We present Nidus, a governance runtime that mechanizes the V-model for AI-assisted software delivery. In the self-hosting deployment, three LLM families (Claude, Gemini, Codex) delivered a 100,000-line system under proof obligations verified against the current obligation set on every commit. The system governed its own construction. Engineering invariants - traced requirements, justified architecture, evidenced deliveries - cannot be reliably maintained as learned behavior; assurance requires enforcement by a mechanism external to the proposer. Nidus externalizes the engineering methodology into a decidable artifact verified on every mutation before persistence. Organizational standards compile into guidebooks - constraint libraries imported by governed projects and enforced by decidable evaluation. Four contributions: (1) recursive self-governance - the constraint surface constrains mutations to itself; (2) stigmergic coordination - friction from the surface routes agents without central control; (3) proximal spec reinforcement - the living artifact externalizes the engineering context that RL and long-chain reasoning try to internalize; the specification is the reward function, UNSAT verdicts shape behavior at inference time, no weight updates; (4) governance theater prevention - compliance evidence cannot be fabricated within the modeled mutation path. The constraint surface compounds: each obligation permanently eliminates a class of unengineered output. The artifact's development history is a formal development - every state satisfies all active obligations, and the obligation set grows monotonically.
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.
Flexible shafts, exhibiting excellent tensile and torsional transmission characteristics, serve as an ideal power transmission medium for the design of flexible continuum robots. This study introduces a novel rigid-flexible hybrid robotic mechanism employing six flexible shaft actuators to achieve seven degrees of freedom (DOFs). The design integrates a dual-segment flexible body with a single-segment rigid body. A comprehensive kinematic model was derived to describe the hybrid structure’s motion. Concurrently, a static model was developed, incorporating driving forces, external loads, gravitational effects, and the combined elastic forces from the flexible driving shafts and spinal rods. Based on this static model, 182 combinations of flexible shaft pulling forces and distal loads were applied to control the robot’s posture. Experimental results indicated that the maximum positional errors for the single- and dual-segment configurations were 2.67% and 5.97% of the manipulator length, respectively. The maximum root mean square error for spatial configuration repeatability is 0.4 and 5.1 mm for single- and dual-segment flexible robots, respectively. The kinematic and static models developed in this research accurately predict the spatial configuration of the flexible continuum robot, which features an integrated elastic arrangement of driving shafts and spinal rods, thereby facilitating rapid deployment in similar flexible robotic systems.
Continuum robots with outstanding compliance, dexterity, and lean bodies are successfully applied in medicine, aerospace engineering, the nuclear industry, rescue operations, construction, service, and manipulation. However, the inherent low stiffness characteristics of continuum bodies make it challenging to develop ultra-long and small-diameter continuum robots. To address this size–scale challenge of continuum robots, we developed an 8 m long continuum robot with a diameter of 23 mm by a tip actuation and growth mechanism. Meanwhile, we also realized the untethered design of the continuum robot, which greatly increased its usable space range, portability, and mobility. Demonstration experiments prove that the developed growing continuum robot has good flexibility and manipulability, as well as the ability to cross obstacles and search for targets. Its continuum body can transport liquids over long distances, providing water, medicine, and other rescue items for trapped individuals. The functionality of an untethered growing continuum robot (UGCR) can be expanded by installing multiple tools, such as a grasping tool at its tip to pick up objects in deep wells, pits, and other scenarios. In addition, we established a static model to predict the deformation of UGCR, and the prediction error of its tip position was within 2.6% of its length. We verified the motion performance of the continuum robot through a series of tests involving workspace, disturbance resistance, collision with obstacles, and load performance, thus proving its good anti-interference ability and collision stability. The main contribution of this work is to provide a technical reference for the development of ultra-long continuum robots based on the tip actuation and steering principle.
Mohamed Hariri Muhammad Hafeez, Joohari Muhammad Imran, Abd Halim Amir Rabani
et al.
The increasing imperative for sustainable energy solutions has significantly amplified the demand for commercial grid-connected photovoltaic (PV) systems, particularly those integrated into rooftop installations within urbanized environments. Malaysia's Net Energy Metering (NEM) 3.0 policy, a cornerstone of the nation's renewable energy strategy, permits commercial establishments to connect up to 75% of their peak electrical demand capacity to the national grid. This strategic allowance empowers property owners to substantially offset their energy expenditures and realize considerable savings on electricity bills over extended periods. The widespread deployment of PV systems leads to complexities notably concerning the grid's power factor which may lead to thermal inefficiencies and potential failures of switching apparatus within the electrical infrastructure. This research presents a detailed design analysis and economic evaluation of a substantial 4324.75 kWp rooftop PV system by utilizing the GCPV system. The study leverages specialized PV system software (PVsyst) to conduct environmental, financial, and technical assessments specifically at the USM Engineering Campus, Penang, Malaysia. Empirical data from 2023 reveal that the USM Engineering Campus achieved an approximate saving of RM 2.2 million during the initial year following the installation of its grid-connected PV system. It is observed that the degradation in the system's power factor from an initial 0.96 to 0.83 was primarily attributed to the suboptimal operational state of the pre-existing capacitor banks. The financial analysis specifically tailored for the commercial buildings operating under the NEM 3.0 framework projects a favorable five-year return on investment (ROI). This research serves as a valuable case study for commercial building owners contemplating the adoption of green energy production and exploring significant avenues for cost reduction.
In response to the increasing demands for cage strength and operational stability of ball bearings in new energy vehicle motors operating under high-speed and light-load conditions, this paper focuses on the 6207 deep groove ball bearing as the research subject. It systematically analyzes the influence of various structural parameters of the crown-type cage, including profile radius, side beam thickness, claw length, and claw radius, on its eccentricity. Furthermore, the paper explores the mechanism by which eccentricity affects the dynamic performance of the cage. By establishing a rigid–flexible coupled dynamics model and conducting simulation analyses, the results indicate that the claw ends of the crown-type cage pockets are the regions of maximum deformation, while the pocket bottom experiences the highest equivalent stress, identifying it as a critical location for fracture failure. The research demonstrates that the impact of eccentricity on performance is non-monotonic: a reduction in eccentricity can significantly diminish the collision force between the balls and the cage, decrease vibration amplitude, and lower equivalent stress; concurrently, the maximum cage deformation and vibration acceleration level increase correspondingly. Additionally, the centrifugal force acting on the cage itself significantly elevates the equivalent stress. Therefore, the optimal design of the crown-type cage necessitates a comprehensive trade-off among multiple objectives, including strength and stability. It is essential to avoid inappropriate eccentricity design that may arise from the pursuit of a single performance indicator (such as friction reduction or weight reduction), thereby providing a theoretical foundation for the refined design of high-performance bearing cages.
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.
This paper focuses on the multi-dimensional health state assessment of complex mechanical equipment, using a fracturing pump as a case study. It proposes a comprehensive assessment framework that integrates the analysis of multiple data dimensions, including real-time operational data, vibration data, and historical maintenance data. A health baseline is constructed by obtaining residual data of the equipment in a healthy state using a Generalized Regression Neural Network (GRNN) and extracting time-frequency domain features. The health assessment model for the vibration data dimension is built by calculating and normalizing the Mahalanobis Distance between the vibration data to be assessed and the health baseline. An equipment operational state evaluation model based on multiple characteristic parameters is established, using these parameters as failure criteria, to quantitatively characterize the Health Index from the real-time operational data dimension. Based on reliability theory, a multi-state transition probability for the main components of the equipment is obtained using a Markov model. The probability is mapped to the Health Index using a weighted average method for defuzzification, thereby constructing a health assessment model for the historical maintenance data dimension. Finally, the health state of the equipment is graded and comprehensively evaluated. This method can provide theoretical support for the preventive maintenance and health management of equipment, while also offering a reference for the intelligent operation and maintenance of complex mechanical equipment.
Compressed sensing (CS) can significantly improve the transmission efficiency of large amounts of vibration data in wireless sensor networks (WSNs) for mechanical vibration monitoring. To address the issue of irrecoverable measurements loss due to unstable communication links in WSN, this article proposes a missing-measurements-tolerant CS (MMTCS) in WSN for mechanical vibration monitoring. First, the embedded compressed sampling (ECS) is designed to compressed sampling the original signals in the acquisition nodes, thereby enhancing transmission efficiency. Moreover, the article analyzes the missing measurements perturbation error caused by compressed sampling and measurements loss in wireless transmission. An objective optimization function is derived for missing measurements. Combined residual adaptive sparse reconstruction (CRASR) is proposed for accurate data reconstruction. The experimental results demonstrate that the proposed method achieves a better trade-off between reconstruction accuracy and reconstruction time in comparison with other popular methods. More importantly, the proposed method can achieve satisfactory fault detection accuracy for rotating machinery under some degree of compressed sampling and missing measurements. This is of great value to practical engineering applications and provides a practical and effective solution.
Journal bearings can suffer damage and reduced performance due to misalignment and impact loads, potentially leading to operational failures. This study addresses the issue by integrating a groove-type flexible structure and rubber into the bearing design to enhance lubrication characteristics under such conditions. The research evaluated various factors, including shaft center trajectory, minimum oil film thickness, maximum deformation, oil film pressure distribution, and deformation distribution under static and dynamic impact loads of varying magnitudes and directions. The results showed that the combination of the flexible structure and rubber significantly improved the lubrication characteristics, particularly under large impact loads and static load conditions. The use of rubber with a low elastic modulus alongside the flexible structure resulted in at least five times increase in the minimum oil film thickness compared to using the flexible structure alone. This enhancement is due to the elastic deformation of the lubricating surface driven by oil film pressure, ensuring sufficient oil film thickness even under substantial impact loads. The findings suggest that this design approach can significantly contribute to stable lubrication in mechanical systems frequently subjected to impact loads, thereby improving their reliability and operational performance. These numerical results are expected to significantly aid in maintaining stable lubrication in mechanical systems that frequently experience impact loads, thereby enhancing their reliability and performance.
Abstract Under the influence of environmental pollution and energy scarcity, integrated energy systems (IES) have received extensive attention in the field of energy supply due to their ability to consume renewable energy and enhance energy utilization. In the context of low‐carbon scheduling for IES, numerous studies calculate the system's carbon emissions based on the carbon emission coefficients of energy devices. However, IES is a multi‐energy coupling system in which a device's energy input can originate from multiple sources with varying degrees of carbon emissions, making it difficult to accurately calculate the resulting carbon emissions using fixed coefficients. Consequently, a carbon emission flow (CEF) model is constructed for the system to calculate carbon emissions. In addition to the basic input–output CEF model, the CEF model for energy storage devices is considered, and carbon emission constraints during system operation are formulated based on the CEF model. Furthermore, many studies on low‐carbon scheduling of IES overlook the uncertainties associated with load and renewable energy. Therefore, a two‐stage scheduling model consisting of day‐ahead stage and intra‐day stage is developed to achieve reliable energy supply. Finally, through experiments, the low‐carbon performance and reliability of the model are validated.
In the 20th century, individual technology products like the generator, telephone, and automobile were connected to form many of the large-scale, complex, infrastructure networks we know today: the power grid, the communication infrastructure, and the transportation system. Progressively, these networked systems began interacting, forming what is now known as systems-of-systems. Because the component systems in the system-of-systems differ, modeling and analysis techniques with primitives applicable across multiple domains or disciplines are needed. For example, linear graphs and bond graphs have been used extensively in the electrical engineering, mechanical engineering, and mechatronic fields to design and analyze a wide variety of engineering systems. In contrast, hetero-functional graph theory (HFGT) has emerged to study many complex engineering systems and systems-of-systems (e.g. electric power, potable water, wastewater, natural gas, oil, coal, multi-modal transportation, mass-customized production, and personalized healthcare delivery systems). This paper seeks to relate hetero-functional graphs to linear graphs and bond graphs and demonstrate that the former is a generalization of the latter two. The contribution is relayed in three stages. First, the three modeling techniques are compared conceptually. Next, these techniques are contrasted on six example systems: (a) an electrical system, (b) a translational mechanical system, (c) a rotational mechanical system, (d) a fluidic system, (e) a thermal system, and (f) a multi-energy (electro-mechanical) system. Finally, this paper proves mathematically that hetero-functional graphs are a formal generalization of both linear graphs and bond graphs.
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 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.