Search Results (3,267)

Buildings are under increasing pressure to address decarbonization and climate adaptation, which is pushing design practice from post hoc performance checks to performance-driven generative design (PDGD). This review maps the current state of PDGD in buildings and proposes an engineering-oriented framework that links research methods to deployable workflows. Using a PRISMA-based systematic search, we identify 153 core studies and code them along five dimensions: design objects and scales, objectives and metrics, algorithms and tools, workflows, and data and validation. The corpus shows a strong focus on facades, envelopes, and single-building massing, dominated by energy, daylight and thermal comfort objectives, and a widespread reliance on parametric platforms connected to performance simulation software with multi-objective optimization. From this evidence we extract three typical workflow routes: parametric evolutionary multi-objective optimization, surrogate or Bayesian optimization, and data- or model-driven generation. Persistent weaknesses include fragmented metric conventions, limited cross-case or field validation, and risks to reproducibility. In response, we propose a harmonized objective–metric system, an evidence pyramid for PDGD, and a reproducibility checklist with practical guidance, which together aim to make PDGD workflows more comparable, auditable, and transferable for design practice. Full article

This paper investigates whether carbon emission intensity influences capital formation in rent-dependent economies, using the Gulf Cooperation Council (GCC) as a case study. In contrast to conventional growth models, the study tests carbon lock-in as a driver, rather than an outcome, of investment in rentier states and links it empirically to resource curse mechanisms. Using panel data for six GCC countries over 2000–2022, we estimate a fixed effects investment model and use System GMM as a robustness check. Results show that a one standard deviation increase in CO₂ intensity is associated with a 2.27 percentage point increase in gross capital formation (GCF) (p < 0.01), consistent with carbon lock-in theory, while oil rents have a significant negative relationship with investment (coefficient = −0.271, p < 0.01), in line with resource curse dynamics. The study contributes by embedding carbon lock-in theory in a standard macro panel investment function, treating emissions intensity as a structural regressor alongside oil rents in the specific context of rentier states. A behavioural interpretation is also offered: high-carbon strategies persist because they continue to yield relatively high short-term returns under existing incentives, so investment systems tend to reinforce carbon-intensive pathways. These insights have implications for both theory and practice, suggesting that screening public projects by emissions intensity, greening sovereign wealth portfolios, and phasing out fossil subsidies may help break carbon-intensive investment inertia. Full article

This study adopts a systems perspective to explain why the embeddedness of foreign-invested enterprises (FIEs) generates divergent effects across countries—promoting upgrading in some while inducing low-end lock-in in others. Based on complex network theory, we construct an industry chain centrality indicator and examine how the embeddedness of FIEs affects functional upgrading in manufacturing, as well as the threshold effect created by industry centrality. Using panel data on manufacturing sectors of 42 economies from 2003 to 2020, we employ a panel threshold model to analyse the nonlinear impact of FIEs embeddedness. The results show a significant single threshold. When industry centrality is low, the positive effect of FIE embeddedness on functional upgrading is not significant; once the threshold is crossed, the effect strengthens markedly. This pattern indicates that industries occupying hub positions in global production networks can better absorb and amplify knowledge and technology spillovers generated by FIEs, promoting upgrading of high value-added functions such as R&D, management, and marketing. Robustness checks confirm these findings, and heterogeneity analysis shows that different types of functional upgrading exhibit distinct threshold levels. Overall, the study highlights that the impact of FIEs depends critically on an industry’s structural position within the global network. Full article

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

Assumptions about environmental and operational conditions play a key role in the design of sensor-driven and cyber–physical systems. When these assumptions later change or prove incorrect, they can cause rework, inconsistency, and other forms of requirements technical debt (RTD). Although prior studies have highlighted this problem conceptually, there has been limited quantitative evidence showing how assumptions volatility contributes to RTD during early system modeling. Objective: This work introduces the concept of assumptions volatility, the degree to which environmental assumptions evolve or become invalid, and provides the first empirical assessment of how these measures relate to RTD indicators in model-based development. Methods: We analyzed 89 environmental assumptions curated from a prior controlled modeling study. For assumptions volatility, we identified three metrics, i.e., assumption change (ACR), invalidation ratio (IR), and dependence density (DD). These measures were compared against three RTD indicators, i.e., rework ratio, inconsistency density, and correction count. Correlation and regression analyses with robustness checks were used to evaluate the strength and consistency of the observed relationships. Results: Our results showed that assumptions with higher volatility were consistently linked to a greater level of RTD, with dependency density showing the most stable associations among the three volatility measures. Conclusions: The findings provide initial quantitative evidence that environmental assumption volatility is associated with RTD during conceptual design and motivate future multi-domain validation in broader Model-based Systems Engineering settings. Full article

This paper addresses the challenge of optimizing cloudlet resource allocation in a code evaluation system. The study models the relationship between system load and response time when users submit code to an online code-evaluation platform, LambdaChecker, which operates a cloudlet-based processing pipeline. The pipeline includes code correctness checks, static analysis, and design-pattern detection using a local Large Language Model (LLM). To optimize the system, we develop a mathematical model and apply it to the LambdaChecker resource management problem. The proposed approach is evaluated using both simulations and real contest data, with a focus on improvements in average response time, resource utilization efficiency, and user satisfaction. The results indicate that adaptive scheduling and workload prediction effectively reduce waiting times without substantially increasing operational costs. Overall, the study suggests that systematic cloudlet optimization can enhance the educational value of automated code evaluation systems by improving responsiveness while preserving sustainable resource usage. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures. Full article

The research process was based on an analysis of an existing building equipped with a heat pump on which photovoltaic panels were installed; then, based on energy consumption, the investment profitability was evaluated. In this research, using the available data, the coefficient of self-consumption of energy from the PV installation, the potential index of the installation’s own needs coverage, and the index of energy use from photovoltaic modules were determined, which in practice is equated with the energy efficiency of the PV installation. The entire investment was subjected to simulation and field tests to determine the energy demand of a single-family building. The main aim of this work was to check whether a system equipped with a heat pump combined with a PV installation is an effective technical solution in the analysed climatic conditions in one of the countries of Central and Eastern Europe. In addition, both positive and negative aspects of renewable energy sources were analysed, including long-term financial savings, energy independence, and reductions in greenhouse gas emissions. It has been shown that the described solution is characterised by high initial costs depending on weather conditions. The installation presented would allow us to avoid 1891 kg/year of CO₂ emissions, which means that with this solution, we contribute to environmental protection activities. Full article

Fill level control is one of the strict checks required when inspecting industrially packaged products. The purpose is both to ensure the content conformity according to the declared label information and to preserve the reliability of brand trust, strongly influenced by the customer’s evenness perception of the marketed items. To this aim, choosing the right technology is not an easy task: content and packaging material properties are essential to establish the suitability of a product to the fill level machine type. In this paper, we propose a novel approach, based on microwaves, to address this issue. The designed microwave inspection system consists of two Vivaldi antennas working between 1 and 18 GHz. We show its applicability to water, oil and alcohol-based products moving on conveyor belts at production speed. The performed experiments demonstrate good accuracy and efficiency of level classification and fault rejection in real-time processing. Full article

This paper explores the integrability of the Akbota equation with various types of solitary wave solutions. This equation belongs to a class of Heisenberg ferromagnet-type models. The model captures the dynamics of interactions between atomic magnetic moments, as governed by Heisenberg ferromagnetism. To reveal its further physical importance, we calculate more solutions with the applications of the logarithmic transformation, the M-shaped rational solution method, the periodic cross-rational solution technique, and the periodic cross-kink wave solution approach. These methods allow us to derive new analytical families of soliton solutions, highlighting the interplay of discrete and continuous symmetries that govern soliton formation and stability in Heisenberg-type systems. In contrast to earlier studies, our findings present notable advancements. These results hold potential significance for further exploration of similar frameworks in addressing nonlinear problems across applied sciences. The results highlight the intrinsic role of symmetry in the underlying nonlinear structure of the Akbota equation, where integrability and soliton formation are governed by continuous and discrete symmetry transformations. The derived solutions provide original insights into how symmetry-breaking parameters control soliton dynamics, and their novelty is verified through analytical and computational checks. The interplay between these symmetries and the magnetic spin dynamics of the Heisenberg ferromagnet demonstrates how symmetry-breaking parameters control the diversity and stability of optical solitons. Additionally, the outcomes contribute to a deeper understanding of fluid propagation and incompressible fluid behavior. The solutions obtained for the Akbota equation are original and, to the best of our knowledge, have not been previously reported. Several of these solutions are illustrated through 3-D, contour, and 2-D plots by using Mathematica software. The validity and accuracy of all solutions we present here are thoroughly verified. Full article

Civilian Global Navigation Satellite Systems (GNSS) play a crucial role in critical infrastructure and safety-critical applications, where their low signal power and open descriptions make them vulnerable to threats such as jamming and spoofing. To address these major challenges and growing concerns, NovAtel’s OEM7 receivers are equipped with an advanced GNSS Resilience and Integrity Technology (GRIT) to identify and respond to GNSS threats effectively. This includes Interference Toolkit (ITK), Spoofing Detection Toolkit (SK) and Robust Dual-Antenna Receiver (RoDAR), which employ a range of countermeasures, from jamming detection and characterization to spoofing detection and mitigation, ensuring solution integrity and reliability. The newly developed Galileo Open Service Navigation Message Authentication (OSNMA) module also offers an additional layer of protection by checking for the authenticity of the navigation message for Galileo E1 signals. This paper evaluates the performance of NovAtel’s OEM7 receivers in detecting and mitigating jamming and spoofing using real event data. Effective jamming detection was achieved through spectrum monitoring across all GNSS bands. The effectiveness of GRIT’s anti-jamming and anti-spoofing technologies was demonstrated in advanced test cases. OSNMA results are discussed, highlighting its role as a complementary protection layer for enhanced GNSS security. Full article

Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight. Objective: We aimed to synthesise empirical evidence on RAG effectiveness versus parametric-only baselines, map datasets/architectures/evaluation practices, and surface limitations and research gaps. Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. We searched the ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and DBLP; all sources were last searched on 13 May 2025. This included studies from January 2020–May 2025 that addressed RAG or similar retrieval-supported systems producing text output, met citation thresholds (≥15 for 2025; ≥30 for 2024 or earlier), and offered original contributions; excluded non-English items, irrelevant works, duplicates, and records without accessible full text. Bias was appraised with a brief checklist; screening used one reviewer with an independent check and discussion. LLM suggestions were advisory only; 2025 citation thresholds were adjusted to limit citation-lag. We used a descriptive approach to synthesise the results, organising studies by themes aligned to RQ1–RQ4 and reporting summary counts/frequencies; no meta-analysis was undertaken due to heterogeneity of designs and metrics. Results: We included 128 studies spanning knowledge-intensive tasks (35/128; 27.3%), open-domain QA (20/128; 15.6%), software engineering (13/128; 10.2%), and medical domains (11/128; 8.6%). Methods have shifted from DPR + seq2seq baselines to modular, policy-driven RAG with hybrid/structure-aware retrieval, uncertainty-triggered loops, memory, and emerging multimodality. Evaluation remains overlap-heavy (EM/$F_1$), with increasing use of retrieval diagnostics (e.g., Recall@k, MRR@k), human judgements, and LLM-as-judge protocols. Efficiency and security (poisoning, leakage, jailbreaks) are growing concerns. Discussion: Evidence supports a shift to modular, policy-driven RAG, combining hybrid/structure-aware retrieval, uncertainty-aware control, memory, and multimodality, to improve grounding and efficiency. To advance from prototypes to dependable systems, we recommend: (i) holistic benchmarks pairing quality with cost/latency and safety, (ii) budget-aware retrieval/tool-use policies, and (iii) provenance-aware pipelines that expose uncertainty and deliver traceable evidence. We note the evidence base may be affected by citation-lag from the inclusion thresholds and by English-only, five-library coverage. Funding: Advanced Research and Engineering Centre. Registration: Not registered. Full article

Recommender systems are widely deployed across digital platforms, yet their opacity raises concerns about auditability, fairness, and user trust. To address the gap between predictive accuracy and model interpretability, this study proposes a glass-box architecture for trustworthy recommendation, designed to reconcile predictive performance with interpretability. The framework integrates interpretable tree ensemble model (Random Forest, XGBoost), an NLP sub-model for tag sentiment, prioritising transparency from feature engineering through to explanation. Additionally, a Reality Check mechanism enforces strict temporal separation and removes already-popular items, compelling the model to forecast latent growth signals rather than mimic popularity thresholds. Evaluated on the MovieLens dataset, the glass-box architectures demonstrated superior discrimination capabilities, with the Random Forest and XGBoost models achieving ROC-AUC scores of 0.92 and 0.91, respectively. These tree ensembles notably outperformed the standard Logistic Regression (0.89) and the neural baseline (MLP model with 0.86). Beyond accuracy, the design implements governance through a multi-layered Governance Stack: (i) attribution and traceability via exact TreeSHAP values, (ii) stability verification using ICE plots and sensitivity analysis across policy configurations, and (iii) fairness audits detecting genre and temporal bias. Dynamic threshold optimisation further improves recall for emerging items under severe class imbalance. Cross-domain validation on Amazon Electronics test dataset confirmed architectural generalisability (AUC = 0.89), demonstrating robustness in sparse, high-friction environments. These findings challenge the perceived trade-off between accuracy and interpretability, offering a practical blueprint for Safe-by-Design recommender systems that embed fairness, accountability, and auditability as intrinsic properties rather than post hoc add-ons. Full article

This study presents SpineCheck, a fully integrated deep-learning-based clinical decision support platform for automatic vertebra segmentation and Cobb angle (CA) measurement from scoliosis X-ray images. The system unifies end-to-end preprocessing, U-Net-based segmentation, geometry-driven angle computation, and a web-based clinical interface within a single deployable architecture. For secure clinical use, SpineCheck adopts a stateless “process-and-delete” design, ensuring that no radiographic data or Protected Health Information (PHI) are permanently stored. Five U-Net family models (U-Net, optimized U-Net-2, Attention U-Net, nnU-Net, and UNet3++) are systematically evaluated under identical conditions using Dice similarity, inference speed, GPU memory usage, and deployment stability, enabling deployment-oriented model selection. A robust CA estimation pipeline is developed by combining minimum-area rectangle analysis with Theil–Sen regression and spline-based anatomical modeling to suppress outliers and improve numerical stability. The system is validated on a large-scale dataset of 20,000 scoliosis X-ray images, demonstrating strong agreement with expert measurements based on Mean Absolute Error, Pearson correlation, and Intraclass Correlation Coefficient metrics. These findings confirm the reliability and clinical robustness of SpineCheck. By integrating large-scale validation, robust geometric modeling, secure stateless processing, and real-time deployment capabilities, SpineCheck provides a scalable and clinically reliable framework for automated scoliosis assessment. Full article