Vehicle-to-infrastructure (V2I) technology enables information interaction between vehicles and among vehicles and infrastructure, significantly enhancing the efficiency of lane-changing processes and stabilising traffic flow. Current research primarily focuses on single lane-changing events in fixed micro-level scenarios or studies involving small-scale vehicle fleets, neglecting the randomness of lane-changing vehicle arrivals and potential conflicts during lane-changing. This paper proposes a lane-changing decision model based on potential conflict analysis, specifically tailored to mandatory lane-changing requirements in high-density traffic conditions. The model comprises sub-models for lane-changing decision triggering, influence range calculation and lane-changing priority determination, capable of dynamically adjusting the lane-changing sequence, mitigating lane-changing conflicts, and improving driving safety and traffic efficiency. Simulation experiments indicate that, when compared to lane-changing patterns in real-world traffic scenarios, this model reduces travel time by 23.30%, delays by 21.95% and the number of stops by 23.84%, thereby providing a novel approach for lane-changing decision-making and control in V2I environments.
This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.
Software engineering concepts and processes are worthy of formal study; and yet we seldom formalize them. This "research ideas" article explores what a theory of software engineering could and should look like. Software engineering research has developed formal techniques of specification and verification as an application of mathematics to specify and verify systems addressing needs of various application domains. These domains usually do not include the domain of software engineering itself. It is, however, a rich domain with many processes and properties that cry for formalization and potential verification. This article outlines the structure of a possible theory of software engineering in the form of an object-oriented model, isolating abstractions corresponding to fundamental software concepts of project, milestone, code module, test and other staples of our field, and their mutual relationships. While the presentation is only a sketch of the full theory, it provides a set of guidelines for how a comprehensive and practical Theory of Software Engineering should (through an open-source community effort) be developed.
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.
[Objective] In evaluating the service life extension of urban rail transit trains, economic calculation serves as a key basis for decision-making and plays a critical role in determining whether to carry out life extension and associated upgrade. Therefore, it is essential to study the economic viability in urban rail transit train life extension based on whole life-cycle cost. [Method] Through calculating the whole life-cycle cost for urban rail transit trains, an economic evaluation method for train service life extension is proposed. Urban rail transit train life cycle is divided into four stages: procurement, operation, maintenance, and recycling/disposal. The costs for each stage are calculated separately, incorporating inflation factors and special expenses such as mid-term overhauls, which enables targeted evaluation for factors like interest rate, inflation, and refurbishment costs under service life extension scenarios. An empirical analysis and parameter study are conducted using a typical case，and multi-dimensional calculation is carried out for two scenarios: retirement at the standard 30-year service life and at the extended 45-year service life. [Result & Conclusion] When excluding the factors such as interest rates, inflation, and residual value at disposal, upgrade and procurement costs are the decisive factors influencing the economic feasibility of service life extension. The higher the procurement cost and the lower the cost, the greater the economic advantage of extending service life. Though interest rate factors significantly affect the overall life-cycle cost of the train, their impact on the economic viability of train delayed upgrade is relatively minor. Inflation factors have a substantial effect on the economics of train life extension; as the inflation rate increases, the cost advantage of life extension diminishes rapidly. When the inflation rate reaches 1.5%, extending train service through upgrade will eliminate any economic advantages.
Nazmul Arefin Khan, Krishna Murthy Gurumurthy, Amir Davatgari
et al.
In recent years, shared E-Scooters (SES) have emerged as one of the most popular and rapidly growing micromobility modes. To better understand the role of SES in urban mobility, it is critical for policymakers and planners to explore the adoption behavior and usage frequency of Shared E-Scooters. This study jointly estimates the Shared E-Scooters' potential adoption and frequency of usage using a zero-inflated ordered probit (ZIOP) model. This approach can be interpreted as whether an individual considers E-scooters as a travel mode alternative, and if so, how frequently they use E-scooters, which also has a zero occurrence. The study uses a dataset from the City of Chicago. The parameter estimation results suggest that various socio-demographics, built environment, accessibility measures and service characteristics have adequate impacts on E-Scooter adoption and usage frequency. This study also implements the model within the POLARIS agent-based transportation system simulator to examine the potential impact of various E-Scooter deployment scenarios. Results suggest that deploying more Shared E-Scooters in the traffic network not only increases the number of E-Scooter trips, but also helps to decrease the person-miles traveled and person-hour traveled. Insights from this study would be useful for planners and policymakers to develop alternative policy strategies associated with emerging mobility.
Quantum Key Distribution (QKD) provides secure communication by leveraging quantum mechanics, with the BB84 protocol being one of its most widely adopted implementations. However, the classical post-processing steps in BB84, such as sifting, error correction, and key verification, often result in significant communication overhead, limiting its efficiency and scalability. In this work, we propose three key optimizations for BB84: (1) PRNG-based predetermined key bit positioning, which eliminates redundant bit exchanges during sifting, (2) hash-based subsequence comparison, enabling lightweight and efficient key verification, and (3) adaptive basis reconciliation, which minimizes the communication costs associated with basis matching. The proposed optimizations achieve a 50% reduction in communication overhead for large key sizes compared to traditional QKD protocols, as demonstrated through rigorous performance analysis. While the focus of this work is on the BB84 protocol, these optimizations are also directly applicable to a broader class of Discrete-Variable QKD (DV-QKD) protocols, such as six-state, B92, and E91, which share a fundamentally similar post-processing structure. This generality highlights the modularity and adaptability of the proposed methods across diverse QKD implementations. The proposed optimizations enhance post-processing efficiency and scalability, enabling practical deployment in bandwidth-limited environments like IoT networks, secure financial systems, and defense communications, thereby supporting broader adoption of quantum communication systems.
Telecommunication, Transportation and communications
This study proposes a semi-analytic harmonic modeling method that significantly improves the accuracy and efficiency of complex magnetic field modeling by integrating numerical and analytical approaches. Compared to traditional methods such as the equivalent charge method and finite element method, this approach optimizes the distribution of surface and body charges in the magnetic dipole model and introduces a finite and variable permeability model to accommodate material non-uniformity. Through harmonic expansion and analytical optimization, the method more accurately reflects the characteristics of real magnets, providing an efficient and precise solution for complex magnetic field problems, particularly in the design of high-performance magnets such as Halbach arrays. In this study, the effectiveness of the new modeling method is verified through the combination of simulation and experiment: the magnetic field distribution of the new Halbach array is accurately simulated, and the applicability of the model in the description of complex magnetic fields is analyzed. The dynamic response ability of the optimized model is verified by modeling and simulating the variation of the permeability under actual conditions. The distribution of scalar potential energy with permeability was simulated to evaluate the adaptability of the model to the real physical field. Through the comparative analysis of simulation and experimental results, the advantages of the new method in modeling accuracy and efficiency are clearly pointed out, and the effectiveness of the semi-analytic harmonic modeling method and its wide application potential in the design of new magnetic fields are proved. In this study, a semi-analytic harmonic modeling method is proposed by combining numerical and analytical methods, which breaks through the efficiency bottleneck of traditional modeling methods, and achieves the unity of high precision and high efficiency in the magnetic field modeling of the new Halbach array, providing a new solution for the study of complex magnetic field problems.
As travel costs fall with new capacity, the quantity of travel increases. This concept—induced travel—has profound implications but remains unevenly embraced in practice. Do instructors teach it in transportation engineering classrooms? What explains their pedagogical decisions? Interviews with university instructors revealed remarkable variation. Whereas some featured induced travel as a key takeaway, others omitted the idea entirely. Instructors also varied in their willingness to critique standard engineering practices; some were largely uncritical while others sought to “counteract conventional wisdom.” In justifying their choices, instructors offered a range of overlapping concerns. Those who “believed” in induced travel but did not teach it often lacked expertise in the area and were uneasy teaching “soft” concepts. Because teaching was seen as a lower priority than conducting research, instructors had little motivation to overcome those challenges. Instructors also advanced pragmatic concerns about the need to prepare students for the Fundamentals of Engineering exam and their careers. Instructors who were more skeptical of induced travel wondered whether seemingly new travel was instead shifted or previously suppressed. Some of these instructors argued that even if new travel was indeed induced, engineers still had a responsibility to accommodate it. Finally, the contested language of induced travel can lead parties to talk past each other. “Believers” and “skeptics” sometimes have more in common than initially thought. However, there are still profound disagreements—about induced travel, standard engineering practices, and indeed the very purpose of engineering. In these debates it will be essential to operate from a shared vocabulary.
Riccardo Dettori, Francesco Siddi, Luciano Colombo
et al.
MoS2 is one of the most investigated and promising transition-metal dichalcogenides. Its popularity stems from the interesting properties of the monolayer phase, which can serve as the fundamental block for numerous applications. In this paper, we propose an atomistic perspective on the modulation of thermal transport properties in monolayer MoS2 through strategic defect engineering, specifically the introduction of sulfur vacancies. Using a combination of molecular dynamics simulations and lattice dynamics calculations, we show how various distributions of sulfur vacancies -- ranging from random to periodically arranged configurations -- affect its thermal conductivity. Notably, we observe that certain periodic arrangements restore the thermal conductivity of the pristine system, due to a minimized interaction between acoustic and optical phonons facilitated by the imposed superperiodicity. This research deepens the understanding of phononic heat transport in two-dimensional materials and introduces a different point-of-view for phonon engineering in nanoscale devices, offering a pathway to enhance device performance and longevity through tailored thermal management strategies.
Sachin B. Chougule, Bharat S. Chaudhari, Sheetal N. Ghorpade
et al.
Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and blockchain in vehicle architecture has increased possibilities towards efficient and sustainable transportation systems. In this article, we present a comprehensive study and analysis of the edge computing paradigm, explaining elements of edge AI. Furthermore, we discussed the edge intelligence approach for deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. It mentions the advantages of edge intelligence and its use cases in smart electric vehicles. It also discusses challenges and opportunities and provides in-depth analysis for optimizing computation for edge intelligence. Finally, it sheds some light on the research roadmap on AI for edge and AI on edge by dividing efforts into topology, content, service segments, model adaptation, framework design, and processor acceleration, all of which stand to gain advantages from AI technologies. Investigating the incorporation of important technologies, issues, opportunities, and Roadmap in this study will be a valuable resource for the community engaged in research on edge intelligence in electric vehicles.
Thawat Sornsadaeng, Peerapon Chanhom, Damrong Amorndechaphon
et al.
This paper introduces a fault-tolerant control method for Cascaded H-Bridge Multilevel Inverters (CHB MLI) using Neutral Voltage Modulation (NVM), enabling reliable operation even when DC source modules are damaged or operating at reduced voltage. The method calculates the inverter’s modulating signals based on the lowest and middle DC source voltages of each phase, followed by the calculation and injection of a common-mode voltage (CMV) modulating signal, which is minimized or nullified. This approach effectively reduces CMV and prevents overmodulation by regulating the control signal to remain within the available module voltage, ensuring balanced three-phase output with low Total Harmonic Distortion (THD). Moreover, the method adapts to unbalanced DC source conditions by dynamically adjusting the inverter’s modulating signals in response to voltage changes, offering a more comprehensive solution compared to traditional NVM techniques. The proposed method is validated through simulations and experiments with an asymmetrical three-phase CHB MLI using Resistive-Inductive (RL) loads. Results confirm its robustness and efficiency across various fault scenarios and operating conditions, demonstrating its potential to significantly enhance system reliability, stability, and performance in industrial applications.
Michael Dorner, Maximilian Capraro, Oliver Treidler
et al.
The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering making it a software engineering problem, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.
The moisture diffusion behaviors of 3D woven composites exhibit non-Fickian properties when they are exposed to a hydrothermal environment. Although some experimental works have been undertaken to investigate this phenomenon, very few mathematical works on non-Fickian moisture diffusion predictions of 3D woven composites are available in the literature. To capture the non-Fickian behavior of moisture diffusion in 3D woven composites, this study first utilized a time fractional diffusion equation to derive the percentage of moisture content of a homogeneous material under hydrothermal conditions. A two-stage moisture diffusion model was subsequently developed based on the moisture diffusion mechanics of both neat resin and 3D woven composites, which describes the initial fast diffusion and the long-term slow diffusion stages. Notably, the model incorporated fractional order parameters to account for the nonlinear property of moisture diffusion in composites. Finally, the weight gain curves of neat resin and the 3D woven composite were calculated to verify the fractional diffusion model, and the predicted moisture uptake curves were all in good agreement with the experimental results. It is important to note that when the fractional order parameter α < 1, the initial moisture uptake will become larger with a later slow down process. This phenomenon can better describe non-Fickian behavior caused by initial voids or complicated structures.