Every development and production process needs to operate within a circular economy to keep the human being within a safe limit of the planetary boundary. Policymakers are in the quest of a powerful and easy-to-use tool for representing the perceived causal structure of a complex system that could help them choose and develop the right strategies. In this context, fuzzy cognitive maps (FCMs) can serve as a soft computing method for modelling human knowledge and developing quantitative dynamic models. FCM-based modelling includes the aggregation of knowledge from a variety of sources involving multiple stakeholders, thus offering a more reliable final model. The average aggregation method for weighted interconnections among concepts is widely used in FCM modelling. In this research, we applied the OWA (ordered weighted averaging) learning operators in aggregating FCM weights, assigned by various participants/ stakeholders. Our case study involves a complex phenomenon of poverty eradication and socio-economic development strategies in rural areas under the DAY-NRLM (Deendayal Antyodaya Yojana-National Rural Livelihoods Mission) in India. Various scenarios examining the economic sustainability and livelihood diversification of poor women in rural areas were performed using the FCM-based simulation process implemented by the “FCMWizard” tool. The objective of this study was three-fold: (i) to perform a brief comparative analysis between the proposed aggregation method called “OWA learning aggregation” and the conventional average aggregation method, (ii) to identify the significant concepts and their impact on the examined FCM model regarding poverty alleviation, and (iii) to advance the knowledge of circular economy in the context of poverty alleviation. Overall, the proposed method can support policymakers in eliciting accurate outcomes of proposed policies that deal with social resilience and sustainable socio-economic development strategies.

Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce efficient and low-power hardware designs. The inference process of a Deep Neural Network (DNN) is considered a computationally intensive process that requires hardware accelerators to operate in real-world scenarios due to the low latency requirements of real-time applications. As a result, High-Level Synthesis (HLS) tools are gaining popularity since they provide attractive ways to reduce design time complexity directly in register transfer level (RTL). In this paper, we implement a MobileNetV2 model using a state-of-the-art HLS tool in order to conduct a design space exploration and to provide insights on complex hardware designs which are tailored for DNN inference. Our goal is to combine design methodologies with sparsification techniques to produce hardware accelerators that achieve comparable error metrics within the same order of magnitude with the corresponding state-of-the-art systems while also significantly reducing the inference latency and resource utilization. Toward this end, we apply sparse matrix techniques on a MobileNetV2 model for efficient data representation, and we evaluate our designs in two different weight pruning approaches. Experimental results are evaluated with respect to the CIFAR-10 data set using several different design methodologies in order to fully explore their effects on the performance of the model under examination.

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.

Current advances in the Internet of Things (IoT) and Cloud involve the presence of an additional layer between them acting as mediator for data transfer and processing in close distance to end users. This mediator is the edge computing (EC) infrastructure. In EC, we can identify an ecosystem of heterogeneous nodes capable of interacting with IoT devices, collecting and locally processing the data they report. The ultimate goal is to eliminate the latency we face when relying on Cloud to perform the desired processing activities. In EC, any processing is performed over a number of geo-distributed datasets formulated by the collected data that exhibit specific statistical characteristics. Processing can have the form of tasks requested by end users or applications. It becomes obvious that in the EC ecosystem, we have to carefully decide the EC nodes that will host and execute any requested task. In this paper, we extend our previous research efforts on the conclusion of efficient task allocations into the available EC nodes. We go a step forward and propose a batch processing model executed over multiple tasks and study two allocation models: a scheme based on an unsupervised machine learning technique and a bio-inspired optimization algorithm. Our models enhance the autonomous behavior of entities performing the envisioned task allocations. We provide the analytical description of the problem, our solution and the advances over the state of the art. We present and evaluate the proposed algorithms and compare them with other efforts in the domain. The pros and cons of our models are revealed through the provided extensive experimental evaluation adopting real and synthetic data.

Due to continuous CMOS technology downscaling, Integrated Circuits (ICs) have become more susceptible to radiation-induced hazards such as soft errors. Thus, to design radiation-hardened and reliable ICs, the Soft Error Rate (SER) estimation constitutes an essential procedure. An accurate SER evaluation is provided based on a SPICE-oriented electrical masking analysis, combined with a TCAD characterization process. Furthermore, the proposed work analyzes the effect of a Static Timing Analysis (STA) methodology and the actual interconnection delay on SER evaluation. An analysis of the generated Single Event Multiple Transients (SEMTs) and the circuit operating frequency that are related to the SER estimation is also discussed. Various benchmarks, synthesized utilizing a 45 nm and 15 nm technology, are employed, and the experimental results demonstrate the SER variation as the device node scales down.