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The purpose of this paper is to discuss an online sensor-based support system which the authors believe can be useful in such scenarios. Persons with a cognitive impairment, such as those with Alzheimer’s disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (IADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going IADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the IADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the IADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the IADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results of this study verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the IADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations.

Duration information to a certain extent has been widely ignored by activity recognition researchers and has received a very limited application within smart environments.

This study concludes that incorporating progressive duration information into partially observed sensor sequences of IADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer’s disease, suffering from deficiencies in cognitive skills which reduce their independence. Such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability, taking into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity; thus, updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.

The purpose of this paper is to propose models of duration for maturity gap risk management in Islamic banks.

A thorough review of literature on duration modeling, duration measurement in Islamic banks and Shariah compliance has been conducted to set parameters to develop Shariah-compliant maturity gap risk management mechanism.

Models based on durations of earning assets and return bearing liabilities using various rates of return earned and paid, benchmark rates and industry standards commonly used by Islamic and conventional banks.

Increased Shariah compliance has threefold impact. Firstly, it will increase trust of customers. Secondly, it will help improve profitability by reducing non-Shariah compliance penalties from the regulators. And finally, it will enhance market capitalization and returns stability to investors because of enhanced customer base, increased level of trust and increased profitability.

This research proposes Shariah-compliant maturity gap risk management models based on the concept of duration according to recommendations of Bank for International Settlements. As there is no such maturity gap risk management mechanism that meets the requirements of Shariah using benchmarks that are common between Islamic and conventional banks; therefore, this research presents risk management solutions that can be applied simultaneously in the entire banking sector.

The purpose of this research is to propose a framework for research on Macaulay duration and establish future research directions.

Thematic, bibliometric and content analyses have been used to review 168 research papers published between 1938 and 2019 taken from ISI Web of Science and Scopus contributed by leading authors, journals and regulatory bodies.

Identification and integration of themes of duration theory, duration model development and duration model implementation leading to unattended research gaps, and framework for research on Macaulay duration.

The study is based on an extensive review of the literature to extract important themes, research gaps and frameworks. It does not empirically investigate significance of Macaulay duration and various sectors.

This research has several aspects that are helpful for practitioners. Macaulay duration has been the subject of empirical research only without any guiding framework. This research provides a platform to initiate profound researches in various areas of finance. Various proposed models are required to be tested under holistic approach in conventional and emerging fields, especially in Islamic settings.

This research highlights, research themes leading to framework, research gaps and factors that are crucial in developing, extending and testing duration models leading to enhancement of theoretical base of Macaulay duration.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

An S-curve is an essential project-management tool. However, it is difficult to adjust S-curve to deal with a force majeure event. The present study develops four valuable adjustment approaches, designed to achieve a compromise between the views of the client and contractor. These can be used to control projects after a force majeure event.

The present study develops four adjustment approaches, which can be used to achieve a compromise between the views of the client and those of the contractor when controlling projects after a force majeure. To determine the S-curves during a force majeure event, two approaches can be selected: BCWS (budgeted cost of scheduled work)-base approach, or BCWP (budgeted cost of work performed)-base approach. To determine the rest of S-curves after a force majeure event, two approaches can be considered: maintaining the original curve of the remaining BCWS, or allocating the original curve of the remaining BCWS. Based on the validation of three empirical cases, drawn from a professional project-management website, this study confirms the feasibility of four proposed empirical approaches and a selection procedure for S-curve adjustment.

The S-curve-adjustment approaches presented here can be used to deal with cases that are ahead of, on and behind schedule. Using the proposed approaches and selection procedure, contractors can easily revise S-curves and control projects more effectively. To deal with a force majeure event, such as COVID-19, they are strongly advised to adopt the approaches labeled SA-A1 (to adjust the S-curve based on the extension ratio multiplied by the difference in progress during the force majeure) and SA-B1 (to maintain the original curve of the remaining BCWS) for the A/E and E/F curves, respectively.

The proposed approaches can be used in cases of continuous construction during force majeure events. If construction work is totally suspended during such an event, it will be necessary to fine-tune the proposed approaches.

Previous studies have used case-oriented or mathematical-simulation approaches to forecast S-curves. The present study proposes simple approaches that allow the client and contractor to adjust the S-curve easily after a force majeure event. These approaches can be used to adjust work and project-completion targets within an extended duration. Selecting the right S-curve adjustment approach can help to control the remainder of the project, reducing the possibility of delay claims.

The purpose of this paper is to show that the duration‐based hedge ratio has many serious defects: first, the yield‐to‐maturity is not the market interest rate, and it cannot even serve as a proxy for the market interest rate. Second, it is difficult to choose an appropriate bond for hedging among available bonds and to calculate duration. Third, duration can only be applied to small changes of interest rate. If there is a large change in interest rate, a duration‐based hedge's performance may be worse than expected. The paper proposes an improving method to solve these problems.

The proposed design is a model‐driven enterprise system.

The proposed system integrates the complex risk management into the enterprise architecture. It can merge, import, and share resource related data across managements.

The paper shows how to manage the risk from both parallel and non‐parallel shifts of interest rates in the proposed system.

The purpose of this study is to develop, test and examine econometric methodology for Sharīʿah-compliant duration models of Islamic banks.

The research evaluates all existing duration models from Sharīʿah’s perspective and develops a four-stage framework for testing Sharīʿah-compliant duration models. The econometric methodology consists of multiple regression, Johansen co-integration, error correction model, vector error correction model (VECM) and threshold vector error models (TVECM).

Regressions analysis suggests that returns on earning assets and interbank offered rates are significant factors for calculating the duration of earning assets, whereas returns paid on return bearing liabilities and average interbank rates of deposits are significant factors for duration of return bearing liabilities. VECM suggests that short run duration converges into long run duration and TVECM suggests that management of assets and liabilities also plays a significant role that can bring about a change of about 15% in respective durations.

Sharīʿah-compliant duration models will improve risk and Sharīʿah efficiency, which will ultimately improve market capitalization and returns stability of Islamic banks in the long run.

Sharīʿah-compliant duration models testing provides insight into how various factors, namely, rates of return, benchmark rates and managerial skills of Islamic bank risk managers impact durations of assets and liabilities. It also explains the future course of action for Sharīʿah-compliant duration model testing.