Quantum computing is increasingly practiced through programming, yet most educational offerings emphasize algorithmic or framework-level use rather than software engineering concerns such as testing, abstraction, tooling, and lifecycle management. This paper reports on the design and first offering of a cross-listed undergraduate--graduate course that frames quantum computing through a software engineering lens, focusing on early-stage competence relevant to software engineering practice. The course integrates foundational quantum concepts with software engineering perspectives, emphasizing executable artifacts, empirical reasoning, and trade-offs arising from probabilistic behaviour, noise, and evolving toolchains. Evidence is drawn from instructor observations, student feedback, surveys, and analysis of student work. Despite minimal prior exposure to quantum computing, students were able to engage productively with quantum software engineering topics once a foundational understanding of quantum information and quantum algorithms, expressed through executable artifacts, was established. This experience report contributes a modular course design, a scalable assessment model for mixed academic levels, and transferable lessons for software engineering educators developing quantum computing curricula.
With the accelerating global transition toward sustainable energy, the role of battery energy storage systems (ESSs) becomes increasingly prominent. This study employs the isothermal battery calorimetry (IBC) measurement method and computational fluid dynamics (CFD) simulation to develop a multi-domain thermal modeling framework for battery systems, spanning from individual cells to modules, clusters, and ultimately the container level. Experimental validation confirms the model’s accuracy, with the simulated maximum cell temperature of 36.2 °C showing only a 1.8 °C deviation from the measured value of 34.4 °C under real-world operating conditions. Furthermore, by integrating on-site calibrated thermodynamic parameters of the container, a battery system energy efficiency model is established. Combined with the battery aging engineering model, a coupled lifetime–energy efficiency model is constructed. Six different control strategies are simulated and analyzed to quantify the system’s comprehensive lifecycle benefits. The results demonstrate that the optimized control strategy enhances the overall energy storage station revenue by 2.63%, yielding an additional cumulative profit of CNY 13.676 million over the entire lifecycle. This research provides an effective simulation framework and decision-making basis for the thermal management optimization and economic evaluation of battery ESSs.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
As distributed photovoltaic and shared energy storage systems expanded on the user side, developing an energy-sharing mechanism across different regions became crucial for fully utilizing local renewable energy resources and maximizing the system’s overall economic performance. This paper established a multi-regional energy operator (MREO) model considering shared energy storage, and a two-layer trading and optimization framework based on a master–slave game was developed. Initially, a trading system was devised to evaluate the interests of the power grid, MREO, and end-users. Next, an optimization model was formulated to capture the dynamic interactions between MREO decisions and user responses. The top-layer model was managed by MREO and focused on energy sharing among regions, which is used to set flexible electricity prices according to regional demand and optimize the use of shared energy storage. Meanwhile, the bottom-layer model addressed user demand response, allowing users to modify their energy consumption and select more advantageous trading areas based on information provided by the MREO. Simulation results confirmed that the proposed model accurately evaluated each party’s income, iteratively balanced their interests, and increased economic returns for both users and MREO. Additionally, the proposed approach supported greater local photovoltaic energy consumption, reduced grid load fluctuations, and fostered mutually beneficial outcomes for all stakeholders.
Production of electric energy or power. Powerplants. Central stations
Nuclear symmetry energy $E_{\mathrm{sym}}(ρ)$ encoding the cost to make nuclear matter more neutron rich has been the most uncertain component of the EOS of dense neutron-rich nucleonic matter. It affects significantly the radii, tidal deformations, cooling rates and frequencies of various oscillation modes of isolated neutron stars as well as the strain amplitude and frequencies of gravitational waves from their mergers, besides its many effects on structures of nuclei as well as the dynamics and observables of their collisions. Siemens (1970s) observed that $E_{\mathrm{sym}}(ρ)$ scales as $(ρ/ρ_0)^{2/3}$ near the saturation density $ρ_0$ of nuclear matter, since both the kinetic part and the potential contribution (quadratic in momentum) exhibit this dependence. The scaling holds if: (1) the nucleon isoscalar potential is quadratic in momentum, and (2) the isovector interaction is weakly density dependent. After examining many empirical evidences and understanding theoretical findings in the literature we conclude that: (1) Siemens' $ρ^{2/3}$ scaling is robust and serves as a valuable benchmark for both nuclear theories and experiments up to $2ρ_0$ but breaks down at higher densities, (2) Experimental and theoretical findings about $E_{\mathrm{sym}}(ρ)$ up to $2ρ_0$ are broadly consistent, but uncertainties remain large for its curvature $K_{\mathrm{sym}}$ and higher-order parameters, (3) Above $2ρ_0$, uncertainties grow due to poorly constrained spin-isospin dependent tensor and three-body forces as well as the resulting nucleon short-range correlations. Looking forward, combining multimessengers from both observations of neutron stars and terrestrial heavy-ion reaction experiments is the most promising path to finally constraining precisely the high-density $E_{\mathrm{sym}}(ρ)$ and the EOS of supradense neutron-rich matter.
Shruti Jadon, Karthikeya Bhat, Karthikeya R. Jenni
et al.
In the current digital landscape, almost everyone is on social media or various social media platforms. People use social media for a plethora of purposes, which include staying connected with friends and family, accessing information and updates about ongoing events, entertainment, networking with professionals, expressing themselves to a wide range of users, promoting businesses, joining online communities and engaging in various activities which has led to an increase in the consumption and usage of online social networks (OSN). One of the reasons for such a growth is their features such as ubiquitous access, on-demand service, friendship networks, user engagement strategies like recommendation engines, etc. However, there are various limitations to the current approach, such as the centralization of control, lack of data ownership, poor access control, fake news, bot accounts, censorship, digital rights management issues, etc. To address these limitations, a paradigm shift is necessary. This paper aims to develop a social media application where every post can be converted to a Non-Fungible Token (NFT) and be sold to earn money. Interplanetary File System (IPFS) is used as the decentralized storage. Algorithms for all the functionalities of the applications are given along with an algorithm for a reputation score for every user and their posts in social media are also proposed.
Aiming at the problem that the Web service quality of service (QoS) estimation methods based on the non-negative latent factorization of tensor model (NLFT) depend heavily on non-negative initial random data and specially designed non-negative training schemes, which lead to low compatibility and scalability, an accelerated unconstrained latent factorization of tensor (AULFT) model was proposed.The proposed model consisted of three main parts.The non-negative constraints from decision parameters were transferred to output latent factors and they were connected through the single-element-dependent mapping function.A momentum-incorporated stochastic gradient descent (MSGD) algorithm was used to effectively improve the convergence rate and estimation accuracy of the proposed AULFT model.The detailed algorithm and result analysis of the proposed AULFT model were presented.The empirical study on two dynamic QoS datasets in real industrial applications demonstrates that the proposed AULFT model has higher computational efficiency and estimation accuracy than the state-of-the-art QoS estimation models.
Mahbub Ul Islam Khan, Md. Ilius Hasan Pathan, Mohammad Mominur Rahman
et al.
Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.
The effectiveness of model-driven software engineering (MDSE) has been successfully demonstrated in the context of complex software; however, it has not been widely adopted due to the requisite efforts associated with model development and maintenance, as well as the specific modelling competencies required for MDSE. Concurrently, artificial intelligence (AI) methods, particularly deep learning methods, have demonstrated considerable abilities when applied to the huge code bases accessible on open-source coding platforms. The so-called big code provides the basis for significant advances in empirical software engineering, as well as in the automation of coding processes and improvements in software quality with the use of AI. The objective of this paper is to facilitate a synthesis between these two significant domains of software engineering (SE), namely models and AI in SE. The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, ai4se. In light of the aforementioned considerations, a vision of AI-assisted big models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development. Finally, the pair modelling paradigm is proposed for adoption by the MDSE industry.
The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, because their effectiveness hinges on the availability of an ample supply of high-quality task-specific labeled data. Moreover, previous works overlook the high-level semantics present in source code and its documentation. Acknowledging the abundance of available source code with documentation, which can be compiled into WebAssembly, we propose to learn representations of them concurrently and harness their mutual relationships for effective WebAssembly reverse engineering. In this paper, we present WasmRev, the first multi-modal pre-trained language model for WebAssembly reverse engineering. WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data. WasmRev incorporates three tailored multi-modal pre-training tasks to capture various characteristics of WebAssembly and cross-modal relationships. WasmRev is only trained once to produce general-purpose representations that can broadly support WebAssembly reverse engineering tasks through few-shot fine-tuning with much less labeled data, improving data efficiency. We fine-tune WasmRev onto three important reverse engineering tasks: type recovery, function purpose identification and WebAssembly summarization. Our results show that WasmRev pre-trained on the corpus of multi-modal samples establishes a robust foundation for these tasks, achieving high task accuracy and outperforming the state-of-the-art ML methods for WebAssembly reverse engineering.
Irfan Ahmed Khan, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor
et al.
The reliability of a power system depends on its ability to handle fluctuations and varying load demands, as uncontrolled frequency deviations can lead to load-shedding and blackouts. Optimally tuned controllers are essential for Load Frequency Control (LFC) applications to efficiently stabilize the power system by minimizing frequency undershoots, overshoots, and settling time. This paper proposed the application of novel Golden Eagle Optimization (GEO) algorithm for the optimal tuning of the LFC controller, which has not been previously employed in any LFC applications. Moreover, this paper presents the first-ever implementation of a hybrid energy storage system consisting of Vanadium Redox Flow Battery (VRFB) and Super Magnetic Energy Storage System (SMES) coupled with AC/HVDC transmission lines in a multi-area power system. A GEO optimized Proportional-Integrative-Derivative (GEO-PID) robust controller is designed with the Integral Time Absolute Error (ITAE) objective function to enhance the power system’s stability. The proposed controller is tested on two and four areas power systems considering the sensitivity and nonlinearity of the power systems. A robustness test is also performed to verify the stability of the system under randomly chosen loading conditions. In comparison with particle swarm optimization, dragonfly algorithm, sine cosine algorithm, ant lion optimization, and whale optimization algorithm, the GEO-PID controller significantly reduced the settling time up to 80% for different area’s frequencies. Simulation results indicate that the proposed controller outperforms other recent optimization algorithms by effectively dampening the frequency and tie-line deviations with less settling times, as well as reduced frequency undershoots and overshoots.
Mohammed Khalid Yousif, Zena Ez Dallalbashi, Shahab Wahhab Kareem
Cloud computing processes vast quantities of data and offers a variety of flexible, secure, on-demand, and cost-effective collaboration options for consumers. Due to the increasing prevalence of hosted services, data security has become an increasingly critical concern. Hadoop, the engine at the heart of cloud computing, causes serious problems for the cloud. Any public, private, or hybrid cloud environment can use this security solution without any hassle (IaaS). Furthermore, it is compatible with the vast majority of Cloud computing's capabilities. Increase cloud security using NTRU encryption. This study made advantage of the (NTRUEncrypt) algorithms residing in Hadoop to speed up the file encryption and decryption processes. If HDFS is engaged in the Map Task, then HDFS will take care of both the encryption and decryption processes. Data on the cloud can be kept private and secure thanks to the proposed protection technique, which makes use of cryptography. Combining the proposed technique with preexisting infrastructure and web-based.
Electric apparatus and materials. Electric circuits. Electric networks
Data-driven engineering refers to systematic data collection and processing using machine learning to improve engineering systems. Currently, the implementation of data-driven engineering relies on fundamental data science and software engineering skills. At the same time, model-based engineering is gaining relevance for the engineering of complex systems. In previous work, a model-based engineering approach integrating the formalization of machine learning tasks using the general-purpose modeling language SysML is presented. However, formalized machine learning tasks still require the implementation in a specialized programming languages like Python. Therefore, this work aims to facilitate the implementation of data-driven engineering in practice by extending the previous work of formalizing machine learning tasks by integrating model transformation to generate executable code. The method focuses on the modifiability and maintainability of the model transformation so that extensions and changes to the code generation can be integrated without requiring modifications to the code generator. The presented method is evaluated for feasibility in a case study to predict weather forecasts. Based thereon, quality attributes of model transformations are assessed and discussed. Results demonstrate the flexibility and the simplicity of the method reducing efforts for implementation. Further, the work builds a theoretical basis for standardizing data-driven engineering implementation in practice.
The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays' systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.
Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek
Software start-ups are new companies aiming to launch an innovative product to mass markets fast with minimal resources. However, most start-ups fail before realizing their potential. Poor software engineering, among other factors, could be a significant contributor to the challenges that start-ups experience. Little is known about the engineering context in start-up companies. On the surface, start-ups are characterized by uncertainty, high risk, and minimal resources. However, such a characterization isn't granular enough to support identification of specific engineering challenges and to devise start-up-specific engineering practices. The first step toward an understanding of software engineering in start-ups is the definition of a Start-Up Context Map - a taxonomy of engineering practices, environment factors, and goals influencing the engineering process. This map aims to support further research on the field and serve as an engineering decision support tool for start-ups. This article is part of a theme issue on Process Improvement.
In the present work, a novel one-step plasma electrolytic oxidation (PEO) method is developed for the formation of PEO/Mg-Al layered double hydroxide (LDH) composite coating on AZ31 Mg alloy. Through the control of electrolyte concentration, a Mg-Al LDH layer was formed on the surface of the resultant PEO coating. The corrosion properties of the composite coating were evaluated by electrochemical analysis, hydrogen evolution measurement and salt spraying testing, which exhibited improved corrosion resistance due to the presence of the Mg-Al LDH layer. This work provides a new strategy to take advantage of alloying elements in Mg alloys in PEO treatment to enhance corrosion resistance.
Materials of engineering and construction. Mechanics of materials
Roberta Alves Gomes Matos, Jonas Mendes, Bruna Horta Bastos Kuffner
et al.
Abstract The Al4.5wt%Cu is an aeronautical and automobile alloy with extensive use in industry for structural purposes. The aim of this work was to evaluate two different solidification processes of the Al4.5wt%Cu alloy, conventional and unidirectional, as well as its recrystallization process. Firstly, the Al4.5wt%Cu alloy was deformed by cold rotary forging and then heat treated at temperatures that varied from 250 to 450 °C. The samples for analysis were obtained after 54, 76 and 91% of reductions in area. Tests of optical microscopy, scanning electron microscopy and Vickers microhardness were performed to evaluate the recrystallization process. The results indicated that the recrystallization started at 350 ºC, being that the conventional samples presented full recrystallization after 5 minutes, while the unidirectional samples presented only partial recrystallization. In general, both solidification processes presented similar results for all of the analysis performed.
Materials of engineering and construction. Mechanics of materials
Abstract In this study, characteristics of charge injection under extra high electric field (above 100 kV/mm) in cross‐linked polyethylene (XLPE) were investigated by experiments of conduction current and space charge. The results show that current density from low electric field to sample breakdown corresponds to space charge limited current (SCLC) theory. More specifically, Schottky current is similar to experiment current before 100 kV/mm, while the J–E curve conforms to a modified SCLC theory after 100 kV/mm. Besides, the non‐linear coefficient of J–E curve from 100 kV/mm to extra high electric field is smaller than theoretical value, and the injection depth of space charge is restricted as the field becomes higher than 100 kV/mm, which may be caused by the negative differential mobility of charge. Driven by extra high electric field, charge collides with lattice of dielectric and scatters. As a result, mean free time of charge decreases and charge mobility is reduced with the increased field. Consequently, considering the decrease in charge mobility, a mobility‐limited charge injection equation is proposed, and the validity of the proposed equation under extra high electric field is demonstrated by space charge simulation.