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© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.With the increase in greenhouse gas emissions, climate change is occurring in the atmosphere. Although the energy production for Turkey is increased at a high rate, the greenhouse gas emissions are still high currently. Problems that seem to be very complex can be predicted with different algorithms without difficulty. Due to fact that artificial intelligence is often included in the studies to evaluate the solution performance and make comparisons with the obtained solutions. In this study, machine learning algorithms are used to compare and predict greenhouse gas emissions. Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are considered direct greenhouse gases originating from the agriculture and waste sectors, energy, industrial processes, and product use, within the scope of greenhouse gas emission statistics. Compared to different machine learning methods, support vector machines can be considered an advantageous estimation method since they can generalize more details. On the other hand, the artificial neural network algorithm is one of the most commonly used machine learning algorithms in terms of classification, optimization, estimation, regression, and pattern tracking. From this point of view, this study aims to predict greenhouse gas emissions using artificial neural network algorithms and support vector machines by estimating CO2, CH4, N2O, and F-gases from greenhouse gases. The data set was obtained from the Turkish Statistical Institute and the years are included between 1990 and 2019. All analyzes were performed using MATLAB version 2019b software.

The efficient and well-designed warehouses can provide critical advantages for retail stores. They also play a crucial role in the achievement of supply chain management. Nowadays, warehouses have become centers where fast access to the customer is provided apart from the storage and protection of the goods. The objective of this study is to find the more efficient and useful warehouse design of A Children Retail Company, which is used for e-Commerce. The best place for the products will be determined by using a combination of such tools like the Pareto Principle, ABC Analysis, Technical Drawing, Simulation, and 5S Model. The optimum layout determined from the analyses enables more efficient work for warehouse employees, and orders to the logistics department are faster and more accurate for the facility. Moreover, an automation system is proposed with the barcode scanning and packaging part for the orders to simplify the existing complexity of the process.

Blockchain technology offers important opportunities for the supply chain management. This paper aims to overview the employment of blockchain technology in the field of the supply chain. Although the technology has been widely associated with cryptocurrencies, non-financial applications such as supply chain, power, and food industry are also promising. Blockchain can provide a permanent, shareable, auditable record of products through their supply chain, which improves product traceability, authenticity, and legality in a more cost-effective way. In this chapter, the potential improvement expectations via blockchain technology for the case of agribusiness were discussed. The proposed case for automotive manufacturing-micro factory with blockchain technology was also introduced.

Online anomaly detection and identification is a major task of many Industry 4.0 applications. Electric motors, being one of the most crucial parts of many products, are subjected to end-of-line tests to pick up faulty ones before being mounted to other devices. With this study, we propose a Syntactic Pattern Recognition based approach to online anomaly detection and identification on electric motors. Utilizing Variable Order Markov Models and Probabilistic Suffix Trees, we apply both unsupervised and supervised approaches to cluster motor conditions and diagnose them. Besides being explainable, the diagnosis method we propose is completely online and suitable for parallel computing, which makes it a favorable method to use synchronously with a physical test system. We evaluate the proposed method on a benchmark dataset and on a case study, which is being worked on within the scope of a European Union funded research project on reliability.