Hasil untuk "Structural engineering (General)"

Alexander D. Taylor, Qing Sun, K. Goetz et al.

Deposition of perovskite films by antisolvent engineering is a highly common method employed in perovskite photovoltaics research. Herein, we report on a general method that allows for the fabrication of highly efficient perovskite solar cells by any antisolvent via manipulation of the antisolvent application rate. Through detailed structural, compositional, and microstructural characterization of perovskite layers fabricated by 14 different antisolvents, we identify two key factors that influence the quality of the perovskite layer: the solubility of the organic precursors in the antisolvent and its miscibility with the host solvent(s) of the perovskite precursor solution, which combine to produce rate-dependent behavior during the antisolvent application step. Leveraging this, we produce devices with power conversion efficiencies (PCEs) that exceed 21% using a wide range of antisolvents. Moreover, we demonstrate that employing the optimal antisolvent application procedure allows for highly efficient solar cells to be fabricated from a broad range of precursor stoichiometries. Thin film deposition of perovskites by antisolvent engineering is commonly used, but the effect of processing parameters is not yet fully understood. Here, the authors identify two key factors that influence the film quality through a detailed structural and compositional study of perovskite layers fabricated by 14 different antisolvents.

D. Mitzi

V. Gharehbaghi, E. Noroozinejad Farsangi, M. Noori et al.

Songhao Guo, Willa Mihalyi-Koch, Yuhong Mao et al.

Designing two-dimensional halide perovskites for high-performance optoelectronic applications requires deep understanding of the structure-property relationship that governs their excitonic behaviors. However, a design framework that considers both intra and interlayer structures modified by the A-site and spacer cations, respectively, has not been developed. Here, we use pressure to synergistically tune the intra and interlayer structures and uncover the structural modulations that result in improved optoelectronic performance. Under applied pressure, (BA)2(GA)Pb2I7 exhibits a 72-fold boost of photoluminescence and 10-fold increase of photoconductivity. Based on the observed structural change, we introduce a structural descriptor χ that describes both the intra and interlayer characteristics and establish a general quantitative relationship between χ and photoluminescence quantum yield: smaller χ correlates with minimized trapped excitons and more efficient emission from free excitons. Building on this principle, we design a perovskite (CMA)2(FA)Pb2I7 that exhibits a small χ and an impressive photoluminescence quantum yield of 59.3%. Guo et al. report enhanced emission and photoconductivity in 2D Ruddlesden-Popper perovskites by synergistically tuning the intra and interlayer structure via pressure. A structure descriptor considering both intra- and interlayer is then introduced for screening perovskite with desired properties.

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Tab, Zhang, Bjorn De Sutter et al.

Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.

Yulu Tang, Canhui Lu, Rui Xiong

Natural bouligand structures enable crustacean exoskeletons and fruits to strike a combination of exceptional mechanical robustness and brilliant chiroptical properties owing to multiscale structural hierarchy. However, integrating such a high strength-stiffness-toughness combination and photonic functionalities into synthetic hydrogels still remains a grand challenge. In this work, we report a simple yet general biomimetic strategy to construct an ultrarobust chiroptical hydrogel by closely mimicking the natural bouligand structure at multilength scale. The hierarchical structural engineering of long-range ordered cellulose nanocrystals' bouligand structure, well-defined poly(vinyl alcohol) nanocrystalline domains, and dynamic interfacial interaction synergistically contributes to the integration of high strength (23.3 MPa), superior modulus (264 MPa), and high toughness (54.7 MJ m-3), as well as extraordinary impact resistance, which far exceed their natural counterparts and synthetic photonic hydrogels. More importantly, seamless chiroptical and solvent-responsive patterns with high resolution can also be scalably integrated into the hydrogel by localized manipulation of the photonic band, while maintaining good ionic conductivity. Such exceptional mechanical-photonic combination holds tremendous potential for applications in wearable sensors, encryption, displays, and soft robotics.

Haixu Chen, Zhengbin Han, Shengliang Wang et al.

Abstract Liquid-liquid phase separation plays an important role in many natural and technological processes. Herein, we implement lateral microphase separation at the surface of oil micro-droplets suspended in water to prepare a range of discrete floating protein/polymer continuous two-dimensional (2D) heterostructures with variable interfacial domain structures and dynamics. We show that gel-like domains of bovine serum albumin (BSA) co-exist with fluid-like polyvinyl alcohol (PVA) regions at the oil droplet surface to produce floating heterostructures comprising a 2D phase-separated protein mesh or an array of discrete mobile protein rafts depending on the conditions employed. Enzymes are embedded in the discontinuous BSA domains to produce droplet-supported microphase-separated 2D reaction scaffolds that can be tuned for interfacial catalysis. Taken together, our work has general implications for the structural and functional augmentation of oil droplet interfaces and contributes to the surface engineering and functionality of droplet-based micro-reactors.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Hao Huang

The requirements for structural performance and seismic performance in the field of civil engineering are increasing. Traditional building structures have certain limitations in extreme conditions such as earthquakes. Therefore, this study discusses the design of modular bi-directional load-bearing and energy dissipating joints in the context of intelligent construction to improve the seismic performance of buildings. The study designs vertical joints and bi-directional joints, and the test results show that the hysteresis curve of the joints is hump-shaped, exhibiting excellent plastic deformation and energy dissipation performance. The introduction of oblique stiffening ribs in the vertical joints significantly improves the load-bearing capacity, and there is no significant decrease in load-bearing capacity when loaded to approximately 32 mm. The maximum energy dissipation coefficient of vertical joint specimen 1 is 1.485. For specimen 2, the maximum values is 1.801, and for the bi-directional joints, the maximum values is 2.156, demonstrating excellent energy dissipation capability. In conclusion, this research is of great significance for the combination of modern building engineering technology and intelligent construction, providing strong support for the seismic performance and overall safety of building structures.

Iryna S. Protsak, Yevhenii M. Morozov

This review summarizes the fundamental concepts, recent advancements, and emerging trends in the field of stimuli-responsive hydrogels. While numerous reviews exist on this topic, the field continues to evolve dynamically, and certain research directions are often overlooked. To address this, we classify stimuli-responsive hydrogels based on their response mechanisms and provide an in-depth discussion of key properties and mechanisms, including swelling kinetics, mechanical properties, and biocompatibility/biodegradability. We then explore hydrogel design, synthesis, and structural engineering, followed by an overview of applications that are relatively well established from a scientific perspective, including biomedical uses (biosensing, drug delivery, wound healing, and tissue engineering), environmental applications (heavy metal and phosphate removal from the environment and polluted water), and soft robotics and actuation. Additionally, we highlight emerging and unconventional applications such as local micro-thermometers and cell mechanotransduction. This review concludes with a discussion of current challenges and future prospects in the field, aiming to inspire further innovations and advancements in stimuli-responsive hydrogel research and applications to bring them closer to the societal needs.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Marios Impraimakis, Evangelia Nektaria Palkanoglou

The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology. The framework outperforms current approaches for fault anomaly detection as no prior information is required for the health state of the system: a topic of high significance for real-world applications. Specifically, current artificial intelligence-based digital twinning approaches suffer from the uncertainty related to obtaining poor predictions when a low number of measurements is available, physics knowledge is missing, or when the damage state is unknown. To this end, an unsupervised framework is examined and validated rigorously on the benchmark structural health monitoring measurements of Z24 Bridge: a post-tensioned concrete highway bridge in Switzerland. In implementing the approach, firstly, different same damage-level measurements are used as inputs, while the model is forced to converge conditionally to two different damage states. Secondly, the process is repeated for a different group of measurements. Finally, the convergence scores are compared to identify which one belongs to a different damage state. The process for both healthy-to-healthy and damage-to-healthy input data creates, simultaneously, measurements for digital twinning purposes at different damage states, capable of pattern recognition and machine learning data generation. Further to this process, a support vector machine classifier and a principal component analysis procedure is developed to assess the generated and real measurements of each damage category, serving as a secondary new dynamics learning indicator in damage scenarios. Importantly, the approach is shown to capture accurately damage over healthy measurements, providing a powerful tool for vibration-based system-level monitoring and scalable infrastructure resilience.

C. Scuro, F. Lamonaca, S. Porzio et al.

Abstract The main aim of using the IoT paradigm is related to the possibility of a connectivity extension of several common SHM devices by means of Internet. The connected devices are thus able to transmit and process data, guaranteeing new scenarios in the design of acquisition systems in different fields of science and engineering. The innovations have led the researchers to extend the application of the IoT paradigm to the SHM of masonry structures making sure that the experiments conducted in this framework are able to ensure good results with promising future improvements. The main uses, to date, have been implemented, for example, to monitor individual structural elements, reducing the risks for users due to partial or total collapses of the structure, or for the identification, detection and characterization of damage and degradation of construction materials. The aim of this work is to expose a general overview of the SHM systems used by the authors for masonry structures belonging to historical and cultural heritage, arguing their use for the protection from earthquakes with related advantages. This will be shown thanks to two preliminary SHM systems designed and implemented by the authors on two case studies using IoT, described in the paper with their related cyber and physical parts.

Chenghao Yu, Renjie Chen, Jinli Chen et al.

Advancements in tissue engineering are crucial for successfully healing tendon-bone connections, especially in situations like anterior cruciate ligament (ACL) restoration. This study presents a new and innovative three-dimensional scaffold, reinforced with nanofibers, that is specifically intended for acellular tendon complexes. The scaffold consists of a distinct layered arrangement comprising an acellular tendon core, a middle layer of polyurethane/type I collagen (PU/Col I) yarn, and an outside layer of poly (L-lactic acid)/bioactive glass (PLLA/BG) nanofiber membrane. Every layer is designed to fulfill specific yet harmonious purposes. The acellular tendon core is a solid structural base and a favorable environment for tendon cell functions, resulting in considerable tensile strength. The central PU/Col I yarn layer is vital in promoting the tendinogenic differentiation of stem cells derived from tendons and increasing the expression of critical tendinogenic factors. The external PLLA/BG nanofiber membrane fosters the process of bone marrow mesenchymal stem cells differentiating into bone cells and enhances the expression of markers associated with bone formation. Our scaffold's biocompatibility and multi-functional design were confirmed through extensive in vivo evaluations, such as histological staining and biomechanical analyses. These assessments combined showed notable enhancements in ACL repair and healing. This study emphasizes the promise of multi-layered nanofiber scaffolds in orthopedic tissue engineering and also introduces new possibilities for the creation of improved materials for regenerating the tendon-bone interface.

LIN Yan, JIN Tingyu, YANG Yuchao

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.

Yiwen Dong, Yuyan Wu, Hae Young Noh

Gait abnormality detection is critical for the early discovery and progressive tracking of musculoskeletal and neurological disorders, such as Parkinson's and Cerebral Palsy. Especially, analyzing the foot-floor contacts during walking provides important insights into gait patterns, such as contact area, contact force, and contact time, enabling gait abnormality detection through these measurements. Existing studies use various sensing devices to capture such information, including cameras, wearables, and force plates. However, the former two lack force-related information, making it difficult to identify the causes of gait health issues, while the latter has limited coverage of the walking path. In this study, we leverage footstep-induced structural vibrations to infer foot-floor contact profiles and detect gait abnormalities. The main challenge lies in modeling the complex force transfer mechanism between the foot and the floor surfaces, leading to difficulty in reconstructing the force and contact profile during foot-floor interaction using structural vibrations. To overcome the challenge, we first characterize the floor vibration for each contact type (e.g., heel, midfoot, and toe contact) to understand how contact forces and areas affect the induced floor vibration. Then, we leverage the time-frequency response spectrum resulting from those contacts to develop features that are representative of each contact type. Finally, gait abnormalities are detected by comparing the predicted foot-floor contact force and motion with the healthy gait. To evaluate our approach, we conducted a real-world walking experiment with 8 subjects. Our approach achieves 91.6% and 96.7% accuracy in predicting contact type and time, respectively, leading to 91.9% accuracy in detecting various types of gait abnormalities, including asymmetry, dragging, and midfoot/toe contacts.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.