Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related with the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems, and define the best procedure for each particular practical situation. For this purpose, a state-of-the-art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focus on extrusion, namely extruders, extrusion dies and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing, with a very good performance and without the need of demanding computation requirements.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%) and range of absolute error for ‘run down‘ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test

Ecological connectivity, as a research method related to spatial ecology and conservation biology, has attracted increasing attention from researchers at home and abroad in recent years. Habitat connectivity, as a key link in ecological connectivity, is of great significance to promote offshore ecological restoration and protection. However, there has been less systematic research about habitat connectivity, which lacks corresponding theories and practices. Therefore, this paper discusses habitat connectivity from three aspects: (1) the concept of habitat connectivity is introduced and clarified, (2) the application of connectivity in artificial habitat and adjacent waters and its relationship with biodiversity conservation are reviewed and illustrated with examples, and (3) the future development trends of this research direction are summarized and prospected, in order to provide a scientific basis and useful reference for the related work of offshore restoration projects in China. Generally, this paper argues that an increase in human behavior irreversibly destroys the connectivity of marine habitats and threatens the temporal scales of biodiversity and ecosystem services. Therefore, the theoretical research results and practical experience of ecological connectivity should be fully applied to marine ecosystems, and the restoration of degraded ecosystems should be encouraged and supported in ways that promote natural recovery.

Artificial intelligence (AI) is a versatile term that is a conclusive remedy to solve the problem using past rational data after deep contemplation using these terms i-e basic statistics, carving data, familiarity with common AI algorithms. Seafood especially tiger prawn export as a busi-ness will provide enormous foreign exchange to any country if the farmers overcome the corre-lated vulnerabilities in prawn farming. This research is elucidating lacking in Tiger prawn (TP) farming like curbing of Oxygen, pH, water temperature, and nutrients, etc. Moreover, hatchery statistics in terms of juveniles will depict this study's clear picture of curbed aquaculture. For normative decisions, the Analytical Hierarchical Process (AHP) is used. The problem which has been faced by local prawn farmers that there is a stagnant TP growth in ponds, the reason is the predominant sensitivity factor in TP. For this reason, they need indemnification of thirteen fac-tors with natural resources to get the plausible results to get calmness in their lives. This study will solely focus on the TP growth model, and the monitoring effect will be established by the Artificial Intelligence algorithm. This study will employ the AHP, 0-1 scaling method, data cura-tion techniques, and ecological statistics. The life of Tiger Prawn (TP) depends upon these factors mainly, a) Physical and b) Chemical parameters. Physical parameters contain environment (E) provided to TP like season (S) and temperature (T) etc. whereas the quality of Ammonia NH3 (N) from fish waste, Oxygen level (O), and water quality hard & soft (W) lies in chemicals do-main. This research will Elucidate the factors which cause conceptual muddles in the aquamarine life of TP, for this reason, Statistical tools will assess the current result, forecast the gap. AHP will analyze the domain inputs, circumspect ramification which will depict visceral factors, later results depict which pond suits the TP. In curtail, these factors will be curbed to improve the growth of TP in a control conditioned environment.

Artificial Intelligence innovations, such as chatbots and specialized education suggestion tools, provide potential interactive and on-demand pedagogical engagement between non-Christians and Christians with Christianity. However, there is little empirical research on the readiness, acceptance, and adoption of A.I.'s involvement in religious education in a secular state, such as Vietnam. This research addresses the literature gap by providing an entrepreneurial analysis and customer perspectives on the ideas of A.I.'s involvement in religious education. Specifically, the study explores whether the Vietnamese across different ages and religious orientations accept and have enough skills to adopt A.I.'s religious education innovation. The interview sample is 32 participants, selected based on their religious orientation (Christians & Non-Christians) and age (Generation X, Generation Y & Generation Z). Most respondents are open to A.I. application in religious education except for the Church's personnel. However, only gen Z generations are fully prepared to adopt this innovation. Theoretically, the research customizes the Unified Theory of Acceptance and Use of Technology model into religious innovation context. Practically, this research acts as market research on the demand for A.I.'s religious innovation in Vietnam, an insight for future religious tech entrepreneurs.

Abstract: Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDM-printed tensile test samples with the desired part thickness. To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering layer thickness, infill percentage, and extruder temperature as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The honeycomb internal fill pattern is applied for the production of light-weight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Indian cities are increasingly becoming susceptible to PM10 induced health effects which have become a matter of concern for the policymakers of the country. Air pollution is engulfing the comparatively smaller cities as the rapid pace of urbanization, and economic development seems not to lose steam ever. A review of air pollution of 28 cities of India, which includes tier-I, II, and III cities of India, found to have grossly violated both WHO and NAAQS standards in respect of acceptable daily average PM10 concentrations by a wide margin. Predicting the city level PM10 concentrations in advance and accordingly initiate prior actions is an acceptable solution to save the city dwellers from PM10 induced health hazards. Predictive ability of three models, linear MLR, nonlinear MLP (ANN), and nonlinear CART, for one day ahead PM10 concentration forecasting of tier-II Guwahati city, were tested with 2016-2018 daily average observed climate data, PM10, and gaseous pollutants. The results show that the non-linear algorithm MLP with feedforward backpropagation network topologies of ANN class, giving the best prediction value when compared with linear MLR and nonlinear CART model. ANN (MLP) approach, therefore, may be useful to effectively derive a predictive understanding of one day ahead PM10 concentration level and thus provide a tool to the policymakers for improving decision-making associated with air pollution and public health.

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

The paper presents a new vision on the energy consumption management in the case of the Small and Medium Enterprises (SMEs), integrated into an advanced decision support platform, with technical and economic benefits on increasing the energy efficiency, which contains modules for database management, profiling, forecasting, and production scheduling. Inside each module, Artificial Intelligence and Data Mining techniques were proposed to remove the uncertainties regarding the dynamic of technological flows. Thus, the data management module includes the Data Mining techniques, that extract the technical details on the energy consumption needed in the development of production scheduling strategies, the profiling module uses an original approach based on clustering techniques to determine the typical energy consumption profiles required in the optimal planning of the activities, the forecasting module contains a new approach based on an expert system to forecast the total energy consumption of the SMEs, and production scheduling module integrates a heuristic optimization method to obtain the optimal solutions in flattening the energy consumption profile. The testing was done for a small enterprise from Romania, belonging to the domain of trade and repair of vehicles. The obtained results highlighted the advantages of the proposed decision support platform on the decrease in the intensity of energy consumption per unit of product, reduction of the purchase costs, and modification of the impact whom the energy bills have on the operational costs.

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction is evaluated. According to the results, ANNGWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

Deep learning models are part of the family of artificial neural networks and, as such, it suffers of catastrophic interference when they learn sequentially. In addition, most of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, in this article we propose the Self-Improving Generative Artificial Neural Network (SIGANN), a type of end-to-end Deep Neural Network system which is able to ease the catastrophic forgetting problem when leaning new classes. In this method, we introduce a novelty detection model to automatically detect samples of new classes, moreover an adversarial auto-encoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder; and a novelty detection module, implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN is able to retain previous knowledge with a gradual forgetfulness for each learning sequence. Moreover, SIGANN can detect new classes that are hidden in the data and, therefore, proceed with incremental class learning.

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

This study was undertaken to forecast waste generation rates of accommodation sector of North Cyprus as a case. Three predictor models including multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD) were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE=1.378) and highest R2 value (0.998) confirmed the accuracy of the model. The analysed wastes were categorised into recyclable, general waste and food residues. The authors estimated the total waste generated during the lean season as 2010.5 kg/day, in which large-sized hotel accounted for largest fraction (66.7%), followed by the medium hotels (19.4%) and guesthouse accounted for smallest part (2.6%). During the peak season, about 49.6% increases in the waste generation rates were obtained. Interestingly, 45% of the wastes were generated by the British tourists while the least waste was generated by African tourists (7.5%). The ANN predicted that the small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

Abstract: New paradigms have emerged in mapping since the 20th century due to theoretical and practical advances in computer technology, communications, and cartography. For the design of the Expert-Topographic Map Service in Mexico, we adopted a theoretical framework of Cybercartography and expanded to incorporate expert systems and eventually other artificial intelligence resources. Using a transdisciplinary approach, our efforts could focus on the interconnection between knowledge and geotechnology to offer solutions to societal complex problems. This paper explores the theoretical pillars supporting the design of an Expert-Topographic Map Service, including cybercartography, knowledge-based systems and surface mathematical modeling. For the first time, cybercartography is applied in the realm of topographic mapping. As shown, a map conceived solely as an information system has been transformed, in the digital era, into a container of knowledge through the abstract representation of features like relief via the Triangular Irregular Network Model. Thereby, we can bridge from digital topographic maps to other knowledge-based models such as expert systems. The societal demand and empirical context for this research demonstrate that topographic maps and services are still at the core of cartographic initiatives in the 21st century.

This article reviews the transformative impact of single-cell whole genome sequencing (scWGS) on cancer biology research. ScWGS has revolutionized our understanding of tumor heterogeneity, clonal evolution, and the tumor microenvironment by enabling high-resolution analysis of individual cells' genomic, transcriptomic, and epigenomic profiles. The technology's unique capabilities, including detection of rare genomic events, simultaneous capture of multiple genomic features, and association with phenotypic data, have opened new avenues for cancer research and precision medicine. Integration of scWGS with other single-cell omics technologies has provided a multidimensional view of cellular states and regulatory mechanisms in cancer. Advanced data analysis tools, including machine learning and AI algorithms, have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and development of predictive models for patient stratification. The article also discusses emerging technologies like spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor spatial organization and cellular interactions. As scWGS and related technologies continue to evolve, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

A possible solution to address the enormous increase in traffic demands faced by network operators is to rely on multi-fiber optical backbone networks. These networks use multiple optical fibers between adjacent nodes, and, when properly designed, they are capable of handling petabits of data per second (Pbit/s). In this paper, an artificial neural network (ANN) model is investigated to estimate both the capacity and cost of a multi-fiber optical network. Furthermore, a fiber assignment algorithm is also proposed to complement the network design, enabling the generation of datasets for training and testing the developed ANN model. The model consists of three layers, including one hidden layer with 50 hidden units. The results show that for a large network, such as one with 100 nodes, the model can estimate performance metrics with an average relative error of less than 0.4% for capacity and 4% for cost, while achieving a computation time nearly 800 times faster than the heuristic approach used in network simulation. Additionally, the network capacity is around 5 Pbit/s.

Atherosclerosis remains a major driver for cardiovascular disease (CVD), despite advancements in traditional risk factor management therapies. Recent evidence emphasizes the crucial role of the gut microbiome in the progression of atherosclerosis and plaque rupture, highlighting a promising therapeutic avenue. This review focuses on the intertwined relationship between the gut microbiome, its metabolites, and atherosclerosis and CVD, also highlighting the potential therapeutic role of probiotics and prebiotics. Given the diverse and unique gut microbiota signatures among individuals, a one-size-fits-all therapeutic approach is unlikely to be effective. Personalized treatment strategies are therefore necessary. Here we discussed how Artificial Intelligence (AI) can be leveraged to analyze individual gut microbiome profiles from microbiome sequencing, predict treatment response, and optimize therapeutic strategies based on individual patients which would significantly improve outcomes of the treatment for atherosclerosis/CVD patients.

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant buses is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

This research explores the feasibility of a data-driven optimization algorithm for placing sparse arrays of artificial hair-cell airflow velocity sensors to enable agile and robust flight-by-feel control of unmanned aircraft. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm exploits the information-rich features of large flow datasets to find a near-optimal set of locations for flow sensors on a wing. The flow information from these sensors can be used to predict flight state parameters such as the angle of attack, mimicking the fly-by-feel control systems observed in nature. We evaluate the effectiveness of this approach for a blunt-edged 45∘ swept delta wing. Exploiting the information in the complex airflow patterns over this wing, the SSPOP algorithm selects a sensor location design point (DP) which reliably ranks within the top 1% of all possible DPs by root mean square error in angle of attack prediction. This performance was consistent across models of various artificial hair lengths, and one model with variable hair lengths. This bioinspired dimension-reducing method for sensor placement optimization exhibits reliability and flexibility benefits when compared with a conventional greedy search algorithm and gradient-based optimization using auto-differentiation. The successful application of SSPOP in complex 3D flows paves the way for experimental evaluation of data-driven sparse sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

The Mining Industry has changed significantly in recent decades with the introduction of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the Mining Industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the Mining Industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

Background: The relationship between the intake of artificial sweetener (AS) and adverse pregnancy outcomes is under-researched, and existing studies yield inconsistent conclusions. Methods/Objectives: A mendelian randomization (MR) approach was employed to investigate the causal relationship between the intake of AS and adverse pregnancy outcomes. Instrumental variables related to the exposure phenotype were selected for analysis. The analysis was conducted using genome-wide association studies summary data from public datasets. Inverse variance weighted, MR-Egger, weighted median, simple mode, and weighted mode were used to evaluate the causal relationship between exposure and outcomes. Sensitivity analysis and multivariable mendelian randomization enrolling body mass index, type 2 diabetes mellitus and fasting glucose was employed to further validate the consistency and robustness of results. Results: The intake of AS added to tea was associated with an increased risk of ectopic pregnancy [OR=1.821 (1.118 - 2.967), p=0.016] in univariable MR. The intake of AS added to cereal was linked to a reduced risk of ectopic pregnancy [OR=0.361 (0.145 - 0.895), p=0.028] and premature rupture of membranes [OR=0.116 (0.019 - 0.704), p=0.019], while the intake of artificial sweetener added to coffee was associated with an increased risk of placenta previa [OR=1.617 (1.042 - 2.510), p=0.032] adjusting for body mass index and type 2 diabetes mellitus in multivariable MR. No causal relationship was identified between the intake of artificial sweetener and the other adverse pregnancy outcomes. Conclusions: The consumption of artificial sweeteners during pregnancy warrants careful consideration.

This study investigates the influence of digital marketing on consumer purchasing decisions, focusing on how various strategies impact consumer behavior. By analyzing data collected from in-depth interviews and thematic analysis, the research highlights several key factors driving consumer engagement and purchases. Personalized content emerged as a significant factor, with tailored recommendations and customized email campaigns creating a sense of individual attention and enhancing consumer involvement. Social media platforms played a pivotal role, with advertisements, influencer endorsements, and user-generated content significantly affecting purchasing decisions. The impact of visual content, including high-quality images and videos, was also notable, as these elements helped consumers better understand products and increased their confidence in making purchases. Email marketing proved effective, particularly when offering exclusive discounts and creating a sense of urgency. Mobile marketing demonstrated its growing influence, with consumers valuing the convenience of mobile-friendly websites and app notifications. The integration of artificial intelligence further improved the shopping experience by providing instant assistance and personalized recommendations. Ethical considerations, such as data privacy and honest advertising, were essential for maintaining consumer trust and loyalty. User-generated content provided authentic insights that influenced purchasing decisions, while omnichannel strategies offered a seamless and flexible shopping experience. Gamification elements, including reward programs and interactive challenges, enhanced consumer engagement and motivation. The study concludes that a multifaceted approach to digital marketing, incorporating these strategies, is crucial for effectively engaging consumers and driving sales. The findings offer valuable insights for marketers aiming to optimize their digital marketing strategies in the evolving digital landscape.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

Comprehensive next-generation sequencing (NGS) assays enable identification of clinically relevant mutations, enhancing the capability for targeted therapeutic interventions. In addition, genomic alterations driving the oncogenic roadmap and lead to resistance mechanisms are reshaping precision oncology. We report, workflow, informatics validation of OncoIndx NGS platform – a comprehensive genomic profiling (CGP)-base assay for pan-cancer investigation. We evaluated the concordance between the OncoIndx® test findings and clinically established hotspot findings using SeraSeq reference standards. OncoIndx is a hybridization capture-based NGS assay for targeted deep sequencing of all exons and selected introns of 1080 cancer related genes. We show the outcome in the form of -tier I and tier II single nucleotide variants (SNVs), copy number alterations (CNAs), and specific gene rearrangements (fusion). OncoIndx® also enables tumor mutational burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), and genomic loss of heterozygosity (gLOH). We show maximum clinical sensitivity of 98% and a positive predictive value (PPV) of 100% for the clinically actionable genomic variants detected by the assay. In addition, we demonstrate analytical validation with performance of the assay, limit of detection (LoD), precision, and orthogonal concordance for various types of SVs, CNAs, genomic rearrangements, and complex biomarkers like TMB, MSI, and HRD. The assay offers reliable genomic predictions with high precision detection of actionable variants, validated by established reference standards.

Background: Ovarian, cervical, and endometrial cancers stand as fatal health killers for women's mental and physical health, especially affecting female reproductive systems. Exploiting weakly supervised learning and explainable AI techniques are crucial for fast, accurate, and robust automatic marker detection, efficient prevention, and primary treatment of gynecological tumors. Methods: With respect to the PRIMSA methodology, weakly supervised learning schemes and deep learning-based schemes are investigated in the cross-subject fields of clinical diagnostic imaging and explainable AI technology. Related methods, opening research problems, and challenging subjects are explored in the screening and treatment of gynecological tumors and performing cancer image diagnosis in the latest study. Results: Keynote approaches combining ultrasound medicine, explainable AI, and clinical imaging technology are summarized. In combination with learning-based methods with medical imaging, prospective insights are put forward for refreshed concepts on treatments in the area of gynecological oncology, with a feasible range of applications for their corresponding AI techniques. Conclusions: Explainable AI and deep learning-based approaches are capable of performing accurate classification between benign and malignant tumors, yielding a pathway to care for women's health, improving the survival rate of patients, pacing with disease prediction, and matching the strategic goal of "Healthy China 2030".

In the last decade, the role of Computed Tomography Pulmonary Angiography (CTPA) in diagnosing and managing pulmonary embolism (PE) has significantly evolved. Once a purely diagnostic tool, CTPA now plays a crucial role in initial emergency department assessments and treatment planning tailored to the patient’s condition. Advances in CTPA technology, including image quality enhancements and artificial intelligence (AI) applications, necessitate a reassessment of its current utility. This narrative review updates on new CTPA tools and techniques and focuses on data extraction challenges in emergency settings. CTPA studies, often conducted with multidetector scanners, provide essential information for risk stratification and treatment planning. The quantification of thrombotic burden using CTPA is vital for predicting mortality and determining appropriate treatments. Classical scoring systems, such as those developed by Qanadli, Mastora, and Ghanima, convert CTPA findings into quantifiable data to assist clinical decision-making. Despite CTPA not being a standard mortality predictor, significant research suggests including CTPA data, especially RV dysfunction indicators, for prognostic purposes. Recent studies highlight automated techniques for quantifying pulmonary perfusion and thrombus composition, enhancing the accuracy of PE severity assessment. The integration of AI in CTPA, particularly through deep learning algorithms, shows promise in automating thrombus load assessment and improving risk stratification. The continuous development of imaging techniques positions CTPA as a potential tool for comprehensive PE management, aiding in diagnosis, prognosis, and treatment decisions.

This research explores the transformative impact of artificial intelligence (AI) on personalizing marketing campaigns, highlighting how AI technologies are reshaping modern marketing practices. AI tools such as machine learning, natural language processing (NLP), predictive analytics, deep learning, and computer vision are integral to enhancing customer engagement and optimizing marketing strategies. Machine learning algorithms enable precise customer segmentation and personalized recommendations, leading to more targeted and effective marketing messages. NLP facilitates the analysis of customer sentiment and enhances communication through advanced language processing and AI-powered chatbots. Predictive analytics and deep learning provide businesses with the capability to forecast customer behaviors and recognize complex data patterns, further refining marketing efforts. Computer vision technology contributes to personalization through innovations like visual search and augmented reality, creating immersive customer experiences. Despite the numerous benefits, the adoption of AI in marketing presents several challenges. Data privacy and security concerns, integration complexities, and the potential for algorithmic bias are critical issues that need to be addressed. Ethical considerations, including transparency and fairness in data usage, are also paramount to maintaining customer trust and ensuring responsible AI application. The future of AI in marketing promises continued advancements, with trends such as hyper-personalization and enhanced integration with technologies like the Internet of Things and voice search expected to drive further innovation. Overall, AI significantly enhances marketing effectiveness by offering tailored solutions and strategic insights while necessitating a thoughtful approach to ethical and practical challenges.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

This study aims at highlighting and illustrating innovative actions for achieving and/or improving accurate medical coding within a DRG system. Already grouped DRG cases recorded in the first DRG implementation year in the region of Crete were examined. A sample of 130,000, from a total of 200,000, cases were analyzed and audited, through a process consisting of three stages: i) Digitalization, ii) Auditor training, and iii) Control & consultation. The results indicated that a significant proportion of DRG coding, with a length of stay exceeding one day, were reclassified into different DRG categories. This was not primarily due to errors in the selection of the principal diagnosis, but resulted from coding errors, including omission of secondary diagnoses coding, medical procedures that should have been included and utilization of less specific codes. The findings indicate that medical coding control, consulting services and the adoption of targeted actions can be instrumental for proper implementation of the DRG coding system, and for ensuring transparent allocation of resources within hospitals. Ultimately, this shortens the coding learning curve, from four years typically reported in the international literature, to a more efficient duration of two years, leading to rational and documented clinical and financial management of hospitals.

This qualitative study explores Supplier Relationship Management (SRM) in the context of digital transformation within e-commerce businesses, focusing on the implications of emerging digital technologies. Through semi-structured interviews and documentary analysis, the study examines how organizations leverage technologies like artificial intelligence (AI), blockchain, and Internet of Things (IoT) to enhance SRM practices. Findings reveal that digitalization enables improved operational efficiencies, real-time data analytics, and expanded supplier networks through platforms such as Amazon Business and Alibaba. Strategic shifts are noted towards collaborative supplier relationships, emphasizing joint innovation, technology co-investment, and shared risk management strategies. However, the implementation of digital SRM strategies presents challenges, including data privacy concerns, integration complexities, cybersecurity threats, and regulatory compliance issues. Organizations must navigate these challenges by developing robust governance frameworks and cybersecurity protocols to protect sensitive information and ensure regulatory adherence. Key performance indicators (KPIs) such as supplier performance, cost savings, innovation impact, risk management effectiveness, and sustainability integration are critical for evaluating the success of digital SRM initiatives. Strategically, businesses are advised to prioritize leadership commitment, cross-functional collaboration, and continuous learning to optimize digital SRM practices effectively. Future research should explore emerging trends in digital SRM, investigate technological impacts on supplier dynamics and organizational performance, and examine ethical considerations in digitalization. By integrating these insights into strategic decision-making, businesses can enhance supplier relationships, mitigate operational risks, and achieve sustainable growth in the global marketplace.

This qualitative research explores the role of digital transformation in procurement through insights gathered from interviews with industry leaders. Digital technologies such as Robotic Process Automation (RPA), Big Data Analytics, Artificial Intelligence (AI), and Blockchain are examined for their impact on operational efficiency, strategic procurement management, supplier relationships, and sustainability practices. Findings reveal that these technologies streamline processes, enhance decision-making capabilities, and improve transparency, thereby enabling organizations to achieve greater agility and competitive advantage. Strategic factors including leadership commitment, strategic investments, and digital skills development emerge as crucial for navigating challenges such as organizational resistance, data security, and integration complexities. The study underscores the transformative potential of digital transformation in procurement, emphasizing its role in driving innovation, fostering sustainability, and aligning procurement practices with broader organizational goals. Looking forward, the research suggests that organizations must continue to innovate and adapt to technological advancements to maintain resilience and leadership in a digital economy. By integrating digital strategies effectively, organizations can enhance operational efficiencies, strengthen supplier relationships, and promote ethical business practices, ultimately positioning themselves for sustained success.

Significant changes in Land Use and Land Cover (LULC) have widespread implications for the environment, economy, and society; influencing future sustainability and development of a region. This study aimed to assess LULC changes for a 30 year period (1990 – 2020) and project future LULC from 2030 to 2100 for the Irrawaddy Delta using remote sensing and simulations with the artificial neural networks-cellular automata method. The findings showed significant LULC changes in the Delta, particularly for the mangrove forests and cropland (rice paddies). Mangrove coverage was 1,471 km² in 1990 but decreased to 1,282 km² in 2020, and simulations predict a further reduction to 1,277 km² in 2100. Cropland areas increased from 10,550 km² to 10,618 km² in 2020, with simulations projecting a slight decrease to 10,586 km² by 2100. Human activities and cyclonic events, especially in the southern part of the region, have led to barren land and impervious areas replacing dense mangrove and forested areas. These changes threaten the ecological integrity of the Delta and impact local livelihoods and biodiversity. The study underscores the need for sustainable land management practices and policies to mitigate the adverse effects of LULC changes and ensure the resilience of the Irrawaddy Delta against ongoing environmental challenges.

This study investigates the adoption of emerging technologies in procurement within supply chain management, focusing on the transformative potential of artificial intelligence (AI) and blockchain. Through a qualitative approach, involving semi-structured interviews with procurement professionals and technology experts, the study explores how these technologies enhance operational efficiency, optimize inventory management, and improve supply chain transparency and trust. AI-driven predictive analytics were found to significantly enhance demand forecasting, inventory optimization, and supplier performance evaluation, fostering data-driven decision-making and proactive risk management. Blockchain technology ensures secure and transparent transactions, facilitating compliance with ethical sourcing standards and reducing risks associated with counterfeit goods. Despite these benefits, organizations face substantial challenges in adopting these technologies, including data security concerns, integration complexities with existing IT infrastructure, and skills shortages in AI and blockchain expertise. Leadership commitment and organizational readiness are identified as crucial enablers for overcoming these barriers and driving successful technology adoption. The study also highlights the strategic importance of supplier relationship management, emphasizing how emerging technologies enhance real-time collaboration, performance insights, and contractual transparency. By addressing these challenges and leveraging technological innovations strategically, organizations can achieve sustainable growth, resilience, and competitive advantage in an increasingly digital and interconnected global economy. The findings contribute to advancing knowledge on procurement innovation and offer valuable insights for organizations seeking to navigate the complexities of technology-driven procurement practices. Future research directions may explore the long-term impact of technology adoption on supply chain dynamics, sustainability initiatives, and organizational performance.

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

53% of adults in the US acknowledge racial bias as a significant issue, 23% of Asian adults experience cultural and ethnic bias, and more than 60% conceal their cultural heritage after racial abuse. AI models like ChatGPT and Google Bard, trained on historically biased data, inadvertently amplify racial and ethnic bias and stereotypes. This paper addresses the issue of racial bias in AI models using scientific, evidence-based analysis and auditing processes to identify biased responses from AI models and develop a mitigation tool. The methodology involves creating a comprehensive database of racially biased questions, terms, and phrases from thousands of legal cases, Wikipedia, and surveys, and then testing them on AI Models and analyzing the responses through sentiment analysis and human evaluation, and eventually creation of an 'AI-BiasAudit,' tool having a racial-ethnic database for social science researchers and AI developers to identify and prevent racial bias in AI models.

The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating Artificial Intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

Accurate and reliable mid-long runoff prediction (MLTRP) is of great importance in water re-sources management. However the MLTRP is not suitable in each basin and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five models is analyzed by applying five models in 222 forecasting scenarios in Australia. The results over 222 forecasting scenarios demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors, and has more significant negative correlation with the RRMSE with the correlation coefficient between -0.62 and -0.85. This means the model's predictive performance will become better along with the increase of TMI, and there-fore the developed TMI index can be used to evaluate the applicability of MLTRP. When TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI is used to partly explain the difference of predictive performance among five models. In general, the complex models, which can better utilize the contained infor-mation, are more sensitive to the TMI, and have more significant improvement in terms of predic-tive performance along with the increase of TMI. This research can provide support for the study of MLTRP.

Artificial intelligence (AI), deep learning (DL) or machine learning (ML) is the ability of computer systems, machines and enginery to work devise procedures like humans. These technologies provide opportunities and possibilities to advance diagnostics and in planning also in the field of human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI, as well as to highlight its uses in maxillofacial surgery. The primary inclusion criterion for this publication was an original paper written in English focusing on the use of AI, DL or ML in maxillofacial surgery. The sources of information were PubMed, Scopus and Web of Science, and the queries were made on 31st of December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery” and “deep learning maxillofacial surgery”. Following the removal of duplicates, all remaining publications that were returned by the searches and were screened by three independent operators to minimize the risk of bias. The analysis of publications from 1992 to 2023 identified certain records, of which 324 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) of R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, artificial intelligence and machine learning have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the set of published papers significantly increased to the number of 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These tools have been found useful for diagnosis, treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and in oral pathology. In orthognathic surgery, they have been utilised for diagnosis, treatment planning, assessment of treatment needs, cephalometric analyses, and orthognathic surgeries, among other applications. The review confirms that the current use of artificial intelligence and machine learning in maxillofacial surgery is focused mainly on the evaluation of digital diagnostic methods especially radiology, treatment plans and postoperative results. However, as these technologies are integrated in maxillofacial surgery and robotic surgery in head and neck region, it is expected that in the future they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

Algorithms, indispensable to understand Artificial Intelligence (AI), are omnipresent in social media but users’ understanding of these computational processes and the way they impact their consumption of information is often limited. There is a need for Media and Information Literacy (MIL) research investigating a) how MIL can support Algorithm Literacy (AL) as a subset of competences, and with what working definition, b) what competences users need in order to evaluate algorithms critically and interact with them effectively, and c) how to design learner-centred interventions that foster increased user understanding of algorithms and better response to disinformation spread by such processes. Based on Crossover project research, this paper looks at four scenarios of use by journalists, developers and MIL experts that mirror users’ daily interactions with social media. The results suggest several steps towards integrating AL within MIL goals, while providing a concrete definition of Algorithm-literacy that is experience-based. The competences and design considerations are organised in a conceptual framework thematically derived from the experimentation. This contribution can support AI developers and MIL educators in their co-design of algorithm-literacy interventions and guide future research on AL as part of a set of nested AI literacies within MIL.

The recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) tools has had a pervasive effect on every sphere of human existence. Libraries and information centres are not immune to these technological advances. These disruptive technologies have transformed the traditional face of libraries and their operations. There are many available AI tools, for example, chatbots, literature review tools, plagiarism detection tools, and several other developments in the field that have perhaps made the functioning of libraries easier. However, there is growing concern and uncertainty among both traditional library and modern professionals regarding the possible job loss and security issues related to these new technologies, which the professionals must deal with. Librarians and information professionals must be ready to handle the unforeseen situation. However, it is to be observed how libraries cope with these uncertainties, and threats to the integrity, availability of data, and normal library housekeeping functions.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

The heavy transition metal tantalum (Ta) offers a wide range of applications, especially in the field of electronics, e.g. Ta capacitors with a comparatively long life-time. Therefore, electronic scrap containing these components is a potentially valuable secondary source of this metal. However, the recovery of Ta, as other critical raw materials, that are lost in pyrometallurgical slags can be a challenging process. As a way of overcome this, the "engineered artificial minerals" EnAM approach is proposed by first searching for suitable host minerals and characterizing them. A favorable prerequisite is the enrichment of the desired element(s) in compounds that have particularly suitable properties for this purpose, such as morphology, crystal size, surface or magnetic properties. Subsequently, the slag system is adapted (e.g. chemism, solidification curve, cooling and holding times) to optimize this compound. This paper presents the results of mineralogical characterization of Ta-containing iron (Fe)-rich synthetic slags in reducing environment under different cooling rates, and describes and discusses the incorporation of Ta into the different compounds detected by powder X-ray diffraction (PXRD) and electron beam microanalysis (EPMA). Additionally, the speciation of Fe and Ta was accessible through X-Ray absorption near edge structure (XANES) spectroscopy. EPMA also provides a first semi-quantitative assessment of the Ta distribution in these individual compounds. According to this, depending on the cooling rate the enrichment factor is highest in tantalite/perovskite-type oxides (FexTayO6, CaxFeyTazO3) with up to 60 wt.% Ta and ‘tantalomagnetite’ (FeII(FeIII(2-5/3xTax)O4) with a maximum of ~ 30 wt.% Ta (only fast cooling). This is followed by a perovskite-like silicon containing oxide (XYO3) with 12-15 wt.-% Ta (only slow cooling), and a hedenbergite-like compound (XYZ2O6) with varying content of 0.3 - 7 wt.-%. The Ta-concentration in pure Fe, Fe(1-x)O, hercynitic spinel and hematite is negligible. Since a larger fraction of potentially amorphous hedenbergitic matrix is formed at faster cooling, in this case the main Ta is enriched in this compound despite the significantly lower Ta content.

This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time-series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time-derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three-machine nine-bus and IEEE 39-bus system models. The mean absolute error (MAE) and trajectory prediction results are analyzed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor-angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. It is revealed that when the ANN architecture and epochs are constrained, the MAE results can be reduced, and the ANN training results can better reflect the original trajectory using the improvement approaches.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate the methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have proved by application of different AI and ML methods with specific HOCR sensors prediction of the structure of the microbial community.

Quantification of erythrocytes and leukocytes is an essential aspect of hemogram diagno- 23 sis in Veterinary Medicine. Flow cytometry analysis, laser scattering, and impedance detection are 24 standard laboratory techniques, verified by manual microscopy counting. Although single-cell scat- 25 tering is already used as a standard technology for differentiating cell counts in flow cytometry, it 26 requires capillary cell separation. The current study investigates the scattering characteristics of 27 whole blood to identify correlations with erythrocytes and leukocytes counts. The scattering infor- 28 mation present in blood samples can be classified into three types: i) geometrical scattering, which 29 occurs when non-absorbed light is reflected and scattered, ii) Mie scattering, which happens when 30 light is scattered by particles of a similar size to the wavelength, and iii) Rayleigh scattering, which occurs when light is scattered by particles that are smaller than the incident light wavelength. In 32 this study, we investigate the scattering correction coefficients of dog blood absorption spectra in 33 the visible-near infrared range, to establish direct correlations with erythrocytes and leukocytes 34 counts, using multivariate linear regression. Our findings demonstrate the possibility of using the 35 scattering properties of dog blood, which is a step towards the existence of a portable and miniatur- 36 ized hemogram diagnosis in Veterinary Clinics worldwide.

The mechanism of brain information processing unfolds within spatial and temporal domains inherently linked to the concept of symmetry. Biological evolution, beginning with the prevalent molecular chirality, results in the handedness of cognitive and psychological functions (the phenomena known as biochirality). The key element in the chain of chirality transfer from the downstream to upstream processes is the pyramidal neuron (PyrN) morphology-function paradigm (archetype). The most apparent landmark of PyrN is the geometry of the cell soma. However, “why/how PyrN soma gains the shape of quasi-tetrahedral symmetry” has never been explicitly articulated. Resolving the above inquiry is only possible based on the broad-view assumption that encoding 3D-space requires specific 3D-geometry of the neuronal detector and corresponding network. Accordingly, our hypothesis states that if the primary function of PyrNs, at the organism level, is sensory space perception, then the pyramidal shape of soma is the best evolutionary-selected geometry to support sensory-motor coupling.

Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, a great deal of mystery still surrounds the design of new models or the modification of existing ones. These tasks require in-depth knowledge of the different components or building blocks as well as their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain in-depth the building blocks to design deep learning models, starting from the artificial neuron to the concepts involved in building deep neural networks. Furthermore, the implementation of each building block is exemplified using the Keras library.

Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This research focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It extended the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thai recruitment practices. The study explores the factors influencing users' intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literatures and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in Bangkok metropolitan area supplied comprehensive responses. The research reveals that several factors, including Perceived Value, Perceived Autonomy, Effort Expectancy, and Facilitating Conditions, significantly impact the intention to adopt AI for recruitment. While Social Influence and Trust in AI Technology do not have a direct influence on intention, Social Influence directly affects Perceived Value. Trust in AI Technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.

Intergenerational Justice entitles the maximum retention of Earth’s Biodiversity. The United Nations COP 15 committed to the protection of 30% of Earth’s terrestrial environments to provide Biospheric Sustainability. However, proactive amphibian sustainability needs support from amphibian Reproduction Technologies, Biobanking, and Conservation Breeding Programs [RBCs]. These technologies include the reliable practice of stimulation of reproduction and the collection of sperm and oocytes, refrigerated and cryopreserved sperm storage, in vitro fertilization. Emerging technologies promise the perpetuation of species solely from biobanked biomaterials. However, although exemplary national programs for amphibian RBCs are now established in the USA and Australia, these potentials are not fully realised resulting in unacceptable biodiversity loss. To fully realise these potentials an independent and democratic global organisation is proposed to, 1) focus on the bioregions with the highest amphibian biodiversity, 2) maximise regional inclusion in international initiatives, and 3) provide for maximum community engagement and financial resources. We present the first overall multidisciplinary review of amphibian RBCs, including their geopolitical, cultural, and scientific background, along with their development and application to economically, efficiently, and reliably, perpetuate amphibian biodiversity. We provide genetic models, the potential for diploid cell cryopreservation to perpetuate species, along with significant new information on methodologies and techniques, and standardise nomenclature for specificity in publications and media consistency.

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

Wide-scale commercialization of cultured meat, produced from in vitro-grown animal stem cells, is still a long way off because of technical, regulatory and, above all, social acceptability constraints. As a result, despite the advancement of knowledge, it is difficult to predict whether this innovation will be commonly accepted. The concept of media framing is key to better understanding individuals’ decision-making and mental interpretative patterns in relation to cultured meat adoption. This research therefore analyzes the role of media framing (namely the social media platform Twitter) on the components of consumer attitudes (cognitive, affective, and conative) in relation to cultured meat. A total of 23 020 publications and 38 531 comments were analyzed qualitatively (content analysis) and quantitatively (MANOVA). This study showed that media-framed posts influenced consumer attitudes more than did non-media-framed posts. Although the results indicate that the different types of media framing (ethical, intrinsic, informational, and belief) do not exert the same influence on each attitude component, they suggest that posts combining the ethical, intrinsic, and informational media frames have a greater impact on the acceptability of cultured meat and that the belief frame is equally important, particularly for the behavioral component. Relevant implications can be drawn for authorities and businesses on the use of differentiated education and marketing strategies

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.

This paper focused on the finite element analysis of structural system in extreme loading condition. Two different stud shape and thicknesses were analyzed under blast. The stud thickness such as 1.19 mm and 1.5 mm were modelled and analyzed using ABAQUS 6.14. FEM is a tool which predicts the engineering physics of the real structure. To validate the finite element modelling performed by authors, a reference work published by earlier researchers on cold-formed steel stud wall is considered and examined in the present study. The novelty of this study was web corrugation and influence of flange width on stud. The models mimic like an air bag in a car to delay the pressure timing inside the stud wall. The mass of explosive used as 1.56 kg at a standard scaled distance. Time versus displacement was captured out at four locations in the stud wall. One of the objectives is to develop mathematical model to validate the deformation of stud under blast loading. Two mathematical models were validated using Artificial Neural Network (ANN). The results captured in ANN model was error histogram, regression plot, best performance fit and training data. The models were capable of resisting the moderate blast load. The response surface methodology (RSM) was employed to evaluate model performance Regression equations are useful for predicting future trends and outcomes, which is crucial for planning and decision-making. The primary goal of this work is to evaluate cold-formed steel stud walls with varying stud sizes under blast loading using finite element analysis and validated by ANN and RSM.

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness [1]. Currently, a popular research area related to artificial neural networks is adversarial attacks. The effect of adversarial attacks on the image is not highly perceptible to the human eye, also it drastically reduces the neural network accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. The approach proposed in this paper can improve the image recognition accuracy in the presence of high-frequency distortions, in particular, caused by adversarial attacks. The proposed technique makes it possible to measure up the logic of artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

With worsening environmental degradation and increasing international demand for human health, plant cell based technology will be more important. The aim of this review is to give an overview regarding key points of current progress and development trend of nature medical and edible plants from the framework of whole plant cell engineering including cell cultures, cell fusion and cell splitting derived biotechnology. Here we sum up three main application trends of medical plants cell cultures covering industrial output, technology upgrading and potential resource development. We also provide classification and typification insights to summarize the development of somatic hybridization in cereal, vegetables crops and other medicinal and edible plants as well as application of artificial polyploidy induction in major medicinal herbs. Development medicinal and edible homologous plants based on modern plant cell engineering will bring us a promising future.

The objective of this study was to apply simulation and computational intelligence techniques using artificial intelligence and genetic algorithm for economic and environmental optimization of the reverse network (manufacturers, waste managers and recyclers in São Paulo, Brazil) of waste electrical and electronic equipment (WEEE) to promote circular economy. For the economic evaluation, the reduction of: fuel, drivers, insurance, depreciation, maintenance and charges was considered and for the environmental evaluation, the environmental impact was measured in the abiotic, biotic, water, land, air and greenhouse gases compartments. It is concluded that the optimized structure of the WEEE reverse chain for São Paulo, Brazil reduced the number of collections, making the most of the cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Thus, the proposed simulation allows replication in organizational practice, mainly to meet the 2030 agenda on reducing the carbon footprint generated in transport in large cities. Thus, this study can guide companies on structuring the reverse WEEE chain in São Paulo, Brazil for economic and environmental optimization, a relevant aspect considering the exponential generation of WEEE, requiring the implementation of the national solid waste policy, and subsequently the signature of the electronics sector agreement in São Paulo.

Switched-mode power supplies (SMPS) often utilize electrolytic capacitors due to their substantial energy density and economical pricing. However, their ability to function at low temperatures is essential for dependable operation in several sectors, including telecommunications, automotive, and aerospace. This study includes an experimental evaluation of how well standard SMPS electrolytic capacitors operate at low temperatures. This paper investigates the suitability of standard electrolytic capacitors used in switched-mode power supplies (SMPS) for low-temperature applications. The experimental evaluation exposed the capacitors to temperatures ranging from - 5 ∘C to - 40 ∘C, assessing capacitance (Cp), impedance (Z), dissipation factor (DF), and equivalent series resistance (ESR) at each temperature. The capacitor’s time-domain electrical signals were analyzed using the Pearson correlation coefficient to extract discriminative features. These features were input into an Artificial Neural Network (ANN) for training and testing. Results indicated a significant impact of low temperatures on capacitor performance. Capacitance decreased with lower temperatures, while ESR and leakage current increased, affecting stability and efficiency. Impedance proved to be a valuable diagnostic tool for identifying potential capacitor failure, showing a 98.44% accuracy drop at - 5 degrees and 88.75% at the peak temperature, indicating proximity to the manufacturer’s specified limit. The study suggests further research and development to improve electrolytic capacitors’ performance in SMPS systems under cold conditions to enhance efficiency and reliability.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific IgG antibody lateral flow assays that enables rapid and accurate evaluation of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in over 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence allowing making decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria was combined with a critical analysis of current and future technologies. The main findings of the review allow answering question what is the role of modern power source technologies, artificial intelligence and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. Novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose: (1) an algorithm for selecting main energy source for robot application, and (2) an algorithm for selecting electrical system power supply. Current mobile robot batteries are, in most cases, their biggest limitation. Progress in batteries development is too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.

Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. Because of organ shortages this treatment can only be applied to carefully selected patients according to clinical guidelines to minimize risk of recurrence. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging like radiology. With the help of these technologies laborious tasks like segmentation can be automated and workflow in radiology departments can be improved. Other roles include in depth pixel-wise analysis of lesions by radiomics or deep learning in order to find new imaging criteria that allow better prediction of treatment response or risk of recurrence. Liver transplant is an ideal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease and artificial intelligence could provide solutions for improving the management of liver transplant candidates to improve survival.

The continuously developing project aims to build an informatics system enabling analysis of spatial and temporal parameters of movement activities occurring in the sport of speed climbing. The monitoring system (climbing information speed system – CISS) is to be used for conducting comprehensive scientific research in the field of speed climbing. The system enables the evaluation of the training process of climbers at various levels of competition. The study analysis was based on video. The video recording with a camera positioned at a short distance (10 m) from the wall. The marker was positioned closest to the centre of mass (gravity) BMC. Results: development of a system for data collection and analysis of the climbing run based on video recording (application of the Kanade-Lucas-Tomasi (KLT) algorithm). Our results showed that used devices can measure a wide range of specific internal and external variables during speed climbing. Some of the analyzed parameters were significantly correlated with speed climbing time. These results could be a theoretical basis for future research and for training program’s preparation.

The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.

Rapid spread of African swine fever virus (ASFV), causing severe disease with often high fatality rates in Eurasian suids, prevails as a threat for pig populations and dependent industries world-wide. Although advancing scientific progress continually enhances our understanding of ASFV pathogenesis, alternative transmission routes for ASFV have yet to be assessed. Here, we demonstrate that ASFV can efficiently be transferred from infected boars to naïve recipient gilts through artificial insemination (AI). In modern pig production, semen from boar studs often supplies many sow herds. Thus, the infection of a boar stud presents the risk of rapidly and widely distributing ASFV within or between countries. Daily blood and semen collection from four boars after intramuscular inoculation with ASFV strain ‘Estonia 2014’ resulted in detection of ASFV genomes in the semen as early as 2 dpi, in blood at 1 dpi while semen quality remained largely unaffected. Ultimately, after insemination with extended semen, 7 of 14 gilts were ASFV positive by 7 days post insemination, and all gilts were ASFV positive by 35 days post insemi-nation. Twelve out of 13 gilts aborted at the onset of fever. A proportion of fetuses originating from the remaining gilt showed both abnormalities and replication of ASFV in fetal tissues. Thus, we present evidence for the efficient transmission of ASFV to gilts via AI and also to im-planted embryos. These results underline the critical role that boar semen could play in ASFV transmission.

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS.

During the COVID-19 pandemic, individuals with symptoms other than cough or fever have refrained from seeking medical advice. However, a delay in treatment might lead to serious consequences. At the same time, digital health initiatives have emerged to overcome this bottleneck of healthcare. Herein, we report the results of a multi-center initiative using a combination of patient history and artificial intelligence (AI) to identify individuals with rare neuromuscular diseases. First, a questionnaire with 46 items was developed by interviewing patients with muscular dystrophies, amyotrophic lateral sclerosis, Morbus Pompe, neuropathies, and myasthenia gravis. Second, patients with proven neurological diseases answered the questionnaire. Third, a combination of classifiers (artificial neural network, support vector, and random forest) was trained and, finally, the system was challenged with new questionnaires. Users with an abnormal questionnaire pattern received a unique code for data privacy and contact details for a neurologist for further advice. The neurologists confirmed or refuted the AI-based diagnosis. The questionnaire was accessed 3122 times, leading to 853 unique codes. Only for a few patients the computer-based diagnoses and the confirmed final diagnoses were reported to us. However, for these few patients, the genetic testing and high CK levels finally ended their long-lasting diagnostic odyssey.

Since 1974, when Heinz von Foerster made the distinction between “the cybernetics of observed systems” as first-order cybernetics (1oC) and “the cybernetics of observing systems” as second-order cybernetics (2oC), cybernetics has been dominated by this observer-centric paradigm that he claimed cannot be extended meaningfully to a third-order. Rather than attempting to extend his paradigm, we derive an alternative, model-centric paradigm from first principles of regulation that naturally extends to three orders, where the third order is ethical regulation. We thus consider a type of regulator that requires a third model and a third observer: If the third model is a model of acceptable (ethical) situations, then a third observer is a necessary element of the system’s “conscience” that prevents or detects any violations of the model of ethical situations. In this paradigm, the cybernetics of systems that are designed to exhibit ethical behaviour can be characterized as third-order cybernetics (3oC). By being able to extend the paradigm to include ethical systems, the model-centric paradigm brings clarity and utility that is not possible using the philosopher-friendly observer-centric paradigm and its under-specified (abstract) observers. Finally new definitions for cybernetics are proposed that clearly differentiate between the science and the philosophy, of cybernetics.

The angle of seismic excitation is a significant factor of the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses using in each one of them different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerograms for the evaluation of seismic response. Thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on Multilayered Feedforward Perceptron Neural Networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class which arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as Pattern Recognition problem. As inputs of networks the ratios of seismic parameters’ values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

The acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are only a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, new novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual-focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available at https://figshare.com/s/ceb58d58e8f5585ce67e.

The strong couplings among ecological, economic, social and technological processes explains the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, the power of information, and the COVID-19 syndemics. Among complexification’s essential features are non-decomposability, asynchronous behavior, components with many degrees of freedom, increased likelihood of catastrophic events, irreversibility, nonlinear phase spaces with immense combinatorial sizes, and the impossibility of long-term, detailed prediction. Sustainability for complex systems implies enough efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with their environments. This in turn means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives and purposes, under dynamic uncertainty, restricted access to materials, energy and information, and a given time horizon, aiming at enhancing the co-evolutionary power of the Biosphere and its human subsystems. Giving the high-stakes, the need for effective, efficient, diverse solutions, their local-global, present-future effects, and their unforeseen short, medium and long-term impacts, achieving sustainable complex systems implies the need for Sustainability-designed Universal Intelligent Agents, harnessing the strong functional coupling between human, artificial and nonhuman biological intelligence in a no-zero-sum game to achieve sustainability.

This article is an effort to scrutinize the role of Information Technology development in the chronological transformation of Urban Planning domain using the exploratory research approach. In this research, it is argued that the theoretical and practical understanding of Urban Planning should absorb and integrate the bright outcome of the rise of Information and Technology to foster congruent future urban development. The article addresses the trends of transformation in the urban planning domain through the myopic lens of the expansion of information and communications technology era followed by investigating the key drivers shaping the interaction between modern-day urban planning and information technology considering both the dark and bright sides into account.

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroen-cephalography for a dataset of 500 subjects. We used Monte-Carlo Feature Selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian ap-proaches. An Artificial Neural Network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is com-parable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using a wearable device in Smart Health.

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. Their main feature is their ability to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video generation systems to explain their execution. This work constitutes a novel approach to get explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. Finally, we apply our approach for creating a complete explainer video about the execution of HNNs on a small recognition problem.

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real- time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Artificial Intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (Industry 4.0), which can be used for the protection of Internet-connected systems from cyber-threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving Machine Learning (ML) and Deep Learning (DL) methods, the concept of Natural Language Processing (NLP), Knowledge Representation and Reasoning (KRR), as well as the concept of knowledge or rule-based Expert Systems (ES) modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an AI-based technical point of view.

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employee to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Extracellular vesicles (EV) such as exosomes, are newly recognized fundamental, natural and physiologic particles of life that seemingly are involved all biologic processes and clinical diseases. Due to their universal involvements, understanding the nature and the potential therapeutic uses of these nano-vesicles requires innovative experimental approaches, in virtually every field. Of the EV group, exosome nano-vesicles and larger companion extracellular micro vesicles (MV) can mediate completely new phenomena dependent on intercellular transfer of proteins and selected RNAs; particularly miRNAs, between donor and targeted cells to elicit epigenetic alterations inducing functional cellular changes. These recipient acceptor cells are nearby (paracrine transfers) or far away after distribution via the circulation (endocrine transfers). The major properties of such vesicles seem to have been conserved over eons, suggesting that they may have ancient evolutionary origins arising perhaps even before cells in the primordial soup from which life evolved. Their potential ancient evolutionary attributes may be responsible for the ability of some modern day exosomes to withstand unusually harsh conditions; perhaps due to unusual membrane lipid compositions. This is exemplified by maternal milk exosome survival of the neonatal acid/enzyme rich stomach. It is postulated that this also applies to their durable presence in phagolysosomes; suggesting unique intracellular release of contents. A major issue discussed is the generally poorly realized superiority of these naturally evolved nano vesicles to therapies compared human engineered artificial nanoparticles; say for treatment of cancers.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

This work presents significant results obtained by the application of global optimization techniques to the design of finite, normal form games with mixed strategies. To that end, the Fuzzy ASA global optimization method is applied to several design examples of strategic games, demonstrating its effectiveness in obtaining payoff functions whose corresponding games present a previously established Nash equilibrium. In other words, the game designer becomes able to choose a convenient Nash equilibrium for a generic finite state strategic game and the proposed method computes payoff functions that will realize the desired equilibrium, making it possible for the players to reach the favorable conditions represented by the chosen equilibrium. Considering that game theory is a very significant approach for modeling interactions between competing agents, and Nash equilibrium represents a powerful solution concept, portraying situations in which joint strategies are optimal in the sense that players cannot benefit from individually modifying their current strategies provided that other players do not change their strategies as well, it is natural to infer that the proposed method may be very useful for strategists in general. In summary, it is a genuine instance of artificial inference of payoff functions after a process of global machine learning, applied to their numerical components.

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for several ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Muscle Thickness, Back Fat amount, and Average Daily Gain serve as significant predictors of conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. I propose a set of logically distinct conceptual components that are necessary and sufficient to 1) ensure various AGI scenarios will not harm humanity and 2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, ‘blockchain’) is integral to this proposal, e.g. ‘smart contracts’ are necessary to address evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: 1) Access to technology by market license. 2) Transparent ethics embodied in DLT. 3) Morality encrypted via DLT. 4) Behavior control structure with values at roots. 5) Individual bar-code identification of critical components. 6) Configuration Item (from business continuity/disaster recovery planning). 7) Identity verification secured via DLT. 8) ‘Smart’ automated contracts based on DLT. 9) Decentralized applications - AI software modules encrypted via DLT. 10) Audit trail of component usage stored via DLT. 11) Social ostracism (denial of resources) augmented by DLT petitions. 12) Game theory and mechanism design.

Luminescence-based sensors for measuring oxygen concentration are widely used both in industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the standard approach, this change is related to an oxygen concentration using the Stern–Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5 % air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

This paper is about to estimate the suspended sediment transport amount in the streams flowing into the Sapanca Lake Basin. There are 12 subsidiary streams flowing into the Sapanca Lake Basin. With the aim of estimating the suspended sediment transport in 2012-204 in these subsidiary streams, measurements belonging the parameters such as level, cross sectioning, flow rate, temperature and suspended sediment were made monthly. Along the measurement period, weather conditions were above seasonal normal and precipitations decreased. In order to estimate the suspended sediment amount by using results of the measurement obtained, Artificial Neuron Network (ANN), Sediment Rating Curve (SRC) and Multiple Linear Regression (MLR) models were used for different scenarios. It was seen that artificial neuron networks yielded the most accurate results among the models.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.