk Index provides an easy-to-understand screening assessment that could support the prioritization of areas for coastal adaptation at the national scale. Moreover, this index allows the direct evaluation of the public value of ecosystems and supports more effective territorial planning and environmental management decisions. In particular, it could support the mainstreaming of ecosystem-based approaches (e.g., ecological engineering and green infrastructures) to mitigate the risks of climate change and extreme events while protecting ecosystems and biodiversity. Integr Environ Assess Manag 2022;18:1564-1577. © 2021 SETAC.

The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19

During the Disease outbreak and in the future, there will be a lot of learning. Since the pandemic has interrupted global schooling, remote learning has emerged as a viable option, depending on machine learning to accomplish its goals. Using the example of ten international science journals that speak out about artificial intelligence in education today and the future of earning, we hope to gain a better understanding of the large extend of the power of artificial intelligence in education, both during the COVID-19 period and during the future learning time frame. Additionally, in addition to evaluating 10 articles, we used an internet search engine to look for relevant material. We conducted searches using terms such as artificial intelligence, learning during a pandemic, and Machine learning, among other things. After that, we used a phenomenological technique to confirm that our results answered the research questions, which was done in accordance with a qualitative approach. Our findings can be summarized by taking into account the evidence from research and literature. Among our findings are that the detailed assessment of artificial intelligence in education, the use of AI in education, typical learning in the pandemic era, and the role of artificial intelligence (AI) disease outbreak learning are all important for both current and future residents. While statistical methods and automated based on learning jobs that are smarter than normal continue to be important, learning is becoming more automated. It helps individuals to be more concentrated on their learning opportunities and to recognize when they do not grasp a subject completely. First and foremost, the instructors provide valuable assistance throughout the assessment process of student learning outcomes.

An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners

Real-time operation is critical for noise reduction in hearing technology. The essential requirement of real-time operation is causality-that an algorithm does not use future time-frame information and, instead, completes its operation by the end of the current time frame. This requirement is extended currently through the concept of "effectively causal," in which future time-frame information within the brief delay tolerance of the human speech-perception mechanism is used. Effectively causal deep learning was used to separate speech from background noise and improve intelligibility for hearing-impaired listeners. A single-microphone, gated convolutional recurrent network was used to perform complex spectral mapping. By estimating both the real and imaginary parts of the noise-free speech, both the magnitude and phase of the estimated noise-free speech were obtained. The deep neural network was trained using a large set of noises and tested using complex noises not employed during training. Significant algorithm benefit was observed in every condition, which was largest for those with the greatest hearing loss. Allowable delays across different communication settings are reviewed and assessed. The current work demonstrates that effectively causal deep learning can significantly improve intelligibility for one of the largest populations of need in challenging conditions involving untrained background noises.

Towards sustainable financing models: A proof-of-concept for a waqf-based alternative financing model for renewable energy investments

This study investigates alternative equity-based financing models and proposes a waqf-owned (that could also be called philanthropy-, endowment-, trust-, foundation-, and third sector-based) financial intermediary (WOFI). We do so by developing the agent-based model and scenario-based computer simulations for financing solar powerplants. For this purpose, we redesign a waqf-based financing system and analyze the changes in wealth inequality, capital pooling, and social prosperity for a future time frame. As a proof-of-concept, the simulation results show that for the given case study, the proposed WOFI reduces wealth inequality dramatically, whereas conventional debt-based financing models raise inequality. Besides, restructuring the waqf-based financial intermediaries also enables the capital pooling essential for large projects and absent in the conventional waqf system. In short, this study proves that waqf-based institutions have a remarkable potential to contribute towards sustainable development goals by taking account of long-run social implications, economic growth, and environment-friendly projects.

Era of Unpredictable Earthquakes to Predictable: A New Perspective to Predict Earthquakes to Mitigate Loss of Life and Destruction of Property in Japan, California and Mexico

The single-most important purpose of this observational successful earthquake prediction research is to mitigate the loss of life and destruction of property by earthquakes. The basic innovative concept is entirely new and related to the tectonic plate activities of epicenter zones, may not familiar to many and may not be readily found in the existing literature. This manuscript is prepared based on 35 years of earthquake prediction study and over 10 years of observation of onshore observable precursors and the corresponding earthquakes. Past precursory phenomena for earthquakes have been observed, recorded, analyzed and can be used to predict future earthquakes for seismically risky regions, Japan, California and Mexico. It is significant to mention that there are more than 250 epicenter zones and equal number of precursor areas identified, centrifugal force due to the orbital motion of the earth is the major driving force of tectonic plates, each and every epicenter zone has its own creation of atmospheric weather anomalies, due to the change in position of the orbital motion of the earth. Though all form of weather anomalies are preceded by earthquakes, among them rainfall is the best earthquake precursor and rainfall location is best to identify the future earthquake direction, location frame, time frame and magnitude frame can be established 15 days before earthquakes occur. Based on this prediction method each and every individual earthquake can be predicted without false alarm in Japan, California and Mexico.

Adverse Health and Psychosocial Repercussions in Retirees from Sports Involving Head Trauma: Looking at the Sport of Boxing

Academic scholarship has steadily reported unfavourable clinical findings on the sport of boxing, and national medical bodies have issued calls for restrictions on the sport. Yet, the positions taken on boxing by medical bodies have been subject to serious discussions. Beyond the medical and legal writings, there is also literature referring to the social and cultural features of boxing as ethically significant. However, what is missing in the bioethical literature is an understanding of the boxers themselves. This is apart from their brain injuries, the debates about the degenerative brain disease known as chronic traumatic encephalopathy (CTE), and related issues about the disease. This article argues that the lives of boxers, their relationships, their careers, and their futures, also requires its own research, particularly in telling stories about their lives, and those lives and futures which boxing affects. The article uses two approaches. First, to imagine a more enduring “whole of life viewpoint” by using an extended future timeframe. Second, to consider perspectives of a person’s significant others. After reviewing the boxing literature, the article discusses social settings and then explores the hidden social relationships in life after boxing. With these longer time and close relationship viewpoints, three important themes emerge: family and kinship; age, stage and career; and the effects of boxing fatalities. These analyses are used in conjunction with relevant clinical findings which complement the telling of stories to improve medical information, and engages professional and public empathy for people’s experience of illness and difficulties in coping.

Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers

We propose a hybrid sequential prediction model called “Deep Sequence”, integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.

2020

At the dawn of a new decade, I cannot help but recall that when I started my academic career in social work in the 1990s, it was common to look ahead to how life would be in the next century. Statistical projections forecast various demographic changes, often using 2020 as the future time frame. Back then, 2020 sounded far away and almost alien. Well folks, the future is here. Now that 2020 has dawned, it seems that the more things change, the more they stay the same. Certainly, the specific issues that social workers address have changed over the decades, and our approaches have been modified to tackle the new issues, but the struggle to understand and meet emerging needs persists. I used to jokingly hear that the ultimate goal of the social work profession was to put ourselves out of business. Given the intransigence of intolerance for difference and the persistent emergence of needs arising from “advances” of modern living, it seems the social justice stance of our profession will never be fully met. Indeed, our social contract is continually expanding.
 In the Fall 2019 issue of Advances in Social Work we are pleased to present 14 papers--11 empirical, 3 conceptual--written by 29 authors from 12 states across the U.S., representing different regions of the country and Ghana. Each paper is briefly introduced below.

A two-stage time series model for monthly hydrological projections under climate change in the Lim River basin (southeast Europe)

ABSTRACTClimate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.

Cloud-Centric IoT-Based Green Framework for Smart Drought Prediction

Drought is a catastrophic natural disaster with significant impact on financial stability, hydrological budget, public health, and agricultural productivity. Numerous drought indices have been introduced to quantify the severity of droughts, but the majority of them are incapable of demonstrating the changes in crucial drought causing elements. Internet of Things (IoT) is appropriate to monitor time-critical environmental parameters. This article proposes an energy-efficient cloud-centric system to assess the drought for the current situation and predict for the future time frame. The architecture determines the active and sleep interval of IoT sensors based on the analysis of data variability using the Bartlett test. The dimensionality of the data about drought causing elements is reduced using kernel principal component analysis (KPCA) at the fog layer. The intensity of drought is determined at the cloud layer using naive-Bayes classifier, and drought severity for different time periods is predicted using the seasonal autoregressive integrated moving average model. Experimentation and performance analysis prove the efficiency of the proposed system in assessing and predicting the drought with better correlations with drought-causing attributes. Furthermore, it shows significant energy savings as compared to other schemes.

Sustainable Financing for Sustainable Development: Agent-Based Modeling of Alternative Financing Models for Clean Energy Investments

Renewable energy investments require a substantial amount of capital to provide affordable and accessible energy for everyone in the world, and finding the required capital is one of the greatest challenges faced by governments and private entities. In a macroeconomic perspective, national budget deficits and inadequate policy designs hinder public and private investments in renewable projects. These problems lead governments to borrow a considerable amount of money for sustainable development, although such excessive debt-based financing pushes them to unsustainable economic development. This substantial amount of borrowing makes a negative contribution to the high global debt concentration, putting countries’ economic and social development at risk. In line with this, excessive debt-based financing causes an increase in wealth inequality, and when wealth inequality reaches a dramatic level, wars and many other social problems are triggered to correct the course of wealth inequality. In this regard, the motivation behind the study is to develop a set of policy guidelines for sustainable financing models as a solution for these intertwined problems, which are: (1) a financial gap in energy investments; (2) an excessive global debt concentration; and (3) a dramatic increase in wealth inequality. To this end, this study presents a quantitative and comparative proof of concept analysis of alternative financing models in a solar farm investment simulation to investigate the change in wealth inequality and social welfare by reducing debt-based financing and increasing public participation. There are many studies in the literature investigating the evolution of wealth inequality throughout history. However, there is a gap in the literature, and investigating the effects of various policy rules on the evolution of wealth inequality in a future time frame needs to be explored in order to discuss possible policy implications beforehand. In this respect, this paper contributes to the literature by developing simulation models for conventional and alternative financing systems. This enables investigating the changes in wealth inequality and social welfare as a result of various policy implications throughout the simulation time.

Time frame as a determinant of accessibility of anaphoric demonstratives in Classical Arabic

Abstract The aim of this paper is to investigate the determinants for choosing nominal anaphoric demonstratives in Classical Arabic (CA) by examining their usage in a corpus of CA texts. The study makes use of Ariel’s (1990; 2001) concept of ‘unity’ as a theoretical framework from which to study the relationship between an anaphoric demonstrative, its antecedent and their shared referent. This study builds on Jarbou and Migdady’s (2012) findings that ‘anaphoric distance’ (Ariel, 1990; 2001) has not been found to be a primary determinant of cognitive accessibility concerning the use of anaphoric demonstratives in CA. The results of this study show that the choice of proximal/distal anaphoric demonstratives in CA depends primarily on the ‘time frame’ of the referent. Anaphoric demonstratives are temporally anchored in the present time of interaction; if a referent existed within a past time frame or is expected to exist within a future time frame (in relation to the interlocutors’ present time), that referent has low accessibility because of non-sharedness of time frame; if a referent existed or is experienced within a present time frame, it has high accessibility due to sharedness of time frame. Temporal distance replaces physical distance as a determinant of accessibility. In the corpus, proximal anaphoric demonstratives have been used in contexts of high accessibility while distal anaphors have been used in those of low accessibility. Findings of this study contribute to the dynamic view of demonstratives that textual/physical distance is not the primary or sole determinant of accessibility concerning demonstratives.