Digital twin is a virtual replica of a physical system that updates in real time using sensor data to enable simulations and predictions. For bridges constructed using rotation construction methods, the rotation phase demands continuous monitoring of structural behavior and coordination with surrounding traffic infrastructure. Therefore, a digital twin system for monitoring rotation construction is vital to ensure safety and schedule compliance. This paper explores the application of model-based systems engineering (MBSE), a modern approach that replaces text-based documentation with visual system models, to design a digital twin system for monitoring the rotation construction of a 90 m + 90 m single-tower cable-stayed bridge. A V-model architecture for the digital twin system, based on requirements analysis, functional analysis, logical design, and physical design analysis (RFLP), is proposed. Based on SysML language, the system’s requirements, functions, behaviors, and other aspects are modeled and analyzed using the MBSE approach, converting all textual specifications into the unified visual models. Compared to the traditional document-driven method, MBSE improves design efficiency by reducing ambiguities in system specifications and enabling early detection of design flaws through simulations. The digital twin system allows engineers to predict potential risks during bridge rotation and optimize construction plans before implementation. These advancements demonstrate how MBSE supports proactive problem-solving (forward design) and provides a robust foundation for future model validation and engineering applications.
Upconversion nanoparticles (UCNPs) are emerging as highly promising nanomaterials due to their exceptional optical properties, enabling diverse applications in biosensing, bioimaging, photodynamic therapy, and drug delivery. However, their potential toxicity should be comprehensively investigated for the safe utilization of UCNPs in several biomedical and environmental applications. This review systematically evaluates the current knowledge on UCNP toxicity from 2008 to 2024, focusing on key toxicological pathways, such as oxidative stress, reactive oxygen species (ROS) production, inflammatory responses, and apoptosis/necrosis, alongside their absorption, distribution, metabolism, and excretion processes and kinetics. Distinctively, this review introduces a bibliometric analysis of UCNP toxicity and biodistribution research, providing a quantitative assessment of publication trends, influential authors, leading institutions, funding agencies, and keyword occurrences. This approach offers a macroscopic perspective on the evolution and current landscape of UCNP safety research, a dimension largely unexplored in existing literature. Furthermore, the review combines mechanistic insights into UCNP toxicity with a critical evaluation of surface modifications, physicochemical properties, and administration routes, presenting a holistic framework for understanding UCNP biosafety. By combining bibliometric data with mechanistic insights, this review provides a data-driven perspective on UCNP-associated risks, actionable strategies for enhancing biosafety through surface engineering, and a forward-looking discussion on regulatory challenges and future directions for UCNP-based technologies. These findings bridge existing gaps in the literature and offer a comprehensive resource for researchers, clinicians, and policymakers, facilitating the safe development and utilization of UCNP-based technologies while establishing robust safety guidelines to mitigate adverse effects on human health and the environment.
Abstract Atmospheric corrosion is one of the major factors leading to the deterioration of steel truss bridges. In order to overcome this problem, protective coatings are generally applied to steel members. Since coatings also can deteriorate over time, investigating the time-dependent structural performance becomes essential. In this paper, a method was developed for the time-dependent advanced analysis of steel truss bridges with and without coatings. To achieve this goal, material and geometric nonlinearity were accounted for evaluating the structural load capacity of the bridges, and consequently, the whole structural behavior was monitored. The effect of atmospheric corrosion was modelled using a relationship based on ISO 9224 for members without coating. Besides, two different coating degradation models were applied for the members with coating. For the evaluation of the proposed method, four steel truss bridges from the literature were accounted and time-dependent structural load capacities were calculated under atmospheric corrosion exposure for 100 years. In order to represent the effects of different environments, each steel truss bridge type was assumed to be built in different countries, and atmospheric corrosion data for each case was acquired from international corrosion databases. Numerical analysis results revealed the importance of detrimental atmospheric corrosion effects in terms of structural load capacity in steel truss bridges. In some cases, reductions in structural capacities were significant and even unexpected failures occurred in aggressive environments. Besides, when coatings were applied to steel truss bridges under atmospheric corrosion, a satisfactory delay in the decrease in load-carrying capacity was achieved throughout the structural lifetime.
Integrating AI into ELT and machine translation marks a major advancement, particularly in the teaching of languages for specific purposes (LSP). This field is crucial in a globalized context, where proficiency in foreign languages and specialized communication is essential. This study explores how AI can serve as a bridge between theory and practice by analyzing tools such as neural machine translation systems (DeepL, Google Translate), pedagogical assistants (ChatGPT), and case studies in specialized educational contexts (medicine and engineering). The findings revealed that AI enables greater personalization of learning, optimization of educational resources, and facilitation of intercultural communication. However, it also raises challenges, such as the reliability of machine translations, algorithmic biases, and excessive dependence on technology. In conclusion, AI offers transformative opportunities for ELT and LSP, but its integration requires a balanced approach, critical teacher training, data protection, and focus on educational equity. Future perspectives include adaptation to underrepresented languages and a study of its long-term impact.
Compared to conventional reinforced concrete (RC) piers, self-centering rocking piers exhibit better seismic resilience and sustain minor damage. However, their construction typically relies on prefabrication. Moving large, prefabricated components can be challenging in mountainous areas with limited transportation access. Therefore, using concrete-filled steel tube (CFST) piers is a practical alternative. The steel tube both serves as a construction permanent formwork and enhances the compressive performance of concrete through confinement effects. To apply CFST self-centering rocking piers in mountainous regions with high seismic intensity, a fast construction system was designed and a 1:4 scale specimen was developed for testing. Lateral cyclic loading tests revealed that the specimen exhibited good deformation and self-centering capabilities, with a residual drift ratio of only 0.17% at a drift ratio of 7.7%. Most of the horizontal displacement was contributed through a rocking gap opening, resulting in minimal damage to the pier itself. The damage was concentrated primarily in the energy-dissipating rebars, while the prestress strands remained elastic, though prestress loss was observed.
Abstract The health of older adults is related to physical activity, and physical activity level (PAL) is associated with various factors such as community environment and psychological factors. Based on the above assumptions, this study constructs Structural Equation Modeling(SEM) to examined the relationship of psychological variables with older adults’ PAL. The study further combines observable variables such as socioeconomic attributes, travel characteristics, and objective community environment elements with latent variables to construct SEM-Logit model to examined the relationship between these factors and PAL. Our results showed that: (1) PAL was related to gender, age, living conditions, self-rated health, PA scope; (2) PAL was positively related to physical activity intention (PAI), and other perceived psychological variables had an indirect effect on PAL through PAI; (3) Older adults living in a walkable community are more likely to improve their PAL. Corresponding strategies were proposed from improving the community environment and guiding psychological perception.
End-to-end reinforcement learning (RL) for motion control trains policies directly from sensor inputs to motor commands, enabling unified controllers for different robots and tasks. However, most existing methods are either blind (proprioception-only) or rely on fusion backbones with unfavorable compute-memory trade-offs. Recurrent controllers struggle with long-horizon credit assignment, and Transformer-based fusion incurs quadratic cost in token length, limiting temporal and spatial context. We present a vision-driven cross-modal RL framework built on SSD-Mamba2, a selective state-space backbone that applies state-space duality (SSD) to enable both recurrent and convolutional scanning with hardware-aware streaming and near-linear scaling. Proprioceptive states and exteroceptive observations (e.g., depth tokens) are encoded into compact tokens and fused by stacked SSD-Mamba2 layers. The selective state-space updates retain long-range dependencies with markedly lower latency and memory use than quadratic self-attention, enabling longer look-ahead, higher token resolution, and stable training under limited compute. Policies are trained end-to-end under curricula that randomize terrain and appearance and progressively increase scene complexity. A compact, state-centric reward balances task progress, energy efficiency, and safety. Across diverse motion-control scenarios, our approach consistently surpasses strong state-of-the-art baselines in return, safety (collisions and falls), and sample efficiency, while converging faster at the same compute budget. These results suggest that SSD-Mamba2 provides a practical fusion backbone for resource-constrained robotic and autonomous systems in engineering informatics applications.
Alexandros Gazis, Ioannis Papadongonas, Athanasios Andriopoulos
et al.
This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with integrated processing capabilities. The article also explores the growing importance of mini-computing devices in educational environments. These devices provide cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, this article examines the most widely used sensors, detailing their properties and modes of operation to help readers understand how sensor systems function. The aim of this study is to provide an overview of the most suitable sensors for various applications by explaining their uses and operations in simple terms. This clarity will assist researchers in selecting the appropriate sensors for educational and research purposes or understanding why specific sensors were chosen, along with their capabilities and possible limitations. Ultimately, this research seeks to equip future engineers with the knowledge and tools needed to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.
Quantum computing has demonstrated the potential to solve computationally intensive problems more efficiently than classical methods. Many software engineering tasks, such as test case selection, static analysis, code clone detection, and defect prediction, involve complex optimization, search, or classification, making them candidates for quantum enhancement. In this paper, we introduce Quantum-Based Software Engineering (QBSE) as a new research direction for applying quantum computing to classical software engineering problems. We outline its scope, clarify its distinction from quantum software engineering (QSE), and identify key problem types that may benefit from quantum optimization, search, and learning techniques. We also summarize existing research efforts that remain fragmented. Finally, we outline a preliminary research agenda that may help guide the future development of QBSE, providing a structured and meaningful direction within software engineering.
The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both. This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders; it then examines the field's recent attention to gender issues and gender bias. It next quantitatively analyzes women's participation as research authors in the field's leading International Conference of Software Engineering (1976-2010), finding a dozen years with statistically significant gender exclusion. Policy dimensions of research on gender bias in computing are suggested.
I. G. Ovchinnikov, I. O. Razov, N. B. Kudaibergenov
Modern bridge construction demands efficient designs, technologies, and materials. Traditional methods tend towards simpler designs, whereas modern approaches enable a wider variety of structures and architectural forms. This research explores successful examples of 3D printing, bionics and fractal approaches in bridge engineering, and analyzes the potential of combining these technologies for optimal results in modern bridge construction.
With the deterioration of bridge performance and ever-increasing amounts of traffic, bridge safety is becoming a concern for the engineering community. A method that can assess a bridge’s condition in real time is urgently needed. The main factors that hinder bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA)-based method is developed for structural damage detection in bridges in operational environments in this paper. Two main operational environmental factors, the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate in assessing the bridge’s condition in operational environments.
C50 manufactured sand concrete requires good workability and strength, and economic efficiency and carbon emissions also need to be considered. This study incorporates sensitivity and significance analyses to recommend the optimal economic mix composition for C50 manufactured sand concrete. The relationship between cost, workability, and mechanical properties was analyzed by considering the water/binder ratio, sand ratio, fly ash content, and superplasticizer dosage. An optimal composition of C50 manufactured sand concrete was recommended. The cost and carbon emissions were quantified at the optimal composition. The results showed that the water/binder ratio had the most significant impact on the cost and carbon emission, while the sand ratio and superplasticizer dosage had the least. All factors significantly affected its cost and carbon emission. Compared to natural sand concrete, manufactured sand concrete achieved a lower cost but higher carbon emissions. Considering the workability, strength, and cost per cubic meter of concrete, the most economical mix proportion for C50 concrete was recommended with a water/binder ratio of 0.36, a fly ash content of 25%, a sand ratio of 0.42, and a superplasticizer dosage of 1.2%. This composition cost 356 yuan, and carbon emission was 352.6 kg CO<sub>2</sub> per cubic meter of concrete. Compared to a composition with a water/binder ratio of 0.34 and fly ash content of 15%, the unit cost can be reduced by 18.4 yuan, and carbon emission can be minimized by 56.6 kg CO<sub>2</sub> e/m<sup>3</sup>. The appropriate water/binder ratio and fly ash content can reduce cost and carbon emissions without compromising the workability, compressive strength, or elastic modulus of C50 concrete. This achieves triple benefits in terms of performance, economy, and the environment when applying C50 manufactured sand concrete.
It has long been recognized that it is not enough for a Recommender System (RS) to provide recommendations based only on their relevance to users. Among many other criteria, the set of recommendations may need to be diverse. Diversity is one way of handling recommendation uncertainty and ensuring that recommendations offer users a meaningful choice. The literature reports many ways of measuring diversity and improving the diversity of a set of recommendations, most notably by re-ranking and selecting from a larger set of candidate recommendations. Driven by promising insights from the literature on how to incorporate versatile Large Language Models (LLMs) into the RS pipeline, in this paper we show how LLMs can be used for diversity re-ranking. We begin with an informal study that verifies that LLMs can be used for re-ranking tasks and do have some understanding of the concept of item diversity. Then, we design a more rigorous methodology where LLMs are prompted to generate a diverse ranking from a candidate ranking using various prompt templates with different re-ranking instructions in a zero-shot fashion. We conduct comprehensive experiments testing state-of-the-art LLMs from the GPT and Llama families. We compare their re-ranking capabilities with random re-ranking and various traditional re-ranking methods from the literature. We open-source the code of our experiments for reproducibility. Our findings suggest that the trade-offs (in terms of performance and costs, among others) of LLM-based re-rankers are superior to those of random re-rankers but, as yet, inferior to the ones of traditional re-rankers. However, the LLM approach is promising. LLMs exhibit improved performance on many natural language processing and recommendation tasks and lower inference costs. Given these trends, we can expect LLM-based re-ranking to become more competitive soon.
Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,ε greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design.
Engineering a sustainable world requires to consider various systems that interact with each other. These systems include ecological systems, economical systems, social systems and tech-nical systems. They are loosely coupled, geographically distributed, evolve permanently and generate emergent behavior. As these are characteristics of systems of systems (SoS), we discuss the engi-neering of a sustainable world from a SoS engineering perspective. We studied SoS engineering in context of a research project, which aims at political recommendations and a research roadmap for engineering dynamic SoS. The project included an exhaustive literature review, interviews and work-shops with representatives from industry and academia from different application domains. Based on these results and observations, we will discuss how suitable the current state-of-the-art in SoS engi-neering is in order to engineer sustainability. Sustainability was a major driver for SoS engineering in all domains, but we argue that the current scope of SoS engineering is too limited in order to engineer sustainability. Further, we argue that mastering dynamics in this larger scope is essential to engineer sustainability and that this is accompanied by dynamic adaptation of technological SoS.
A laser welding technology for the dissimilar materials 316L stainless steel (316L ss) and polylactic acid (PLA) was investigated to analyze the process parameters, which have a large influence on the joint quality. Orthogonal tests, single-factor tests, response surface method (RSM), and Box–Behnken design (BBD) were utilized to optimize the experimental design. A metallographic microscopy analysis was conducted to classify the joint morphology into two categories: effective and ineffective. The effective area ratio was established as an effective judgment method for the joint mechanical properties. Mathematical relations between the process parameters and the mechanical properties of the joints were investigated and the process parameters were optimized and validated. The test values were in excellent agreement with the actual values, thus demonstrating the reliability of the proposed model.
Polyurethane (PU)-based Mortar (PUM) has a short curing time and high bonding ability as a new potential repair material. However, its low strength significantly limits its application in maintaining civil engineering structures. In this paper, the effect of micro steel fiber (MSF) with different aspect ratio and different dosages on the compressive strength, flexural strength, and elastic modulus of PUM was investigated. The performance of fiber at different embedment depths on the interfacial bond properties between the PU-based mortar mixture and steel fiber was explored through the fiber pull-out test. Additionally, steel fiber’s reinforcing and toughening effects were analyzed, and scanning electron microscopy (SEM) was used to analyze the microstructure of PU-based mortar reinforced with steel fiber. The results showed that the mechanical performance of PUM incorporated with two fiber types up to 3% was significantly improved. The bond strength between the micro steel fiber and PUM mixture improved with the increase in fiber volume fraction in the PU mixture. Similarly, the pull-out load and the pull-out work are increased with the increase in depth, while the interface bond strength is decreased. Additionally, based on the result obtained, the reinforcing effect of MSF in PUM can effectively refine the pores and bridge the crack formation.
Materials of engineering and construction. Mechanics of materials
The Tuojiang Bridge of Jinjianren Expressway Phase II Project is located in Jianyang City and the Eastern New District of Chengdu, all the section length is 4.3 km with a total construction and installation cost of about 1.74 billion yuan. The road is classed as both a first-class highway and an urban expressway, with a design speed of 80 km/h for the main line and 40 km/h for the auxiliary lanes. Tongji University Architectural Design (Group) Co., Ltd. designed the main bridge which is an urban landscape bridge with a length of 963 m. The main bridge adopts 45 + 185 + 238 + 45𝑚 single-tower double-cable-plane cablestayed span layout, and the approach bridge adopts prestressed small box girder structure.