Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Anomaly Detection Based on Local-Tensor-Nuclear-Norm

This paper proposes a novel anomaly detection method for hyperspectral (HS) images degraded by non-sparse noise. To characterize the spatial local low-rankness of a background component, we first introduce a regularization function, called Local Tensor Nuclear Norm (LTNN). Then, we formulate a minimization problem based on Tensor Robust Principal Component Analysis (TRPCA) using LTNN, which can decompose an observed HS image into a low-rank background component, a sparse anomaly component, and a noise component. The proposed problem can be solved by applying the alternating direction method of multipliers (ADMM). Experiments show the effectiveness of the proposed method compared to RX-based methods and existing TRPCA-based methods.
Automated Identification and Classification of Teeth Fossils

This study explores the performance of modern, accurate deep-learning algorithms on the classification of fossil teeth in different types of animals such as sharks, elephants, hyrax, and Afradapis. Sharks teeth are typically the most common fossils found in Wadi Al-hitan and they often provide valuable information about the diets and trophic interactions of past organisms, which can be used to infer aspects of the environments they lived in. Taxonomic identification of fossils is usually performed manually in a time-consuming manner and requires considerable expertise due to the vast quantity and high diversity of teeth fossils. Using modern teeth with known categories, deep-learning algorithms can be trained to identify, classify, and extract information about teeth fossils such as determining whether the animal is herbivorous or carnivorous. It also can be applied in other niches such as comparative anatomy. In this paper, a method for fully automated teeth fossils identification and classification is introduced and validated. A pipeline is proposed that begins with the image-capturing process and ends with a classification result. To assess the method performance and dataset representability that was created in cooperation with Mansoura University vertebrae paleontology lab (MUVP). Extensive experiments were performed on our dataset of more than 50,000 images of the above-mentioned 4 classes. Compared with Resnet50 model 82% precision, 80% recall, and an F1 score of 80%, VGG16 model 79% precision, 78% recall, and an F1 score of 78%, the Mobilenetv2 model 70% precision, 66 % recall, and an F1 score of 66 %, and Convolutional Neural Networks (CNN) 60% precision, 57% recall, and an F1 score of 58%. The ResNet50 model had the highest accuracy (80 % ) on the experimental dataset. The system provides a reliable approach for the detection of fossil teeth from 2D images that can be applied in a range of morphology and paleontology contexts for research purposes.
Exogenous Application of Zinc Oxide Nanoparticles Improved Antioxidants, Photosynthetic, and Yield Traits in Salt-Stressed Maize

Salinity is one of the most threatening abiotic stresses to agricultural production, alarmingly expanding both through natural salinization phenomena and anthropogenic activities in recent times. The exploration of sustainable and eco-friendly strategic approaches for mitigating the negative impact of salinity on food crops is of vital importance for future food security. Therefore, our study aimed to evaluate zinc oxide nanoparticles (ZnO-NPs) as potent salinity mitigators in maize (Zea mays L.). Three ZnO-NPs foliar treatments (i.e., 0, 50, and 100 mg/L) were applied 40, 55, and 70 days after sowing on maize plants exposed to continuous salinities of 0 mM NaCl (S0), 60 mM NaCl (S1), and 120 mM NaCl (S3) in a semi-automated greenhouse facility. Results showed that the highest salinity (i.e., 120 mM NaCl) significantly affected plant growth attributes, physiological performance, nutrient profiles, antioxidant activity, plant yield, and yield-contributing characteristics of maize plants. Thus, 120 mM NaCl resulted in −53% number of grains per cob (NG), −67% grains weight per cob (GW), −36% 100-grains weight (HGW), and −72% grain yield per plant (GY) compared to controls. However, foliar treatment of maize plants with ZnO-NPs successfully mitigated salinity and significantly improved all studied parameters, except transpiration rate (TR) and intrinsic water use efficiency (iWUE). Foliar application of 100 mg/L of ZnO-NPs alleviated NG, GW, HGW, and GY by 31%, 51%, 13%, and 53%, respectively. Furthermore, principal component analysis (PCA) and Pearson’s correlation further strengthened the significance of ZnO-NP application as salinity mitigators.
Aberrant histone modification in CD19 + B cells of patients with chronic lymphocytic leukemia

: The aim of this study was to detect the alterations in histone methylation and acetylation in patients with chronic lymphocytic leukemia (CLL). Global histone H3/H4 acetylation and H3K4/H3K9 methylation were detected by the EpiQuik™ global histone H3/H4 acetylation and H3K4/H3K9 methylation assay kits. The mRNA expression of selected chromatin modifier genes was measured by real-time polymerase chain reaction (RT-PCR). Our results found that the global histone H3/H4 hypoacetylation in the CD19 + B cells of patients with CLL ( P = 0.028 and P = 0.03, respectively) and the global histone H3K9 methylation in patients with CLL were significantly increased compared with controls ( P = 0.02), while there was no significant difference in the global histone H3K4 methylation between the two groups. The level of SIRT1 and EZH2 mRNA expression was upregulated in patients with CLL ( P = 0.03 and P = 0.02, respectively), which increased significantly with progression from Binet stage A to stage C ( P = 0.015 and P = 0.01, respectively) and Rai good to high risk stage ( P = 0.007 and P = 0.008, respectively). The level of HDAC1 and HDAC7 mRNA expression was significantly increased ( P = 0.02 and P = 0.008, respectively) and HDAC2 and P300 mRNA expression was reduced in patients with CLL ( P = 0.002 and P = 0.001, respectively). In conclusion, it is observed that the aberrant histone modification plays an important role in the pathogenesis of CLL.
Modeling and simulation of cardio electrical activity for ischemia using comsol multiphysics tool

The circulatory system revolves around the heart, which is of blood vessels network that transports blood to all part of the body. All organs of human rely on blood for transport of oxygen and some other vital nutrients, ensuring that they remain good healthy and properly function. The heart is very important for blood pumping throughout the circulatory system. When the supply of blood to tissues is restricted, oxygen lack for cellular metabolism occurs (to keep tissue alive). Ischemia is a vascular disease in which arterial blood �ow is interrupted to a tissue, organ, or extremity, resulting in tissue death if left untreated. It can be caused by embolism, atherosclerotic artery thrombosis, or trauma. Acute arterial ischemia can be caused by venous issues such as venous out�ow blockage and low-ow conditions. One of the most common causes of acute arterial ischemia is an aneurysm. The importance of the heart structure on COMSOL Multiphysics is highlighted as it can be modeled and simulated for cardiac contraction and relaxation. The heart cavity structure is implemented using the FHN (Fitzhugh Nagumo equation) and LG (Landau Ginzberg equation) equations along with the corner cases in this paper. The model of the heart is divided into 4 different models to demonstrate the blood �ow. The changes for the 4 models are in terms of mesh and the cavity change of heart structure which indirectly demonstrates the blood movement of the heart. The observed plots are in terms of spiral waves and also the waves for the dependent variables are plotted.
KetoacidosisSpectroscopy in Children with Diabetic Cerebral Proton Magnetic Resonance

N.S. Glaser BACKGROUND AND PURPOSE: Subclinical cerebral edema occurs in many, if not most, children with diabetic ketoacidosis (DKA) and may be an indicator of subtle brain injury. Brain ratios of N- acetylas-partate (NAA) to creatine (Cr), measured by proton MR spectroscopy, decrease with neuronal injury or dysfunction. We hypothesized that brain NAA/Cr ratios may be decreased in children in DKA, indicating subtle neuronal injury. MATERIALS AND METHODS: Twenty-nine children with DKA underwent cerebral proton MR spectroscopy during DKA treatment (2–12 hours after initiating therapy) and after recovery from the episode (72 hours or more after the initiation of therapy). We measured peak heights of NAA, Cr, and choline (Cho) in 3 locations within the brain: the occipital gray matter, the basal ganglia, and periaqueductal gray matter. These regions were identified in previous studies as areas at greater risk for neurologic injury in DKA-related cerebral edema. We calculated the ratios of NAA/Cr and Cho/Cr and compared these ratios during the acute illness and recovery periods. RESULTS: In the basal ganglia, the ratio of NAA/Cr was significantly lower during DKA treatment compared with that after recovery (1.68 (cid:1) 0.24 versus 1.86 (cid:1) 0.28, P (cid:2) . 005). There was a trend toward lower NAA/Cr ratios during DKA treatment in the periaqueductal gray matter (1.66 (cid:1) 0.38 versus 1.91 (cid:1) 0.50, P (cid:3) . 06) and the occipital gray matter (1.97 (cid:1) 0.28 versus 2.13 (cid:1) 0.18, P (cid:3) . 08). In contrast, there were no significant changes in Cho/Cr ratios in any region. CONCLUSIONS: NAA/Cr ratios are decreased in children during DKA and improve after recovery. This finding suggests that during DKA neuronal function or viability or both are compromised and improve after treatment and recovery.
On the Wind-Driven Circulation of the Uncoupled and Coupled NCAR Climate System Ocean Model*

Abstract The time-mean wind-driven circulation of the uncoupled and coupled National Center for Atmospheric Research Climate System Model ocean model is investigated. Although the coupled surface wind stress and wind stress curl magnitudes are, in general, larger than the uncoupled distributions, the coupled wind stresses are realistic, given the substantial uncertainties in the observational estimates. The Sverdrup balance, which represents a simple dynamical relation, can indeed describe the model depth-integrated transports to a large degree and shows that the increase in the coupled ocean barotropic transports is mainly due to the larger wind stress curl of the coupled system. Another simple dynamical tool, the Ekman transport analysis, shows that the coupled ocean upwelling and downwelling velocities are, in general, larger than in uncoupled ocean, consistent with the larger wind stress curl. Both models have similar upper-ocean upwelling magnitudes in the equatorial Atlantic. In the equatorial Pacif.
AN ANALYSIS ON ADVANCES IN LUNG CANCER DIAGNOSIS WITH MEDICAL IMAGING AND DEEP LEARNING TECHNIQUES: CHALLENGES AND OPPORTUNITIES

This abstract provides an overview of numerous studies on the identification and diagnosis of lung cancer using medical imaging and deep learning. However, techniques like the VGG16 (Visual geometry group) model, SSD (Social Ski-Driver), SVM (Support Vector Machines), CNN (Convolutional Neural Network), and CSA (crow search algorithm) have demonstrated encouraging results in accurately identifying and classifying lung cancer from CT images. Accuracy still needs to be improved. Pre-processing operations including edge identification, picture resampling, and segmentation are crucial for improving the visual appeal of input photos. The use of computer-aided diagnosis (CAD) systems can significantly increase the efficacy of diagnostic classifiers and pre-processing methods. For the identification of lung cancer, the use of deep learning models and meta-heuristic-based optimisation techniques can produce accurate and durable diagnosis models. Effective lung cancer treatment depends on early detection. The limits of the current histology-based diagnostic methods are also covered in the paper, as well as the potential of molecular biomarkers for classification. In categorising the morphology of lung cancer, selecting the optimal course of action, and forecasting the outcomes of systemic therapy for non-small cell lung cancer, machine learning algorithms have demonstrated success. A larger dataset is needed for the veracity of these conclusions, but the paper claims that doctors are increasingly employing machine learning to better understand their patients and create tailored treatment regimens.
ASPECT-ORIENTED SUGGESTION MINING FROM OPINION REVIEWS

Suggestion Mining refers to extracting suggestions from the opinionated text. The majority of existing research in suggestion mining does the binary classification of the given opinion review text as a suggestion or non-suggestion. The pioneering work ignored the fine-grained analysis, such as identifying the target towards which aspect the suggestion was mentioned or the target audience towards whom it intended to. However, such fine-grained analysis is more important in various use cases. A novel end-to-end hybrid model for fine-grained analysis of suggestions with aspect orientation has been proposed in this paper. We have utilized two different datasets of SemEval-2019 Task 9. The performance has been evaluated using other models from machine learning, neural network, and transfer learning with combinations of word embeddings. The exper-iment results demonstrate that our approaches worked well for aspect-oriented suggestion mining.
Refining Background Corrections for Radiocarbon Dating of Bone Collagen at Orau

During the laboratory pretreatment of samples for radiocarbon dating, small amounts of carbon may be added to a sample. Contamination can be incorporated at any stage: during chemical pretreatment, combustion to CO2, graphitization, or accelerator mass spectrometry (AMS) measurement. Such carbon contamination is often modern in age, and so can have an especially severe effect on samples older than ∼25 ka BP. During the extraction of collagen from bone using the ultrafiltration protocol at the Oxford Radiocarbon Accelerator Unit (ORAU), small amounts of young carbon are added to the sample. Currently, this contamination is poorly characterized when less than 10 mg of collagen is extracted from a bone. Demand to date small collagen samples with 14C concentrations that approach the detection limit of AMS measurement has increased recently with the growing interest in, for example, directly dating Neanderthal remains and Upper Paleolithic bone artifacts. This paper aims to reduce the minimum collagen sample size required to produce a reliable date from 10 to 5 mg by re-examining the combustion background and subsequently the pretreatment background for bone. The average of 136 measurements of directly combusted nylon suggests that 0.0007 ± 0.001 mg of modern carbon is added to each sample, although the distribution is positively skewed. Regression analysis of the measurements of 52 collagen samples extracted from a bone of background age results in a background of just less than 50,000 BP for bone treated at ORAU.
Optimizing Problem of Brain Tumor Detection Using Image Processing

- Tumor is the one of the most common brain disease and this is the reason for the diagnosis & treatment of the brain tumor has vital importance. MRI is the technique used to produce computerized image of internal body tissues. Cells are growing in uncontrollable manner this results in mass of unwanted tissue which is called as tumor. CT-Scan and MRI image which are diagnostic technique are used to detect brain tumor and classifies in types malignant & benign. This is difficult due to variations hence techniques like image preprocessing, feature extraction are used, there are many methods developed but they have different results. In this paper we are going to discuss the methods for detection of brain tumor and evaluate them.
Potential Effects of Sea-Level Rise on Coastal Wetlands in Southeastern Louisiana

ABSTRACT Glick, P.; Clough, J.; Polaczyk, A. ; Couvillion, B. , and Nunley, B. , 2013. Potential effects of sea-level rise on coastal wetlands in southeastern Louisiana. In: Brock, J.C; Barras, J.A. , and Williams, S.J. (eds.), Understanding and Predicting Change in the Coastal Ecosystems of the Northern Gulf of Mexico, Journal of Coastal Research, Special Issue No. 63, pp. 211-233, Coconut Creek (Florida), ISSN 0749–0208. Coastal Louisiana wetlands contain about 37% of the estuarine herbaceous marshes in the conterminous United States. The long-term stability of coastal wetlands is often a function of a wetland's ability to maintain elevation equilibrium with mean sea level through processes such as primary production and sediment accretion. However, Louisiana has sustained more coastal wetland loss than all other states in the continental United States combined due to a combination of natural and anthropogenic factors, including sea-level rise. This study investigates the potential impact of current and accelerating sea-level rise rates on key coastal wetland habitats in southeastern Louisiana using the Sea Level Affecting Marshes Model (SLAMM). Model calibration was conducted using a 1956–2007 observation period and hindcasting results predicted 35% versus observed 39% total marsh loss. Multiple sea-level-rise scenarios were then simulated for the period of 2007–2100. Results indicate a range of potential wetland losses by 2100, from an additional 2,188.97 km2 (218,897 ha, 9% of the 2007 wetland area) under the lowest sea-level-rise scenario (0.34 m), to a potential loss of 5,875.27 km2 (587,527 ha, 24% of the 2007 wetland area) in the highest sea-level-rise scenario (1.9 m). Model results suggest that one area of particular concern is the potential vulnerability of the region's baldcypress-water tupelo (Taxodium distichum-Nyssa aquatica) swamp habitat, much of which is projected to become permanently flooded (affecting regeneration) under all modeled scenarios for sea-level rise. These findings will aid in the development of ecosystem management plans that support the processes and conditions that result in sustainable coastal ecosystems.
ROLE OF EXTERNAL FINANCE AND INNOVATION IN ACHIEVING ECO-EFFICIENCY AND SUSTAINABLE DEVELOPMENT GOALS

Eco-efficiency is a biggest universal challenge and one-fourth CO₂ emission produce by World Transport sector. UN Sustainable development Goals Agenda-2030; Paris agreement of 2015 was most successful agreement between the advancing Global World to set a target, to meet Social-economic-environment success. The main objective of this study is to examine the effect of External finance and Power Diversity on Eco-efficiency and sustainable Development Goals; to identify the best financial and transport eco-power source. This study used two ways of data analysis; first, past and future trend analysis, and second, Regression, GMM and Robust measurement; first, External finance Trend of four countries, Japan has least dependent country, china only face dependency in FDI, while Pakistan major dependency on Remittances and ODA-Foreign-Aid. Fuel Consumption Trend; Japan Eco-efficiency policy is better than other three countries, but still away from Paris Agreement-Target-2030. Second; Regression and GMM results indicates external finance i.e. FDI, Remittances, ODA and Transport Power Source i.e. Oil and Electric high significant impact on eco-efficiency and SDGs; GDP highly significant inverse, health negative and Per-Capita GDP, Government Consumption positive relationships exist with Eco-efficiency and SDGs. The robust result indicates of validity and the sustainability. Study implication and recommendation are cleared; external finance has a modern finance source for technology enhancement and economy. Transport Cargo system need to be design according eco-friendly, UN SDGs Agenda-2030. Government need to enacting policies to encourage foreigner to invest eco-friendly project like modern technology enhancement, people to move away from gasoline and diesel transportation until modern technology eco-efficient, Electric power system is an alternative of fossil power generation toward CO₂ emission reduction.
Platinum-group mineralization in the Tertiary Igneous Province: new data from Mull and Skye, Scottish Inner Hebrides, UK

In recent years, platinum-group mineral deposits have been reported from several of the Tertiary igneous complexes in East Greenland. These intrusions form part of the same igneous province as the Tertiary igneous centres in northwest Scotland, and recent work confirmed the presence of abundant platinum-group minerals in the Rum Central Complex. In this paper we report for the first time the presence of abundant and diverse platinum-group minerals from the Tertiary Ben Buie intrusion on Mull and the Cuillin Complex of Skye, Inner Hebrides, Scotland. The platinum-group minerals in the Ben Buie intrusion are associated with chromitite layers in ultramafic cumulates. The grains are small (1–2 μm), locally very abundant, and dominated by Pd–Bi–Te–Sb phases. In the Peridotite Series of the Cuillin Complex, the platinum-group minerals are dominated by laurite commonly enclosed within chromite or silicate. A combined orthomagmatic–hydromagmatic origin is proposed for the mineralization. However, the style and type of mineralization differs between each of the igneous centres. Platinum-group element mineralization is a ubiquitous feature of the Tertiary Igneous Province in northwest Scotland and in Greenland and is intimately linked to the regional tectonic setting.
Transitional continental–oceanic structure beneath the Norwegian Sea from inversion of surface wave group velocity data

SUMMARY We have analysed the fundamental mode of Love and Rayleigh waves generated by 12 earthquakes located in the mid-Atlantic ridge and Jan Mayen fracture zone. Using the multiple filter analysis technique, we isolated the Rayleigh and Love wave group velocities for periods between 10 and 50 s. The surface wave propagation paths were divided into five groups, and average group velocities calculated for each group. The average group velocities were inverted and produced shear wave velocity models that correspond to a quasi-continental oceanic structure in the Greenland‐Norwegian Sea region. Although resolution is poor at shallow depth, we obtained crustal thickness values of about 18 km in the Norwegian Sea area and 9 km in the region between Svalbard and Iceland. The abnormally thick crust in the Norwegian Sea area is ascribed to magmatic underplating and the thermal blanketing eVect of sedimentary layers. Maximum crustal shear velocities vary between 3.5 and 3.9 km s’1 for most paths. An average lithospheric thickness of 60 km was observed, which is lower than expected for oceanic-type structure of similar age. We also observed low shear wave velocities in the lower crust and upper mantle. We suggest that high heat flow extending to depths of about 30 km beneath the surface can account for the thin lithosphere and observed low velocities. Anisotropy coeYcients of 1‐5 per cent in the shallow layers and >7 per cent in the upper mantle point to the existence of polarization anisotropy in the region.