The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

Recent studies have shown the rapid adoption of digital health software applications worldwide. However, researchers are yet to fully understand users’ rationale of eHealth systems. Therefore, the objective of this study is to analyze user attitudes to eHealth applications in China and eHealth system in Ukraine, and then provide insights and suggestions to the development of an eHealth application (eZdorovya) for health information services in general. The study includes a survey conducted by Chinese and Ukrainian users, after which thorough data analyses were conducted. Based on the Technology Acceptance Model (TAM), this research framework explores the influence of socio-technical factors affecting user’s adoption of eHealth functionalities. Serial Multiple Mediator Model 6 (SMMM6) and a deep neural network-based approach were used to analyze the eHealth software users’ rationale with the sample size of survey 236 end-users from China and 124 end-users from Ukraine. The key findings from the data analysis are: (1) if the software application is covering an important service function and is interesting to use, Chinese users will continue using it, (2) given an eHealth software with important or interesting function, it is inconclusive whether Ukrainian users will switch to use the application. (3) Deep neural network shows highly accurate prediction results and was given applied suggestions for Chinese and Ukrainian providers in the case of improving eHealth systems based on a raw prediction.