Search Results (72)

The Matra As–Sb deposit (Alpine Corsica, France) is hosted in the normal N–S trending Matra Fault. Sulfide minerals in ore consist of realgar, stibnite, and pyrite with minor orpiment and hörnesite. The gangue includes quartz, dolomite, and calcite. In this study, the microstructural analysis of selected ore samples has been combined with the geochemical characterization of the sulfides. The results depict a succession of events that record the evolution of the ore deposit related to fault movement. In the pre–ore stage, plumose, crustiform, jigsaw, and feathery textures of quartz testify to a short–lived boiling event. The mineral assemblage of the main–ore stage includes an Fe(–Zn) substage dominated by the formation of different textures of pyrite. In general, pyrite samples contain significant concentrations of As (≤32,231 ppm) and Sb (≤10,684 ppm), with lesser amounts of by Tl (≤1257 ppm) and Ni (≤174 ppm). This is followed by an Sb–As–Fe substage of pyrite–stibnite–realgar ±orpiment. The precipitation of the sulfides was mainly driven by changes in ƒS₂. The increasing level of oxidation is attributed to a progressive influx of meteoric water resulting from reactivation of the Matra Fault. Full article

Tracking the precise movement of surgical tools is essential for enabling automated analysis, providing feedback, and enhancing safety in robotic-assisted surgery. Accurate 3D tracking of surgical tooltips is challenging to implement when using monocular videos due to the complexity of extracting depth information. We propose a pipeline that combines state-of-the-art foundation models—Florence2 and Segment Anything 2 (SAM2)—for zero-shot 2D localization of tooltip coordinates using a monocular video input. Localization predictions are refined through supervised training of the YOLOv11 segmentation model to enable real-time applications. The depth estimation model Metric3D computes the relative depth and provides tooltip camera coordinates, which are subsequently transformed into world coordinates via a linear model estimating rotation and translation parameters. An experimental evaluation on the JIGSAWS Suturing Kinematic dataset achieves a 3D Average Jaccard score on tooltip tracking of 84.5 and 91.2 for the zero-shot and supervised approaches, respectively. The results validate the effectiveness of our approach and its potential to enhance real-time guidance and assessment in robotic-assisted surgical procedures. Full article

Lilium holds significant horticultural and ecological importance. Understanding the morpho-anatomical diversity of the stems can provide insights into taxonomy and breeding strategies. This study comprehensively examined the stem morpho-anatomy of 71 Lilium taxa to elucidate taxonomic and structural differences. For the first time, four distinct jigsaw-puzzle-shaped shapes of epidermal cells (Ep) in monocot stems, novel I-shaped and Co-xylem (O-, X-, W-, Q-shaped) vascular bundles (Vb) in Lilium stems, and quantitative characteristics (Vb density, xylem/phloem area ratio, etc.) were systematically discovered and analyzed. Asiatic (A) and Longiflorum × A (LA) hybrids displayed epidermal appendages, while Oritenal × Trumpet (OT) hybrids featured thicker sclerenchymatous rings (Sr). Collateral Vb in hybrids visually displayed bicollateral with degraded bundle sheaths (Bs), contrasting with intact circular Bs in wild species. Ward.D clustering categorized Lilium taxa into group A (Oritenal and OT hybrids) and B (A, LA, Trumpet, Longiflorum × Oriental hybrids and wild species), with Mantel’s test identified height, Ep shape, Ep length/width ratio, cortex/Sr thickness ratio and Bs integrity as key discriminators. Bending stems exhibited a higher Vb area. These findings establish a comprehensive pheno-anatomical framework for Lilium, which can guide future breeding programs and ecological studies. Full article

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of $\mathcal{O}(LlogL)$. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery. Full article

Action Quality Assessment (AQA)—the task of evaluating how well human actions are performed—is essential in domains such as sports and medicine. Existing AQA methods typically rely on score regression following feature extraction but often neglect the ambiguity inherent in extracted features. In this work, we introduce a novel AQA framework that incorporates a modified attention module to better capture relevant information. Our approach segments video data into clips, extracts features using the I3D network, and applies attention mechanisms to highlight salient features while suppressing irrelevant ones. To assess feature quality, we employ score distribution regression and propose an uncertainty-aware score distribution learning strategy that models features as Gaussian distributions. We further leverage Variational Autoencoders (VAEs) to capture complex latent representations and quantify uncertainty. Extensive experiments on the MTL-AQA and JIGSAWS datasets demonstrate the effectiveness and robustness of our proposed method. Full article

The aim of this paper was to reduce the current spikes in battery-powered saw motors by designing and implementing a sliding mode model-following adaptive controller. The proposed controller reduces overcurrent consumption, improves system energy efficiency, and effectively maximizes battery runtime, especially under high-load conditions. By applying nonlinear compensation techniques, the controller can ensure smooth motor operation, reduce mechanical stress, and prolong tool life. The results showed that this control strategy is particularly suitable for hand tools, where a long battery life is essential for efficient operation. Full article

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications. Full article

We propose an enhanced privacy-preserving method for image classification using ConvMixer, which is an extremely simple model that is similar in spirit to the Vision Transformer (ViT). Most privacy-preserving methods using encrypted images cause the performance of models to degrade due to the influence of encryption, but a state-of-the-art method was demonstrated to have the same classification accuracy as that of models without any encryption under the use of ViT. However, the method, in which a common secret key is assigned to each patch, is not robust enough against ciphertext-only attacks (COAs) including jigsaw puzzle solver attacks if compressible encrypted images are used. In addition, ConvMixer is less robust than ViT because there is no position embedding. To overcome this issue, we propose a novel block-wise encryption method that allows us to assign an independent key to each patch to enhance robustness against attacks. In experiments, the effectiveness of the method is verified in terms of image classification accuracy and robustness, and it is compared with conventional privacy-preserving methods using image encryption. Full article

This study assessed the influence of sediment and water redistribution in host sediments on peperite formation by examining the peperites at the boundary between Cretaceous lacustrine sedimentary successions and intruding dikes (D1 and D2). The peperite zones occur along the dike margins and consist of fluidal juvenile fragments, classified as Type A and Type B perperite zones based on lateral extent of the peperite zones. Type A peperite zone, the dominant type, exhibites a narrow distribution (<20 cm), whereas Type B peperite zone sporadically occurs along D1 with a wider width (<1 m). Type B peperite zone is laterally linked with clastic dikes. These dikes containi fluidal shaped dike fragments with jigsaw-fit textures, indicating syn-magmatic fluidization and the resultant formation of the clastic dike via heat transfer. During dike emplacement, the interaction between the host sediments and the intruding magma formed Type A along the margins. Simultaneously, the clastic dikes, composed of fluidized sediments and water, supplied additional water and sediments, enhancing magma-host sediment intermingling and leading to the wide lateral extent of Type B. Our findings demonstrate that sediment and water redistribution via syn-magmatic fluidization is crucial in peperite formation, influencing the initial processes of phreatomagmatic volcanism. Full article

Surgical gesture recognition is an important research direction in the field of computer-assisted intervention. Currently, research on surgical gesture recognition primarily focuses on robotic surgery, with a lack of studies in traditional surgery, particularly open surgery. Therefore, this study established a dataset simulating open surgery for research on surgical gesture recognition in the field of open surgery. With the assistance of professional surgeons, we defined a vocabulary of 10 surgical gestures based on suturing tasks in open procedures. In addition, this paper proposes a surgical gesture recognition method that integrates the R3D network with a multi-head attention mechanism (R3D-MHA). This method uses the R3D network to extract spatiotemporal features and combines it with the multi-head attention mechanism for relational learning of these features. The effectiveness of the R3D-MHA method in the field of open surgery gesture recognition was validated through two experiments: offline recognition and online recognition. The accuracy at the gesture instance level for offline recognition was 92.3%, and the frame accuracy for online recognition was 73.4%. Finally, its performance was further validated on the publicly available JIGSAWS dataset. Compared to other online recognition methods, the accuracy improved without using additional data. This work lays the foundation for research on surgical gesture recognition in open surgery and has significant applications in process monitoring, surgeon skill assessment and educational training for open surgeries. Full article

Agate veins and nodules occur in the Lece Volcanic Complex (Oligocene-Miocene) situated in the south of Serbia and occupying an area of 700 km². This volcanic complex is composed predominantly of andesites, with sporadic occurrences of andesite-basalts, dacites and latites, and features agate formations that have been very little investigated. This study focuses on five selected agate occurrences within the Lece Volcanic Complex, employing optical microscopy, scanning electron microscopy (SEM), X-ray powder diffraction analysis, inductively coupled plasma mass spectrometry (ICP-MS), and Fourier transform infrared spectroscopy (FTIR). In three localities (Rasovača, Mehane, and Ždraljevići), agate mineralization is directly related to distinct fault zones with strong local brecciation. In the other two localities (Vlasovo and Sokolov Vis), the agate is found in nodular form and does not show any connection with fracture zones. The silica phases of the Lece volcanic agates consist of cristobalite and tridymite, length-fast chalcedony, quartzine (length-slow chalcedony), and macrocrystalline quartz. Vein agates show a frequent alternation between length-fast chalcedony and quartz bands. Nodular agates consist primarily of length-fast chalcedony, occasionally containing notable quantities of opal-CT, absent in vein agates. Microtextures present in vein agates include crustiform, colloform, comb, mosaic, flamboyant, and pseudo-bladed. Jigsaw puzzle quartz microtexture supports the recrystallization of previously deposited silica in the form of opal or chalcedony from hydrothermal fluids. Growth lines in euhedral quartz (Bambauer quartz) point to agate formations in varying physicochemical conditions. These features indicate epithermal conditions during the formation of hydrothermal vein agates. Due to intense hydrothermal activity, vein agate host rocks are intensively silicified. Vein agates are also enriched with typical ore metallic elements (especially Pb, Co, As, Sb, and W), indicating genetic relation with the formation of polymetallic ore deposits of the Lece Volcanic Complex. In contrast, nodular agates have a higher content of major elements of host rocks (Al₂O₃, MgO, CaO, Na₂O, and K₂O), most probably mobilized from volcanic host rocks. Organic matter, present in both vein and nodular agate with filamentous forms found only in nodular agate, suggests formation in near-surface conditions. Full article

Virtual reality (VR) enables the development of virtual training frameworks suitable for various domains, especially when real-world conditions may be hazardous or impossible to replicate because of unique additional resources (e.g., equipment, infrastructure, people, locations). Although VR technology has significantly advanced in recent years, methods for evaluating immersion (i.e., the extent to which the user is engaged with the sensory information from the virtual environment or is invested in the intended task) continue to rely on self-reported questionnaires, which are often administered after using the virtual scenario. Having an objective method to measure immersion is particularly important when using VR for training, education, and applications that promote the development, fine-tuning, or maintenance of skills. The level of immersion may impact performance and the translation of knowledge and skills to the real-world. This is particularly important in tasks where motor skills are combined with complex decision making, such as surgical procedures. Efforts to better measure immersion have included the use of physiological measurements including heart rate and skin response, but so far they do not offer robust metrics that provide the sensitivity to discriminate different states (idle, easy, and hard), which is critical when using VR for training to determine how successful the training is in engaging the user’s senses and challenging their cognitive capabilities. In this study, electroencephalography (EEG) data were collected from 14 participants who completed VR jigsaw puzzles with two different levels of task difficulty. Machine learning was able to accurately classify the EEG data collected during three different states, obtaining accuracy rates of 86% and 97% for differentiating easy versus hard difficulty states and baseline vs. VR states. Building on these results may enable the identification of robust biomarkers of immersion in VR, enabling real-time recognition of the level of immersion that can be used to design more effective and translative VR-based training. This method has the potential to adjust aspects of VR related to task difficulty to ensure that participants are immersed in VR. Full article

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data. Full article

Medical education acknowledges our need to teach our physicians about “social determinants of health” and “health care disparities”. However, educators often lack actionable training to address this need. We describe a faculty development activity, a health disparities journal club, using the jigsaw strategy with the intent of increasing awareness, encouraging self-directed learning, and inspiring future teaching of the subject to health professional learners. We completed six workshops at six individual hospitals, with 95 total attendees in medicine and numerous other health professions. Our evaluation asked trainees to: report the number of journal articles about health disparities they had read, excluding the assigned journal club articles, in the past 12 months, and to predict future plans for reading about health disparities. In total, 28.9% responded they had “never read” a prior article on health or healthcare disparities, while 54.2% responded “1–5 articles”. Many (60%) reported they would continue to investigate this topic. Our experience has demonstrated the utility and positive impact of a “flipped classroom” jigsaw method, showing it can be used successfully in Inter-Professional (IPE) Faculty Development to increase active exposure and discussion of the content. Additionally, this method promotes individual reflection and enhances continued collective engagement. Full article

Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images. Full article