This paper investigates the benefits of node-based user interfaces in Digital Humanities (DH). It highlights the importance of a qualitative versus a quantitative approach for text comparisons generated by Artificial Intelligence (AI). Since thinking processes in text analysis are lost in generative outputs within current AI text-based User Interfaces (UI), keeping track of them allows for an interpretive approach. Within the fields of User Experience (UX) and Data Visualization Design we are developing uBIQUity, a platform that identifies intertextual references in Christian and Islamic sacred texts. The platform uses a node-based UI to manage, display data, and compare references. It emphasizes collaborative knowledge-building, and supports different cognitive learning styles. By combining philology, computer science, computational linguistics, and design, it overcomes the limitations of tools based solely on quantitative analysis, offering flexible interactions with digitized texts.
Today, culture is considered the fourth pillar of sustainability. It is therefore essential to address this theme in relation to museums, which are bastions of culture and potentially a driving sector for global sustainable development. The recent inclusion of sustainability in 2022 ICOM’s definition of a museum highlights the need to rethink exhibition design as a systemic design lever. On the one hand, museums are undergoing a radical transition facing many challenges; on the other, we must ask how to intervene more effectively. This paper analyzes the field of temporary museum exhibitions and identifies recurring principles, the ‘three codes of exhibit design’, emerging from the history of exhibition design discipline. In line with Bruno Zevi’s view of history as a tool for present-day action, this study examines 102 case studies (1927 to 2022) through the contemporary lens of sustainability. The goal is to define a fourth, essential design code: the Environment Code, which complements and integrates the existing three. This research proposes a counter-historical reading of exhibition projects as a method- ological and operative tool to define a new sustainable methodology, establishing the Environment Code as a generative hypothesis for the future of exhibition design.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.
Within the modern commercial printing press, a common problem is the ef-ficient management of the maintenance of different machines of newspaper printing press. If Effective Maintenance Management is applied, productivity of the machines can be increased by reducing breakdown time of the ma-chine. Productivity Management is an organizational framework that helps machines to improve productivity. Productivity of a machine is dependent on the failure probability which can be controlled by technical and management actions. The present investigation is established by the analysis of productivi-ty, effectiveness and failure probability on the basis of Pareto Analysis. Pareto chart is also developed to understand the actual scenario where highest prior-ity events are sequentially arranged. It has been observed that the web-offset printing machine has the highest productivity and effectiveness with less failure probability while the exposure unit has the highest failure probability having low productivity and effectiveness. Based on the reduction of proba-bility of failure to meet the acceptable criteria, further maintenance planning can be suggested. This approach confirms that productivity and effectivity of the machines of newspaper printing press can be increased by considering consequences of the machines and their corresponding failure assessments.
This paper focuses the relationship between data technologies, meaning the set of tools and devices of different nature, and the transformation of reality into data, from their collection to their use. We confine our reflection to the use of data related to the body and the ability to establish its state of health or illness. A critical stance is adopted, reflecting on the impact that data have - and may have - on biological, legal, cultural, and social bodies. It aims to highlight problems and potential side effects, emphasising the need to create ‘connections’ between the individual device and the large technical system; between the personal data and the system of collected data; between the present and the future scenarios towards which it is desirable to orient design. The paper aims to open up questions on the relationship between us and the knowledge of our body, beyond the promises of idyllic worlds presented by data technologies. And what role can Design play in this context?
Design is often viewed as an activity focused on shaping futures or, more precisely, on creating ‘better’ futures. However, an important yet often overlooked reality is that the future is uncertain, unpredictable, and fundamentally unknown. Based on this premise, the Open Debate section seeks to explore the relationship between Design and Unknowns. By examining its philosophical, ethical, cosmological, practical, and pedagogical implications, it offers new perspectives for design research: slowness, as a way to approach the encounter with the unknown, as well as a way to design it; problematization, as a rethinking of what we know; possible, as potential realities to be shaped by the unknowns, that also deals with non-linearity; de-scripting, as a way to de-colonise futures; inevitabilism, that strips potential from the unknown. These concepts introduce the voices that populated the section, exploring different dimensions of the relationships between Design and Unknowns.
Following recent social and global transformations, cultural organizations have undergone a major redefinition in their role as keepers and promoters of knowledge and heritage, developing virtuous examples and reference models for the free, open, and autonomous enjoyment of all audiences. This contribution - contextualized in a current doctoral research on the enhancement of cultural heritage, promotion of institutional and territorial identities - is presented as a reflection on the recent awareness and attention to the issues of inclusiveness and accessibility in places of culture, especially higher education institutions. Through an analytical work on numerous university case studies, focused on institutional merchandising products, it was possible to outline common attitudes and effective communication strategies that promote core values related to multiculturalism, body positivity, gender equality, women’s empowerment, and integration of ethnic minorities.
Volodymyr Vanin, Gennadii Virchenko, Petro Yablonskyi
et al.
Mechanical engineering is one of the basic branches of world industry. The formation of our state as a component of the community of European countries is closely related to it. Therefore, the further development of mechanical engineering by making appropriate improvements to it is quite relevant. A generally recognized direction for improving product quality is computer-aided design, which is quite common in the specified field of activity. This is evidenced by the widespread use of various CAD/CAM/CAE/PDM (Computer-Aided Design/Computer-Aided Manufacturing/Computer-Aided Engineering/Product Data Management) computer systems. Without the latter, it is almost impossible to create world-class technical facilities. This is due to the fact that the specified means make it possible to significantly shorten the design time of products, increase their quality, and reduce manufacturing and operating costs. It is known that geometric modeling is a fundamental component for the creation of many types of industrial products and is an integral element of many modern CAD systems, i.e. automated design systems. Consequently, the corresponding characteristics of the created technical objects significantly depend on the perfection of computer shaping. There are many scientific and methodological approaches to its effective practical implementation. One of them is the methodology of structural-parametric geometric modeling proposed by the scientific school of applied geometry of the National Technical University of Ukraine "Ihor Sikorskyi Kyiv Polytechnic Institute". A progressive direction in mechanical engineering is a modular approach, which is used during the design, manufacture and operation of technical objects. Many issues at different stages of the life cycle are closely related to the geometric parameters of products. Therefore, the scientific and applied task of developing appropriate tools for computer shaping is relevant. This publication is aimed at defining a strategy for solving the described problem. The concept presented in it is based on the principles of structural-parametric methodology
Daichi Haraguchi, Naoto Inoue, Wataru Shimoda
et al.
Recent advancements in foundation models show promising capability in graphic design generation. Several studies have started employing Large Multimodal Models (LMMs) to evaluate graphic designs, assuming that LMMs can properly assess their quality, but it is unclear if the evaluation is reliable. One way to evaluate the quality of graphic design is to assess whether the design adheres to fundamental graphic design principles, which are the designer's common practice. In this paper, we compare the behavior of GPT-based evaluation and heuristic evaluation based on design principles using human annotations collected from 60 subjects. Our experiments reveal that, while GPTs cannot distinguish small details, they have a reasonably good correlation with human annotation and exhibit a similar tendency to heuristic metrics based on design principles, suggesting that they are indeed capable of assessing the quality of graphic design. Our dataset is available at https://cyberagentailab.github.io/Graphic-design-evaluation .
Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi
et al.
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.
As computer-aided design (CAD) tools have become an essential aspect of modern mechanical engineering design, the demand for CAD experts has increased significantly. The development from novice, to proficient, to expert user is of particular interest to the industrial and academic design communities. Yet little is known about the development or characteristics of expert CAD skill; much of the past work that reports user action data is based on student or novice data. We compared the CAD modelling process across nine proficient and ten expert designers as they were tested to complete the same design task. Under identical conditions – the same time constraints in the same CAD platform and with the same task -- the expert users were able to complete a larger proportion of the task with higher dimensional accuracy. While the experts were able to dissect and retrieve geometries from manufacturing drawings more efficiently than proficient users, they were also able to plan a modelling strategy that required less effort and revisions. With our experimental findings, we identify the demand for procedural knowledge-building for young engineers, with the ultimate goal of more effectively developing experts in engineering design with CAD.
This paper presents a theoretical calculation of the vacuum energy density by summing the contributions of all quantum fields’ vacuum states, which turns out to indicate that there seems to be a missing bosonic contribution in order to match the predictions of the current cosmological models and all observational data to date. The basis for this calculation is a new Zeta function regularization method used to tame the infinities present in the improper integrals of power functions. The paper also presents a few other contributions in the area of vacuum energy.
Mechanical drawing. Engineering graphics, Physical and theoretical chemistry
In this paper, we have studied an anisotropic Bianchi-I cosmological model in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>f</mi><mo>(</mo><mi>R</mi><mo>,</mo><mi>T</mi><mo>)</mo></mrow></semantics></math></inline-formula> gravity. To obtain the exact solutions of the field equations, we have used the condition <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>σ</mi><mo>/</mo><mi>θ</mi></mrow></semantics></math></inline-formula> to be a function of the scale factor (<b>IJTP, 54, 2740-2757, 2015</b>). Our model possesses an initial singularity. It initially exhibits decelerating expansion and transits to accelerating expansion at late times. We have also discussed the physical and geometrical properties of the model.
Mechanical drawing. Engineering graphics, Physical and theoretical chemistry
A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.
Vector graphics are an industry-standard way to represent and share visual designs. Designers frequently source and incorporate styles from existing designs into their own work. Unfortunately, popular design tools aren't well suited for this task. We present VST, Vector Style Transfer, a novel design tool for flexibly transferring visual styles between vector graphics. The core of VST lies in leveraging automation while respecting designers' tastes and the subjectivity inherent to style transfer. In VST, designers tune a cross-design element correspondence and customize which style attributes to change. We report results from a user study in which designers used VST to control style transfer between several designs, including designs participants created with external tools beforehand. VST shows that enabling design correspondence tuning and customization is one way to support interactive, flexible style transfer. We also find that someone using VST can significantly reduce the time and work for style transfer compared to experienced designers using industry-standard tools.
The development of tissue-engineered cardiovascular implants can improve the lives of large segments of our society who suffer from cardiovascular diseases. Regenerative tissues are fabricated using a process called tissue maturation. Furthermore, it is highly challenging to produce cardiovascular regenerative implants with sufficient mechanical strength to withstand the loading conditions within the human body. Therefore, biohybrid implants for which the regenerative tissue is reinforced by standard reinforcement material (e.g. textile or 3d printed scaffold) can be an interesting solution. In silico models can significantly contribute to characterizing, designing, and optimizing biohybrid implants. The first step towards this goal is to develop a computational model for the maturation process of tissue-engineered implants. This paper focuses on the mechanical modeling of textile-reinforced tissue-engineered cardiovascular implants. First, we propose an energy-based approach to compute the collagen evolution during the maturation process. Then, we apply the concept of structural tensors to model the anisotropic behavior of the extracellular matrix and the textile scaffold. Next, the newly developed material model is embedded into a special solid-shell finite element formulation with reduced integration. Finally, we use our framework to compute two structural problems: a pressurized shell construct and a tubular-shaped heart valve. The results show the ability of the model to predict collagen growth in response to the boundary conditions applied during the maturation process. Consequently, we can predict the implant's mechanical response, such as the deformation and stresses of the implant.
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.
The most important and significant research topic in mechanical and industrial engineering is the fluid flow with heat transport by a stretched surface because of the numerous applications. The impact of heat transport on product quality can be noticed in the field of chemical engineering, polymer processing, glass fiber production, hot rolling, metal extrusion, production of paper, and drawing of plastic films and wires. In light of such foregoing applications, an attempt is made to model the thermal and solutal diffusion phenomena in Oldroyd-B nanofluid flow over a stretching cylinder by using Buongiorno's model and Cattaneo-Cristov theory. To explore the heat flow mechanism in the flow, the effects of heat source/sink with ohmic heating are also considered. Additionally, the influence of chemical reactions is used to investigate the solutal transport process in nanofluid flow. The mathematical formulation section of the manuscript depicts the mathematical modeling of momentum, heat, and mass diffusion equations. The effect of dimensionless physical constraints on the flow, temperature, and concentration distributions of Oldroyd-B nanofluid flow are investigated using the homotopy analysis method (HAM) in Wolfram Mathematica. In the results and discussion section, graphical findings are displayed and physically justified. A section of concluding remarks is added at the end of the text to emphasize the study's major findings.
Drawing as an activity aids problem solving, collaboration, and presentation in design, science, and engineering and artistic creativity as well as expression in the arts. Unfortunately, blind, and partially sighted learners still lack an inclusive and effective drawing tool, even in the digital age. In response, this research aims to explore what an effective drawing tool for blind and partially sighted individuals (BPSI) would be. Raised-line drawing kits aim to provide this, but in prior work, our usability tests of raised line graphics with blind and partially sighted participants rated the raised line graphics that we tested as barely comprehensible relative to 3D models, which they rated as highly comprehensible. Semi-structured interviews with our participants afterward suggest that they found 3D models to be more comprehensible because these are consistent with haptic principles of perception whereas conventions of raised line graphics, such as a line representing a surface edge, replicate visual cues of source images and thereby violate haptic principles of perception. Therefore, we hypothesize that a drawing tool for blind and partially sighted drawers could be effective by recruiting affordances of 3D models. Through co-design sessions conducted during the Covid-19 pandemic with blind and partially sighted drawers (BPSD), we prototyped a tangible 3D model construction kit for non-visual haptic drawing with a digital interface to a 3D virtual environment. Our current investigation of user needs is informing us of our ongoing iterative development of an accessible 3D scanning application that is enabling blind and partially sighted individuals to build and scan in 3D models constructed from a more flexible range of materials beyond what was possible with our previous prototype.