Search Results (55)

With an anticipated 3.6 million Americans who will be living with limb loss by 2050, the demand for active assistive devices is rapidly increasing. This study investigates the feasibility of leveraging a ChatGPT-based (Version 4o) model to predict motion based on input electroencephalogram (EEG) signals, enabling the non-invasive control of active assistive devices. To achieve this goal, three objectives were set. First, the model’s capability to derive accurate mathematical relationships from numerical datasets was validated to establish a foundational level of computational accuracy. Next, synchronized arm motion videos and EEG signals were introduced, which allowed the model to filter, normalize, and classify EEG data in relation to distinct text-based arm motions. Finally, the integration of marker-based motion capture data provided motion information, which is essential for inverse kinematics applications in robotic control. The combined findings highlight the potential of ChatGPT-generated machine learning systems to effectively correlate multimodal data streams and serve as a robust foundation for the intuitive, non-invasive control of assistive technologies using EEG signals. Future work will focus on applying the model to real-time control applications while expanding the dataset’s diversity to enhance the accuracy and performance of the model, with the ultimate aim of improving the independence and quality of life of individuals who rely on active assistive devices. Full article

This study investigates the integration of ChatGPT in Philippine higher education institutions (HEIs) through narrative inquiry, employing Clandinin and Connelly’s three-dimensional framework (temporality, sociality, place) to explore the lived experiences of 18 participants (10 students, 8 faculty). The research identifies three global themes: (1) the need for strong ethical guidelines amid widespread but tacit “silent acceptance” of AI use, (2) faculty efforts to adapt traditional pedagogy while addressing concerns about critical thinking erosion, and (3) strategies to optimize ChatGPT’s utility without exacerbating inequities. Participant narratives reveal divergent adoption patterns: urban stakeholders leverage ChatGPT for efficiency and learning augmentation, while rural counterparts face infrastructural barriers that deepen the urban–rural divide. Students report evolving ethical engagement, from initial dependency to reflective use, whereas faculty grapple with academic integrity and assessment redesign. The findings underscore how cultural resistance, institutional policy gaps, and technological disparities shape ChatGPT’s uneven adoption, reinforcing existing educational inequalities. This study contributes to the literature on AI in education by proposing context-sensitive strategies for equitable integration, including offline AI tools for rural areas, faculty training programs, and transparent policy frameworks. By centering stakeholder narratives, the research advocates for culturally grounded AI adoption that balances innovation with pedagogical integrity, offering a model for Global South contexts facing similar challenges. Full article

This study uses generative AI to investigate the influence of building façade geometry on human physiological and psychological health. Employing Christopher Alexander’s fifteen fundamental properties of living geometry and a set of ten emotional descriptors {beauty, calmness, coherence, comfort, empathy, intimacy, reassurance, relaxation, visual pleasure, well-being} in separate tests, ChatGPT 4.5 evaluates simple, contrasting window designs. AI analyses strongly and consistently prefer traditional window geometries, characterized by symmetrical arrangements and coherent visual structure, over fragmented or minimalist–modernist alternatives. These results suggest human cognitive–emotional responses to architectural forms are hardwired through evolution, privileging specific geometric patterns. Finally, ChatGPT o3 formulates ten detailed geometric rules for empathetic window design and composition. It then applies these criteria to select contemporary window typologies that generate the highest anxiety. The seven most anxiety-inducing designs are the most favored today worldwide. The findings challenge contemporary architectural preferences and standard window archetypes by emphasizing the significance of empathetic and health-promoting façade designs. Given the general suspicion among many readers of the frequently manipulative and unreliable use of AI, its use in this experiment is not to validate design decisions directly, which would put into question what the AI is trained with, but to prove a correlation between two established methodologies for evaluating a design. AI is used as an analytical tool to show that Alexander’s geometric rules (the guidelines proposed beforehand) closely match emotional reactions (the desirable outcomes observed afterward). This novel use of AI suggests integrating neurodesign principles into architectural education and practice to prioritize urban vitality through psychological well-being. Full article

Generative AI leads to designs that prioritize cognition, emotional resonance, and health, thus offering a tested alternative to current trends. In a first AI experiment, the large language model ChatGPT-4o generated six visual environments that are expected to boost creative thinking for their occupants. The six test cases are evaluated using Christopher Alexander’s 15 fundamental properties of living geometry as criteria, as well as ChatGPT-4o, to reveal a strong positive correlation. Living geometry is a specific type of geometry that shows coherence across scales, fractal structure, and nested symmetries to harmonize with human neurophysiology. The human need for living geometry is supported by interdisciplinary evidence from biology, environmental psychology, and neuroscience. Then, in a second AI experiment, ChatGPT-4o was asked to generate visual environments that suppress creativity for comparison with the cases that boost creative thinking. Checking these negative examples using Alexander’s 15 fundamental properties, they are almost entirely deficient in living geometry, thus confirming the diagnostic model. Used together with generative AI, living geometry therefore offers a useful method for both creating and evaluating designs based on objective criteria. Adopting a hybrid epistemological framework of AI plus living geometry as a basis for design uncovers a flaw within contemporary architectural practice. Dominant design styles, rooted in untested aesthetic preferences, lack the empirical validation required to address fundamental questions of spatial quality responsible for human creativity. Full article

Beautimeter is a new tool powered by generative pre-trained transformer (GPT) technology, designed to evaluate architectural and urban beauty. Rooted in Christopher Alexander’s theory of centers, this work builds on the idea that all environments possess, to varying degrees, an innate sense of life. Alexander identified 15 fundamental properties, such as levels of scale and thick boundaries, that characterize living structure, which Beautimeter uses as a basis for its analysis. By integrating GPT’s advanced natural language processing capabilities, Beautimeter assesses the extent to which a structure embodies these 15 properties, enabling a nuanced evaluation of architectural and urban aesthetics. Using ChatGPT4o, the tool helps users generate insights into the perceived beauty and coherence of spaces. We conducted a series of case studies, evaluating images of architectural and urban environments, as well as carpets, paintings, and other artifacts. The results demonstrate Beautimeter’s effectiveness in analyzing aesthetic qualities across diverse contexts. Our findings suggest that by leveraging GPT technology, Beautimeter offers architects, urban planners, and designers a powerful tool to create spaces that resonate deeply with people. This paper also explores the implications of such technology for architecture and urban design, highlighting its potential to enhance both the design process and the assessment of built environments. Full article

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend into regular network activity, which makes detection by automated security systems challenging. The study seeks to determine whether LLMs can reliably generate effective queries for security tools, enabling organisations with limited budgets and expertise to conduct threat hunting. A testing environment was created to simulate LotL techniques, and LLM-generated queries were used to identify malicious activity. The results demonstrate that LLMs do not consistently produce accurate or reliable queries for detecting these techniques, particularly for users with varying skill levels. However, while LLMs may not be suitable as standalone tools for threat hunting, they can still serve as supportive resources within a broader security strategy. These findings suggest that, although LLMs offer potential, they should not be relied upon for accurate results in threat detection and require further refinement to be effectively integrated into cybersecurity workflows. Full article

PaxosStore is a high-availability storage system developed to support the comprehensive business of WeChat. With the widespread application of WeChat, it is particularly important to verify the safety of PaxosStore. This work proposes a formal model for the storage system PaxosStore using the process algebra Communicating Sequential Processes (CSP) to clearly reflect the interactions of the components in PaxosStore. More importantly, we utilize the model checker Process Analysis Toolkit (PAT) to simulate and verify the constructed CSP model. We specifically verify the validity of six properties: deadlock-freeness, divergence-freeness, robustness, consistency, nontriviality and liveness. Through the verification results, we demonstrate that our formalization model successfully satisfies these properties, confirming the correctness and effectiveness of the framework in ensuring secure interactions among the PaxosStore storage system components. Full article

The emergence of generative artificial intelligence (GAI) has revolutionized numerous aspects of our lives and presents significant opportunities in education. However, specific digital competencies are essential to effectively leverage this technology’s potential. Notably, prompt engineering proficiency presents a significant barrier to achieving optimal outcomes. In response, various solutions are being developed, including custom GPTs available through OpenAI’s ChatGPT platform. This study validates ‘GamifIcA Edu’, a specialized GPT-based assistant for gamification and serious games, designed to enable educators to implement these pedagogical approaches without requiring advanced prompt engineering expertise. This is achieved through the utilization of pre-designed instructional frameworks. The assistant’s effectiveness was evaluated using a comprehensive rubric across five distinct use-case scenarios. Each scenario underwent four different tests, representing varied learning contexts across multiple academic disciplines. The validation methodology involved a systematic assessment of the assistant’s performance in diverse educational settings. The findings demonstrate the successful implementation of this custom-designed GPT, which generated contextually appropriate responses through natural language interactions, thus eliminating the need for complex prompt structures. This research highlights the potential of instruction-based design in the development of AI assistants that empower users with limited prompt engineering knowledge to achieve expert-level results. These findings have significant implications for the democratization of AI-enhanced educational tools. Full article

Nowadays, technology allows people to connect and communicate with each other even from miles away, no matter the distance. With the increased use of social networks that were rapidly adopted in human beings’ lives, they can chat and share different media files. While the intent for which they have been created may be positive, they can be abused and utilized in a negative way. One form in which they can be maliciously used is represented by cyberbullying. This is a form of bullying where an aggressor shares, posts, or sends false, harmful, or negative content about someone else by electronic means. In this paper, we propose a solution for bullying detection in GIFs. We employ a hybrid architecture that comprises a Convolutional Neural Network (CNN) and three Recurrent Neural Networks (RNNs). For the feature extractor, we used the DenseNet-121 model that was pre-trained on the ImageNet-1k dataset. The obtained results give an accuracy of 99%. Full article

Introduction: The corpus callosum is one of the five main cerebral commissures. It is key to combining sensory and motor functions. Its structure can be pathological (dysgenesis) or completely absent (agenesis). The corpus callosum dys- or agenesis is a rare disease (1:4000 live births), but it can have serious mental effects. Methods: In our study, we processed the data of 64 pregnant women. They attended a prenatal diagnostic center and genetic counseling from 2005 to 2019 at the Department of Obstetrics and Gynecology at Semmelweis University. Results: The pregnancies had the following outcomes: 52 ended in delivery, 1 in spontaneous abortion, and 11 in termination of pregnancy (TOP) cases (n = 64). The average time of detection with imaging tests was 25.24 gestational weeks. In 16 cases, prenatal magnetic resonance imaging (MRI) was performed. If the abnormality was detected before the 20th week, a genetic test was performed on an amniotic fluid sample obtained from a genetic amniocentesis. Karyotyping and cytogenetic tests were performed in 15 of the investigated cases. Karyotyping gave normal results in three cases (46,XX or XY). In one of these cases, postnatally chromosomal microarray (CMA) was later performed, which confirmed Aicardi syndrome (3q21.3–21.1 microdeletion). In one case, postnatally, the test found Wiedemann–Rautenstrauch syndrome. In other cases, it found X ring, Di George syndrome, 46,XY,del(13q)(q13q22) and 46,XX,del(5p)(p13) (Cri-du-chat syndrome). Edwards syndrome was diagnosed in six cases, and Patau syndrome in one case. Conclusions: We found that corpus callosum abnormalities are often linked to chromosomal problems. We recommend that a cytogenetic test be performed in all cases to rule out inherited diseases. Also, the long-term outcome does not just depend on the disease’s severity and the associated other conditions, and hence proper follow-up and early development are also key. For this reason, close teamwork between neonatology, developmental neurology, and pediatric surgery is vital. Full article

Family caregivers are vital to enabling people with dementia to live longer in their own homes. For these caregivers, chatting with clinicians—being listened to empathetically and receiving reassurance—can be seen as not incidental but important to supporting them. This paper considers and identifies the significance of this relational work for family carers by re-examining data originally collected to document caregivers’ perspectives on quality in crisis response teams. This reveals that chatting, for family caregivers, comprises three related features: (i) that family caregivers by responding to a person’s changing and sometimes challenging needs and behaviors inhabit a precarious equilibrium; (ii) that caregivers greatly appreciate ‘chatting’ with visiting clinicians; and (iii) that while caregivers appreciate these chats, they can be highly critical of the institutionalized character of a crisis response team’s involvement with them. Full article

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk. Full article

This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane’s architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community. Full article

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development. Full article

Introduction: The designer benzodiazepine (DBZD) market continues to expand whilst evading regulatory controls. The widespread adoption of social media by pro-drug use communities encourages positive discussions around DBZD use/misuse, driving demand. This research addresses the evolution of three popular DBZDs, etizolam (E), flubromazepam (F), and pyrazolam (P), available on the drug market for over a decade, comparing the quantitative chemical analyses of tablet samples, purchased from the internet prior to the implementation of the Psychoactive Substances Act UK 2016, with the thematic netnographic analyses of social media content. Method: Drug samples were purchased from the internet in early 2016. The characterisation of all drug batches were performed using UHPLC-MS and supported with ¹H NMR. In addition, netnographic studies across the platforms X (formerly Twitter) and Reddit, between 2016–2023, were conducted. The latter was supported by both manual and artificial intelligence (AI)-driven thematic analyses, using numerous.ai and ChatGPT, of social media threads and discussions. Results: UHPLC-MS confirmed the expected drug in every sample, showing remarkable inter/intra batch variability across all batches (E = 13.8 ± 0.6 to 24.7 ± 0.9 mg; F = 4.0 ± 0.2 to 23.5 ± 0.8 mg; P = 5.2 ± 0.2 to 11.5 ± 0.4 mg). ¹H NMR could not confirm etizolam as a lone compound in any etizolam batch. Thematic analyses showed etizolam dominated social media discussions (59% of all posts), with 24.2% of posts involving sale/purchase and 17.8% detailing new administration trends/poly-drug use scenarios. Artificial intelligence confirmed three of the top five trends identified manually. Conclusions: Purity variability identified across all tested samples emphasises the increased potential health risks associated with DBZD consumption. We propose the global DBZD market is exacerbated by surface web social media discussions, recorded across X and Reddit. Despite the appearance of newer analogues, these three DBZDs remain prevalent and popularised. Reporting themes on harm/effects and new developments in poly-drug use trends, demand for DBZDs continues to grow, despite their potent nature and potential risk to life. It is proposed that greater controls and constant live monitoring of social media user content is warranted to drive active regulation strategies and targeted, effective, harm reduction strategies. Full article