Andrii Herasymenko, Leonid Shyrin, Rostyslav Yegorchenko
et al.
This article presents a comprehensive study on the management and optimization of frame-anchor support systems for seam preparatory excavations designed for the transportation of large-tonnage cargo via suspended monorail transport. With the intensification of underground mining operations and the increasing use of heavy mechanized transport, ensuring the stability and safety of mine workings under dynamic load conditions has become a critical challenge for engineering. The research proposes an innovative support technology based on the combined fastening of monorail systems to the crowns of metal arches and directly to the roof using deep-embedded anchors. This approach aims to reduce dynamic impacts on the excavation roof and improve the overall reliability of the support system. To evaluate the effectiveness of the proposed support design, a numerical modelling method was employed to simulate the interaction of components within the dynamic system “suspended monorail – support – rock mass.” The stress-strain behaviour of the frame-anchor structure under real load scenarios was analyzed using SolidWorks Simulation software. During the simulation, various parameters were systematically varied, including the spacing of support frames, the length and anchorage depth of the rock bolts, and the mechanical properties of the surrounding rock mass. The results of the analysis enabled the identification of rational design parameters that minimize deformation and enhance load-bearing capacity. In particular, optimal combinations of frame spacing and anchor configurations were found to significantly reduce stress concentrations and improve the stability of preparatory workings under dynamic loading from moving monorail trains. The study demonstrates that effective management of support system parameters can lead to improved safety, reduced material consumption, and faster development of mining panels. The findings have practical significance for the design of underground transport routes and can be incorporated into normative documents governing support systems in dynamically loaded mine environments.
Replication packages are crucial for enabling transparency, validation, and reuse in software engineering (SE) research. While artifact sharing is now a standard practice and even expected at premier SE venues such as ICSE, the practical usability of these replication packages remain underexplored. In particular, there is a marked lack of studies that comprehensively examine the executability and reproducibility of replication packages in SE research. In this paper, we aim to fill this gap by evaluating 100 replication packages published in ICSE proceedings over the past decade (2015 - 2024). We assess the (1) executability of the replication packages, (2) efforts and modifications required to execute them, (3) challenges that prevent executability, and (4) reproducibility of the original findings for those that are executable. We spent approximately 650 person-hours in total to execute the artifacts and reproduce the study findings. Our analysis shows that only 40 of the 100 evaluated artifacts were fully executable. Among these, 32.5% ran without any modification. However, even executable artifacts required varying levels of effort: 17.5% required low effort, while 82.5% required moderate to high effort to execute successfully. We identified five common types of modifications and 13 challenges that lead to execution failure, encompassing environmental, documentation, and structural issues. Among the executable artifacts, only 35% (14 out of 40) reproduced the original results. These findings highlight a notable gap between artifact availability, executability, and reproducibility. Our study proposes three actionable guidelines to improve the preparation, documentation, and review of research artifacts, thereby strengthening the rigor and sustainability of open science practices in SE research.
IntroductionA reasonable advanced support system is crucial for ensuring stable excavation in tunnels with weak surrounding rock. This study aims to address the problems of instability, large deformation, and prolonged construction period in the sidewalls of tunnel faces in plateau high-geostress carbonaceous slate tunnels in western mountainous areas.MethodsBased on lithology analysis and creep tests, we propose enhanced over-head pre-support, rapid initial support closure, and increased steel arch stiffness. The proposed approach was validated via numerical modeling and field tests.ResultsResults show that compared to no forepoling (Case I), Cases II–V reduced deformation by 17%, 21%, 44%, and 46%, respectively. The additional arch over-support improves stability and reduces sidewall deformation. The combined "arch-wall over-support + HW175 steel arch + 6m anchors" system shortened initial support formation from 20 to 12 days.DiscussionThese findings support the design of active-passive support systems for high-stress weak rock tunnels and offer insights for similar projects.
Task-specific off-highway vehicles are typically produced in small volumes, so limited resources must be used in their design. The fuel efficiency benefits of hybridizing an off-highway vehicle are typically in the range of 10–30%, meaning that a simulation tool should ideally be able to predict fuel usage within about ±10%, to support stage-gate design decisions. However, such simulation tools typically require significant cost, setup effort, and simulation expertise. A wheel loader and four agricultural tractors were analyzed with a new tool, “ePOP Concept (v1.0)” from ZeBeyond Ltd. of Leamington Spa, UK, to estimate the benefits of electrification. This method is quick to set up, requiring minimal data preparation and simulation expertise. The results were compared with measured fuel consumption data, and with those of commercially available analysis tools. The errors deriving from ePOP Concept’s BSFC assumptions alone were large at 17% RMS when using a generic value for engine BSFC, but could be improved to 6.7% RMS when applying a readily available minimum BSFC value in the model setup. For future development, a target accuracy of ±10% could potentially be achieved with one-dimensional loss models, requiring minimal extra setup effort, while reducing the subject BSFC errors to 3.9% RMS.
Offshore wind turbines positioned in deepwater areas are increasingly favored due to them providing superior and stable wind resources. However, the dynamic stability of floating offshore wind turbines (FOWTs) under complex environmental loading remains challenging. This study proposes a novel semi-submersible platform featuring a fractal structure inspired by the venation of Victoria Amazonica and investigates the effects of fractal branching level and biomimetic structural height on platform motions, with the aim of enhancing the overall system stability of FOWTs. Within a high-fidelity computational fluid dynamics (CFD) framework coupled with a dynamic fluid–body interaction (DFBI) model and a volume-of-fluid (VOF) wave model, the dynamic responses of the biomimetic platform are investigated under varying fractal dimensions (<i>D</i><sub>f</sub>) and structural heights. The results indicate that increasing fractal complexity enhances the local wall viscosity effect, significantly improving energy dissipation capabilities within the fractal cavities. Specifically, an eight-level fractal structure shows optimal performance, achieving reductions of approximately 16.94%, 23.26%, and 35.63% in heave, pitch, and rotational energy responses, respectively. Additionally, the increasing fractal height further strengthens energy dissipation, markedly enhancing stability, particularly in pitch motion. These findings underscore the substantial potential of biomimetic fractal designs in enhancing the dynamic stability of FOWTs.
Abstract Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.
Objective Traditional methods for detecting the installation effect of SmMSC (switch machine movable/static contact) groups are slow, inaccurate, and susceptible to human errors. It is necessary to introduce a fast and high-precision image detection technology based on key point recognition, aiming to make research on the installation effect detection method for SmMSC groups. Method The detection process of the above-mentioned detection method is described in detail. 24 key recognition points of the SmMSC groups are introduced, along with the calculation method for the contact depth of moving contacts and spacing between the bases. Through test analysis of different combination models, the selected optimal key point recognition model is based on the pose detection algorithm in the YOLOv8 visual framework, incorporating the BiFormer dual-encoder attention mechanism and SCConv (spatial and channel reconstruction convolution) efficient convolution module. The functions of the auxiliary shooting frame and perspective correction transformation are also described. Result & Conclusion Recognition time of the optimal key point recognition model is only 1.3 milliseconds, with a recognition accuracy reached 96.3%. The installation effect inspection method of SmMSC groups based on key point recognition achieves an image recognition rate of 99.8% for the dynamic and static contact groups, with a calculation accuracy of ±0.1 mm, and an average recognition error lower than 0.3%, only 2 seconds for each group′s detection. It′s obvious that this method is significantly more intelligent and efficient compared to manual detection methods.
This paper evaluates the contributing factors to maritime dangerous goods (DG) transport accidents by integrating the Entropy Weight (EW) and Grey Relational Analysis (GRA) methods. For this purpose, investigation reports of maritime DG transport accidents that occurred worldwide between 2000 and 2023 are derived from the International Maritime Organization’s Integrated Shipping Information System (IMO GISIS) database’s Marine Casualties and Incidents (MCI) module. Eleven main ship operations and thirteen primary causes were selected by analysing accident investigation reports. The weights of main ship operations are calculated utilizing the EW method. The correlational degrees of the primary causes are then calculated using the GRA method. Most maritime DG transport accidents occur during unberthing, bunkering, and pilotage operations. The most common contributing factors of maritime DG transport accidents are collisions and occupational accidents. Specifically, maritime DG transport accidents are most likely to be caused by collisions during sailing, passage, maneuvering, and bunkering operations, as well as occupational accidents during cargo loading, anchoring, berthing, and mooring operations. The results of this paper can support stakeholders in developing the needed policies to guarantee the safety of maritime DG transport.
Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.
Takamasa Hirai, Toshiaki Morita, Subrata Biswas
et al.
Thermal conductivity, a fundamental parameter characterizing thermal transport in solids, is typically determined by electron and phonon transport. Although other transport properties including electrical conductivity and thermoelectric conversion coefficients have material-specific values, it is known that thermal conductivity can be modulated artificially via phonon engineering techniques. Here, we demonstrate another way of artificially modulating the heat conduction in solids: magnonic thermal transport engineering. The time-domain thermoreflectance measurements using ferromagnetic metal/insulator junction systems reveal that the thermal conductivity of the ferromagnetic metals and interfacial thermal conductance vary significantly depending on the spatial distribution of nonequilibrium spin currents. Systematic measurements of the thermal transport properties with changing the boundary conditions for spin currents show that the observed thermal transport modulation stems from magnon origin. This observation unveils that magnons significantly contribute to the heat conduction even in ferromagnetic metals at room temperature, upsetting the conventional wisdom that the thermal conductivity mediated by magnons is very small in metals except at low temperatures. The magnonic thermal transport engineering offers a new principle and method for active thermal management.
Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar
et al.
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
Sanjita Prajapati, Tanu Singh, Chinmay Hegde
et al.
Recent developments in vision language models (VLM) have shown great potential for diverse applications related to image understanding. In this study, we have explored state-of-the-art VLM models for vision-based transportation engineering tasks such as image classification and object detection. The image classification task involves congestion detection and crack identification, whereas, for object detection, helmet violations were identified. We have applied open-source models such as CLIP, BLIP, OWL-ViT, Llava-Next, and closed-source GPT-4o to evaluate the performance of these state-of-the-art VLM models to harness the capabilities of language understanding for vision-based transportation tasks. These tasks were performed by applying zero-shot prompting to the VLM models, as zero-shot prompting involves performing tasks without any training on those tasks. It eliminates the need for annotated datasets or fine-tuning for specific tasks. Though these models gave comparative results with benchmark Convolutional Neural Networks (CNN) models in the image classification tasks, for object localization tasks, it still needs improvement. Therefore, this study provides a comprehensive evaluation of the state-of-the-art VLM models highlighting the advantages and limitations of the models, which can be taken as the baseline for future improvement and wide-scale implementation.
The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.
In this paper, reinforcement learning (RL) for network slicing is considered in next generation (NextG) radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt NextG network slicing. The adversary observes the spectrum and builds its own RL based surrogate model that selects which RBs to jam subject to an energy budget with the objective of maximizing the number of failed requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of both the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense schemes such as suspending the RL algorithm's update once an attack is detected, introducing randomness to the decision process in RL to mislead the learning process of the adversary, or manipulating the feedback (NACK) mechanism such that the adversary may not obtain reliable information are introduced to show that it is viable to defend NextG network slicing against this attack, in terms of improving the RL algorithm's reward.
Transportation engineering, Transportation and communications