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This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.

Purpose: Assembly Line Balancing (ALB) is critical to manufacturing efficiency and productivity. It involves assigning tasks to workstations to optimise performance while satisfying task priority and cycle time constraints. The Simple ALBP (SALBP) is a simplified version of the general problem that has received considerable research interest. Many academic works have been published on this topic, using a variety of methods, including exact, heuristic, and metaheuristic approaches. Therefore, the purpose of this research is to present a comprehensive evaluation of the literature on the methods used to solve the SALBP.Design/methodology/approach: A comprehensive literature review was conducted to identify, select, analyse, and summarise 126 papers on SALBPs. The study started with the selection of relevant keywords. The selected papers were then narrowed down using various criteria.Findings: The analysis showed that SALBP-1 and SALBP-2 are the most common types, with metaheuristic approaches being the most widely used. Despite extensive research, there is a significant need for studies focusing on SALBPs for multi- and mixed-models, particularly in the context of U-shaped and two-sided lines.Originality/value: This literature review contributes to the identification of key areas for improvement in the SALBP and provides insight into potential directions for future research.

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.

The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960–2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal images are the main interest. IR thermography is discussed in view of novel applications of machine learning methods for improved diagnostic analysis and medical treatment. The AI approach aims to improve image quality by denoising thermal images, using applications of AI super-resolution algorithms, removing artifacts, object detection, face and characteristic features localization, complex matching of diagnostic symptoms, etc.

Water quality evaluation is critically important for the protection and sustainable management of groundwater resources, which are variably vulnerable to ever-increasing human-induced physical and chemical pressures (e.g., overexploitation and pollution of aquifers) and to climate change/variability. Preceding studies have applied a variety of tools and techniques, ranging from conventional to modern, for characterization of the groundwater quality worldwide. Recently, geographic information system (GIS) technology has been successfully integrated with the advanced statistical/geostatistical methods, providing improved interpretation capabilities for the assessment of the water quality over different spatial scales. This review intends to examine the current standing of the GIS-integrated statistical/geostatistical methods applied in hydrogeochemical studies. In this paper, we focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment. In addition, we provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe. Then, limitations and research gaps of the past studies are outlined and perspectives of the future research needs are discussed. It is revealed that comprehensive applications of the GIS-integrated advanced statistical methods are generally rare in groundwater quality evaluations. One of the major challenges in future research will be implementing procedures of statistical methods in GIS software to enhance analysis capabilities for both spatial and temporal data (multiple sites/stations and time frames) in a simultaneous manner.

Currently, one of the topical areas of application of artificial intelligence methods in industrial production is neural networks, which allow for predicting the performance properties of products and structures that depend on the characteristics of the initial components and process parameters. The purpose of the study was to develop and train a neural network and an ensemble model to predict the mechanical properties of lightweight fiber-reinforced concrete using the accumulated empirical database and data from construction industry enterprises, and to improve production processes in the construction industry. The study applied deep learning and an ensemble of regression trees. The empirical base is the result of testing a series of experimental compositions of fiber-reinforced concrete. The predicted properties are cubic compressive strength, prismatic compressive strength, flexural tensile strength, and axial tensile strength. The quantitative picture of the accuracy of the applied methods for strength characteristics varies for the deep neural network method from 0.15 to 0.73 (MAE), from 0.17 to 0.89 (RMSE), and from 0.98% to 6.62% (MAPE), and for the ensemble of regression trees, from 0.11 to 0.62 (MAE), from 0.15 to 0.80 (RMSE), and from 1.30% to 3.4% (MAPE). Both methods have shown high efficiency in relation to such a hard-to-predict material as concrete, which is so heterogeneous in structure and depends on many factors. The value of the developed models lies in the possibility of obtaining additional useful information in the process of preparing highly functional lightweight fiber-reinforced concrete without additional experiments.

The COVID-19 has placed pandemic modeling at the forefront of the whole world’s public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic.

Recently, novel approaches have been developed for simulating bubbly flow as well as distributed and constant phase evolution by means of a two-phase reactor. Among these approaches, the Eulerian–Eulerian method and soft computing approaches can be mentioned. Since complex numerical methods (for example, multidimensional Eulerian–Eulerian method) require several runs for fluid conditions optimization, a method which can decrease these runs can be very useful and practical. This method is provided by joining computational fluid dynamic (CFD) to the adaptive neuro-fuzzy inference system (ANFIS). In this technique, valuable information is provided for a careful analysis of fluid conditions. Also, it can facilitate a vast amount of data categorization in synthetic neural network nodes, which eliminates the need for a complex nonstructured CFD mesh. Moreover, a neural geometry can be provided, in which no limitation of mesh numbers in the fluid domain would exist. The key CFD parameters in the scale-up of the reactorstaken into consideration in the current research are gas and liquid circulations. These factors are applied as output factors for prediction tool in various dimensions in the ANFIS method. The results obtained in this study show appropriate conformity concerning ANFIS and CFD results depending on multiple dimensions. In this study, the grouping of CFD and multifunction the ANFIS method delivers the nondiscrete domain in different dimensions and presents an intelligent instrument for the local prediction of multiphase flow. The result shows that three inputs, which represent the dimension of the reactor, and learning stage of the ANFIS method provide a better understanding of flow characteristics in the two-phase reactor, while the two-dimensional ANFIS method even with multistructured functions cannot predict well the multiphase flow in the reactor.

This paper presents a new generative artificial intelligence (AI) approach for creating modular skeletal frameworks, using vernacular bamboo stilt houses as examples to investigate an innovative methodological perspective. By transforming building skeletons to connected graphs, our method uses Variational Graph Autoencoders (VGAE) and Graph Sample and Aggregate (GraphSAGE) to generate 3D modular components based on spatial constraints set by users, such as axis grids and chosen room areas. The graph representation encodes structural elements as edges and their connections as nodes, maintaining critical dimensional constraints and spatial relationships. Using data from bamboo stilt houses built without architects, we make a specialized dataset of geometric skeletons for model training. Experimental results demonstrate the effectiveness of our approach in capturing the distribution of featured elements in building frameworks and in generating structurally sound designs, with GraphSAGE showing better performance compared to alternative methods. The probabilistic edge prediction approach allows for a collaborative human-AI design process, empowering designers while utilizing computational capabilities. The inherent flexibility of the graph-based representation makes it adaptable to a wide range of materials and scales.