Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
Alessandro Franco, Carlo Carcasci, Andrea Ademollo
et al.
This study evaluates the performance and feasibility of hybrid photovoltaic–hydrogen systems integrated with 4.2 MW PV installations, focusing on the interplay between electrolyzer capacity, energy storage, and hydrogen production. Key findings reveal that downsizing electrolyzers, such as using a 1 MW unit instead of a 2 MW model, increases operational efficiency by extending nominal power usage, though it reduces total hydrogen output by approximately 50%. Meanwhile, expanding energy storage systems show diminishing returns, with added capacity offering minimal gains in hydrogen production and raising economic concerns. The system’s performance is highly weather-dependent, with daily hydrogen production ranging from 26 kg on cloudy winter days to 375 kg during sunny summer conditions. Surplus energy export to the grid peaks at 3300 kWh during periods of high solar generation but is minimal otherwise. For economic and operational viability, the system design must prioritize directing a majority of PV energy to hydrogen production while minimizing grid export, requiring a minimum of 50% PV energy allocation to the hydrogen value chain. Cost analysis estimates a Levelized Cost of Hydrogen (LCOH) as low as €6/kg with an optimized configuration of a 2 MW electrolyzer and 2 MWh battery. Although high production costs challenge economic sustainability, careful component optimization and supportive policies can enable competitive hydrogen pricing and a positive net present value (NPV) over the system’s lifetime.
Ali Zaenal Abidin, I Ketut Agung Enriko, Aloysius Adya Pramudita
Energy audits play a pivotal role in improving energy efficiency and reducing carbon emissions in office buildings. However, conventional audits often suffer from fragmented insights, lack of system-level monitoring, establishing energy baseline, and insufficient incorporation of occupant behavior. To address these challenges, this study conducts a systematic literature review of recent applications of Internet of Things (IoT), machine learning (ML), and digital twin (DT) technologies in the energy audit domain. The review, guided by PRISMA methodology, analyzes eleven selected studies published between 2022 and 2024, revealing that while ML dominates in predictive modeling, IoT and DT remain underutilized in delivering integrated, efficiency recommendations. The analysis identifies three key engineering gaps: limited use of occupant behavior data, absence of continuous energy baseline modeling, and lack of systems capable of generating real-time efficiency recommendations. In response, this paper proposes a novel AIoT-based energy audit framework that combines real-time monitoring via IoT with ML-driven analytics and optimization, supported optionally by DT-based simulation. The proposed framework aims to enable continuous, system-level audits aligned with ISO 50000 standards, offering practical pathways for building managers to diagnose inefficiencies and implement energy-saving actions. Validating the model in real-world office environments, expanding input variables, and integration strategy with building automation systems are further important study to realize intelligent and scalable energy audit solutions.
The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a population of neurons is often needed to encode the complete range of input signals. However, using population encoding immensely increases the total number of neurons required for a particular application, thus increasing the power consumption and on-board resource utilization. A transition from two neurons to a population of neurons for the linear quadratic regulator (LQR) control of a cartpole is shown in this work. The near-linear behavior of a leaky-integrate-and-fire neuron can be exploited to achieve the LQR control of a cartpole system. This has been shown in simulation, followed by a demonstration on a single-neuron hardware, known as Lu.i. The improvement in control performance is then demonstrated by using a population of varying numbers of neurons for similar control in the Nengo neural engineering framework (NEF), on CPU and on Intel’s Loihi neuromorphic chip. Finally, linear control is demonstrated for four multi-linked pendula on cart systems, using a population of neurons in Nengo, followed by an implementation of the same on Loihi. This study compares LQR control in the NEF using 7 control and 7 neuromorphic performance metrics, followed by a comparison with other conventional spiking and non-spiking controllers.
Ardaneswari Dyah Pitaloka Citraresmi, Sri Gunani Partiwi, Ratna Sari Dewi
The creative industry has experienced rapid expansion in emerging economies, substantially contributing to employment and economic growth. However, despite this expansion, understanding how multiple workforce-related factors jointly influence creative performance remains limited. This study’s main contribution is to offer an integrated perspective on how workforce resilience, sustainability, and digital readiness collectively shape the creative output of Micro, Small, and Medium Enterprises (MSMEs). We used a mixed-methods design to collect data through surveys and in-depth interviews with owners and employees to capture insights on adaptability, well-being, and digital competencies. Results derived from Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that resilient and sustainable workforces positively affect creative performance, with digital readiness as a crucial mediator. This study highlights the importance of digital adoption strategies and workforce preparedness in an evolving industry landscape. Importance-Performance Map Analysis further identifies psychosocial risk management, employee well-being, and workplace safety as high-priority yet underdeveloped areas requiring immediate attention. By clearly articulating how an integrated approach to resilience, sustainability, and digital readiness advances theoretical and practical discourse, this work provides actionable insights for policymakers and MSMEs practitioners seeking to enhance innovation and maintain competitiveness in the face of ongoing digital disruption.
This paper presents Carleman-Fourier linearization for analyzing nonlinear real dynamical systems with periodic vector fields. Using Fourier basis functions, this novel framework transforms such dynamical systems into equivalent infinite-dimensional linear dynamical systems. In this paper, we establish the exponential convergence of the primary block in the finite-section approximation of this linearized system to the state vector of the original nonlinear system. To showcase the efficacy of our approach, we apply it to the Kuramoto model, a prominent model for coupled oscillators. The results demonstrate promising accuracy in approximating the original system's behavior.
Model reconstruction is a method used to drive the development of complex system development processes in model-based systems engineering. Currently, during the iterative design process of a system, there is a lack of an effective method to manage changes in development requirements, such as development cycle requirements and cost requirements, and to realize the reconstruction of the system development process model. To address these issues, this paper proposes a model reconstruction method to support the development process model. Firstly, the KARMA language, based on the GOPPRR-E metamodeling method, is utilized to uniformly formalize the process models constructed based on different modeling languages. Secondly, a model reconstruction framework is introduced. This framework takes a structured development requirements based natural language as input, employs natural language processing techniques to analyze the development requirements text, and extracts structural and optimization constraint information. Then, after structural reorganization and algorithm optimization, a development process model that meets the development requirements is obtained. Finally, as a case study, the development process of the aircraft onboard maintenance system is reconstructed. The results demonstrate that this method can significantly enhance the design efficiency of the development process.
Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic
et al.
Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability.
The rapid advancement of electronic communication technology has greatly aided human productivity and quality of life, but it has also resulted in significant electromagnetic pollution issues. Traditional metals and alloys are often used for electromagnetic interference (EMI) shielding due to their excellent electrical conductivity. However, they have drawbacks such as being heavy, expensive, and having low corrosion resistance, which limits their application in electromagnetic shielding. Therefore, it is crucial to develop novel EMI shielding materials. Polymers, being highly flexible, corrosion-resistant, and possessing high specific strength, are frequently employed in electromagnetic shielding materials. In this review, we firstly introduce the basic theory of electromagnetic shielding. Then, we outline the processing methods and recent developments of polymer-based electromagnetic shielding composites, including uniform-, foam-, layered-, and segregated structures. Lastly, we present the challenges and prospects for the field, aiming to provide direction and inspiration for the study of polymer-based electromagnetic shielding composite materials.
Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini
et al.
Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category.
China’s public cultural service system transitioned from a centrally controlled model to a more complex one due to the gradual introduction of market forces. This change brought new challenges and opportunities, making the role of market forces a practical concern. By analyzing data from 282 public and 153 private children’s libraries in China, this study investigates how market forces compensate for the government’s capacity limitations in constructing public cultural service systems. Results show that market factors within the scope of our study do not negatively impact the system but instead promote synergy between government and market entities to meet children’s cultural needs. It is essential not to sever the role of the market from its interdependent relationship with the government, as this stance is based on unrealistic assessments of how policies function in practice, potentially leading to inadequate public cultural services. This study provides novel empirical evidence from China by confirming the interdependent relationship between the market and the government in constructing public cultural service systems and highlights the significance of applying complexity thinking. Overall, understanding the complexity of the role of market forces is essential for the construction of a robust and inclusive public cultural service system.
Abstract Fully convolutional neural networks‐based salient object detection has recently achieved great success with its performance benefits from the effective use of multi‐layer features. Based on this, most of the existing saliency detectors designed complex network structures to fuse the multi‐level features generated by the backbone network. However, the variable scale and complex shape of the target are always a great challenge for saliency detection tasks. In this paper, the authors propose a Rich‐scale Feature Fusion Network (RFFNet) for salient object detection. The authors design a rich‐scale feature interactive fusion module to obtain more efficient features from the multi‐scale features. Moreover, the global feature enhance module is used to extract features with better characterization for the final saliency prediction. Extensive experiments performed on five benchmark datasets demonstrate that the proposed method can achieve satisfactory results on different evaluation metrics compared to other state‐of‐the‐art salient object detection approaches.
Hyunki Seong, Chanyoung Chung, David Hyunchul Shim
In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.
Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.
This study proposes an implementation method of a hardware-oriented restricted Boltzmann machine (RBM) without random number generators (RNGs) that employ cut-off bits, which are obtained from fixed-point binary arithmetic operations on digital hardware, such as field-programmable gate arrays (FPGAs), instead of random numbers. Most FPGA circuits employ fixed-point binary arithmetic operations to improve hardware resource efficiency. Therefore, the proposed method applies the unique feature of the operation, which is bit width extension and cut-off bits. Stochastic neural networks, including RBMs, employ sampling processes based on a probability distribution associated with the network, and the processes require many random numbers. However, implementing RNGs in hardware is costly because it requires considerable hardware resources. The proposed method can mitigate this requirement. To validate the proposed method, we implement an RBM with the proposed method on the software, emulate fixed-point binary arithmetic operations, and train the RBM using the MNIST and Fashion MNIST datasets. Furthermore, we apply the chi-square goodness-of-fit test to evaluate the uniformity of the cut-off bits. Additionally, we compare hardware resource requirements and power consumption for the proposed method and some major RNGs, a linear feedback shift register (LFSR), and a xorshift. Experimental results showed that it was possible to use the cut-off bits for training the RBM using the datasets and clarified the properties of the cut-off bits using statistical analyses. Moreover, hardware implementation of the proposed method involved the lowest hardware resource requirements and power consumption among the RNGs compared in this study.