Search Results (730)

The recent demand for 8-bit floating-point (FP) formats is driven by their potential to accelerate domain-specific applications with intensive vector computations (e.g., machine learning, graphics, and data compression). This paper presents the design, implementation, and application of the software model of an 8-bit FP vector arithmetic operation set, compliant with the RISC-V vector instruction set architecture. The model has been developed as an extension of the SoftFloat library and integrated into the RISC-V reference instruction-level simulator Spike, providing the first open-source 8-bit SoftFloat extension for an instruction-set simulator. Based on the SoftFloat library templates for standard FP formats, the proposed extension implements the two widely used 8-bit formats E4M3 and E5M2 in both Open Compute Project (OCP) and IEEE 754 variants. In host-time micro-kernels, FP8 delivers +2–4% more elements per second versus FP32 (across vfadd/vfsub/vfmul ) and ≈ 5% lower RSS; E4M3 and E5M2 perform similarly. Enabling FP8 in Spike increases the stripped binary by ~1.8% (mostly .text). The proposed extension was used to fully verify and correct errors in the vector FP unit design for the eProcessor European project, and continues to be used to verify other 8-bit FP unit implementations. Full article

This study presents a holistic framework for adaptive thermal energy storage (A-TES) in solar-assisted systems. This framework aims to support a reliable industrial energy supply, particularly during periods of limited sunlight, while also facilitating industrial decarbonization. In previous studies, the focus was not on addressing the framework of the entire problem, but rather on specific parts of it. Therefore, the innovation in this study lies in bringing these aspects together within a unified framework through a data-driven approach that combines the analysis of efficiency, technology, environmental impact, sectoral applications, operational challenges, and policy into a comprehensive system. Sensible thermal energy storage with an adaptive approach can be utilized in numerous industries, particularly concentrated solar power plants, to optimize power dispatch, enhance energy efficiency, and reduce gas emissions. Simulation results indicate that stable regulations and flexible incentives have led to a 60% increase in solar installations, highlighting their significance in investment expansion within the renewable energy sector. Integrated measures among sectors have increased energy availability by 50% in rural regions, illustrating the need for partnerships in renewable energy projects. The full implementation of novel advanced energy management systems (AEMSs) in industrial heat processes has resulted in a 20% decrease in energy consumption and a 15% improvement in efficiency. Making the switch to open-source software has reduced software expenditure by 50% and increased productivity by 20%, demonstrating the strategic advantages of open-source solutions. The findings provide a foundation for future research by offering a framework to analyze a specific real-world industrial case. Full article

Soft open point (SOP) offers a viable alternative to traditional tie switches for optimizing power flow distribution between connected feeders, thereby improving power quality and enhancing the reliability of distribution networks (DNs). Among existing medium-voltage (MV) SOP demonstration projects, the modular multilevel converter (MMC) back-to-back voltage source converter (BTB-VSC) is the most commonly adopted configuration. However, MMC BTB-VSC suffers from high cost and significant volume, with device requirements increasing substantially as the number of feeders grows. To address these challenges, this paper proposes a novel star-connected cascaded H-bridge (CHB) STATCOM SOP (SCS-SOP). The SCS-SOP integrates the static synchronous compensator (STATCOM) and low-voltage (LV) BTB-VSC into a single device, enabling reactive power support within feeders and active power exchange between feeders, while achieving reduced component cost and volume, simplified power decoupling control, and increasing power quality management capabilities. The topology derivation, configuration, operational principles, and control strategies of the SCS-SOP are elaborated. Finally, simulation and experimental models of a two-port 3 Mvar/300 kW SCS-SOP are developed, with results validating the theoretical analysis. Full article

Efforts to lower the barriers for 5G uptake have brought forth accelerated innovation rates in a wide range of verticals. Consequently, the demands for 5G experimentation facilities have recently emerged from small- and medium-sized enterprises (SMEs) and 3rd party developers, requiring the abstraction of the complexities of the underlying infrastructure, platform, and related management and orchestration (MANO) systems, especially in multi-domain scenarios. This paper proposes a novel approach towards cross-domain service orchestration, which combines the flexibility of supporting different 5G Service Orchestrators (SOs) in various domains, while preserving compatibility through NetApps. The Cross-domain Service Orchestrator (CDSO) is based on ETSI’s Open Source MANO (OSM), with a Requests Handler module on top, which acts as the main integration point to the different domains’ 5G SOs and other custom systems that are northbound. This would facilitate the interworking among independently orchestrated domains in supporting multi-domain services. The EU Horizon 2020 project, 5GMediaHUB, is presented as a use case, together with a first implementation of the Requests Handler and corresponding integrations. Nonetheless, the proposed approach is vertical-agnostic and is foreseen to accelerate service innovation and digitalization in any industry by laying the foundations in terms of service management and interconnectivity. Full article

This study presents a detailed analysis of the energy performance and thermal comfort conditions in four existing school buildings located in Madrid, Spain. Dynamic simulations were conducted using TeKton3D—(iMventa Ingenieros, Málaga, Spain)- an open-source tool based on the EnergyPlus engine—to model four improvement scenarios: (I) current state, (II) envelope retrofitting with ETICS and high-performance glazing, (III) solar control strategies, and (IV) incorporation of mechanical ventilation with heat recovery. Each building was simulated under both current and projected 2050 climate conditions. The case studies were selected to represent different construction periods and urban contexts, including varying levels of exposure to the urban heat island effect. This approach allows the results to reflect the diversity of the existing school building stock and its different vulnerabilities to climate change. The results show that envelope retrofitting substantially reduces heating demand but may increase cooling needs, particularly under warmer future conditions. Solar control strategies effectively mitigate overheating, while mechanical ventilation with heat recovery contributes to improved comfort and overall efficiency. This study highlights the trade-offs between energy savings and indoor environmental quality, underlining the importance of integrated renovation measures. The study provides relevant data for decision-making in climate-resilient building renovation, aligned with EU goals for nearly zero and zero-emission buildings. Full article

This study investigates the numerical prediction of the sonic boom phenomenon in supersonic aircraft by evaluating the near-field pressure signatures of three different aeroshapes. Two computational fluid dynamics (CFD) solvers, the open-source SU2 Multiphysics code and ANSYS Fluent, were employed to assess their effectiveness in modeling the aerodynamic flow field. A preliminary validation of numerical methods was conducted against numerical data available from the Sonic Boom Prediction Workshops (SBPW) organized by NASA, ensuring simulation reliability. Particular attention is paid to the topology of the mesh grid, exploring hybrid approaches that combine structured and unstructured grids to optimize the accuracy of pressure wave transmission. In addition, different numerical schemes were analyzed to determine the best practices for sonic boom simulations. The proposed methodology was finally applied to three supersonic aircraft developed within the European project MORE&LESS, demonstrating the capability of the model to estimate shock wave generation, evaluate the aeroacoustic performance of different supersonic aeroshapes from Mach 2 to Mach 5, and provide predictions to support ground-level noise assessment. The findings of this study contribute to the definition of a comprehensive workflow for sonic boom evaluation, providing a reliable methodology for exploring future supersonic aircraft designs. Full article

This paper presents a flexible approach to a multipurpose, heterogeneous archive and data management system model that merges the robustness of legacy grid-based technologies with modern cloud and edge computing paradigms. It leverages innovations driven by big data, IoT, AI, and machine learning to create an adaptive data storage and processing framework. In today’s digital age, where data are the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and data misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common single points of failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. The principal goal of this work is to outline best practices in archival and data management projects—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents and Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use. Full article

GitHub hosts over 10 million repositories, providing researchers with vast opportunities to study diverse software engineering problems. However, as anyone can create a repository for any purpose at no cost, open-source platforms contain many non-cooperative or non-developmental noise projects (e.g., repositories of dotfiles). When selecting open-source projects for analysis, mixing collaborative coding projects (e.g., machine learning frameworks) with noisy projects may bias research findings. To solve this problem, we optimize the Semi-Automatic Decision Tree Method (SADTM), an existing Collaborative Coding Project (CCP) classification method, to improve its generality and accuracy. We evaluate our method on the GHTorrent dataset (2012–2020) and find that it effectively enhances CCP classification in two key ways: (1) it demonstrates greater stability than existing methods, yielding consistent results across different datasets; (2) it achieves high precision, with an F-measure ranging from 0.780 to 0.893. Our method outperforms existing techniques in filtering noise and selecting CCPs, enabling researchers to extract high-quality open-source projects from candidate samples with reliable accuracy. Full article

One of the key components of the software quality model is reliability. Its importance has grown with the increasing use and reuse of open-source components in software development. Software reliability growth models are commonly employed to address this aspect by predicting future failure rates and estimating the number of remaining defects throughout the development process. This paper investigates two software reliability growth models derived from the Verhulst model, with a particular focus on a structural property known as Hausdorff saturation. We provide analytical estimates for this characteristic and propose it as an additional criterion for model selection. The models are evaluated using four open-source datasets, where the Hausdorff saturation metric supports the conclusions drawn from standard goodness-of-fit measures. Furthermore, we introduce an interactive software reliability assessment tool that integrates with GitHub, enabling expert users to analyze real-time issue-tracking data from open-source repositories. The tool facilitates model comparison and enhances practical applicability. Overall, the proposed approach contributes to more robust reliability assessment by combining theoretical insights with actionable diagnostics. Full article

Collective robotics systems hold great potential for future education and public engagement; however, only a few are utilized in these contexts. One reason is the lack of accessible tools to convey their complex, embodied interactions. In this work, we introduce the Light-Augmented Reality System (LARS), an open-source, marker-free, cross-platform tool designed to support experimentation, education, and outreach in collective robotics. LARS employs Extended Reality (XR) to project dynamic visual objects into the physical environment. This enables indirect robot–robot communication through stigmergy while preserving the physical and sensing constraints of the real robots, and enhances robot–human interaction by making otherwise hidden information visible. The system is low-cost, easy to deploy, and platform-independent without requiring hardware modifications. By projecting visible information in real time, LARS facilitates reproducible experiments and bridges the gap between abstract collective dynamics and observable behavior. We demonstrate that LARS can serve both as a research tool and as a means to motivate students and the broader public to engage with collective robotics. Its accessibility and flexibility make it an effective platform for illustrating complex multi-robot interactions, promoting hands-on learning, and expanding public understanding of collective, embodied intelligence. Full article

Deep learning has emerged as a prominent focus in both academia and industry, with a wide range of models being applied across diverse domains. Fast and efficient model inference is essential for the practical deployment of deep learning models. Under specific hardware constraints, accelerating inference remains a key research challenge. Common techniques for model acceleration include quantization, pruning, and vectorization. Although quantization and pruning primarily reduce model precision or complexity to enhance efficiency, this paper concentrates on vectorization, a technique that accelerates models by increasing the parallelism of operator execution. Based on the open-source Buddy-MLIR project, this work implements vectorization optimizations for Matmul, Conv2d, and Max Pooling operations to improve inference performance. These optimizations are designed as compiler passes and integrated into the Buddy-MLIR framework, offering a general solution for vectorizing such operators. Two optimization approaches are proposed: general vectorization and adaptive vectorization. Compared to the standard MLIR lowering pipeline and the fully optimized LLVM backend, the proposed general and adaptive vectorization methods reduce the inference latency of LeNet-5 by $26.7\%$ and $37.3\%$, respectively. For the more complex ResNet-18 model, these methods achieve latency reductions of $79.9\%$ and $82.6\%$, respectively. Full article

The spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain’s integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats. Full article

Recent advances in large language models and retrieval-augmented generation, a method that enhances language models by integrating retrieved external documents, have created opportunities to deploy AI in secure, offline environments. This study explores the feasibility of using locally hosted, open-weight large language models with integrated retrieval-augmented generation capabilities on CPU-only hardware for tasks such as question answering and summarization. The evaluation reflects typical constraints in environments like government offices, where internet access and GPU acceleration may be restricted. Four models were tested using LocalGPT, a privacy-focused retrieval-augmented generation framework, on two consumer-grade systems: a laptop and a workstation. A technical project management textbook served as the source material. Performance was assessed using BERTScore and METEOR metrics, along with latency and response timing. All models demonstrated strong performance in direct question answering, providing accurate responses despite limited computational resources. However, summarization tasks showed greater variability, with models sometimes producing vague or incomplete outputs. The analysis also showed that quantization and hardware differences affected response time more than output quality; this is a tradeoff that should be considered in potential use cases. This study does not aim to rank models but instead highlights practical considerations in deploying large language models locally. The findings suggest that secure, CPU-only deployments are viable for structured tasks like factual retrieval, although limitations remain for more generative applications such as summarization. This feasibility-focused evaluation provides guidance for organizations seeking to use local large language models under privacy and resource constraints and lays the groundwork for future research in secure, offline AI systems. Full article

This paper examines some of the most popular protocols for transmitting sensor data to cloud structures from publish/subscribe and request/response IoT models. The selection of a highly efficient message transmission protocol is essential, as it depends on the specific characteristics and purpose of the developed IoT system, which includes communication requirements, message size and format, energy efficiency, reliability, and cloud specifications. No standardized protocol can cover all the diverse application scenarios; therefore, for each developed project, the most appropriate protocol must be selected that meets the project’s specific requirements. This work focuses on finding the most appropriate protocol for integrating sensor data into a suitable open-source IoT platform, ThingsBoard. First, we conduct a comparative analysis of the studied protocols. Then, we propose a project that represents an experiment for transmitting data from a stationary XBee sensor network to the ThingsBoard cloud via HTTP, MQTT-SN, and CoAP protocols. We observe the parameters’ influence on the delayed transmission of packets and their load on the CPU and RAM. The results of the experimental studies for stationary sensor networks collecting environmental data give an advantage to the MQTT-SN protocol. This protocol is preferable to the other two protocols due to the lower delay and load on the processor and memory, which leads to higher energy efficiency and longer life of the sensors and sensor networks. These results can help users make operational judgments for their IoT applications. Full article

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic CAE software integrated development platform to examine how open-source strategies shape collaborative mechanisms and innovation outcomes. The analysis reveals that firms adopt both formal (behavioral and outcome coordination) and informal (relationship and empowerment coordination) strategies, fostering high-level complementary collaboration in data, technology, institution, and human resources. These mechanisms significantly enhance R&D efficiency and quality, drive technological innovation, and create new market innovation, thereby improving collaborative performance. The study contributes to theory by linking open-source-driven digital–real integration with industrial software innovation catch-up and offers practical governance recommendations for strengthening China’s industrial software autonomy and ecosystem sustainability. Full article