Adrian Esser, Fabian Müller, Julia Manczurowsky
et al.
The Myoshirt, an active exosuit, provides gravity compensation for the shoulders. This study evaluated the impact of the Myoshirt on range of motion (ROM), endurance, and activities of daily living (ADLs) performance through tests involving nine participants with varying levels of arm impairments and diverse pathologies. Optical motion capture was used to quantify ROM of the shoulder and elbow joints during isolated movements and functional tasks. Endurance was quantified through a timed isometric shoulder flexion task, and a battery of ADL tasks was used to measure the perceived support of the exosuit, along with changes in movement quality. Feedback and usability insights were gathered with surveys. The Myoshirt did not significantly improve ROM during isolated movements (shoulder flexion, shoulder abduction, and elbow flexion/extension), but during the reaching phase of a functional drinking task elbow extension increased significantly by 13.5% (t = 7.52, p = .002). Participants could also keep their arms elevated 78.7% longer (t = 1.942, p = .047). Patients also reported less perceived difficulty with ADLs while using the device, and a therapist reported improved execution quality. Participants who self-reported severe impairment levels tended to derive greater benefits compared to those with milder impairments. These findings highlight the potential of the Myoshirt as an assistive device, particularly for individuals with severe impairments, while emphasizing the need for further refinement.
Sahar Ghatrehsamani, Mohammad Silani, Saleh Akbarzadeh
et al.
Tribological phenomena such as wear, friction, and lubrication critically influence the performance and durability of agricultural machinery components. This paper presents a comprehensive review of various computational methods employed for predicting tribological behavior in agricultural equipment. The surveyed techniques include mathematical modeling, finite element analysis (FEA), discrete element method (DEM), computational fluid dynamics (CFD), and artificial intelligence (AI)-based approaches such as artificial neural networks (ANN) and machine learning (ML). The review summarizes key findings, advantages, and limitations of each method, highlighting their applicability to different components and operating conditions. Additionally, the paper discusses future research directions including green tribology and continuous damage mechanics, aiming to support sustainable and efficient agricultural machinery design. This work serves as a valuable resource for researchers and engineers seeking advanced predictive tools in agricultural tribology.
Engineering design risks could cause unaffordable losses, and thus risk assessment plays a critical role in engineering design. On the other hand, the high complexity of modern engineering designs makes it difficult to assess risks effectively and accurately due to the complex two-way, dynamic causal-effect risk relations in engineering designs. To address this problem, this paper proposes a new risk assessment method called token fuzzy cognitive map (Token-FCM). Its basic idea is to model the two-way causal-risk relations with the FCM method, and then augment FCM with a token mechanism to model the dynamics in causal-effect risk relations. Furthermore, the fuzzy sets and the group decision-making method are introduced to initialize the Token-FCM method so that comprehensive and accurate risk assessments can be attained. The effectiveness of the proposed method has been demonstrated by a real example of engine design for a horizontal directional drilling machine.
In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this paper presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.
Sebe Vanbrabant, Gustavo Rovelo Ruiz, Davy Vanacken
While the increased integration of AI technologies into interactive systems enables them to solve an increasing number of tasks, the black-box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models. While this makes individual AI models clearer, the overarching system architecture remains opaque. This challenge not only pertains to standard XAI techniques but also to human examination and conversational XAI approaches that need access to model internals to interpret them correctly and completely. To this end, we propose conceptually representing such interactive systems as sequences of structural building blocks. These include the AI models themselves, as well as control mechanisms grounded in literature. The structural building blocks can then be explained through complementary explanatory building blocks, such as established XAI techniques like LIME and SHAP. The flow and APIs of the structural building blocks form an unambiguous overview of the underlying system, serving as a communication basis for both human and automated agents, thus aligning human and machine interpretability of the embedded AI models. In this paper, we present our flow-based approach and a selection of building blocks as MATCH: a framework for engineering Multi-Agent Transparent and Controllable Human-centered systems. This research contributes to the field of (conversational) XAI by facilitating the integration of interpretability into existing interactive systems.
Maria Veronica Lipreri, Marilina Tamara Totaro, Julia Alicia Boos
et al.
The advancement of personalized treatments in oncology has garnered increasing attention, particularly for rare and aggressive cancer with low survival rates like the bone tumors osteosarcoma and chondrosarcoma. This study introduces a novel PDMS–agarose microfluidic device tailored for generating patient-derived tumor spheroids and serving as a reliable tool for personalized drug screening. Using this platform in tandem with a custom imaging index, we evaluated the impact of the anticancer agent doxorubicin on spheroids from both tumor types. The device produces 20 spheroids, each around 300 µm in diameter, within a 24 h timeframe, facilitating assessments of characteristics and reproducibility. Following spheroid generation, we measured patient-derived spheroid diameters in bright-field images, calcein AM-positive areas/volume, and the binary fraction area, a metric analyzing fluorescence intensity. By employing a specially developed equation that combines viability signal extension and intensity, we observed a substantial decrease in spheroid viability of around 75% for both sarcomas at the highest dosage (10 µM). Osteosarcoma spheroids exhibited greater sensitivity to doxorubicin than chondrosarcoma spheroids within 48 h. This approach provides a reliable in vitro model for aggressive sarcomas, representing a personalized approach for drug screening that could lead to more effective cancer treatments tailored to individual patients, despite some implementation challenges.
Soft computing techniques, with their self-learning capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling complex machining processes. This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. The hybrid nanofluid was created by combining 50�50% multi-walled carbon nanotubes and aluminum oxide nanoparticles with vegetable-based palm oil. ANFIS and ANN models were constructed with data from well-designed machining trials. The ANFIS model predicted machining performance using fuzzy logic, whereas the ANN model employed a feedforward neural network design. The results showed that both models were able to accurately predict the machining performance. However, ANFIS outperforms ANN in terms of accuracy, with prediction errors of 4.47% and 10.97% for surface roughness, and 6.05% and 9.86% for tool life, respectively. However, the accuracy of cutting force prediction was slightly higher with the ANN. This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718. However, this study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques.
Mechanical engineering and machinery, Structural engineering (General)
AbstractWhen the loader boom is lifted and lowered, the hydraulic pump operates at high peak power levels. In this process, the hydraulic valve port dissipates the gravitational potential energy of the boom. Consequently, the dissipated energy is converted into thermal energy, resulting in elevated hydraulic oil temperature and reduced energy efficiency. This paper proposes a gravitational potential energy recovery system based on three-chamber cylinder and a hydraulic accumulator. The system utilizes the hydraulic accumulator to balance the weight of the loading boom and achieve energy recovery for the boom. The co-simulation model of wheel loader based on the three-chamber cylinder was built in SimulationX, then the energy consumption was analyzed under two kinds of typical operating modes. The simulation results illustrate that when the initial pressures of accumulators are 6 and 8 MPa with heavy-load and without load, the system has the highest energy utilization rate. By establishing a loader prototype and investigating the operating characteristics and energy efficiency of the boom driven by a two-chamber cylinder and a three-chamber cylinder, the experimental results illustrate that the new system operates smoothly, reducing energy consumption by 39.24% and peak power by 27.41%.
AbstractThe movable pulley block failure can lead to catastrophic crane accidents, so the dynamic performance of a defective movable pulley block system is essential. Based on ANSYS contact analysis through APDL command, a pulley block simulation platform is developed, which can be used for dynamic analysis of defective pulley systems. Firstly, a parameterized 2D contact model is established and validated through static analysis. Then, dynamic simulation analysis for the intact pulley block under two working conditions is performed: lifting from the ground and sudden unloading. Finally, COMBIN 37 is used as a defective element, and dynamic simulations are analyzed for the faulty pulley system with hook or wire rope rupture. The analysis results show that the longer the unloading time and the shorter the wire rope length will lead to a minor impact under hook rupture and sudden unloading conditions. Meanwhile, if the unloading time is consistent, the dynamic analysis of the intact pulley block under sudden unloading and defective pulley block with hook rupture are equivalent. It is worth noting that when the steel wire ropes on both sides of the moving pulley break simultaneously in a defective system, it will cause a more significant horizontal impact force. This study demonstrates that ANSYS contact analysis with COMBIN 37 as the defective element can accurately and efficiently apply the dynamic simulation of a crane movable pulley system with defects.
The effectiveness of model-driven software engineering (MDSE) has been successfully demonstrated in the context of complex software; however, it has not been widely adopted due to the requisite efforts associated with model development and maintenance, as well as the specific modelling competencies required for MDSE. Concurrently, artificial intelligence (AI) methods, particularly deep learning methods, have demonstrated considerable abilities when applied to the huge code bases accessible on open-source coding platforms. The so-called big code provides the basis for significant advances in empirical software engineering, as well as in the automation of coding processes and improvements in software quality with the use of AI. The objective of this paper is to facilitate a synthesis between these two significant domains of software engineering (SE), namely models and AI in SE. The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, ai4se. In light of the aforementioned considerations, a vision of AI-assisted big models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development. Finally, the pair modelling paradigm is proposed for adoption by the MDSE industry.
We consider a quantum Otto-type heat engine constructed in an optomechanical system with which the cavity is chosen as the working substance. The cavity can effectively be coupled with hot thermal baths in nonequilibrium steady-states via optomechanical interaction. The mechanical mode with parametric drive fuels the cavity, and the utilization efficiency of energy is discussed. To obtain higher efficiency in finite time evolution, we use the shortcuts-to-adiabaticity method in work generation processes. The modified thermal efficiencies are obtained by numerical simulation. Such a system provides potential applications in quantum heat transfer and energy utilization in quantum devices.
The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, because their effectiveness hinges on the availability of an ample supply of high-quality task-specific labeled data. Moreover, previous works overlook the high-level semantics present in source code and its documentation. Acknowledging the abundance of available source code with documentation, which can be compiled into WebAssembly, we propose to learn representations of them concurrently and harness their mutual relationships for effective WebAssembly reverse engineering. In this paper, we present WasmRev, the first multi-modal pre-trained language model for WebAssembly reverse engineering. WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data. WasmRev incorporates three tailored multi-modal pre-training tasks to capture various characteristics of WebAssembly and cross-modal relationships. WasmRev is only trained once to produce general-purpose representations that can broadly support WebAssembly reverse engineering tasks through few-shot fine-tuning with much less labeled data, improving data efficiency. We fine-tune WasmRev onto three important reverse engineering tasks: type recovery, function purpose identification and WebAssembly summarization. Our results show that WasmRev pre-trained on the corpus of multi-modal samples establishes a robust foundation for these tasks, achieving high task accuracy and outperforming the state-of-the-art ML methods for WebAssembly reverse engineering.