Search Results (142)

Uncertainty quantification (UQ) is essential for the robust and competitive design of climate-friendly transportation systems, such as aircraft and space launch systems. However, supporting software applications for UQ are fragmented across numerous open-source libraries, often require in-depth knowledge of the mathematics underlying UQ, and commercial solutions often involve licensing costs. This can make it difficult for design experts to take uncertainties into account. To address this issue, we propose a modular, web-based framework that will guide practitioners through the most common UQ processes, such as statistical sampling, propagation through design workflows, and statistical analysis of the results. Adopting a modern client-server architecture, a backend service, called uqFramework, wraps relevant software libraries for each of the aforementioned steps. The current version focuses on probabilistic approaches, enabling the generation of Design-of-Experiment (DOE) inputs via Quasi-Monte Carlo, Latin Hypercube, and Low Discrepancy Sequence sampling methods. Furthermore, it enables the parallel execution of design and analysis workflows via DLR’s Remote Component Environment (RCE) or Python scripts. Finally, uqFramework performs global sensitivity analyses using Sobol, FAST, or Morris techniques. An interactive front-end application called uqStudio connects to uqFramework through a Representational State Transfer (REST) interface. It guides users through the UQ process via an intuitive, step-by-step interface. Interactive visualizations enable detailed exploration of each step. The framework’s capabilities are illustrated through two examples, the Ishigami function and a multidisciplinary UAV design study, verifying its precision, adaptability, and user-friendliness. We demonstrate that uqStudio enables researchers to conduct integrated UQ studies covering uncertainty specification, propagation, and sensitivity analysis without the difficulty of installing and properly using fragmented libraries. Future work includes extending visualization capabilities and integrating surrogate-modeling capabilities to enable faster workflow execution. Full article

Enhancing the accuracy of short-term wind power forecasting helps mitigate the adverse impacts of prediction errors on grid dispatch. Wind power exhibits a significantly nonlinear dependence on multiple influencing factors. However, existing methods struggle to effectively resolve multi-dimensional feature redundancy and multi-scale non-stationary evolutionary characteristics inherent in far-offshore wind power forecasting tasks. This leads to bottlenecks such as insufficient feature discriminability and temporal dependency focus shift under complex marine environments, ultimately limiting further improvements in prediction accuracy. To address these challenges, this paper proposes a federated learning-based adaptive multi-head attention model for wind power forecasting (Fed-AMHA). The proposed framework operates as follows: First, each wind farm client utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network to model input sequences bidirectionally, capturing long-term temporal dependencies. Subsequently, linear projection and parallel one-dimensional convolution operations are introduced to mine multi-scale local temporal features from each time step and its neighborhood. Building upon this, channel attention and multi-head temporal feature attention mechanisms are stacked. The model adaptively adjusts the weights of different time slices and feature channels by learning the importance of each channel to the forecasting task. The central server then aggregates the model parameters uploaded by the clients via averaging, enabling cross-site collaborative training without directly sharing raw data. Simulation results based on public datasets and actual wind farm data under various short-term forecasting scenarios demonstrate that the proposed model consistently achieves lower prediction errors and superior stability compared to existing forecasting models under identical settings. Full article

The Bacillus Calmette–Guérin (BCG) vaccine, originally developed for tuberculosis (TB) prevention, has recently attracted attention due to its broader immunomodulatory properties. In addition to its role in TB control, BCG induces trained immunity, a process involving epigenetic and metabolic reprogramming of innate immune cells that leads to altered systemic inflammatory responses. Increasing evidence suggests that these long-term immune adaptations may influence the central nervous system by modulating microglial activation and neuroinflammatory pathways implicated in neurodegenerative diseases. In parallel, chronic infections such as TB are associated with persistent systemic inflammation and immune dysregulation, which may contribute to microglial priming and increased vulnerability to neurodegeneration. This narrative review, based on a targeted literature search of PubMed, Scopus, Web of Science, Embase, and relevant preprint servers, synthesizes current evidence on the relationships between BCG vaccination, trained immunity, and neuroimmune interactions. We focus on studies addressing systemic immune reprogramming, microglial responses, and neuroinflammatory mechanisms relevant to neurodegenerative disorders. The available data suggest that BCG-induced immune modulation may exert context-dependent effects on the brain, with potential neuroprotective implications under certain conditions. However, the evidence remains heterogeneous and largely observational, and causality cannot yet be established. Further mechanistic and prospective studies are required to clarify whether BCG-induced trained immunity can modify the risk or progression of age-related neurodegenerative diseases. Full article

Edge computing has emerged as a promising paradigm to minimize latency and energy consumption while improving computational efficiency for mobile devices. Latency-sensitive applications such as autonomous driving, augmented reality, and industrial automation require ultra-low response times, making efficient task offloading a necessity in edge computing. However, distributing optimally computational tasks among edge servers remains a challenge, especially when considering latency, energy consumption, and workload balancing simultaneously. Although existing approaches have focused on one or two of these objectives, they do not provide a holistic solution that incorporates all three factors. In addition, some existing solutions do not take advantage of parallelism at the edge layer, resulting in bottlenecks and inefficient resource usage. In this paper, we propose a novel learning-based task offloading model that integrates parallel processing at the edge layer, adaptive workload balancing, and joint latency–energy optimization. Moreover, by dynamically adjusting the number of selected edge servers for parallel execution, our approach achieves optimal trade-offs between performance and resource efficiency. Our experimental setup includes several edge servers and several randomly deployed devices. It employs Apache HTTP Benchmark (AB) to generate realistic Mobile Edge Computing workloads. The obtained results show that our method outperforms existing approaches by reducing latency, lowering energy consumption, and maintaining a balanced workload across edge nodes. Full article

Modern web mapping technologies implement web standards that make the visualization of geoscience data on the web possible using various methods, offering a high degree of customizability for creating web maps. In meteorology, synoptic surface weather charts serve as crucial products to communicate observed surface weather at a point in time. To convey such information, these maps implement complex symbology, such as a multi-element surface station model symbol to indicate station data, isobars, and special line symbology to visualize weather fronts. Synoptic messages (SYNOP standard numerical code by WMO) are periodic meteorological reports of weather observations, exchanged by national meteorological services around the globe. This study focuses on visualizing surface weather data decoded from SYNOP reports. The paper introduces an open-source JavaScript module, which handles data decoding and dynamic symbol generation, using a WMO-compliant method for creating station model vector symbols for observational GeoJSON data on the client-side, in an interactive web mapping environment. Its output is compatible with popular, open-source web mapping libraries. It runs Python in the browser with Pyodide and makes use of the Web Workers API for parallelization, speeding up the decoding and visualization process without blocking the user interface thread. The developed module intends to help with easy representation of surface weather observations on web maps used in meteorology, which can also be implemented in a dynamically updated server–client architecture. The code is presented with a ready-to-use wrapper for Leaflet. Full article

In the era of big data, data is key to the accuracy of analytical models, and cloud computing services are often used to efficiently process large volumes of data. However, outsourcing sensitive data to a third-party cloud service provider results in a loss of direct control over the data, raising serious security concerns. The target of this study is to propose highly efficient and privacy-preserving protocols that compute the maximum/minimum value in large-scale data. To achieve the improvements in efficiency, the proposed protocols reuse the intermediate results generated in independent subprotocols. Existing privacy-preserving maximum/minimum protocols are based on approximation methods that sacrifice accuracy or reveal information during execution. They use costly comparison operations that are proportional to the size of the input data and are not suitable for large-scale data applications. In contrast, the proposed protocols theoretically reduce the number of communication rounds by 25%, the communication size by 50%, and the computational cost by 42% compared to the existing protocols. Nevertheless, the accuracy and privacy are fully maintained. In order to demonstrate these efficiency improvements concretely, we conducted experiments and demonstrated that the proposed protocols reduce the communication volume by half and the execution time by 22%. Because the proposed protocols support parallel execution, their performance can be substantially enhanced in cloud environments that provide large-scale parallel processing resources. Even data owners with restricted computational capabilities can use the protocols without exposing their information. Under the secure version, even cloud servers executing the protocol learn nothing about the input data or the computation results. Full article

To support multimedia and deep learning applications running on containers within a server, both processor cores and main memory space are critical resources for performance tuning. With the growing memory demands of applications to maintain intermediate data, installing additional dynamic random-access memory (DRAM) modules increases not only hardware costs but also the static and dynamic energy consumption of a server. In this study, both DRAM and non-volatile memory (NVM) are leveraged to provide short access latency and large main memory capacity for a server running multiple containers with diverse applications. Contention for memory space and processor cores among containers is jointly modeled as part of the performance optimization problem for the hybrid memory system of the server. Our memory and computing resource scheduling algorithms are thus developed to judiciously balance the usage of cores and DRAM space among tasks, while NVM is utilized to increase the degree of parallelism to reduce the Makespan of task batches. Benchmark programs were used to generate the input task set, and experimental results show that our solution outperforms others by achieving at least an 18.34% reduction in Makespan when 100 distinct containerized tasks are executed on a system with 512 gigabytes (GB) of NVM, 32 GB of DRAM, and eight cores. Full article

We present a combined ML/NLP (Machine Learning, Natural Language Processing) pipeline for protecting cloud-based APIs (Application Programming Interfaces), which works both at the level of individual HTTP (Hypertext Transfer Protocol) requests and at the access log file reading mode, linking explicitly technical anomalies with business risks. The system processes each event/access log through parallel numerical and textual branches: a set of anomaly detectors trained on traffic engineering characteristics and a hybrid NLP stack that combines rules, TF-IDF (Term Frequency-Inverse Document Frequency), and character-level models trained on enriched security datasets. Their results are integrated using a risk-aware policy that takes into account endpoint type, data sensitivity, exposure, and authentication status, and creates a discrete risk level with human-readable explanations and recommended SOC (Security Operations Center) actions. We implement this design as a containerized microservice pipeline (input, preprocessing, ML, NLP, merging, alerting, and retraining services), orchestrated using Docker Compose and instrumented using OpenSearch Dashboards. Experiments with OWASP-like (Open Worldwide Application Security Project) attack scenarios show a high detection rate for injections, SSRF (Server-Side Request Forgery), Data Exposure, and Business Logic Abuse, while the processing time for each request remains within real-time limits even in sequential testing mode. Thus, the pipeline bridges the gap between ML/NLP research for security and practical API protection channels that can evolve over time through feedback and retraining. Full article

The MTBseq pipeline, published in 2018, was designed to address bioinformatics challenges in tuberculosis (TB) research using whole-genome sequencing (WGS) data. It was the first publicly available tool on GitHub to perform full analysis of WGS data for Mycobacterium tuberculosis complex (MTBC) encompassing quality control through mapping, variant calling for lineage classification, drug resistance prediction, and phylogenetic inference. However, the pipeline’s architecture is not optimal for analyses on high-performance computing or cloud computing environments that often involve large datasets. To overcome this limitation, we developed MTBseq-nf, a Nextflow wrapper that provides parallelization for faster execution speeds in addition to several other significant enhancements. The MTBseq-nf wrapper can run several instances of the same step in parallel, fully utilizing the available resources, unlike the linear, batched analysis of samples in the TBfull step of the MTBseq pipeline. For evaluation of scalability and reproducibility, we used 90 M. tuberculosis genomes (European Nucleotide Archive—ENA accession PRJEB7727) for the benchmarking analysis on a dedicated computational server. In our benchmarks, MTBseq-nf in its parallel mode is at least twice as fast as the standard MTBseq pipeline for cohorts exceeding 20 samples. Through integration with the best practices of nf-core, Bioconda, and Biocontainers projects MTBseq-nf ensures reproducibility and platform independence, providing a scalable and efficient solution for TB genomic surveillance. Full article

Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at scale. The proposed framework enables concurrent performance evaluation of multiple edge nodes, drastically reducing the time-to-deploy (TTD) for benchmarking tasks compared to traditional sequential methods. SHEAB’s architecture leverages containerized microservices for orchestration and result aggregation, integrated with multi-layer security (firewalls, VPN tunneling, and SSH) to ensure safe operation in untrusted network environments. We provide a detailed system design and workflow, including algorithmic pseudocode for the SHEAB process. A comprehensive comparative review of related work highlights how SHEAB advances the state-of-the-art in edge benchmarking through its combination of secure automation and scalability. We detail a real-world implementation on eleven heterogeneous edge devices, using a centralized 48-core server to coordinate benchmarks. Statistical analysis of the experimental results demonstrates a 43.74% reduction in total benchmarking time and a 1.78× speedup in benchmarking throughput using SHEAB, relative to conventional one-by-one benchmarking. We also present mathematical formulations for performance gain and discuss the implications of our results. The framework’s effectiveness is validated through the concurrent execution of standard benchmarking workloads on distributed edge nodes, with results stored in a central database for analysis. SHEAB thus represents a significant step toward efficient and reproducible Edge AI performance evaluation. Future work will extend the framework to broader workloads and further improve parallel efficiency. Full article

Private Information Retrieval (PIR) is a cryptographic protocol that allows users to retrieve data from one or more databases without revealing any information about their queries. Among existing PIR protocols, single-server schemes based on the Learning With Errors (LWE) assumption currently constitute the most practical class of constructions. However, existing schemes continue to suffer from high client-side preprocessing complexity and significant server-side storage overhead, leading to degraded overall performance. We propose ShufflePIR, a single-server protocol that marks the first introduction of an SM3-based pseudorandom function into the PIR framework for shuffling during preprocessing and utilizes cryptographic hardware to accelerate computation, thereby improving both efficiency and security. In addition, the adoption of a parallel encryption scheme based on the LWE assumption significantly enhances the client’s computational efficiency when processing long-bit data. We evaluate the performance of our protocol against the latest state-of-the-art PIR schemes. Simulation results demonstrate that ShufflePIR achieves a throughput of 9903 MB/s on a 16 GB database with 1 MB records, outperforming existing single-server PIR schemes. Overall, ShufflePIR provides an efficient and secure solution for privacy-preserving information retrieval in a wide range of applications. Full article

The Industrial Internet of Things (IIoT) increasingly relies on vector embeddings for analytics and AI-driven applications such as anomaly detection, predictive maintenance, and sensor fusion. Efficient approximate nearest neighbor search (ANNS) is essential for these workloads. Graph-based methods are among the most representative methods for ANNS. However, most existing graph-based methods, such as Hierarchical Navigable Small World (HNSW), are designed for CPU execution on high-end servers and give little consideration to the unique characteristics of edge devices. In this work, we present ON-NSW, a GPU-optimized design of HNSW optimized for edge devices. ON-NSW employs a flat graph structure derived from HNSW to fully exploit GPU parallelism. In addition, it carefully places HNSW components in the unified memory architecture of NVIDIA Jetson Orin Nano. Also, ON-NSW introduces warp-level parallel neighbor exploration and lightweight synchronization to reduce search latency. Our experimental results on real-world high-dimensional datasets show that ON-NSW achieves up to 1.44× higher throughput than the original HNSW on the NVIDIA Jetson device while maintaining comparable recall. These results demonstrate that ON-NSW provides an effective design for enabling efficient and high-throughput vector search on embedded edge platforms. Full article

This paper proposes a scalable serverless architecture for real-time natural language processing (NLP) on large datasets using Amazon Web Services (AWS). The framework integrates AWS Lambda, Step Functions, and S3 to enable fully parallel sentiment analysis with Transformer-based models such as DistilBERT, RoBERTa, and ClinicalBERT. By containerizing inference workloads and orchestrating parallel execution, the system eliminates the need for dedicated servers while dynamically scaling to workload demand. Experimental evaluation on the IMDb Reviews dataset demonstrates substantial efficiency gains: parallel execution achieved a 6.07× reduction in wall-clock duration, an 81.2% reduction in total computing time and energy consumption, and a 79.1% reduction in variable costs compared to sequential processing. These improvements directly translate into a smaller carbon footprint, highlighting the sustainability benefits of serverless architectures for AI workloads. The findings show that the proposed framework is model-independent and provides consistent advantages across diverse Transformer variants. This work illustrates how cloud-native, event-driven infrastructures can democratize access to large-scale NLP by reducing cost, processing time, and environmental impact while offering a reproducible pathway for real-world research and industrial applications. Full article

Multi-variant execution (MVX) is an active defense technique that can detect unknown attacks by comparing the outputs of redundant program variants. Despite notable progress in MVX techniques in recent years, current approaches for recovery of abnormal variants still face fundamental challenges, including state inconsistency, low recovery efficiency, and service disruption of an MVX system. Therefore, a record–replay-based state recovery approach for variants in MVX systems is proposed in this paper. First, a Syscall Coordinator (SSC), composed of a recording module, a classification module, and a replay module, is designed to enable state recovery of variants. Then, a synchronization and voting algorithm is presented. When an anomaly is identified through voting, the abnormal variant is handed over to the SSC for state recovery, while the Synchronization Queue is updated accordingly. Furthermore, to ensure uninterrupted system service, we introduce a parallel grouped recovery mechanism, which enables the execution of normal variants and the recovery of abnormal variants to run in parallel. Experimental results on SPEC CPU 2006 benchmark and server applications show that the proposed approach achieves low overhead in both the recording and replay phases while maintaining high state recovery accuracy and supports uninterrupted system service. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article