A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.
Abstract As artificial intelligence transitions from industry-exclusive tool to public-facing technology, society faces critical decisions about its integration into socioecological systems. This paper proposes a reimagining of AI as a synthetic participant in the circular bioeconomy (CBE)—a regenerative model emphasizing cyclical flows of resources, information, and energy. Drawing on Bruno Latour’s Actor-Network Theory and Donna Haraway’s posthumanism, we reconceptualize AI as a non-living organism capable of functioning within multispecies systems, analogous to viruses that shape ecosystems without conventional life. Conventional, in that it meets the standard biological criteria for like: metabolism, reproduction, and homeostasis. AI, like viruses, does not meet this biological criteria. Current AI applications in CBE—from biowaste recycling to precision agriculture—demonstrate both transformative potential and ethical concerns. While AI enables unprecedented efficiency through advanced algorithms and embodied robotics, it risks perpetuating extractive logics that treat information as a resource to be mined rather than circulated. Critical ethical challenges emerge including algorithmic bias amplifying inequalities, epistemic opacity eroding stakeholder trust, blurred accountability for AI-driven harm, displacement of human labor, and marginalization of indigenous and local ecological knowledge. Through examples in medicine and remote sensing, we argue that AI becomes a “friend” to the Circular Bioeconomy (CBE) only when designed as circular and relational rather than linear and extractive. This requires synthetic datasets preserving privacy, multimodal architectures enabling dimensional understanding, and human-machine-ecosystem feedback loops replacing terminal outputs with ongoing accountability. Ultimately, AI’s role depends on intentional design grounded in justice and multispecies dignity—transforming it from extractive tool into participant in shared regenerative futures.
Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi></mrow></semantics></math></inline-formula> is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi></mrow></semantics></math></inline-formula> is selected from the control matrix set, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi></mrow></semantics></math></inline-formula> is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced.
Mechanical engineering and machinery, Machine design and drawing
With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception.
Mechanical engineering and machinery, Machine design and drawing
This study focuses on the delivery routing problem faced by a transport company located in Phuket, Thailand. The goal of this study is to find a daily optimum route in order to minimize the total transportation cost, which comprises fixed costs associated with vehicle rental and variable costs calculated based on factors of travel distance, fuel prices, and fuel consumption. The complexity of this problem is compounded by the fact that customer demand often exceeds a vehicle capacity, in terms of weight and volume. In addition, delivery must be made within specific time windows. To tackle this issue, the delivery routing problem is classified as a multi-trip capacitated vehicle routing problem with time window (MTCVRPTW). Since the problem is NP-hard, an application of metaheuristic is more practical to determine the delivery routing of the company within a reasonable computing time. In this study, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are applied to solve MTCVRPTW. The numerical results show that DE provides better solution quality compared to those obtained from PSO and company current practices.
Machine design and drawing, Engineering machinery, tools, and implements
The aim of digitalising the fashion industry was to streamline the design, production and business of physical products for the real world and to achieve sustainability with the help of different digital tools. However, with the recent emergence of the metaverse, the parallel world in virtual reality, a new horizon of digital fashion has been opened. In general, the innovations in digital fashion can be clustered into the following four themes – (1) Digital design and e-prototyping, (2) Digital business and promotion, (3) Digital human and metaverse, and (4) Digital apparel and smart e-technology (Figure 1). This special issue presents eight research articles and two reviews covering the first two themes of digital fashion innovations – (1) Digital design and e-prototyping and (2) Digital business and promotion. Computer-aided design (CAD) is among the first few digital elements entered into the fashion industry and education. There are many CAD systems for two-dimensional (2D) and three-dimensional (3D) design development that are being used in the industry today (Sayem, Kennon, & Clarke, 2010). Traditionally, the face-to-face teaching approach has been most effective for teaching these CAD software packages in academic set-ups. However, the COVID-19 pandemic forced us to move to a virtual mode of teaching across the world for the most of 2020 and 2021. Likewise, the tutors of fashion CAD had to adopt the new normal quickly and deliver the practical teaching elements of CAD over the online platforms, such as Zoom, MS Teams and Google Meet, etc. Lee (2021) looked into the effectiveness of online fashion CAD teaching in South Korea during the pandemic. They compared the grades and results of two groups of fashion CAD students: one group was taught 2D CAD systems offline in 2019, and the other group was taught the same systems online in 2020. Their finding is promising, and it shows that higher scores and grades were achieved by the online-taught students than the offline-taught cohorts. Although we do not have readily available similar studies from other countries to compare the finding, Lee’s (2021) study will give some confidence to the employers and educators about the knowledge and skills gained by students taught remotely during the pandemic. Pattern cutting is the first technical step in the apparel manufacturing process that starts materialising a design into a real wearable product in a set of technical drawings. In a mass production scenario, the pattern pieces of multiple sizes of same style of a garment are arranged into a rectangular area, known as a marker, matching the dimension of the cutting table and fabric width. The pattern cutting process, including marker making, is one of the most labour-intensive and least efficient processes in terms of waste generation within the fashion design and development cycle and is responsible for an average of fifteen percent fabric wastage (Ramkalaon and Sayem 2020). ElShishtawy, Sinha, and Bennell (2021) reviewed the works done on computational methods for the cutting problem and zero-waste design thinking. They highlighted the application of the CAD technique by Ramkalaon and Sayem (2020) and Weng and Kuo (2011) for zero-waste marker generation and stressed the importance of collaboration between the fields of cutting and packing (C&P) operational research and zero-waste fashion design (ZWFD). Being the first to cover the C&P and ZWFD research, the review article by ElShishtawy et al. (2021) provokes new research thinking among the academics and researchers in fashion and computer disciplines. The term ‘cyborg’, a portmanteau of cybernetic and organism, was first coined by Clynes and Kline (1960) to refer to an organism with enhanced capabilities through the integration of any artificial component or technology. Later, Haraway (1985) established the depiction of technology-dependent humanity as an existing version of a cyborg in her ‘Cyborg Manifesto’. The article by Särmäkari and Vänskä (2021) hosted in this special issue identified tomorrow’s fashion designers as cyborgs and it proposed a concept of ‘cyborg designer 4.0’, which refers to a physical and digital craftsperson, through the analysis from two case studies – one on generative clothing design involving machine learning and another on artificial intelligent (AI)-aided fashion sketching. They nicely echoed the footsteps of the blockchain technology, especially non-fungible tokens (NFTs), approaching towards the deisgn and development of digital-only garments as tradable assets, and provided an excellent food for thought for educators and industry leaders to figure out the construct of tomorrow’s fashion designers. Three-dimensional (3D) printing technology is a direct approach to converting a digital entity into a physical entity efficiently. Although this ‘digi-physi’ approach has been around for a fair amount of time, it has been more successful in designing and prototyping fashion accessories than in producing drape-able garments (Dip et al., 2020). Rolling (2021) looked into the designers’ perceptions of this technology and identified the efficient and inefficient
Ganegoda V. C. Rasanga, Kengo Hiraishi, Ryuichi Hodoshima
et al.
Abstract WORMESH-II, which is the second prototype in the WORMESH series, is inspired by a flatten and soft-bodied fatworm, and its uniqueness is the use of multiple travelling waves for locomotion. In this paper, the sidewinding locomotions for WORMESH-II are talked about. This is because sidewinding is one of the most effective ways to traverse sandy terrain. The mathematical model of the sidewinding locomotion kinematics of WORMESH-II explains how synchronous multiple sidewinding waves can be used to control the movement of the robot effectively. Unlike WORMESH’s pedal-wave locomotion, sidewinding gaits allow the robot to be manoeuvred in any direction without changing the joint sequence. Relative to the wave propagation direction, velocity in the longitudinal direction is dependent on the vertical component of sidewinding travelling waves. Moreover, velocity in the transverse direction depends on the horizontal component of sidewinding travelling waves. The velocity in the longitudinal direction becomes zero when the phase shift of the travelling waves equals $$\pi $$ π rad. The angular velocity around the instantaneous centre of rotation depends on the wave amplitude of the horizontal component of the sidewinding travelling wave along the kinematic chains, and the turning radius is proportional to the amplitude gradient along the kinematic chains. The dynamic simulation of WORMESH-II and testing with the WORMESH-II prototype confirmed the proposed method, which was based on the metamathematical explanation of locomotion.
Die Prüfverfahren von Applikationstechnik für Pflanzenschutzmittel sind aufgrund der meist erforderlichen Praxisversuche sehr aufwendig. Aus diesem Grund ist der Einsatz von Simulationsmethoden wünschenswert. Ein Gesamtmodell zur Abbildung einer Pflanzenschutzdüse und des zugehörigen Spritzfächers konnte bisher nicht entwickelt werden. Jedoch existieren für einzelne Teilprozesse zur Simulation der Abdrift in der CFD (Computational Fluid Dynamics) bereits Modelle, die aber oft nicht vollständig validiert sind. Für ein Gesamtmodell eignet sich die Diskrete-Elemente-Methode (DEM) u. a. aufgrund der Analogie zwischen Tropfen und den simulierten Partikeln sowie der einfachen Kontakterkennung bei der Benetzung. Die Abbildung des Sprühkegels mit der DEM stellt die erste Herausforderung dar, ein Ansatz hierfür wird im vorliegenden Beitrag vorgestellt. Auf Messdaten basierend wird in der Simulation ein Tropfenspektrum 100 mm unterhalb der Düse erzeugt und zur Validierung das Spektrum sowie die Querverteilung 500 mm unterhalb der Düse gemessen. Das Tropfenspektrum zeigt eine hohe Übereinstimmung, während bei der Querverteilung leichte Abweichungen zur Messung vorliegen.
Currently, in the course of the German mobility transition, an increasing number of disused rail lines are already being or intended to be reactivated in order to increase capacities, decrease transport-related emissions and reconnect rural areas to passenger rail services, thus creating a more comprehensive rail service. However, the use of state-of-the-art regional railcars on old rural infrastructure often leads to problems since they are often worn out and do not meet today’s technical standards. This applies, for example, to the axle loads and dimensions of the vehicles but also to operational aspects, such as the vehicle’s passenger capacity and accessibility. First, this work gives an overview of the available rolling stock and the given infrastructure, as well as an analysis of the (system) interfaces. Subsequently, various challenges for the re-connection of peripheral areas to the rail network were identified through data research and comparison of the vehicle and infrastructure parameters. In addition, the requirements related to possible autonomous operation and the related absence of the driver and crew personnel in the vehicle, which require new solutions in terms of safety, were taken into consideration. Orientation of future rolling stock generations towards the existing infrastructure and the required transport needs, including lower axle loads, accessibility and smaller capacities, can contribute to the economic operation of low-capacity lines and bring more passengers to public transport.
Mechanical engineering and machinery, Machine design and drawing
Alex Coiret, Pierre-Olivier Vandanjon, Romain Noël
Energy moderation of the road transportation sector is required to limit climate change and to preserve resources. This work is focused on the moderation of vehicle consumption by optimizing the speed policy along an itinerary while taking into account vehicle dynamics, driver visibility and the road’s longitudinal profile. First, a criterion is proposed in order to detect speed policies that are impeding drivers’ eco-driving ability. Then, an energy evaluation is carried out and an optimization is proposed. A numerical application is performed on a speed limiting point with 20 usage cases and 5 longitudinal slope values. In the hypothesis of a longitudinal slope of zero, energy savings of 27.7 liter per day could be realized by a speed sign displacement of only 153.6 m. Potential energy savings can increase to up to 308.4 L per day for a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> slope case, or up to 70.5 L per day for an ordinary <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> slope, with a sign displacement of only 391.5 m. This results in a total of 771,975 L of fuel savings over a 30 year infrastructure life cycle period. Therefore a methodology has been developed to help road managers optimize their speed policies with the aim of moderating vehicle consumption.
Mechanical engineering and machinery, Machine design and drawing
This paper presents a novel method for measuring the data for evaporation estimation as the key ingredient for the final decision of the reclamation form in the area of the Most Basin. The area has been intensively mined for many decades, resulting in significant landscape devastation, loss of natural habitats, and negative environmental impact. Currently, it is assumed that by 2050, three large-scale reclamation projects will be implemented in the area and it is necessary to decide which form of reclamation to choose. Whether to build lakes according to the currently valid rehabilitation and reclamation plan or to leave the area of the quarries in succession with the support of spontaneous inflow of water up to a naturally sustainable water level. Whether the first or second option is approved, or a combination of both, the prediction of evaporation from the free water surface will always be of great importance. To deal with this goal, the available meteorological data must be combined with a suitable calculation method. In our work, we suggest utilizing a measuring network of meteorological devices that describe the character of the weather in a given area of interest in a long-term time series. Together with the state-of-the-art calibration of models for calculating evaporation, the measurement network helps to provide more accurate evaporation data for a given area. Based on the analysis of research results, it will be possible to choose a specific right decision and thus contribute to the long-term sustainability of these reclamations.
Machine design and drawing, Engineering machinery, tools, and implements
Mohammadreza Ebrahimi, Yidong Chai, Hao Helen Zhang
et al.
Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.
Digitization of scanned Piping and Instrumentation diagrams(P&ID), widely used in manufacturing or mechanical industries such as oil and gas over several decades, has become a critical bottleneck in dynamic inventory management and creation of smart P&IDs that are compatible with the latest CAD tools. Historically, P&ID sheets have been manually generated at the design stage, before being scanned and stored as PDFs. Current digitization initiatives involve manual processing and are consequently very time consuming, labour intensive and error-prone.Thanks to advances in image processing, machine and deep learning techniques there are emerging works on P&ID digitization. However, existing solutions face several challenges owing to the variation in the scale, size and noise in the P&IDs, sheer complexity and crowdedness within drawings, domain knowledge required to interpret the drawings. This motivates our current solution called Digitize-PID which comprises of an end-to-end pipeline for detection of core components from P&IDs like pipes, symbols and textual information, followed by their association with each other and eventually, the validation and correction of output data based on inherent domain knowledge. A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper. In addition, we have created an annotated synthetic dataset, Dataset-P&ID, of 500 P&IDs by incorporating different types of noise and complex symbols which is made available for public use (currently there exists no public P&ID dataset). We evaluate our proposed method on this synthetic dataset and a real-world anonymized private dataset of 12 P&ID sheets. Results show that Digitize-PID outperforms the existing state-of-the-art for P&ID digitization.
This systematic literature review explores the evolution, application, and performance of credit risk assessment models in emerging economies, with a focused lens on Bangladesh’s commercial banking sector. In an environment marked by institutional constraints, limited data infrastructure, and evolving regulatory frameworks, selecting the appropriate credit risk model is critical for financial stability and inclusion. Drawing from a total of 98 peer-reviewed studies published up to 2022, this review synthesizes evidence from academic and applied research to evaluate traditional statistical models—such as logistic regression and discriminant analysis—as well as machine learning approaches including support vector machines, decision trees, and neural networks. The review follows the PRISMA 2020 guidelines to ensure transparency, replicability, and methodological rigor throughout the review process. Key findings indicate that while machine learning models consistently outperform traditional models in terms of predictive accuracy, they are rarely adopted at scale due to concerns about model interpretability, regulatory acceptance, and institutional readiness. Furthermore, the review identifies major gaps in sector-specific model development, integration of alternative and real-time data, and post-deployment performance monitoring. The synthesis reveals that most models are designed generically, with limited adaptation to specific industries such as garments, agriculture, SMEs, and microfinance, thereby reducing their predictive relevance in context. Additionally, institutional barriers including lack of analytical expertise, fragmented IT infrastructure, and vague regulatory guidelines hinder the operationalization of advanced credit risk tools. The findings emphasize the necessity of aligning model sophistication with contextual realities, and the importance of balancing predictive performance with explainability and institutional capacity. This review offers an evidence-based foundation for policymakers, banking professionals, and researchers seeking to develop more inclusive, accurate, and operationally viable credit risk models in emerging-market financial ecosystems.
Abstract Metal additive manufacturing (AM) is revered for the design freedom it brings, but is it environmentally better or worse than conventional manufacturing? Since few direct comparisons are published, this study compared AM data from life-cycle assessment literature to conventional manufacturing data from the Granta EduPack database. The comparison included multiple printing technologies for steel, aluminum, and titanium. Results showed that metal AM had far higher CO2 footprints per kg of material processed than casting, extrusion, rolling, forging, and wire drawing, so it is usually a less sustainable choice than these. However, there were circumstances where it was a more sustainable choice, and there was significant overlap between these circumstances and aerospace industry use of metal AM. Notably, lightweight parts reducing embodied material impacts, and reducing use-phase impacts through fuel efficiency. Finally, one key finding was the irrelevance of comparing machining to AM per kg of material processed, since one is subtractive and the other is additive. Recommendations are given for future studies to use more relevant functional units to provide better comparisons.
Martin Dorynek, Lisa-Theres Derle, Martin Fleischer
et al.
Due to changes in mobility and the emergence of new services, it is becoming necessary to establish new vehicle classes between conventional buses and privately owned vehicles. New mobility scenarios need concrete specifications to develop the most user-centered shuttle buses. As a result, we are looking for the requirements and needs of operators and customers. Initially, we want to determine the status quo, as there is no preliminary work in this regard. During the course of extensive literature research, expert interviews, and follow-up workshops, the respective solution space was highlighted and narrowed down. Services such as ride-pooling require adapted vehicle concepts to ensure optimal implementation of their offer. Due to its optimized processes, the automotive industry depends on producing vehicles in a certain quantity and manner. Faster changes and extensive experiments are not possible with the current production approach. Purpose-built vehicle concepts can make mobility services more attractive to customers while facilitating business operations. For instance, potential improvements can be identified in the seating concept.
Mechanical engineering and machinery, Machine design and drawing
Eugenio Fernández, Abel Ortego, Alicia Valero
et al.
The measurement of NO<sub>x</sub> emissions in vehicles has so far been exclusively carried out during the type-approval process. For this purpose, high-precision gas measurement laboratory equipment and Portable Emission Measurement Systems (PEMS) are used. Both types of equipment are costly in terms of price, maintenance, complexity, and time of use (calibration and maintenance requirements). Currently, NO<sub>x</sub> emissions measurements in Periodic Technical Inspections (PTIs) are being considered, but PEMS or laboratory equipment is unsuitable for this function, and PTI-grade equipment has to be used. Although CO and O<sub>2</sub> are currently being reliably measured with this equipment, there is not enough information about its accuracy for NO<sub>x</sub> measurements. Accordingly, in this paper, simultaneous measures have been performed over the same engine in a test cell, with a laboratory and a PTI gas analyser to assess the accuracy of the latter. When performing the test with the most similar conditions found in PTI, our results show that the PTI gas analyser shows an average deviation of 2.6 ppm and 9% rel. with respect to high-precision laboratory equipment for concentrations below 700 ppm NO<sub>x</sub>, which can be considered acceptable for periodic technical inspections.
Mechanical engineering and machinery, Machine design and drawing