Search Results (142)

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article

Blood typing remains a manual, subjective procedure when not reliant on centralized laboratory infrastructure. This study presents an automated blood typing system for point-of-care deployment, developed in collaboration with CRIAM, whose portable device captures reaction images for in vitro diagnostics. The system integrates computer vision and artificial intelligence to classify these reactions automatically. Fourteen classification pipelines were trained and evaluated with a 3090-image dataset, encompassing fine-tuned convolutional neural networks, raw pixel-based classifiers, and hybrid architectures pairing pretrained embeddings from DINOv2 and EfficientNet-B4 with lightweight classifiers. Embedding-based approaches consistently outperformed alternatives in accuracy and cross-fold stability. The best pipeline, in terms of performance and suitability for low-power devices, combined DINOv2-small embeddings with logistic regression, achieving 99.87 ± 0.12% mean accuracy. After 8-bit integers (INT8) quantization and retraining with data augmentation, accuracy improved to 99.97 ± 0.03%, surpassing the uncompressed baseline. All misclassifications were traced to borderline weak-positive Rh/D reactions, confirming errors are localized and explainable. Held-out validation on 856 images yielded 99.53% accuracy, with the single error attributed to a lighting artifact. While deployment on a legacy 32-bit CPU prototype processes four images in approximately 4.7 min, hardware benchmarking confirmed feasibility, from a Raspberry Pi Zero 2W to high-end mobile processors. These results establish quantized embedding-driven architectures as a solution for automated blood typing in point-of-care and resource-limited settings. Full article

The digital integration of existing industrial equipment (retrofitting) is a central element of the Industry 4.0 paradigm, wherein the energy efficiency of Internet of Things (IoT) gateways is a decisive design consideration. This research aims to experimentally compare various wireless and wired communication protocols—ESP-NOW, Bluetooth Low Energy (BLE), Bluetooth Classic (Serial Port Profile, SPP), Message Queuing Telemetry Transport (MQTT), and S7 Protocol—within a legacy Programmable Logic Controller (PLC)-based environment. A dedicated testbed was developed using Siemens S7-300 PLCs and ESP32-based gateway devices to ensure measurement reproducibility. Energy consumption was determined using a high-precision power profiler with payloads ranging from 50 to 15,000 bytes, applying the trapezoidal rule while considering both active transaction and standby states. The specific energy consumption metric (μJ/byte) introduced in this study highlights the distinct scaling limitations of the protocols. While ESP-NOW proved highly efficient for small telemetry packets, Bluetooth Classic exhibited superior scalability for bulk data volumes. Furthermore, a critical energetic crossover point was identified for ESP-NOW due to hardware fragmentation limits, whereas MQTT demonstrated massive energetic overhead for small payloads. Standby measurements confirmed that the continuous baseline consumption of the wired Ethernet interface significantly dominates the energy budget compared to wireless alternatives. These empirical findings are synthesized into a formal Qualitative Decision Matrix to help engineers optimize protocol selection based on the expected duty cycle, facilitating the development of sustainable industrial digitalization solutions. Full article

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

Petroleum hydrocarbon contamination of soils remains a persistent environmental problem in Arctic and sub-Arctic regions, where oil extraction, pipeline transportation, fuel storage, industrial legacy sites, and diesel-dependent infrastructure coexist with fragile cold-climate ecosystems. Remediation in these regions is constrained by low temperatures, short thaw seasons, permafrost, waterlogged active layers, slow vegetation recovery, limited infrastructure, and high mobilization costs, which limit the direct transferability of conventional temperate-zone technologies. This study presents a structured narrative review of international and Russian evidence on petroleum-contaminated soil management in cold regions, focusing on monitoring as a basis for remediation decision-making. Peer-reviewed studies, technical guidance documents, regulatory frameworks, and regional case studies were analyzed across key domains, including environmental constraints, hydrocarbon behavior, monitoring methodologies, and remediation technologies. Particular attention is given to chemical analysis, hydrocarbon fractionation, bioavailability-oriented methods, ecotoxicological bioassays, and microbial indicators as tools linking contamination assessment with remediation strategy selection. Reliance on total petroleum hydrocarbon (TPH) concentration as a primary endpoint is shown to be insufficient, especially in cold-region soils where strong sorption and limited mass transfer decouple concentration from biological exposure. Multi-endpoint monitoring systems provide a more reliable basis for assessing contaminant risk, treatment effectiveness, and soil recovery. For the Russian Arctic, the integration of national recultivation frameworks with risk-based assessment and ecotoxicological monitoring is identified as a key pathway for improving remediation outcomes. A decision-oriented framework is proposed that links environmental conditions, contaminant properties, and monitoring data to support the selection and optimization of remediation strategies. This study supports a transition from concentration-based cleanup toward risk-informed and ecosystem-oriented management of petroleum-contaminated soils in Arctic and sub-Arctic environments. Full article

Urban environments in developing countries remain affected by legacy asbestos-containing materials, yet integrated assessments of multi-pathway asbestos release and environmental mobilization integrated with demographic distribution remain limited. This study aimed to develop a spatially explicit framework to assess environmental deterioration and asbestos-related environmental hazard where multiple asbestos release pathways converge in a post-ban urban setting, using Cartagena, Colombia, as a case study. A multi-pathway approach was implemented, combining source characterization of asbestos-cement (AC) roofs through microvacuum sampling, analysis of roof runoff and drinking water, spatial distribution of AC pipelines, and demographic data at the neighborhood scale. A total of 72 roof surface samples were collected, of which 92% showed detectable asbestos fibers, with concentrations reaching up to 326 × 10⁶ structures/cm². Runoff water analysis indicated 85% detection, with average concentrations of 3.5 ± 3.14 million fibers per liter (MFL). Drinking water samples showed 11% positivity, with lower concentrations (mean 1.01 ± 1.59 MFL). Spatial analysis revealed that approximately 9.5% of the urban area exhibited high airborne release potential and 3.1% exhibited high runoff-related hazard, while integrated spatial prioritization identified 5.59% of the city as high priority for intervention. Results indicated that less deteriorated roofs exhibited higher surface fiber availability, suggesting that emission potential is not directly proportional to visible degradation. The integration of environmental and demographic data supported the identification of critical hotspots where multiple asbestos release pathways converge. The proposed methodology provides a novel framework for multi-pathway asbestos spatial prioritization in urban environments and highlights the need for source-based monitoring approaches. These findings support the development of targeted mitigation strategies in cities with widespread legacy asbestos infrastructure. Full article

Fifth-generation (5G) mobile networks are widely positioned as key enablers of industrial digital transformation. However, despite extensive coverage expansion, the deployment landscape remains dominated by Non-Standalone (NSA) architectures integrated with legacy 4G cores, limiting the practical availability of advanced capabilities such as Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), and network slicing. This has contributed to a disparity between projected 5G functionality and realised industrial utility. This paper investigates the economic and structural factors constraining advanced 5G adoption and examines their implications for emerging sixth-generation (6G) frameworks. We conceptualise the current stagnation as arising from concurrent supply-side and demand-side constraints: elevated Radio Access Network (RAN) capital expenditure relative to previous generations, and limited demonstrable return on investment (ROI) for advanced service capabilities. To evaluate these dynamics empirically, a regional stakeholder study was conducted across industrial and public sector organisations in Ayrshire, Scotland. Data were collected through structured surveys and workshop-based questionnaires involving 34 participants, with proportional sectoral analysis performed to assess representativeness. The results indicate that high initial deployment costs and ROI uncertainty are the primary adoption barriers, with 45.83% of respondents reporting no immediate operational requirement for advanced 5G features. The findings identify an implementation gap in which economic viability, rather than technical feasibility, limits progression beyond basic 5G deployment. The paper argues that unless cost-efficiency and sector-specific value articulation are addressed, similar adoption constraints may extend into 6G development. These results provide empirically grounded insights to inform more economically aligned next-generation network planning. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

The increasing resolution of global ocean circulation models has made data output an important constraint on runtime efficiency and operational timeliness. The current dedicated-process asynchronous I/O scheme in the Mass Conservation Ocean Model (MaCOM) sends output data from compute processes to a group of reserved I/O processes. Although this design separates part of the writing work from the main time-stepping loop, it still introduces centralized data aggregation, additional I/O process management, and high memory pressure on the I/O side at large process counts. This paper presents MaCOM–PAIO, a PAIO-enabled asynchronous I/O optimization for MaCOM. Built on the existing PAIO/PAIOM asynchronous I/O stack, MaCOM–PAIO implements a thread-based asynchronous output path, adapts the PnetCDF execution path used by MaCOM to route selected collective writes to PAIO, and uses PAIOM asynchronous zones to submit history and restart output operations as background tasks. The implementation keeps the numerical solver unchanged and preserves the PnetCDF-style calling path at the application level, while replacing the dedicated I/O process path with I/O–thread-based asynchronous execution on the allocated HPC nodes. Experiments were conducted on a ${\mathrm{1/12}}^{\circ }$ global MaCOM configuration. Strong-scaling tests show that, at 1646 compute processes, MaCOM–PAIO reduces the total runtime from 1167.45 s to 276.53 s and lowers the compute-side I/O blocking ratio from 67.2% to 4.9% under the tested configuration. In an independent bandwidth test at 1080 compute processes, the measured write bandwidth increases from approximately 0.10 GiB/s to 0.90 GiB/s for output volumes of about 82 GiB. The maximum memory footprint of the I/O entities is also reduced from approximately 18.2 GiB in the legacy dedicated-I/O scheme to approximately 1.9 GiB in MaCOM–PAIO. These results demonstrate that PAIO-based integration is a practical approach for improving MaCOM I/O performance under the evaluated hardware/software environment and workload. Full article

The Yangtze River Economic Belt supports over 400 million people and contributes nearly half of China’s GDP, yet decades of industrialization, urbanization, and agricultural intensification have resulted in severe contamination and pressing environmental challenges. This systematic review synthesizes three decades of peer-reviewed and governmental data to examine the spatiotemporal distribution, sources, and ecological and human health risks of major pollutants, including heavy metals, microplastics, persistent organic pollutants, and excess nutrients. While point-source emission of heavy metals such as cadmium, lead, and mercury have decreased by 35–42% since 2013 following policy interventions like the 10-Point Water Plan and the Yangtze River Protection Law, legacy contaminants in sediments and diffuse agricultural inputs continue to pose significant risks. Cadmium levels in rice still exceed food safety standards, arsenic in groundwater surpasses health guidelines, and microplastic flux into the East China Sea has reached 8.3 × 10¹² particles per year. Nutrient surpluses also drive extensive algal blooms, causing substantial economic losses. This review evaluates remediation strategies such as dredging, phytoremediation, wetland restoration, and AI-enhanced monitoring, which show removal efficiencies of 60–90% at reduced costs. However, critical gaps remain in understanding chronic mixture toxicity, the long-term fate of emerging contaminants, and pollutant–climate interactions. We propose an integrated basin-wide roadmap combining zero-liquid-discharge mandates, green infrastructure, and adaptive, performance-based governance to secure the Yangtze’s ecological and economic sustainability. This framework offers a transferable model for large-scale watershed management worldwide. Full article

Polychlorinated biphenyls (PCBs) remain a major concern in marine ecosystems, where bioaccumulation in fish occurs as complex congener mixtures whose dynamics challenge conventional indicator approaches. This study develops and evaluates a data-driven framework for refining mixture-based indicators of PCB contamination by integrating ensemble machine learning with explainable artificial intelligence. Focusing on PCB-138 as a target indicator of cumulative PCB burden, we analyse concentrations of 24 organochlorines together with biological covariates in four Mediterranean edible pelagic fish species (sardine, anchovy, horse mackerel, and chub mackerel). Comparative evaluation of indicator performance shows that alternative congener combinations, including i4 PCBs (-138, -153, -170, -180), i6 PCBs (-138, -153, -170, -180, -118, -123), and mixtures incorporating DDD and DDE, more effectively represent total PCB burden than traditional indicator groups. Clustering identifies two distinct bioaccumulation settings, characterized by high-concentration coherent congener effects and low-concentration heterogeneous responses, demonstrating that indicator performance depends on concentration range and mixture context. The study illustrates how interpretable machine learning approaches can serve as formal tools for indicator evaluation and optimisation, strengthening long-term monitoring and management of legacy contaminants in marine ecosystems, particularly under conditions of persistent exposure and renewed inputs from sediment remobilization and riverine transport. Full article

X-ray free-electron lasers (XFELs) have revolutionized structural biology by enabling “diffraction-before-destruction” and capturing the ultrafast dynamics of life. However, the intrinsic sparsity and noise of XFEL diffraction snapshots, often complicated by multi-lattice overlaps, create a formidable computational bottleneck that limits data utilization and structural fidelity. Here, we present MCDPS-SFX, a robust indexing framework based on a reference-based, whole-pattern matching principle integrated with parallelized iterative refinement. By exhaustively sampling orientation space and progressively rejecting outliers, MCDPS-SFX significantly outperforms legacy algorithms—more than doubling crystal yields in heterogeneous datasets (e.g., 21,807 vs. 8792 for MOSFLM)—and achieves highly competitive yields comparable to state-of-the-art indexers, such as extracting over 90,000 lattices in the lysozyme benchmark. We demonstrate its efficacy on standard benchmarks and technically demanding G-protein-coupled receptor (GPCR) systems, including the rhodopsin–arrestin complex and the glucagon receptor. MCDPS-SFX consistently produces high-quality data statistics, enabling the high-resolution visualization of functionally critical, flexible regions such as phosphorylated receptor tails. Our results provide a powerful tool for enhancing the scientific output of XFEL experiments, offering a robust alternative for maximizing information recovery from weakly diffracting or overlapping crystalline samples. Full article

Having long served as the backbone of automotive communication, the Controller Area Network utilizes error handling mechanisms under the ISO 11898 standard for communication reliability. However, these legacy error types do not explicitly distinguish between simple electrical noise and malicious intent. To address this structural limitation, we propose a sixth error type as a specialized protocol extension considering cybersecurity along with an error frame designed to notify other controllers and the driver of cybersecurity attacks. By defining a specific detection logic capable of identifying impersonation and replay attacks and introducing a specialized frame structure, this study enables the data link layer to take immediate defensive action without complex cryptographic overhead. Through FPGA based prototyping and Vector CANoe testing, we demonstrated that this mechanism successfully invalidates malicious attempts while preserving compatibility with the existing CAN error-handling mechanism. This research argues that cybersecurity can no longer be treated as an add-on but should be embedded within the protocol itself. Our findings provide a technical foundation for the next evolution of the ISO 11898 standard and toward security integrated CAN communication. Full article

This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting the baseband chain. A context-aware pre-selection unit dynamically selects among hardware cipher primitives based on latency constraints, security requirements, and channel conditions. The current prototype implements and synthesizes AES-128 as the primary block cipher, while ASCON (NIST lightweight AEAD) and Keccak (SHA-3 foundation) are validated through RTL simulation and architectural integration, demonstrating crypto-agility across block, AEAD, and sponge-based primitives. DES is retained solely as a legacy reference for backward-compatibility evaluation and is not recommended for secure V2X deployment. The design adopts a modular decoupling strategy in which cryptographic engines interface with a unified buffering and interleaving subsystem, enabling hardware-based single-cycle cipher switching without partial reconfiguration. FPGA results demonstrate sub-microsecond cryptographic processing latencies with moderate resource utilization, preserving the timing budget of latency-sensitive vehicular services. AES-128 provides standard-strength encryption, while ASCON and Keccak offer lightweight and sponge-based alternatives suited to constrained IoV platforms. Specifically, the implemented AES-128 core achieves a throughput of 1.02 Gbps with a switching latency of 86 ns, verified across 10 randomized transitions with a 99.99% success rate and zero data corruption. The ASCON and Keccak cores attain throughput-to-area efficiencies of 2.01 and 1.47 Mbps/LUT, respectively, at a unified clock frequency of 50 MHz. All acronyms are defined at first use and a complete list of abbreviations is provided prior to the reference section. Full article