Miracle-dz/UniFAR-FTUs huggingface dataset:
Intensive allochthonous inputs along the Ganges River and their effect on microbial community composition and dynamics

Little is known about microbial communities in the Ganges River, India and how they respond to intensive anthropogenic inputs. Here we applied shotgun metagenomics sequencing to study microbial community dynamics and function in planktonic samples collected along an approximately 700 km river transect, including urban cities and rural settings in upstream waters, before and after the monsoon rainy season. Our results showed that 11%-32% of the microbes represented terrestrial, sewage and human inputs (allochthonous). Sewage inputs significantly contributed to the higher abundance, by 13-fold of human gut microbiome (HG) associated sequences and 2-fold of antibiotic resistance genes (ARGs) in the Ganges relative to other riverine ecosystems in Europe, North and South America. Metagenome-assembled genome sequences (MAGs) representing allochthonous populations were detectable and tractable across the river after 1-2 days of (downstream) transport (> 200 km apart). Only approximately 8% of these MAGs were abundant in U.S. freshwater ecosystems, revealing distinct biodiversity for the Ganges. Microbial communities in the rainy season exhibited increased alpha-diversity and spatial heterogeneity and showed significantly weaker distance-decay patterns compared with the dry season. These results advance our understanding of the Ganges microbial communities and how they respond to anthropogenic pollution.
Indonesian Traditional Shadow Puppet Image Classification: A Deep Learning Approach

Puppet is one of Indonesian traditional art which is an art performance performed by a puppeteer. The puppets have their own features and character according to the puppet character. So one way to preserve it, we do research to recognize and classify the type of puppets. So with this can be known the types of puppet based on shades and shapes. With the differences of these characteristics, the writer made the classification of puppets by using Deep Learning method which is currently popular on computer vision. In this study we used Deep Convolutional Neural Network architecture such as AlexNet and VGG-16. By using our own dataset, we classify the types of puppets based on the puppet feature with the number of databases that we have after going through the data augmentation is 5130. The best result we made is using architecture VGG-16.
Error Analysis of ZUPT-Aided Pedestrian Inertial Navigation

We present, for the first time, an analytical relation between the navigation errors during Zero-Velocity-Update (ZUPT) aided pedestrian inertial navigation and Inertial Measurement Unit (IMU) performances. Angle Random Walk of gyroscopes and Velocity Random Walk of accelerometers in IMU, velocity measurement uncertainty during the stance phase, and the sampling frequency of IMU are studied and their effects on the overall navigation accuracy are analyzed. Numerical simulation of the ZUPT-aided navigation algorithm is conducted based on a generated pedestrian trajectory to verify the analytical results with the discrepancy less than 30%, showing fidelity of the results. In this study, a foot motion model was developed according to the ambulatory gait analysis as a basis of the numerical simulation. This study may serve as a benchmark for analysis of errors in pedestrian inertial navigation.
Moving Object Tracking Based on High Precision Opto-Acoustic BPSK-CDMA Distance Measurement

Opto-acoustic indoor localization systems based on ultrasound and infrared allow the tracking of tools and hand movements in complex manual industrial assembly and handling processes, e. g. the application of glue or adhesive sealing material. However, this requires a localization system that is able to track the position of multiple objects simultaneously during movement when the system is affected by Doppler effects and noise. The amplitude shift keying (ASK) modulation method within the code division multiple access (CDMA) environment using Gold codes is focused in previous publications for unilateral distance measurement of non-moving objects. The novelty of this contribution is the implementation of the binary phase shift keying (BPSK) modulation method and the consideration of moving objects with a Doppler effect mitigation algorithm. This algorithm can be performed in real time with an acquisition and a tracking step. Moreover, BPSK provides a permanently available phase information that can be used for exact positioning of multiple moving transmitters and ensures more robustness to modeling errors and noise. In this publication unilateral distance measurement results for up to four moving objects simultaneously with various speeds up to 0.54 m/s are presented showing the superiority of BPSK-CDMA with Doppler effect mitigation (mean errors below 6.9 mm) compared to ASK-CDMA and BPSK-CDMA without Doppler effect consideration.
Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime Version (K-SADS-PL), DSM-5 update: translation into Brazilian Portuguese

On February 23, the World Health Organization (WHO) published a survey on the two most common psychiatric illnesses affecting the world population: depressive and anxiety disorders. According to this survey, Brazil leads the world in prevalence of anxiety disorders and ranks fifth in depression rates. Surveys of this kind are highly relevant, as they are often the basis of actions by the government and society and serve as guides for public policy-making for prevention and/or treatment. Whatever the use and attention that the data from this survey might have in the future, some reflections seem timely for an informed debate on this topic. Compared to the preceding survey, released in 2005, the latest WHO report describes an increase in the frequency of both depressive and anxiety disorders worldwide, pointing to population growth and increased longevity as contributing factors for the current picture. Although the latter factors are always involved in discussions about the prevalence of any disease, other potential confounders must be taken into account in this debate, including methodological discrepancies in diagnosis and data recording across countries, cultural differences in help-seeking behaviors, and differential availability and organization of mental health services, to name a few. In 2007, the British Medical Journal invited two eminent researchers to answer the question ‘‘Is depression overdiagnosed? ’’ in their section ‘‘Head to Head. ’’ Advocating that depression is overdiagnosed as a result of the medicalization of sadness, Parker stated that ‘‘a low threshold for diagnosing clinical depression [. ] risks normal human emotional states being treated as illness, challenging the model’s credibility and risking inappropriate management. ’’ In the opposite direction, Hickie wrote at the time that ‘‘From a health and economic perspective, we can give a clear answer – more adults are alive and well, and we can easily afford to treat more. ’’ Both authors supported their views with solid evidence. Whatever the precise extent of the problem in Brazil and around the globe, it is already a collective issue, and the personal and financial burden of anxiety and depression demands prompt and coordinated action from the scientific community and government agencies. In order to answer some of the questions raised by the WHO reports on mental health, research efforts should be directed to three main targets: 1) furthering our understanding of the etiological factors of anxiety and depressive disorders; 2) elucidating their social, cultural, and geographic determinants and impacts; and 3) increasing diagnostic and therapeutic accuracy. If the spread of these disorders is not checked – whether through preventative or therapeutic action – and the needs of affected individuals are not met, the productivity and social-security systems of many countries could be seriously jeopardized in the near future.
A New Methodology for Long-Term Maintenance of WiFi Fingerprinting Radio Maps

One of the main problems of Indoor Positioning Systems (IPSs) based on WiFi fingerprinting is the radio map maintenance. It is well known that the creation of the radio map is a tedious and long-time task. In addition, if sometime after its creation, some access points are removed from the environment the accuracy of the IPS can be dramatically affected. This paper presents a new methodology to deal with this problem using imputation based techniques. An extensive set of experiments, comparing different imputation techniques, has been performed to demonstrate the benefits of using the proposed approach, showing that the proposed method is able to reduce the localization error in almost one meter with respect to a well-known solution.
Delay-Constrained Utility Maximization for Video Ads Push in Mobile Opportunistic D2D Networks

It is a significant challenge for device-to-device (D2D) networks to deliver mobile videos among mobile users due to the highly nondeterministic and intermittent connectivity. In this paper, we propose to integrate the random mobility of users in mobile opportunistic D2D networks with crowdsourcing to push mobile video Ads. Incentives are key to the success of video Ads push as it heavily depends on how actively mobile users participate in it. To stimulate users to perform mobile video Ads push tasks, we model the interaction between depositories and the Ad provider as a reverse auction. More specifically, we try to maximize the utility of Ad provider by selecting a subset of depositories before a specified deadline. We first propose an online auction (OA) algorithm, which runs efficiently in polynomial time, guarantees individual rationality, profitability. However, it does not guarantee truthfulness and thus limits its practicality. We then introduce two truthful OA algorithms, i.e., TOA and TOA-MM. We carry out trace-driven simulation to verify the three OA algorithms. The simulation results corroborate that the proposed algorithms have superior performance and efficiently stimulate mobile users to make contribution to video Ads push.
Clinical Whole Body CBM Parametric PET with Flexible Scan Modes

We implemented clinical whole body parametric PET with flexible CBM scan modes. We designed a method to accurately track dynamic CBM scans and calculate time information for axial image slices based on bed position tags. This approach of calculating time information based on finelysampled bed position tags can be readily applied to flexible CBM scan modes with various scan speeds over different body regions. We applied the calculated image slice time information for whole body parametric PET using a linear Patlak model. Parametric images were reconstructed directly from dynamic sinogram data with a nested EM algorithm. We demonstrated that parametric images may provide more information in addition to SUV images for tumor detection and diagnosis.
Particulate Matter Air Pollution: Effects on the Cardiovascular System

Air pollution is a complex mixture of gaseous and particulate components, each of which has detrimental effects on human health. While the composition of air pollution varies greatly depending on the source, studies from across the world have consistently shown that air pollution is an important modifiable risk factor for significantly increased morbidity and mortality. Moreover, clinical studies have generally shown a greater impact of particulate matter (PM) air pollution on health than the gaseous components. PM has wide-ranging deleterious effects on human health, particularly on the cardiovascular system. Both acute and chronic exposure to PM air pollution is associated with increased risk of death from cardiovascular diseases including ischemic heart disease, heart failure, and ischemic/thrombotic stroke. Particulate matter has also been shown to be an important endocrine disrupter, contributing to the development of metabolic diseases such as obesity and diabetes mellitus, which themselves are risk factors for cardiovascular disease. While the epidemiological evidence for the deleterious effects of PM air pollution on health is increasingly accepted, newer studies are shedding light on the mechanisms by which PM exerts its toxic effects. A greater understanding of how PM exerts toxic effects on human health is required in order to prevent and minimize the deleterious health effects of this ubiquitous environmental hazard. Air pollution is a growing public health problem and mortality due to air pollution is expected to double by 2050. Here, we review the epidemiological evidence for the cardiovascular effects of PM exposure and discuss current understanding about the biological mechanisms, by which PM exerts toxic effects on cardiovascular system to induce cardiovascular disease.
Delphi study to identify the young people priorities about digital society

This paper aims to present an advance of the results of the application of the Delphi technique, with the aim of gathering the opinion of experts or stakeholders on the topics of priority interest of young people in Europe, especially on the digital society. It is a work framed within the Horizon 2020 WYRED Project (netWorked Youth Research for Empowerment in the Digital Society). The WYRED project, and in this specific case the Networking process, aims in bringing together children, young people, other stakeholders and policymakers around Europe. The Delphi technique is one of the prospective methods that study the future, concerning the evolution of the factors of the techno-socio-economic environment and the interactions between these factors. The characteristics that define this technique are anonymity, iteration and controlled feedback, and presentation of group data in the statistical form. The progress of the presented results allows us to corroborate the agreement of experts about the topics that have been raised. According to experts, the concern of youth towards issues related to the digital society does not appear among the first concerns of young people neither in Spain nor the rest of the countries surveyed.
SmartLET: Learning analytics to enhance the design and orchestration in scalable, IoT-enriched, and ubiquitous Smart Learning Environments

This paper presents the SmartLET project, a coordinated research project funded by the Spanish Ministry of Science, Innovation and Universities, which just started in 2018. The main aim of this project is to provide support for the design and orchestration of Smart Learning Environments (SLEs) with the support of learning analytics and the Internet of Things. This paper gives an overview of our conception of SLEs based on previous works, provides some ideas about the connection of learning design and orchestration with SLEs, and analyses different ethical and privacy issues for SLEs. In addition, an initial hypothesis and some specific objectives for a support environment for SLEs are proposed.
Quantitative evaluation of breast CT reconstruction by means of figures of merit based on similarity metrics

Iterative CT reconstruction algorithms coupled with edge-preserving filters are attracting a growing interest in the field of biomedical X-ray imaging. In many cases such algorithms demonstrate improved reconstruction quality compared with analytical reconstruction algorithms, for instance in the case of measurements with a reduced number of projections. Their performance is often evaluated on test phantoms using conventional figures of merit, as for example contrast-to-noise ratio or spatial resolution at sharp edges. However, this approach can lead to an optimistic evaluation of the reconstruction quality: compared to test phantoms, biomedical images typically present complex structures and strong inhomogeneities. The reconstruction algorithms can generate artifacts that can hide small details, produce fake structures or alter the shape of actual ones. Figures of merit based on similarity metrics are a valuable tool for an objective evaluation of the reconstruction quality, in particular in the case of biomedical images.In this work we present an application of such figures of merit to breast-CT reconstruction with a simultaneous algebraic reconstruction technique (SART) algorithm combined with a bilateral filter, at varying number of projections. The results show that, for a given reconstruction, the figures of merit are fairly stable for a wide range of variations of the bilateral filter parameters, thus showing the robustness of the reconstruction technique.
EMG Classification by using Swarm Intelligence for Myoelectric Prosthetic Hand

In recent years, myoelectric prosthetic hand (MPH) has been extensively studied due to the spread of 3D printers. However, it cannot do precisely movement now because it is difficult to identify electromyogram (EMG) by using existing method. The reasons for this are as follows; Hand movement is too complicated to use it as label for supervised learning method, EMG change its characteristics gently with time. Accordingly, we need to develop a new method adapted to MPH. In this study we developed an identification method using swarm intelligence which was optimized to the characteristic of EMG. To verify the function of the method, experiments were conducted. For some subjects, identification rates were high. Moreover, we discussed how to improve the method and conducted some experiments to verify it. It has been considered effective to investigate the optimization method of particle swarms.
Acute aerobic exercise improves memory across intensity and fitness levels

ABSTRACT The present study was designed to examine the impact of exercise intensity and aerobic fitness on free recall, judgments of learning (JOLs), and metacognitive accuracy. In Experiment 1, 30 college students engaged in either (1) no exercise, (2) light exercise (55% of predicted maximal heart rate), or (3) moderate exercise (75% of predicted maximal heart rate) on three different days. In Experiment 2, 29 high-fit students (VO2 max ≥ 70th percentile) and 28 low-fit students (VO2 max ≤ 50th percentile) completed sedentary and light exercise conditions. In both experiments, free recall scores significantly increased in the exercise conditions compared with the sedentary condition, but JOL magnitude and metacognitive accuracy were largely unaffected. These results demonstrate that exercise can improve recall at both light and high intensities, and that the benefit can be obtained by individuals regardless of their fitness level.
Resistive Switching Memory Effect and Conduction Mechanism in Nano-Silver Incorporated Type-A Gelatin Films

This paper presents the conduction mechanisms and the observation of bipolar resistive switching in nano silver incorporated gelatin (AgG) composite films. Different concentrations of commercially purchased silver nanoparticles (0.3 w/v%, 0.5 w/v%, 0.7 w/v%) were incorporated in gelatin and AgG films were spin-coated on ITO substrates. We did systematic study of I- V characteristics in these films. The film with 0.5 w/v% exhibits an abrupt increase in current at 6 V with ON/OFF ratio of more than 3 orders of magnitude. Further, the I- V characteristics revealed O-type hysteresis behaviour along with hopping type of conduction for higher nano particle concentrations of 0.5 and 0.7w/v%. However, for much dilute concentration of Ag (0.3 w/v%), the conduction is of ohmic type
IoT Communications Network for Wireless Power Transfer System State Estimation and Stabilization

Wireless energy harvesting has emerged as an appealing solution to prolong the lifespan of energy-constrained wireless networks and been regarded as a main functional unit for almost all perpetual communications. This paper proposes a channel coding-based Internet of Things communication network, and state estimation as well as stabilization algorithms for wireless power transfer systems. After modeling the energy harvesting module like wireless energy transfer systems as a state-space framework, Web-enabled smart sensors are deployed to obtain measurements. The sensing information is transmitted to the nearby base station for collection and digital communication under the condition of packet losses. A recursive systematic convolutional code is used to add redundancy into the quantize system states. After digital modulation, the signal is transmitted to a control center for system state estimation and stabilization. The optimal gain of the state estimation scheme is determined after minimizing the mean squared errors. Afterward, a stabilization algorithm is designed considering packet losses in the communication networks using the semidefinite programming approach. Furthermore, the convergence of the developed estimation algorithm is proved. Numerical simulation results demonstrate, considering packet loss, sensor faults, disturbances, and delay into account, that the proposed algorithms can estimate and stabilize the system states within a short period of time.
Single Image Haze Removal using Image Processing Algorithms

The pictures taken in awful climatic conditions will adversely influence the picture quality. Fog and haze degrades the picture standard, air-light lessens whiteness of the image and attenuation lessens its contrast. This paper provides colour attenuation prior for haze removal of the image. The main aim is to solve the problem of reduced visibility by excellent image processing strategy and the purpose is to improve the quality of images that lack the shading, complication and depth when perceived by the human eye. By creating a straight model for display the scene extent of the hazy image, we can easily determine the transmission and resume the scene radiance via the atmospheric scattering model.
Comparative Study of X- and γ-Ray Detectors with MoOx, TiOx and TiN Schottky Contacts

Electrical characteristics of the Mo-MoO<inf>x</inf>/p-CdTe/MoO<inf>x</inf>-Mo, Ti-TiO<inf>x</inf>/p-CdTe/MoO<inf>x</inf>-Mo and Ti-TiN/p-CdTe/MoO<inf>x</inf>-Mo structures, manufactured by magnetron sputtering of the thin films on CdTe semi-insulating crystals, produced by Acrorad Co. Ltd were studied. The optimization of conditions of the substrate pretreatment and the contacts deposition allowed to reduce the dark current of the Mo-MoO<inf>x</inf>/p-CdTe/MoO<inf>x</inf>-Mo detectors compared to earlier analogs and, consequently, to improve its spectrometric characteristics. There was shown that the structure Mo-MoO<inf>x</inf>/p-CdTe/MoO<inf>x</inf>-Mo, TiTiO<inf>x</inf>/p-CdTe/MoO<inf>x</inf>-Mo and Ti-TiN/p-CdTe/MoO<inf>x</inf>-Mo can be used for practical applications in the X- and γ-ray detectors.
Application and Resulting Suitability of a Genetic Algorithm in the Design of FGMOS-based CMOS-MEMS Transducers

In this work an initial approach and requirements overview is performed in order to promote awareness on the compatibility between CMOS – MEMS sensor devices design and the group of heuristic techniques known as genetic algorithms. As might be known, genetic algorithms (GAs) main application is in the field of multivariable functions optimization and this kind of iterative procedures in their simplest forms may be suitable to serve as a tool in the automation of design of integrated CMOS devices and technologies where the variables and restrictions are known. FGMOS-based devices along with MEMS structures when embedded in a single-chip platform (CMOS – MEMS technology) are expected to be in compliance with a set of design rules for both their mechanical and electrical properties, making easier to code and decode variables for their use in a GA in a discrete always-positive basis. In this approach, parasitic and process-resolution-related issues are neglected and further analysis based in a more detailed modeling and parameter restrictions is encouraged.