Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network

Abstract. We introduce a new machine learning approach to retrieve cloud optical thickness (COT) fields from visible passive imagery. In contrast to the heritage Independent Pixel Approximation (IPA), our Convolutional Neural Network (CNN) retrieval takes the spatial context of a pixel into account, and thereby reduces artifacts arising from net horizontal photon transfer, commonly known as independent pixel (IP) bias. The CNN maps radiance fields acquired by imaging radiometers at a single wavelength channel to COT fields. It is trained with a low-complexity and therefore fast U-Net architecture where the mapping is implemented as a segmentation problem with 36 COT classes. As a training data set, we use a single radiance channel (600 nm) generated from a 3D radiative transfer model using Large Eddy Simulations (LES) from the Sulu Sea. We study the CNN model under various conditions based on different permutations of cloud aspect ratio and morphology, and use appropriate cloud morphology metrics to measure the performance of the retrievals. Additionally, we test the general applicability of the CNN on a new geographic location with LES data from the equatorial Atlantic. Results indicate that the CNN is broadly successful in overcoming the IP bias and outperforms IPA retrievals across all morphologies. In the Atlantic, the CNN tends to overestimate the COT but shows promise in regions with high cloud fractions and high optical thicknesses, despite being outside the general training envelope. This work is intended to be used as a baseline for future implementations of the CNN that can enable generalization to different regions, scales, wavelengths, and sun-sensor geometries with limited training.
Laparoscopy versus Laparotomy for the Treatment of BCLC Stages 0 or A Hepatocellular Carcinoma Associated with or without Microvascular Invasion: A Multicenter, Propensity Score Matching Analysis

Purpose: To analyze the long-term prognoses of Barcelona Clinic Liver Cancer (BCLC) stages 0 or A hepatocellular carcinoma (HCC) patients associated with or without microvascular invasion (MVI) treated with laparoscopy versus laparotomy using propensity score matched analysis.Methods: The clinicopathological data of HCC patients at BCLC stages 0 or A from 4 major medical centers were retrospectively reviewed, and sub-grouped in terms of the presence of MVI to explore the effect of MVI on the long-term surgical outcomes. Propensity score matching (PSM) analysis was used to build a matched group of HCC patients for comparison of follow-up indicators between the laparoscopy and laparotomy groups.Results: 495 HCC patients at BCLC stages 0 or A were enrolled, including 243 in the laparoscopic group and 252 in the laparotomic group. Laparoscopic resection group had a shorter operative time, less blood loss, and lower frequencies of blood transfusion and postoperative complication rates. After PSM, the laparoscopic group had a significantly better overall survival (OS) and recurrence-free survival (RFS) than the laparotomic group. Subgroup analysis demonstrated OS and RFS of patients without MVI were also significantly improved in the laparoscopic group compared with the laparotomic group. However, no significant difference of OS and RFS between two groups was found in patients with MVI after PSM.Conclusions: Pure laparoscopic hepatectomy for HCC patients at early stages can be performed safely with favorable short-term and long-term outcomes at high-volume liver cancer centers, regardless of the presence of MVI.
Impact Properties of Parts Manufactured from Fiberglass and Kevlar Composite Panels

This paper presents a method of finite element modelling used to study the effect of laminates orientation and thickness on impact properties of a composite sandwich panel made of glass and kevlar fibers in an epoxy resin matrix. In this research, the composite sandwich panel consists of a fuselage skin panel from an aircraft having two configurations: (0/90/0/90/core/90/0/90/0) and (0/90/45/-45/core/-45/45/90/0). This panel is loaded with one uniform distributed abuse loading case and the stress variation within the composite panel for each configuration is determined.
Integrated Analysis of RNA Binding Protein-Related lncRNA Prognostic Signature for Breast Cancer Patients

Long non-coding RNAs (lncRNAs) have been well known for their multiple functions in the tumorigenesis, development, and prognosis of breast cancer (BC). Mechanistically, their production, function, or stability can be regulated by RNA binding proteins (RBPs), which were also involved in the carcinogenesis and progression of BC. However, the roles and clinical implications of RBP-related lncRNAs in BC remain largely unknown. Therefore, we herein aim to construct a prognostic signature with RBP-relevant lncRNAs for the prognostic evaluation of BC patients. Firstly, based on the RNA sequencing data of female BC patients from The Cancer Genome Atlas (TCGA) database, we screened out 377 differentially expressed lncRNAs related to RBPs. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were then performed to establish a prognostic signature composed of 12-RBP-related lncRNAs. Furthermore, we divided the BC patients into high- and low-risk groups by the prognostic signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that of the low-risk group. Moreover, the 12-lncRNA signature exhibited independence in evaluating the prognosis of BC patients. Additionally, a functional enrichment analysis revealed that the prognostic signature was associated with some cancer-relevant pathways, including cell cycle and immunity. In summary, our 12-lncRNA signature may provide a theoretical reference for the prognostic evaluation or clinical treatment of BC patients.
Mechanism and regulation of filamentous algal‐bacterial symbiosis based on microbiological quorum sensing

In algae‐bacteria symbiotic systems, algae and bacteria work cooperatively to reduce aeration demand and carbon emissions during wastewater treatment. However, controlling and stabilizing microbial communities in the conventional algae‐bacteria symbiotic systems are complicated and unstable. We, therefore, developed a novel sewage treatment system based on a filamentous algae‐bacterial symbiotic granule system (AB) in a photo‐sequencing batch reactor (PSBR). The exogenous signal molecules (i.e., N‐acyl‐homoserine lactones [AHLs]) were applied to promote the algae‐bacteria consortia formation. The characteristics and performance of the mature algae‐bacterial granular consortia were investigated and then compared with that of the activated sludge (AS) system under identical operating conditions. The chemical oxidation demand (COD) removal efficiencies were greater than 90% in the dynamic synergistic investigation, even without aeration. However, the addition of AHLs (5, 50, and 200 nmol/L) had a negligible effect on treatment performance. In static conditions, 10−9 nmol/L of AHLs can significantly regulate the algae‐bacteria symbiotic system, and AHLs (<50 nmol/L) improved COD and NH4+ removal efficiencies by up to 90%. Moreover, the addition of AHLs augmented polysaccharide secretion but had little effect on protein secretion. In comparison to the AS system, the AB system demonstrated promise in terms of pollution removal efficiency and microbial diversity enhancement.
PMDRnet: A Progressive Multiscale Deformable Residual Network for Multi-Image Super-Resolution of AMSR2 Arctic Sea Ice Images

The extent of the area covered by polar sea ice is an important indicator of global climate change. Continuous monitoring of Arctic sea ice concentration (SIC) primarily relies on passive microwave images. However, passive microwave images have coarse spatial resolution, resulting in SIC production with significant blurring at the ice–water divides. In this article, a novel multi-image super-resolution (MISR) network called progressive multiscale deformable residual network (PMDRnet) is proposed to improve the spatial resolution of sea ice passive microwave images according to the characteristics of both passive microwave images and sea ice motions. To achieve image alignment with complex and large Arctic sea ice motions, we design a novel alignment module that includes a progressive alignment strategy and a multiscale deformable convolution alignment unit. In addition, the temporal attention mechanism is used to adaptively fuse the effective spatiotemporal information across image sequence. The sea ice-related loss function is designed to provide more detailed sea ice information of the network to improve super-resolution performance and further benefit finer Arctic SIC results. Experimental results demonstrate that PMDRnet significantly outperforms the current state-of-the-art MISR methods and can generate super-resolved SIC products with finer texture features and much sharper sea ice edges. The code and datasets of PMDRnet are available at https://doi.org/10.5061/dryad.k3j9kd590.
Adaptive Segmentation for Discontinuity Detection on Karstified Carbonate Outcrop Images From UAV-SfM Acquisition and Detection Bias Analysis

The identification of fractures and discontinuities has great importance on the fluid flow estimation in hydrocarbon reservoirs since they influence the properties of porosity and permeability. Due to the inaccessibility and sparsity of reservoir data, the fracture characterization is generally assessed through the study of outcrop analogues using remote sensing or in situ observations by a specialist. Considering the remote sensing methods, the unmanned Aerial Vehicle (UAV) acquisition combined with Structure from Motion (SfM) photogrammetry is a low-cost way to generate products like orthorectified images, allowing manual and automated methods of fracture trace detection. Automatic approaches, commonly used to address this problem, present some known limitations and disadvantages due to the nature of the outcrops and weather conditions during UAV acquisitions. In this work, we focus on fracture detection over karstic regions that are highly fractured. For this, we evaluated a series of adaptive segmentation methods based on thresholding. The Sauvola local adaptive segmentation presented the best result when compared to a manually annotated ground truth. The segmentation results were further improved by the use of the binary denoising method Non-Local means. We also carried an evaluation of the influence of the sun position in the fracture detection, and to reduce this inherent bias we combined three UAV acquisitions done over the karstic carbonate outcrop, namely Rosário pavement in the Jandaíra formation northeast Brazil. With the proposed methodology we acquired more accurate fracture data over the study area, which follows the directional statistics of previous works carried out in the region.
Genome assembly of the numbat (Myrmecobius fasciatus), the only termitivorous marsupial

The numbat (Myrmecobius fasciatus) is a critically endangered Australian marsupial, and the last surviving member of the Myrmecobiidae family. The numbat regularly undergoes torpor and is unique amongst marsupials as it is the only diurnal and termitivorous species. Here we sequenced the first draft genome of the numbat using 10× Genomics chromium linked-read technology, resulting in a 3.42 Gbp genome with a scaffold N50 of 223 Kbp. A global transcriptome from liver, lung and tongue was also generated to aid genome annotation with Fgenesh++, identifying 21,465 protein-coding genes and 78.7% complete mammalian BUSCOs. To investigate adaptation to the numbat’s termitivorous diet and arid/semi-arid range, we interrogated the most highly expressed transcripts within the tongue and manually annotated taste, vomeronasal and aquaporin gene families. Antimicrobial proteins and proteins involved in digestion were highly expressed in the tongue, as expected. Similarly, umami taste receptors were also expressed in the tongue, however sweet taste receptors were not expressed in this tissue. This pattern of taste receptor expression, combined with the putative contraction of the bitter taste receptor gene repertoire in the numbat genome, may indicate a potential evolutionary adaptation to their specialized termitivorous diet. Vomeronasal and aquaporin gene repertoires were similar to other marsupials and have not undergone expansion or contraction. The draft numbat genome is a valuable tool for conservation and can be applied to population genetics/genomics studies and to investigate the unique biology of this interesting species.
Estratégias de transição para alta hospitalar utilizadas por enfermeiros: revisão integrativa

Objetivo: identificar estratégias de transição do cuidado na alta hospitalar, utilizadas por enfermeiros para o fortalecimento da continuidade do cuidado, disponíveis na literatura científica. Método: revisão integrativa da literatura, que incluiu estudos completos, tendo como referência as estratégias "care transition" OR "Continuity of Patient Care" OR "care continuity" AND nurse para as bases de dados BVS, PubMed, SCOPUS e WoS e "care transition" OR "care continuity" AND nurse para CINAHL, publicados em inglês, espanhol ou português, entre 2016 e 2020, que responderam à questão de revisão. Resultados: foram selecionados 23 artigos, e as estratégias de transição do cuidado para a alta hospitalar usadas por enfermeiros para o fortalecimento do cuidado foram educação em saúde, reconciliação medicamentosa, telemonitoramento, planejamento da alta, contrarreferência e visita domiciliar. Conclusão: o enfermeiro é responsável pelo desenvolvimento de estratégias de transição interconectadas para o fortalecimento do cuidado, desenvolvendo ações diversificadas que qualificam a atenção.
A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.
Industrial and Automation Control System Cyber Range Prototype for Offensive Capability Development

Industrial and automation control systems (IACS) are broadly utilized in sectors, such as manufacturing processes control and energy transmission. Attacks on these systems may have devastating effects. Moreover, current IACS systems are interconnected with conventional IT infrastructures thus increasing potential adversary access to industrial systems. This research describes and offers a prototype for a realistic and easily reproducible IACS cyber range for offensive exercise development. An extensive study of various technical aspects and scenarios of existing IACS cyber ranges is conducted to create the knowledgebase for such range development. Created IACS cyber range use is validated by conducting practical offensive capability development training for a target audience. This work concludes, that IACS cyber ranges are a viable tool for understanding and developing offensive tactics and techniques used to gain access to the IACS network and damage physical processes.
A Resilient power opportunity in Hybrid Grid including DGs and Smart Appliance

The electrical industry is dominated by AC supply medium for more than a century now. In last few decades, the environmental pollution and limited resources for non-renewable energy led us to shift towards more sustainable renewable energy resources and smart efficient appliance for electricity saving. In modern electricity system Hybrid grid is much more energy efficient and reliable technology. In Hybrid system has both types of AC and DC supply and appliance are included with proper power modulator. In this paper optimized a hybrid grid with different types of DGs and AC-DC loads in linear platform using MILP algorithm with taken the example of a home as well as compared the electricity bill with previous condition. Here we done the work for automation of home as well as optimized hybrid grid and saw many benefits over the conventional grid.
Design of a hybrid hierarchical demand response control scheme for the frequency control

Conventionally, the frequency of the power system is controlled by matching the electric supply to the varying demand. Some recent studies demonstrated that demand response (DR) can also contribute to the frequency control. However, most of the research works focus on the overall control strategy of DR, whereas the DR parameters are usually randomised rather than carefully designed. In this study, a hybrid hierarchical DR control scheme is designed to support frequency control. The parameters settings are discussed in detail. Instead of randomising the parameters for individual DR controllers, this study proposes a systematic way to design the parameters such as frequency threshold and minimum off-time for individual controllers, so that aggregation of DR can provide primary frequency control smoothly just like thermal generators. Furthermore, the control system is formulated as a multi-objective optimisation problem. The parameters such as the recovery rate of DR and the integral gain of secondary frequency control is optimised so that the maximum deviation, overshoot and oscillations of the frequency can be properly minimised. The proposed method is verified on numerical examples.
Large Scale Multilingual Sticker Recommendation In Messaging Apps

Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except for validation, our system does not require human labeled data, leading to a fully auto-matic tuning pipeline. We propose a hybrid message prediction model, which can easily run on low-end phones. We discuss message cluster to sticker mapping, addressing the multilingual needs of our users, automated tuning of the system and also propose a novel application of community detection algorithm. As of November 2020, our system contains 100k+ stickers, has been deployed for 15+ months, and is being used by millions of users.
Towards a smart University in the light of 21st century skills

In the recent years, the accelerated development of knowledge and the changeable demands of the technological learning put the educators on the face of many challenges. The 21 century skills promote the competitiveness and innovation capabilities of the organization. These skills are crucial for sustainability and development of organization. The main objective of this study is to evaluate the ownership degree of the faculty members at Al-Aqsa University of 21 century skills to propose a vision to transform Al-Aqsa University into a smart university in the light of the 21 century skills. The results show that (79.3%) of faculty members have high ownership degree of 21 century skills regardless of the specialization and academic degree; these skills are one of the most important elements for the transformation to smart universities. Keyword: 21 Century Skills, Al-Aqsa University Faculty Members, Smart University. صخلم ملعتلل ةريغتملا تابلطتملاو ةفرعملل عراستملا روطتلا عضو ،ةريخلأا تاونسلا يف . تايدحتلا نم ديدعلا ةهجاوم يف نييوبرتلا يجولونكتلا ربتعتو نرقلا تاراهم 21 تاراهملا مهأ نم 1 Abed Moneim: Towards a smart University in the light of 21st century skills Published by Arab Journals Platform, 202
A Review on Recent Advances in Recurrent Neural Networks

- Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.
MODELOVANIE ŠUMOVÝCH PROCESOV MODELLING OF THE NOISE PROCESSES MODELOVANIE ŠUMOVÝCH PROCESOV MODELLING OF THE NOISE PROCESSES

Problém potláčania šumu je jednou z troch hlavných úloh spracovania signálov, ktoré sa zaoberajú odstraňovaním interferencie, analýzou a extrakciou charakteristík signálov a transformáciou signálov na formu vhodnejšiu pre ďalšie spracovanie. Dôvod prečo dochádza k interferencii šumu a užitočnej informácie sú rôzne [3], [4], [10], [17], [18], [24], napr. chyby prenosového kanála, starnutie záznamových médií, tepelné degenerácie, či človekom spôsobené rušenie.
I hear you feel confident

Here I explore a new line of evidence for belief-credence dualism, the thesis that beliefs and credences are distinct and equally fundamental types of mental states. Despite considerable recent disagreement over this thesis, little attention has been paid in philosophy to differences in how our mindreading systems represent the beliefs and credences of others. Fascinatingly, the systems we rely on to accurately and efficiently track others’ mental states appear to function like belief-credence dualists: Credence is tracked like an emotional state, composed of both representational and affective content, whereas belief is tracked like a bare representational state with no affective component. I argue on a preliminary basis that, in this particular case, the mechanics of mentalizing likely pick out a genuine affective dimension to credence that is absent for belief, further strengthening the converging case for beliefcredence dualism.
Comment on nhess-2021-121 Anonymous Referee

This study aims to provide further insights on the selection of Global Climate Models (GCMs) -Regional Climate Models (RCMs) combinations, according not only to their skills to reproduce the local climate during the selected historical period but also the local hydrology. Concretely, the authors calculate an error index for basic and drought statistics and use it to classify the GCMs-RCMs combinations according to their reliability for the assessment of meteorological and hydrological impacts. The selected methodology involves the bias correction of climate models' outputs through a quantile mapping (QM) approach based on empirical quantiles, the use of a lumped rainfall-runoff model to simulate monthly inflows from climate data and the use of standardized indices for drought characterization (namely the Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSI)).