Miracle-dz/UniFAR-FTUs huggingface dataset:
Positioning of AUV in the Presence of Underwater Currents with Visual Servoing

The problems of dynamic positioning (DP) of autonomous underwater vehicles (AUV) with a control system are considered. The image-based visual servo control (IBVS) architecture for the DP equipped with a monocular video camera is used. The IBVS approach directly uses the coordinates of the object in the image plane as feedback and, thus, is inherently resistant to errors in the calibration of the camera and the target model. One of the main difficulties in designing the IBVS control system for underwater vehicles arises from uncertainties related to both the image Jacobi matrix and the dynamics of the object. The DP problem is discussed for the AUV moving in a horizontal plane, controlled either using surge or sway or surge and yaw velocities. The main attention is paid to the study of the influence of kinematics and dynamics of the AUV on the properties of a closed control system with an unlimited increase in time. The proposed results are illustrated by computer modeling.
DNA-Depth: A Frequency-Based Day-Night Adaptation for Monocular Depth Estimation

Autonomous driving necessitates ensuring safety across diverse environments, particularly in challenging conditions like low-light or nighttime scenarios. As a fundamental task in autonomous driving, monocular depth estimation has garnered significant attention and discussion. However, current monocular depth estimation methods primarily rely on daytime images, which limits their applicability to nighttime scenarios due to the substantial domain shift between daytime and nighttime styles. In this article, we propose a novel Day-Night Adaptation method (DNA-Depth) to realize monocular depth estimation in a night environment. Specifically, we simply use Fourier Transform to address the domain alignment problem. Our method does not require extra adversarial optimization but is quite effective. The simplicity of our method makes it easy to guide day-to-night domains. To the best of our knowledge, we are the first to utilize fast Fourier transformation for nighttime monocular depth estimation. Furthermore, to alleviate the problem of mobile light sources, we utilize an unsupervised joint learning framework for depth, optical flow, and ego-motion estimation in an end-to-end manner, which is coupled by 3-D geometry cues. Our model can simultaneously reason about the camera motion, the depth of a static background, and the optical flow of moving objects. Extensive experiments on the Oxford RobotCar, nuScenes, and Synthia datasets demonstrate the accuracy and precision of our method by comparing it with those state-of-the-art algorithms in depth estimation, both qualitatively and quantitatively.
Crystal chemistry of clinopyroxenes from Linosa Volcano, Sicily Channel, Italy: implications for modelling the magmatic plumbing system

Abstract The Islandof Linosa is a small part of the large submarine volcanic complex which is locatedat the SW edge of the Linosa Graben, Sicily Channel. The island was formed between 1.06±0.10 and 0.53±0.07 Ma, through three main stages of activity: Paleo-Linosa, Arena Bianca and Monte Bandiera. Major and trace element data show that the compositional variability of the three activity stages is limited, with most of the rocks showing basaltic to hawaiitic composition. Evolved benmoreites and trachytes are foundas lithics in some pyroclastic units of Paleolinosa. The mafic rocks of the three stages show porphyritic texture, with phenocryst assemblages characterizedby olivine, clinopyroxene andplagioclase. The volume ratio of olivine vs. clinopyroxene decreases from early to late stages of activity in mafic rocks with comparable major element composition. Clinopyroxene phenocrysts from mafic rocks of the three stages have poorly variable composition, clustering in the augite field. Phenocrysts from the first activity stage (Paleo-Linosa), show a slight increase in TiO2, Al2O3 and CaO, and a decrease of Fe2O3 (total) with the increasing SiO2 content of the host rocks. Crystals from the second and the third stage (Arena Bianca and Monte Bandiera) display a slightly more restricted range of FeOtot, frequently with very high MgO, Al2O3 and TiO2 contents. Crystal chemical investigation of clinopyroxenes from rocks of the three stages with comparable degrees of evolution, revealed significant variation of structural parameters, in particular VM1 and Vcell. These show a consistent decrease, passing from clinopyroxenes of the early stage to crystals extracted from the mafic lavas of stages 2 and 3. Given the similar compositional ranges of the host rocks, structural variations of clinopyroxenes are interpreted to reveal modifications of crystallization pressure, which increased, passing from Paleo-Linosa to the Arena Bianca and Monte Bandiera stages. Given this information, the observed crystal-chemical variations provide information on the depth of magma reservoirs and on the evolution of the plumbing system of Linosa volcano.
Anthropocentrism and Environmental Wellbeing in AI Ethics Standards: A Scoping Review and Discussion

As AI deployment has broadened, so too has an awareness for the ethical implications and problems that may ensue from this deployment. In response, groups across multiple domains have issued AI ethics standards that rely on vague, high-level principles to find consensus. One such high-level principle that is common across the AI landscape is ‘human-centredness’, though oftentimes it is applied without due investigation into its merits and limitations and without a clear, common definition. This paper undertakes a scoping review of AI ethics standards to examine the commitment to ‘human-centredness’ and how this commitment interacts with other ethical concerns, namely, concerns for nonhumans animals and environmental wellbeing. We found that human-centred AI ethics standards tend to prioritise humans over nonhumans more so than nonhuman-centred standards. A critical analysis of our findings suggests that a commitment to human-centredness within AI ethics standards accords with the definition of anthropocentrism in moral philosophy: that humans have, at least, more intrinsic moral value than nonhumans. We consider some of the limitations of anthropocentric AI ethics, which include permitting harm to the environment and animals and undermining the stability of ecosystems.
PiCovS: Pixel-Level With Covariance Pooling Feature and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have exhibited exceptional performance, owing to their hierarchical nonlinear modeling. However, their fixed square receptive field constrains their ability to effectively handle irregular image regions. Graph convolutional networks (GCNs) have been introduced to learn irregular regions through correlations between adjacent pixels modeled as superpixel-based nodes, yet they lack pixel-level information. We propose a novel approach Pixel-level with Covariance Pooling feature and Superpixel-level feature Fusion for HSI Classification (PiCovS). Our method harnesses complementary spectral–spatial features at both pixel-level and superpixel-level to capture characteristics of both small-scale regular and large-scale irregular regions. We introduce a hybrid network that integrates and propagates features between image-level pixels and graph-level nodes using a graph encoder–decoder, effectively reconciling the differences between regular CNN and irregular GCN data representations. To enhance superpixel boundary learning, we modify the manifold simple linear iterative clustering (M-SLIC) algorithm by incorporating texture feature information, resulting in refined superpixel representations. In addition, we propose a novel covariance pooling mechanism with an attention mechanism within the CNN branch, enabling the capturing and utilization of holistic HSI information along spectral and spatial dimensions by exploiting second-order statistics throughout the network. Our comprehensive experiments showcase the efficiency and robustness of the proposed framework, achieving an impressive overall accuracy of 99.84%, 99.97%, 99.98%, and 81.96% on the Indian Pines, University of Pavia, Salinas, and Houston University datasets, respectively. Remarkably, PiCovS excels even with limited training samples, outperforming other state-of-the-art methods in accuracy.
Identification of error sources in convective planetary boundary layer cloud forecasts using SIRTA observations

In this study, errors in cloud base height predicted by two numerical weather prediction models is evaluated using observations from the French instrumented site SIRTA. Results show that a significant portion of the error is due to errors in predicted surface relative humidity. These errors are in turn thought to be caused by the convection schemes.
An Evaluation of the Supplementation of Rumen-Protected Lysine and Methionine on the Lactation Performance of Fall Parturition Grazing Holstein Cows in Southern Chile

Simple Summary This study assessed the supplementation of two limiting milk production amino acids for dairy cows (methionine and lysine) on the lactational performance of fall-calving grazing dairy cows from Chile. Four groups were compared and 26 cows per group were randomly assigned to the following groups: (i) a control group without supplementation; (ii) a lysine group; (iii) a methionine group; and (iv) a lysine plus methionine group. Supplementation was conducted from 2 to 70 days postpartum. The group supplemented with both amino acids produced more milk protein and fat than the control group. In conclusion, the supplementation of methionine and lysine improved milk protein and fat yield in grazing dairy cows with fall parturitions; therefore, they can be used as a strategy to improve the performance of lactation in cows. Abstract The aim of this study was to evaluate the effect of the supplementation of rumen-protected (RP) methionine and lysine on milk yield, solids, and body weight over time on fall-calving grazing multiparous Holstein cows from Chile. Four treatment groups were studied and compared for the outcomes over time. The treatments were as follows: (i) CON: control (n = 26); (ii) RP lysine group (LYS; 20 g per cow per day; n = 26); (iii) RP methionine group (MET; 20 g per cow per day; n = 26); and (iv) LYS × MET (RP lysine and RP methionine 20 g of each amino acid per cow per day; n = 26). Data were analyzed with general linear mixed model ANOVAs for repeated measures to primarily test the main effects of each amino acid and their interactions. The supplementation of the amino acids was conducted from 2 to 70 days postpartum. Overall, milk production tended to be higher in the MET and in the LYS × MET group when compared to the control group. Furthermore, CON produced significantly less milk protein (kg) and milk fat (kg) than the LYS, MET, and LYS × MET groups. Milk urea tended to be lower during the entire study in the CON group than the rest of the groups. There was a trend for a reduction in the losses of postpartum body weight in the LYS × MET than the CON. In conclusion, RP methionine and lysine improved milk fat and protein yield in grazing multiparous cows with fall parturitions; consequently, both RP amino acids can be used as a strategy for improving grazing cows’ production performance.
Permeability imaging in granitic rocks based on surface resistivity profiling

In order to image the distribution of permeability in granitic rocks, we carried out two-dimensional (2D) resistivity profiling, together with in-situ permeability tests, electrical logging of boreholes, and resistivity measurements of rock core samples in a laboratory. Based on the electrical logging and in-situ permeability data from boreholes, we obtained empirical equations which relate resistivity and permeability of the granitic rocks in the area studied. We then applied the empirical equation to a 2D resistivity section, to produce a 2D permeability section of the granitic rocks. In this paper, we present details of the field data and of the procedure for conversion from the resistivity section to a permeability section. The observed relationship between resistivity and permeability of the rocks is also discussed.
Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Background Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.
Production Optimization under Constraints Using Adjoint Gradients

The introduction of controllable downhole devices has greatly improved the ability of the reservoir engineer to implement complex well control strategies to optimize hydrocarbon recovery. The determination of these optimal control strategies, subject to limitations imposed by production and injection constraints, is an area of much active research and generally involves coupling some form of control logic to a reservoir simulator. Some of these strategies are reactive: interventions are made when conditions are met at particular wells or valves, with no account taken for the effect on the future lifetime of the reservoir. Moreover, it may be too late to prevent unwanted breakthrough when the intervention is applied. Alternative proactive strategies may be applied to the lifetime of the field and fluid flow controlled early enough to delay breakthrough. This paper presents a proactive, gradient-based method to optimize production throughout the field life. This method requires the formulation of a constrained optimization problem, where bottomhole pressure or target flow rates of wells, or flow rates of groups, represent the controllable parameters. To control a large number of wells or groups at a reasonably high frequency, efficient calculation of accurate well sensitivities (gradients) is required. Hence, the adjoint method has been implemented in a commercial reservoir simulator to compute these gradients. Once these have been calculated, the simulator can be run in optimization mode to find a locally optimal objective function (e.g., cumulative production). This optimization procedure usually involves progressively activating constraints, with each new constraint representing a significant improvement in the objective. Proper management of degrees of freedom of the parameters is essential when calculating the constrained optimization search direction. Adjoint methods have already been used for production optimization within reservoir simulation; however, an accurate analysis of optimal management of active and inactive constraints for different type of recovery processes in field-like cases has not been discussed to our knowledge.
p53 in the Molecular Circuitry of Bone Marrow Failure Syndromes

Mice with a constitutive increase in p53 activity exhibited features of dyskeratosis congenita (DC), a bone marrow failure syndrome (BMFS) caused by defective telomere maintenance. Further studies confirmed, in humans and mice, that germline mutations affecting TP53 or its regulator MDM4 may cause short telomeres and alter hematopoiesis, but also revealed features of Diamond–Blackfan anemia (DBA) or Fanconi anemia (FA), two BMFSs, respectively, caused by defects in ribosomal function or DNA repair. p53 downregulates several genes mutated in DC, either by binding to promoter sequences (DKC1) or indirectly via the DREAM repressor complex (RTEL1, DCLRE1B), and the p53-DREAM pathway represses 22 additional telomere-related genes. Interestingly, mutations in any DC-causal gene will cause telomere dysfunction and subsequent p53 activation to further promote the repression of p53-DREAM targets. Similarly, ribosomal dysfunction and DNA lesions cause p53 activation, and p53-DREAM targets include the DBA-causal gene TSR2, at least 9 FA-causal genes, and 38 other genes affecting ribosomes or the FA pathway. Furthermore, patients with BMFSs may exhibit brain abnormalities, and p53-DREAM represses 16 genes mutated in microcephaly or cerebellar hypoplasia. In sum, positive feedback loops and the repertoire of p53-DREAM targets likely contribute to partial phenotypic overlaps between BMFSs of distinct molecular origins.
Value Relevance of Accounting Information: Evidence from Banking Industry in ASEAN

Previous research on the value relevance of accounting information and stock prices had different results. The differences in the results of this study indicate that further studies on the value relevance of accounting information and stock prices are still needed. This study investigates the relationship between the capital markets and accounting information in the banking sector of ASEAN countries, namely Indonesia, Malaysia, Singapore, Thailand and the Philippines. Specifically, this study looks at the relationship between earnings per share (EPS) and book value per share (BVPS) on the balance sheet value. Using a sample of listed banks from 2017 to 2019, this study documents accounting information in the context of emerging markets by using stock prices as the dependent variable three months after the end of the year is taken into consideration. The sample determination method for this study used a dedicated sampling method to obtain 82 samples that met the criteria. This study uses a panel data regression technique. The results of this study show that earnings and book value have a statistically positive impact on stock prices. This study also shows that revenue is more related to value than other variables
Theoretical Prediction of Electrical Conductivity in Saturated and Unsaturated Soil

A conceptual model is proposed for the prediction of the electrical conductivity of bulk soil in saturated and in unsaturated states. The model is based on the hypothesis that the tortuosity factor affecting the bulk soil electrical conductivity is identical to that defined for prediction of the soil hydraulic conductivity. Simple mathematical functions are derived to forecast the soil electrical conductivity as a function of the water content. Experimental results of seven previous studies carried out on 26 soils were used for validation tests. The model's efficiency as a predictive tool, in cases where limited experimental data are available, is found to be generally good.
Validation of a SARS-CoV-2 Surrogate Neutralization Test Detecting Neutralizing Antibodies against the Major Variants of Concern

SARS-CoV-2 infection and/or vaccination elicit a broad range of neutralizing antibody responses against the different variants of concern (VOC). We established a new variant-adapted surrogate virus neutralization test (sVNT) and assessed the neutralization activity against the ancestral B.1 (WT) and VOC Delta, Omicron BA.1, BA.2, and BA.5. Analytical performances were compared against the respective VOC to the reference virus neutralization test (VNT) and two CE-IVD labeled kits using three different cohorts collected during the COVID-19 waves. Correlation analyses showed moderate to strong correlation for Omicron sub-variants (Spearman’s r = 0.7081 for BA.1, r = 0.7205 for BA.2, and r = 0.6042 for BA.5), and for WT (r = 0.8458) and Delta-sVNT (r = 0.8158), respectively. Comparison of the WT-sVNT performance with two CE-IVD kits, the “Icosagen SARS-CoV-2 Neutralizing Antibody ELISA kit” and the “Genscript cPass, kit” revealed an overall good correlation ranging from 0.8673 to −0.8773 and a midway profile between both commercial kits with 87.76% sensitivity and 90.48% clinical specificity. The BA.2-sVNT performance was similar to the BA.2 Genscript test. Finally, a correlation analysis revealed a strong association (r = 0.8583) between BA.5-sVNT and VNT sVNT using a double-vaccinated cohort (n = 100) and an Omicron-breakthrough infection cohort (n = 91). In conclusion, the sVNT allows for the efficient prediction of immune protection against the various VOCs.
Remote detection of vegetation stress for mineral exploration

Remote detection of geochemically-enriched and/or mineralized zones is invariably obstructed by tropical vegetation. Plants, however, take up from the substrate nutrients that influence their spectral characteristics. In this study, Landsat TM data are used to study the spectral characteristics of vegetation in stream sediment sample catchment basins and in mineralized zones in Catanduanes, Philippines. Vegetated areas are extracted from a classified image of the TM data. NDVI is also calculated. The mean NDVI and mean spectral band pixel values of vegetation are determined for the stream sediment sample catchment basins and the 30-m interval buffer zones around mineralized zones. Correlation analysis is performed on the geochemical and spectral data. Plots of mean NDVI and mean pixels values of vegetation versus element concentration levels and distance from mineralized zones are constructed. The results of analyses show that vegetation in and around geochemically-enriched and mineralized zones is stressed. A method to detect vegetation-covered mineralized zones is demonstrated.