Hasil untuk "Structural engineering (General)"

S. Kiranyaz, Onur Avcı, Osama Abdeljaber et al.

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

Sihong Wang, Jie Xu, Weichen Wang et al.

Junyang He, Hao Wang, Hailong Huang et al.

A. Chopra

A. Oron, S. H. Davis, S. Bankoff

S. Doebling, C. Farrar, M. Prime

J. K. Mitchell

Hui Peng, Mengran Wang, Ling Jiang et al.

For arch dam seismic safety evaluation, the finite element equivalent stress method has been widely used, and existing studies have realized mature equivalent stress calculation along the foundation surface path. However, from the scientific research perspective, there is a lack of a full dam surface equivalent stress characterization method for arch dams under seismic action; from the engineering practice perspective, the traditional path method cannot fully reflect the overall stress distribution of the dam, leading to insufficient comprehensive safety evaluation. To accurately assess the impact of seismic action on the overall structural safety of arch dams and address the above limitations, this study develops a methodology for calculating equivalent stress across the entire dam surface of arch dams under seismic action. Taking a concrete arch dam as the research object, a seismic wave input method based on viscoelastic artificial boundaries is employed. Three-dimensional finite element analysis of the arch dam is performed using ABAQUS, integrated with Python-based secondary development to extract stress along the integration path of each arch ring layer and calculate sectional internal forces. The equivalent stress of each arch ring layer integration path is then processed using the material mechanics method to obtain the equivalent stress distribution across the entire dam surface. A comparative analysis is conducted between the equivalent stress on the entire dam surface and that along paths on the foundation surface regarding the seismic dynamic response and behavioral patterns of the dam. The results demonstrate that the full dam surface equivalent stress approach not only accurately captures the extreme tensile and compressive stress values in the downstream foundation area but also identifies stress extrema in the upstream dam crest region, thereby achieving comprehensive characterization of the dam stress field under seismic action and enhancing both the efficiency and accuracy of equivalent stress calculations for arch dams. This method provides more comprehensive and reliable data support for seismic design optimization and reinforcement of arch dams. Compared with the traditional foundation surface path method, the proposed method achieves 100% identification of the whole dam surface stress extremum areas, with a maximum relative error of only 1.62% in the overlapping calculation area.

Changyuan Zhao, Jiacheng Wang, Yunting Xu et al.

Future wireless networks demand increasingly powerful intelligence to support sensing, communication, and autonomous decision-making. While scaling laws suggest improving performance by enlarging model capacity, practical edge deployments are fundamentally constrained by latency, energy, and memory, making unlimited model scaling infeasible. This creates a critical need to maximize the utility of limited inference-time inputs by filtering redundant observations and focusing on high-impact data. In large language models and generative artificial intelligence (AI), context engineering has emerged as a key paradigm to guide inference by selectively structuring and injecting task-relevant information. Inspired by this success, we extend context engineering to wireless systems, providing a systematic way to enhance edge AI performance without increasing model complexity. In dynamic environments, for example, beam prediction can benefit from augmenting instantaneous channel measurements with contextual cues such as user mobility trends or environment-aware propagation priors. We formally introduce wireless context engineering and propose a Wireless Context Communication Framework (WCCF) to adaptively orchestrate wireless context under inference-time constraints. This work provides researchers with a foundational perspective and practical design dimensions to manage the wireless context of wireless edge intelligence. An ISAC-enabled beam prediction case study illustrates the effectiveness of the proposed paradigm under constrained sensing budgets.

Se-jeong Kim, Eun Mi Kim, Masaya Yamamoto et al.

Multi‐cellular spheroids are formed as a 3D structure with dense cell–cell/cell–extracellular matrix interactions, and thus, have been widely utilized as implantable therapeutics and various ex vivo tissue models in tissue engineering. In principle, spheroid culture methods maximize cell–cell cohesion and induce spontaneous cellular assembly while minimizing cellular interactions with substrates by using physical forces such as gravitational or centrifugal forces, protein‐repellant biomaterials, and micro‐structured surfaces. In addition, biofunctional materials including magnetic nanoparticles, polymer microspheres, and nanofiber particles are combined with cells to harvest composite spheroids, to accelerate spheroid formation, to increase the mechanical properties and viability of spheroids, and to direct differentiation of stem cells into desirable cell types. Biocompatible hydrogels are developed to produce microgels for the fabrication of size‐controlled spheroids with high efficiency. Recently, spheroids have been further engineered to fabricate structurally and functionally reliable in vitro artificial 3D tissues of the desired shape with enhanced specific biological functions. This paper reviews the overall characteristics of spheroids and general/advanced spheroid culture techniques. Significant roles of functional biomaterials in advanced spheroid engineering with emphasis on the use of spheroids in the reconstruction of artificial 3D tissue for tissue engineering are also thoroughly discussed.

Brigitte Walla, Anna-Maria Dietrich, Edwin Brames et al.

Industrial biotechnology offers a potential ecological solution for PET recycling under relatively mild reaction conditions via enzymatic degradation, particularly using the leaf branch compost cutinase (LCC) quadruple mutant ICCG. To improve the efficient downstream processing of this biocatalyst after heterologous gene expression with a suitable production host, protein crystallization can serve as an effective purification/capture step. Enhancing protein crystallization was achieved in recent studies by introducing electrostatic (and aromatic) interactions in two homologous alcohol dehydrogenases (<i>Lb</i>/<i>Lk</i>ADH) and an ene reductase (<i>Nsp</i>ER1-L1,5) produced with <i>Escherichia coli</i>. In this study, ICCG, which is difficult to crystallize, was utilized for the application of crystal contact engineering strategies, resulting in ICCG mutant L50Y (ICCGY). Previously focused on the Lys-Glu interaction for the introduction of electrostatic interactions at crystal contacts, the applicability of the engineering strategy was extended here to an Arg-Glu interaction to increase crystallizability, as shown for ICCGY T110E. Furthermore, the rationale of the engineering approach is demonstrated by introducing Lys and Glu at non-crystal contacts or sites without potential interaction partners as negative controls. These resulting mutants crystallized comparably but not superior to the wild-type protein. As demonstrated by this study, crystal contact engineering emerges as a promising approach for rationally enhancing protein crystallization. This advancement could significantly streamline biotechnological downstream processing, offering a more efficient pathway for research and industry.

Sandeep Neema, Susmit Jha, Adam Nagel et al.

We discuss the challenges and propose a framework for evaluating engineering artificial general intelligence (eAGI) agents. We consider eAGI as a specialization of artificial general intelligence (AGI), deemed capable of addressing a broad range of problems in the engineering of physical systems and associated controllers. We exclude software engineering for a tractable scoping of eAGI and expect dedicated software engineering AI agents to address the software implementation challenges. Similar to human engineers, eAGI agents should possess a unique blend of background knowledge (recall and retrieve) of facts and methods, demonstrate familiarity with tools and processes, exhibit deep understanding of industrial components and well-known design families, and be able to engage in creative problem solving (analyze and synthesize), transferring ideas acquired in one context to another. Given this broad mandate, evaluating and qualifying the performance of eAGI agents is a challenge in itself and, arguably, a critical enabler to developing eAGI agents. In this paper, we address this challenge by proposing an extensible evaluation framework that specializes and grounds Bloom's taxonomy - a framework for evaluating human learning that has also been recently used for evaluating LLMs - in an engineering design context. Our proposed framework advances the state of the art in benchmarking and evaluation of AI agents in terms of the following: (a) developing a rich taxonomy of evaluation questions spanning from methodological knowledge to real-world design problems; (b) motivating a pluggable evaluation framework that can evaluate not only textual responses but also evaluate structured design artifacts such as CAD models and SysML models; and (c) outlining an automatable procedure to customize the evaluation benchmark to different engineering contexts.

Timothé Boulet, Xavier Hinaut, Clément Moulin-Frier

Software Engineering Agents (SWE-Agents) have proven effective for traditional software engineering tasks with accessible codebases, but their performance for embodied tasks requiring well-designed information discovery remains unexplored. We present the first extended evaluation of SWE-Agents on controller generation for embodied tasks, adapting Mini-SWE-Agent (MSWEA) to solve 20 diverse embodied tasks from the Minigrid environment. Our experiments compare agent performance across different information access conditions: with and without environment source code access, and with varying capabilities for interactive exploration. We quantify how different information access levels affect SWE-Agent performance for embodied tasks and analyze the relative importance of static code analysis versus dynamic exploration for task solving. This work establishes controller generation for embodied tasks as a crucial evaluation domain for SWE-Agents and provides baseline results for future research in efficient reasoning systems.

Jungho Kim, Sang-ri Yi, Ziqi Wang

Accurate prediction of nonlinear structural responses is essential for earthquake risk assessment and management. While high-fidelity nonlinear time history analysis provides the most comprehensive and accurate representation of the responses, it becomes computationally prohibitive for complex structural system models and repeated simulations under varying ground motions. To address this challenge, we propose a composite learning framework that integrates simplified physics-based models with a Fourier neural operator to enable efficient and accurate trajectory-level seismic response prediction. In the proposed architecture, a simplified physics-based model, obtained from techniques such as linearization, modal reduction, or solver relaxation, serves as a preprocessing operator to generate structural response trajectories that capture coarse dynamic characteristics. A neural operator is then trained to correct the discrepancy between these initial approximations and the true nonlinear responses, allowing the composite model to capture hysteretic and path-dependent behaviors. Additionally, a linear regression-based postprocessing scheme is introduced to further refine predictions and quantify associated uncertainty with negligible additional computational effort. The proposed approach is validated on three representative structural systems subjected to synthetic or recorded ground motions. Results show that the proposed approach consistently improves prediction accuracy over baseline models, particularly in data-scarce regimes. These findings demonstrate the potential of physics-guided operator learning for reliable and data-efficient modeling of nonlinear structural seismic responses.

Ahmed Chead, Ahmed Abbas Obaid, Abdulrazzaq Abdzaid Hussein

The design of a controller for a chemical reactor was studied. Based on the input-output feedback linearization, the controller was designed for a situation where the output of the system is the concentration. The reaction in the reactor is of the first-order type. First, the reactor is modeled and presented, and then a controller for this system is designed. The control system was implemented in Simulink MATLAB. The simulation results show that the designed controller is able to control the concentration in a wide range and its performance is desirable for changing either the disturbances or the set point.

Mays Mousawi, Ali Abdul Razzaq Altahir, Asseel Majeed AL- Gaheeshi

Abstract In both the agricultural and industrial sectors, pumping water is essential. Due to its arid climate, our Saharan area has a substantial solar energy resource, making constructing a PV solar irrigation system possible. Given solar resources' unpredictable and intermittent nature, it is imperative to set up a system that permits optimal usage. This study aims to design and simulate a solar pumping system's effective nonlinear direct torque command. A solar generator provides an asynchronous three-phase machine coupled to a centrifugal pump as part of the system. MPPT monitors the boost converter via duty cycle. The solar generator's functioning at full power will be ensured. The first uses PSO in the MPPT system to supply the motor pump with three phases of power. The second one employs direct torque command (DTC) to regulate the operation of a centrifugal pump coupled with an induction machine. More advancements will be made to the DTC scenario. The proposed mathematical model of the solar irrigation system was simulated using a MATLAB simulation environment, adopting accurate data provided by Fadak Farm. The maximum power extracted from PV tends to peak at 280 Wp with the assistance of particle swarm optimization that energizes the use of the centrifugal pump for the proposed irrigation system. It is evident from numerical simulation results based on accurate input data that the power extracted from PV solar is positively affected by solar radiation and surface temperature variations. It is clear from simulation results that the speed of three–phase I.M motor converges to 22.5 rad/sec, and water flow pumping by centrifugal is decreased and increased, converging to 7.5 *10-8 m/s with the variation of solar irradiance.

Anabela Veiga, Inês V. Silva, Juliana R. Dias et al.

Silk proteins have been highlighted in the past decade for tissue engineering (TE) and skin regeneration due to their biocompatibility, biodegradability, and exceptional mechanical properties. While silk fibroin (SF) has high structural and mechanical stability with high potential as an external protective layer, traditionally discarded sericin (SS) has shown great potential as a natural-based hydrogel, promoting cell–cell interactions, making it an ideal material for direct wound contact. In this context, the present study proposes a new wound dressing approach by developing an SS/SF bilayer construct for full-thickness exudative wounds. The processing methodology implemented included an innovation element and the cryopreservation of the SS intrinsic secondary structure, followed by rehydration to produce a hydrogel layer, which was integrated with a salt-leached SF scaffold to produce a bilayer structure. In addition, a sterilization protocol was developed using supercritical technology (sCO₂) to allow an industrial scale-up. The resulting bilayer material presented high porosity (>85%) and interconnectivity while promoting cell adhesion, proliferation, and infiltration of human dermal fibroblasts (HDFs). SS and SF exhibit distinct secondary structures, pore sizes, and swelling properties, opening new possibilities for dual-phased systems that accommodate the different needs of a wound during the healing process. The innovative SS hydrogel layer highlights the transformative potential of the proposed bilayer system for biomedical therapeutics and TE, offering insights into novel wound dressing fabrication.

Ruchi Kansal, Mahtab Ahmed

The paper discusses the role of social marketing in preventing health-related harmful habits such as tobacco consumption and smoking. These habits are the causes of deadly diseases such as lung cancer, tuberculosis, and other chronic infections which are detrimental to life of the people. Children fall prey to the wrong habits in the wrong company and become tobacco addicts. So many cases of teen drug addicts are reported in a large number. They have a lack of conscience at a tender age and negligence of their counselling and awareness increases the number of smokers, drunkards, and drug addicts. Once they are afflicted with the diseases they must run for medicines and treatment. Therefore, it should be prevented before suffering as the saying goes, “Prevention is better than cure “. They are unaware that they are prevented not only by clinical treatment and medicines but also by social awareness and education. Social mobilization of the people through awareness programs, education, camps, campaigns, etc. is known as social marketing. The significance of social marketing is its effects on the prevention of physically detrimental habits in the youth which contributed a lot to the reduction of cases of diseases. The role of government programs, educational and medical institutions, social workers, and NGOs is worth applauding in India which undertake and complete projects, organize awareness camps, and educate parents and youths to save themselves from the consumption of harmful substances. It has also produced good output in India that the cases of smoking and drug addiction have reduced to support the country’s development as India is advancing towards becoming the third largest economy and a developed country by 2030 and 2047 respectively.

Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham

Large Language Models (LLMs) have demonstrated impressive performance in software engineering tasks. However, improving their accuracy in generating correct and reliable code remains challenging. Numerous prompt engineering techniques (PETs) have been developed to address this, but no single approach is universally optimal. Selecting the right PET for each query is difficult for two primary reasons: (1) interactive prompting techniques may not consistently deliver the expected benefits, especially for simpler queries, and (2) current automated prompt engineering methods lack adaptability and fail to fully utilize multi-stage responses. To overcome these challenges, we propose PET-Select, a PET-agnostic selection model that uses code complexity as a proxy to classify queries and select the most appropriate PET. By incorporating contrastive learning, PET-Select effectively distinguishes between simple and complex problems, allowing it to choose PETs that are best suited for each query's complexity level. Our evaluations on the MBPP and HumanEval benchmarks using GPT-3.5 Turbo and GPT-4o show up to a 1.9% improvement in pass@1 accuracy, along with a 74.8% reduction in token usage. Additionally, we provide both quantitative and qualitative results to demonstrate how PET-Select effectively selects the most appropriate techniques for each code generation query, further showcasing its efficiency in optimizing PET selection.