During the last decade, the demand for electricity has increased significantly, both for companies and consumers. Therefore, in every country, there are companies developing and functioning to provide various forms of electric energy. The quality of the services that they provide has been of major concern for these companies for the last few years. The objective of this study is to examine residential customers’ satisfaction of electricity providers in Greece regarding various factors, such as the products, services, customer service, and the pricing policy. The present research was conducted with the use of a specially developed website questionnaire; 689 questionnaires were collected from January to June, 2019. The results were analyzed with the multicriteria satisfaction analysis (MUSA) method, which is considered as an aggregation–disaggregation approach developed on the qualitative analysis regression. The results of the study showed that the residential customers were quite satisfied. More specifically, the average global satisfaction index of the residential customers was about 52.15%. Using the results of this study, electricity providers will have the chance to frame their future products and services so as to keep their industrial customers satisfied. This empirical study may serve as a reference for other electricity providers who desire to carry out similar studies in the future.

Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product integrity, leading to deterioration and an increased risk of foodborne illness. Monitoring agrifood supply chains is essential, from packaging to last-mile delivery. Distribution methods that rely on non-automated monitoring systems, such as manual temperature measurements, are error-prone due to the failure of manual treatments and increase the likelihood of product deterioration. Emerging sensor technologies and the rapid development of Information and Communication Technologies offer new possibilities for real-time tracking, enabling stakeholders to maintain optimal conditions and monitor aesthetic, physicochemical, and nutritional quality. This paper proposes a cost-effective temperature and humidity traceability system that utilises wireless sensor networks (WSN) and Internet of Things (IoΤ) services to monitor perishable products within the agrifood supply chain ecosystem. It also provides an overview of recent innovations in sensor technologies, along with food quality indicators relevant to real-time monitoring of food quality. The proposed research examines the available sensor technologies and methodologies that enable continuous monitoring of agrifood supply chains. Moreover, the paper presents a pilot full-scale project from both functional and technological perspectives.

This aim of this paper is to measure industrial consumer satisfaction in the natural gas sector in Greece. By using the Multicriteria Satisfaction Analysis (MUSA) method, the paper measures industrial customer satisfaction based on criteria concerning the provided products and services, communication and collaboration with providers’ staff, customer service, pricing policy and website. The research results that are based on the analysis of 95 questionnaires collected during the period between June 2017 and October 2017 show that the index of the global customer has a good performance as its value is about 74.99%. Furthermore, the satisfaction criterion with the highest performance is the one concerning communication and collaboration with natural gas providers’ staff. It should be noted that the criterion concerning the provided products and services criterion is the only one with high performance and importance—meaning that it should be in the spotlight of the natural gas providers. The paper concludes that there is considerable space for improvements to be made. Customer satisfaction is of great importance for every company, as it can be highly connected with its performance. Using the results of this study, natural gas providers will have the chance to frame their future actions in order to keep their industrial customers satisfied. Taking into account both the fact that industrial customers’ share in the Greek natural gas market is about 25% and that this market has been recently liberalized, it is of vital importance for natural gas providers to have sufficient information about their industrial customers’ satisfaction.

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

The recycling of used refractory materials in the heavy industry constitutes one of the significant environmental problems in the industry related to environmental and financial issues. This study proposes a multicriteria methodological frame to characterize the refractory material waste and identify the recycling capabilities. Considering the chemical and physical analysis of the refractory material wastes, the proposed methodological frame progresses into a two-phase procedure. The first phase includes an on/off approach that allows discretizing the refractory material wastes to compatible or not compatible as far as their recycling prospects. Then, an additive value model is utilized, including (a) the marginal value functions used for every criterion related to critical environmental factors, and (b) the weight vector reflecting the relative importance of the criteria used. A group of experts concerning the environment and the refractory materials was employed to estimate the additive value model. The assessment of the marginal value function is achieved using the module of the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), which is based on a modification of the mid-value split point technique incorporating focused dialogues, artificial intelligence, and visual techniques. The weight vector was assessed using the weight assessment through prioritization method (WAP), which concludes with the estimation of the weights based on the criteria ranking and the pairwise expression of the strength of preferences for the consecutive criteria according to their ranking. The outcome of this approach is to introduce an environmental appropriateness index for refractory materials based on their chemical composition and the judgement of an expert group. The main findings of this research may be useful for engineers, decision-makers, and scientists in the field of circular economy and waste management.

The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predictive analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies’ funding proposals on their financial results after the finalization of the co-financed program.