Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevention of Mountain Disasters and Maintenance of Residential Area through Real-Time Terrain Rendering

Climate change increases the frequency of localized heavy rains and typhoons. As a result, mountain disasters, such as landslides and earthworks, continue to occur, causing damage to roads and residential areas downstream. Moreover, large-scale civil engineering works, including dam construction, cause rapid changes in the terrain, which harm the stability of residential areas. Disasters, such as landslides and earthenware, occur extensively, and there are limitations in the field of investigation; thus, there are many studies being conducted to model terrain geometrically and to observe changes in terrain according to external factors. However, conventional topography methods are expressed in a way that can only be interpreted by people with specialized knowledge. Therefore, there is a lack of consideration for three-dimensional visualization that helps non-experts understand. We need a way to express changes in terrain in real time and to make it intuitive for non-experts to understand. In conventional height-based terrain modeling and simulation, there is a problem in which some of the sampled data are irregularly distorted and do not show the exact terrain shape. The proposed method utilizes a hierarchical vertex cohesion map to correct inaccurately modeled terrain caused by uniform height sampling, and to compensate for geometric errors using Hausdorff distances, while not considering only the elevation difference of the terrain. The mesh reconstruction, which triangulates the three-vertex placed at each location and makes it the smallest unit of 3D model data, can be done at high speed on graphics processing units (GPUs). Our experiments confirm that it is possible to express changes in terrain accurately and quickly compared with existing methods. These functions can improve the sustainability of residential spaces by predicting the damage caused by mountainous disasters or civil engineering works around the city and make it easy for non-experts to understand.
Estimating infiltration front depth using time-lapse multioffset gathers obtained from ground-penetrating-radar antenna array

Nondestructive and noninvasive visualization and quantification of dynamic subsurface hydrologic processes are needed. Using a ground-penetrating-radar (GPR) antenna array, time-lapse common-offset gather and common midpoint (CMP) data can be collected by fixing the antenna at a given location when scanning the subsurface. We have determined wetting front depths continuously during a field infiltration experiment by estimating electromagnetic (EM) wave velocities at given elapsed times using GPR antenna array data. A surface GPR antenna array system, consisting of 10 transmitters (Tx) and 11 receivers (Rx), that can scan each Tx-Rx combination in 10 s at a millisecond scale has been used to acquire all 110 Rx-Tx combinations in approximately 1.5 s. The field infiltration experiment has been conducted at an experimental field near the Tottori Sand Dunes in Japan. Using the estimated EM wave velocity from the CMP data, the depth to the wetting front is computed every minute. The estimated wetting front arrival time agrees with the time at which a sudden increase in the moisture sensor output is observed at a depth from 20 cm and below. We determine that time-lapsed CMP data collected with the GPR antenna array system could be used to estimate EM wave velocities continuously during the infiltration. The GPR antenna array is capable of accurate and quantitative tracking of the wetting front.
Gender and ethnicity in social assistance assessments of single applicants with substance abuse problems

ABSTRACT In Sweden, trained social workers ultimately determine the right to social assistance (SA). This paper examines how a substance-abusing SA applicant’s gender and ethnicity affect assessments on eligibility. It further addresses whether professionals’ gender and migration background have bearing for such patterns. Social work professionals (n = 910) spread over 43 social service offices in 19 Swedish municipalities conducted assessments based on a vignette. Four versions of the vignette were used, in which the name of the applicant was varied. The names were two Swedish-sounding names and two Arabic-sounding names: one of each gender respectively. Data were analysed by means of cross tabulations and multilevel logistic regression analysis. The Swedish-sounding names were linked to a higher chance of granting compared to the Arabic-sounding names, whereas females more often were seen as eligible than males. The major finding was the combined impact of gender and ethnicity: Applicants attributed with an Arabic-sounding male name were less often seen as eligible than those with a Swedish-sounding female name. This bias was specifically manifest among female professionals. Further, native-born professionals generally viewed the applicant as eligible to a higher extent than foreign-born.
MaxDEL: Accurate and Efficient Calling Genomic Deletions From Single Molecular Real-Time Sequencing Using Integrated Method

Background: Single-molecule real-time (SMRT) sequencing data are characterized by long reads and high read depth. Compared with next-generation sequencing (NGS), SMRT sequencing data can present more structural variations (SVs) and has greater advantages in calling variation. However, there are high sequencing errors and noises in SMRT sequencing data, which brings inaccurately on calling SVs from sequencing data. Most existing tools are unable to overcome the sequencing errors and detect genomic deletions. Methods and results: In this investigation, we propose a new method for calling deletions from SMRT sequencing data, called MaxDEL. MaxDEL can effectively overcome the noise of SMRT sequencing data and integrates new machine learning and deep learning technologies. Firstly, it uses machine learning method to calibrate the deletions regions from variant call format (VCF) file. Secondly, MaxDEL develops a novel feature visualization method to convert the variant features to images and uses these images to accurately call the deletions based on convolutional neural network (CNN). The result shows that MaxDEL performs better in terms of accuracy and recall for calling variants when compared with existing methods in both real data and simulative data. Conclusions: We propose a method (MAXDEL) for calling deletion variations, which effectively utilizes both machine learning and deep learning methods. We tested it with different SMRT data and evaluated its effectiveness. The research result shows that the use of machine learning and deep learning methods has great potential in calling deletion variations.
Optimization of Vineyard Water Management: Challenges, Strategies, and Perspectives

Water availability is endangering the production, quality, and economic viability of growing wine grapes worldwide. Climate change projections reveal warming and drying trends for the upcoming decades, constraining the sustainability of viticulture. In this context, a great research effort over the last years has been devoted to understanding the effects of water stress on grapevine performance. Moreover, irrigation scheduling and other management practices have been tested in order to alleviate the deleterious effects of water stress on wine production. The current manuscript provides a comprehensive overview of the advances in the research on optimizing water management in vineyards, including the use of novel technologies (modeling, remote sensing). In addition, methods for assessing vine water status are summarized. Moreover, the manuscript will focus on the interactions between grapevine water status and biotic stressors. Finally, future perspectives for research are provided. These include the performance of multifactorial studies accounting for the interrelations between water availability and other stressors, the development of a cost-effective and easy-to-use tool for assessing vine water status, and the study of less-known cultivars under different soil and climate conditions.
BIOFILM FORMATION AND QUORUM SENSING ANALYSIS OF UROPATHOGENIC MULTIDRUG RESISTANCE ESCHERICHIA COLI

Objective: Escherichia coli (E. coli) are gram-negative facultative anaerobes which are commonly found in the lower intestine. Biofilm production in E. coli promotes colonization and leads to an increase rate of infections, and such infections may be difficult to treat as they exhibit multidrug resistance (MDR). Methods: 50 strains of uropathogenic E. coli were collected from Clinical Microbiology laboratory at Saveetha medical college and hospital for a time period of 3 mo. Strains were identified by conventional biochemical methods. Biofilm formation and quorum sensing analysis were performed by the Microtitre plate method and Thin Layer Chromatography method (TLC), respectively. Results: In this study, 46 (92%) of E. coli strains were strong, 3(6%) were intermediate and 1(2%) were weak biofilm producers. From TLC analysis, 34 (68%) of the strains produced Acyl Homoserine Lactone molecules. Out of which, 16 isolates were shown unknown analytes of Retardation factor (Rf) value greater than 1. The Rf values identified were 3 unsubstituted C4 (5), 3 unsubstituted C6 (3), 3 oxo C8 (3), 3 oxo C4 (4), 3 oxo C6 (2), 3 oxo C1 (1). Conclusion: In this study, 100% of isolates were biofilm producers. Of which 18 strains produced known Acyl Homoserine Lactone molecules and 16 isolates produced unknown analytes. Thus, quorum sensing molecules plays a major role in biofilm formation.
An improved framework for polyp image segmentation based on SegNet architecture

A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir‐SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polyp image. This paper has demonstrated state‐of‐the‐art performance on both VGG‐16, and VGG‐19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG‐19 model has outperformed the VGG‐16 model via all evaluation parameters except sensitivity for the polyp segmentation on the Kvasir‐SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time.
The impact of the changes in surface properties of yeast biomass, Saccharomyces cerevisiae, on Pb2+ biosorption

: This study examines changes that occur in yeast cell surface properties during physical, chemical and mechanical treatment and their potential impact on the biomass uptake capacity for lead. As a result of biomass treatment, the negative surface charge decreased and relative hydrophobicity increased. A strong negative correlation was discovered between the negative surface charge of variously modified yeast cells and their relative hydrophobicity. Despite the lower negative surface charge of treated in comparison to untreated yeast cells, the treated biomass had higher lead sorption capability. No relationship was found between the effectiveness of lead sorption by treated biomass and its surface charge or relative hydrophobicity.
Kinetic Modeling of Endosulfan Degradation by Mixed Bacterial Culture

ABSTRACT A consortium of already isolated strains has been applied to degrade endosulfan. These were identified by 16S rRNA technique as Stenotrophomonas maltophilia EN-1, Pseudomonas sp. TAH and Enterobacter sp EN-2. Batch experiments were conducted to access the endosulfan degradation efficiency of consortium while using various concentrations of endosulfan (5, 25, 50, 75, 100 mg/L). Maximum degradation of about 85–90% was observed at a concentration of 100 mg/L after 96 hours of incubation while no degradation was observed in untreated ones (without consortium). Cell growth kinetic models were applied to explain the kinetic behaviors of bacterial consortium in batch study. Various kinetic parameters such as ‘µ max ’ and ‘Ks’ were determined having values of 0.9 h−1 and 7.9 mg/L respectively, that depicts the faster rate of endosulfan degradation by the selected bacterial consortium. Conversely, first-order kinetics was also well fitted in the experimental data using least squares regression (R 2) ranged from 0.09 to 0.95, which indicates that the endosulfan utilization was stimulated by a synergistic interaction of each bacterial strain in the consortium.
Propensity-Score Analysis Reveals that Sex is Not a Prognosis Factor for Mortality in Intensive Care Unit-Admitted Patients with Sepsis

Purpose: Classically, men have been considered to have a higher incidence of infectious diseases, with controversy over the possibility that sex could condition the prognosis of the infection. The aim of the present work was to explore this assumption in patients admitted to the ICU with sepsis using a robust statistical analysis.Methods: Retrospective analysis (2006-2017) in patients with microbiologically confirmed bacteremia (n=440) by majoritarian bacterial pathogens. Risk of ICU and in-hospital mortality in males respect to females was compared by an univariant analysis and a propensity score correspondence analysis integrating their clinical characteristics. Results: Relevant differences were related to the infection source: urinary origin for females (28.7% vs 19.8%) and abdominopelvic surgery for males (8.8% vs 4.8%). Sepsis occurred more frequently in males (80.2% vs 76.1%) as well as in-hospital (48.0% vs 41.3%) and ICU (39.9% vs 36.5%) mortality. Escherichia coli was 2 times more frequent in survivors whereas Staphylococcus aureus was 3 times more frequent in deceased patients. Univariate analyses showed that males had a higher Charlson comorbidity index, a poorer McCabe prognostic score; however the propensity score in 296 patients demonstrated that females had higher risk of both ICU (OR 0.72; 95% CI 0.46 to 1.13), and in-hospital mortality (OR 0.84; 95% CI 0.55 to 1.30) but without statistical significance. Conclusion: Men with sepsis have worse clinical characteristics when admitted to the ICU, but sex has no influence on the prognosis of mortality. Our data contributes to help reduce the sex-dependent gap present in health care provision.
A Comparison of Partial Admission Axial and Radial Inflow Turbines for Underwater Vehicles

The metal fueled steam Rankine cycle has been successfully applied to Unmanned Underwater Vehicles. However, the suitable turbine configuration is yet to be determined for this particular application. In this paper, the mean-line design approach based on the existing empirical correlations is first described. The corresponding partial admission axial and radial inflow turbines are then preliminarily designed. To assess the performance of designed turbines, the three-dimensional Computational Fluid Dynamics (CFD) simulations and steady-state structural analysis are performed. The results show that axial turbines are more compact than radial inflow turbines at the same output power. In addition, since radial inflow turbines can reduce the exit energy loss, this benefit substantially offsets the increment of the rotor losses created by the low speed ratios and supersonic rotor inlet velocity. On the contrary, due to the large volume of dead gas and strong transient effects caused by the high rotor blade length of radial inflow turbines, the overall performance between axial and radial inflow turbines is comparable (within 4%). However, the strength of radial inflow turbines is slightly superior because of lower blade inlet height and outlet hub radius. This paper confirms that the axial turbine is the optimal configuration for underwater vehicles in terms of size, aerodynamics and structural performance.
Performance Analysis of Maximum Power Point Tracking for Two Techniques with Direct Control of Photovoltaic Grid -Connected Systems

ABSTRACT The present study presents two techniques of Maximum Power Point Tracking (MPPT) via DC/DC converter to enhance the performance of the grid-connected Photovoltaic (PV) generation system to participate effectively within microgrids. The two techniques of MPPT are Perturb-Observe (P&O) and Incremental Conductance (IC). The variation of the solar radiation and temperature is considered during employing the two MPPT techniques. Besides, the performance of the system under the random variation of solar radiation was investigated. The authors used two types of controllers at the three-phase inverter, Proportional Integral (PI) and H-infinity Control (H∞C). The output voltage, current, and power at each type of inverter controller are compared along with the two techniques of MPPT. The effectiveness of the developed controllers together with MPPT techniques is demonstrated by comparing the obtained results with some previously reported research in the literature. In the case of MPPT via P&O and IC techniques along with the H∞C controller, the results show that the technique of IC is more robust, and the obtained output power is well matched with the references one as compared with the P&O technique which records a 7% error from MPP. Besides, the P&O technique has a high voltage and current ripple with a percentage of 20% at the starting time of the simulation.
EARLY ACTION PREDICTION USING VGG16 MODEL AND BIDIRECTIONAL LSTM

Action prediction plays a key function, where an expected action needs to be identified before the action is completely performed. Prediction means inferring a potential action until it occurs at its early stage. This paper emphasizes on early action prediction, to predict an action before it occurs. In real time scenarios, the early prediction can be very crucial and has many applications like automated driving system, healthcare, video surveillance and other scenarios where a proactive action is needed before the situation goes out of control. VGG16 model is used for the early action prediction which is a convolutional neural network with 16 layers depth. Besides its capability of classifying objects in the frames, the availability of model weights enhances its capability. The model weights are available freely and preferred to used in different applications or models. The VGG-16 model along with Bidirectional structure of Lstm enables the network to provide both backward and forward information at every time step. The results of the proposed approach increased observation ratio ranging from 0.1 to 1.0 compared with the accuracy of GAN model.
A Comparative Study of Energy Consumption and Recovery of Autonomous Fuel-Cell Hydrogen–Electric Vehicles Using Different Powertrains Based on Regenerative Braking and Electronic Stability Control System

Today, with the increasing transition to electric vehicles (EVs), the design of highly energy-efficient vehicle architectures has taken precedence for many car manufacturers. To this end, the energy consumption and recovery rates of different powertrain vehicle architectures need to be investigated comprehensively. In this study, six different powertrain architectures—four independent in-wheel motors with regenerative electronic stability control (RESC) and without an RESC, one-stage gear (1G) transmission, two-stage gear (2G) transmission, continuously variable transmission (CVT) and downsized electric motor with CVT—were mathematically modeled and analyzed under real road conditions using nonlinear models of an autonomous hydrogen fuel-cell electric vehicle (HFCEV). The aims of this paper were twofold: first, to compare the energy consumption performance of powertrain architectures by analyzing the effects of the regenerative electronic stability control (RESC) system, and secondly, to investigate the usability of a downsized electrical motor for an HFCEV. For this purpose, all the numerical simulations were conducted for the well-known FTP75 and NEDC urban drive cycles. The obtained results demonstrate that the minimum energy consumption can be achieved by a 2G-based powertrain using the same motor; however, when an RESC system is used, the energy recovery/consumption rate can be increased. Moreover, the results of the article show that it is possible to use a downsized electric motor due to the CVT, and this powertrain significantly reduces the energy consumption of the HFCEV as compared to all the other systems. The results of this paper present highly significant implications for automotive manufacturers for designing and developing a cleaner electrical vehicle energy consumption and recovery system.
Symbolic ethnicity, cultural and linguistic landscape: remnants of ‘Little Europe’ in the Valcanale (Northeast Italy)

ABSTRACT This study investigates ‘Little Europe’, the rural, mountainous Italian region of the quadrilingual Valcanale, bordering Austria and Slovenia. The combined analysis of cultural landscape, linguistic landscape (LL) and symbolic ethnicity provides a new concept to examine multilingual regions. In this once Austrian valley, where German was the official language before WWI, Italian now dominates over minority languages, as exemplified by official signs. However, elements of the area's cultural and linguistic landscape reveal unexpected linguistic practices and visible, locatable artefacts of the Valcanale's ethnolinguistic heritage, which we assume to play a key role in the preservation of symbolic ethnicity.
Beyond ethnic solidarity: the diversity and specialisation of social ties in a stigmatised migrant minority

ABSTRACT Whether presented as ethnic ‘solidarity’ or ‘segregation’, the idea that migrants’ social world is dominated by tightly-knit, homogeneous, and supportive networks of kin and co-ethnics is common in scholarly and public discourse around migration, particularly for minorities with a history of marginalisation, segregation, and stigmatisation. We test this idea using results from the first survey of personal networks in one of the most stigmatised immigrant minorities in the Western world: Roma migrants in Europe. Analysing data on 119 Romanian Roma migrants in France and their 3,570 social ties, we identify typical structures of personal communities, describe the distribution and association of different dimensions of social support, and estimate multilevel models to identify determinants of support in this population. We find that, even in contexts of strong marginalisation and stigmatisation, the hypotheses of ethnic solidarity, sociodemographic homophily, and network closure are inadequate to explain the way migrants obtain social support. Instead, Romanian Roma in France appear much closer to the model of ‘networked individualism’ and similar to middle classes in Western ethnic majorities, as they strategically maintain diverse and far-flung networks, choose forms of elective belonging in local contexts, and mobilise different social ties for different, specialised types of support.