Hasil untuk "Transportation engineering"

A. Kaufmann, M. Gupta

Sharadhi Gunathilake, Ampalavanapillai Nirmalathas, Kosala Herath et al.

The growing passion for indoor optical wireless networks reflects their immense capability to deliver consistent high-quality data connectivity across diverse indoor environments. This study examines how polarization can be engineered to enhance near-field beam focusing in optical wireless indoor networks using a modular clustered optical phased array aperture. The aperture follows a ceiling-mounted phased array embedded within a phased array layout, associating planar clusters of dipole nano-emitters, supported by a dual-carrier architecture for grating lobe mitigation. We introduce a polarization-adaptive synthesis strategy that determines the cluster-level emitter polarization to replicate any desired polarization at the focal spot. The optimization model accommodates both unconstrained and constrained modes, enabling electric (E)-field matching from linear to general elliptical receiver states. Moreover, we analyze how quantization can be applied to these optimized orientations and how it affects the final performance. To learn the benefits of polarization orthogonality in a multi-receiver environment, we extend the aperture to simultaneously manage multiple focused beams via sub-cluster segmentation. At this level, we apply two polarization control strategies: continuous-domain polarization optimization and binary polarization assignment to match user-specific polarization states and suppress inter-user interference. Numerical estimates of per-receiver signal-to-interference-plus-noise ratio (SINR), E-field patterns, beam characteristics, and mean SINR trends with increasing user availability confirm the superior performance of the proposed techniques over systems that do not consider polarization. Under realistic system- and hardware-level constraints, our results deepen understanding of polarization-engineered modular optical phased arrays and demonstrate their potential for efficient and secure next-generation indoor networks.

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

ZHENG Junqiu, MA Hui, QIAO Yuelai et al.

To accurately assess the impact of structural cracks on the strength of pavement structures and establish corresponding evaluation indicators based on deflection response, field tests were conducted on multiple sections of expressways in Jiangsu Province. A falling weight deflectometer was used to test a total of 15 measurement points within a 3-meter range on both sides of each crack. The center deflection values at each measurement point were plotted into curves, and three evaluation indicators for cracks, namely deflection range, maximum influence distance on one side of the crack, and influence area, were proposed based on the characteristics of the curves. Core samples were taken directly above the selected cracks within the test sections to verify the crack types and development layers. According to the distribution and cracking conditions of the cracks in the core samples, six types of structural cracks were classified. The results show that the proposed indicators can effectively distinguish fatigue cracks from structural cracks. The coefficient of determination (R2) between deflection range and influence area is 0.75, and the Pearson correlation coefficient is 0.88. Both indicators have a good correspondence with the various stages of crack development and can be used in combination to evaluate the severity of cracks, providing practical guidance for pavement maintenance and construction.

Vitou That, Kimchheang Chhea, Jung-Ryun Lee

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the Whale Optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian Optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

Roham Koohestani, Philippe de Bekker, Begüm Koç et al.

Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich et al.

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

Li Gao, Mei-Ling Zhuang, Qunqun Zhang et al.

Mechanically connected precast piles are a type of precast piles that utilise snap-type mechanical connectors to restrain the pile ends of two identical or different precast piles at the top and bottom so as to quickly realise the purpose of the connection. However, the gap problem in the connectors of mechanically connected piles can lead to uneven and uniform deformation of the piles under horizontal loading, resulting in additional displacements and rotation angles of the piles at the connection. Solving the problem of calculating the internal force response of discontinuous deformed piles is a prerequisite for promoting and applying mechanically connected precast piles. Firstly, the theoretical derivation of mechanically connected piles with fixed constraints at the pile bottom is carried out. Secondly, the pile response equations of mechanically connected piles are established, and the theoretical solutions of pile displacement and internal force response of mechanically connected piles under horizontal loading are derived. Thirdly, the pile-soil model of the test pile is established using ABAQUS software (ABAQUS 2016) in combination with the design data of the test pile. The numerical simulation displacements and angles of rotation are compared with the test results. Finally, the theoretical and numerical simulation displacements and internal forces of the ordinary pile and the mechanically connected pile are compared. The relative errors of the displacements and angles of rotation of the established pile-soil model are less than 10%, indicating that the established model has good accuracy. The relative errors of the theoretical and numerical simulation displacements and internal forces of the mechanically connected pile are less than 10%, proving the correctness of the theoretical calculation by the m-method. This study can provide effective theoretical support and methodological guidance for the displacement and internal force response of discontinuous piles.

Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais et al.

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.

Nathan Huynh, Majbah Uddin, Chu Cong Minh

With the growth of intermodal freight transportation, it is important that transportation planners and decision makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities to intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolution, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation system. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques through relatively simple examples, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.

Adebola Olutayo, Yanjie Dong, Julian Cheng et al.

Performance of wireless powered wireless systems is analyzed. The wireless devices in such systems scavenge energy from sources in downlinks and use the energy to communicate in uplinks. We introduce two new models for these energy harvesters to consider their nonlinear circuitry and their functioning over multiple line-of-sight and non-line-of-sight channels. The newly proposed Beaulieu-Xie fading model is used to characterize this manifold of channels. Performance analyses of average harvested energy and transmission outage probability show good fit between the proposed models and measured data.

Yeong-Min Kim, Phat Tien Nguyen, Tam Minh Phan et al.

The objective of this study was to identify the most effective chip seal design method and chip sizes by conducting a modified Hamburg wheel tracking test and sweep test. Three chip seal design methods, including Austroads, McLeod, and Vietnam methods, were evaluated to design several chip seal mixtures with varying aggregate sizes (e.g., 12.5, 9.5, 4.75, and 2.36 mm) and binder types. To assess the performance of the chip seal mixtures, aggregate loss and bleeding susceptibility were measured. The sweep test was utilized to determine the amount of aggregate loss caused by the sweeping effect, while the modified Hamburg wheel tracking (HWT) test was developed to estimate the aggregate loss caused by the braking effect using a fixed pneumatic rubber tire. Additionally, a modified Hamburg wheel tracking test with a rolling pneumatic rubber tire was used to assess bleeding susceptibility, and the bleeding area was quantified using an image analysis process. The results indicated that the Austroads design method exhibited the lowest aggregate loss compared to the McLeod and Vietnam methods. Moreover, the consistent findings from both the sweep test and modified HWT test demonstrated the feasibility of using the modified HWT test to simulate aggregate loss caused by the braking effect. The bleeding percentage was found to be affected not only by the binder application rate but also by the aggregate size and the number of layers applied. The Austroads and McLeod chip seal mixtures exhibited an approximately 20 % lower bleeding percentage than that of the Vietnam mixtures. Finally, for a single chip seal, a 9.5 mm chip aggregate was determined to be the optimal size, while for a double chip seal, 4.75 mm and 2.36 mm chips were recommended.

Zhiyu Fan, Yannic Noller, Ashish Dandekar et al.

The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including interdisciplinary students who are not CS majors. The high teacher-to-student ratio makes it challenging to provide timely and high-quality feedback. Meanwhile, software engineering education comes with inherent difficulties like acquiring industry partners and the dilemma that such software projects are often under or over-specified and one-time efforts within one team or one course. To address these challenges, we designed a novel foundational SE course. This SE course envisions building a full-fledged Intelligent Tutoring System (ITS) of Programming Assignments to provide automated, real-time feedback for novice students in programming courses over multiple years. Each year, SE students contribute to specific short-running SE projects that improve the existing ITS implementation, while at the same time, we can deploy the ITS for usage by students for learning programming. This project setup builds awareness among SE students about their contribution to a "to-be-deployed" software project. In this multi-year teaching effort, we have incrementally built an ITS that is now deployed in various programming courses. This paper discusses the Intelligent Tutoring System architecture, our teaching concept in the SE course, our experience with the built ITS, and our view of future computing education.

Xiaofeng Wang, Henry Edison, Dron Khanna et al.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Srijoni Majumdar, Soumen Paul, Debjyoti Paul et al.

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects and an additional dataset generated individually by teams using large language models. Overall 56 experiments have been submitted by 17 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The labels generated from large language models increase the bias in the prediction model but lead to less over-fitted results.

Wouter Groeneveld, Laurens Luyten, Joost Vennekens et al.

Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem solving in higher education, we have included a list of implications for educational practice.

Xuanhua Lin, Xiaohui Lin, Kelian Chen

MFD is widely used in traffic state evaluation because of its description of the macro level of urban road net-work. Aiming at the control strategy optimization problem of urban arterial road network under saturated traffic flow state, this study analyzes the MFD characteristics of a typical three-segment "ascending-stable-descending segment" and its advantages in characterizing the macroscopic operation efficiency of the road network, a arte-rial coordination control strategy considering MFD is proposed. According to the characteristics of MFD, it is proposed that the slope of the ascending segment and the capacity of the road network represent the operating efficiency of the free flow and saturated flow of the road network respectively. The traffic flow and density data of road segment are obtained by the road detector through Vissim simulation software. Aiming at the problem that the MFD is too discrete due to unreasonable control strategy or traffic condition, and in order to extract the MFD optimization target indicators, it is proposed to extract the key boundary points of the MFD by the “tic-tac-toe” method and divide the MFD state by Gaussian mixture clustering. The genetic algorithm integrates the multi-objective particle swarm algorithm as the solution algorithm, and the simulation iterative process is com-pleted through Python programming and the com interface of Vissim software. In order to verify the validity of the model and algorithm, the actual three-intersections arterial road network is used for verification, and the model in this study is compared with the optimization model without considering MFD, the model solved by traditional algebraic method, and the optimization model solved by typical multi-objective particle swarm. Re-sults show that the model in this research performs well in efficiency indicators such as total delay, average delay, and queue coefficient. At the same time, the MFD form has highest stability, the control effect is the best in the saturated state. The solution algorithm GA-MOPSO also has a better solution effect.

Ian A. Cosden, Kenton McHenry, Daniel S. Katz

As software has become more essential to research across disciplines, and as the recognition of this fact has grown, the importance of professionalizing the development and maintenance of this software has also increased. The community of software professionals who work on this software have come together under the title Research Software Engineer (RSE) over the last decade. This has led to the formalization of RSE roles and organized RSE groups in universities, national labs, and industry. This, in turn, has created the need to understand how RSEs come into this profession and into these groups, how to further promote this career path to potential members, as well as the need to understand what training gaps need to be filled for RSEs coming from different entry points. We have categorized three main classifications of entry paths into the RSE profession and identified key elements, both advantages and disadvantages, that should be acknowledged and addressed by the broader research community in order to attract and retain a talented and diverse pool of future RSEs.

Md Jobair Hossain Faruk, Santhiya Subramanian, Hossain Shahriar et al.

Software Engineering is the process of a systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software product development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of the software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented Software Engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current software engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futuristic technology would be challenging and promising research is needed extensively towards addressing and improving state-of-the-art software engineering processes and methodology for novel technologies.