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The purpose of this study is to develop new type of reverse mortgage contract. How to provide adequate services and housing for an increasing number of people that are dependent on the help of others is a crucial question in the European Union (EU). The housing stock in Europe is not fit to support a shift from institutional care to the home-based independent living. Some 90% of houses in the UK and 70%–80% in Germany are not adequately built, as they contain accessibility barriers for people with emerging functional impairments. The available reverse mortgage contracts do not allow for relocation to their own adapted facilities. How to finance the adaptation from housing equity is discussed.

The authors have extended the existing loan reverse mortgage model. Actuarial methods based on the equivalence of the actuarial present values and the multiple decrement approach are used to evaluate premiums for flexible longevity and lifetime long-term care (LTC) insurance for financing adequate facilities.

The adequate, age-friendly housing provision that is appropriate to support the independence and autonomy of seniors with declining functional capacities can lower the cost of health care and improve the well-being of older adults. For financing the development of this kind of facilities for seniors, the authors developed the reverse mortgage scheme with embedded longevity and LTC insurance as a possible financial instrument for better LTC services and housing with care in assisted-living facilities. This kind of facilities should be available for the rapid growth of older cohorts.

The numerical example is based on rather crude numbers, because of lack of data, as the developed reverse mortgage product with LTC insurance is a novelty. Intensity of care and probabilities of care in certain category of care will change after the introduction of this product.

The model results indicate that it is possible to successfully tie an insurance product to the insured and not to the object.

The introduction of this insurance option will allow many older adult with low pension benefits and a substantial home equity to safely opt for a reverse mortgage and benefit from better social care.

While currently available reverse mortgage contracts lapse when the homeowner moves to assisted-living facilities in any EU Member State, in the paper a new method is developed where multiple adjustments of housing to the functional capacities with relocation is possible, under the same insurance and reverse mortgage contract. The case of Slovenia is presented as a numerical example. These insurance products, as a novelty, are portable, so the homeowner can move in own specialised housing unit in assisted-living facilities and keep the existing reverse mortgage contract with no additional costs, which is not possible in the current insurance products. With some small modifications, the method is useful for any EU Member State.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The purpose of this paper is to study the concept of purging and present a comparative study of the existing purging methodologies prevailing in the market with a view to evolving a more effective method of capturing the entire impure income to be purged.

To illustrate the present discussion, a case study of purging based on numerical examples has been included. The argument has also been supported with empirical data related to the universe of Sharīʿah-compliant stocks listed on Indian stock exchanges.

During the study, it was found that the existing purging methodologies of calculating impure income to be purged have conceptual and practical shortcomings.

The scope of the current research is limited to calculation of impure income which accrues on account of Sharīʿah non-compliant investments directly or indirectly. It does not try to quantify the benefit which may be imputed in the form of capital gains made in trading of the investee company shares due to higher market value of the shares as a result of the impure income earned by the investee company. The paper has focused on identifying and calculating the impure income on account of interest. Impure income earned from specific Sharīʿah non-compliant products or services has not been considered directly. The reason for this is that companies dealing in such products or services are generally excluded at the business screening stage itself. In the case of those companies which derive a relatively small proportion of their total income from such activities and pass the business screening stage, the quantum of the impure income is not generally reported separately in company accounts.

The result of adopting the proposed methodology will lead to complete purging of impure income (to the extent that is possible under present Company Law and stock exchange reporting regulations). Implementation of the proposed method requires a proper understanding of the working of listed companies and either a sound mathematical background or access to a software application to calculate the impure income to be purged.

The current paper is original and based on the authors’ personal understanding and experience of providing Sharīʿah consultancy services related to Sharīʿah-compliant investments.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

This study aims to validate the content of an instrument which identifies the organizational, sociocultural and technological characteristics that foster digital transformation (DT) in higher education institutions (HEIs) through the Delphi method.

The methodology is quantitative, non-experimental, and descriptive in scope. First, expert judges were selected; Second, Aiken's V coefficients were obtained. Nine experts were considered for the validation.

This study’s findings show that the instrument has content validity and there was strong consensus among the judges. The instrument consists of 29 questions; 13 items adjusted and 2 merged.

A novel instrument for measuring the DT at HEIs was designed and has content validity, evidenced by Aiken's V coefficients of 0.91 with a 0.05 significance, and consensus among judges evidenced by consensus coefficient of 0.81.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

The operation state of lithium-ion battery for vehicle is unknown and the remaining life is uncertain. In order to improve the performance of battery state prediction, in this paper, a joint estimation method of state of charge (SOC) and state of health (SOH) for lithium-ion batteries based on multi-scale theory is designed.

In this paper, a joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is designed. The venin equivalent circuit model and fast static calibration method are used to fit the relationship between open-circuit voltage and SOC, and the resistance and capacitance parameters in the model are identified based on exponential fitting method. A battery capacity model for SOH estimation is established. A multi-time scale EKF filtering algorithm is used to estimate the SOC and SOH of lithium-ion batteries.

The SOC and SOH changes in dynamic operation of lithium-ion batteries are accurately predicted so that batteries can be recycled more effectively in the whole vehicle process.

A joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is accurately predicted and can be recycled more effectively in the whole vehicle process.

This paper seeks to define a theoretical model for the urban regeneration of mass housing areas based on citizen initiative, self-management and self-financing in the form of the neighbourhood cooperative. This paper aims to identify mechanisms for economic resource generation that enable the improvement of the urban surroundings and its buildings without assuming disproportionate economic burdens by the local residents based on two principles, the economies of scale and service provision.

The research is structured in three phases: a literature review of the different trends in self-financing for urban regeneration and the conceptual framework for the definition of a cooperative model; the definition of theoretical model by analysing community ecosystem, neighbourhood-based services and the requirements for its economic equilibrium; and the discussion of the results and the conclusions.

The results show the potential of the cooperative model to generate a social economy capable of reducing costs and producing additional resources to finance the rehabilitation process. The findings show not only the extent of economic advantages but also multiple social, physical and environmental benefits. Its implementation involves the participation of multiple actors, which is one of its significant advantages.

The main contribution is to approach comprehensive urban rehabilitation from a collaborative understanding, overcoming the main financing difficulties of the current practices based on public subsidy policies. The model also allows an ethical relationship to be built with supplier companies by means of corporate social responsibility.

The underrepresentation of women in engineering has important consequences for meeting the need for a larger, talented scientific and technological labor force. Increasing the proportion of women faculty in engineering will help increase the persistence probabilities of women undergraduate and graduate students in engineering, as well as contribute to the range and diversity of ideas toward innovations and solutions to the greatest engineering challenges. This study aims to examine the association among gender, family formation and post-PhD employment patterns of a cohort of engineering doctorates.

Using the National Science Foundation’s Survey of Doctorate Recipients data, 2001–2010, descriptive and multinomial logit regression analyses are conducted to illustrate the career trajectories of engineering PhDs over a ten-year period.

The career trajectories of engineering PhDs are nonlinear, and transitions between employment sectors commonly occur over the ten-year time period studied. Although women engineering PhDs with young dependents are less likely to be employed initially after PhD completion, they tend to enter the workforce in the academic sector as time progresses. Early post-PhD employment as a postdoctoral researcher or in the academic sector contributes to the pursuit of the professoriate downstream.

While previous studies tend to focus on the early career outcomes of science and engineering students, this study contributes to the literature by focusing on the long-term career outcomes of engineering doctorates. Research findings provide engineering PhD students and PhDs with more information regarding potential post-PhD career trajectories, highlighting the multitude of career options and transitions that occur over time. Research findings also provide higher education administrators and doctoral program stakeholders with foundational information toward designing and revitalizing professional development programs to help PhD students prepare for the workforce. The findings have the potential to be applied toward helping increase diversity by shaping policies and programs to encourage multiple alternative career pathways to the professoriate.