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The work of text summarization in question-and-answer systems has gained tremendous popularity recently and has influenced numerous real-world applications for efficient decision-making processes. In this regard, the exponential growth of COVID-19-related healthcare records has necessitated the extraction of fine-grained results to forecast or estimate the potential course of the disease. Machine learning and deep learning models are frequently used to extract relevant insights from textual data sources. However, in order to summarize the textual information relevant to coronavirus, we have concentrated on a number of natural language processing (NLP) models in this research, including Bidirectional Encoder Representations of Transformers (BERT), Sequence-to-Sequence, and Attention models. This ensemble model is built on the previously mentioned models, which primarily concentrate on the segmented context terms included in the textual input. Most crucially, this research has concentrated on two key variations: grouping-related sentences using hierarchical clustering approaches and the distributional semantics of the terms found in the COVID-19 dataset. The gist evaluation (ROUGE) score result shows a significant and respectable accuracy of 0.40 average recalls. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle's historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models. [ABSTRACT FROM AUTHOR]

Synovial joints are unique biological tribo-systems that allow for efficient mobility. Most of the synovial joint activities in the human body are accomplished due to the presence of synovial fluid. As a biological lubricant, synovial fluid lubricates the articular cartilage to minimize wear and friction. The key components of synovial fluid that give it its lubricating ability are lubricin, hyaluronic acid (HA), and surface-active phospholipids. Due to age and activities, synovial fluid and articular cartilages lose their properties, restricting synovial joint mobility and resulting in articular cartilage degradation, leading to the pathological synovial joint, which is a major cause of disability. In this context, synovial joint research remains significant. Even though synovial joint lubrication has been investigated, several problems linked to squeeze film lubrication need greater attention. The Newtonian model of squeeze film lubrication in synovial joints must be studied more extensively. This work aims to investigate squeeze film lubrication in diseased synovial joints. The lubrication and other properties of synovial fluid and the flow of synovial fluid in a diseased human knee joint are investigated theoretically in this work. We have investigated the effect of the synovial fluid viscosity and the effects of permeability and thickness of articular cartilage on squeeze film properties. Moreover, we have also investigated the effect of squeeze velocity and film thickness on the characteristics of the squeeze film formed between the articular cartilages of a diseased human knee joint. In this work, the articular cartilages were treated as a rough, porous material, and the geometry was approximated as parallel rectangular plates, while the synovial fluid flow is modeled as a viscous, incompressible, and Newtonian fluid. The modified Reynolds equation is obtained using the principles of hydrodynamic lubrication and continuum mechanics, and it is solved using the appropriate boundary conditions. The expressions for pressure distribution, load-bearing capacity, and squeezing time are then determined, and theoretical analysis for various parameters is conducted. Pressure is increased by squeeze velocity and viscosity, while it is decreased by permeability and film thickness, leading to an unhealthy knee joint and a reduction in knee joint mobility. The load capacity of the knee joint decreases with permeability and increases with viscosity and squeezing velocity, resulting in a reduction in the load-carrying capacity of the knee joint in diseased conditions. Synovial knee joint illness is indicated by increased pressure and squeeze time. The squeeze film properties of synovial joints are important for maintaining joint health and function. Joint diseases such as osteoarthritis, rheumatoid arthritis, and gout can affect the composition and production of synovial fluid, leading to changes in squeeze film properties and potentially causing joint damage and pain. Understanding these relationships can help in the development of effective treatments for joint diseases. [ABSTRACT FROM AUTHOR]

The ES structure described by soft subsets or soft M-subsets does not yield a lattice structure due to its restriction on parameter sets, and so cannot be used in information theory. This study proposes a new ES structure on soft sets that addresses the deficiencies of the prior structure. Using mathematical concepts, we can construct and entirely new system of soft sets. As a result, the ES structure is derived from a finite collection of basic soft sets and offers complicated soft sets via its ES components, allowing for it to be operated by computers, as this is more acceptable to conventional mathematical viewpoints. We rewrote this using a soft J-subset and demonstrated that (ES, ∨ ˜ E S , ∧ ˜ E S ) is a distributive lattice. This will play an important role in decision-making problems and contribute to a better understanding of human recognition processes. During the process of reaching a decision, several groups of parameters develop, and the ES structure in this article takes these parameters into consideration in order to handle the intricate issues that arise. In soft set theory, this research gives insight into the cognitive field. [ABSTRACT FROM AUTHOR]

Purpose: The recent development in logistics due to the dawn of Logistics 4.0 has made global logistics providers more dependent on intelligent technologies. In this era, these technologies assist in data collection and transmission of logistical data and pose many security and privacy threats in logistics management systems. The customer's private information, which is shared among the logistics stakeholders for optimal operation, faces unauthorized access due to a lack of privacy. This, amongst others, is a critical problem that needs to be addressed with blockchain. Blockchain is a disruptive technology that is transforming different sectors, and it has the potential to provide a solution to the issues mentioned above, with its unique features such as immutability, transparency, and anonymity. Method: This study designed a blockchain-based logistics management architecture on a decentralized peer-2-peer network using Ethereum smart contracts. The proposed system deployed the Rivest–Shamir–Adleman (RSA) asymmetric encryption method to protect the logistics system from cyber-attacks and secure customers' private information from unauthorized access. Findings: Furthermore, the security and privacy of the proposed system are evaluated based on the theorem. The proof shows that the system can provide security to the logistics system and privacy to customers' private data. The performance evaluation is based on throughput and latency. It shows that the proposed system is better than the baseline system, and the comparatives analysis shows that the proposed system is more secure and efficient than the existing systems. Implication and Limitation: The proposed system offers a better solution to the security/privacy of the logistics management system and provides recommendations to key stakeholders involved in the logistics industry while adopting blockchain technology. Apart from the study's methodological limitation, it is also limited by a lack of reference materials. [ABSTRACT FROM AUTHOR]

The recommendation services are critical for IoT since they provide interconnection between various devices and services. In order to make Internet searching convenient and useful, algorithms must be developed that overcome the shortcomings of existing online recommendation systems. Therefore, a novel Stein Variational Recommendation System algorithm (SVRS) is proposed, developed, implemented and tested in this paper in order to address the long-standing recommendation problem. With Stein's identity, SVRS is able to calculate the feature vectors of users and ratings it has generated, as well as infer the preference for users who have not rated certain items. It has the advantages of low complexity, scalability, as well as providing insights into the formation of ratings. A set of experimental results revealed that SVRS performed better than other types of recommendation methods in root mean square error (RMSE) and mean absolute error (MAE). [ABSTRACT FROM AUTHOR]

This paper empirically studies the differences among the systematic risks of three asset pricing models, namely; the mean–variance capital asset pricing model (MV‐CAPM), AS‐CAPM and FH‐CAPM. The last two are derived by replacing variance with the Aumann‐Serrano (AS) index and the Foster‐Hart (FH) as the risk measure in MV‐CAPM. We use the Dow Jones Industrial Average (DJIA) index as a proxy for the market portfolio, and its component stocks to check if the systematic risks and the Treynor measures are different. The monthly return data from January 1997 to October 2017 are used for empirical estimations. The results show that the three systematic risks are highly correlated. Similarly, high correlation is also found for the three Treynor measures. It seems that even though they are derived under different risk measures, they produce almost the same systematic risk and performance measure for individual stocks. Therefore the findings of the present study suggest that any of the above measures can be used in empirical finance in the area of risk management. As this finding is different from those of other studies in the existing literature in this area, this study makes a contribution to the finance literature. [ABSTRACT FROM AUTHOR]

This paper develops a new cooperative dynamic time consistent model for studying regional air pollution management issues in a cooperative game framework for formulating pollution control policies and dynamically consistent compensation mechanisms. As air pollution is a transboundary issue, unilateral response on the part of one region is generally ineffective. Regional cooperation is essential to resolve serious environmental problems. In addition, the long-term environmental impacts are closely related to the building up existing air pollution stocks in Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Respirable suspended particulates (RSP) and Ozone (O3). A cooperative dynamic game with different types of pollutants is developed. We characterize the non-cooperative outcomes, and examine the cooperative arrangements, group optimal actions, and individually rational imputations. In particular, an air pollution levy consisting of four components involving damage charges on emissions of sulfur dioxide, nitrogen dioxide, respirable suspended particulates and ozone depletion materials. Cooperative games offer the possibility of socially optimal and group efficient solutions to the lack of cooperation among different regions involving decision problems among strategic actors. This paper makes a valuable contribution to the literature as this is the first cooperative dynamic time consistent model for regional management of different types of air pollutants. [ABSTRACT FROM AUTHOR]

Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature. [ABSTRACT FROM AUTHOR]

This paper considers the Flexible Job-shop Scheduling Problem with Operation and Processing flexibility (FJSP-OP) with the objective of minimizing the makespan. A Genetic Algorithm based approach is presented to solve the FJSP-OP. For the performance improvement, a new and concise Four-Tuple Scheme (FTS) is proposed for modeling a job with operation and processing flexibility. Then, with the FTS, an enhanced Genetic Algorithm employing a more efficient encoding strategy is developed. The use of this encoding strategy ensures that the classic genetic operators can be adopted to the utmost extent without generating infeasible offspring. Experiments have validated the proposed approach, and the results have shown the effectiveness and high performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The purpose of this research is to solve the problems of unreasonable layout of the production plant, disorder of the logistics process, and unbalanced production line in discrete manufacturing plants. By analyzing the production process and characteristics, the timed Petri net model is constructed according to the function and connection of each production unit, which is then used to generate a FlexSim simulation model of the production plant logistics system with a simulation software. Therewith the FlexSim simulation model is used to simulate the original layout of the plant, and to analyse the simulation data synthetically to put forward an improvement strategy. Combined with the use of the systematic layout planning method to analyze the overall layout of the plant and logistics relations, we infer the relevant drawings between the production units and determine the improved layout of the facilities. Finally, by comparing the before and after improvement simulation results, it is verified that the combination of timed Petri nets and systematic layout planning is effective to ameliorate the layout of the plant facilities and the logistics system. This method makes up for the factors that traditional methods have not considered, achieves the goal of reducing the cross circuitous route of the plant and the idle rate of equipment, and improving the efficiency of production. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with loading constraints (VRPLC) is related to real‐life transportation problems and integrates two of the most important activities in distribution logistics: packing of items inside vehicles and planning of delivery routes. In spite of its relevance, literature on VRPLCs is still limited. The majority of the solution approaches have concentrated on heuristic solution methods, and few have presented mathematical optimization models to help characterize the problem. Furthermore, few studies have considered several practical loading and routing constraints that could be used to approximate the problem toward more realistic situations. To help fill this gap in the literature, this article extends an existing VRPLC optimization model to a nonlinear optimization model that considers weight‐bearing strength of three‐dimensional items, vehicle weight capacity, weight distribution inside vehicles, delivery time windows, and a balanced fleet of vehicles. The model is solved by applying a simple procedure that isolates the nonlinearity of the model. Computational experiments show that the new proposed model gives a more streamlined formulation than the model it extended on, and that the addition of practical loading constraints can improve the solutions of the original model by reducing the measure of tardiness due to late deliveries and by producing cargo patterns with better weight distribution. [ABSTRACT FROM AUTHOR]

This study compares the strength of PPP financing approach in a developed country (United Kingdom) and the limitations in a developing country (Nigeria), the largest economy in Africa by GDP. It observes the missing gap between the practices and successes of both countries with the aim of fostering positive outcomes for PPP in Nigeria. Results from the literature analysis assert the critical success factors in the UK as: transparent procurement, quality private consortium, public support, strong political support, apt risk allocation, etc. These present clues to be adopted by the Nigerian economy in maximizing the PPP approach to infrastructure financing. [ABSTRACT FROM AUTHOR]

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Innovation is the engine of development for enterprises, and there is an increasing trend to adopt an open innovation strategy. However, how to manage external resources in an open, collaborative and complementary manner, and in a shared environment that will yield the greatest networking effects, it is a challenging task. Because there is no such a satisfactory model for an open innovation strategy that combine operational mechanisms with the management of external resources. This article tries to fill the gap by adopting a resource-based perspective to construct an overall open innovation (OOI) business model. In this model, external resources are classified as industrial and non-industrial entities, to enable the identification of the interaction methods between manufacturing enterprises and external resources. The management of external resources involved in a Technology Open Innovation (TOI) cycle is given particular attention that includes: 1) the classification of the external resources of a TOI, 2) the general mechanisms extracted to promote qualified resources in and unqualified resources out, and 3) a business model to conceptualize the collaboration between enterprises and external resources. A case study of TOI is also provided to empirically verify its feasibility. This paper contributes to the literature by providing an original operational model and mechanism design for an open innovation strategy that is capable of managing external resources effectively. [ABSTRACT FROM AUTHOR]

The model in this paper is similar to Brailsford and Faff (1997), using a conditional CAPM model with the GARCH-M framework, but with a significant additional dummy term (in the conditional mean of the share return) that will help explain the models better in both economic and statistical sense. The relatively simpler asymmetric model in this paper is compatible to other more complex asymmetric models and hence should be easier to model and explain for practical purposes. The model in this paper is also a more effective model, in both economical and statistical terms, as compared to some other models in the GARCH family as it captures the asymmetric effect in the modeling process in both the conditional first and second moments. The findings in this paper have contributed in re-evaluating the nature and process of time varying behavior of time series of stock returns and will provide researchers and practitioners additional options and incentives to explore for future research. We have also provided statistical and practical reasons to support these findings. [ABSTRACT FROM AUTHOR]

A series of stock prices typically shows a large trend and smaller fluctuations. These two parts are often studied together, as if parts of a single process; but they appear to be separately caused. In this paper, the two parts are analyzed separately, so that one does not distort the other, and some spurious interaction terms are avoided. This contributes a model, in which a wide range of features of stock price behavior are identified. With logarithms of stock prices, the two parts become of more comparable size. This is found to lead to a simpler additive model. On a logarithmic scale, the stock prices show the trend as a straight line (which can be extrapolated), with added fluctuations filling a narrow band. The trend and fluctuations are thus separated. The trend appears to be largely generated by a positive feedback process, describing investor behavior. The width of the fluctuation band does not grow with time, so positive feedback is not its cause. The movement of stock prices can be understood by analyzing the trend and fluctuations as separate processes; the latter considered as a stationary stochastic process with a scale factor. This analysis is applied to a historical dataset index of daily prices from February 1928). Here, the fluctuations are autocorrelated over short time intervals; there is little structure, except for market crash periods, when variability increases. The slope of the trend showed some jumps, not predictable from price history. This approach to modeling describes many aspects of stock price behavior, which are usually discussed in behavioral finance. [ABSTRACT FROM AUTHOR]

Both mathematical characteristics and computational aspects of dynamic optimization in finance have potential for extensions. Various proposed extensions are presented in this paper for dynamic optimization modelling in finance, adapted from developments in other areas of economics and mathematics. They show the need and potential for further areas of study and extensions in financial modelling. The extensions discussed and made concern (a) incorporation of the elements of a dynamic optimization model, (b) an improved model including physical capital, (c) some computational experiments. These extensions make dynamic financial optimisation relatively more organized, coherent and coordinated. These extensions are relevant for applications of financial models to academic and practical exercises. This paper reports initial efforts in providing some useful extensions; further work is necessary to complete the research agenda. [ABSTRACT FROM AUTHOR]

Modigliani and Miller's argument of the irrelevance of the debtequity ratio to the value of the firm implies that capital structure has no impact on the value of the firm (irrelevance result). In the existing work, the proof or disproof of the Modigliani and Miller theorem is based critically on some specific assumptions, not general enough to be always valid in practical finance, and including especially a constant interest rate for borrowing. This paper develops another optimal financing model, whose assumptions differ from those in previous models for the Modigliani and Miller theorem. If the borrowing rate increases with the amount borrowed, there is a unique optimal ratio of debt to equity, determining the optimal capital structure. Therefore the debtequity ratio does affect the value of the firm, and hence the need for good corporate financial management to maximize the value of the firm, by choosing the optimal debt. Some important issues of sensitivity are also analysed. The proposed model should apply to more real situations, and therefore makes an original contribution to finance. [ABSTRACT FROM AUTHOR]

Understanding of factors like economic fundamentals or bubbles that normally determine the returns of stock in any emerging market such as the Thai stock market is essential for academic, investment planning and public policy reasons. An empirical study of the existence of rational speculative bubbles in the Thai stock market is undertaken by using the Weibull Hazard model. The conventional Weibull Hazard model is used as a benchmark model for other speculative bubble models. Empirical results suggest the presence of rational speculative bubbles in the Thai stock market, especially during the pre-crisis period. While rational speculative bubbles were not present immediately after the post-crisis period, some were observed a few years after the crisis. A possible explanation for such a result concerning rational speculative behaviour and bubbles in the emerging stock markets could be attributed to the presence of market imperfections in emerging stock markets, requiring institutional and policy developments to ensure efficient operation of the stock market. [ABSTRACT FROM AUTHOR]

A dominant objective within the public policies of all SE Asian countries has been the achievement of economic growth. The issue of sustainability has serious implications for this policy objective. Pursuit of economic growth is concerned solely with the present, whilst sustainability is concerned with ensuring the current generation meets its present needs without threatening future generations' ability to do likewise. National accounts, such as gross domestic product, can measure healthy economies, but they can not measure sustainability. This paper, however, sets out a conceptual approach that describes the misalignment of national accounting measures with sustainability objectives and provides empirical evidence of how this misalignment can be partially overcome. An empirical approach is developed whereby certain adjustments to national accounts, based on normative social choice theory, are introduced to indicate how a partial measure of sustainability can be determined using national accounting aggregates as a base. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

This paper presents a new measure of sustainability within a welfare economics framework. Gross domestic product (GDP) can be used as an indicator of sustainability if the GDP estimates are undertaken within a cost–benefit analysis framework based on social choice perspectives. Sustainability is dependent on a healthy and functioning socio-economic and environmental (SEE) system. Economic development can damage the SEE system through resource degradation, over-harvesting and pollution. This paper addresses the tensions between economic development and sustainability by undertaking a number of SEE-based adjustments to GDP based on social choice perspectives in order to measure sustainability. These adjustments include the environmental and social costs caused by economic development such as water pollution, the depletion of non-renewable resources, and deforestation. Thailand is used as a case study for a 25 year period (1975–1999). The results show a divergence in terms of GDP per capita and the SEE-adjusted GDP per capita figure. The paper concludes that, with increasing environmental and social costs of economic development, pursuing such extreme high growth objectives without due environmental and social considerations can threaten present social welfare and future sustainability. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs. [ABSTRACT FROM AUTHOR]