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The burgeoning adoption of Robotic and Autonomous Systems with Artificial Intelligence (RAS-AI) necessitates standardized approaches for Systems Engineering (SE) and assurance of RAS-AI systems. Unlike crewed systems, RAS-AI systems lack real-time human control, posing unique challenges yet to be addressed by existing regulations and SE frameworks. This paper proposes the "Autonomous Delta" approach for the Assurance of RAS-AI systems (As4AI), focusing on Maritime Autonomous Systems (MAS). It comprises seven parts, contextualizing RAS-AI assurance in an Australian maritime context, providing key definitions, a literature review, and outlining the research methodology. The core of the paper (Part 5) presents an As4AI framework, integrating concepts from SE and high-fidelity flight simulation. A prototype instantiation at the Australian Institute of Marine Science confirmed the framework's utility and identified areas for refinement. The paper concludes with a summary and suggestions for future research enabling operational deployment of MAS specifically and autonomous systems in general. [ABSTRACT FROM AUTHOR]

For assessing the overall impact of Artificial Intelligence (AI), it is crucial to continuously monitor large corporations. This paper delves into the examination of 42 corporations that rank among the world's largest investors in R&D, accounting for over one-third of AI patents globally. The focus is on their post-patenting performance, specifically in terms of employment changes, and comparing it with the outcomes of 42 similar companies operating in the same sectors. The latter also recorded substantial levels of R&D expenditures but were not significantly involved in AI patenting. The key findings reveal that, in the medium – and high-tech manufacturing sectors, companies with the highest proportions of AI patents incurred in employment reductions. Conversely, IT services companies experienced substantial employment growth. Along with tentative explanations of these findings, advantages, limitations, and possible developments of this type of analysis are illustrated in the concluding section. [ABSTRACT FROM AUTHOR]

AbstractA seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems. The IoT sensors collect information about patients’ health conditions, and the AI employs the information in disease prediction. TriKSV utilizes multiple kernel functions, including linear, polynomial, and radial basis functions, to classify features more effectively. By learning from different representations of the data, TriKSV better handles variations and complexities within the dataset, leading to more robust disease prediction models. The Levy Flight strategy with Gazelle optimization algorithm tunes the hyperparameters and balances the exploration and exploitation for optimal hyperparameter configurations in predicting chronic kidney disease (CKD) and heart disease (HD). Furthermore, TriKSV's incorporation of multiple kernel functions, combined with the Gazelle optimization strategy, helps mitigate overfitting by providing a more comprehensive search space for optimal hyperparameter selection. The proposed TriKSV-LG method is applied to two different datasets, namely the CKD dataset and the HD dataset, and evaluated using performance measures such as AUC-ROC, specificity, F1-score, recall, precision, and accuracy. The results demonstrate that the proposed TriKSV-LG method achieved an accuracy of 98.56% in predicting kidney disease using the CKD dataset and 98.11% accuracy in predicting HD using the HD dataset. [ABSTRACT FROM AUTHOR]

The European Commission (EC) has recently developed its AI strategy which aims to develop and mobilize for a European version of AI. This article seeks to generate an in-depth understanding of how the EC frames its AI version as distinctively European. A frame and content analysis methodology is used for understanding how the EC struggles to reconcile AI and European identity. European identity consists of European historical narratives, different societal and political purposes and values. It is found that the EC seeks to legitimize the European AI alternative through beneficial outputs and narratives based on historic technological leadership and that via framing practices the EC tries to foster European integration. This paper concludes that the EC's communication efforts are inconsistent and might not be sufficient to generate trust in AI among the European public. [ABSTRACT FROM AUTHOR]

This paper proposes a new enterprise intelligentization framework, by making the transition from process transformation to artificial intelligence (AI) transformation. The novel transformation framework can be decomposed into the conceptual model of AI strategic planning, the procedural model, the operational model, and the analytics model. For leading-edge microchip production, a new AI transformation project regarding the reticle SMIF pod (RSP) transport system designed by a medium-sized semiconductor tool vendor in Taiwan is presented. The technical advantages, gained from the implementation of the presented AI transformation project, over the existing RSP systems are manifold. The throughput and yield rate significantly increase on a semiconductor-fabrication-plant basis. The clean room construction costs less by approximately 3 million dollars per FAB, mainly attributed to the redesigned automatic optical inspection flow. The proposed model-based framework proves to be a viable tool from the process transformation to the AI transformation in the semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is not a new technology, as many industries have been using it for years. Recently, it has become a more available asset to university students and faculty. Due to this interest, guidelines and practices are being established in universities and academic libraries on how best to use AI. This paper covers some of the challenges currently facing academia when it comes to AI and the various ways AI can benefit academic libraries in teaching, collection services, and aspects of accessibility. [ABSTRACT FROM AUTHOR]

Microwave drying (MWD) is an efficient dielectric drying method in food, with advantages such as volumetric heating, fast drying, safety, and good product quality. As a key indicator of a dryer's market value, energy efficiency is of concern to sellers and dryer manufacturers. This paper systematically reviewed the quantification methods and influencing factors of energy efficiency of microwave drying in food application from different perspectives. Mechanisms and possible improvements of these factors are highlighted. Future trends in improving the energy efficiency of MWD are proposed. Energy consumption of MWD depends on a variety of factors such as equipment structure, drying conditions (microwave power, frequency, temperature, and air velocity), material properties, and combined/hybrid drying technologies. The drying system can be effectively improved if these parameters are adjusted appropriately and taking the processing cost into consideration. Although a good product can be obtained by pretreatment or combined/hybrid drying method, it may consume more energy. Future research should develop artificial intelligence, renewable energy, and computational fluid dynamics technology to pave the way for large-scale application of MWD and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has an increasing presence in scholarship, posing new challenges and opportunities for qualitative researchers. Generative AI, such as Chat-GPT, can supposedly produce humanlike responses, which has implications for online qualitative research, which relies on human participation. In this paper, we contribute to debates about AI as a research participant ('AI-as-participant') and the threat of imposter participation in qualitative research. We share our unexpected encounter with AI during our story completion study on mobile dating during the COVID-19 pandemic and discuss how we identified AI responses within our dataset. Central to our analysis was our theoretical grounding of feminist new materialism, which attuned us to the affective and discursive qualities of our participant data. Using our theoretical lens, in tandem with other strategies, we examined the affective forces that signalled stark differences between previous, human-generated data and that of the current study. Analysing the discursive construction of narratives further alerted us to the absence of humans within our data. We conclude that AI cannot sufficiently replicate affect or capture the richness of human experience that is central to qualitative research, and offer recommendations for future researchers to anticipate and check for AI as an unwelcome research participant. [ABSTRACT FROM AUTHOR]

This paper presents a source degenerated cascode low noise amplifier (LNA) design using a regulated cascode active inductor (AI). The AI has an inductance value of up to 6.28 nH, a Q-value of up to 4750, and a resonant frequency of up to 9.758 GHz. The proposed AI replaces the passive source degenerated inductor and load inductor in the LNA. Further, an active balun is used to have a differential signal at the LNA output to support the mixer stage. The gain and noise figure of the LNA is 22.2 and 5.6 dB respectively. The 3rd order input intercept (IIP3) value is 1.92 dBm and the return losses at the input port and two output ports are less than −10 dB. The power consumption of the circuit is 27 mW at a 1.8 V supply and the figure of merit (FoM) is 9.17. The area occupied by the AI is 39 × 72 µm2 and balun-LNA is 0.872 × 0.62 mm2. The complete proposed circuit is meant for wireless local area network (WLAN) applications in the 2.4 GHz frequency band. [ABSTRACT FROM AUTHOR]

The introduction of ChatGPT 3.5 in November 2022 ignited a sensation in the academic community, leaving many astounded by its capabilities. This new release more closely emulates human responses than its predecessors. Among its remarkable capabilities, it can answer questions, catalog items in MARC21, recommend reading lists, and make suggestions on a wide array of topics. To assess ChatGPT’s efficacy in aiding library users, the authors of this paper conducted an experiment comparing ChatGPT’s performance with that of librarians in answering reference questions. Thirty questions were randomly selected from the transaction log of the reference inquiries between June 1, 2023 to July 31, 2023 at the Rider University Libraries. These queries constituted 34% of the total user questions during this two-month period. The authors compared the answers by ChatGPT and those by reference librarians for their accuracy, relevance, and friendliness. The findings indicate that reference librarians markedly outperformed their robotic counterpart. An evident issue arises from ChatGPT’s deficiency in understanding local policies and practices. This consequently hinders its ability to provide satisfactory answers in those areas. OpenAI posits that ChatGPT’s proficiency can be enhanced through targeted fine-tuning using locally specific information. At the moment, ChatGPT remains a great tool for librarians. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

The emerging concept of ‘smart’ in the field of cultural heritage in China is shaping new relationships between cultural representation, production, consumption and regulation. The study conducted an urban anthropological exploration of the transformation and construction of smart cultural heritage in Hangzhou, China in 2022–2023. Over 40 stakeholders were interviewed, including officials, heritage experts, museum curators and directors, technicians, and museum visitors. The paper explores the smart heritage politics in China from the macro-political, meso-institutional and micro-user perspectives. This study illustrates the role of smart cultural heritage as a ‘social technological imagination’ and highlights the existence of intercultural communication deficits and disparities between stakeholders. It reveals inconsistencies between the official Chinese technological narratives and the reality of the perceived impacts. The top-down proliferation of smart heritage discourses in China, driven by the authoritarian bureaucracy and leading-edge ICT companies, has overshadowed the voices and experiences of grassroots practitioners and users. [ABSTRACT FROM AUTHOR]

Landscapes are composed of physical places, affording meaning-making and value creation from everyday heritage based on personal experiences, life histories, memories, traditions and heritage practices. Individually held values form the basis for attachment and connection between people and places. Place attachment develops into a sense of place, belonging and identity. Despite the Burra Charter and Faro Convention's aspiration to include people in the assessment process, individual, subjective or emotional connections to place are often overlooked within heritage decision-making. When places are altered, neglected or damaged, such connections can be lost, and the quality of place diminished. Most changes to landscapes happen as part of the planning process, which is not currently able to account for individual connections but based on views expressed in the language of the Authorised Heritage Discourse (AHD). This paper presents a method to meaningfully integrate insider or individual knowledge into the framework of local planning and decision-making while at the same time addressing subtleties and fluidity of such personal views. The people and place-centred method of Social Landscape Characterisation collects, analyses and visualises invisible or hidden value communities based on the same meaning (category value) or location (place value) as shared values across wider landscapes. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

In recent years the popularity of smartphones has been on the rise, the importance of user experience is being discussed more frequently, and interface design is determined as a crucial technique in the interactive design of mobile devices. As both supply and demand for smartphones have grown, the interactive interface designs are obtained as significant, and attention is paid to their effects. The traditional method is only applicable to computers, has a limited user base, and has poor efficiency and effectiveness. However, the operation and functionality of mobile phones are diminished due to the installation of unwanted applications. Hence it is particularly evident in the interactive art approach in which there has been limited innovation in core capabilities and few distinguishing features among more established programmes. Inorder to perform speedy progress, the Convolutional Neural Network (CNN) and Deep Learning (DL) are widely determined. Furthermore, Style Transfer and Target Detection are emerging as popular because of their significance to the field of visual research. So this paper proposes the prevalent quasi- and flat-materialized style and analyzes the skills necessary for applications. The proposed method achieved superior performance when validated with MPI Sintel and Train2020 MSCOCO datasets. [ABSTRACT FROM AUTHOR]

Extant literature has not fully accounted for the changes underway in China's perspectives on the ethical risks of artificial intelligence (AI). This article develops a community-of-practice (CoP) approach to the study of Chinese policymaking in the field of AI. It shows that the Chinese approach to ethical AI emerges from the communication of practices of a relatively stable group of actors from three domains—the government, academia, and the private sector. This Chinese CoP is actively cultivated and led by government actors. The paper draws attention to CoP configurations during collective situated-learning and problem-solving among its members that inform the evolution of Chinese ethical concerns of AI. In so doing, it demonstrates how a practice-oriented approach can contribute to interpreting Chinese politics on AI governance. [ABSTRACT FROM AUTHOR]

The use of optical coherence tomography angiography (OCTA) holds significant promise for optometrists in the diagnosis and management of glaucoma. It offers reliable differentiation of glaucomatous eyes from healthy ones and extends monitoring capabilities for advanced cases. OCTA represents a valuable addition to traditional assessment methods, particularly in complex cases. Glaucoma, a major cause of irreversible blindness, is traditionally diagnosed using structural and functional metrics. With growing interest, OCTA is being explored to diagnose, monitor, and manage glaucoma. This review focuses on the application of OCTA in glaucoma patients. A database search was carried out using Embase Elsevier (n = 664), PubMed (n = 574), and Cochrane Central Register of Controlled Trials (n = 19) on 15 August 2023. After deduplication and screening, 272 original papers were included in the narrative review. Inclusion criteria comprised English-language original studies on OCTA use in human glaucoma patients, with or without healthy controls. Exclusion criteria encompassed animal studies, in-vivo/in-vitro research, reviews, and congress abstracts. OCTA has good repeatability and reproducibility. OCTA metrics have good discriminatory power to differentiate glaucomatous eyes from healthy eyes and show strong associations with structural changes and visual field defects. OCTA can extend the monitoring of advanced glaucoma, addressing the 'floor effect' of traditional structural measurements. OCTA metrics can be affected by the choice of OCTA machine, post-image processing algorithms, systemic diseases, and ocular factors. Image artefacts can affect the accuracy of OCTA measurements, and proper scan quality evaluation is crucial to ensure reliable results. Additionally, artificial intelligence techniques offer promise for enhancing the diagnostic accuracy of OCTA by combining data from various retinal layers and regions. OCTA complements traditional methods in assessing glaucoma, especially in challenging cases, providing valuable insights for detection and management. Further research and clinical validation are needed to integrate OCTA into routine practice. [ABSTRACT FROM AUTHOR]

The algorithmic turn has fundamentally transformed journalistic work. Academic interest in the implication of automated algorithms for journalism has grown hand-in-hand with their everyday use. This paper presents a literature review of peer-reviewed research reports (N = 62) on automated algorithms in the context of journalistic work. Our review focuses on the first decade (2010–2019) during which automated journalism gained traction. The study identifies the most prominent perspectives or themes that studies in automated journalism have explored and the future directions for research that researchers have proposed. Based on the analysis, the dominant themes that studies in automated journalism have covered include (1) testing and developing algorithmic tools, (2) developing practices and policies for journalistic work, (3) attitudes and technology acceptance, and (4) societal and macro-level discourses concerning AI and journalism. The new directions for research that studies on automated algorithms have recognized relate to (1) target groups and stakeholders—that is, who to study in the future; (2) emergent themes and phenomena—that is, what to study in the future; and (3) approaches and methodologies—that is, how to study these topics in the future. These findings help create a holistic picture of possible future directions for the field. [ABSTRACT FROM AUTHOR]

ChatGPT, an artificial intelligence large language model, took the world by storm in November 2022. Its launch was met with excitement, and it garnered over 1 million users within the first five days of its release, surpassing social media giants like Facebook, Instagram, Twitter and even the top search engine, Google. Having been trained on volumes of data, ChatGPT has displayed the ability to produce human-like responses to questions. Concerns have been raised among members of the academic and research community regarding the ethical usage of ChatGPT and language models in education and academic/scientific research. This paper examines the development and rise of ChatGPT and similar AI tools, their pros and cons, their impact on academic research and possible intellectual property conflicts. It concludes that ChatGPT and language models are here to stay and makes recommendations for its ethical usage to prevent and detect plagiarism and protect intellectual property rights. [ABSTRACT FROM AUTHOR]

The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care. [ABSTRACT FROM AUTHOR]

This article examines the potential impact of AI technology on psychotherapy and counseling. It discusses the use of AI chatbots as companions, including for romantic and sexual purposes, and raises questions about the changing nature of therapy in the digital age. The article explores the concept of "technical thinking" and its effects on human psychology and therapeutic practices. It also discusses the history of therapy using communication technologies and the emergence of AI bots in the field. The article concludes by considering the role of traditional therapy in the digital age and the potential benefits and drawbacks of online therapy. Additionally, it highlights a recent study that found face-to-face communication to be more important for mental health during lockdowns, but also acknowledges the positive impact of digital text-based communication. The author emphasizes the need for further research on the effects of the digital age on psychological therapies and invites submissions to their journal. The article provides summaries of several papers included in the journal, covering topics such as online therapy, the impact of technology on the therapeutic relationship, and the experiences of students in remote learning. The author concludes by emphasizing the importance of considering the effects of the digital age on both psychotherapy provision and individuals involved in therapy. [Extracted from the article]

Digital technology has a ubiquitous presence in psychotherapy. While we recognize its immediate impact on our work, we are less attuned to its symbolic—yet highly consequential—implications for our field, much less its penetrating grip on the human psyche. The eight papers to follow offer us a beginning blueprint that invites us to revisit and re-calibrate our conception of psychotherapy in the digital age. They identify limitations and potential benefits of relating through the screen across multiple theoretical, clinical and training contexts, including a commentary on AI and the future of our field. Rather than shrinking ourselves to adjust to the screen's limits, we need to expand our understanding of its impact both inside and outside of the therapeutic context. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. Milestones like AlexNet and AlphaGo established deep learning as a dominant AI approach. Transfer learning enabled models pre-trained on diverse corpora to excel at specialised downstream tasks. The scope of AI expanded from niche applications like playing chess to multifaceted capabilities in computer vision, natural language processing and dialogue agents. However, current AI still needs to catch up to human intelligence in aspects like reasoning, creativity, and empathy. Addressing limitations around real-world knowledge, biases, and transparency remains vital for further progress and aligning AI with human values. This survey provides a comprehensive overview of the evolution of AI and documents innovations that shaped its advancement over the past six decades. [ABSTRACT FROM AUTHOR]

PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach. [ABSTRACT FROM AUTHOR]

The primary goal of this research paper is to inculcate AI approaches in thermal power plants to raise automation at power plants by expanding the integrated control idea to thermal power plants and implementing it using AI approaches. The recommended control method will be analysed and contrasted with established strategies. The crucial condition will be evaluated even though the suggested type of control system doesn't come up with an effective result. The development of the conceptual model is predicated on the state of power generation technologies. There will be no suggestion to use newer or different technology, such as fluidised bed combustion, sliding pressure units or ultra-supercritical plants, in the process of producing electricity. This study specifically focuses on the operational features of the control system instead of the hardware technologies. [ABSTRACT FROM AUTHOR]

Cyberattacks are becoming more and more intense in the banking industry (Ryzhkova et al. 2020). Banking industry is trying to adopt artificial intelligence to create cyber defence system so that the unauthorized access and cyber attacks would be minimized. Banks in Qatar acknowledge the threat of cybercrimes and the role of cybersecurity in sustainable growth. Meanwhile, the banking industry is experiencing a major technological disruption. It becomes important to understand the implications of such technologies as artificial intelligence (AI) on the cybersecurity of banks. The present paper aims to explore the impacts of AI on the cybersecurity of banks in Qatar. A thematic analysis of interviews of 9 experts in the banking industry of Qatar was conducted. A qualitative thematic analysis was conducted using NVIVO 12 tool. Four key themes were identified: (1) AI is a major tool for enhancing the cybersecurity of banks in Qatar; (2) banks face challenges in using AI for improving cybersecurity; (3) AI can be used destructively and thus poses a threat to cybersecurity in banks of Qatar; (4) employed AI-based tools have vulnerabilities that can be exploited. Overall, Qatar banks can be expected to face new challenges in the future, due to changes in regulatory frameworks and increasing availability of AI-powered malware. [ABSTRACT FROM AUTHOR]

Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPS's information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven. [ABSTRACT FROM AUTHOR]

Evacuation planning and emergency routing systems are crucial in saving lives during disasters. Traditional emergency routing systems, despite their best efforts, often struggle to accurately capture the dynamic nature of flood conditions, road closures, and other real-time changes inherent in urban disaster logistics. This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL). The model incorporates a unique RL environment that considers multiple criteria, such as traffic conditions, hazardous situations, and the availability of safe routes. The RL agent in this model learns optimal actions through interaction with the environment, receiving feedback in the form of rewards or penalties. The ReinforceRouting model excels in executing prompt and accurate route planning on large road networks, outperforming traditional RL algorithms and shortest-path-based algorithms. A higher safety score and episode reward of the model are demonstrated when compared to these classical methods. This innovative approach to disaster evacuation planning offers a promising avenue for enhancing the efficiency, safety, and reliability of emergency responses in dynamic urban environments. [ABSTRACT FROM AUTHOR]

The article discusses the recent research progress in bridge-related topics, including safety, maintenance, management, life-cycle, resilience, and sustainability. It highlights the International Conference on Bridge Maintenance, Safety, and Management (IABMAS) and the papers presented at the conference. The article covers various aspects such as bridge design, structural monitoring, load testing, connectivity assessment, service life extension, refurbishment, digital twinning, and climate change considerations. It also explores new technologies for bridge inspection and monitoring, drive-by bridge condition monitoring, and the use of sensors in bridge monitoring. The article concludes by emphasizing the importance of bridge condition monitoring and the potential benefits of digital twins in asset management and condition monitoring. [Extracted from the article]

This paper details a doctoral student research peer support group that was established consisting of three social work researchers at various stages (doctoral student, doctoral candidate, and postdoctoral fellow) as they trained for qualitative simulation-based research within a research lab at a school of social work in Canada. Grounded in mutual aid, this paper highlights the development of this student research peer support group which focused on experiential, collaborative, and reflective learning. Benefits of establishing similar groups are discussed, and suggestions for doctoral students and faculty interested in developing similar groups are provided. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e. controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future. [ABSTRACT FROM AUTHOR]

This paper examines myths envisaging a "balancing" of commercial, state and citizen interests through a market-led adjustment process underpinned by regulatory interventions in digital technology markets. It considers how these myths operate to downplay or conceal a persistent commitment to digital innovation that privileges the interests of technology companies and states. The key assumptions of the prevailing imaginary of technology innovation—emphasising investment in artificial intelligence and commercial datafication with harms mitigated by regulatory "guardrails"—are contrasted with an alternative imaginary that pays attention to uneven power relations and their consequences for the protection of citizens' fundamental rights. Drawing upon Gramsci's insights into the role of myth, the paper emphasises the need to examine the implementation of regulation and its outcomes if we are to understand how myth operates by concealing outcomes that leave the prevailing imaginary robustly intact. In arguing that myth suppresses possibilities for resistance to the capitalist exploitation of digital technology innovation, the conclusion discusses why a reframing of digital technology innovation processes and their regulation is essential if digital systems are to operate in ways that are consistent with human autonomy, dignity and democracy. [ABSTRACT FROM AUTHOR]

The use of automated decision-making systems is increasing in education. While the potential impacts of ADM are becoming widely known amongst experts, the perspectives of those impacted by ADM remain peripheral. To broaden expertise and participation, this paper proposes that ADM needs to be considered as a sociotechnical controversy, as part of a technical democracy approach that utilises hybrid forums. Following Callon and colleagues, in this paper, technical democracy refers to the process of learning through uncertainty about sociotechnical controversies, and hybrid forums refer to the specific sites of democratisation. This paper first identifies key uses and concerns with ADM in education. Second, it proposes that restricted capacity for participation can be addressed through technical democracy. Last, it proposes that hybrid forums can create moments of democratisation through shared uncertainty, material politics, and collective experimentation. [ABSTRACT FROM AUTHOR]

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical 'AI' (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to impact educational governance. This trend will be firstly shown to align with a narrow form of ethics - deontology - and overlook other potential ways ethical reasoning might contribute to thinking about 'AI'. Secondly, the attention to ethical principles will be suggested to focus excessively on the technology itself, with the effect of masking political concerns for equity and justice. Thirdly and finally, the paper will propose a more radical form of participation in ethical decision-making that not only challenges the assumption of universal consensus, but also draws more authentically on the capacities for debate, contestation, and exchange inherent in the educational institution. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been widely explored across the world over the past decades. A particularly emerging topic is the application of AI in e-learning (AIeL) to improve the effectiveness of teaching and learning in precision education. This study aims to systematically review publication patterns for AIeL research with a focus on leading journals, countries, disciplines, and applications. In addition, a co-citation network analysis was conducted to explore the invisible relationships among the core papers of AIeL to reveal directions for future research. The analysis is based on a total of 86 core AIeL papers accompanied by 1149 citations in follow-up studies obtained from the Web of Science. It was found that a majority of AIeL studies focused on the development and applications of intelligent tutoring systems, followed by using AI to facilitate assessment and evaluation in e-learning contexts. For field researchers, the visualized network diagram serves as a map to explore the invisible relationships among the core AIeL research, providing a structural understanding of AI-supported research in e-learning contexts. A further investigation of the follow-up studies behind the highly co-cited links revealed the extended research directions from the AIeL mainstreams, such as adaptive learning-based evaluation environments. Implications are discussed. [ABSTRACT FROM AUTHOR]

The paper argues that the material scope of AI regulations should not rely on the term 'artificial intelligence (AI)'. The argument is developed by proposing a number of requirements for legal definitions, surveying existing AI definitions, and then discussing the extent to which they meet the proposed requirements. It is shown that existing definitions of AI do not meet the most important requirements for legal definitions. Next, the paper argues that a risk-based approach would be preferable. Rather than using the term AI, policy makers should focus on the specific risks they want to reduce. It is shown that the requirements for legal definitions can be better met by defining the main sources of relevant risks: certain technical approaches (e.g. reinforcement learning), applications (e.g. facial recognition), and capabilities (e.g. the ability to physically interact with the environment). Finally, the paper discusses the extent to which this approach can also be applied to more advanced AI systems. [ABSTRACT FROM AUTHOR]

This paper aimed to evaluate the research trends in the discipline of library and information science (LIS) originating from the group of nations known as the BRICS (Brazil, Russia, India, China, and South Africa). The Web of Science core collection database (WoSCC) has been used to extract the published data during the period 1989–2020. Furthermore, VoSviewer and Bibliometrix R package software have been employed for bibliometric analysis of data and data visualization. Results from data analysis indicate that China has the highest number of publications. There is a positive upward trend in scientific publications and citation patterns. Scientometrics is the most influential journal with the highest number of publications. Wuhan University is the most productive institution. Fourie I (n = 233) attains the first rank as the most prolific author based on the highest number of research publications. The overall collaboration rate of LIS publications among BRICS countries is moderate. Deep learning, machine learning, sentiment analysis, altmetrics, and artificial intelligence are the leading topics in LIS research among BRICS nations. The study will be of interest or helpful for researchers and professionals in the LIS research field who are interested in learning more about research trends in LIS in the BRICS nations. [ABSTRACT FROM AUTHOR]

In Aion, C. G. Jung demonstrated an important benefit of viewing a continual process of unfolding that does so over vast periods of time, namely the unfolding of the Self during the last two millennia. Careful attention to such time periods can reveal patterns and connections that might otherwise have gone unnoticed. Alternatively, it can deepen one's experience of a pattern that is already known but perhaps not as highly valued in a given tradition. It is the latter that this paper intends to address—AI (Artificial Intelligence) and the pattern of increasing abstraction. Crucially, the advent of AI has long been prepared for. It emerges in Western culture, whose history displays the critical pattern of rapidly elevating abstraction, a pattern that has only partially been the result of human effort and intention. It is as if the unconscious itself had not only condoned this pattern but was in fact its author. It is the achievement of a certain level of abstraction that makes AI possible. If, indeed, the unconscious is the author of this pattern, then it is plausible that there is deeper purpose in AI's manifestation at this time. The cultures of the past have had to, in their unique ways, endure hope and fear and take responsibility for their work on artificial life. This responsibility now falls on us, and it is critical that we understand artificial life in the modern form of AI and its deeper purpose in the 21st century. [ABSTRACT FROM AUTHOR]

Recently, ionic liquids as a new group of solvent have successfully showed high capacity for applications in chemical engineering industries because of their desirable properties so knowledge of their properties such refractive indices of the binary system of alcohols and ionic liquids have high importance. This paper presents predictive tool based Least Squares Support Vector Machine optimized by Particle swarm optimization to determined refractive index of this binary system. In order to prepare the mentioned algorithm, a comprehensive databank contains 688 experimental refractive indices of the binary system of different alcohols and ionic liquids is used. The developed method shows satisfying results based on statistical and graphical comparisons. Furthermore the effects of different inputs on refractive index term has been investigated by a novel sensitivity analysis. [ABSTRACT FROM AUTHOR]

The ability to create art and advance science are distinguishing features of the human species. Currently, these fields are rarely interconnected. This publication presents liquid crystal textures in terms of their aesthetics. This manuscript covers issues from various fields of science and culture: physics, artificial intelligence, and art. The presented publications focuses on the textures of the following phases: nematic, smectic A, smectic B, smectic C, smectic B, smectic I, smectic F, smectic G, smectic H, ferroelectic smectic C and antiferroelectric smectic C. Images of these textures can serve as inspiration for creating new artwork using neural networks. Neural network algorithms like style transfer and DeepDream modified selected liquid crystal textures. The paper discusses the potential applications of the images obtained this way. [ABSTRACT FROM AUTHOR]

This special issue highlights state-of-the-art research in intelligent systems and technology for people with diverse abilities. To control scope, we pay particular attention to cognitive diversity, including but not limited to, neurodevelopmental disorders and autism, cognitive and learningdisabilities, and dementia. We introduce the papers in this special issue by contextualizing them according to different research areas. By curating leading-edge contributions in this area, we aim to raise awareness about research challenges and requirements inherent in the development and assessment of systems designed for these categories of users. [ABSTRACT FROM AUTHOR]

The paper examines the knowledge and practices of catalogers at the City of Cape Town Metropolitan Public Libraries (CCTML). The study aims to understand the extent to which catalogers understand current and emerging cataloging practices and to make recommendations for improving practice. The extent of access and the use of library resources depends greatly upon the quality of the library catalog. The following research questions were formulated: what cataloging knowledge do catalogers of CCTML have? How is cataloging carried out in CCTML? What challenges do catalogers of CCTML experience in cataloging library resources? Qualitative approach and case study design were employed. The population of the study was made of 6 catalogers. Data was collected through the focus group interview. Findings indicated that catalogers had knowledge of cataloging standards. They downloaded the catalog records from the OCLC although, they also created original catalog records in case they did not find the matching manifestations on OCLC. Moreover, they experienced challenges of assigning subject headings for non-roman scripts, allocation of the correct classification notation and the advanced use of the cataloging standards. The study recommends the exchange programmes in cataloguing, use of technological tools, artificial intelligence in cataloging. [ABSTRACT FROM AUTHOR]

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research. [ABSTRACT FROM AUTHOR]

The paper introduces partial set-theoretic relations for metasets and investigates their properties. Metaset is a concept of imprecise set designed to represent vague notions in Artificial Intelligence, whose idea stems from Boolean-valued techniques in classical set theory. The basic set relations are extended to functions valued in a Boolean algebra or unit interval. Important classical set theory axioms: extensionality and comprehension are formulated for metasets. The latter enables formal definitions of collections with blurred boundaries by using set-theory formulae. This facilitates representing and reasoning about imprecise data. [ABSTRACT FROM AUTHOR]

Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers' like SCIP capability. [ABSTRACT FROM AUTHOR]

The concept of personhood has been central to bioethics debates about abortion, the treatment of patients in a vegetative or minimally conscious states, as well as patients with advanced dementia. More recently, the concept has been employed to think about new questions related to human-brain organoids, artificial intelligence, uploaded minds, human-animal chimeras, and human embryos, to name a few. A common move has been to ask what these entities have in common with persons (in the normative sense), and then draw conclusions about what we do (or do not) owe them. This paper argues that at best the concept of "personhood" is unhelpful to much of bioethics today and at worst it is harmful and pernicious. I suggest that we (bioethicists) stop using the concept of personhood and instead ask normative questions more directly (e.g., how ought we to treat this being and why?) and use other philosophical concepts (e.g., interests, sentience, recognition respect) to help us answer them. It is time for bioethics to end talk about personhood. [ABSTRACT FROM AUTHOR]

Most manufactured electrical cables suffer from reductions in their physical, mechanical and electrical properties. These setbacks are mainly attributed to the improper control of wire tension during the cable manufacturing process. Hence, this paper systematically reviewed different control algorithms involved in controlling tension in moving webs, which include conventional control, advanced control, observer-based control, artificial intelligence-based control and hybrid control techniques. Thus, the review provided information about existing tension control techniques in moving webs, including their strengths and weaknesses. It was observed in this review that although a significant research effort has been made on web tension control systems, a thorough literature review is still lacking. It was concluded that controller optimisation using hybrid control algorithms is gaining popularity in web tension control due to their improved control response. Hence, its application in wire tension control can better help cable manufacturers improve the quality of manufactured cables. [ABSTRACT FROM AUTHOR]

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It's even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. [ABSTRACT FROM AUTHOR]