This engineering textbook is designed to introduce advanced control systems for vehicles, including advanced automotive concepts and the next generation of vehicles for Intelligent Transportation Systems (ITS). For each automotive-control problem considered, the authors emphasize the physics and underlying principles behind the control-system concept and design. This is an exciting and rapidly developing field for which many articles and reports exist but no modern unifying text. An extensive list of references is provided at the end of each chapter for all topics covered. This is currently the only textbook, including problems and examples, that covers and integrates the topics of automotive powertrain control, vehicle control, and ITS. The emphasis is on fundamental concepts and methods for automotive control systems rather than the rapidly changing specific technologies. Many of the text examples, as well as the end-of-chapter problems, require the use of MATLAB and/or Simulink.
The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years.
This study tackles the critical challenges of stress evolution and pillar optimization in underground potash mining, with a focus on the 351-stope of Kaiyuan Mining in Laos. Integrating theoretical calculations, large-scale 3D numerical modeling, and an AHP-Fuzzy comprehensive evaluation, we systematically analyze the complex mechanical behaviors of the mining environment. Applying key stratum theory, we reveal the unique mechanism by which overlying hard rock bends without fracturing in carnallite layers under room-and-pillar conditions. Comparative numerical simulations of four pillar-width schemesβinvolving 8 m rooms with 10 m, 8 m, 6 m, and 4 m pillarsβshow that reducing pillar width markedly increases vertical stress, triggers exponential roof subsidence, and expands pillar failure zones. Using an AHP-Fuzzy method that incorporates safety, technical, and economic factors, the Simultaneous Backfilling with 8 m Mining and 6 m Pillar Retention is identified as the optimal scheme. This configuration demonstrates superior stability, exhibiting an average pillar stress of 9.3 MPa and only limited plastic failure zones at pillar ends. These findings offer a robust scientific and technical foundation for enhancing the safety, efficiency, and sustainability of underground potash mining operations.
This study addresses two major limitations in the current evaluation system for urban rail train driversβ emergency handling capability: the lack of clearly defined criteria, and an overemphasis on technical skills to the neglect of psychological factors. We innovatively construct a multidimensional evaluation framework based on the Physio-Psycho-Machine-Environment-Management (PPMEM) model. Through a systematic analysis of the core components of emergency response capability and its influencing factors, a mechanism model rooted in βHuman-Machine-Environment-Managementβ theory is established. Empirically, 30 key influencing factors were identified and categorized into seven dimensions: cognitive, physiological, skill-based, psychological, equipment, environmental, and managerial. A mixed-methods approach was adopted. During the qualitative phase, a system of influencing factors was determined through field studies and in-depth expert interviews. In the quantitative phase, a questionnaire survey was administered to employees of Kunming Rail Transit Operations Co., Ltd. (N = 538 valid responses), and a multidimensional evaluation model was developed using structural equation modeling (SEM) with Amos 26 Graphics. The results indicate that the total effects of latent variables on emergency handling capability, in descending order, are: psychological factors (Ξ² = 0.214) > physiological factors (Ξ² = 0.212) > environmental factors (Ξ² = 0.205) > equipment status (Ξ² = 0.126) > cognitive factors (Ξ² = 0.105) = skill-based factors (Ξ² = 0.105) > managerial factors (Ξ² = 0.102). Notably, psychological, physiological, and environmental factors all exhibited effect sizes exceeding the significant threshold of 0.2, constituting a core group of determinants for emergency response performance. Therefore, metro operators should prioritize improvements in driversβ workload management, mental health support, and environmental adaptability, supplemented by targeted skill and cognitive training, as well as policy refinement. These measures will contribute to a systematic enhancement of emergency response capabilities. The findings provide both a theoretical foundation and practical guidance for strengthening emergency management systems in urban rail transit.
Jonathan Ullrich, Matthias Koch, Andreas Vogelsang
With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.
Engineering information systems for scientific data analysis presents significant challenges: complex workflows requiring exploration of large solution spaces, close collaboration with domain specialists, and the need for maintainable, interpretable implementations. Traditional manual development is time-consuming, while "No Code" approaches using large language models (LLMs) often produce unreliable systems. We present iProg, a tool implementing Interactive Structured Inductive Programming. iProg employs a variant of a '2-way Intelligibility' communication protocol to constrain collaborative system construction by a human and an LLM. Specifically, given a natural-language description of the overall data analysis task, iProg uses an LLM to first identify an appropriate decomposition of the problem into a declarative representation, expressed as a Data Flow Diagram (DFD). In a second phase, iProg then uses an LLM to generate code for each DFD process. In both stages, human feedback, mediated through the constructs provided by the communication protocol, is used to verify LLMs' outputs. We evaluate iProg extensively on two published scientific collaborations (astrophysics and biochemistry), demonstrating that it is possible to identify appropriate system decompositions and construct end-to-end information systems with better performance, higher code quality, and order-of-magnitude faster development compared to Low Code/No Code alternatives. The tool is available at: https://shraddhasurana.github.io/dhaani/
Ahmad Rahdari, Elham Keshavarz, Ehsan Nowroozi
et al.
The increasing need to process large, high-dimensional datasets and the substantial computational power required have made the use of distributed cloud servers essential. These servers provide cost-effective solutions that make storage and computing accessible to ordinary users. However, they might face significant vulnerabilities, including data leakage, metadata spoofing, insecure programming interfaces, malicious insiders, and denial of service. To gain public trust in distributed computing, addressing concerns related to privacy and security while ensuring high performance and efficiency is crucial. Multiparty computation, differential privacy, trusted execution environments, and federated learning are the four major approaches developed to address these issues. This survey paper reviews and compares these four approaches based on a structured framework, by highlighting recent top-tier research papers published in prestigious journals and conferences. Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. Finally, the survey discusses future research directions for connecting academic innovations with real-world industrial applications.
Telecommunication, Transportation and communications
Transportation mode recognition (TMR) is a critical component of human activity recognition (HAR) that focuses on understanding and identifying how people move within transportation systems. It is commonly based on leveraging inertial, location, or both types of signals, captured by modern smartphone devices. Each type has benefits (such as increased effectiveness) and drawbacks (such as increased battery consumption) depending on the transportation mode (TM). Combining the two types is challenging as they exhibit significant differences such as very different sampling rates. This paper focuses on the TMR task and proposes an approach for combining the two types of signals in an effective and robust classifier. Our network includes two sub-networks for processing acceleration and location signals separately, using different window sizes for each signal. The two sub-networks are designed to also embed the two types of signals into the same space so that we can then apply an attention-based multiple-instance learning classifier to recognize TM. We use very low sampling rates for both signal types to reduce battery consumption. We evaluate the proposed methodology on a publicly available dataset and compare against other well known algorithms.
Yvonne Dittrich, Johan Bolmsten, Catherine Seidelin
Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners. However, as the research moves beyond the observational approach, it requires a different kind of interaction with the software development organisation. This makes action research a challenging endeavour, and it makes it difficult to teach action research through a course that goes beyond explaining the principles. This chapter is intended to support learning and teaching action research, by providing a rich set of examples, and identifying tools that we found helpful in our action research projects. The core of this chapter focusses on our interaction with the participating developers and domain experts, and the organisational setting. This chapter is structured around a set of challenges that reoccurred in the action research projects in which the authors participated. Each section is accompanied by a toolkit that presents related techniques and tools. The exercises are designed to explore the topics, and practise using the tools and techniques presented. We hope the material in this chapter encourages researchers who are new to action research to further explore this promising opportunity.
Writing secure code is challenging and so it is expected that, following the release of code-generative AI tools, such as ChatGPT and GitHub Copilot, developers will use these tools to perform security tasks and use security APIs. However, is the code generated by ChatGPT secure? How would the everyday software or security engineer be able to tell? As we approach the next decade we expect a greater adoption of code-generative AI tools and to see developers use them to write secure code. In preparation for this, we need to ensure security-by-design. In this paper, we look back in time to Saltzer & Schroeder's security design principles as they will need to evolve and adapt to the challenges that come with a world of AI-generated code.