Search

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]

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]

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]

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]

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]

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]

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]

AbstractHorizon scanning is a process to generate foresight – insight into how the future could unfold. Horizon scanning involves systematically searching a wide range of information sources for early signals of change, collecting those signals in a database, and exploring their possible implications for the future. The focus is often on external change in the broad contextual environment of a field or organization, i.e., the social, economic, technological, and governance domains. External change could be highly disruptive and is unlikely to be on the radar of decision makers. Early awareness of changes and monitoring their development over time can help natural resource planners, managers, and policymakers prepare for emerging challenges and opportunities. This paper introduces horizon scanning, characterizes different approaches and techniques, outlines the basic process, and briefly describes artificial intelligence in horizon scanning. [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]

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]

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]

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]

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]

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 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]

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]

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]

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]

AbstractWith the increasing application of Natural Language Processing (NLP) in Medicine at large, medical educators are urged to gain an understanding and implement NLP techniques within their own education programs to improve the workflow and make significant and rapid improvements in their programs. This paper aims to provide twelve essential tips inclusive of both conceptual and technical factors to facilitate the successful integration of NLP in medical education program evaluation. These twelve tips range from advising on various stages of planning the evaluation process, considerations for data collection, and reflections on preprocessing of data in preparation for analysis and interpretation of results. Using these twelve tips as a framework, medical researchers, educators, and administrators will have an understanding and reference to navigating applications of NLP and be able to unlock its potential for enhancing the evaluation of their own medical education programs. [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 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]

The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography. [ABSTRACT FROM AUTHOR]

AbstractAdvances in Artificial Intelligence (AI) have led to AI systems’ being used increasingly in medical education research. Current methods of reporting on the research, however, tend to follow patterns of describing an intervention and reporting on results, with little description of the AI in the system, or the many concerns about the use of AI. In essence, the readers do not actually know anything about the system itself. This paper proposes a checklist for reporting on AI systems, and covers the initial protocols and scoping, modelling and code, algorithm design, training data, testing and validation, usage, comparisons, real-world requirements, results and limitations, and ethical considerations. The aim is to have a systematic reporting process so that readers can have a comprehensive understanding of the AI system that was used in the research. [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]

This research paper includes two different topologies for the operation of both grounded and floating active inductor (AI) realization. Both structures use a single Z-Copy Voltage Differencing Current Conveyor (ZC-VDCC) and an external grounded capacitor (C). The operational behavior of the proposed design is free from the component matching constraint. In addition, an application workability of the AI is tested for filter frequency response that comprises band pass filter (BPF) and band rejection filter (BRF) using grounded as well as floating active inductor, respectively. The performance test of the proposed active inductor is successfully validated in SPICE computer simulation using 0.18 µm TSMC parameter for ZC-VDCC. Moreover, an experimental verification is also performed using easily available Current Feedback Operational Amplifier (CFOA/IC-AD844) and Operational Transconductance Amplifier (OTA/IC-CA3080) for the implementation of ZC-VDCC. Both simulation and experimental observation are well verified to justify the theoretical concept behind the proposed AI. [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]

The rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity. [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]

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 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]

The concepts of means of warfare and combatants are not comparable or on the same scale in IHL. Yet the human-like performances of AI technologies, such as independent decision-making, may blur the line between these two concepts. This may also lead one to compare the technology with a human combatant rather than with other means of warfare. In this context, this paper questions the attributability of combatant status to military AI technologies by concentrating on the scope of the combatant concept. Contrary to some existing studies that found combatant status insufficient for machines based on ethics or behavioural human-machine differences, this study examines why combatant status is unsuitable for military AI technologies from a legal conceptual perspective, even in their most intelligent and independent forms by visiting terms—membership to armed forces, armed forces and prisoners of war (POW)—that are relevant to disclose the scope of the term combatant. [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]

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]

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 role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

The article discusses a paper on the need for a checklist for artificial intelligence (AI) research in medical education. Topics covered include its importance in ensuring transparency, credibility, and repeatability in research, and the value of the paper's content to readers. Also noted are key points in the paper that would benefit from discussion and clarification.

This document, titled "AI Fairness in Action: A Human-Computer Perspective on AI Fairness in Organizations and Society," discusses the increasing use of artificial intelligence (AI) systems in decision-making processes within organizations and society. The authors highlight the importance of fairness in AI decision-making and the negative consequences of unfair practices. They argue that while technical improvements to AI models are commonly pursued to enhance fairness, there is a need to also consider the subjective nature of fairness perceptions. The document introduces several papers that explore different aspects of AI fairness, including perceptions of fairness, biases in AI system creators, counterfactual thinking in ethical dilemmas, perceptions of advice seekers, socio-algorithmic construction of fairness, and procedural justice in online platforms. The authors conclude that AI fairness is influenced by the interaction between AI and human end-users, and organizations and societies must approach AI adoption and implementation in a way that is perceived as fair. [Extracted from the article]

AbstractComposition instructors are currently contending with a new technology that seems to threaten the foundation of teaching writing: ChatGPT, which arrived on the digital scene in November 2022, is an A.I. bot that produces writing that closely resembles human-authored works. Not surprisingly, writing teachers have been expressing concerns about plagiarism and even an eventual obsolescence of writing-intensive classes. While this sentiment of fear is grounded in the proof that students are already eagerly using this technology to write their papers, I argue that this technology concurrently offers teachers a golden opportunity to teach rhetorical analysis of the texts it produces. In a class experiment, my students discovered that ChatGPT struggles to establish connections to audience through shared experience. ChatGPT can only convey the impression of personal experience from the text it outsources. Through analysis, we can invite our students to trace the rhetorical dimensions of the texts this bot produces and re-emphasize the role of lived experience, personal examples, and real-life proof in forming connections to audience. [ABSTRACT FROM AUTHOR]