Cyber-Physical Systems (CPS) produce behavior through execution on substrates coupling computation with physical processes. However, usual engineering approaches do not treat execution semantics as first-class engineering entities. Formal verification reasons about model behaviors under fixed semantic assumptions that are not revisable and do not account for physical execution constraints. Simulation-based validation explores scenarios under execution semantics that are implicitly determined by the simulation engine. In both cases, physical constraints of the execution substrate are addressed as implementation details rather than as semantic boundary conditions. In this article, it is hypothesized that making execution semantics explicit as first-class engineering entities is necessary and sufficient to bridge the gap between verified model behaviors and validated executed behaviors in CPS. To test this hypothesis, Modeling and Simulation Based Engineering (MSBE) is proposed: a methodology grounded in the Theory of Modeling and Simulation. MSBE formalizes execution conditions as four components: execution semantics, activity (behaviorally meaningful changes), admissibility constraints (physical bounds), and specified properties (behavioral guarantees). MSBE organizes engineering around an iterative cycle alternating formal execution, experimental execution, verification, and activity-mediated validation. Executability is defined as stabilization of execution conditions and the induced admissible model space. The cycle is applied to four CPS classes (human-centric, biophysical, technological, and digital twins). These applications show that the framework generalizes beyond CPS to any system whose behavior depends on explicitly defined execution conditions. Modeling and Simulation-Based Engineering
Software engineers use regular expressions (regexes) across a wide range of domains and tasks. To support regexes, software projects must integrate a regex engine, whether provided natively by the language runtime (e.g., Python's re) or included as an external dependency (e.g., PCRE). However, these engines may contain bugs and introduce vulnerabilities. A common strategy for testing regex engines involves differential testing -- comparing outputs across different implementations. However, this approach is concerning because regex syntax and semantics vary significantly between dialects (e.g., POSIX vs. PCRE). Fuzzing is also utilized to ease testing of feature-rich regex implementations to expose defects, but naive byte-level mutations generate syntactically invalid inputs that exercise only parsing logic, not matching internals. In this work, we describe our progress towards ReTest, a framework that systematically tests regular expression engines by combining grammar-aware fuzzing for high code coverage with metamorphic testing to generate dialect-independent test oracles. So far, we have surveyed testing practices across 22 regex engines, analyzed 1,007 regex engine bugs and 156 CVEs to characterize failure modes, and curated 16 metamorphic relations for regexes derived from Kleene algebra. Our preliminary evaluation on PCRE shows that ReTest achieves 3x higher edge coverage than existing fuzzing approaches and has identified three new memory safety defects. We conclude by describing our next steps toward our ultimate goal: helping regex engine developers identify bugs without depending on a consistent cross-implementation standard.
Global freshwater scarcity continues to escalate due to pollution, climate change, and population growth, making innovative sustainable desalination technologies increasingly vital. Solar stills offer a simple and eco-friendly method for freshwater production by utilizing renewable energy, yet their low productivity remains a major limitation. This study experimentally evaluates and quantifies several established enhancement techniques under real climatic conditions to improve evaporation and condensation efficiency. The integration of porous materials, such as black rocks, significantly improves thermal energy storage and management by retaining absorbed heat during the daytime and releasing it gradually, resulting in an average 30% increase in daily distillate production (SD = 6 mL). Additionally, forced convection using small fans enhances humid air removal and evaporation rates, increasing the average yield by approximately 11.4% (SD = 2 mL). Optical concentration through lenses intensifies solar irradiation on the evaporation surface, achieving the highest performance with an average 50% improvement in water output (SD = 5 mL). The incorporation of Phase Change Materials (PCM) is further proposed to extend thermal stability during off-sunshine hours, with materials selected based on a melting point range of 38–45 °C. To minimize nocturnal heat loss, future designs may integrate radiative cooling materials for passive night-time condensation support, by applying a radiative cooling coating to the condenser plate to enhance passive heat rejection to the sky. Overall, the validated combined use of renewable energy-driven desalination, thermal storage media, and advanced strategies presents a practical pathway toward high-efficiency solar stills suitable for sustainable buildings and decentralized water supply systems in arid regions.
Engineering machinery, tools, and implements, Technological innovations. Automation
Aparoop Das, Partha Protim Borthakur, Dibyajyoti Das
et al.
Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for the precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions.
Dragan Vujadinović, Milorad Tomić, Vesna Gojković Cvjetković
et al.
In submitting conference proceedings to <i>Engineering Proceedings</i>, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...]
System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber- Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.
This pilot study presents a small-scale but carefully annotated benchmark of Named Entity Recognition (NER) performance across six systems: three non-LLM NLP tools (NLTK, spaCy, Stanza) and three general-purpose large language models (LLMs: Gemini-1.5-flash, DeepSeek-V3, Qwen-3-4B). The dataset contains 119 tokens covering five entity types (PERSON, LOCATION, ORGANIZATION, DATE, TIME). We evaluated each system's output against the manually annotated gold standard dataset using F1-score. The results show that LLMs generally outperform conventional tools in recognizing context-sensitive entities like person names, with Gemini achieving the highest average F1-score. However, traditional systems like Stanza demonstrate greater consistency in structured tags such as LOCATION and DATE. We also observed variability among LLMs, particularly in handling temporal expressions and multi-word organizations. Our findings highlight that while LLMs offer improved contextual understanding, traditional tools remain competitive in specific tasks, informing model selection.
In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this paper presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.
Maintainable source code is essential for sustainable development in any software organization. Unfortunately, many studies show that maintainability often receives less attention than its importance warrants. We argue that requirements engineering can address this gap the problem by fostering discussions and setting appropriate targets in a responsible manner. In this preliminary work, we conducted an exploratory study of industry practices related to requirements engineering for maintainability. Our findings confirm previous studies: maintainability remains a second-class quality concern. Explicit requirements often make sweeping references to coding conventions. Tools providing maintainability proxies are common but typically only used in implicit requirements related to engineering practices. To address this, we propose QUPER-MAn, a maintainability adaption of the QUPER model, which was originally developed to help organizations set targets for performance requirements. Developed using a design science approach, QUPER-MAn, integrates maintainability benchmarks and supports target setting. We posit that it can shift maintainability from an overlooked development consequence to an actively managed goal driven by informed and responsible engineering decisions.
This paper introduces ModeliHub, a Web-based, federated analytics platform designed specifically for model-based systems engineering with Modelica. ModeliHub's key innovation lies in its Modelica-centric, hub-and-spoke federation architecture that provides systems engineers with a Modelica-based, unified system model of repositories containing heterogeneous engineering artifacts. From this unified system model, ModeliHub's Virtual Twin engine provides a real-time, interactive simulation environment for deploying Modelica simulation models that represent digital twins of the virtual prototype of the system under development at a particular iteration of the iterative systems engineering life cycle. The implementation of ModeliHub is centered around its extensible, Modelica compiler frontend developed in Isomorphic TypeScript that can run seamlessly across browser, desktop and server environments. This architecture aims to strike a balance between rigor and agility, enabling seamless integration and analysis across various engineering domains.
Umar Farooq Shafi, Imran Sarwar Bajwa, Waheed Anwar
et al.
The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.
Engineering machinery, tools, and implements, Technological innovations. Automation
Mamun Al Asad, Surya Afrin Shorna, Abu Saim Mohammad Saikat
et al.
<i>Acinetobacter baumannii</i> (<i>A. baumannii</i>) is an example of an opportunistic pathogen that is generally harmless to healthy individuals but can cause serious infections, such as ventilator-associated pneumonia, wound infections, and bacteremia, in critically ill hospital patients. <i>A. baumannii</i> produces many proteins within its genome. By analyzing its structural and functional interpretations, bioinformatics techniques can make it easier to understand this organism. The protein is still unclear, though. As a result, this study developed an in-silico method for functional and structural characterization of the uncharacterized protein (accession ID: SSI32830.1). These provide many characteristics in silico viewpoints, such as the protein’s physiochemical qualities, subcellular localization, three-dimensional structure, and protein-protein interactions. Protein-protein interactions are explained using the STRING software. The projected tertiary structure evaluation was conducted using the Swiss Model. The best materials are chosen utilizing structural analyses based on Ramachandran plot analysis. This research sought to understand the function of <i>A. baumannii</i>. Therefore, this investigation will increase our understanding of pathophysiology and allow us to target the protein complex specifically.
Jiin-Chyuan Mark Lai, Chiung-Ling Wang, Ming-Yuan Hsieh
Due to the rapid innovative applications of technological education as a result of the swift development and popularity of telecommunication and wireless technologies, ever-more diversified methods of technological education play a critical role in various education and lecturing activities, without time and distance restrictions. Currently, in order to bridge the urban–rural development gap, the Taiwanese Government has introduced a lot of policies to encourage teenagers to return their hometowns; however, these policies have not achieved their goals, as the most of Taiwanese community development associations faced scarcities of professional knowledge without any educational support during the development of their various schemes, such as economic development, environmental protection, cultural heritage, public services, etc. As a result, local development depends on entire community development associations, especially in the most mountain regions of Taiwan. Moreover, community development associations have a lot of social responsibilities, such as age-related caring activities, driving local economies and industrial development, environmental protection and education, maintaining and developing traditional cultures and arts, etc. Due to the digital characteristics of technological education, most Taiwanese rural community development associations are able to obtain professional information and data about courses without space and time restrictions. Thus, after strengthening and satisfying professionals’ demands, local economies will achieve growth and, therefore, younger persons would be encouraged to stay their original area, thus stimulating the development of rural community development associations. Eventually, the positive development cycle is predicted to increased and, as a result, the urban–rural disparities will be directly diminished by the diversified application of technological education in Taiwan.
As a digital innovation tool in the field of architecture and engineering, BIM technology can improve the efficiency and quality of projects and also realize all-round information sharing and collaboration throughout the project life cycle. Most of the ancient buildings in China are wooden structures, which often face problems such as damage, corrosion, and insect infestation, but because of their high cultural value, the maintenance of ancient buildings is always subject to problems. The effective combination of BIM technology and ancient buildings is conducive to the all-round, detailed, and in-depth maintenance and protection of ancient buildings. The Han family compound is located in Shangli Ancient Town, Sichuan Province, which is a well-preserved Qing dynasty architectural complex in an ancient town. However, under the dual effects of nature and man, a fire in the Republic of China period, an earthquake in 2008, and tourism development in recent years have caused the ancient buildings to become damaged and collapse, and they tend to assimilate with modern buildings, facing the dilemmas of maintenance and protection. Therefore, this paper is dedicated to exploring the feasibility of combining BIM technology with the maintenance and protection of the Han Family Courtyard and summarizing the specific application of BIM technology in the repair and protection of the Han Family Courtyard through domestic and international cases, so that the Han Family Courtyard can be developed in a sustainable way.
Javier Ramírez-Zelaya, Vanessa Jiménez, Paola Barba
et al.
The tectonic activity produced by the interaction between the Eurasian and African plates continually generates high seismic activity and the possibility of tsunamis occurring in the Gulf of Cadiz, Spain. The occurrence of these phenomena and the associated threat implies the need to implement a seismogeodesic system made up of a GNSS receiver, a seismograph–accelerograph, and an inclinometer that allows for us to study the behavior of tectonic activity in the Gulf and adjacent areas. This system is installed in the Doñana biological station, Huelva, Spain, and sends continuous records to the control center located in the University of Cadiz, generating GNSS, seismic, accelerometric, and inclinometric time series, which, together with the implementation of geodetic and geophysical techniques, is capable of providing information on tectonic activity immediately. In this manuscript, the time series generated by the system have been analyzed, in addition to a specific seismic event that occurred in the study area.
This article describes the resistance of chemically durable enamelled surfaces against the effects of chemical leachate and enzymes of bacteria <i>Acidithiobacillus ferrooxidans</i>, which are commonly used in biohydrometallurgy today during the biochemical leaching of ore. For many years now, the Vítkovice ENVI company has successfully used steel parts protected by the tested enamel in the construction of wastewater treatment plants and biogas stations. This article summarizes the results of a study that dealt with verifying the possibility of usage of widely applied enamel in the field of biochemical leaching during the treatment of raw materials. From the performed experiments, it follows that enamel can endure the effects of leaching solutions, but on poorly treated areas such as the edges and corners of parts, a quick onset of corrosion occurs.
The Exascale Computing Project (ECP) was one of the largest open-source scientific software development projects ever. It supported approximately 1,000 staff from US Department of Energy laboratories, and university and industry partners. About 250 staff contributed to 70 scientific libraries and tools to support applications on multiple exascale computing systems that were also under development. Funded as a construction project, ECP adopted an earned-value management system, based on milestones. and a key performance parameter system based, in part, on integrations. With accelerated delivery schedules and significant project risk, we also emphasized software quality using community policies, automated testing, and continuous integration. Software Development Kit teams provided cross-team collaboration. Products were delivered via E4S, a curated portfolio of libraries and tools. In this paper, we discuss the organizational and management elements that enabled the efficient and effective delivery of ECP libraries and tools, lessons learned and next steps.
Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the creation industry is enormous. AI can transform how we layout, build, and function buildings by automating ordinary duties, optimising electricity intake, and enhancing safety. However, ensuring that AI is used ethically and responsibly and that the advantages are shared fairly throughout the enterprise is essential.
Benoit Figuet, Manuel Waltert, Michael Felux
et al.
Global navigation satellite systems technology is at the core of modern air traffic navigation. Aircraft use it to estimate their position, while air navigation service providers rely on services such as automatic dependent surveillance broadcast which have been enabled by this technology. Since satellite signals are very low in power, they are susceptible to radio frequency interference activities, which can have a significant impact on aviation. This paper illustrates how crowd-sourced automatic dependent surveillance data transmitted by aircraft can be used to gain situational awareness about radio frequency interference and how air traffic over Eastern Europe has been impacted by interference activities over a period spanning from February to August 2022. The results suggest that satellite navigation signals were subject to interference of varying strength and duration. We observed several days when more than 1000 flights were affected, representing 60% of the daily traffic in the analysed area. Furthermore, the extent of the interference impact on aviation depends on the altitude of the aircraft, as low-flying aircraft tend to be less affected by interference than the ones flying at higher altitudes. Consequently, this paper contributes to a better understanding of how civil aviation is affected by radio frequency interference and where such disturbances may occur.