Hasil untuk "Machine design and drawing"

Natalie Shapira, Mosh Levy, S. Alavi et al.

The escalating debate on AI’s capabilities warrants developing reliable metrics to assess machine “intelligence.” Recently, many anecdotal examples were used to suggest that newer Large Language Models (LLMs) like ChatGPT and GPT-4 exhibit Neural Theory-of-Mind (N-ToM); however, prior work reached conflicting conclusions regarding those abilities. We investigate the extent of LLMs’ N-ToM through an extensive evaluation of 6 tasks and find that while LLMs exhibit certain N-ToM abilities, this behavior is far from being robust. We further examine the factors impacting performance on N-ToM tasks and discover that LLMs struggle with adversarial examples, indicating reliance on shallow heuristics rather than robust ToM abilities. We caution against drawing conclusions from anecdotal examples, limited benchmark testing, and using human-designed psychological tests to evaluate models.

Guorui Li, Tuck-Whye Wong, Benjamin Shih et al.

The deep ocean, Earth’s untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines. This perspective addresses challenges, recent strides, and design strategies for bioinspired deep-sea soft robots. Drawing from abyssal life, it explores the actuation, sensing, power, and pressure resilience of multifunctional deep-sea soft robots, offering game-changing solutions for profound exploration and operation in harsh conditions.

M. K. K. Rony, Mst. Rina Parvin, Md. Wahiduzzaman et al.

Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers’ concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers’ concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient–provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient–provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.

P. Helm, G. Bella, Gertraud Koch et al.

It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought to address the “digital language divide” by extending the reach of large language models to “underserved languages.” We show how some of these efforts tend to produce flawed solutions that adhere to a hard-wired representational preference for certain languages, which we call language modeling bias. Language modeling bias is a specific and under-studied form of linguistic bias were language technology by design favors certain languages, dialects, or sociolects with respect to others. We show that language modeling bias can result in systems that, while being precise regarding languages and cultures of dominant powers, are limited in the expression of socio-culturally relevant notions of other communities. We further argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader ethico-political implications and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs of marginalized language communities. Finally, we present an alternative socio-technical approach that is designed to tackle some of the analyzed problems.

Makoto Jinno, Daisuke Kawasaki, Ryosuke Nonoyama

Abstract Polymerase chain reaction (PCR) testing was widely used for diagnosing infectious diseases, particularly during the novel coronavirus disease (COVID-19) pandemic. The automation of this process has been limited by a severe lack of compact microtube cappers/decappers that accommodate a wide array of microtubes. To address this issue, we developed an automatic microtube capper/decapper (AMC/D) system, which we call the single-AMC/D (S-AMC/D) because it handles individual tubes one at a time. Subsequently, recognizing that microtube handling is a fundamental operation not only in PCR testing but also in various clinical examinations and biological experiments, and considering the strong demands of clinical laboratory and biological laboratory personnel, we developed a manually operated microtube equipped with the AMC/D. We also identified a significant demand among laboratory automation system developers and automatic pipetting equipment manufacturers for multistation AMC/Ds capable of handling numerous microtube caps simultaneously to increase throughput. In this study, we propose a multistation automatic microtube capper/decapper (M-AMC/D) with improved throughput for automating clinical experiments and bioexperiments that use microtubes. We adopted two design concepts to develop this M-AMC/D: According to the first one, eight microtubes can be opened and closed at one process. According to the second one, eight microtubes can be driven by staggering the timings of their opening and closing operations so that the loads generated by the torques of multiple opening and closing operations are not applied to the drive system or structural system at the same time. Based on the design concepts, we performed the basic design of an eight-station AMC/D (8-AMC/D) with a new driving mechanism using cams and cam followers. The installation space (249 mm × 93 mm) of this system is approximately 35% of the installation space required for the eight S-AMC/Ds (124 mm × 78 mm × 8 units). Finally, we developed a prototype of the proposed M-AMC/D and conducted a function confirmation experiment and a verification experiment to improve the throughput. The experimental results of throughput improvement showed that the operation time of the 8-AMC/D was 110.5 s—a significant reduction of 343.3 s (or 75.7%) from the S-AMC/D operation time of 453.9 s. Thus, the effectiveness of the proposed 8-AMC/D was verified. The proposed device enables simultaneous handling, opening, and closing of eight microtubes in a compact and efficient configuration. Although initially motivated by the urgent demand for PCR testing during the pandemic, the proposed system addresses a fundamental challenge in laboratory automation involving press-type microtubes and is applicable to a broad range of laboratory procedures involving microtubes.

Haoxiang Xue, Gabriele Fichera, Massimiliano Gobbi et al.

Evaluating noise, vibration, and harshness (NVH) performance is crucial in vehicle development. However, NVH evaluation is often subjective and challenging to achieve through numerical simulation, and typically prototypes are required. Dynamic driving simulators are emerging as a viable solution for assessing NVH performance in the early development phase before physical prototypes are available. However, most current simulators can reproduce vibrations only in a single direction or within a limited frequency range. This paper presents a comprehensive design optimization approach to enhance the dynamic response of a full-spectrum driving simulator, addressing these limitations. Specifically, in complex driving simulators, vibration crosstalk is a critical and common issue, which usually leads to an inaccurate dynamic response of the system, compromising the realism of the driving experience. Vibration crosstalk manifests as undesired vibration components in directions other than the main excitation direction due to structural coupling. To limit the system crosstalk, a flexible multibody dynamics model of the driving simulator has been developed, validated, and employed for a global sensitivity analysis. From this analysis, it turns out that the bushings located below the seat play a crucial role in the crosstalk characteristics of the system and can be effectively optimized to obtain the desired performances. Bushings’ stiffness and locations have been used as design variables in a multiobjective optimization with the aims of increasing the direct transmissibility of the actuators’ excitation and, at the same time, reducing the crosstalk contributions. A surrogate model approach is employed for reducing the computational cost of the process. The results show substantial crosstalk reduction, up to 57%. The proposed method can be effectively applied to improve the dynamic response of driving simulators allowing for their extensive use in the assessment of vehicles’ NVH performances.

Erwin de Gelder, Tajinder Singh, Fouad Hadj-Selem et al.

Ensuring the safety of Automated Driving Systems (ADSs) requires structured and transparent validation processes. Scenario-based testing has emerged as a widely adopted approach, enabling the targeted assessment of system behavior under diverse and challenging conditions. To offer a structured approach for scenario-based safety assurance, the European SUNRISE project developed the Safety Assurance Framework (SAF), which comprises stages such as scenario creation, allocation, execution, evaluation, decision-making, and in-service monitoring and reporting. Central to the SAF are scenario metrics, which quantify aspects such as coverage, criticality, and complexity and support evidence for safety cases. This paper provides a comprehensive overview of scenario-based scenario metrics relevant to ADS safety assessments. We categorize six core metric types: completeness, coverage, criticality, diversity/dissimilarity, exposure, and complexity. We explain their roles across the difference SAF components. This paper also discusses interdependencies among metrics, implementation challenges, and gaps where further research is needed, particularly in metric validation, aggregation, and standardization. By clarifying the landscape of scenario metrics and their application within the SAF, this work aims to support both practitioners and researchers in advancing scalable, data-driven safety assurance for ADSs.

Mohammed Mahdi Abdulla, Seraj Alzhrani, Khalid Juhany et al.

This work investigates how the vehicle-to-tube suspension gap governs compressible flow physics and operating margins in Hyperloop-class transport at 10 kPa. To our knowledge, this is the first study to apply adjoint aerodynamic optimization to mitigate gap-induced choking and shock formation in a full pod–tube configuration. Using a steady, pressure-based Reynolds-averaged Navier-Stokes (RANS) framework with the GEnerlaized K-Omega (GEKO) turbulence model, a simulation for the cruise conditions was performed at M = 0.5–0.7 with a mesh-verified analysis (medium grid within 0.59% of fine) to quantify gap effects on forces and wave propagation. For small gaps, the baseline pod triggers oblique shocks and a near-Kantrowitz condition with elevated drag and lift. An adjoint shape update—primarily refining the aft geometry under a thrust-equilibrium constraint—achieves 27.5% drag reduction, delays the onset of choking by ~70%, and reduces the critical gap from <i>d</i>/<i>D</i> ≈ 0.025 to ≈0.008 at M = 0.7. The optimized configuration restores a largely subcritical passage, suppressing normal-shock formation and improving gap tolerance. Because propulsive power at fixed cruise scales with drag, these aerodynamic gains directly translate into operating-power reductions while enabling smaller gaps that can relax tube-diameter and suspension mass requirements. The results provide a gap-aware optimization pathway for Hyperloop pods and a compact design rule-of-thumb to avoid choking while minimizing power.

Ruizhe Chen, Dongyu Xue, Xiangxin Zhou et al.

Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm.

Maxence Bouvier, Ryan Amaudruz, Felix Arnold et al.

As AI workloads proliferate, optimizing arithmetic units is becoming increasingly important for reducing the footprint of digital systems. Conventional design flows, which often rely on manual or heuristic-based optimization, are limited in their ability to thoroughly explore the vast design space. In this paper, we introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units, with a focus on multipliers. At the core of GENIAL is a Transformer-based surrogate model trained in two stages, involving self-supervised pretraining followed by supervised finetuning, to robustly forecast key hardware metrics such as power and area from abstracted design representations. By inverting the surrogate model, GENIAL efficiently searches for new operand encodings that directly minimize power consumption in arithmetic units for specific input data distributions. Extensive experiments on large datasets demonstrate that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs. This enables deployment of a high-effort logic synthesis optimization flow in the loop, improving the accuracy of the surrogate model. Notably, GENIAL automatically discovers encodings that achieve up to 18% switching activity savings within multipliers on representative AI workloads compared with the conventional two's complement. We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines, highlighting GENIAL's applicability for a wide spectrum of logic functions. Together, these advances mark a significant step toward automated Quality-of-Results-optimized combinational circuit generation for digital systems.

Aditya Gunturu, Yi Wen, Nandi Zhang et al.

We introduce Augmented Physics, a machine learning-integrated authoring tool designed for creating embedded interactive physics simulations from static textbook diagrams. Leveraging recent advancements in computer vision, such as Segment Anything and Multi-modal LLMs, our web-based system enables users to semi-automatically extract diagrams from physics textbooks and generate interactive simulations based on the extracted content. These interactive diagrams are seamlessly integrated into scanned textbook pages, facilitating interactive and personalized learning experiences across various physics concepts, such as optics, circuits, and kinematics. Drawing from an elicitation study with seven physics instructors, we explore four key augmentation strategies: 1) augmented experiments, 2) animated diagrams, 3) bi-directional binding, and 4) parameter visualization. We evaluate our system through technical evaluation, a usability study (N=12), and expert interviews (N=12). Study findings suggest that our system can facilitate more engaging and personalized learning experiences in physics education.

Emmanuel Senior Tenakwah, C. Watson

Purpose This paper aims to highlight the crucial role of strategic human resource management and leadership in preparing workforces for the artificial intelligence (AI) and automation age. Design/methodology/approach The paper adopts a conceptual approach, reviewing existing literature, drawing insights from industry experts, and real-world examples to develop a framework for preparing and sustaining workforces for the AI era. Findings The paper finds that successfully integrating AI and automation in the workforce requires a proactive and strategic approach from HR leaders, emphasising the critical importance of aligning AI and automation strategies with overall business goals through strategic workforce planning. Developing an AI-literate and adaptable workforce is crucial for embracing AI-driven changes, necessitating the creation of new AI-centric roles and career pathways, innovative job models, and comprehensive upskilling programs. HR must act as a translator between humans and machines, fostering seamless collaboration, addressing cultural and ethical implications, and leading the charge. Research limitations/implications The paper relies primarily on conceptual arguments and anecdotal evidence from industry experts. Practical implications The paper provides actionable insights for HR leaders to foster sustainable AI transitions within workforces. Social implications The paper highlights the potential social implications such as job displacement concerns and the need for reskilling and upskilling initiatives. It emphasises the importance of proactively addressing these concerns through clear communication, job security measures, and learning and development opportunities. Originality/value The paper offers a fresh perspective on the role of HR in the AI era, positioning HR leaders as strategic enablers of sustainable human-machine collaboration. It synthesises insights from various sources to provide a comprehensive framework for workforce preparation, emphasising the importance of aligning AI adoption with workforce development initiatives.

Akilu Ismail, Abdulrahaman Aliu, Mansur Ibrahim et al.

Artificial Intelligence (AI) is designed to create intelligent systems capable of performing tasks traditionally dependent on human intellect. Its integration into the field of education presents both opportunities and challenges as it is quickly expanding. Preparing teachers for this rapidly advancing technological shift is essential for success, as education itself is not static. This position paper adopts the methodology of synthesizing existing literature on innovative strategies for integrating AI into the preparation of Teachers of the Future. The concept of Teachers of the Future was introduced in this paper, addressing concerns surrounding AI’s potential to replace teachers. The paper recognized the irreplaceable roles of teachers in providing emotional and moral support as well as nurturing critical thinking among learners. It further explored the importance of AI for effective application in teaching and learning processes. Drawing upon the synthesis of literature collected from the review of related works, strategies for preparing Teachers of the Future in the Era of AI can be realized by implementing approaches such as development of AI literacy, integrating AI into teacher training courses, promoting collaborative learning among teachers in training, offering continuing education opportunities, and nurturing a positive attitude towards AI utilization. The paper suggested, among others, that Teachers of the Future should be provided with foundational training in AI application for teaching and learning processes within teacher education programmes offered by teacher training institutions.

Shiyan Jiang, Jeanne McClure, Can Tatar et al.

This study addresses the need for inclusive AI education by focusing on marginalized female students who historically lack access to learning opportunities in computing. It applies the theoretical framework of intersectionality to understand how gender, race and ethnicity intersect to shape these students' learning experiences and outcomes. Specifically, this study investigated 27 high‐school students' cognitive engagement in machine learning practices. We conducted the Wilcoxon–Mann–Whitney test to explore differences in cognitive engagement between marginalized female students and their peers, employed comparative content analysis to delve into significant differences and analysed interview data thematically to gain deeper insights into students' machine learning model development processes. The findings indicated that, when engaging in machine learning practices requiring drawing diverse cultural perspectives, marginalized female students demonstrated significantly higher performance compared to their peers. In particular, marginalized female students exhibited strengths in holistic language analysis, paying attention to writers' intentions and recognizing cultural nuances in language. This study suggests that integrating language analysis and machine learning across subjects has the potential to empower marginalized female students and amplify their perspectives. Furthermore, it calls for a strengths‐based approach to reshape the narrative of underrepresentation and promote equitable participation in machine learning and AI. What is already known about this topic Female students, particularly those from underrepresented groups such as African American and Latina students, often experience low levels of cognitive engagement in computing. Marginalized female students possess unique strengths that, when nurtured, have the potential to not only transform their own learning experiences but also contribute to the advancement of the computing field. It is critical to empower marginalized female students in K‐12 AI (ie, a subfield of computing) education, seeking to bridge the gender and racial disparity in AI. What this paper adds Marginalized female students outperformed their peers in responding to machine learning questions related to feature analysis and feature distribution interpretation. When responding to these questions, they demonstrated a holistic approach to analysing language by considering interactions between features and writers' intentions. They drew on knowledge about how language was used to convey meaning in different cultural contexts. Implications for practice and/or policy Educators should design learning environments that encourage students to draw upon their cultural backgrounds, linguistic insights and diverse experiences to enhance their engagement and performance in AI‐related activities. Educators should strategically integrate language analysis and machine learning across different subjects to create interdisciplinary learning experiences that support students' exploration of the interplay among language, culture and AI. Educational institutions and policy initiatives should adopt a strengths‐based approach that focuses on empowering marginalized female students by acknowledging their inherent abilities and diverse backgrounds.

Federico Castagna, Nadin Kökciyan, I. Sassoon et al.

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

Ieee Junhui Zhao Senior Member, Qiuping Li, Xiaoting Ma et al.

Drivenby the increasing need of massive data analysis and the rising concern about data privacy, implementing machine learning (ML) at network edge is drawing increasing attention, where local users are expected to process massive raw data without sharing data to a remote central server. However, due to the limited computing power of user equipments, how to deal with the rich data is a critical problem for each user. Based on computation offloading and edge learning, we propose an edge intelligence (EI) learning framework in two-tier heterogeneous networks to alleviate the computing pressure of users. Focusing on the minimum time delay of model training, we analyze the completion time of local learning in parallel manner and obtain the optimal offloading ratio in the proposed EI framework. Aiming at the strict interference constraint of the macrocell base station (MBS), a priority-based power allocation algorithm is designed. The analysis and simulation results verify the proposed algorithm can improve the data transmission rate and reduce the task completion time while satisfying the interference constraints of the MBS and maximum tolerable delay of learning tasks. In addition, the partial computation offloading can effectively improve the learning accuracy within a given learning time budget.

Syed Babar Abbas, Iljoong Youn

This study presents an effective control strategy for improving the dynamic performance index of a two degrees-of-freedom (DOF) quarter-car model equipped with an active suspension system that collaborates with an active aerodynamic surface, using optimal control theory. The model takes several road excitations as input and applies an optimal control law to improve the ride comfort and road-holding capability, which are otherwise in conflict. <span style="font-variant: small-caps;">MATLAB^® (R2024a)</span> simulations are carried out to evaluate the time and frequency domain characteristics of the quarter-car active suspension system. Individual performance indices in the presence of an active aerodynamic surface are calculated based on mean squared values for different sets of weighting factors and compared with those of passive and active suspension systems. From the viewpoint of total performance, the overall results show that the proposed control strategy enhances the performance index by approximately 70–80% compared to the active suspension system.

Czerwińska-Lubszczyk Agnieszka, Jagoda-Sobalak Dominika, Owczarek Tomasz

Despite the abundance of researches on innovation and green innovation, there remains a necessity to further research in this field. This is particularly crucial in regions like Central and Eastern Europe, including Poland. This publication is a part of research on business innovation utilizing public funds. The paper aims to pinpoint directions for further empirical research on innovation within enterprises funded publicly. Empirical research was conducted using a database of 95 projects, all of which were included in the lists of projects selected for funding under the Opolskie Voivodeship Regional Operational Programme 2014-2020 (Enterprise investments in innovation).

Bart Jacobs

Drawing (a multiset of) coloured balls from an urn is one of the most basic models in discrete probability theory. Three modes of drawing are commonly distinguished: multinomial (draw-replace), hypergeometric (draw-delete), and Polya (draw-add). These drawing operations are represented as maps from urns to distributions over multisets of draws. The set of urns is a metric space via the Kantorovich distance. The set of distributions over draws is also a metric space, using Kantorovich-over-Kantorovich. It is shown that these three draw operations are all isometries, that is, they exactly preserve the Kantorovich distances. Further, drawing is studied in the limit, both for large urns and for large draws. First it is shown that, as the urn size increases, the Kantorovich distances go to zero between hypergeometric and multinomial draws, and also between Pólya and multinomial draws. Second, it is shown that, as the drawsize increases, the Kantorovich distance goes to zero (in probability) between an urn and (normalised) multinomial draws from the urn. These results are known, but here, they are formulated in a novel metric manner as limits of Kantorovich distances. We call these two limit results the law of large urns and the law of large draws.

Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud

Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer vision to predict living conditions in areas with limited data, but recent studies increasingly focus on causal analysis. Despite this shift, the use of EO-ML methods for causal inference lacks thorough documentation, and best practices are still developing. Through a comprehensive scoping review, we catalog the current literature on EO-ML methods in causal analysis. We synthesize five principal approaches to incorporating EO data in causal workflows: (1) outcome imputation for downstream causal analysis, (2) EO image deconfounding, (3) EO-based treatment effect heterogeneity, (4) EO-based transportability analysis, and (5) image-informed causal discovery. Building on these findings, we provide a detailed protocol guiding researchers in integrating EO data into causal analysis -- covering data requirements, computer vision model selection, and evaluation metrics. While our focus centers on health and living conditions outcomes, our protocol is adaptable to other sustainable development domains utilizing EO data.