Problem Presented case study enhances environmental research on the impact of climate change by means of application of stet-of-the-art multimodal Gaidai risk and natural hazard assessment method to the in-situ measured wave-height dynamics. Existing reliability methods do not always cope easily with multivariate dynamic environmental systems with high-dimensionality. Method Multimodal Gaidai risk and natural hazard assessment technique is especially well-suited for environmental multimodal dynamic systems, that are either numerically simulated or physically observed over a representative time period, producing coherent jointly quasi-ergodic time series. Data Offshore ocean and sea waves are essential for operational safety and dependable operation/production process of offshore installations such as offshore wind turbines, oil and gas platforms. Key results State-of-the-art non-parametric deconvolution extrapolation-type scheme had been utilized to generate extreme areal wave-heights forecasts. Obtained forecasts had been verified versus parametric 4 parameter Weibull fit. Current study advocates generic, yet efficient reliability and risk assessment approach for failure (damage or natural hazard) risk evaluations for spatiotemporal multi-dimensional, nonlinear environmental multimodal dynamic wave-driven systems.

The installation of surveillance systems in public or private settings is increasingly integrated with intelligent and real-time video anomaly detection to proactive threat consideration. A framework is proposed for real-time abnormal behavior detection for streaming video data based on Dynamic Graph Neural Networks (DGNNs). Unlike traditional frame-based models, the DGNN is not restricted to a static set of nodes; instead, it treats each frame as a graph, where nodes denote detected physical objects such as people and vehicles. DGNN introduces real-time frame-wise graph updates unlike prior methods with static or slow graph refresh using motion, pose, and interaction cues, supported by a lightweight spatio-temporal GCN optimized for live anomaly detection. The results convey high performance for the model, which recorded an accuracy of 99.65%, along with the corresponding precision, recall, and F1-score. An FNR and FPR of only 0.35% and 0.03%, respectively, highlighted its strong performance in discriminating normal from anomalous behaviors. The ROC and precision-recall AUC scores zoomed close to 1.0000 in almost all categories, thereby attesting to the system’s robustness where even slight stakes are involved. Following this was the fusion of TensorRT optimization in ensuring the real-time processing capability of the model, with an inference latency of fewer than 200 milliseconds per segment. The DGNN system, therefore, represents a scalable, low-latency option that is capable of real-time detection and classification of diverse and complex real-world abnormal behaviors, making it a novel approach toward smart public safety systems with very little dependency on huge annotated datasets.

Non-intrusive load monitoring (NILM) improves energy efficiency and mitigates the greenhouse effect by effectively monitoring energy consumption. However, many methods fail to fully utilize raw signal information and require large amounts of labeled data for training. In this paper, we propose a novel method for NILM. First, raw load data are transformed into two views using strong and weak augmentations, with data locality enhanced through patching to capture more comprehensive load features. Next, an unsupervised contrastive learning framework is designed based on patch and context contrast, aiming to maximize similarity between different views of the same sample while minimizing similarity between different samples. This framework enables the encoder to learn effective feature representations from unlabeled load samples. Then, the learned representations are used by a classifier for load identification, relying on a small amount of labeled data for supervised training. Ultimately, the paper achieves a semi-supervised model that combines a self-supervised encoder with a classifier guided by partial label information to enhance NILM performance. Experiments on PLAID, WHITED and REDD public datasets demonstrate the effectiveness of the proposed algorithm, which outperforms comparative algorithms in performance.

Multivariate time series forecasting is crucial for constructing digital twin systems for urban utility tunnels. We collect diverse environmental sensor data for multivariate time series forecasting, ensuring tunnel safety. However, static sensor networks usually utilize fixed distances to reflect the spatial correlation, making it struggle to capture the dynamic spatial correlations among them. In addition, different environmental factors in utility tunnels exhibit temporal features of different periods. To overcome these challenges, we propose a spatio-temporal model, which can adaptively learn graph structures to grasp dynamic spatial correlations among different environmental factors and employ a temporal convolutional module to capture various temporal features among different environmental factors. Additionally, we integrate a linear capture module with a nonlinear feature in parallel, forming the graph and temporal convolution-linear capture networks (GTCLNs). We carry out experiments utilizing real-world datasets from Suzhou utility tunnels, demonstrating that our model outperforms baseline models and provides support for the precise construction of digital twin systems.

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to benign inputs. Notably, adversarial examples generated on white-box models often exhibit black-box transferability. Targeted attacks, which require fooling a model into predicting a specific target class, are more challenging than non-targeted attacks. A representative approach to targeted attacks is the Self-Universality (SU) method, which improves targeted transferability by enhancing the universality of adversarial perturbations. SU achieves this by maximizing the feature similarity between adversarially perturbed global images and randomly cropped local regions. However, as the pair of images used for similarity calculation is derived from the same domain, the natural high similarity between local regions and global images diminishes the prominence of the dominant features introduced by the perturbations. This limitation compromises universality, ultimately reducing targeted transferability. To address these issues, we propose Style Augmentation Domain-Universality (SADU), a method that enhances perturbation universality across domain-augmented images of the same source image. Specifically, we apply style augmentation to the source domain images and mix them with generated images to create style domain images. We then introduce a feature similarity loss that maximizes the feature similarity between adversarially perturbed source domain images and style domain images, encouraging the learned perturbations to be more universal. This approach amplifies the dominance of features introduced by adversarial perturbations compared to SU, thereby improving perturbation universality and targeted transferability. Experiments on the ImageNet-Compatible dataset demonstrate the effectiveness of SADU, boosting the average targeted attack success rate from 25.6% to 36.8% compared to state-of-the-art methods.

Cracks pose a significant threat to road and building safety, making effective detection of cracks on road surfaces a focus of research both domestically and internationally. Deep learning-based methods often require extensive pixel-level annotations, posing significant labor costs. We propose a single-stage weakly supervised crack segmentation model based on multi-scale feature fusion. The model is built on a single-stage weakly supervised segmentation framework, which reduces model complexity. It utilizes a multi-scale feature fusion module (PPM) to integrate features at different scales, enhancing the ability to extract features from cracks of various sizes. The combination of the Domain Restriction Suppression (DRS) module and pixel affinity convolution is employed to optimize pseudo-pixel annotations. In addition, we propose a joint loss function to mitigate sample imbalance between crack and non-crack pixels. Compared to other two-stage weakly supervised segmentation models, our model is simpler and more effective, achieving excellent results on the Deep Crack and Crack500 datasets, surpassing most weakly supervised crack segmentation models in terms of Recall (Re), F-score (F1), and mean Intersection-over-Union (mIoU), achieving similar effects to fully supervised crack segmentation models. This demonstrates the effectiveness and robustness of our model.

Business intelligence (BI) is a compilation of business decision-support technologies that help skilled professionals such as supervisors, managers, and experts make real-time decisions. The number of products and services that the industry has supplied and the use of these technologies have witnessed a significant decreasing the acquisition and storage of vast quantities of data from various sources, which has driven this increase. This paper proposes big data querying and correlative analytical techniques (BDQCA) to evaluate complex and interactive queries where typical query processing systems cannot handle big data queries. The MapReduce paradigm is built to analyze and search web documents from query logs in business analysis. The online Analytic processing(OLAP) framework is introduced to promote operations for multiple database perspectives, such as filtering, aggregation, roll-up, and drill-down. Therefore, this research identifies different big data strategies for a business and provides several corporate business continuity factors that influence strategic decisions. The simulation evaluations are performed based on the robustness of the proposed framework, the performance ratio of 97.1%, and the efficiency ratio of 95.5%.

In this cognitive era, vast amount of data are accumulated every day. Analysing such unstructured information and obtaining insights will be challenging. To address this, Large language models have been developed to support analysis of extensive data corpora. However, it tends to cause hallucination due to a lack of proper knowledge sources. If the analysis has to be performed with respect to the health care domain or finance domain, the challenge is raised because of the lack of domain specificity. COVID-19 sentiment analysis is one of the complex responsibilities of the government since it needs to know the opinions of people to take necessary measures. This paper presents COVID-19 retrieval augmented and fine-tuning (RAFT), a novel framework that includes the analysis of COVID-19 vaccine tweets through retrieval augmented-based approaches. This integrated domain-specific knowledge through a retrieval-augmented generation-based approach with external knowledge sources. We employed a transformer-based semantic approach in embedding generation via vector database. Furthermore, this framework exhibited generalizability when integrated with domain knowledge. It uses parameter efficient fine tuning with quantization to use a large language model with a reduced number of parameters, which will allow a model to be used in low-resource-constrained devices. This framework achieved an accuracy of 0.886 on the Twitter dataset containing tweets specific to Indian region and 0.912 on the Twitter dataset with tweets from Global region.

Temporal Knowledge Graph (TKG) representation learning aims to project entities and relations from high-dimensional spaces into low-dimensional ones while preserving dynamic relational characteristics. However, many existing methods primarily focus on single-time-stamp Knowledge Graphs, neglecting the importance of time in capturing the evolving relationships within TKGs. To address this limitation, we introduce TAR-TKG (Temporal-Aware Representation for Temporal Knowledge Graph), a novel framework that consists of three core modules. The first module, the Temporal Dynamics Steering Module, enhances dynamic temporal features by employing a multi-time-awareness network to capture changes between time stamps, thereby improving the understanding of temporal data evolution. The second module, the Cross-Time Domain Gating Module, applies cross-time domain graph convolution and adaptive gating to learn relationships between different time stamps, facilitating the integration of information across multiple time spans to improve the accuracy of temporal reasoning. The third module, the Temporal Adaptive Relation Perception Module, combines temporal embeddings, causal reasoning, and multi-modal relation fusion to enhance the model’s ability to perceive temporal relationships, particularly in managing causal dependencies and complex time-based interactions. Experimental results demonstrate that TAR-TKG outperforms existing baseline methods on three real-world datasets, proving its effectiveness in capturing dynamic relationship evolution and improving temporal reasoning within TKGs.

Accurate solar energy prediction is critical for optimising renewable energy systems, particularly in regions with diverse climatic conditions. This study investigates the impact of humidity and temperature on solar energy prediction accuracy in Ghanaian climates. It uses a hybrid Dilated Temporal Convolutional Network and Long Short-Term Memory (DTCN-LSTM) model to capture diurnal patterns and normalised temperature, humidity, and solar irradiance data from 2010–2022. The results show exceptional predictive accuracy, with R 2 values exceeding 99.86 and near-zero RMSE values of less than 0.0055 kWh across all areas studied, representing 0.098 percent of the designed 560Wh of the solar energy simulated. Temperature exclusion caused the largest performance decline, while humidity exclusion had negligible beneficial effects. The DTCN-LSTM model exhibited strong generalisation, with minimal training-testing discrepancies R 2 ≤ 0.07%, and maintained high R 2 accuracy of greater than 99.87% even when both temperature and humidity were excluded, highlighting its adaptability to sparse sensor environments. The findings show that prioritising temperature data collection over humidity improves solar energy prediction accuracy, and the DTCN-LSTM robustness across diverse climates is a vital tool for enhancing grid stability and energy storage planning in variable environments. This work further advances adaptive machine learning frameworks for renewable energy systems, emphasising scalability and operational practicality in global solar forecasting applications.