The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into professional workflows is increasingly reshaping software engineering practices. These tools have lowered the cost of code generation, explanation, and testing, while introducing new forms of automation into routine development tasks. In contrast, most of the software engineering and computer engineering curricula remain closely aligned with pedagogical models that equate manual syntax production with technical competence. This growing misalignment raises concerns regarding assessment validity, learning outcomes, and the development of foundational skills. Adopting a conceptual research approach, this paper proposes a theoretical framework for analyzing how generative AI alters core software engineering competencies and introduces a pedagogical design model for LLM-integrated education. Attention is given to computer engineering programs in Turkey, where centralized regulation, large class sizes, and exam-oriented assessment practices amplify these challenges. The framework delineates how problem analysis, design, implementation, and testing increasingly shift from construction toward critique, validation, and human-AI stewardship. In addition, the paper argues that traditional plagiarism-centric integrity mechanisms are becoming insufficient, motivating a transition toward a process transparency model. While this work provides a structured proposal for curriculum adaptation, it remains a theoretical contribution; the paper concludes by outlining the need for longitudinal empirical studies to evaluate these interventions and their long-term impacts on learning.
Despite advances of single-atom catalysts (SACs) in sodium–sulfur (Na–S) batteries, their symmetric coordination geometry (e.g., M–N4) fundamentally restricts orbital-level modulation of sulfur redox kinetics. Herein, we demonstrate that hetero-diatomic Co–Y sites with Co–N4–Y–N4 coordination on N-doped carbon (Co–Y/NC) break the M–N4 symmetry constraint through d–d orbital hybridization, which is confirmed by an implementation of advanced characterizations, including the high-angle annular dark-field scanning transmission electron microscopy and x-ray absorption fine structure spectroscopy. In practical operation, the Co–Y/NC@S cathode with 61% sulfur mass fraction delivers a superior capacity (1,109 mAh/g) at 0.2 A/g, outperforming that of Co or Y SAC and further setting a new benchmark of diatomic catalysts for Na–S battery systems. Furthermore, the theoretical calculations show a hybridization-induced d-band splitting energy (ΔE = 0.5 eV), which induces electron-deficient Y sites for polysulfide adsorption (Na2S6) and electron-rich Co sites for S–S scission (barrier energy = 0.28 eV) via the d-p orbital hybridization of an asymmetric configuration. Our work establishes a strategy based on rare-earth-transition metal orbital hybridization to design asymmetric active sites for promoting multielectron sulfur redox reactions.
Materials of engineering and construction. Mechanics of materials, Renewable energy sources
Bastiaan Heeren, Fabiano Dalpiaz, Mazyar Seraj
et al.
Software engineering educators strive to continuously improve their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them by benchmarking against observed practices, and ultimately enhance their curricula. In this study, we aim to provide an encompassing analysis of higher education on software engineering by considering the higher educational offering of an entire European country, namely the Netherlands. We leverage a crowd-sourced analysis process by considering 10 Dutch universities and 207 university courses. The courses are analysed via knowledge areas adopted from the SWEBOK. The mapping process is refined via homogenisation and internal consistency improvement phases, and is followed by a data analysis phase. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively covered at Master level. We identify three clusters of tightly coupled knowledge areas: (i) requirements, architecture, and design, (ii) testing, verification, and security, and (iii) process-oriented and DevOps topics. Dutch universities generally cover all knowledge areas uniformly, with minor deviations reflecting institutional research strengths. Our results highlight correlations among key knowledge areas and their potential for enhancing integrated learning. We also identify underrepresented areas, such as software engineering economics, which educators may consider including in curricula. We invite researchers to use our research method in their own geographical region, in order to contrast software engineering education programs across the globe.
Abstract Ultra-high-voltage (UHV) autotransformers are widely employed in long-distance power transmission systems. Their operation involves complex energy conversion and coupling mechanisms, including high-intensity magnetic induction energy and strong induced currents. From the perspective of power systems and automation control, it is essential to construct a comprehensive equivalent control circuit for UHV autotransformers, integrating the analysis of induced current and magnetic flux density into the domain of analog electronics. Numerical analysis has become a core approach for investigating the external thermal physical characteristics of transformer power and various thermal management strategies. In this paper, the Message Passing Interface (MPI) and Portable, Extensible Toolkit for Scientific Computation (PETSc) parallel computing framework is adopted to compute and analyze the electro-thermal coupling in a UHV autotransformer. The dielectric loss of transformer components is thoroughly examined. A linear numerical simulation method for evaluating dielectric loss is assessed through parallel computation and validated via the design of a three-dimensional coupling model for leakage flux and core temperature rise. The dielectric loss calculation is applied to the transformer. Magnetostriction measurements under rated output power and various current and voltage conditions reveal the correlation between the coupled data and the thermal topology. The MPI-PETSc framework significantly enhances the computational efficiency of three-dimensional electro-thermal coupling problems in UHV autotransformers through distributed computing and efficient numerical solving, making it suitable for large-scale, high-precision engineering simulations.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Anomaly detection in rotating machinery often faces challenges in threshold determination and false alarms, which not only restrict the generalization ability of models but also lead to unnecessary downtime. To address this issue, this study explores a probabilistic expression framework suitable for anomaly detection of rotating machinery under constant-speed and stable operating conditions. Specifically, the reconstruction error (RE) based on Variational Autoencoder (VAE) is first adopted as a health indicator. Aiming at the problems of threshold setting and false alarms in its application, a probabilistic expression framework based on RE and Kernel Density Estimation (KDE) is proposed. This framework can directly provide anomaly probability density, and distinguish between interference and anomalies by quantifying the impact of abnormal values on the shape of the probability density curve, thereby avoiding threshold dependency and the risk of false alarms. The performance of this indicator is validated using experimental data of gears and bearings. The empirical relationship between the coefficient of variation (CV) of reconstruction error and the KDE window h is analyzed and presented, and a comparative analysis with traditional methods is also conducted. The research shows that this framework does not require setting an anomaly detection threshold for reconstruction error, nor does it issue alarms for non-abnormal states, providing a new solution for anomaly detection in rotating machinery.
To improve the adaptability of variable universe fuzzy controllers (VUFC) in independent metering control (IMC) hydraulic systems, this paper proposes an enhanced controller by integrating a Cycle Reservoir with Jumps (CRJ) online neural network. Although conventional adaptive neuro-fuzzy inference system (ANFIS) based controllers enable offline optimization of fuzzy rules, their rule structures and parameters remain fixed after design, which limits their adaptability. In contrast, the proposed CRJ-based fuzzy controller replaces the fuzzy system in the universe adjustment layer with a neural network capable of online learning. By dynamically updating the weights of its nodes during system operation, the CRJ network generates more effective universe adjustment factors, thereby improving control performance. A key advantage of the CRJ fuzzy controller is that it does not require offline training or dataset preparation. Once the basic network structure is configured, it autonomously adapts to changes in system state, such as temperature rise in hydraulic oil or mechanical wear over extended operation. The test results demonstrate that the proposed controller achieves superior performance in both system stability and motion accuracy, validating its effectiveness for adaptive control in IMC hydraulic systems.
Allysson Allex Araújo, Marcos Kalinowski, Daniel Graziotin
This paper explores the intricate challenge of understanding and measuring software engineer behavior. More specifically, we revolve around a central question: How can we enhance our understanding of software engineer behavior? Grounded in the nuanced complexities addressed within Behavioral Software Engineering (BSE), we advocate for holistic methods that integrate quantitative measures, such as psychometric instruments, and qualitative data from diverse sources. Furthermore, we delve into the relevance of this challenge within national and international contexts, highlighting the increasing interest in understanding software engineer behavior. Real-world initiatives and academic endeavors are also examined to underscore the potential for advancing this research agenda and, consequently, refining software engineering practices based on behavioral aspects. Lastly, this paper addresses different ways to evaluate the progress of this challenge by leveraging methodological skills derived from behavioral sciences, ultimately contributing to a deeper understanding of software engineer behavior and software engineering practices.
Alvaro M. Aparicio-Morales, Enrique Moguel, Luis Mariano Bibbo
et al.
Quantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers' capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the establishment of a specialized field known as Quantum Software Engineering. Recognizing the regional focus on Latin America within this special issue, we have boarded on an in-depth inquiry encompassing a systematic mapping study of existing literature and a comprehensive survey of experts in the field. This rigorous research effort aims to illuminate the current landscape of Quantum Software Engineering initiatives undertaken by universities, research institutes, and companies across Latin America. This exhaustive study aims to provide information on the progress, challenges, and opportunities in Quantum Software Engineering in the Latin American context. By promoting a more in-depth understanding of cutting-edge developments in this burgeoning field, our research aims to serve as a potential stimulus to initiate pioneering initiatives and encourage collaborative efforts among Latin American researchers.
While the methodological rigor of computing research has improved considerably in the past two decades, quantitative software engineering research is hampered by immature measures and inattention to theory. Measurement-the principled assignment of numbers to phenomena-is intrinsically difficult because observation is predicated upon not only theoretical concepts but also the values and perspective of the research. Despite several previous attempts to raise awareness of more sophisticated approaches to measurement and the importance of quantitatively assessing reliability and validity, measurement issues continue to be widely ignored. The reasons are unknown, but differences in typical engineering and computer science graduate training programs (compared to psychology and management, for example) are involved. This chapter therefore reviews key concepts in the science of measurement and applies them to software engineering research. A series of exercises for applying important measurement concepts to the reader's research are included, and a sample dataset for the reader to try some of the statistical procedures mentioned is provided.
Taiwo A. Adebiyi, Nafeezat A. Ajenifuja, Ruda Zhang
Digital twin (DT) technology has received immense attention over the years due to the promises it presents to various stakeholders in science and engineering. As a result, different thematic areas of DT have been explored. This is no different in specific fields such as manufacturing, automation, oil and gas, and civil engineering, leading to fragmented approaches for field-specific applications. The civil engineering industry is further disadvantaged in this regard as it relies on external techniques by other engineering fields for its DT adoption. A rising consequence of these extensions is a concentrated application of DT to the operations and maintenance phase. On another spectrum, Building Information Modeling (BIM) is pervasively utilized in the planning/design phase, and the transient nature of the construction phase remains a challenge for its DT adoption. In this paper, we present a phase-based development of DT in the Architecture, Engineering, and Construction industry. We commence by presenting succinct expositions on DT as a concept and as a service, and establish a five-level scale system. Furthermore, we present separately a systematic literature review of the conventional techniques employed at each civil engineering phase. In this regard, we identified enabling technologies such as computer vision for extended sensing and the Internet of Things for reliable integration. Ultimately, we attempt to reveal DT as an important tool across the entire life cycle of civil engineering projects, and nudge researchers to think more holistically in their quest for the integration of DT for civil engineering applications.
Resilient cyber-physical systems comprise computing systems able to continuously interact with the physical environment in which they operate, despite runtime errors. The term resilience refers to the ability to cope with unexpected inputs while delivering correct service. Examples of resilient computing systems are Google's PageRank and the Bubblesort algorithm. Engineering for resilient cyber-physical systems requires a paradigm shift, prioritizing adaptability to dynamic environments. Software as a tool for self-management is a key instrument for dealing with uncertainty and embedding resilience in these systems. Yet, software engineers encounter the ongoing challenge of ensuring resilience despite environmental dynamic change. My thesis aims to pioneer an engineering discipline for resilient cyber-physical systems. Over four years, we conducted studies, built methods and tools, delivered software packages, and a website offering guidance to practitioners. This paper provides a condensed overview of the problems tackled, our methodology, key contributions, and results highlights. Seeking feedback from the community, this paper serves both as preparation for the thesis defense and as insight into future research prospects.
To fabricate oxide thin-film transistors (TFTs) with high performance and excellent stability, preparing high-quality semiconductor films in the channel bulk region and minimizing the defect states in the gate dielectric/channel interfaces and back-channel regions is necessary. However, even if an oxide transistor is composed of the same semiconductor film, gate dielectric/channel interface, and back channel, its electrical performance and operational stability are significantly affected by the thickness of the oxide semiconductor. In this study, solution process-based nanometer-scale thickness engineering of InZnO semiconductors was easily performed via repeated solution coating and annealing. The thickness-controlled InZnO films were then applied as channel regions, which were fabricated with almost identical film quality, gate dielectric/channel interface, and back-channel conditions. However, excellent operational stability and electrical performance suitable for oxide TFT backplane was only achieved using an 8 nm thick InZnO film. In contrast, the ultrathin and thicker films exhibited electrical performances that were either very resistive (high positive <i>V<sub>Th</sub></i> and low on-current) or excessively conductive (high negative <i>V<sub>Th</sub></i> and high off-current). This investigation confirmed that the quality of semiconductor materials, solution process design, and structural parameters, including the dimensions of the channel layer, must be carefully designed to realize high-performance and high-stability oxide TFTs.
The building sector accounts for 36% of energy consumption and 39% of energy-related greenhouse-gas emissions. Integrating bifacial photovoltaic solar cells in buildings could significantly reduce energy consumption and related greenhouse gas emissions. Bifacial solar cells should be flexible, bifacially balanced for electricity production, and perform reasonably well under weak-light conditions. Using rigorous optoelectronic simulation software and the differential evolution algorithm, we optimized symmetric/asymmetric bifacial CIGS solar cells with either (i) homogeneous or (ii) graded-bandgap photon-absorbing layers and a flexible central contact layer of aluminum-doped zinc oxide to harvest light outdoors as well as indoors. Indoor light was modeled as a fraction of the standard sunlight. Also, we computed the weak-light responses of the CIGS solar cells using LED illumination of different light intensities. The optimal bifacial CIGS solar cell with graded-bandgap photon-absorbing layers is predicted to perform with 18%–29% efficiency under 0.01–1.0-Sun illumination; furthermore, efficiencies of 26.08% and 28.30% under weak LED light illumination of 0.0964 mW cm ^−2 and 0.22 mW cm ^−2 intensities, respectively, are predicted.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Fault detection and diagnostics (FDD) have great potential to enable safety, efficiency, and reliability measures of critical machinery systems. However, it is clear that there is a lack of systematic literature review to identify and classify the FDD studies conducted within the scope of marine engineering. This paper offers a systematic review of FDD models particular to marine machinery and systems. The numbers of 72 core articles were highlighted through a comprehensive literature review conducted in the 2002–2022 period. The studies are classified based on the mostly utilized methods such as data-driven, model-based, knowledge-based, and new generation-hybrid. In addition, new generation and hybrid methods are discussed in detail. The experimental environment (i.e. shipboard, labs, simulator) and technical details of the conducted studies are extensively discussed. While 56.94% of the examined studies are related to the main engine, 43.06% of them are related to auxiliary engines. In addition, the main and auxiliary engine studies are also divided into subject headings and examined in detail. Given the recent developments in green and smart maritime concepts, a future research agenda of the FDD studies on marine machinery systems is then pinpointed. Consequently, the study stimulates scholars interested in FDD while it enables innovative ideas for marine engineers, technology providers, ship operators, and maritime entrepreneurs.
In today's world, many cities are embracing cutting-edge technology and transforming into "smart cities". These emerging innovations are revolutionizing the standard of living for people, and as a result, smart city infrastructure development has become a major focus for city planners and policymakers worldwide. The goal is to create more livable, sustainable, and efficient urban environments, and software engineering plays a crucial role in achieving this. In this article, we will delve into what makes a city "smart" and what it means for the future. We will explore the software engineering roadmap for smart city infrastructure development, highlighting the goals and challenges that come with this innovative approach to urban planning. Our aim is to provide valuable insights into the importance of software engineering in achieving successful smart city infrastructure development. As cities continue to grow and evolve, it is essential to adopt new technologies that can help us build smarter, more sustainable communities. Smart city initiatives are paving the way for a brighter future, and software engineering is at the forefront of this movement. By understanding the software engineering roadmap for smart city infrastructure development, we can work towards creating more livable, efficient, and sustainable urban environments for generations to come.
A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
This article reports a two-stage differential structure power amplifier based on a 130 nm SiGe process operating at 77 GHz. By introducing a tunable capacitor for amplitude and phase balance at the center tap of the secondary coil of the traditional Marchand balun, the balun achieves amplitude imbalance less than 0.5 dB and phase imbalance less than 1 degree within the operating frequency range of 70–85 GHz, which enables the power amplifier to exhibit comparable output power over a wide operating frequency band. The power amplifier, based on a designed 3-bit digital analog convertor (DAC)-controlled base bias current source, exhibits small signal gain fluctuation of less than 5 dB and saturation output power fluctuation of less than 2 dB near the 80 GHz frequency point when the ambient temperature varies in the range of −40 °C to 125 °C. Benefiting from the aforementioned design, the tested single-path differential power amplifier exhibits a small signal gain exceeding 16 dB, a saturation output power exceeding 18 dBm, and a peak saturation output power of 19.1 dBm in the frequency band of 70–85 GHz.
Alessia Broccoli, Anke R. Vollertsen, Pauline Roels
et al.
The local integration of metal nanoparticle films on 3D-structured polydimethylsiloxane (PDMS)-based microfluidic devices is of high importance for applications including electronics, electrochemistry, electrocatalysis, and localized Raman sensing. Conventional processes to locally deposit and pattern metal nanoparticles require multiple steps and shadow masks, or access to cleanroom facilities, and therefore, are relatively imprecise, or time and cost-ineffective. As an alternative, we present an aerosol-based direct-write method, in which patterns of nanoparticles generated via spark ablation are locally printed with sub-mm size and precision inside of microfluidic structures without the use of lithography or other masking methods. As proof of principle, films of Pt or Ag nanoparticles were printed in the chambers of a multiplexed microfluidic device and successfully used for two different applications: Screening electrochemical activity in a high-throughput fashion, and localized sensing of chemicals via surface-enhanced Raman spectroscopy (SERS). The versatility of the approach will enable the generation of functional microfluidic devices for applications that include sensing, high-throughput screening platforms, and microreactors using catalytically driven chemical conversions.
Betul Ari, Mehtap Sahiner, Selin Sagbas Suner
et al.
Here, super-macroporous cryogel from a natural polysaccharide, pullulan was synthesized using a cryo-crosslinking technique with divinyl sulfone (DVS) as a crosslinker. The hydrolytic degradation of the pullulan cryogel in various simulated body fluids (pH 1.0, 7.4, and 9.0 buffer solutions) was evaluated. It was observed that the pullulan cryogel degradation was much faster in the pH 9 buffer solution than the pH 1.0 and 7.4 buffer solutions in the same time period. The weight loss of the pullulan cryogel at pH 9.0 within 28 days was determined as 31% ± 2%. To demonstrate the controllable drug delivery potential of pullulan cryogels via degradation, an antibiotic, ciprofloxacin, was loaded into pullulan cryogels (pullulan-cipro), and the loading amount of drug was calculated as 105.40 ± 2.6 µg/mg. The release of ciprofloxacin from the pullulan-cipro cryogel was investigated in vitro at 37.5 °C in physiological conditions (pH 7.4). The amount of drug released within 24 h was determined as 39.26 ± 3.78 µg/mg, which is equal to 41.38% ± 3.58% of the loaded drug. Only 0.1 mg of pullulan-cipro cryogel was found to inhibit half of the growing <i>Escherichia coli</i> (<i>E. coli</i>) and <i>Staphylococcus aureus</i> (<i>S. aureus</i>) colonies for 10 min and totally eradicated within 2 h by the release of the loaded antibiotic. No significant toxicity was determined on L929 fibroblast cells for 0.1 mg drug-loaded pullulan cryogel. In contrast, even 1 mg of drug-loaded pullulan cryogel revealed slight toxicity (e.g., 66% ± 9% cell viability) because of the high concentration of released drug.