Exosomes are cell-derived nanovesicles that are involved in the intercellular transportation of materials. Therapeutics, such as small molecules or nucleic acid drugs, can be incorporated into exosomes and then delivered to specific types of cells or tissues to realize targeted drug delivery. Targeted delivery increases the local concentration of therapeutics and minimizes side effects. Here, we present a detailed review of exosomes engineering through genetic and chemical methods for targeted drug delivery. Although still in its infancy, exosome-mediated drug delivery boasts low toxicity, low immunogenicity, and high engineerability, and holds promise for cell-free therapies for a wide range of diseases.
Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.
High-performance conductors are essential for economically and environmentally sustainable ways of electricity transfer in modern infrastructure, manufacturing and transportation, including electric vehicles. This report reviews the aluminum conductors, their fundamentals, classification and utilization markets, focusing on metallurgical characteristics of present commercial solutions and the strategy of future development directions. The inherent features of aluminum, both beneficial and detrimental, for electrical engineering are emphasized along with alloying concepts that provide the accelerated decomposition of matrix solid solution to minimize the electron scattering. Development activities are assessed of new generation of aluminum conductors that in addition to alloying utilize novel processing techniques such as ultra-fast crystallization, severe plastic deformation and complex thermomechanical treatments aiming at grain reduction to nanometer scale, crystallographic texture control and grain boundary engineering. Transition metals and rare earths are considered as the promising alloying candidates for high-strength conductors having superior thermal stability with extra importance given to immiscible systems of Al–Ce, Al–La and Al–Y along with multiply additions, combined to generate the synergy effects. The composites with cladding configuration and particulate reinforcement including via carbon-type strengtheners are discussed as the effective solutions of advanced conductors. A variety of strategies that aim at overcoming the strength–conductivity trade-off in conductor materials are presented throughout the report.
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo
et al.
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.
Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre
et al.
The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.
ObjectiveCompared with traditional concrete construction, the application of prefabricated assembly construction based on digital twin technology in urban rail transit station construction can effectively ensure component production quality, reduce environmental pollution and lower resource consumption. Therefore, an in-depth research on digital twin technology suitable for prefabricated assembly station construction should be conducted. MethodFirst, in station construction, the overall architecture featuring "4 horizontal + 4 vertical + N platforms" for the application of digital twin technologies, such as BIM (building information modeling) and IoT (Internet of things) is proposed. Second, the modeling process and methodology of BIM are presented. By adopting methods such as mathematical model separation, lightweight processing, and mathematical model association, the established BIM data are imported into the platform, and a technical workflow for uploading IoT monitoring data to the BIM platform is established. Finally, taking a certain underground prefabricated assembly superimposed station in the Phase I project of Jinan Urban Rail Transit Line 8 as a case study, the application effect of the digital twin technology for prefabricated assembly superimposed stations based on BIM+IoT integration is analyzed. Result & Conclusion The proposed digital twin technology shows good application effects in the case station, achieving design goals such as construction progress query, structural safety monitoring, quality management control, and process auxiliary design, and realizing data management interaction and sharing throughout the components full life cycle.
Kampala faces increasing congestion, air pollution, and dependence on fossil fuels, driven by widespread reliance on diesel minibuses and motorcycle taxis. Existing models—KAMPALA-TIMES, KLAP-TIMES, and GKMA-TIMES–CGE—show strong potential for electrified mass transit to reduce emissions, change commuter behavior, and boost macroeconomic welfare. However, these studies assume electric-bus reliability without examining the mechanical conditions needed to achieve their projected outcomes. This study combines system-level modeling insights with vehicle-level engineering analysis to identify key mechanical factors necessary for the successful deployment of electric Bus Rapid Transit (e-BRT) in Kampala. It considers drivetrain torque for steep gradients, battery thermal management in hot equatorial climates, and regenerative braking efficiency in traffic congestion, alongside policy, infrastructure, and grid readiness. Mechanical performance links modeling to implementation—adequate torque, thermal stability, and regenerative braking efficiency directly affect service reliability, headway adherence, fleet uptime, and lifecycle costs. These operational factors influence commuter mode choices, the realism of bottom-up pathways, and the broader economic benefits predicted in top-down scenarios. Engineering reliability must be a core policy consideration, guiding procurement standards, charging infrastructure design, and multisector coordination among KCCA, MoWT, MEMD, and Uganda’s power utilities. Incorporating mechanical parameters into future bottom-up or hybrid models, combined with digital-twin testing and degradation-aware analytics, will enable Kampala to serve as a living laboratory for low-carbon mobility transitions across Sub-Saharan Africa.
On-demand restaurant meal delivery platforms, such as DoorDash and Meituan, have recently introduced a multi-order delivery service: Customers may combine delivery requests from different restaurants in a single multi-order with the service promise of a synchronized delivery. However, the platform must not only ensure the synchronization of multi-orders but also improve punctuality and freshness for all customers. This is challenging because, as we show, synchronization, delay, and freshness are conflicting objectives. Uncertainty in the delivery process and unknown future orders further complicate the decision making. This raises several research questions: How does the introduction of a multi-order service affect the overall delivery operations with regard to service quality and operational expenses? How should a multi-order service be strategically rolled out? How can we balance the competing objectives of synchronizing deliveries while minimizing delay and maximizing freshness? To answer the research questions, we propose an effective policy that allows for a careful and controlled balance between the competing objectives and employ it in an extensive computational study. We evaluate the effect of different trade-offs between delay, freshness, and synchronization on delivery operations over varying demand for multi-orders. We observe that enforcing strict synchronization of multi-orders by assigning each multi-order to a single delivery driver is hardly operational feasible. Occasionally using split-deliveries provides the flexibility to better balance all objectives. Our detailed experiments further generate insights on how platforms may roll-out multi-orders as a new service offering without negatively affecting their existing delivery operation while benefiting from reduced operational expenses.
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.
A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.
［Objective］ By establishing a numerical seepage analysis model that aligns with real drainage systems and introducing the concept of a ′virtual permeability coefficient′ for secondary lining, the objective is to delve into the correlation between numerical methods and theoretical formulas, with expectation to leverage the efficiency and practicality of theoretical formulas in predicting external water pressure. ［Method］ Based on the principle of equivalent stable drainage volume in underwater tunnels, the concept of a ′virtual permeability coefficient′ for the secondary lining is introduced. On this basis, key factors, including the spacing of circumferential drainage blind pipes, the thickness of geotextiles, and their permeability coefficients, are selected as primary research factors. By adjusting these factors, multiple numerical seepage analysis models consistent with real drainage systems are established. ［Result & Conclusion］ The actual external water pressure acting on the secondary lining exhibits significant spatial distribution characteristics. Longitudinally, the variation in external water pressure displays periodic fluctuations corresponding to the spacing of circumferential drainage blind pipes. Circumferentially, the closer the position is to the longitudinal drainage blind pipe, the lower the external water pressure, with maximum circumferential water pressure occurring at the arch vault, followed by the inverted arch, and the smallest pressure on sidewalls. The reduction coefficients of external water pressure calculated with theoretical formulas are generally smaller than those derived from numerical methods. The stronger the drainage capacity of the design parameters, the smaller the difference between the two calculation results. The reduction coefficient consistently follows a decreasing trend from the vault to the invert to the sidewalls. When applying theoretical formulas directly in quantitative engineering design, it is necessary to introduce a comprehensive correction factor greater than 1.0 to ensure engineering safety. The value of comprehensive correction factor should be determined based on the specific structural location, with zones divided by the sidewalls. For the upper structure, a range of 1.48-1.97 is recommended, while a proper range of 1.21-1.39 for the lower structure
A REVIEW OF METHODOLOGICAL DEVELOPMENTS Zhaoqi Zang , Xiangdong Xu b, , Kai Qu , Ruiya Chen , Anthony Chen c a School of Civil and Environmental Engineering, Nanyang Technological University, Singapore b College of Transportation Engineering, Tongji University, Shanghai, China c Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China ABSTRACT The unavoidable travel time variability in transportation networks, resulted from the widespread supply-side and demand-side uncertainties, makes travel time reliability (TTR) be a common and core interest of all of the stakeholders in transportation systems, including planners, travelers, service providers, and managers. This common and core interest stimulates extensive studies on modeling travel time reliability. Researchers have developed a range of theories and models of TTR, many of which have been incorporated into transportation models, transport policies, and project appraisals. Adopting the network perspective, this paper aims to provide an integrated framework for summarizing the methodological developments of modeling TTR in transportation networks, including its characterization, evaluation and valuation, and traffic assignment. Specifically, the TTR characterization provides a whole picture of travel time distribution in transportation networks; TTR evaluation and TTR valuation interpret abstract characterized TTR in a simple and intuitive way in order to be well understood by different stakeholders of transportation systems; and lastly TTR-based traffic assignment investigates the effects of TTR on the individual users’ travel behavior and consequently the collective network flow pattern. As the above three topics are mainly separately studied in different disciplines and research areas, the integrated framework allows to better understand their relationships and may contribute to developing more possible combinations of TTR modeling philosophy. Also, the network perspective enables to pay more attention to some common challenges of modeling TTR in transportation networks, especially the uncertainty propagation from the uncertainty sources to the TTR at various spatial levels including link, route, and the entire network. Some potential directions for future research are discussed in the era of new data environment, applications, and emerging technologies.
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.
Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach
et al.
Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.
With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development of V2G applications, based on the current research status of V2G technology. Firstly, the standards on V2G applications and some pilot projects involving more representative V2G systems are introduced. Comparing V2G applications with ordered charging and unordered charging, the social and economic benefits of V2G applications are highlighted. Analysis of the social benefits of V2G applications concerns three points: the grid demand response, personalized charging, and the coordination of renewable energy sources. And analysis of the economic benefits of V2G applications is divided into three parties: the grid, the aggregator, and individuals. From the perspective of innovative EVs expanding the application scenarios through V2G technology, V2G applications for commercial EVs, emergency power applications, and vehicle-to-vehicle energy trading are introduced. The current challenges related to V2G applications are presented: users’ willingness to participate in V2G applications, battery loss, charging and discharging tariffs, privacy and security, and power loss. Finally, some research recommendations for the development of V2G applications are given and the current state of research in regard to those recommendations is presented.
Vision monitoring of distress has emerged as a new trend in intelligent transportation infrastructure systems, including roads and bridges. Recently, transfer learning methods and lightweight networks have been used to realize efficient distress identification without large amount of human-labor work. This paper proposed an engineering approach that integrated transfer learning with lightweight models to classify and detect concrete bridge distresses. Two datasets named Distress Dataset of Asphalt Pavement (DDAP) with 2500 asphalt pavement distresses images and Distress Dataset of Concrete Bridge (DDCB) with 906 concrete bridge distresses images were used. The lightweight models MobileNet and MobileNet-SSD were employed to conduct 6 comparative experiments for exploring the model performance with different transfer learning modes (Mode I and II) and procedures (one-step and two-step procedure) in classification and detection tasks. Based on the comparison of the results, the optimum model for two tasks was respectively recognized. For classification task, the model directly transferred specific parameters of partial convolution layers from ImageNet-based model achieved the highest accuracy of 97.8%. For detection task, the two-step transfer learning model using an intermediate transfer learning step trained by DDAP reached a mean average precision of 87.16%. The proposed approach has the application potential for practical road inspection work in intelligent transportation infrastructure maintenance.