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.
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.
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.
•Decentralized decision-making could result in the distortion of order quantity and a loss of supply chain profit.•Asymmetric demand information is the main reason of slow-moving events.•For fresh ...agricultural products with higher quantity/quality elasticity, the utility of freshness-keeping is reduced.•TPLSP's optimal freshness-keeping effort is the same in both decentralized and centralized decision modes.•Revenue and cost sharing contracts can eliminate motivation to hide needs, increase sales, and improve expected profits.
This paper studies the coordination problem of a three-echelon supply chain system consisting of one supplier, third-party logistics service providers (TPLSP) and one retailer that provides seasonal fresh agricultural products to customers. The market demand for the retailer is assumed to be influenced by the retail price, the product's freshness and other random variables. Both quantity and quality losses are viewed as endogenous variables of the freshness-keeping effort, which is decided by the TPLSP. Dynamic game models for both the decentralized decision mode and the centralized decision mode are developed, and asymmetric demand information is considered in the decentralized decision mode. The analysis shows that decentralized decision making could result in the distortion of the order quantity and selling price and could ultimately result in a loss of supply chain profit. The TPLSP is motivated to exaggerate the demand, which could seriously damage the supplier's interests. Based on an analysis of the major influencing factors in the supply chain system, a coordination contract based on cost and revenue sharing (RS) is designed for the two transaction processes in the three-echelon supply chain system. We illustrate the proposed models with a numerical study and conduct a sensitivity analysis of some of the key parameters in the models. It is proven that with the designed contract, the sales volume can be significantly expanded, all the supply chain members can benefit from Pareto improvement, and both the retailer and the TPLSP have no incentive to exaggerate the market demand.
Abstract
With the optimal operating cost and optimal carbon emission target of the chemical logistics companies, a low-carbon routing optimisation with a multi-energy type vehicle combined problem is ...proposed by considering the concept of the logistics companies’ low-carbon behaviour. An integrated decision-making of multi-energy type vehicles combined strategy and route optimisation based on customer demand is presented, and an improved genetic algorithm is designed. A case study is then applied based on the data collected from the case research. The effectiveness of the improved genetic algorithm is tested. The two joint objectives of operating cost and carbon emission are examined through the cost analysis of environmental energy vehicles and traditional energy vehicles in different combination scenarios. The case analysis shows that a rational multi-energy type vehicle combination with route optimisation has a significant correlation with the operating cost and carbon emissions, while the environmental vehicle purchasing cost reduction and subsidy policy affect the operating cost.
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.
The current global interest in improving the use of ever-scarcer natural resources calls for the re-alignment of supply chain operations to include not only economic factors, but environmental and ...social factors as well. Two of the most important supply chain activities that logistics managers have to deal with are the planning and improvement of the packing and distribution of products. Although the optimization of these two activities has been thoroughly studied by means of Vehicle Routing Problems and Packing Problems, their analysis is often done separately and, in most cases, they consider only the economic decisions. Independent optimization of these two operations may overlook the structural dependencies between them, resulting in impractical solutions; while the consideration of only the economic criteria can overlook the environmental and social impacts of distribution activities, in the scope of sustainable supply chains. With the objective of improving distribution logistics, the aim of this review is to provide an overview of recent optimization developments for integrating packing and routing problems, in order to propose a simple classification scheme for re-aligning the optimization criteria and operational constraints, taking into consideration the issues of sustainability.
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.
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.
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, ∨˜ES, ∧˜ES) 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.
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