José Peixoto, Alexis Gonzalez, Janki Bhimani
et al.
Programmable caching engines like CacheLib are widely used in production systems to support diverse workloads in multi-tenant environments. CacheLib's design focuses on performance, portability, and configurability, allowing applications to inherit caching improvements with minimal implementation effort. However, its behavior under dynamic and evolving workloads remains largely unexplored. This paper presents an empirical study of CacheLib with multi-tenant settings under dynamic and volatile environments. Our evaluation across multiple CacheLib configurations reveals several limitations that hinder its effectiveness under such environments, including rigid configurations, limited runtime adaptability, lack of quality-of-service support and coordination, which lead to suboptimal performance, inefficient memory usage, and tenant starvation. Based on these findings, we outline future research directions to improve the adaptability, fairness, and programmability of future caching engines.
Federico Allione, Maria Lazzaroni, Antonios E. Gkikakis
et al.
Musculoskeletal disorders, particularly low back pain, are some of the most common occupational health issues globally, causing significant personal suffering and economic burdens. Workers performing repetitive manual material handling tasks are especially at risk. FleXo, a lightweight (1.35 kg), flexible, ergonomic, and passive back-support exoskeleton is intended to reduce lower back strain during lifting tasks while allowing full freedom of movement for activities like walking, sitting, or side bending. FleXo’s design results from an advanced multi-objective design optimization approach that balances functionality and user comfort. In this work, validated through user feedback in a series of relevant repetitive tasks, it is demonstrated that FleXo can reduce the perceived physical effort during lifting tasks, enhance user satisfaction, improve employee wellbeing, promote workplace safety, decrease injuries, and lower the costs (both to society and companies) associated with lower back pain and injury.
Mechanical engineering and machinery, Electronic computers. Computer science
ObjectiveTo simulate the meshing state of gear teeth under actual working conditions and reveal the meshing transmission mechanism of gears under actual working conditions, the flow field model of gear meshing process was established based on the multiphase flow model and the dynamic mesh method.MethodsThe flow field model was calculated and solved. The initial calculation parameters such as oil pressure, tooth surface temperature and convective heat transfer coefficient were provided for the thermal-solid coupling analysis of titanium alloy gears. The grain anisotropy distribution information inside the gear was introduced through the nanoindentation. Finally, the gear fluid-thermal-solid multi-field microcrystal model was established based on the Voronoi method and the Python secondary development, and the flow field, temperature field and stress field during the gear meshing transmission were calculated. The difference between the traditional homogeneous finite element model and the microcrystalline heterogeneous finite element model in the maximum contact stress and peak temperature of the tooth surface was compared and analyzed.ResultsThe results show that the meshing thermal stress calculated by the microcrystalline model is smaller, and the stress distribution is more dispersed than that by the traditional finite element model. Because the influence of grain inhomogeneity on the temperature and stress in the meshing process is fully considered in the modeling, the microcrystalline model can more truly reflect the meshing state of the gear teeth in the actual working condition.
Meeting the needs for both renewable energy production and increased food supply to sustain growing communities remains a global challenge. Agrivoltaic greenhouses can meet these dual needs in one plot of land, mitigating land competition. Luminescent solar concentrators (LSCs) benefit these systems by providing additional design flexibility for crop-specific spectrum modification while allowing sufficient light transmission for crop growth. Silicon quantum dots (Si QDs) have received growing interest as a material candidate for LSC greenhouses as well. We present an investigation into the impact of Si QD film concentration on the energy demands of an LSC greenhouse in Phoenix, Arizona through a comprehensive modelling framework. We then expand upon one Si QD concentration and simulate LSC greenhouses in 48 locations across the United States. We demonstrate LSC greenhouses can supply their annual energy demands in warm climates, where greenhouse heating demands remain low. LSC greenhouses can also be as profitable as the conventional glass greenhouse if the crop yield remains comparable or if the greenhouse can benefit from net metering.
Michael Schwingshackl, Fabio Francisco Oberweger, Markus Murschitz
This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network (GCN) to accurately segment machinery parts. By providing 1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset depicting a truck-mounted loading crane, achieves effective segmentation across various levels of detail. Training times are kept under five minutes on consumer GPUs. The model demonstrates robust generalization to real data, achieving a qualitative synthetic-to-real generalization with a $J\&F$ score of 92.2 on real data using 10 synthetic support samples. When benchmarked on the DAVIS 2017 dataset, it achieves a $J\&F$ score of 71.5 in semi-supervised video segmentation with three support samples. This method's fast training times and effective generalization to real data make it a valuable tool for autonomous systems interacting with machinery and infrastructure, and illustrate the potential of combined and orchestrated foundation models for few-shot segmentation tasks.
Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineering approach that transforms a passive monitoring system into an interactive Type 2 DT with real-time control capabilities for pressurized systems operating at seven bar. We contribute details of multi-layered safety protocols, hardware-software integration strategies across Arduino controllers and Unity visualization, and real-time synchronization solutions. We document specific engineering challenges and solutions spanning interdisciplinary integration, demonstrating how our use of the constellation reporting framework facilitates cross-domain collaboration. Key findings include the critical importance of safety-first design, simulation-driven development, and progressive implementation strategies. Our work thus provides actionable guidance for practitioners developing DTs requiring bidirectional control in safety-critical applications.
Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the work of researchers and practitioners in learning about the coverage and main findings of existing work as well as identifying relatively unexplored niches of research that need further attention. We performed a tertiary study on quality in MDE, in order to gain a better understanding of its most prominent findings and existing challenges, as reported in the literature. We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. Eighty out of 83 research questions in the selected secondary studies have a structure that is more often associated with mapping existing research than with answering more concrete research questions (e.g., comparing two alternative MDE approaches with respect to their impact on a specific quality attribute). We briefly outline the main contributions of each of the selected literature reviews. In the collected studies, we observed a broad coverage of software product quality, although frequently accompanied by notes on how much more empirical research is needed to further validate existing claims. Relatively, little attention seems to be devoted to the impact of MDE on the quality in use of products developed using MDE.
Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.
AbstractThe display console and vehicle platform designed in this paper adopt an integrated structure design, the structure is simple and beautiful, easy to operate and easy to maintain, and the standard function module is selected as far as possible to meet the standardization requirements. Based on the design idea of generalization, serialization and combination, the display console is divided into unit design, which can be successively divided into: display unit, control unit, display and control processing unit and cabinet unit. Among them, the display unit and control unit learn from the mature design technology to ensure the design quality; The display and control processing unit is designed separately, with rigid connections to both the display unit and control unit externally, and the internal air duct is set up to meet the heat dissipation requirements. The cabinet unit is arranged under the display and control processing unit and the two are rigidly connected to ensure the overall structural rigidity and provide required installation space for user devices. The display console adopts split designs to support quick installation, disassembly and handling of four units. 3D design software is used to analyse the installation space, visual field, hand operation space, knee space and maintenance space of the display console to ensure that the display console has a good human–computer interaction function. The internal space of the display console is designed with three-dimensional wiring, the routing path of each cable in each unit is planned, and the cable can be customized in advance, which greatly improves the efficiency of on-site electrical assembly. Mechanical analysis software is used to simulate the main structure of the display console to ensure that the whole display console meets the mechanical performance requirements. The above design and analysis have certain guiding significance for similar design work of vehicle display console.
Advancements in data-driven predictive maintenance have significantly improved digital twin applications for rotating machinery, offering robust solutions for smart manufacturing challenges. These improvements are crucial since equipment failures can cause extensive and costly disruptions to both maintenance schedules and operations. As precision and reliability are critical in production processes, undetected fluctuations in operating frequencies can swiftly escalate to complete part failure, leading to prolonged repairs and productivity loss. This study explores an integrated dataflow pipeline, specifically through Siemens’ MindSphere, to enable continuous predictive maintenance and enhance data acquisition and management. Particularly, conditions such as normal operation, mass balance, rotating imbalance, and mechanical looseness are classified using support vector machine (SVM), neural network (NN), and K-Nearest Neighbor (KNN) methods for the purpose of comparing results. Our results highlight the efficacy of ensemble techniques in collecting and diagnosing vibration signatures, thereby enabling proactive maintenance. To classify various failure signatures, we have proposed a framework to interpret time-series and frequency-dependent data for determining failure types. This research exemplifies how merging data-driven methods with digital twin can improve the accuracy and reliability of condition monitoring. Additionally, we introduce a cloud-based architecture for the diagnosis of rotating machinery, utilizing Application Programming Interface (API) configurations, and develop a real-time dashboard for streaming and visualizing classified data, fostering immediate and informed decision-making.
The operating conditions of the production process significantly influence the resulting dimensional and form accuracy of the workpiece. The operating conditions include the position of the workpiece location, with internal and external heat sources influenced not only by the machine location but also by its operation. In addition, there are the cutting conditions and the feed rate requirements of CNC machine tools. These changes, such as workpiece position, feed rates, and machine heat load, are further reflected in the ability of the machine to run at the position required and interpolate within the given tolerances of circularity. For the accuracy and repeatability of positioning, the machine was set up according to ISO 230-2 and for the circular interpolation tests according to ISO 230-4. The obtained results show the importance of attention to the appropriate setting of the operating conditions of the production process, where the knowledge of the geometric accuracy of the CNC machine tool in its working space can systematically increase the manufacturing accuracy itself or be another tool suitable for predicting the dimensional and form accuracy of workpieces.
In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing.
Twisted and coiled polymer actuators (TCPAs) generate large contractile mechanical work mimicking natural muscles, which makes them suitable for robotics and health-assistive devices. Understanding the mechanism of nylon TCPA remains challenging due to the interplay between their intricate geometry, chirality, residual stresses, and material microstructure. This study integrates a material microstructure model with rod theory to analytically predict the equilibrium helical shape of the nylon TCPA after fabrication and to explain the observed contraction mechanism upon stimulation. The first ingredient of the model is to treat nylon as a two-phase thermomechanical microstructure system capable of storing strain energy and exchanging it among the two phases. This is validated by characterizing the torsional actuation response of twisted and annealed nylon fibers. The second ingredient of the model is to use the classic Kirchhoff Rod Theory and add a necessary term that couples the bending and twisting energy. Validation with experiments shows that the model captures the equilibrium and longitudinal stiffness of the TCPA in both active and passive states, and the stimulated contraction under external load. Importantly, the model quantifies the influence of the stored energy level on the actuation performance. These concepts can be extended to other types of TCPAs and could enable new material design.
AbstractThis paper focused on the rupture problem of precooler outlet compensation pipe of civil aircraft, the design concerning was discussed. Based on the design proposal, aging mechanical from two aspects of temperature and high velocity sand dust flow were calculated and tested. Root cause of pipe rupture is identified and the result shows silicon rubber could not suffer high temperature up to 250 ℃ and velocity up to 150 m/s.
AbstractAiming at a series hydraulic hybrid vehicle, the mathematical model and Matlab/Simulink simulation model of the vehicle are established, and the energy management strategy based on rules is put forward. The simulation results show that the control strategy not only realizes four working modes of hydraulic hybrid vehicle, but also improves its fuel economy by 11.9%. It is found that the volume of accumulator and the working pressure range of accumulator are two important parameters that affect the fuel economy of hydraulic hybrid vehicle. Under the condition of a certain accumulator volume, increasing the working pressure range of the accumulator can not only increase the energy storage of the accumulator, but also reduce the idling times of the engine and further improve the fuel economy of the vehicle.
We describe a method, based on Jennifer Nado's proposal for classification procedures as targets of conceptual engineering, that implements such procedures by prompting a large language model. We apply this method, using data from the Wikidata knowledge graph, to evaluate stipulative definitions related to two paradigmatic conceptual engineering projects: the International Astronomical Union's redefinition of PLANET and Haslanger's ameliorative analysis of WOMAN. Our results show that classification procedures built using our approach can exhibit good classification performance and, through the generation of rationales for their classifications, can contribute to the identification of issues in either the definitions or the data against which they are being evaluated. We consider objections to this method, and discuss implications of this work for three aspects of theory and practice of conceptual engineering: the definition of its targets, empirical methods for their investigation, and their practical roles. The data and code used for our experiments, together with the experimental results, are available in a Github repository.
Carbon-nanotube (CNT) is a promising material owing to its compelling mechanical, thermal and electrical properties and has been applied in a broad variety of fields such as composite, fiber, film and microelectronic. Although the introductions of CNT have brought huge improvement for many applications, these properties of macrostructures prepared by CNTs still cannot meet those of individual CNT. Disordered alignment of CNTs in the matrix results in degradation of performance and hinders further application. Nowadays, quantities of methods are being researched to realize alignments of CNTs. In this paper, we introduce the application of CNTs and review some typical pathways for vertical and horizontal alignment, including chemical vapor disposition, vertical self-assembly, external force, film assisted, electric field, magnetic field and printing. Besides that, advantages and disadvantages of specific methods are also discussed. We believe that these efforts will contribute to further understanding the nature of aligned CNT and generating more effective ideas to the relevant workers.