Search Results (110)

Enhanced Geothermal Systems (EGS) extend geothermal energy beyond conventional hydrothermal resources but face challenges in creating sustainable heat exchangers in low-permeability formations. This review synthesizes achievements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a field laboratory advancing EGS readiness in 175–230 °C granitic basement. From 2017 to 2025, drilling, multi-stage hydraulic stimulation, and monitoring established feasibility and operating parameters for engineered reservoirs. Hydraulic connectivity was created between highly deviated wells with ~300 ft vertical separation via hydraulic and natural fracture networks, validated by sustained circulation tests achieving 10 bpm injection at 2–3 km depth. Advanced monitoring (DAS, DTS, and microseismic arrays) delivered fracture propagation diagnostics with ~1 m spatial resolution and temporal sampling up to 10 kHz. A data infrastructure of 300+ datasets (>133 TB) supports reproducible ML. Geomechanical analyses showed minimum horizontal stress gradients of 0.74–0.78 psi/ft and N–S to NNE–SSW fractures aligned with maximum horizontal stress. Near-wellbore tortuosity, driving treating pressures to 10,000 psi, underscores completion design optimization, improved proppant transport in high-temperature conditions, and coupled thermos-hydro-mechanical models for long-term prediction, supported by AI platforms including an offline Small Language Model trained on Utah FORGE datasets. Full article

Detecting surface defects in forgings is crucial for ensuring the reliability of automotive components such as steering knuckles. In fluorescent magnetic particle inspection (FDMPI) images, normal forging surfaces generally exhibit locally symmetric texture patterns, whereas cracks and other flaws appear as locally asymmetric regions. Traditional FDMPI inspection relies on manual visual judgement, which is inefficient and error-prone. This paper introduces SDDNet, a symmetry-aware deep learning model for surface defect detection in FDMPI images. A dedicated FDMPI dataset is constructed and further expanded using a denoising diffusion probabilistic model (DDPM) to improve training robustness. To better separate symmetric background textures from asymmetric defect cues, SDDNet integrates a UPerNet-based segmentation layer for background suppression and a Scale-Variant Inception Module (SVIM) within an RTMDet framework for multi-scale feature extraction. Experiments show that SDDNet effectively suppresses background noise and significantly improves detection accuracy, achieving a mean average precision (mAP) of 45.5% on the FDMPI dataset, 19% higher than the baseline, and 71.5% mAP on the NEU-DET dataset, outperforming existing methods by up to 8.1%. Full article

With the rapid advancement of generative AI (AIGC) technology, synthetic images are increasingly approaching real pictures in terms of resolution and semantic consistency. Traditional detection methods face numerous challenges, such as insufficient cross-modal generalization capabilities and difficulty in identifying hidden generative traces. Existing solutions primarily design feature extractors for single generative models, struggling to address the complexity of multimodal forgeries. Therefore, we propose a multi-domain feature fusion Transformer network that integrates spatial, frequency, and wavelet transform features and introduce a cross-domain feature fusion module (CDAF) to detect subtle forgery traces in deepfake images. This model demonstrates superior detection performance on current forged images generated by generative adversarial networks (GANs) and diffusion models while exhibiting enhanced robustness. Full article

The quantity, size, and distribution of non-metallic inclusions in High-Strength Low-Alloy (HSLA) steel critically influence its service performance. Conventional detection methods often fail to adequately characterize extreme inclusion distributions in large-section components. This study developed an integrated full-process inclusion analysis system combining high-precision motion control, parallel optical imaging, and laser spectral analysis technologies to achieve rapid and automated identification and compositional analysis of inclusions in meter-scale samples. Through systematic investigation across the industrial process chain—from a dia. 740 mm consumable electrode to a dia. 810 mm electroslag remelting (ESR) ingot and finally to a dia. 400 mm forged billet—key process-specific insights were obtained. The results revealed the effective removal of Type D (globular oxides) inclusions during ESR, with their counts reducing from over 8000 in the electrode to approximately 4000–7000 in the ingot. Concurrently, the mechanism underlying the pronounced enrichment of Type C (silicates) in the ingot tail was elucidated, showing a nearly fourfold increase to 1767 compared to the ingot head, attributed to terminal solidification segregation and flotation dynamics. Subsequent forging further demonstrated exceptional refinement and dispersion of all inclusion types. The billet tail achieved exceptionally high purity, with counts of all inclusion types dropping to extremely low levels (e.g., Types A, B, and C were nearly eliminated), representing a reduction of approximately one order of magnitude. Based on these findings, enhanced process strategies were proposed, including shallow molten pool control, slag system optimization, and multi-dimensional quality monitoring. An intelligent analysis framework integrating a YOLOv11 detection model with spectral feedback was also established. This work provides crucial process knowledge and technological support for achieving the quality control objective of “known and controllable defects” in HSLA steel. Full article

Current tumor diagnostics rely on fluorodeoxyglucose (FDG)-PET imaging, but FDG’s short half-life and high cost limit its widespread use. Infrared (IR) probes are emerging as non-radioactive alternatives to conventional tracers for tissue section and other in vitro imaging applications. Because cells and tissues are relatively free of absorption peaks between 1800 and 2200 cm⁻¹, metal-carbonyl complexes, especially cyclopentadienylrhenium(I) tricarbonyl (Cp[Re(CO)₃]) derivatives, absorb strongly in this window and provide robust platforms for bioconjugation. Furthermore, Cp[Re(CO)₃] fragments can be introduced into organic substrates via an elegant three-component reaction that simultaneously forges the cyclopentadienyl-metal and cyclopentadienyl-substituent bonds. As a result, the functionalized half-sandwich complex is obtained in a single step without any special handling issues. We have therefore properly modified a glucose molecule with that complex and developed a novel glucoconjugated Cp[Re(CO)₃] probe that enables IR-based visualization of diseased cells at 2100 cm⁻¹, offering a non-invasive, non-radioactive histological tool and a promising basis for future medical imaging devices. Full article

This paper addresses the critical challenge of detecting content-aware image manipulations, specifically focusing on seam carving forgery. While deep learning models, particularly Convolutional Neural Networks (CNNs), have shown promise in this area, their black-box nature limits their trustworthiness in high-stakes domains like digital forensics. To address this gap, we propose and validate a framework for interpretable forgery detection, termed E-XAI (Ensemble Explainable AI). Conceptually inspired by Ensemble Learning, our framework’s novelty lies not in combining predictive models, but in integrating a multi-perspective ensemble of explainability techniques. Specifically, we combine SHAP for fine-grained, pixel-level feature attribution with Grad-CAM for region-level localization to create a more robust and holistic interpretation of a single, custom-trained CNN’s decisions. Our approach is validated on a purpose-built, balanced, binary-class dataset of 10,300 images. The results demonstrate high classification performance on an unseen test set, with a 95% accuracy and a 99% precision for the forged class. Furthermore, we analyze the model’s robustness against JPEG compression, a common real-world perturbation. More importantly, the application of the E-XAI framework reveals how the model identifies subtle forgery artifacts, providing transparent, visual evidence for its decisions. This work contributes a robust end-to-end pipeline for interpretable image forgery detection, enhancing the trust and reliability of AI systems in information security. Full article

With the widespread use of audio editing software and artificial intelligence, it has become very easy to forge audio files. One type of these forgeries is copy-move forgery, which is achieved by copying a segment from an audio file and placing it in a different place in the same file, where the aim is to take the speech content out of its context and alter its meaning. In practice, forged recordings are often disguised through post-processing steps such as lossy compression, additive noise, or median filtering. This distorts acoustic features and makes forgery detection more difficult. This study introduces a robust keypoint-based approach that analyzes Mel-spectrograms, which are visual time-frequency representations of audio. Instead of processing the raw waveform for forgery detection, the proposed method focuses on identifying duplicate regions by extracting distinctive visual patterns from the spectrogram image. We tested this approach on two speech datasets (Arabic and Turkish) under various real-world attack conditions. Experimental results show that the method outperforms existing techniques and achieves high accuracy, precision, recall, and F1-scores. These findings highlight the potential of visual-domain analysis to increase the reliability of audio forgery detection in forensic and communication contexts. Full article

This study presents a dual-module architecture for image forgery detection in the context of cyber fraud investigation, designed to provide interpretable and court-admissible forensic evidence. The forgery segmentation module built on an encoder–decoder structure segments forged regions at the pixel level to produce a binary mask. The forgery classification module with two-stream structure integrates contextual and noise-residual cues from the raw image and the binary mask to determine the designated forgery method. The segmentation module achieves an F1-Score of 0.875 and an IoU of 0.78, while the classification module reaches an F1-Score of 0.94. The combined system attains an end-to-end F1-Score of 0.855 and AUC of 0.91, demonstrating reliable detection performance and enhanced explainability. These results highlight the framework’s potential for forensic image analysis and its practical applicability to real-world cyber fraud investigations. Full article

This paper presents a rare example of the conservation of a piece of marine oval-shaped tableware, commonly known as a ‘cloche’, made of nickel silver with silver electroplating that was recovered in 2006 from the 19th-century Patris paddle-wheel shipwreck in Greece. Our study found that the cloche is made of two components of differing compositions of nickel-silver alloy, also known as German silver: a forged body and a cast handle, joined by lead soldering. The body also has an impressed decorative stamp bearing the ‘Greek Steamship’ signature in Greek. The condition assessment found the object was covered in thick concretion formations and suffered galvanic corrosion, along with dealloying, resulting in redeposition of copper. The conservation treatment carried out in 2007 is detailed along with diagnostic examination using microscopic analysis, radiographic imaging, and chemical analysis of the corrosion and metal, using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and portable X-ray fluorescence (pXRF). The conservation of the object involved mechanical and chemical methods (formic acid 5–10% v/v, stabilisation treatment with sodium sesquicarbonate 1% w/v), including spot electrolysis, and the object was coated with 15% w/v Paraloid B72 in acetone. Since its conservation, the object has been on display in the Industrial Museum of Hermoupolis in Syros. In 2025, the object was inspected for its coated surface as well as to carry out pXRF again with a more advanced system to better understand the alloy composition of the object. These results are presented here for this unique object. Full article

In the realm of image classification, the capsule network is a network topology that packs the extracted features into many capsules, performs sophisticated capsule screening using a dynamic routing mechanism, and finally recognizes that each capsule corresponds to a category feature. Compared with previous network topologies, the capsule network has more sophisticated operations, uses a large number of parameter matrices and vectors to express picture attributes, and has more powerful image classification capabilities. However, in the practical application field, the capsule network has always been constrained by the quantity of calculation produced by the complicated structure. In the face of basic datasets, it is prone to over-fitting and poor generalization and often cannot satisfy the high computational overhead when facing complex datasets. Based on the aforesaid problems, this research proposes a novel enhanced capsule network topology. The upgraded network boosts the feature extraction ability of the network by incorporating a multi-scale feature extraction module based on proprietary star structure convolution into the standard capsule network. At the same time, additional structural portions of the capsule network are changed, and a variety of optimization approaches such as dense connection, attention mechanism, and low-rank matrix operation are combined. Image classification studies are carried out on different datasets, and the novel structure suggested in this paper has good classification performance on CIFAR-10, CIFAR-100, and CUB datasets. At the same time, we also achieved 98.21% and 95.38% classification accuracy on two complicated datasets of skin cancer ISIC derived and Forged Face EXP. Full article

With the rapid advancement of image processing techniques, digital image forgery detection has emerged as a critical research area in information forensics. This paper proposes a novel deep learning model based on Multi-view Multi-dimensional Forgery Detection Networks (MMFD-Net), designed to simultaneously determine whether an image has been tampered with and precisely localize the forged regions. By integrating a Multi-stream Edge Feature Learning module with a Multi-dimensional Information Fusion module, MMFD-Net employs joint supervised learning to extract semantics-agnostic forgery features, thereby enhancing both detection performance and model generalization. Extensive experiments demonstrate that MMFD-Net achieves state-of-the-art results on multiple public datasets, excelling in both pixel-level localization and image-level classification tasks, while maintaining robust performance in complex scenarios. Full article

Recent advancements in text-to-image models, such as Stable Diffusion, have showcased their ability to create visual images from natural language prompts. However, existing methods like DreamBooth struggle with capturing arbitrary art styles due to the abstract and multifaceted nature of stylistic attributes. We introduce Single-StyleForge, a novel approach for personalized text-to-image synthesis across diverse artistic styles. Using approximately 15 to 20 images of the target style, Single-StyleForge establishes a foundational binding of a unique token identifier with a broad range of attributes of the target style. Additionally, auxiliary images are incorporated for dual binding that guides the consistent representation of crucial elements such as people within the target style. Furthermore, we present Multi-StyleForge, which enhances image quality and text alignment by binding multiple tokens to partial style attributes. Experimental evaluations across six distinct artistic styles demonstrate significant improvements in image quality and perceptual fidelity, as measured by FID, KID, and CLIP scores. Full article

The rapid advancement of generative models, such as GAN and diffusion architectures, has enabled the creation of highly realistic forged images, raising critical challenges in key domains. Detecting such forgeries is essential to prevent potential misuse in sensitive areas, including healthcare, financial documentation, and identity verification. This study addresses the problem by deploying a vision transformer (ViT)-based multiclass classification framework to identify image forgeries across three distinct domains: invoices, human faces, and medical images. The dataset comprises both authentic and AI-generated samples, creating a total of six classification categories. To ensure uniform feature representation across heterogeneous data and to effectively utilize pretrained weights, all images were resized to 224 × 224 pixels and converted to three channels. Model training was conducted using stratified K-fold cross-validation to maintain balanced class distribution in each fold. Experimental results of this study demonstrate consistently high performance across three folds, with an average training accuracy of 0.9983 (99.83%), validation accuracy of 0.9620 (96.20%), and test accuracy of 0.9608 (96.08%), along with a weighted F1 score of 0.9608 and exceeding 0.96 (96%) for all classes. These findings highlight the effectiveness of ViT architectures for cross-domain forgery detection and emphasize the importance of preprocessing standardization when working with mixed datasets. Full article

Continuous authentication allows devices to keep checking that the active user is still the rightful owner instead of relying on a single login. However, current methods can be tricked by forging faces, revealing personal data, or draining the battery. Additionally, the environment where the user plays a vital role in determining the user’s online security. Thanks to several security attacks, such as impersonation and replay, the user or the device can easily be compromised. We present a lightweight system that pairs face recognition with complex environmental sensing, i.e., the phone validates the user when the surrounding light or noise changes. A convolutional network turns each captured face into a 128-bit code, which is combined with a random “nonce” and protected by hashing. A camera–microphone module monitors light and sound to decide when to sample again, reducing unnecessary checks. We verified the protocol with formal security tools (Scyther v1.1.3.) and confirmed resistance to replay, interception, deepfake, and impersonation attacks. Across 2700 authentication cycles on a Snapdragon 778G testbed, the median decision time decreased from 61.2 ± 3.4 ms to 42.3 ± 2.1 ms (p < 0.01, paired t-test). Data usage per authentication cycle fell by an average of 24.7% ± 1.8%, and mean energy consumption per cycle decreased from 21.3 mJ to 19.8 mJ (∆ = 6.6 mJ, 95% CI: 5.9–7.2). These differences were consistent across varying lighting (≤50, 50–300, >300 lux) and noise conditions (30–55 dB SPL). These results show that smart-sensor-triggered face recognition can offer secure and energy-efficient continuous verification, supporting smart imaging and deep-learning-based face recognition. Full article

This study investigates the influence of temperature measurement accuracy on tool failure mechanisms in industrial hot forging processes. Challenges related to extreme operational conditions, including high temperatures, limited access to measurement surfaces, and optical interferences, significantly hinder reliable data acquisition. Thermal imaging, pyrometry, thermocouples, and finite element modeling were employed to characterize temperature distributions in forging tools and billets. Analysis of multi-stage forging of stainless steel valve forgings revealed significant discrepancies between induction heater settings and actual billet surface temperatures, measured by thermal imaging. This thermal non-uniformity led to localized underheating and insufficient dissolution of hard inclusions, confirmed by dilatometric tests, resulting in billet jamming and premature tool failure. In slender bolt-type forgings, excessive or improperly controlled billet temperatures increased adhesion between the forging and tool surface, causing process resistance, billet sticking, and accelerated tool degradation. Additional challenges were noted in tool preheating, where non-uniform heating and inaccurate temperature assessment compromised early tool performance. Measurement errors associated with thermal imaging, particularly due to thermal reflections in robotic gripper monitoring, led to overestimated temperatures and overheating of gripping elements, impairing forging manipulation accuracy. The results emphasize that effective temperature measurement management, including cross-validation of methods, is crucial for assessing tool condition, enhancing process reliability, and preventing premature failures in hot forging operations. Full article