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A computational methodology, based on the coupling of the finite element and boundary element methods, is developed for the solution of magnetothermal problems. The finite element formulation and boundary element formulation, along with their coupling, are discussed. The coupling procedure is also presented, which entails the application of the LU decomposition to eliminate the need for the direct inversion of matrices resulting from FE‐BE formulation, thereby saving computation time and storage space. Corners for both FE‐BE interface and BE regions, where discontinuous fluxes exist, are treated using the double flux concept. Numerical results are presented for three different systems and compared with analytical solutions when available. Numerical experiments suggest that for magnetothermal problems involving small skin depths, a careful mesh distribution is critical for accurate prediction of the field variables of interest. It is found that the accuracy of the temperature distribution is strongly dependent upon that of the magnetic vector potential. A small error in the magnetic vector potential can produce significant errors in the subsequent temperature calculations. Thus, particular attention must be paid to the design of a suitable mesh for the accurate prediction of vector potentials. From all the cases examined, 4‐node linear elements with adequate progressive coarsening of meshes from the surface gave the results with best accuracy.

This study investigates the level of stock return synchronicity in African markets with the aim of establishing whether, contrary to conventional wisdom, stock return synchronicity can be low in countries with relatively weak information environments.

The authors use a sample of five African countries (Botswana, Ghana, Kenya, Nigeria and South Africa) and a total of 616 firms over the period 2005–2015. This study's main measure of synchronicity is the R² from a regression of stock returns on index returns. The authors also carry out regression analysis to investigate the main firm-level drivers of synchronicity.

On average, firms in African markets do not exhibit high levels of stock return synchronicity, providing support for the view that stock return synchronicity can be low in markets with relatively weak transparency. The authors, however, observe an increase in the level of synchronicity during the global financial crisis, notably for Ghana and Kenya. In the regression analysis, the main firm-level driver of synchronicity is firm size, while contrary to some previous studies, ownership structure has no impact. The authors also find evidence of the impact of changes in accounting regulation, notably the mandatory adoption of IFRS, on the level stock synchronicity.

This study contributes to the understanding of stock return synchronicity and how price discovery can vary between different information environments. The authors argue that stock returns in African countries may not always fit the stereotypical view that they are synchronous. The level of synchronicity among firms suggests that corporate events may carry some stock price implications.

The deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs).

RSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.

The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets.

The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.

UK policy seeks to shift commissioning of services ‘closer to the people’ with a view to establishing shared visions of local care services grounded in the opinions and priorities of the public. The participation of older people with mental health problems and their carers in the strategic planning process has been patchy, however. This article compares practitioner and public perspectives of the services that should be provided for older people with mental health problems in an area of North West England. Significant differences were found in the services the various stakeholder groups prioritised for development, and in their views on how they should be organised. The implications for commissioning are discussed.

This paper aims to explore the pathways of carbon transfer in 200 US corporations along with the motivations that drive such transfers. The particular focus is on each firm’s embeddedness in the global value chain (GVC) and the influence of environmental law, operational costs and corporate social responsibility (CSR). The insights gleaned bridge a gap in the literature surrounding GVCs and corporate carbon transfer.

The methodology comprised a two-step research approach. First, the authors used a two-sided fixed regression to analyse the relationship between each firm’s embeddedness in the GVC and its carbon transfers. The sample consisted of 217 US firms. Next, the authors examined the influence of environmental law, operational costs and CSR on carbon transfers using a quantitative comparison analysis. These results were interpreted through the theoretical frameworks of the GVC and legitimacy theory.

The empirical results indicate positive relationships between carbon transfers and GVC embeddedness in terms of both a firm’s position and its degree. From the quantitative comparison, the authors find that the pressure of environmental law and operational costs motivate these transfers through the value chain. Furthermore, CSR does not help to mitigate transfers.

The findings offer insights for policymakers, industry and academia to understand that, with globalised production and greater value creation, transferring carbon to different parts of the GVC – largely to developing countries – will only become more common. The underdeveloped nature of environmental technology in these countries means that global emissions will likely rise instead of fall, further exacerbating global warming. Transferring carbon is not conducive to a sustainable global economy. Hence, firms should be closely regulated and given economic incentives to reduce emissions, not simply shunt them off to the developing world.

Carbon transfer is a major obstacle to effectively reducing carbon emissions. The responsibilities of carbon transfer via GVCs are difficult to define despite firms being a major consideration in such transfers. Understanding how and why corporations engage in carbon transfers can facilitate global cooperation among communities. This knowledge could pave the way to establishing a global carbon transfer monitoring network aimed at preventing corporate carbon transfer and, instead, encouraging emissions reduction.

This study extends the literature by investigating carbon transfers and the GVC at the firm level. The authors used two-step research approach including panel data and quantitative comparison analysis to address this important question. The authors are the primary study to explore the motivation and pathways by which firms transfer carbon through the GVC.

This study seeks to explore the presence and the relative strength of market efficiency in the onshore and offshore Renminbi (RMB) forward markets.

In the onshore and offshore foreign exchange markets, the RMB forward contracts are designed in similar ways. However, the underlying economic forces and regulatory frameworks are very different in these two markets. We first analyze the functioning of each market, by examining the covered interest rate parity (CIRP) conditions. Second, we explore the CIRP deviations in the two markets and quantify the role of market frictions and government interventions.

We find that the CIRP condition does not hold in either the onshore or the offshore RMB forward markets. We also find that the offshore market is more efficient than the onshore market in conveying private information about investors’ expectation.

Our results reveal that the onshore RMB forward market provides an imperfect platform for investors to manage their currency exposures. We suggest that by opening the offshore market to domestic participants and the onshore market to more foreigners, the forward rates may become more informative with a greater investor mix. These liberalization efforts are important steps in the right directions to improve market efficiency in the Chinese FOREX market.

In this paper, a superconvergent patch recovery method is proposed for superconvergent solutions of modes in the finite element post-processing stage of variable geometrical Timoshenko beams. The proposed superconvergent patch recovery method improves the solution speed and accuracy of the finite element analysis of a curved beam. The free vibration and natural frequency of the beam were considered for studying forced vibrations and structural resonance. Beam vibration mode analysis was performed for high-precision vibration mode solutions and frequency values. The proposed method can be used to compute beam vibration modes of beams with different shapes and boundary conditions as well as variable cross sections and curvatures. The purpose of this paper is to address these issues.

An adaptive method was proposed to analyse the in-plane and out-of-plane free vibrations of the variable geometrical Timoshenko beams. In the post-processing stage of the displacement-based finite element method, the superconvergent patch recovery method and high-order shape function interpolation technique were used to obtain the superconvergent solution of mode (displacement). The superconvergent solution of mode was used to estimate the error of the finite element solution of mode in the energy form under the current mesh. Furthermore, an adaptive mesh refinement was proposed by mesh subdivision to derive an optimised mesh and accurate finite element solution to meet the preset error tolerance.

The results computed using the proposed algorithm were in good agreement with those computed using other high-precision algorithms, thus validating the accuracy of the proposed algorithm for beam analysis. The numerical analysis of parabolic curved beams, beams with variable cross sections and curvatures, elliptically curved beams and circularly curved beams helped verify that the solutions of frequencies were consistent with the results obtained using other specially developed methods. The proposed method is well suited for the mesh refinement analysis of a curved beam structure for analysing the changes in high-order vibration mode. The parts where the vibration mode changed significantly were locally densified; a relatively fine mesh division was adopted that validated the reliability of the mesh optimisation processing of the proposed algorithm.

The proposed algorithm can obtain high-precision vibration solutions of variable geometrical Timoshenko beams based on more optimized and reasonable meshes than the conventional finite element method. Furthermore, it can be used for vibration problems of parabolic curved beams, beams with variable cross sections and curvatures, elliptically curved beams and circularly curved beams. The proposed algorithm can be extended for application in superconvergent computation and adaptive analysis of finite element solutions of general structures and solid deformation fields and used for adaptive analysis of more complex plates, shells and three-dimensional structures. Additionally, this method can analyse the vibration and stability of curved members with crack damage to obtain high-precision vibration modes and instability modes under damage defects.

This study examines whether investors value the timing and/or information of mandatory disclosures in a unique research setting of listed companies in Thailand.

The authors adopt an event-study based approach. Abnormal stock returns are calculated using an OLS market model to measure market reactions to three types of mandatory reports issued by listed Thai firms: financial statements, Form 56-1 and Form 56-2. These reports are released sequentially but contain overlapping information content. Multivariate regression models are employed to examine the market reactions to these regulatory reports and explore the characteristics of firms that affect the market response.

The stock market reacts differentially to these reports. The financial statements, which are filed the earliest and are the most concise, prompt the strongest reaction. Investors similarly react significantly to Form 56-1 and Form 56-2, although Form 56-2 provides additional information beyond Form 56-1. The market reactions to small firms are stronger. Collectively, equity investors focus on the timeliness of disclosures rather than the information disclosed in the mandatory reports.

The evidence provides support for ongoing regulatory initiatives aimed at improving the timeliness of mandatory disclosures in emerging economies.

Prior studies on disclosure regulation investigate either the effect of information content or the timing of mandatory disclosures in isolation. The authors differentiate the effect of information content from disclosure timing and extend the literature by suggesting that investors incrementally value timeliness of disclosures. Investors perceive the benefit of the timely release of quantitative information compared to subsequent narrative disclosures. Between Form 56-1 and Form 56-2, the earlier release of the narrative non-financial information is incrementally traded into share prices.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

Outpatient care delivery is one of the key revenue sources of a hospital which plays a salient role in timely care delivery. The key purpose of the study is to propose a multi-objective simulation-based decision support model that considers the cost of care delivery and patient dissatisfaction as its two key conflicting objectives. Patient dissatisfaction considers service fairness. Patient idiosyncrasies such as no-show, unpunctuality and balking have been considered in the model involving multiple classes of patients.

A model has been designed using data collected from field investigations. In the first stage, queuing theory based discrete event simulation model has been developed. Genetic algorithm has been used to solve the scalarized problem and obtain actionable insights. In the second stage, non-dominated sorting genetic algorithm II (NSGA-II) has been involved to achieve the Pareto optimal fronts considering equal priority of the two objectives.

The computational results considering various parameter settings can help in efficient resource planning while ensuring better care delivery. The model proposed in the study provides structural insights on the business strategy of healthcare service providers on optimizing the dual goals of care delivery cost and service fairness.

The study is one of the early works that helps to improve the care delivery process by taking into consideration the environmental factors as well as service fairness. The study demonstrates the usage of simulation-based multi-objective optimization to provide a more sustainable patient centric care delivery.