The Multi-Sectoral Water Circularity Assessment (MSWCA) is a methodological framework developed for circularity assessment of the Water-Energy-Food-Ecosystems nexus. It involves five methodological steps and includes an indicators list for the selection of case-specific indicators. This study expands the MSWCA to provide a systematic approach for selecting indicators, considering system's circular actions and multi-functionality, the capture of implemented changes, the three CE principles and the sustainable development goals. Furthermore, this study differentiates between benchmark and dynamic circularity assessment and applies the expanded MSWCA in a water system of the HYDROUSA H2020 project. The benchmark assessment indicates that the HYDROUSA system achieves a 75% increase of water circularity, 76-80% increase of nutrients circularity and 14% reduction of operational `carbon footprint compared to the baseline scenario. The dynamic assessment highlights that additional measures can improve the system's circularity performance (e.g. water circularity can reach 94%) and mitigate risks occurring from uncontrollable changes.

A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.

Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy.

An attempt is made to translate five generic key performance indicators (KPIs) into a continuous real-time cost function in a batch order-based manufacturing environment. The challenge of controlling and optimizing resource utilization, production efficiency, product-process quality, environmental impact, and inventory was specified by microelectronics and hard metal composite manufacturers. The motivation is to facilitate decision-making by converting operations management data into dynamic financial cost models. The process of interpreting engineering data of the physical level and operations management level into financial metrics creates a common language between engineers, managers, and financial departments of the company whose common objective is the profitability of the company, each with their own priorities. The proposed method provides a realistic representation of the performance of the system in monetary value. The integration may become an instrument of effective and efficient tactical and strategic collective decision-making. The main outcome is a near real-time formulation and prediction of manufacturing cost with respect to the five KPIs. The resultant cost function is verified according to several production scenarios. The case study demonstrating the proposed cost modeling methodology utilizes real-time and historical information from two different industrial partners in Tungsten metallurgy and electronic circuit manufacturing industries.

Hydrogen cyanide gas is a dangerous and fatal gas that is one of the causes of air pollution in the environment. A small percentage of this gas causes poisoning and eventually death. In this paper, a new PCF is designed that offers high sensitivity and low confinement loss in the absorption wavelength of hydrogen cyanide gas. The proposed structure consists of circular layers that are located around the core, which is also composed of circular microstructures. The finite element method (FEM) is used to simulate the results. According to the results, the PCF gives a high relative sensitivity of 65.13% and a low confinement loss of 1.5 × 10⁻³ dB/m at a wavelength of 1.533 µm. The impact of increasing the concentration of hydrogen cyanide gas on the relative sensitivity and confinement loss is investigated. The high sensitivity and low confinement losses of the designed PCF show that this optical structure could be a good candidate for the detection of this gas in industrial and medical environments.

In recent years, the topic of contactless biometric identification has gained considerable traction due to the COVID-19 pandemic. One of the most well-known identification technologies is iris recognition. Determining the classification threshold for large datasets of iris images remains challenging. To solve this issue, it is essential to extract more discriminatory features from iris images. Choosing the appropriate loss function to enhance discrimination power is one of the most significant factors in deep learning networks. This paper proposes a novel iris identification framework that integrates the light-weight MobileNet architecture with customized ArcFace and Triplet loss functions. By combining two loss functions, it is possible to improve the compactness within a class and the discrepancies between classes. To reduce the amount of preprocessing, the normalization step is omitted and segmented iris images are used directly. In contrast to the original SoftMax loss, the EER for the combined loss from ArcFace and Triplet is decreased from 1.11% to 0.45%, and the TPR is increased from 99.77% to 100%. In CASIA-Iris-Thousand, EER decreased from 4.8% to 1.87%, while TPR improved from 97.42% to 99.66%. Experiments have demonstrated that the proposed approach with customized loss using ArcFace and Triplet can significantly improve state-of-the-art and achieve outstanding results.