Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach.

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision - making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

An extended multi-criteria group decision-making method with psychological factors and bidirectional influence relation for emergency medical supplier selection.

The COVID-19 pandemic outbreak spread rapidly worldwide, posing a severe threat to human life. Due to its unpredictability and destructiveness, the emergency has aroused great common in society. At the same time, the selection of emergency medical supplier is one of the critical links in emergency decision-making, so undertaking appropriate decision-making using scientific tools becomes the primary challenge when an emergency outbreak occurs. The multi criteria group decision-making (MCGDM) method is an applicable and common method for choosing supplier. Nevertheless, because emergency medical supplier selection should consider regarding many aspects, it is difficult for decision makers (DMs) to develop a comprehensive assessment method for emergency medical supplier. Therefore, few academics have focused on emergency situation research by the MCGDM method, and the existing MCGDM method has some areas for improvement. In view of this situation, in this study, we propose a new MCGDM method, which considers the bidirectional influence relation of the criteria, consensus and the psychological factors of DMs. It providers a good aid in emergency decision-making and it could apply to other types of MCGDM research. Firstly, DMs give their assessment in interval type-2 fuzzy sets (IT2FSs). Secondly, an extended IT2FSs assessment method and a novel ISM-BWM-Cosine Similarity-Max Deviation Method (IBCSMDM) are used for weighing all alternatives. The TODIM (an acronym for interactive and multi-criteria decision-making in Portuguese) can obtain the ranking results under different risk attenuation factors. Eventually, this extended IT2FSs-IBCSMDM-TODIM method is applied in a real case in Wuhan in the context of COVID-19 to illustrate the practicability and usefulness.

Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study.

COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.

Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels.

In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since the optimization problem is non-convex and difficult to solve directly, by considering the network with the relatively high signal-to-interference-plus-noise ratio (SINR), the non-convex optimization problem can be transformed into a convex optimization problem by convex approximation. We attain the properties of the optimal solution by Lagrange duality and solve the convex optimization problem by the CVX solver. The experimental results demonstrate that the total delay of the energy harvesting WSNs with interference channels is more than that in the orthogonal channel; the total network delay increases with the increasing data flow for the fixed energy arrival rate; and the energy transfer can help to decrease the total delay.

An Extension to Deng's Entropy in the Open World Assumption with an Application in Sensor Data Fusion.

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.

An improved Physarum polycephalum algorithm for the shortest path problem.

Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm.

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

Upstream-Downstream Joint Carbon Reduction Strategies Based on Low-Carbon Promotion.

A differential game model is established to analyze the impact of emissions reduction efforts and low-carbon product promotion on the reduction strategies of low-carbon product manufacturers (subsequently referred to as manufacturers) and the retailers of such products in a dynamic environment. Based on this model, changes in emissions reduction efforts and promotional efforts are comparatively analyzed under three scenarios (retailers bearing the promotional cost, manufacturers bearing the promotional cost, and centralized decision-making). The results are as follows: (1) the trajectory of carbon emissions reduction per product unit is the highest when the supply chain is under centralized decision-making, followed by when manufacturers bear the promotional cost, and lastly when retailers bear the cost; (2) when manufacturers bear the promotional cost, the market demand, emissions reduction effort, and promotional effort are higher, although the unit retail price is higher than when retailers bear the promotional cost; and (3) under centralized decision-making, the unit retail price is the lowest; however, sales volume, the emissions reduction effort, and the promotional effort are all higher than those in the other scenarios.

A biologically inspired network design model.

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach.