Hasil untuk "Machine design and drawing"

Aditya Kishor Deshmukh, Ajay Rajendra Kadam, Om Ratan Dhokale, Sangle P. V, Mayur Manohar Naik

This paper presents the development of an Automatic Writing Machine designed to automate basic handwriting tasks using an Arduino-based control system. The proposed system converts programmed instructions into precise pen movements on paper. It utilizes an Arduino microcontroller, motor drivers, and servo motors to control motion along horizontal and vertical axes. By coordinating motor movements, the machine is capable of writing letters, words, and simple shapes with consistent spacing and alignment. The primary objective of this project is to reduce manual effort in repetitive writing activities such as form filling, multiple document preparation, and pattern drawing. The system is developed with a focus on simplicity, affordability, and ease of implementation, making it suitable for academic and small-scale applications. Testing results indicate stable motor control, smooth pen operation, and satisfactory writing accuracy. The project demonstrates how embedded systems and mechanical components can be effectively combined to create a compact and efficient automated writing solution.

Yi Wang, Ying Xin

Visual social commerce platforms now mediate much of brand communication and conversion, yet managers still lack clear guidance on how brands and creators should technically design posts that consistently achieve high user engagement under budget and platform constraints. Prior research explains why users engage with brands online, but it mainly focuses on individual motives and message features and largely treats the brand–creator–platform relationship and the post-design process as a black box. Drawing on the Technology Affordance Actualization (TAA) framework—which conceptualizes how platform-provided action possibilities (affordances) are selectively enacted through user practices—we develop a Creator–Content–Timing (CCT) perspective on how brands and creators actualize visibility, interactivity, and commercial collaboration affordances into user engagement outcomes. We analyze 138,713 image–text posts from 100 beauty brands on Xiaohongshu using machine learning, text mining, computer vision, and regression and clustering models. The results show that creator tier, brand status, sponsorship, content cues, and posting time have systematic effects on both engagement intensity and a cost-normalized metric, Int_per_cost (interactions per 1000 CNY of estimated advertising cost). Smaller creators and non-sponsored posts achieve higher engagement per impression and higher Int_per_cost than top-tier creators and sponsored posts; moderate text length, non-exclusive brand mentions, human faces, and specific temporal windows are also associated with superior outcomes. The study extends TAA to a creator–brand–platform context by operationalizing affordance actualization as observable CCT configurations at the post level and provides configuration-level guidance on how brands can align creator selection, content design, and scheduling to improve engagement on visual social commerce platforms.

Colin John Greengrass

Clinical reasoning is foundational to medical practice, requiring clinicians to synthesise complex information, recognise patterns, and apply causal reasoning to reach accurate diagnoses and guide patient management. However, human cognition is inherently limited by factors such as limitations in working memory capacity, constraints in cognitive load, a general reliance on heuristics; with an inherent vulnerability to biases including anchoring, availability bias, and premature closure. Cognitive fatigue and cognitive overload, particularly apparent in high-pressure environments, further compromise diagnostic accuracy and efficiency. Artificial intelligence (AI) presents a transformative opportunity to overcome these limitations by supplementing and supporting decision-making. With AI's advanced computational capabilities, these systems can analyse large datasets, detect subtle or atypical patterns, and provide accurate evidence-based diagnoses. Furthermore, by leveraging machine learning and probabilistic modelling, AI reduces dependence on incomplete heuristics and potentially mitigates cognitive biases. It also ensures consistent performance, unaffected by fatigue or information overload. These attributes likely make AI an invaluable tool for enhancing the accuracy and efficiency of diagnostic reasoning. Through a narrative review, this article examines the cognitive limitations inherent in diagnostic reasoning and considers how AI can be positioned as a collaborative partner in addressing them. Drawing on the concept of Mutual Theory of Mind, the author identifies a set of indicators that should inform the design of future frameworks for human–AI interaction in clinical decision-making. These highlight how AI could dynamically adapt to human reasoning states, reduce bias, and promote more transparent and adaptive diagnostic support in high-stakes clinical environments.

Marcel Simeonov, Andrei Kurdiumov, Milan Dado

Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo depth estimation model. At low resolutions, FoundationStereo achieves real-time performance (up to 26 FPS) on embedded platforms like NVIDIA Jetson AGX Orin with TensorRT acceleration and power-of-two input sizes, enabling deployment in roadside cameras and in-vehicle systems. For Full HD stereo pairs, the same model delivers dense and precise environmental scans, complementing LiDAR while maintaining a high level of accuracy. YOLO11 object detection and segmentation is deployed in parallel for object extraction. Detected objects are removed from depth maps generated by FoundationStereo prior to point cloud generation, producing cleaner 3D reconstructions of the environment. This approach demonstrates that advanced stereo networks can operate efficiently on embedded hardware. Rather than replacing LiDAR or radar, it complements existing sensors by providing dense depth maps in situations where other sensors may be limited. By improving depth completeness, robustness, and enabling filtered point clouds, the proposed system supports safer navigation, collision avoidance, and scalable roadside infrastructure scanning for autonomous mobility.

Yunze Xiao, Lynnette Hui Xian Ng, Jiarui Liu et al.

Large Language Models (LLMs) increasingly exhibit \textbf{anthropomorphism} characteristics -- human-like qualities portrayed across their outlook, language, behavior, and reasoning functions. Such characteristics enable more intuitive and engaging human-AI interactions. However, current research on anthropomorphism remains predominantly risk-focused, emphasizing over-trust and user deception while offering limited design guidance. We argue that anthropomorphism should instead be treated as a \emph{concept of design} that can be intentionally tuned to support user goals. Drawing from multiple disciplines, we propose that the anthropomorphism of an LLM-based artifact should reflect the interaction between artifact designers and interpreters. This interaction is facilitated by cues embedded in the artifact by the designers and the (cognitive) responses of the interpreters to the cues. Cues are categorized into four dimensions: \textit{perceptive, linguistic, behavioral}, and \textit{cognitive}. By analyzing the manifestation and effectiveness of each cue, we provide a unified taxonomy with actionable levers for practitioners. Consequently, we advocate for function-oriented evaluations of anthropomorphic design.

Nnam Onwuka, Uzoechi Stanley Goodnews, Otuu Ogbonnia Inya et al.

Aim and Objectives of the Project: The goal of this work is to enhance the production efficiency of a rice threshing machine by making appropriate modifications to the design of the current model. The study aims to develop a mobile, motorized rice threshing machine that delivers high performance and quality output while minimizing time and cost. Study Design: The Sieve holes was made to enter each grain of rice given room for adequate passage of the grains, Suspending the sieve with relief springs to enable it sieve during vibration and also proper selection of the construction materials were the three significant areas considered for modification. Mild steel was chosen for the fabrication of the parts due to its reliability, durability, and resistance to corrosion. Place and Duration: Akanu Ibiam Federal Polytechnic, Unwana, Ebonyi State, Nigeria, between October 2023 and September 2024 Methodology: Detailed machine design both parts and assembled were drawn using Solidworks 2015 version. The machine component parts were marked and cut out from the parent materials as specified in the detailed drawing. The cut-out parts were joined using Arc welding process, why some parts were fastened using bolt and nut. A gasoline engine of 3.5 hp, running at and 1500 rpm transmitting a torque of 4.06 Nm causing a rotational motion on the shaft for effective and efficient threshing of the rice. Results: the performance analysis showed that the enlarged sieve holes, provision of suspension spring and closeness of the spikes significantly affected the threshing and sieving rate of the machine. The machine recorded a threshing efficiency of 95.94% and throughput of 46.99 kg/h. when compared with the existing threshing machine. The new machine saved 0.07 h when threshing 30 kg of rice paddy. The result showed that the machine has optimal performance and produces the intended quantity with the recorded efficiency. Conclusion: this study highlights the crucial role of rice in Nigeria’s economy and the challenges faced by local rice producers, particularly in processing good quantity. The design and fabrication of the mechanized rice threshing machine represent a significant advancement in addressing these challenges. By improving threshing efficiency, reducing grain damage, and cutting labor costs, the machine offers a practical solution to the inefficiencies of traditional rice processing methods. With a threshing efficiency of 95.94% and throughput of 46.99 kg/hr., the machine demonstrates a notable improvement in rice processing. Utilizing locally sourced materials, it further promotes local content and self-reliance in machine development. Ultimately, this project stands to enhance rice production in Nigeria, improve productivity for farmers, and bolster the overall economic potential of the rice farming sector.

Lamiae Ben Moussa, Ahmed El Khalfi, Abbass Seddouki et al.

The study focuses on an advanced numerical framework designed to optimize an electric car’s aerodynamic efficiency through the slanting front bumper. The study begins with a comparative analysis of four angular configurations (−4°, 0°, 4°, and 8°) using computational fluid dynamics (CFD). It concludes that an angle of 4° improves resource productivity and dynastic balance by reducing drag (Cd = 0.26) and guaranteeing controlled lift (Cl = 0.030). In order to further this research, ANSYS DesignXplorer 2019 R3 was used for parametric optimization, which included direct parameterization of the angle in the simulation process. A quadratic response surface was constructed using the CFD findings, and an optimality point with a Cd value of 0.2601 and a Cl value of 0.0302 was found at 3.9998°. Because this solution is part of the Pareto front, its use demonstrates the significance of the chosen geometric configuration. The approach is innovative because it combines a simple geometric transformation with an automated, repeatable simulation method to a degree appropriate for an industrial setting. The results show that modifying the front bumper in a particular way is a successful way to improve the aerodynamic performance of electric vehicles, with the added potential to function at other required locations on the vehicle body.

Shuai Hao, Liping Liu, Guogang Wang et al.

Narrowing the urban–rural income gap in a sustainable and inclusive manner remains a longstanding concern in development economics. This study investigates how entrepreneurial activity can contribute to narrowing the urban–rural income gap in China, with a focus on technological spillovers and structural transformation. Drawing on a county-level panel dataset from 2000 to 2022, we apply a Double Machine Learning (DML) framework for causal inference. The empirical results show that entrepreneurship significantly reduces the urban–rural income gap, and the findings are robust to a series of validity checks. Mechanism analysis reveals two key pathways through which entrepreneurship helps narrow the income gap. First, it enhances resource allocation efficiency via knowledge and technology spillovers. Second, it promotes industrial upgrading in rural areas. Heterogeneity analysis shows that the effects are particularly pronounced in central and western regions. Across industries, labor-intensive entrepreneurship exerts the strongest equalizing effect, while technology-intensive sectors rely more on spillover channels. The impact of resource-intensive entrepreneurship is comparatively weaker and may be accompanied by negative externalities. This study provides novel empirical evidence on how entrepreneurship can support coordinated urban–rural development and informs the design of regionally and sectorally differentiated innovation policies.

Ocieczek Wioletta, Kraśniewski Damian, Furman Joanna

In a dynamically changing market, manufacturing companies must meet the growing expectations of customers in terms of manufacturing costs, high product quality, and order fulfillment time. Therefore, they continually improve their production processes, using various methods, tools, and techniques that can reduce or eliminate losses and increase operational efficiency. One such solution is the SMED (Single Minute Exchange of Die) method, which enables shortening the machine changeover time, thereby increasing the process flexibility and reducing Lead Time. The article is a case study and presents the results of implementing the SMED method at the welding station on the production line for bridge gratings. The research was carried out in four stages, by the SMED methodology. As a result of implementing SMED, the welding machine changeover time was shortened by 61%. Thanks to the introduced changes, over 40 minutes of production time were recovered, which resulted in the additional production of 4 pieces of bridge gratings (achieving an increase in efficiency by 4.77%).

Victor Cai, Jennifer Zhou, Haebin Do et al.

3D die-stacked DRAM has emerged as a key technology for delivering high bandwidth and high density for applications such as high-performance computing, graphics, and machine learning. However, different applications place diverse and sometimes diverging demands on power, performance, and area that cannot be universally satisfied with fixed commodity DRAM designs. Die stacking creates the opportunity for a large DRAM design space through 3D integration and expanded total die area. To open and navigate this expansive design space of customized memory architectures that cater to application-specific needs, we introduce DreamRAM, a configurable bandwidth, capacity, energy, latency, and area modeling tool for custom 3D die-stacked DRAM designs. DreamRAM exposes fine-grained design customization parameters at the MAT, subarray, bank, and inter-bank levels, including extensions of partial page and subarray parallelism proposals found in the literature, to open a large previously-unexplored design space. DreamRAM analytically models wire pitch, width, length, capacitance, and scaling parameters to capture the performance tradeoffs of physical layout and routing design choices. Routing awareness enables DreamRAM to model a custom MAT-level routing scheme, Dataline-Over-MAT (DLOMAT), to facilitate better bandwidth tradeoffs. DreamRAM is calibrated and validated against published industry HBM3 and HBM2E designs. Within DreamRAM's rich design space, we identify designs that achieve each of 66% higher bandwidth, 100% higher capacity, and 45% lower power and energy per bit compared to the baseline design, each on an iso-bandwidth, iso-capacity, and iso-power basis.

Wenqian Zhang, Weiyang Liu, Zhen Liu

The design of complex machines stands as both a marker of human intelligence and a foundation of engineering practice. Given recent advances in large language models (LLMs), we ask whether they, too, can learn to create. We approach this question through the lens of compositional machine design: a task in which machines are assembled from standardized components to meet functional demands like locomotion or manipulation in a simulated physical environment. With this simplification, machine design is expressed as writing XML-like code that explicitly specifies pairwise part connections. To support this investigation, we introduce BesiegeField, a testbed built on the machine-building game Besiege, which enables part-based construction, physical simulation and reward-driven evaluation. Using BesiegeField, we benchmark state-of-the-art LLMs with agentic workflows and identify key capabilities required for success, including spatial reasoning, strategic assembly, and instruction-following. As current open-source models fall short, we explore reinforcement learning (RL) as a path to improvement: we curate a cold-start dataset, conduct RL finetuning experiments, and highlight open challenges at the intersection of language, machine design, and physical reasoning.

Yuqing Wang, Xiao Yang

Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management. Organizations must continuously meet evolving regulatory requirements such as GDPR and ISO 27001, yet traditional manual review processes have become increasingly inadequate for modern business scales. This paper presents a novel machine learning-based framework for automating cloud computing compliance processes, addressing critical challenges including resource-intensive manual reviews, extended compliance cycles, and delayed risk identification. Our proposed framework integrates multiple machine learning technologies, including BERT-based document processing (94.5% accuracy), One-Class SVM for anomaly detection (88.7% accuracy), and an improved CNN-LSTM architecture for sequential compliance data analysis (90.2% accuracy). Implementation results demonstrate significant improvements: reducing compliance process duration from 7 days to 1.5 days, improving accuracy from 78% to 93%, and decreasing manual effort by 73.3%. A real-world deployment at a major securities firm validated these results, processing 800,000 daily transactions with 94.2% accuracy in risk identification.

D. Gáli, Ć. ZvezdanSTOJANOVI, Ć. ElvirČAJI

: Artificial neural networks find extensive applications in various fields, including complex robotics, computer vision, and classification tasks. They are designed to mimic the highly complex, nonlinear, and parallel computational abilities of the human brain. Just like neurons in the brain, artificial neural networks can be organized to perform rapid and specific computations, such as perception and motor control. Drawing insights from the behavior of biological neural networks and their learning and adaptive capabilities, these technical counterparts have been developed to simulate the properties of biological systems. This paper concentrates on two main areas. Firstly, it explores the approximation of image recognition for healthy individuals using artificial neural networks. Secondly, it investigates the identification of kidney conditions associated with common kidney diseases that affect the global population. Specifically, the paper examines polycystic kidney disease, kidney cysts, and kidney cancer. The ultimate goal is to utilize machine learning algorithms to aid in diagnosing kidney diseases by analyzing various samples.

Stefan P Schmid, Leon Schlosser, F. Glorius et al.

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Yeon Chae, Samraat Gupta, Youngjib Ham

In construction teleoperation, particularly in disaster restoration, delicate manipulation of heavy machinery is crucial, based on a thorough understanding of the surroundings. Current practices have utilized multiple viewpoints to facilitate a thorough understanding of the site's 3D spatial layout. However, challenges might arise as visual cues within the surroundings could create distractions for teleoperators. Drawing from visual search theory and Gibson’s perception theory, exploring visual prompts in teleoperation interface could enhance performance by directing attention to key visual cues, reducing cognitive workload. Nonetheless, the evaluation of different visual prompts from human factors perspectives has been underexplored. Addressing challenges of potential distraction from multiple viewpoints and inappropriate visual prompts, this study emphasizes the necessity of exploring different visual prompts to most effectively guide operators’ attention in given work environments. The experiment, designed with low and high visual cue environments, focuses on debris removal and extended 3D Fitts' law tasks, evaluating spatial awareness and depth perception during teleoperation. The experiments were conducted with participants in construction-related fields with industrial experience. Performance measurements and subjective ratings with open-ended discussion was conducted. The findings show visual prompts' effects on distraction and visibility conditions concerning task-oriented difficulty levels in teleoperation. The experimental results can inform the optimal design of visual prompts in human-machine interface for teleoperation for complicated construction environments, highlighting the importance of the considerations of environments and task characteristics.

Jack Forman, Ozgun Kilic Afsar, Sarah Nicita et al.

We present FibeRobo, a thermally-actuated liquid crystal elastomer (LCE) fiber that can be embedded or structured into textiles and enable silent and responsive interactions with shape-changing, fiber-based interfaces. Three definitive properties distinguish FibeRobo from other actuating threads explored in HCI. First, they exhibit rapid thermal self-reversing actuation with large displacements (∼40%) without twisting. Second, we present a reproducible UV fiber drawing setup that produces hundreds of meters of fiber with a sub-millimeter diameter. Third, FibeRobo is fully compatible with existing textile manufacturing machinery such as weaving looms, embroidery, and industrial knitting machines. This paper contributes to developing temperature-responsive LCE fibers, a facile and scalable fabrication pipeline with optional heating element integration for digital control, mechanical characterization, and the establishment of higher hierarchical textile structures and design space. Finally, we introduce a set of demonstrations that illustrate the design space FibeRobo enables.

Yong Peng, Wenjing Zhu, Dennis Z. Yu et al.

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.

Emani Dotch, Vitica Arnold

We explore ideas and inclusive practices for designing and testing child-centered artificially intelligent technologies for neurodivergent children. AI is promising for supporting social communication, self-regulation, and sensory processing challenges common for neurodivergent children. The authors, both neurodivergent individuals and related to neurodivergent people, draw from their professional and personal experiences to offer insights on creating AI technologies that are accessible and include input from neurodivergent children. We offer ideas for designing AI technologies for neurodivergent children and considerations for including them in the design process while accounting for their sensory sensitivities. We conclude by emphasizing the importance of adaptable and supportive AI technologies and design processes and call for further conversation to refine child-centered AI design and testing methods.

Berre Su Yanlıç, Aykut Coşkun

Sustainability-focused design research is witnessing a change in approach with the emergence of More-than-human Design (MTHD), which challenges human-centered thinking by incorporating nonhuman perspectives into the design process. However, implementing MTHD presents challenges for design researchers and practitioners, such as understanding non-verbal species. Despite the techniques developed to facilitate such an understanding (e.g. contact zone), the growing literature on MTHD lacks studies reflecting on how these techniques are utilized in the design process. In this paper, we present a case study on designing olive harvesting tools from a MTH lens, where designers used contact zone, plant interviews, plant persona, and experience map to explore the perspectives of olive trees and incorporate them into ideas in collaboration with farmers and agricultural engineers. The results indicate the significance of reconsidering decentralization in MTHD from the standpoint of entanglements among techniques and incorporating various knowledge types to manage tensions arising from perspective shifts.

Aman Gupta, Aditi Sheshadri, Sujit Roy et al.

Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the "single-column approximation" which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.