Search Results (19)

Linear Fresnel Reflectors (LFR) are a type of concentrated solar collector that uses direct solar radiation (DSR) to provide useful heat for a range of applications, including power generation, heating and cooling. This study introduces a simplified analytical method for predicting the optical and thermal performance of LFR systems using only the geometric parameters and meteorological data of a location. In this analysis, the Incident Angle Modifier (IAM) and DSR are calculated using long-term data from open-access weather datasets, offering analysis from minute-scale to annual resolution, without the requirement of computationally expensive methods such as ray-tracing or extensive onsite measurements. The model is validated against experimental data from an installed LFR system at the Cyprus Institute (CyI) in Nicosia, Cyprus, demonstrating good agreement in both solar and useful energy output. The latter is derived as the product of the predicted DSR, which closely matches pyrheliometer measurements over a four-year period, and the accurately reproduced IAM. Overall, the method provides a reliable estimation of the thermal performance, offering a practical tool for early-stage design for any location of interest, yielding results significantly faster and at a fraction of the computational cost compared to ray-tracing and CFD approaches. Full article

Due to the explosive growth in cloud-native infrastructures, the attack surface has dramatically increased in modern enterprise identity systems, where privilege escalation has become a major security risk. Conventional rule-based intrusion detection systems fall short in identifying multi-hop privilege inheritance paths and lateral movements over heterogeneous and dynamic identity graphs. This study introduces PEGraphSec-Net, a graph-theoretical framework for detecting privilege-escalation-relevant identity behavior, modeling cloud identity interactions as dynamic heterogeneous graphs of users, services, roles, tokens, and workloads. The core contribution of this framework is a graph-based detection pipeline—an Identity Relationship Graph Constructor, a Privilege-Escalation Path Encoder, and a Temporal Graph Attention Detection layer—evaluated on privilege-escalation-relevant attack categories using a documented proxy identity-graph construction derived from the UNSW-NB15 network-traffic benchmark, and benchmarked against six non-graph tabular classifiers (CNN, LightGBM, XGBoost, Random Forest, SVM, and MLP) trained under identical preprocessing; this pipeline achieves 98.78% accuracy, a weighted F1-score of 0.98692 (macro F1-score of 0.91828), and an AUC of 1.000 on the held-out test partition. PEGraphSec-Net is further benchmarked against three graph neural network baselines (GCN, GAT, and GraphSAGE) trained on the identical identity-graph topology and node attributes; all three substantially underperform PEGraphSec-Net (best case, GraphSAGE: 63.66% accuracy, 0.239 macro F1-score), indicating that a large share of PEGraphSec-Net’s performance derives from its explicit privilege-path encoding and temporal attention mechanisms rather than from the graph topology alone. An Adaptive Containment and Isolation Engine and a Mitigation Policy Reinforcement Optimizer are further proposed as risk-scoring and reward-driven policy-learning components, whose contribution is validated through module-wise ablation on classification performance; live containment action and reinforcement-learning-specific evaluation are left for future validation. The term “privilege escalation” is used throughout to denote the evaluated proxy attack categories (Exploits, Backdoor/Backdoors, and Reconnaissance) under a documented, decade-old (2015) network-intrusion benchmark, rather than production cloud-native IAM behavior, for which native-dataset validation remains an open direction. SHAP-based interpretability analysis links the model’s top-ranked traffic-level features back to the identity-graph risk, role, and trust-transition attributes they populate, evidencing that the learned representation captures semantically meaningful identity-behavior patterns within this proxy setting. Full article

Security datasets are central to the evaluation of intrusion detection and prevention systems, but their suitability differs substantially across domains, data sources, attack scenarios, labeling practices, and reproducibility conditions. This study presents a structured evidence-mapping review of security datasets reported in intrusion detection and prevention research between 2018 and 2025. The final evidence map includes 42 primary dataset-use publication records, 82 dataset or evidence-source mentions, and 69 unique datasets, corpora, or evidence sources after duplicate, retracted, and irrelevant records were removed. The review organizes datasets across network-based IDS, IPS, firewall, VPN, WAF, endpoint, email filtering, IAM, DDoS, Windows, Linux-Apache, NetFlow, cloud, and IoT/IIoT security settings. In addition to adoption-frequency analysis, the study assesses representative dataset families in terms of documentation, labeling information, feature representation, class balance, public availability, reproducibility, and practical usability limitations. The findings show that established benchmarks remain widely reused, but recent studies increasingly rely on domain-specific datasets for IoT/IIoT, DDoS, cloud, edge, host, and cyber-physical environments. The review also proposes and illustrates a dataset-selection framework that links dataset choice to security objectives, operational context, telemetry requirements, attack coverage, and evaluation protocol. The results support more transparent, context-aware, and reproducible dataset selection for intrusion detection and prevention research. Full article

Accurate building roof classification from airborne LiDAR point clouds is fundamental to reliable three-dimensional (3D) urban reconstruction. While supervoxel-based methods offer efficiency and resilience to uneven point density, their performance is critically undermined by cross-boundary segmentation errors—a direct consequence of random seed initialization that merges geometrically similar yet semantically distinct objects. To address this root cause, this study proposes Interior-Anchored Mean-Shift (IAMS), a novel supervoxel segmentation framework that rethinks seed placement as a geometry-aware interior localization problem. By integrating local geometric consistency point density, and spatial correlation into a unified kernel density estimator, supplemented by density-adaptive voxel weighting and a semi-variogram-driven bandwidth, IAMS reliably anchors seeds within object interiors, yielding highly homogeneous supervoxels without post-processing. Extensive experiments on three diverse airborne LiDAR datasets demonstrated that IAMS consistently outperformed state-of-the-art baselines. On the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark, our approach improved roof classification completeness, correctness, and quality by up to 7.1% (per-object) over the conventional Voxel Cloud Connectivity Segmentation (VCCS) algorithm while being significantly faster than recent boundary-preserving alternatives. Critically, IAMS maintains robust performance under challenging conditions, including sparse sampling and dense vegetation occlusion, making it a practical solution for real-world urban remote sensing. Full article

A comprehensive two-dimensional liquid chromatography platform (LC × LC) was developed and validated for dermal permeability studies. In this implementation, the two separation dimensions were applied to mimic the layered structure of human skin: a ceramide-like stationary phase in the first dimension (¹D) to simulate the lipid-rich epidermis, and an immobilized artificial membrane (IAM) phase in the second (²D) to emulate the dermis. Experimental conditions were optimised to reflect the microenvironment of the in vivo skin. For validation purposes, 43 pharmaceutical and cosmetic compounds whose transdermal permeability coefficients (log K_p) were known from the scientific literature were selected as model solutes. A good degree of separation was achieved across the whole dataset, and affinity profiles correlated with transdermal passage properties, suggesting that retention within specific chromatographic ranges may be predictive of skin permeation. To complement this approach, mass diffusion measurements were also conducted using Permeapad^® 96-well plates and LC was performed on a narrow bore column in MS-friendly conditions. These log K_p values were compared against both in vivo and chromatographic retention data. The combined use of these techniques offers a strategic framework for profiling new chemical entities for their dermal absorption in a manner that is both ethically compliant and eco-sustainable. Full article

Effectively classifying each pixel in an image is an important research topic in semantic segmentation. The Existing methods typically require the network to directly generate a feature map of the same size as the original image and classify each pixel, which makes it difficult for the network to fully leverage the representations from the backbone. To handle this challenge, this paper proposes a method named mask classification combined with an adaptive dilated convolution network (MCDCNet). Firstly, a Vision Transformer (ViT)-based module is employed to capture contextual features as the backbone. Secondly, the Spatial Extraction Module (SEM) is proposed to extract multi-scale spatial information through adaptive dilated convolution while preserving the original feature size. This spatial information is then integrated into the corresponding contextual features to enhance the representation. Finally, a novel inference process is proposed that incorporates the instance activation map (IAM)-based decoder for semantic segmentation, thereby enhancing the network’s capability to capture and comprehend semantic features. The experimental results demonstrate that our network significantly outperforms other per-pixel classification networks across several semantic segmentation datasets. In particular, on Cityscapes, MCDCNet achieves 80.3 mIoU with 11.8 M Params, demonstrating that the network is able to deliver a strong segmentation performance while maintaining a relatively low parameter count. Full article

Infrared-visible image fusion (IVIF) is an important part of multimodal image fusion (MMF). Our goal is to combine useful information from infrared and visible sources to produce strong, detailed, fused images that help people understand scenes better. However, most existing fusion methods based on convolutional neural networks extract cross-modal local features without fully utilizing long-range contextual information. This limitation reduces performance, especially in complex scenarios. To address this issue, we propose TCTFusion, a three-branch cross-modal transformer for visible–infrared image fusion. The model includes a shallow feature module (SFM), a frequency decomposition module (FDM), and an information aggregation module (IAM). The three branches specifically receive input from infrared, visible, and concatenated images. The SFM extracts cross-modal shallow features using residual connections with shared weights. The FDM then captures low-frequency global information across modalities and high-frequency local information within each modality. The IAM aggregates complementary cross-modal features, enabling the full interaction between different modalities. Finally, the decoder generates the fused image. Additionally, we introduce pixel loss and structural loss to significantly improve the model’s overall performance. Extensive experiments on mainstream datasets demonstrate that TCTFusion outperforms other state-of-the-art methods in both qualitative and quantitative evaluations. Full article

While a flurry of studies and Integrated Assessment Models (IAMs) have independently investigated the impacts of switching mitigation policies in response to different climate scenarios, little is understood about the feedback effect of how human risk perceptions of climate change could contribute to switching climate mitigation policies. This study presents a novel machine learning approach, utilizing a probabilistic structural equation model (PSEM), for understanding complex interactions among climate risk perceptions, beliefs about climate science, political ideology, demographic factors, and their combined effects on support for mitigation policies. We use machine learning-based PSEM to identify the latent variables and quantify their complex interaction effects on support for climate policy. As opposed to a priori clustering of manifest variables into latent variables that is implemented in traditional SEMs, the novel PSEM presented in this study uses unsupervised algorithms to identify data-driven clustering of manifest variables into latent variables. Further, information theoretic metrics are used to estimate both the structural relationships among latent variables and the optimal number of classes within each latent variable. The PSEM yields an R² of 92.2% derived from the “Climate Change in the American Mind” dataset (2008–2018 [N = 22,416]), which is a substantial improvement over a traditional regression analysis-based study applied to the CCAM dataset that identified five manifest variables to account for 51% of the variance in policy support. The PSEM uncovers a previously unidentified class of “lukewarm supporters” (~59% of the US population), different from strong supporters (27%) and opposers (13%). These lukewarm supporters represent a wide swath of the US population, but their support may be capricious and sensitive to the details of the policy and how it is implemented. Individual survey items clustered into latent variables reveal that the public does not respond to “climate risk perceptions” as a single construct in their minds. Instead, PSEM path analysis supports dual processing theory: analytical and affective (emotional) risk perceptions are identified as separate, unique factors, which, along with climate beliefs, political ideology, and race, explain much of the variability in the American public’s support for climate policy. The machine learning approach demonstrates that complex interaction effects of belief states combined with analytical and affective risk perceptions; as well as political ideology, party, and race, will need to be considered for informing the design of feedback loops in IAMs that endogenously feedback the impacts of global climate change on the evolution of climate mitigation policies. Full article

Hydraulic systems are critical components of mechanical equipment, and effective fault diagnosis is essential for minimizing maintenance costs and enhancing system reliability. In practical applications, data from hydraulic systems are collected with varying sampling frequencies, coupled with complex interdependencies within the data, which poses challenges for existing fault diagnosis algorithms. To solve the above problems, this paper proposes an intelligent fault diagnosis of a hydraulic system based on a multiscale one-dimensional convolution neural network with a multiattention mechanism (MA-MS1DCNN). The proposed method first extracts features from multirate data samples using a parallel 1DCNN with different receptive fields. Next, a Hybrid Attention Module (HAM) is proposed, consisting of two submodules: the Correlation Attention Module (CAM) and the Importance Attention Module (IAM), which aim to meticulously and comprehensively model the complex relationships between channel features. Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. Finally, the proposed method is evaluated and experimentally compared using the UCI hydraulic system dataset. The results demonstrate that, compared to existing methods such as Shapelet, MCIFM, and CNNs, the proposed method shows superior diagnostic performance. Full article

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture. Full article

An Integrated Assessment Model (IAM) was employed to develop a Narrative Policy Framework (NPF) and a quantitative model to investigate the changes in land use within the Brazilian Amazon. The process began by creating a theoretical NPF using a ‘systems thinking’ approach. Subsequently, a ‘system dynamic model’ was built based on an extensive review of the literature and on multiple quantitative datasets to simulate the impacts of the NPF, specifically focusing on the conversion of forests into open land for ranching and the implementation of soil management practices as a macro-level policy aimed at preserving soil quality and ranching yields. Various fallow scenarios were tested to simulate their effects on deforestation patterns. The results indicate that implementing fallow practices as a policy measure could reduce deforestation rates while simultaneously ensuring sustainable long-term agricultural productivity, thus diminishing the necessity to clear new forest land. Moreover, when combined with payments for avoided deforestation, such as REDD+ carbon offsets, the opportunity costs associated with ranching land can be utilized to compensate for the loss of gross income resulting from the policy. A sensitivity analysis was conducted to assess the significance of different model variables, revealing that lower cattle prices require resources for REDD+ payments, and vice-versa. The findings indicate that, at the macro level, payments between USD 2.5 and USD 5.0 per MgC ha⁻¹ have the potential to compensate the foregone cattle production from not converting forest into ranching land. This study demonstrates that employing an IAM with a systems approach facilitates the participation of various stakeholders, including farmers and landowners, in policy discussions. It also enables the establishment of effective land use and management policies that mitigate deforestation and soil degradation, making it a robust initiative to address environmental, climate change, and economic sustainability issues. Full article

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods. Full article

Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles. In a deep convolutional neural network, writer recognition based on supervised learning has shown great success. These supervised methods typically require a lot of annotated data. However, collecting annotated data is expensive. Although unsupervised writer recognition methods may address data annotation issues significantly, they often fail to capture sufficient feature relationships and usually perform less efficiently than supervised learning methods. Self-supervised learning may solve the unlabeled dataset issue and train the unsupervised datasets in a supervised manner. This paper introduces Self-Writer, a self-supervised writer recognition approach dealing with unlabeled data. The proposed scheme generates clusterable embeddings from a small fixed-length image frame such as a text block. The training strategy presumes that a small image frame of handwritten text should include the writer’s handwriting characteristics. We construct pairwise constraints and nongenerative augmentation to train Siamese architecture to generate embeddings depending on such an assumption. Self-Writer is evaluated on the two most widely used datasets, IAM and CVL, on pairwise and triplet architecture. We find Self-Writer to be convincing in achieving satisfactory performance using pairwise architectures. Full article

At the crossroads of sociology and international relations, this interdisciplinary and comparative research article explores how the COVID-19 outbreak has impacted China–Europe relations. Unfolding the critical moments of the COVID-19 outbreak, this article characterizes the evolution of China–Europe relations with regard to the facemask. This simple object of self-protection against the coronavirus strikingly became a source of contention between peoples and states. In the face of this situation, we argue that the facemask is the prism through which to illustrate (1) the transnational links between China and its overseas population, (2) the changing social perceptions of China and Chinese-looking people in European societies, and (3) the advent of China’s health diplomacy and its reception in Europe. Comparing two European settings—France and the United Kingdom (UK)—the common denominator appears to be the reduced trust, if not outright distrust, between individuals and communities in the French and British contexts, and in Sino–French and Sino–British relations at the transnational level. Combining critical juncture theory and (dis)trust in international relations as our analytical framework, this article examines how the facemask became a politicized object, both between states and between Mainland China and its overseas population, as the epidemic unfolded throughout Europe. Adopting a qualitative approach, our dataset comprises the analysis of official speeches and statements; press releases; traditional and social media content (especially through hashtags such as #JeNeSuisPasUnVirus, #IAmNotAVirus, #CoronaRacism, etc.); and interviews with Chinese, French, and British community members. Full article

Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas. Full article