Matheus de Morais Leca, Kim Johnston, Ronnie de Souza Santos
\textbf{Context:} Empathy is increasingly recognized as a critical human capability for software engineers, supporting collaboration, ethical awareness, and user-centered design. While many disciplines have long explored empathy as part of professional formation, its incorporation into software engineering education remains fragmented. \textbf{Aim:} This study investigates how empathy has been used, taught, and discussed in general engineering and software engineering education, with the goal of identifying pedagogical practices, outcomes, and disciplinary differences that inform the structured integration of empathy into software curricula. \textbf{Method:} Following established guidelines for systematic reviews in software engineering, we conducted a comprehensive search across six databases and analyzed 43 primary studies published between 2001 and 2025. Data were coded and synthesized using descriptive and thematic analysis to capture how empathy is conceptualized, fostered, and assessed across educational contexts. \textbf{Findings:} Our findings show that engineering programs frame empathy as an ethical and reflective capacity linked to social responsibility, whereas software engineering translates empathy into structured, design-oriented, and measurable practices. Across both domains, empathy teaching enhances collaboration, ethical reasoning, bias awareness, and motivation, but remains limited by low curricular prioritization, measurement challenges, and resource constraints. \textbf{Conclusion:} Empathy is evolving from a peripheral soft skill into a measurable pedagogical construct in software engineering education. Embedding empathy as a continuous, assessable component of design and development courses can strengthen inclusivity, ethical reflection, and responsible innovation in future software professionals.
Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency.
Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana
et al.
Context: Early detection of ophthalmic diseases, such as drusen and glaucoma, can be facilitated by analyzing changes in the retinal microvascular structure. The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. However, the complexity of these algorithms increases with the diversity of pathologies to be classified. In this study, we introduce a new lightweight algorithm based on CNNs for the classification of multiple categories of eye diseases, using discrete wavelet transforms to enhance feature extraction. Methods: The proposed approach integrates a simple CNN architecture optimized for multi-class and multi-label classification, with an emphasis on maintaining a compact model size. We improved the feature extraction phase by implementing multi-scale decomposition techniques, such as biorthogonal wavelet transforms, allowing us to capture both fine and coarse features. The developed model was evaluated using a dataset of retinal images categorized into four classes, including a composite class for less common pathologies. Results: The feature extraction based on biorthogonal wavelets enabled our model to achieve perfect values of precision, recall, and F1-score for half of the targeted classes. The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. This advancement not only enhances the accuracy of real-time diagnoses but also supports the development of sophisticated tools for the detection of a wide range of retinal pathologies, thereby improving clinical decision-making processes.
This study presents a deep learning model that integrates Vision Transformers (ViT) with Fourier spectral filtering for remote sensing lithology classification. The model automates the process of identifying and classifying various rock types in remote sensing images, addressing a multi-class classification challenge. It utilizes ViT for feature extraction, enhanced by pretrained weights for improved efficiency and accuracy in recognizing geographical features. Fourier spectral filtering further augments the model by leveraging frequency domain information for accurate classification. The model preprocesses images, extracts spatial features, applies spectral filtering, and employs a classification head to predict rock types. Optimization of parameters through backpropagation and gradient descent methods, coupled with regularization strategies, aims to prevent overfitting and ensure generalizability. This approach combines deep learning’s capability for feature extraction with the analytical power of signal processing, offering a significant advancement for automatic rock type classification in remote sensing.
Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing assumptions in requirements engineering, including functional decomposability and behavioral predictability. This paper investigates this problem and advocates for a rethinking of existing requirements engineering methodologies. We propose a conceptual framework tailored to requirements engineering of pretrained-model-enabled software systems and outline several promising research directions within this framework. This vision helps provide a guide for researchers and practitioners to tackle the emerging challenges in requirements engineering of pretrained-model-enabled systems.
Sliding Mode Control (SMC) is a popular nonlinear controller for quadrotor UAVs due to its robustness, fast response, and ability to handle complex dynamics. However, it suffers from chattering, sensitivity to modeling errors, and poor performance in the presence of unmatched uncertainties. In this paper, a novel Neuro-fuzzy adaptive sliding mode control is designed and proposed for the position and attitude control of quadrotor UAVs. The proposed method combines SMC with the learning capabilities of Artificial Neural Networks (ANN) and the decision-making abilities of Fuzzy Logic Control (FLC). Firstly, the quadrotor flight dynamics is derived using Newton’s quaternion formalism. Secondly, conventional SMC is designed, and the system’s stability is validated using Lyapunov stability analysis. Finally, the designed SMC equivalent control part is estimated online by ANN, while its switching control part is estimated by FLC. To verify the controller’s performance, extensive software-in-the-loop simulations have been conducted in various scenarios. The results show that the proposed controller effectively tolerates matched and unmatched uncertainties and has better tracking and disturbance rejection capabilities with minimal control effort compared to fuzzy-based SMC and conventional SMC. Therefore, the suggested controller is very suitable for quadrotor UAV applications that require high tracking precision despite varying operating conditions.
The growing emphasis on studying equity, diversity, and inclusion within software engineering has amplified the need to explore hidden populations within this field. Exploring hidden populations becomes important to obtain invaluable insights into the experiences, challenges, and perspectives of underrepresented groups in software engineering and, therefore, devise strategies to make the software industry more diverse. However, studying these hidden populations presents multifaceted challenges, including the complexities associated with identifying and engaging participants due to their marginalized status. In this paper, we discuss our experiences and lessons learned while conducting multiple studies involving hidden populations in software engineering. We emphasize the importance of recognizing and addressing these challenges within the software engineering research community to foster a more inclusive and comprehensive understanding of diverse populations of software professionals.
The landscape of software engineering is evolving rapidly amidst the digital transformation and the ascendancy of AI, leading to profound shifts in the role and responsibilities of software engineers. This evolution encompasses both immediate changes, such as the adoption of Language Model-based approaches in coding, and deeper shifts driven by the profound societal and environmental impacts of technology. Despite the urgency, there persists a lag in adapting to these evolving roles. By fostering ongoing discourse and reflection on Software Engineers role and responsibilities, this vision paper seeks to cultivate a new generation of software engineers equipped to navigate the complexities and ethical considerations inherent in their evolving profession.
Digital Engineering currently relies on costly and often bespoke integration of disparate software products to assemble the authoritative source of truth of the system-of-interest. Tools not originally designed to work together become an acknowledged system-of-systems, with their own separate feature roadmaps, deprecation, and support timelines. The resulting brittleness and conglomeration of disparate interfaces in the Digital Engineering Ecosystem of an organization drains resources and impairs efficiency and efficacy. If Model-Based Systems Engineering were applied to this problem, a complete system architecture model would be defined, and a purpose-built computing system-of-systems would be constructed to satisfy stakeholder needs. We have decades of research in computer science, cybersecurity, software and systems engineering, and human-computer interaction from which to draw that informs the design of a Seamless Digital Engineering tooling system, but it would require starting from a clean slate while carefully adopting existing standards. In this paper, this problem space and solution space are characterized, defining and identifying Seamless Digital Engineering as a grand challenge in Digital Engineering research.
Óscar Pedreira, Félix García, Mario Piattini
et al.
Gamification has been applied in software engineering to improve quality and results by increasing people's motivation and engagement. A systematic mapping has identified research gaps in the field, one of them being the difficulty of creating an integrated gamified environment comprising all the tools of an organization, since most existing gamified tools are custom developments or prototypes. In this paper, we propose a gamification software architecture that allows us to transform the work environment of a software organization into an integrated gamified environment, i.e., the organization can maintain its tools, and the rewards obtained by the users for their actions in different tools will mount up. We developed a gamification engine based on our proposal, and we carried out a case study in which we applied it in a real software development company. The case study shows that the gamification engine has allowed the company to create a gamified workplace by integrating custom developed tools and off-the-shelf tools such as Redmine, TestLink, or JUnit, with the gamification engine. Two main advantages can be highlighted: (i) our solution allows the organization to maintain its current tools, and (ii) the rewards for actions in any tool accumulate in a centralized gamified environment.
The accuracy and efficiency of current leakage fault diagnosis methods in rural areas are low. In view of this defect, a rural power leakage diagnosis system based on narrowband Internet of Things is studied and designed. Aiming at the defect that the magnetic modulation sensor with single magnetic core structure is prone to zero drift and temperature drift, which increases the measurement error, a magnetic modulation sensor with double magnetic core structure is proposed. Aiming at the problem that the output voltage waveform of the sensor in the square wave excitation scheme is not ideal and the calculation is complex, a half-wave excitation scheme is proposed to make the output voltage waveform of the sensor more ideal and reduce the calculation complexity. The experimental results show that the diagnostic accuracy of the leakage diagnosis system of rural power grid designed according to the research has reached 95%. To sum up, the rural power grid leakage diagnosis system can effectively improve the diagnosis accuracy of power grid leakage, and provide guarantee for rural people’s livelihood and economic development.
Swati A. Patil, G. Pradeepini, Thirupathi Rao Komati
Abstract Music Genre Classification (MGC) is a crucial undertaking that categorizes Music Genre (MG) based on auditory information. MGC is commonly employed in the retrieval of music information. The three main stages of the proposed system are data readiness, feature mining, and categorization. To categorize MG, a new neural network was deployed. The proposed system uses features from spectrographs derived from short clips of songs as inputs to a projected scheme building to categorize songs into an appropriate MG. Extensive experiment on the GTZAN dataset, Indian Music Genre(IMG) dataset, Hindustan Music Rhythm (HMR) and Tabala Dataset show that the proposed strategy is more effective than existing methods. Indian rhythms were used to test the proposed system design. The proposed system design was compared with other existing algorithms based on time and space complexity.
Computer engineering. Computer hardware, Information technology
Cr12MoV flat steel mainly adopts the long process of die casting ingot， multi-fire forging and rolling， which has low production efficiency， low yield， high cost and high energy consumption. In order to solve the problems of poor thermal conductivity and ductility of cold working die steel， a short process of 90 t EBT-LF-VD-150 mm×630 mm continuous casting rectangular billet and one-fire heating +15-pass rolling was designed. The rectangular continuous casting billet and rolled 19 mm thick flat steel products of Cr12MoV steel were successfully developed， continuous casting center porosity 1.5 grade， center segregation ≤1.0， the finished flat steel eutectic carbide unevenness level ≤3， the defects inspection quality grade reaches grade A， and the performance indexes meet the standard requirements. Cr12MoV cold working die steel products have achieved mass production and achieved good economic benefits.
Materials of engineering and construction. Mechanics of materials, Technology
Aleksandar Nikolov, Lili Dobreva, Svetla Danova
et al.
Many antimicrobial coatings deliver a peak release of antimicrobial agent at an early age, after which they lost antimicrobial activity over time. In the present study a novel geopolymer paints with long term antimicrobial activity were developed based on natural zeolite modified with silver and copper ions. The obtained geopolymer paints were applied by brushing on concrete, ceramic, gypsum paperboard and steel. The coating was characterized by excellent adhesive strength and hiding properties. The long-term antimicrobial effect was evaluated by accelerated aging in carbonation chamber. Microstructural changes were analyzed by powder X-ray diffraction and Fourier transformed infrared spectroscopy. Cytotoxicity, antibacterial, antifungal and virucidal properties were investigated on raw and carbonated geopolymer paints. Geopolymer paints based on modified natural zeolite seems promising antimicrobial coating material that can be implemented in the global fight against the spread of diseases and pathogens.
Materials of engineering and construction. Mechanics of materials
Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on applying and evaluating LLMs in software engineering. Therefore, this paper comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases and selected 123 timely papers published starting from 2022 for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, guiding researchers and developers to optimize.
The purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should feel when using the system. Differently from functional and quality (non-functional) requirements, emotional requirements have received relatively less attention from the RE community. This study is motivated by the need to explore and map the literature on emotional requirements. The study applies the systematic mapping study technique for surveying and analyzing the available literature to identify the most relevant publications on emotional requirements. We identified 34 publications that address a wide spectrum of practices concerned with engineering emotional requirements. The identified publications were analyzed with respect to the application domains, instruments used for eliciting and artefacts used for representing emotional requirements, and the state of the practice in emotion-related requirements engineering. This analysis serves to identify research gaps and research directions in engineering emotional requirements. To the best of the knowledge by the authors, no other similar study has been conducted on emotional requirements.
Paloma Guenes, Rafael Tomaz, Marcos Kalinowski
et al.
The Impostor Phenomenon (IP) is widely discussed in Science, Technology, Engineering, and Mathematics (STEM) and has been evaluated in Computer Science students. However, formal research on IP in software engineers has yet to be conducted, although its impacts may lead to mental disorders such as depression and burnout. This study describes a survey that investigates the extent of impostor feelings in software engineers, considering aspects such as gender, race/ethnicity, and roles. Furthermore, we investigate the influence of IP on their perceived productivity. The survey instrument was designed using a theory-driven approach and included demographic questions, an internationally validated IP scale, and questions for measuring perceived productivity based on the SPACE framework constructs. The survey was sent to companies operating in various business sectors. Data analysis used bootstrapping with resampling to calculate confidence intervals and Mann-Whitney statistical significance testing for assessing the hypotheses. We received responses from 624 software engineers from 26 countries. The bootstrapping results reveal that a proportion of 52.7% of software engineers experience frequent to intense levels of IP and that women suffer at a significantly higher proportion (60.6%) than men (48.8%). Regarding race/ethnicity, we observed more frequent impostor feelings in Asian (67.9%) and Black (65.1%) than in White (50.0%) software engineers. We also observed that the presence of IP is less common among individuals who are married and have children. Moreover, the prevalence of IP showed a statistically significant negative effect on the perceived productivity for all SPACE framework constructs. The evidence relating IP to software engineers provides a starting point to help organizations find ways to raise awareness of the problem and improve the emotional skills of software professionals.
Abstract Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li3ClO antiperovskite. By leveraging ab initio, machine learning, and force-field descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.
Materials of engineering and construction. Mechanics of materials, Computer software