Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-disaster Management based Machine Learning, IoT and Big Data: Survey and future direction

Due to climate changes, the world has experienced in recent years violent natural disasters such as fires, floods and typhoons. Since Disasters are difficult to cope with and stop, researchers have focused on the pre-disaster management phase to reduce damage. Pre-disaster management is the first phase in a disaster management cycle, it uses available data collected from different resources, to detect and predict disaster cases in order to give rescue teams more time to prepare and make decisions.With development of modern technologies and equipment many systems and approaches were proposed based on this development to improve the effectiveness and performance of disaster management and early warning systems. This paper aims to provide an overview of last years studies, focusing on new technologies (IoT, Machine learning, Big Data) for pre-disaster management.
Particle Ecient Importance Sampling

The ecient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by recognising that the EIS framework has an oine sequential Monte Carlo interpretation. The particle EIS method is based on non-standard resampling weights that take into account the look-ahead construction of the importance sampler. We apply the method for a range of univariate and bivariate stochastic volatility specications. We also develop a new application of the EIS approach to state space models with Student’s t state innovations. Our results show that the particle EIS method strongly outperforms both the standard EIS method and particle lters for likelihood evaluation in high dimensions. Moreover, the ratio between the variances of the particle EIS and particle lter methods remains stable as the time series dimension increases. We illustrate the eciency of the method
Phytochemical Composition of Combretum molle (R. Br. ex G. Don.) Engl. & Diels Leaf and Stem Extracts

The demand for medicinal plants is on a rise due to their affordability, accessibility and relatively non-toxic nature. Combretum molle (Combretaceae) is used in African traditional medicine to treat a number of diseases. This study aimed to screen the phytochemical composition of the hexane, chloroform and methanol extracts of C. molle leaves and stems using qualitative phytochemical screening. Additionally, the study aimed to identify the functional phytochemical groups, determine the elemental composition and provide a fluorescence characterization of the powdered leaves and stems by performing Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray (EDX) microanalyses and fluorescence microscopy. Phytochemical screening revealed the presence of alkaloids, flavonoids, phenolic compounds, polyphenols, terpenoids, tannins, coumarins, saponins, phytosterols, gums, mucilage, carbohydrates, amino acids and proteins within all leaf and stem extracts. Lipids and fixed oils were additionally present within the methanol extracts. FTIR demonstrated significant peaks in absorption frequency in the leaf at wavelengths of 3283.18, 2917.81, 1617.72, 1318.83, 1233.97, 1032.32 and 521.38 cm−1, and in the stem at 3318.91, 1619.25, 1317.13, 1032.68, 780.86 and 516.39 cm−1. These corresponded to the functional groups of chemical compounds including alcohols, phenols, primary amines, alkyl halides, alkanes and alkyl aryl ethers, corroborating the presence of the detected phytochemicals within the plant. EDX microanalyses showed the elemental composition of the powdered leaves (68.44% C, 26.72% O, 1.87% Ca, 0.96% Cl, 0.93% Mg, 0.71% K, 0.13% Na, 0.12 % Mn and 0.10% Rb) and stems (54.92% C, 42.86% O, 1.7% Ca, 0.43% Mg and 0.09% Mn). Fluorescence microscopy provided a characteristic evaluation of the plant in its powdered form and revealed distinct colour changes in the material when treated with various reagents and viewed under ultraviolet light. In conclusion, the phytochemical constituents of the leaves and stems of C. molle confirm the suitability of this species for use in traditional medicine. The findings from this study suggest the need to validate the use of C. molle in the development of modern medicines.
A novel feature point matching method of remote sensing images

The method of feature-based registration has been successful applied in registration of multi-source remote sensing images. Unfortunately, the mismatching still exists due to the complex textures, spectrum variation, nonlinear distortion and the large scale change. In this paper, we proposed a novel feature point matching method of multi-source remote sensing images. Firstly, the Fast-Hessian detector is to extract the feature points which are described by the SURF descriptor in the following step. After that, we analyze the local neighborhood structures of the feature points, and formulate point matching as an optimization problem to preserve local neighborhood structures. The shape context distances of the feature points are utilized to initialize matching probability matrix. Then relaxation labeling is adopted to update the probability matrix and refine the matching, which is aimed to maximize the value of the object function deduced based on preserving local neighborhood structures. Subsequently, the mismatching elimination method based on affine transformation and distance measurement is used to eliminate the residual mismatching points. During the abovementioned matching produce, the multi-resolution analysis method is adopted to decrease the scale difference between the multi-source remote sensing images. Also the mutual information method is utilized to match the feature points of the down sampling and the original images. The experimental results are shown that the proposed method was robust and efficient for registration of multi-source remote sensing images.
Maximum Magnitude of Completeness in a Salt Mine

Abstract In this study, we analyze acoustic emission (AE) data recorded at the Morsleben salt mine, Germany, to assess the catalog completeness, which plays an important role in any seismicity analysis. We introduce the new concept of a magnitude completeness interval consisting of a maximum magnitude of completeness ( M c(max) ) in addition to the well‐known minimum magnitude of completeness. This is required to describe the completeness of the catalog, both for the smallest events (for which the detection performance may be low) and for the largest ones (which may be missed because of sensors saturation). We suggest a method to compute the maximum magnitude of completeness and calculate it for a spatial grid based on (1) the prior estimation of saturation magnitude at each sensor, (2) the correction of the detection probability function at each sensor, including a drop in the detection performance when it saturates, and (3) the combination of detection probabilities of all sensors to obtain the network detection performance. The method is tested using about 130,000 AE events recorded in a period of five weeks, with sources confined within a small depth interval, and an example of the spatial distribution of M c(max) is derived. The comparison between the spatial distribution of M c(max) and of the maximum possible magnitude ( M max ), which is here derived using a recently introduced Bayesian approach, indicates that M max exceeds M c(max) in some parts of the mine. This suggests that some large and important events may be missed in the catalog, which could lead to a bias in the hazard evaluation.
Prognostic Value of Nutritional Assessments on Overall Survival in Head and Neck Cancer Survivors with Radiation-Induced Brain Necrosis

Malnutrition is related to worsened prognosis, but the association between nutritional risk status and overall survival in radiation-induced brain necrosis (RN) has never been studied. We included consecutive patients who had received radiotherapy for head and neck cancer (HNC) and subsequently developed RN from 8 January 2005 through to 19 January 2020. The primary outcome was overall survival. We utilized three commonly-used nutritional assessments: the Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and the COntrolling NUTritional Status (CONUT) measure, to quantify the baseline nutritional risk. A total of 398 eligible patients were included. During a median follow-up of 2.3 years, 42 (10.6%) patients died of any cause. Malnutrition at admission was associated with an increased risk of future death, as assessed by the GNRI (per 1-point decreased, HR 1.05, 95%CI 1.02–1.09, p = 0.001), the PNI (per 1-point decreased, HR 1.07, 95%CI 1.03–1.12, p = 0.002), and the CONUT (per 1-point increased, HR 1.22, 95%CI 1.08–1.37, p = 0.001). There were no nonlinear correlations between all three indices and post-RN survival. Among HNC survivors with RN, the assessment of nutritional risk by composite indices upon admission could help identify patients who might be at high risk of future death and deliver better nutritional management.
What You See and What You Get: Direct and Indirect Political Dividends of Public Policies

Abstract We investigated both the direct and indirect political dividends of public policies by examining Minha Casa, Minha Vida, a housing programme in Brazil that selects its beneficiaries by lottery. We surveyed the lottery participants and found that the winners were not more likely to support the incumbent politicians. Non-beneficiaries, a much larger group, were aware of the programme and thought well of it while the beneficiaries' responses to the programme were sometimes underwhelming. However, politicians considered the programme to be an electoral asset, and a difference-in-differences analysis of electoral results leveraging the roll-out of the programme across municipalities found that presidential and mayoral incumbent candidates performed better in localities that had implemented MCMV. Overall, when the beneficiaries formed a relatively small group, the benefits were conspicuous and the programme's objectives were widely supported. Government programmes can create electoral payoffs independently of how programmes are perceived or experienced by beneficiaries.
Registration of point cloud data for HDD stamped base inspection

As a part of the HDD manufacturing process, HDD stamped base, an exterior container, is one of the most essential components in which various parts become assembled to compose a hard disk drive (HDD). Height errors that are caused by pressing, breaking or cracking can occur on the base, because it is designed by a stamping method. In order to detect the height errors, the inspection process is essential in the production fields. In the current industry, CMM (Coordinate Measurement Machine) is one of the representative machines that inspect certain regions on the product. The machine probes a designated point by an operator and judges the defect by comparing the height of the point to the originally designed height. However, the method takes much time to inspect each designated point resulting in a total of 17 minutes. In order to reduce the total inspection time, we propose an inspection method using 3D point cloud data acquired from a holographic sensor. To compare the height from acquired 3D point cloud data with the one from the originally designed CAD data, the exact point cloud registration is important. There are differences between 2D image registration and 3D point cloud registration, such as translation on each plane, rotation, tilt, and nonlinear transformations. The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation. If the projective transformation matrix between the two is obtained, 3D point cloud data registration can be performed. In order to calculate 3D projective transformation matrix, corresponding points between 3D point cloud data and CAD data are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. The height map has pixel intensity from the height value of each point. If the height maps from 3D point cloud data and CAD data are matched, corresponding points can be estimated. As one of the features of the HDD stamped base, there are multiple circles on the base. In this paper, we find the corresponding points between 3D point cloud data and CAD data using circle fitting, and obtain 2D Affine transformation matrix from the corresponding points. By applying 2D Affine transformation matrix to height map, the corresponding points on the 3D coordinate can be obtained. Using such points, we propose the method designed to achieve 3D projective transformation matrix. To find the proper 3D projective transformation matrix, we formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D projective transformation matrix can be calculated by minimizing the cost function. Then the 3D point cloud data can be matched to CAD data and the height values of each point of 3D point cloud can be compared to the CAD data.
AAGDN: Attention-Augmented Grasp Detection Network Based on Coordinate Attention and Effective Feature Fusion Method

High-precision robotic grasping is necessary for extensive grasping applications in the future. Most previous grasp detection methods fail to pay enough attention to learn grasp-related features and the detection accuracy is limited. In this letter, a novel attention-augmented grasp detection network (AAGDN) is presented to generate accurate grasp poses for unknown objects. The proposed AAGDN has three elaborate designs making it achieve higher accuracy than existing methods. First, we construct a coordinate attention residual module to extract positional information and improve the spatial sensitivity of features. Then, we propose an effective feature fusion module to bridge the resolution and semantic gaps of different-level features and obtain efficient feature representations. Lastly, a feature augmentation pyramid module is developed to enhance grasp-related features as needed and reduce the loss of information. Extensive experiments on three public datasets and various real-world scenes prove that the proposed AAGDN achieves better performance than current methods. Our model obtains the state-of-the-art 99.3% and 96.2% grasp detection accuracy on the Cornell and Jacquard dataset, respectively. Moreover, in physical grasping experiments, the AAGDN attains the 94.6% success rate for unseen objects in cluttered scenes, which further demonstrates the accuracy and robustness of our method to grasp novel objects.
Recent Advances in Antimicrobial Peptide Hydrogels

Advances in the number and type of available biomaterials have improved medical devices such as catheters, stents, pacemakers, prosthetic joints, and orthopedic devices. The introduction of a foreign material into the body comes with a risk of microbial colonization and subsequent infection. Infections of surgically implanted devices often lead to device failure, which leads to increased patient morbidity and mortality. The overuse and improper use of antimicrobials has led to an alarming rise and spread of drug-resistant infections. To overcome the problem of drug-resistant infections, novel antimicrobial biomaterials are increasingly being researched and developed. Hydrogels are a class of 3D biomaterials consisting of a hydrated polymer network with tunable functionality. As hydrogels are customizable, many different antimicrobial agents, such as inorganic molecules, metals, and antibiotics have been incorporated or tethered to them. Due to the increased prevalence of antibiotic resistance, antimicrobial peptides (AMPs) are being increasingly explored as alternative agents. AMP-tethered hydrogels are being increasingly examined for antimicrobial properties and practical applications, such as wound-healing. Here, we provide a recent update, from the last 5 years of innovations and discoveries made in the development of photopolymerizable, self-assembling, and AMP-releasing hydrogels.
Point Cloud Segmentation Using Euclidean Cluster Extraction Algorithm With the Smoothness

An Euclidean cluster extraction algorithm with the smoothness parameter for Kinect point cloud is presented to achieve fast,accurate segmentation.First,the Kinect point cloud collection and filtering method is introduced,then an Euclidean cluster extraction algorithm with the smoothness is proposed by adding a smoothness threshold to traditional Euclidean clustering algorithm,which can prevent the problem of over-segmentation or inadequate split,and maintain a rapid segmentation speed.The experiments of valve point cloud data acquired by an industrial robot prove the effectiveness of the algorithm.
Outlier detection algorithm based on Reverse K Nearest Neighbors

This paper proposes a new outlier detection algorithm ODRKNN based on reverse K nearest neighbors.ODRKNN counts the number of each point's reverse K nearest neighbors to reflect its isolation degree,experimental results on synthesized and real world dataset show that ODRKNN can efficiently detect outliers and has higher efficiency than outliers detection algorithm LOF and LSC.
A New Quantitative Analyzing Method for Doppler Tissue Image Sequences

Objective To develop a new method to obtain mechanical motion information of all the points in region of interest(ROI) from 2dimensional Bscanned Doppler tissue images,and as an application,it is used to describe the activation process of sinusatria node (SAN) quantitatively. Method By decomposing and analyzing the color components in the Color Bar provided by ultrasound system,the quantitative relationship of the color components and acceleration was derived. Colorcoded Doppler shift information was separated from the final captured images and the acceleration value can be obtained using the relationship. The method was used to analyze DTI image sequences for a description of the SAN motion and activation process of dogs. Result (1) Acceleration values of all the points (pixels in the image) in ROI were obtained through the proposed method. (2) Acceleration cumulative distribution function(CDF)time curves of the sinusatria node were given. Conclusion (1) Quantitative description of tissue motion can be derived from the 2D Bscanned Doppler tissue image sequences,which can be used to study the tissue motion in the cardiac conduct pathway. (2) Because the proposed method is based on the analysis of Color Bar that is common in most ultrasound system,it can also be used for Doppler tissue velocity image and for general Bscanned DTI image sequences.
Subclinical Myocardial Injury in Patients Recovered from COVID-19 Pneumonia: Predictors and Longitudinal Assessment

(1) Background: Emerging data regarding patients recovered from COVID-19 are reported in the literature, but cardiac sequelae have not yet been clarified. To quickly detect any cardiac involvement at follow-up, the aims of the research were to identify: elements at admission predisposing subclinical myocardial injury at follow up; the relationship between subclinical myocardial injury and multiparametric evaluation at follow-up; and subclinical myocardial injury longitudinal evolution. (2) Methods and Results: A total of 229 consecutive patients hospitalised for moderate to severe COVID-19 pneumonia were initially enrolled, of which 225 were available for follow-up. All patients underwent a first follow-up visit, which included a clinical evaluation, a laboratory test, echocardiography, a six-minute walking test (6MWT), and a pulmonary functional test. Of the 225 patients, 43 (19%) underwent a second follow-up visit. The median time to the first follow-up after discharge was 5 months, and the median time to the second follow-up after discharge was 12 months. Left ventricular global longitudinal strain (LVGLS) and right ventricular free wall strain (RVFWS) were reduced in 36% (n = 81) and 7.2% (n = 16) of the patients, respectively, at first the follow-up visit. LVGLS impairment showed correlations with patients of male gender (p 0.008, OR 2.32 (95% CI 1.24–4.42)), the presence of at least one cardiovascular risk factor (p < 0.001, OR 6.44 (95% CI 3.07–14.9)), and final oxygen saturation (p 0.002, OR 0.99 (95% CI 0.98–1)) for the 6MWTs. Subclinical myocardial dysfunction had not significantly improved at the 12-month follow-ups. (3) Conclusions: in patients recovered from COVID-19 pneumonia, left ventricular subclinical myocardial injury was related to cardiovascular risk factors and appeared stable during follow-up.
LANGUAGE AND MATHEMATICS

The paper presents the theoretical construct “mathematical reflection“ and elaborates its specificity with regard to the epistemological conditions of mathematical knowledge. This construct of “mathematical reflection” is the key concept in a wider research project. A conceptual grid with fundamental categories is developed that serves to carefully characterize the important components of “mathematical reflection” and that is used as an instrument for qualitatively analyzing students' mathematical collaboration in clinical interviews and for identifying different types of “mathematical reflection” in interaction.
Linkages between bacteria and nutrient availabilities in slash‐and‐burn tropical soils vary with feeding‐habit ants

As important structuring forces in forest ecosystems, ants can drive above‐ and belowground food‐webs and ecosystem functionality. It remains uncertain, however, about how different foraging‐habit ants shape symbiotic bacterial assemblies and nutrient cycling in nest soils. This study quantified the impacts of predatory and honeydew‐feeding ants on soil bacterial communities and nutrient pools in Xishuangbanna tropical forests restored from slash‐and‐burn agriculture. In contrast to reference soils, all ant nests increased the relative abundances of dominant copiotrophs and decreased those of preponderant oligotrophs in the bacterial communities, which was closely associated with the high levels of carbon (C) and nitrogen (N) in the nests. Honeydew‐feeding ants harbored a high abundance of bacterial trophic groups (i.e., γ‐Proteobacteria, Firmicutes, and Bacteroidetes) preferring low‐quality resource, owing to high increment (103%) in C concentration and low increase (69%) in N level compared to reference soils. In contrast, predatory ants sheltered a high assemblage of bacterial taxa (i.e., β‐Proteobacteria and δ‐Proteobacteria) favoring high‐quality resource, due to increased N level (135%) and decreased C:N ratio (35%) compared to reference soils. We concluded that the foraging‐habit ants shaped symbiotic bacterial assemblages primarily through differential modifications on level and allocation of soil C and N pools. Our results would further the understanding of the fauna roles in driving soil food‐webs as well as nutrient cycling.