Hasil untuk "Machine design and drawing"

Baoquan Wei, Kai Zhen, Fangming Deng et al.

Sliding contact wear at the pantograph–catenary interface directly impacts the current collection performance and power supply reliability of electrified railways. Addressing the challenges in multi-environmental wear studies—namely, fragmented modeling chains, inconsistent parameter calibrations, and prohibitive computational costs that hinder horizontal comparisons—this study develops an equivalent parameterized modeling framework tailored for engineering assessment. The framework encapsulates environmental effects as equivalent load increments and interface coefficient corrections, facilitating efficient multi-scenario parameter scanning within a 3D contact model. Findings reveal that environmental factors drive wear through a distinct “pressure-wear” nonlinear decoupling mechanism. In sandy environments, abrasive-mediated micro-cutting dominates, leading to a monotonic surge in wear depth as sand concentration increases, despite a buffered contact pressure response. In icing conditions, the synergy of low-temperature brittleness and geometric impact renders hotspot wear highly sensitive to temperature fluctuations. For salt spray conditions, the environmental impact is represented via equivalent corrections to the interfacial parameters; within this equivalent framework, the results suggest that salt spray intensity has a more pronounced effect on wear accumulation than humidity alone. This work reveals the divergence of dominant damage pathways across environments, offering a quantitative basis for the differentiated maintenance and remaining life estimation of pantograph–catenary systems in extreme climates.

Nicolae Filip, Calin Iclodean, Marius Deac

The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI (Artificial Intelligence)-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The obtained results contribute to a better understanding of urban traffic dynamics under extreme mobility disruptions and provide a transferable methodological framework for probabilistic traffic modeling, resilience-oriented urban mobility planning, and data-driven traffic management.

Xuekai Han, Yingchun Qi, Yuqiong Li et al.

Vehicle repeated passes over soft terrain alter the soil’s bearing and shear behavior, thereby affecting vehicle mobility and energy consumption. To address this issue, this study conducted cyclic compression and shear tests on beach sand with moisture contents of 5%, 15%, and 25%. A constitutive model incorporating the coupling effects of loading cycles (N) and moisture content (ω) was developed based on the Bekker and Janosi model framework. The model expresses compression parameters as functions of N and ω, and describes shear behavior through the strength evolution function k(N,ω) and deformation modulus function h(N,ω). Results show excellent agreement between the model predictions and experimental data (R² > 0.92). Furthermore, a vehicle–soil coupled dynamics model was established based on the proposed constitutive model, forming a comprehensive analytical framework that integrates soil meso-mechanics with full vehicle–terrain interaction. This work provides valuable theoretical and technical support for predicting vehicle trafficability on coastal soft soils and optimizing vehicle suspension systems.

N. Madathil, F. Dankar, Marton Gergely et al.

Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.

Xinxing Gao, Qingming He, Hailong Chen et al.

ω-Transaminases are biocatalysts capable of asymmetrically synthesizing high-value chiral amines through the reductive amination of carbonyl compounds, and they are ubiquitously distributed across diverse microorganisms. Despite their broad natural occurrence, the industrial utility of naturally occurring ω-transaminases remains constrained by their limited catalytic efficiency toward sterically bulky substrates. Over recent decades, the use of structure-guided molecular modifications, leveraging three-dimensional structures, catalytic mechanisms, and machine learning-driven predictions, has emerged as a transformative strategy to address this limitation. Notably, these advancements have unlocked unprecedented progress in the asymmetric synthesis of bulky chiral amines, which is exemplified by the industrial-scale production of sitagliptin using engineered ω-transaminases. This review systematically explores the structural and mechanistic foundations of ω-transaminase engineering. We first delineate the substrate binding regions of these enzymes, focusing on their defining features such as substrate tunnels and dual pockets. These structural elements serve as critical targets for rational design to enhance substrate promiscuity. Next, we dissect the catalytic and substrate recognition mechanisms of (<i>S</i>)- and (<i>R</i>)-ω-transaminases. Drawing on these insights, we consolidate recent advances in engineering ω-transaminases to highlight their performance in synthesizing bulky chiral amines and aim to guide future research and the industrial implementation of tailored ω-transaminases.

Shivashankar Hiremath, Kevin Amith Mathias, Tae-Won Kim

Abstract Soft gripping technologies have attracted significant attention due to their potential to advance mechatronics and human-machine interaction. Among various soft actuation methods, 3D-printed, pneumatic-based soft actuators stand out for their versatility and adaptability. This study investigates a unique semi-oval-shaped groove design, featuring a hollow 3D-printed structure made from soft material, and analyses its performance under varying pneumatic pressures. Soft actuators with different groove geometries were fabricated using material extrusion techniques. Their compliance, deformation behavior, and gripping capabilities were evaluated through experimental testing. The outcome shows that the actuator exhibits increased deflection with rising pneumatic pressure, highlighting its high sensitivity. At an applied pressure of 5 bar, a maximum deformation of 72.0 mm was recorded. Furthermore, numerical simulations closely matched the experimental results within a certain pressure range. The actuator’s ability to bend and conform to objects of various shapes and sizes demonstrates its excellent compliance and adaptability. These findings confirm that an optimal pressure level enables reliable object gripping using a Thermoplastic polyurethane-based soft actuator. As soft gripping technologies advance, such actuators are poised to play a crucial role in revolutionizing industries like manufacturing, logistics, and robotics by offering innovative solutions for diverse gripping challenges.

Siwiec Dominika, Pacana Andrzej, Gavurová Beáta et al.

Sustainable development has caused companies to try to adapt their activities to changing customer expectations, but also to climate change. This poses a particular challenge for SMEs from developing countries. Therefore, the objective of the investigation was to analyse the qualitative-environmental aspects of the improvement of the products in SMEs from the countries of the Visegrad Group (V4). The results analysed constituted a research sample of 379 companies in the electrical machinery industry (machine processing industry), which were obtained in the period from March to September 2023 through a targeted survey. The area of analysis included, e.g.: (i) environmental issues of selected areas of activity, (ii) measures of environmental activity, and (iii) selected qualitative-environmental aspects. Analyses of the research results were carried out using quantitative and qualitative analyses, including comparative analyses, e.g. regarding the implementation status of ISO 14001, EMAS, and ISO 9001. These techniques were used: word cloud, ANOVA test and Mann Whitney U test at the significance level of α=0.05. It has been shown that SMEs in V4 countries focus their activities on improving products to improve their quality rather than limiting their negative environmental impact. Originality of the research is the identification of significant differences in the qualitative-environmental aspects undertaken when SMEs from V4 countries. Research results may contribute to the development activities more effective and coherent in the V4 countries, to achieve a stable and competitive advantage on the market as part of the qualitative and environmental improvement of the products. The research results and the conclusions drawn from them can be used by scientists and practitioners to shape the target states of enterprises in the period of increasing involvement in proecological ideas.

Stefan P. Schmid, Leon Schlosser, Frank 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.

László Pokorádi

Modern automotive systems must satisfy strict reliability requirements. Most real vehicle systems and safety-critical networks have complex interconnections. The sensitivities and probabilistic uncertainties of the reliability of systems with complex interconnections (SwCIs) can be investigated by Monte-Carlo Simulation (MCS). This paper focuses on the sensitivities and parametrical uncertainties of SwCIs’ reliability. The proposed method can be implemented in the investigation of the uncertainties of SwCI reliability, i.e., in the determination of critical system elements and the estimation of the required number of spare parts (<i>RNSP</i>) of the system, which depends on the probability of allowable spare equipment shortage.

János Szőts, Zoltán Gyenes, Emese Gincsainé Szádeczky-Kardoss et al.

Abstract We introduce an algorithm that maneuvers a vehicle through an area with randomly moving pedestrians. In non-critical situations, our strategy is to avoid pedestrians by steering, whereas dangerously moving pedestrians are avoided by braking, possibly coming to a complete stop. The distinction between non-critical and dangerous situations, as well as proof of safety, is based on a continuous optimization problem that we define. In this abstract problem, called Emergency Braking Game, one pedestrian is actively trying to collide with a continuously decelerating car. We show how to determine the outcome of the game based on the initial states of the car and the pedestrian. Using this information, our algorithm can initiate deceleration in the real scenario in time to avoid collision. The method’s safety is proven theoretically, and its efficiency is shown in simulations with randomly moving pedestrians.

Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein et al.

We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.

P. Krutz, A. Leonhardt, A. Graf et al.

Given the use of high-strength steels to achieve lightweight construction goals, conventional shear-cutting processes are reaching their limits. Therefore, so-called high-speed impact cutting (HSIC) is used to achieve the required cut surface qualities. A new machine concept consisting of linear motors and an impact mass is presented to investigate HSIC. It allows all relevant parameters to be flexibly adjusted and measured. The design and construction of the test bench, as well as the mechanism for coupling the impact mass, are described. To validate the theoretically determined process speeds, the cutting process was recorded with high-speed cameras, and HSIC with a mild deep-drawing steel sheet was performed. It was discovered that very good cutting edges could be produced, which showed a significantly lower hardening depth than slowly cut reference samples. In addition, HSIC was numerically modelled in LS-DYNA, and the calculated cutting edges were compared with the real ones. With the help of adaptive meshing, a very good agreement for the cutting edges could be achieved. The results show the great potential of using a linear motor in HSIC.

Jonathan Soper, Jennifer Smith, T. Browne et al.

The digital transformation of the marine industry presents opportunities and challenges for engineers, designers, and seafarers alike. These challenges are specifically acute in the context of the Arctic, where the presence of sea ice, severe metocean conditions, and limitations to modern charting and navigational aids present increased navigational challenges. Integration and further development of digital bridge tools can improve safety, but must take into account human factors concerns by drawing from literature and case studies where the human-machine interaction has broken down, leading to incidents. In this paper, the challenges of using digital bridge tools to support safe Arctic navigation are discussed as they relate to safe human-machine interaction. A case study of a passenger ship grounding in the Canadian Arctic is presented to demonstrate how failures in bridge equipment design and operation can contribute to accidents. This case study is discussed in relation to literature on human factors engineering and compared to other incidents where human factors was listed as a potential cause. These findings can be used to inform designers of marine navigation systems of the best practices to be aware of when implementing new technologies on the bridge of ships. Additionally, the implications of these findings on autonomous ship development and operation are discussed.

Gavardashvili Givi, Vartanov Martin

Based on theoretical and long-term field researche, the article provides assessment of the management of engineering and technical structures of the Zhinvali hydroengeneering complex. The methodology and quantitative assessment of the risk of loss resulting from accidents at hydraulic structures are described. The implementation of the recommendations of scientific researche related to the protection of the waters of the Zhinvali reservoir will allow to extend its service life for at least another twenty years, which, subject to one-time-only investments in protection measures in the amount of 35 million GEL (1 USA Dollar - 2,90 GEL), will provide an opportunity to bring the amount of direct and indirect loss prevented to 25 million GEL per year. The calculation showed that rational management of the reservoir will allow, with an internal rate of return (IRR) of 42%, to accumulate net present value (NPV) in the amount of 87.6 million GEL over twenty additional years of operation, which in turn indicates a high efficiency of investments in the protection of reservoir waters.

Mohamed-Lamine Benzagouta, Hasnaâ Aniss, Hacène Fouchal et al.

Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.

Forrest Fabian Jesse, Carla Antonini, Mercedes Luque-Vilchez

This paper proposes to use a type of machine learning network called artificial neural networks to design a circularity accounting network. The network is composed of human and non-human actors and accounts for the impact of products’ CO2 emissions and sequestration along global supply chains.  The network serves to connect people and other actors that share a CO2 indicator and allows users to visualize the level of (un-) circularity of different products through specific diagrams calculated by a CO2 estimator drawing on insights from actor-network theory. Unlike most previous circular economy accounting studies that develop some type of framework or indicator that represent measurements at micro, meso or macro levels, the circularity accounting network is not confined to a particular level of analysis but is designed to build relationships between multiple users at different levels (e.g., government, corporate or consumer actors). The paper presents the conceptual design and a preliminary test of the network using real data, helping to advance the underexplored potential of artificial intelligence in the field of circular economy accounting. The main contribution of this network is that data provided by the indicator: (i) is derived from the network itself learning from open sources, the network (ii) is not static but keeps flowing as new relationships are built within the network, moving toward self-regulating, (iii) contemplates both emissions and sequestrations along supply chains.

Lea von Berg, Jonas Frank, Manuela Sann et al.

Wiesen und Weiden (Grünland) sind vom Menschen geschaffene Agrarflächen, die unterschiedlich – intensiv bis extensiv – bewirtschaftet werden. In Deutschland finden sich etwa ein Drittel dieser Flächen als Dauergrünland. Je nach Nutzungsintensität und -form stellt Dauergrünland einen wichtigen Lebensraum vieler Tier- und Pflanzenarten dar und kann entscheidend zum Erhalt biologischer Diversität beitragen. In dieser Literaturrecherche legen wir besonderes Augenmerk auf die Mahd und deren Auswirkungen auf Gliederfüßer (Arthropoden), einer ökologisch bedeutsamen Tiergruppe. Die Mahd trägt durch verschiedene Faktoren indirekt zum allgemeinen Arthropodenrückgang bei, wirkt aber vor allem auch direkt durch das Abtöten von Arthropoden im Mähwerk. Es werden unterschiedliche Mähwerke aus technischer Sicht beleuchtet und deren Wirkung auf die Arthropodenfauna von Grünflächen evaluiert. Zudem stellen wir bereits existierende arthropodenschonende Alternativen zur konventionellen Mähtechnik vor und diskutieren deren Potenzial. Wissenschaftliche Studien zeigen, dass unabhängig von der untersuchten Arthropodengruppe Balkenmähwerke und insbesonders Doppelmesserbalken eine geringere Mortalität verursachen als Rotationsmähwerke. Aufbereiter und Schlegelmähwerke verursachen die höchsten Schädigungen. Die konsequente Verwendung sogenannter Insektenscheuchen oder modifizierter Rotationsmähwerke könnte mit geringen wirtschaftlichen Einbußen vermutlich zu einer verminderten Arthropodenmortalität beitragen.

S. Thomas, A. Abraham, Jeremy Baldwin et al.

A. Gerdes

ABSTRACT Data-driven artificial intelligence (AI) based on machine learning techniques (ML) has increasingly become an enabler in critical societal domains. However, the introduction of ML systems is often accompanied by unjustified, biased, and discriminated outcomes with severe consequences for the individuals affected. Consequently, in recent years value-based design methods have sought to anticipate and mitigate moral wrongdoing by drawing attention to ethical and epistemic challenges related to the design of AI systems. This article presents a participatory data-centric approach to AI Ethics by Design by promoting and refining insights from contributions within the family of value-sensitive design methods. The approach provides a practicable outlook on addressing epistemic and ethical issues related to data activities in early ML development project stages. Hence, the article seeks to enhance opportunities for ethically informed AI design by stressing the need for bridge building to cultivate a shared understanding among system developers and domain experts about a given data domain and its relatedness to a specific practice.

Jeonghwan Jeon, S. Geetha, D. Kang et al.

ABSTRACT The development of artificial intelligence (AI) made human feel the pressure of machine competition. The architectural industry focuses on whether the AI will replace manpower. This study is an exploratory one. The problems that AI will have in the practice of architectural design are discussed through semi-structured interviews with architects, draftsmen, drawing reviewers, construction company owners, and professors of architecture. This study proposes an extended robotic architectural technology acceptance model with five facets and ten elements. This model highlights two dimensions, namely, specialised field diversity and controllable flexibility. This study provides new three implications in the future, namely, development direction, theoretical framework, and industry guidance, in the architectural design with artificial intelligence. Diversity and flexibility are important research directions for the development of AI robotic architects, just as fluctuations phenomenon in human capabilities can lead to a mutation effect in the design. Human beings need to contribute their own emotional intelligence, and replace competitive relationship with complementary mode of extended intelligence. Similar to any new technology, AI may create many jobs no less than it replaces.