Search

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A smart aquaculture management system (SAMS) based on the Internet of Things (IoT) has recently gained much attention for fulfilling the growing demand for aquaculture products. The SAMS uses cutting-edge sensing technologies with a modern networking system to continuously monitor water quality, animal health, and feeding behavior to improve the production yield. This review aims to comprehensively summarize and discussed the recent advancements in IoT-based technologies, e.g., sensors nodes, software interfacing, artificial intelligence, cloud computing, and storage, to develop reliable and robust SAMS for small- and large-scale aquaculture production modules. A future perspective section is included at the end of this review to recommend the needed research to be done to scale up the related digital technologies for application in aquaculture farms. The knowledge in this thorough review can be useful for the scientific community for needful advancements and modifications of the IoT technologies and for existing and future aquaculture farmers to adopt modern SAMS for improving the benefit-cost ratios. [ABSTRACT FROM AUTHOR]

This essay zooms into the topic of art forgeries and how the refinement of authentication methods and the dissemination of the resulting expert knowledge has led to detectives and forgers alike being better prepared. Following a brief navigation into the mechanics of art forensics, it discusses several relevant case studies that display the importance of authority delegation in solving the puzzle of authentication. In art attribution, a consensus needs to be reached between the main forgery detection “sensors”: the eyes of art historian for stylistic diagnostic; the eyes of hardware analytical and optical devices that gather scientific evidence; the eyes of the scientific experts to interpret the latter properly. Particular focus is given to the correlated effects activated by the increasing popularity of digitization of cultural heritage (CH) objects. Digital repositories of cultural heritage scientific data have themselves become a subject for forgery and contamination. This introduces the need to dive into a new typology of sensors: forensic software, that can identify the alteration of scientific images. Nonetheless, software may also be employed by forgers to maliciously modify digital data in order to influence a certain diagnostic. This essay frames all the above-mentioned aspects in a cyclic adversarial process, where the progress of sensors determines that of forgers and vice versa. Finally, ethical and legal considerations are explored with respect to the prevention of art forgeries and the reciprocal influence between researchers and forgers. [ABSTRACT FROM AUTHOR]

Background and Objectives: Measuring joint range of motion (ROM) is essential for diagnosing and treating musculoskeletal diseases. However, most clinical measurements are conducted using conventional devices, and their reliability may significantly depend on the tester. This study implemented an RGB-D (red/green/blue-depth) sensor-based artificial intelligence (AI) device to measure joint ROM and compared its reliability with that of a universal goniometer (UG). Materials and Methods: A single-center study was conducted from January 2022 to December 2022 on participants visiting the Chung-nam National University Hospital to compare the reliability of the RGB-D sensor-based AI device with that of the UG for measuring ROM. The ROM of the shoulder, hip, and lumbar spine joints was measured in 35 healthy participants in our hospital. The ROM was measured during active motion by the participants in the standing position. The ROM was measured twice consecutively using the RGB-D sensor-based AI device, and the mean values were obtained along with other values. A clinician also measured the ROM twice using a UG. Bland–Altman analysis was performed to evaluate the reliability of the measurements, which was assessed using intra-class correlation coefficient (ICC). An ICC value greater than 0.90 indicates excellent reliability. Results: Both methods achieved good-to-excellent intra-test reliability results (ICC > 0.75) for all the joints, with the reliability being slightly higher for the RGB-D sensor-based AI method than for the UG measurements. Moreover, for both methods, the inter-test reliability was higher than good (ICC > 0.75) for shoulder and lumbar joint ROM measurements but lower than good (ICC < 0.75) for hip ROM measurements. Conclusions: This study compared the efficacies of the RGB-D sensor-based AI method and UG in measuring ROM. In the future, this RGB-D sensor-based AI method should be technologically improved, and the measurement methods and protocols should be standardized. [ABSTRACT FROM AUTHOR]

In India, there is an urgent need to meet the food production due to the country's rapidly expanding population. There is a downturn in the farming sector and the farmers are shifting to other firms due to insufficient output in the sector. Furthermore, traditional agricultural practices followed in India are insufficient to meet the increasing food demand. The costs and operational times generally increase as the majority of agricultural activities depend significantly on human labor. The increasing prevalence of machinery on farms is not a substitute for the necessity for human labor completely. This highlights the critical importance of agricultural automation. This study highlights the significant impact of UAVs on precision and smart agriculture through automation. The implementation of drones in diverse agricultural tasks has the potential to reduce labor demands and streamline operational efficiency. This review offers a comprehensive overview of conceptual design, command flow operation, micro-controller boards, remote-control systems, and attachments such as sensors, cameras, and motors in UAVs, aimed at automating agricultural activities. The integration of the Internet of Things (IoT) in unmanned aerial vehicles (UAVs), alongside artificial intelligence and machine learning, enhances accuracy and precision in agricultural operations. This study also addresses future advancements, limitations, and challenges that farmers face in adapting to UAVs. [ABSTRACT FROM AUTHOR]

Current advances and trends in the fields of mechanical, material, and software engineering have allowed mining technology to undergo a significant transformation. Aiming to maximize the efficiency and safety of the mining process, several enabling technologies, such as the recent advances in artificial intelligence, IoT, sensor fusion, computational modeling, and advanced robotics, are being progressively adopted in mining machine manufacturing while replacing conventional parts and approaches that used to be the norm in the rock ore extraction industry. This article aims to provide an overview of research trends and state-of-the-art technologies in face exploration and drilling operations in order to define the vision toward the realization of fully autonomous mining exploration machines of the future, capable of operating without any external infrastructure. As the trend of mining at large depths is increasing and as the re-opening of abandoned mines is gaining more interest, near-to-face mining exploration approaches for identifying new ore bodies need to undergo significant revision. This article aims to contribute to future developments in the use of fully autonomous and cooperative smaller mining exploration machines. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is being ubiquitously adopted to automate processes in science and industry. However, due to its often intricate and opaque nature, AI has been shown to possess inherent vulnerabilities which can be maliciously exploited with adversarial AI, potentially putting AI users and developers at both cyber and physical risk. In addition, there is insufficient comprehension of the real-world effects of adversarial AI and an inadequacy of AI security examinations; therefore, the growing threat landscape is unknown for many AI solutions. To mitigate this issue, we propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. The framework provides operators with a proactive (secure by design) and reactive (post-deployment evaluation) response to securing AI technology today and in the future. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI, ranging from poisoning to adversarial patch attacks. The lessons learned from systematic AI red teaming can help prevent MAS-related catastrophic events in a world with increasing uptake and reliance on mission-critical AI. [ABSTRACT FROM AUTHOR]

Background/Objectives: The growing incidence of cognitive impairment among older adults has a significant impact on individuals, family members, caregivers, and society. Current conventional cognitive assessment tools are faced with some limitations. Recent evidence suggests that automating cognitive assessment holds promise, potentially resulting in earlier diagnosis, timely intervention, improved patient outcomes, and higher chances of response to treatment. Despite the advantages of automated assessment and technological advancements, automated cognitive assessment has yet to gain widespread use, especially in low and lower middle-income countries. This review highlights the potential of automated cognitive assessment tools and presents an overview of existing tools. Methods: This review includes 87 studies carried out with non-neuroimaging data alongside their performance metrics. Results: The identified articles automated the cognitive assessment process and were grouped into five categories either based on the tools' design or the data analysis approach. These categories include game-based, digital versions of conventional tools, original computerized tests and batteries, virtual reality/wearable sensors/smart home technologies, and artificial intelligence-based (AI-based) tools. These categories are further explained, and evaluation of their strengths and limitations is discussed to strengthen their adoption in clinical practice. Conclusions: The comparative metrics of both conventional and automated approaches of assessment suggest that the automated approach is a strong alternative to the conventional approach. Additionally, the results of the review show that the use of automated assessment tools is more prominent in countries ranked as high-income and upper middle-income countries. This trend merits further social and economic studies to understand the impact of this global reality. [ABSTRACT FROM AUTHOR]

The intelligence of ultra-precision machining processes has become a research focus in the field of precision and ultra-precision manufacturing. Scholars have conducted some fragmented studies on the intelligence of ultra-precision machining processes; however, a systematic review and summary of the intelligent systems and architectures for such processes are still lacking. Therefore, this paper is devoted to reviewing the intelligent systems and architectures for ultra-precision machining processes, focusing on three aspects: machining environment monitoring, cutting process analysis, and intelligent machining system frameworks. The paper first provides an overview of environmental intelligence monitoring from the perspective of the machining environment and then discusses and summarizes monitoring processes, such as tool errors, tool wear, tool setting, and surface measurement, from the perspective of machining process analysis. The intelligent machining system framework is then analyzed and summarized from the perspective of process control. Finally, the paper outlines the overall framework of the intelligent system for ultra-precision machining processes and analyzes its components. This paper provides guidance for the development of intelligent systems in ultra-precision machining processes. [ABSTRACT FROM AUTHOR]

The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a widely adopted nondestructive and efficient method. Previous research has employed artificial intelligence to automate GPR interpretation by identifying special radargram textures, but the irregular GPR responses of degraded subsurface soils present a challenge. Therefore, this study proposes a method for preliminary diagnosing large-scale culverts based on GPR wave attributes. The method comprises three parts: (1) evaluating soil compactness by estimating the tortuosity of GPR responses at the culvert top; (2) investigating soil moisture using the attenuation rate of GPR responses; and (3) overlaying the results of the incompact and moist soils to pinpoint potential degradations. Two long-distance culverts were inspected and diagnosed using the proposed method, and three abnormal sections were identified and confirmed by excavation evaluation. The case studies indicate that the proposed method can quickly provide reference for further in-depth investigation, thereby improving GPR survey efficiency and reducing workload by narrowing the investigation scope. Consequently, this study can facilitate large-scale GPR culvert surveys and further safeguard the water system. [ABSTRACT FROM AUTHOR]

The novel Coronavirus (COVID-19) is caused by the newly identified strain of the coronavirus family severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), whose target organ is the lungs. It has become a global pandemic, and COVID-19 poses so far unprecedented challenges to healthcare systems around the globe, particularly to those with weakened immune systems. Effective methods for managing, diagnosing, and lessening the effects of COVID-19 are critical because, by 2024, the virus has already caused over 7 million deaths. In this study, we anatomize the impacts of the latest COVID-19 virus on patients with the help of computational intelligence, like Machine learning, artificial intelligence, and IoT-enabled technologies for managing, analyzing, diagnosing, and predicting COVID-19. With tools for early identification, risk assessment, and therapy optimization, machine learning and artificial intelligence have shown tremendous promise in the healthcare industry. These tools can examine big datasets to find patterns and trends that might not be noticeable to human observers. Additionally, IoT will enable healthcare firms to monitor patient scenarios properly and reduce the readmission of COVID-19 patients. Wearable sensors and remote monitoring systems are two examples of IoT-enabled gadgets that are vital for tracking the COVID-19 virus's spread and keeping an eye on its sufferers. These gadgets can gather data in real-time on environmental variables, symptoms, and vital signs, giving medical professionals important insights into the state of their patients' health and the course of their diseases. This study will play a vital role in competing for safety considerations of reducing SARS-CoV-2, COVID-19, and exposure with the assistance of smart technology and provide much-needed information regarding the impact of COVID-19 on patients that will benefit globally. [ABSTRACT FROM AUTHOR]

Soil monitoring is essential for promoting sustainability in agriculture, as it helps prevent degradation and optimize the use of natural resources. The introduction of innovative technologies, such as low-cost sensors and intelligent systems, enables the acquisition of real-time data on soil health, increasing productivity and product quality while reducing waste and environmental impact. This study examines various agricultural monitoring technologies, focusing on soil moisture sensors and nutrient detection, along with examples of IoT-based systems. The main characteristics of these technologies are analyzed, providing an overview of their effectiveness and the key differences among various tools for optimizing agricultural management. The aim of the review is to support an informed choice of the most appropriate sensors and technologies, thus contributing to the promotion of sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is being trained, referred to as the worker's digital twins. The framework incorporates novel work activity recognition using generative adversarial network (GAN) and machine learning (ML) models for recognising the types and sequences of work activities by analysing an individual's kinematic model. Finally, the development of skill proficiency ML is proposed to evaluate each trainee's proficiency in work activities and the overall task. To illustrate DigitalUpSkilling from wearable IoT-sensor-driven kinematic models to GAN-ML models for work activity recognition and skill proficiency assessment, the paper presents a comprehensive study on how specific meat processing activities in a real-world work environment can be recognised and assessed. In the study, DigitalUpSkilling achieved 99% accuracy in recognising specific work activities performed by meat workers. The study also presents an evaluation of the proficiency of workers by comparing kinematic data from trainees performing work activities. The proposed DigitalUpSkilling framework lays the foundation for next-generation digital personalised training. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey's 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management. [ABSTRACT FROM AUTHOR]

Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence, there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns. Due to vehicles' increasingly complex and autonomous nature, there is a growing urgency to investigate novel diagnosis methodologies for improving safety, reliability, and maintainability. While Artificial Intelligence (AI) has provided a great opportunity in this area, a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis (VFD) systems is unavailable. Therefore, this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques. We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines, lifting systems (suspensions and tires), gearboxes, and brakes, among other vehicular subsystems. We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars. The review elucidates the transformation of VFD systems that consequently increase accuracy, economization, and prediction in most vehicular sub-systems due to AI applications. Indeed, the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations: The integration shows that a single technique or method fails its expectations, which can lead to more reliable and versatile diagnostic support. By synthesizing current information and distinguishing forthcoming patterns, this work aims to accelerate advancement in smart automotive innovations, conforming with the requests of Industry 4.0 and adding to the progression of more secure, more dependable vehicles. The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis. [ABSTRACT FROM AUTHOR]

Swarm robotics involves coordinating the behaviour of multiple robots to perform complex tasks collaboratively. Our study explores using low-cost NodeMCU ESP8266 devices to establish master-slave communication in swarm robotics utilizing the Internet of Things (IoT), focusing on different network topologies: star, mesh, and ring. Unlike existing research that uses more expensive or less accessible technologies, our approach demonstrates that these affordable devices can effectively manage communication and coordination in swarm systems. Our experiments reveal that the star topology is the most efficient for master-slave communication, allowing for aggregation, pattern formation, and coordinated motion tasks. This research highlights the potential of using low-cost devices for efficient communication in swarm robotics, offering significant implications for various industries. [ABSTRACT FROM AUTHOR]

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data. [ABSTRACT FROM AUTHOR]

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems. [ABSTRACT FROM AUTHOR]

Precision Livestock Farming (PLF) is a technology-driven approach comprising sensors, cameras, global positioning system tracking, and data analytics to collect real-time data on animal behavior, health, welfare, and performance that enables feedyard operations to make informed decisions and take proactive measures to ensure optimized management and the well-being of their livestock and production efficiency. PLF systems leverage advanced analytical tools and techniques, such as artificial intelligence (AI), including machine learning and deep learning, by integrating data provided by various sensors and software with related datasets and standard AI models to obtain unique data-driven decisions on a range of management practices. The most imminent benefits of PLF include reduced environmental impact and increased profitability. Current PLF tools for climatesmart feedyard operations include 1) feed management systems that use data from feed intake and animal behavior to optimize feed efficiency and minimize waste, which has the potential to decrease the amount of greenhouse gas (GHG) emissions linked to livestock feeding and feed production; 2) environmental monitoring systems to collect local temperature, humidity, and air quality to optimize the pen environment, which can reduce stress on animals, reduce diseases, such as the incidence of respiratory and foot infections, and improve their overall health; 3) feeding systems to use data from individual animals to provide individualized feed and nutrient programs, thus, helping to improve and select for feed efficiency and reduce the amount of feed wasted, assisting with mitigating GHG emissions associated with livestock feeding and feed production; 4) individual animal monitoring systems to access behavior, health and well-being of individual animals that, through early disease symptoms detection, can prevent and reduce recovery time, and reduce the amount of medication applied to animals, reducing GHG gas emissions associated with pharmaceutical products; and 5) waste management systems to help reduce GHG emissions associated with manure production by optimizing manure management and reducing the amount of waste, which can also help reduce unpleasant odors and improve the overall environment conditions in and around feedyards. Waste management systems could also measure the soil and water contamination with fugitive nutrients, feed additives, implants, antibiotics, and other pharmaceuticals administrated to animals to maintain health standards. The adoption of PLF tools in feedyard operations aims to create a sustainable and efficient livestock industry by reducing waste, optimizing resources, and improving animal welfare through advanced technologies and analytical tools. Additionally, PLF tools enable feedyards to optimize feeding and breeding programs, minimize waste and environmental pollution, and improve the quality and safety of animal products. In today's fast-paced industry, PLF tools are essential for feedyard operations to monitor, analyze, and make informed decisions about the well-being and production efficiency of their livestock, ultimately contributing to a more sustainable and efficient livestock industry. [ABSTRACT FROM AUTHOR]

The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials. [ABSTRACT FROM AUTHOR]

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations. [ABSTRACT FROM AUTHOR]

The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnostics, but also for urban railways such as trams and metros, ensuring a high level of passenger safety. The introduction of artificial intelligence in the railway sector is a key step towards modernisation, improving efficiency, resource optimization and safety. Although still in its infancy, artificial intelligence already shows great potential in transforming the railway sector towards a more efficient, reliable and sustainable future. [ABSTRACT FROM AUTHOR]

This study conducts a literature review to analyse the incorporation of Industry 4.0 in the construction sector, known as Construction 4.0, in developing countries. This study utilises an effective technique, encompassing academic databases, journals, and conference proceedings, to carefully examine relevant studies published with respect to developing countries. The primary areas of emphasis involve the definition of Construction 4.0. The technologies of execution include six cutting-edge technologies such as Building Information Modelling (BIM), Internet of Things (IoT), robotics, 3D printing, UAVs, and artificial intelligence in construction procedures. This analysis also explores the awareness and understanding of Industry 4.0 in the construction sector (Construction 4.0) in developing countries before identifying where it is being applied therein. Furthermore, obstacles that impede the mainstream adoption in developing countries are identified, including but not limited to such things as insufficient technological infrastructure, skill deficiencies, and budgetary limitations. This review consolidates various studies to provide a thorough comprehension of the present condition of Construction 4.0 in developing nations. As such, this paper aims to provide a guide for future research, policy making, and industry practices in order to promote sustainable and technologically advanced construction methods in these settings. [ABSTRACT FROM AUTHOR]

This paper introduces non-linear approaches which include neural networks and ANFIS to identify and control heat transfer within a chamber. Initially, traditional linear models are obtained using transfer functions with delays through MATLAB identification tools. However, this traditional linear model failed to faithfully represent the system when the input was changed. This outcome was expected since linear models are reliable only within specific operational ranges. To create a novel model that is applicable across the entire state space, two alternative identification methods, utilizing neural networks and an adaptive neuro-fuzzy inference system were introduced. After testing them with input data not used during the training, the models were compared and all of them showed satisfying results. In the continuation of the research, control techniques based on these techniques were presented. After assigning an arbitrary temperature as a reference signal, inverse models were made and four controllers in direct inverse control scheme were compared: three feedforward neural networks with different numbers of neurons in the hidden layer and the adaptive neuro-fuzzy inference controller. The results and possible improvements are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

The term "artificial intelligence" (AI) refers to "smart" high-tech that is mindful of and able to learn from its surroundings. It is the most revolutionary technology that humans have ever created. Common AI approaches involving machine learning and deep learning techniques can be effectively applied to resolve today's various cybersecurity issues. Furthermore, the metaverse is all about how people communicate and engage with one another through technology. This survey explores the role of AI with its emerging applications and their various technologies, such as the metaverse, healthcare, IoT, gaming, and many more. To determine the strengths, flaws, opportunities, and risks that are inherent in artificial intelligence technologies, using an extensive literature survey, the SWOT (Strengths, Weaknesses, Opportunities, and Threats) assessments have been undertaken in this survey paper. Finally, the survey paper summarises the current state of knowledge of AI applications and discusses the findings present in recent research to ensure a favourable change in artificial intelligence advances and applications. Some technical AI challenges, like high-speed, high-performance hardware and reducing the amount of training data, etc., are also discussed with future scope. [ABSTRACT FROM AUTHOR]

In today's world, where artificial intelligence and digital technologies are widely used, this study seeks to investigate their impact on food security activities. The key role of digital technology in this area is highlighted, in particular in addressing challenges and providing opportunities for safe and affordable food for the whole population. The article discusses how supply chain monitoring and tracking, smart agriculture implementation, and the use of data analytics and artificial intelligence can all help to ensure the food system's sustainability and efficiency. These can be supplemented successfully with communication and awareness via mobile applications and online platforms that increase consumer engagement and responsibility. On the other hand, the article also highlights the challenges associated with the intensive use of artificial intelligence and digital technologies in terms of accessibility and data security. To summarize, rational use of digital technologies and artificial intelligence is critical for developing a sustainable, safe, and crisis-resilient food system. [ABSTRACT FROM AUTHOR]

Changing customer experiences and conventional business models, the convergence of cutting-edge technologies like blockchain, chatbots, and the Internet of Things (IoT) has triggered revolutions across industries. Customers want personalized, easy, and meaningful interactions in today's digital world, so traditional retail and service models are changing to online platforms and round-the-clock digital support. Blockchain-integrated chatbots give users a reliable platform for communication and transactions by utilizing the security and transparency of blockchain technology. These artificial intelligence (AI)-driven chatbots are revolutionizing digital interactions by conversing with users in natural language, managing transactions, and providing tailored support. IoT devices, on the other hand, bring in a new era of connectedness by giving commonplace things the ability to sense, process, and communicate. IoT devices optimize delivery processes and guarantee that the quality and safety criteria are satisfied in the food delivery industry. Blockchain-integrated chatbots are a disruptive force that improves delivery process efficiency, transparency, and trust when integrated with IoT devices. This study examines how blockchain, chatbots, and IoT technologies might be integrated into the food delivery sector, emphasizing the advantages and potential ramifications of doing so. [ABSTRACT FROM AUTHOR]

With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production. [ABSTRACT FROM AUTHOR]

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain. [ABSTRACT FROM AUTHOR]

The Metaverse is regarded as a brand-new virtual society constructed by deep media, and the new media art produced by new media technology will gradually replace the traditional art form and play an important role in the infinite Metaverse in the future. The maturity of the new media art creation mechanism must also depend on the help of artificial intelligence (AI) and Internet of Things (IoT) technology. The purpose of this study is to explore the image style transfer of digital painting art in new media art, that is, to reshape the image style by neural network technology in AI based on retaining the semantic information of the original image. Based on neural style transfer, an image style conversion method based on feature synthesis is proposed. Using the feature mapping of content image and style image and combining the advantages of traditional texture synthesis, a richer multi-style target feature mapping is synthesized. Then, the inverse transformation of target feature mapping is restored to an image to realize style transformation. In addition, the research results are analyzed. Under the background of integrating AI and IoT, the creation mechanism of new media art is optimized. Regarding digital art style transformation, the Tensorflow program framework is used for simulation verification and performance evaluation. The experimental results show that the image style transfer method based on feature synthesis proposed in this study can make the image texture more reasonably distributed, and can change the style texture by retaining more semantic structure content of the original image, thus generating richer artistic effects, and having better interactivity and local controllability. It can provide theoretical help and reference for developing new media art creation mechanisms. [ABSTRACT FROM AUTHOR]

This paper introduces the Blockchain-Based Trustworthy Artificial Intelligence (AI) Supported by Stakeholders-In-The-Loop Model (BCTrustAI.SL) that incorporates sociotechnical components to ensure legal compliance and conformity with the emerging trustworthiness standards for AI systems. BCTrustAI.SL combines features of the Blockchain Framework for Trustworthy AI (BF. TAI), the BlockIoTIntelligence architectural model and the Society-In-The-Loop (SITL) framework, to solve the intangibility problem of the foundational normative notions of robustness, ethicality and lawfulness, and human-centredness, and help them become applicable in practice. The work contributes to the specification and operationalisation of trustworthiness as an AI attribute, highlighting the importance of a precise understanding of its institutional foundations, high order principles and concrete key requirements (data protection, data governance, technical robustness and safety, transparency, accountability, diversity and non-discrimination, and human agency and oversight) deriving from them. On a practical/technical level, BCTrustAI.SL showcases the strengths of combining Blockchain (BC) and AI to address their individual limitations, laying the groundwork for future advancements and practical applications. [ABSTRACT FROM AUTHOR]

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries. Design/methodology/approach: The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model. Findings: This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention. Practical implications: This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers. Originality/value: This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders. [ABSTRACT FROM AUTHOR]

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle's own as well as surrounding objects' relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe. [ABSTRACT FROM AUTHOR]

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, "smart" technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. "Smart Agriculture/farming" (SA) or "Digital Agriculture" (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA's ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users' facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language. [ABSTRACT FROM AUTHOR]

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions. [ABSTRACT FROM AUTHOR]

With the environmental and societal changes, the achievement of sustainable development goals (SDGs) and the realization of sustainability in general is now more important than ever. Through a bibliometric analysis and scientific mapping analysis, this study aims to explore and provide a review regarding the role of artificial intelligence (AI), the Internet of Things (IoT), and artificial intelligence of things (AIoT) in realizing sustainable development and achieving SDGs. AIoT can be defined as the combination of AI with IoT to create more efficient and data-driven interconnected, intelligent, and autonomous IoT systems and infrastructure that use AI methods and algorithms. The analysis involved 9182 documents from Scopus and Web of Science (WoS) from 1989 to 2022. Descriptive statistics of the related documents and the annual scientific production were explored. The most relevant and impactful authors, articles, outlets, affiliations, countries, and keywords were identified. The most popular topics and research directions throughout the years and the advancement of the field and the research focus were also examined. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. Based on the results of this study, AIoT emerged as an important contributor in ensuring sustainability and in achieving SDGs. [ABSTRACT FROM AUTHOR]

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing is a Direct Energy Deposition additive technology that uses the principle of wire welding to deposit layers of material to create a finished component. This technology is finding an increasing interest in the manufacturing industry, especially thanks the low cost and the possibility to build large-scale components. Nowadays, the boosting to transition into smart manufacturing systems and the increasingly computational resources allowed the development of intelligent applications for smart production systems for both in situ inspection and process parameter control. This paper aims to provide an review of applications developed using artificial intelligence techniques for Wire Arc Additive Manufacturing, with particular focus on defect detection software modules, feedback generation for control system and innovative control strategies as reinforcement learning to overcome problems related to model non-linearity and uncertainties. [ABSTRACT FROM AUTHOR]

JPL's Descent Image Motion Estimation System (DIMES) is the first machine-vision system for estimating lander velocity during planetary descent. Composed of sensors and software, DIMES features a descent imager, a radar altimeter, an inertial-measurement unit, and an algorithm for combining sensor measurements to estimate horizontal velocity-the speed across the planet's surface the lander travels as its descends. The algorithm and flight software that combines the sensors are new. This algorithm combines radar, image and inertial data in a novel way to create a low-cost, robust and computationally efficient solution to the horizontal-velocity-estimation problem.

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles' fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle's energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers' selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers. [ABSTRACT FROM AUTHOR]

This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In this way, meaningful insights for immediate and long-term decision-making can be derived. This article can be useful to airport management, blockchain application developers, academics, and researchers who are interested in automating airport terminal systems. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges. [ABSTRACT FROM AUTHOR]