Miracle-dz/UniFAR-FTUs huggingface dataset:
Kenya’s perspective on Ecosan

Around one billion people of the world are slum dwellers, which live in inadequate housing, under health and life-threatening circumstances that perpetuate cycles of poverty (UNPF, 2007). There is an increasing recognition of the links between poverty and poor sanitation. Most of the rural population in Africa, including Kenya does not have access to safe and reliable toilets, which are vital to provide a barrier to diseases carried in fecal matter. Diarrhea caused by feces contaminating food and water kills nearly two million children every year, due to the fact that one gram of untreated feces can contain 10 million viruses, 1 million bacteria, 1 thousand parasite cysts and a hundred worm eggs (UN, 2006). Sanitation coverage in Kenya is estimated to be only 50%, and 11% of Kenyans (about 6 million) do not have access to any kind of toilet and practice open defecation in fields, rivers, buckets, plastic bags, in gutters and by train tracks (MWI, 2009). The novel misconception exists that human excretes are wastes with no useful purpose. Ecological sanitation (EcoSan) challenges this mistaken belief. EcoSan has three main features: the containment of human excreta, its sanitization and recycling back to the soil. This paper emphasizes EcoSan in Kenya, including topical situation of sanitation sector, excreta disposal attitudes and practices, alongside with the different types of selected projects. Associated to EcoSan background history activities achievements problems and examples of acceptance are also reviewed.
Synthesis and Antifungal Activity of Some New Fluorine-Substituted 4-Thiazolidinone Bearing 1,2,4-Triazinone

Fluorine substituted 4-thiazolidinone 5 bearing 1,2,4-triazinone obtained from the condensation of 3-Amino-6(2’-aminophenyl)-1,2,4-triazin-5(4H)-one (2) with an aromatic aldehyde followed by cycloaddition with mercaptoacetic acid afforded the thiazolidinone (4), and treatment with ethyl trifluoroacetate. Structure of the products has been deduced from their correct elemental analysis and spectral measurements. The antifungal activity of the new fluorinated target also has been evaluated.
The Moderating Effect of Subjective Norm on Cloud Computing Users’ Perceived Risk and Usage Intention

The high popularity and high demand of internet applications lead to faster development of cloud computing services that make network access become more convenient. However, using a new technology can cause users’ perceived risk because they worry about internet security and invasion of privacy. The study aims to explore the influence of end users’ perceived risk on usage intention of cloud computing services and the interactive effect of perceived risk and subjective norm on usage intention. The study dispatched 350 questionnaires to internet users. The amount of valid questionnaires is 238 and the effective response rate is 68%. The results showed that both perceived risk and subjective norm are significantly affected to usage intention, and subjective norm has a moderating effect on the relationship between perceived risk and usage intention.
Fully-integrated LDO voltage regulator for digital circuits

Abstract. Low-dropout (LDO) voltage regulators are widely used to supply low-voltage digital circuits. For recent ultra-low-power microcontroller systems, a fully-integrated LDO without any external capacitance is preferred in order to achieve a fast and energy-efficient wake-up. Commonly, an LDO is specified, designed and verified for DC load currents. In contrast, a digital load creates large current spikes. As an LDO designed for low quiescent current is too slow to react on fast current spikes, a minimum on-chip capacitance is required to keep the supply voltage within a certain error window. Different fully-integrated LDO topologies are investigated regarding their suitability to supply low-voltage digital circuits. The any-load stable LDO topology is selected and implemented on a 0.13 μm test-chip. The LDO is able to provide a maximum load current of 2.5 mA while consuming a quiescent current of 17 μA.
The Oxidative Stress Balance Measured in Humans with Different Markers, Following a Single Oral Antioxidants Supplementation or a Diet Poor of Antioxidants

Four different markers of oxidative stress [OS] (8-OHdG in urine, 8-Iso-PGF, hydroperoxides and carbonylated proteins in plasma, a new marker of antioxidant capacity (AC) in plasma/urine/saliva, and hs-CRP were determined concomitantly in twelve apparently healthy volunteers. All the markers were determined at 8 am, 10 am, 12 am in three different moments: after a week of normal diet (baseline), after an acute supplementation with an antioxidant pool, and finally following a week of a diet poor in antioxidant. The supplementation of antioxidants determined a significant (t test p < 0.05) decrease up to 12% of 8-OHdG in urine and up to 46% of carbonylated proteins in plasma, whereas hydroperoxides and 8-Iso-PGF were unmodified; the antioxidant capacity increased significantly (t test p < 0.05) up to 19%, 78%, and 67%, respectively in plasma, urine and saliva. Hs-CRP was unchanged.The diet poor in antioxidant caused significant increases (t test p < 0.01) of hydroperoxides (up to 24%), 8-Iso-PGF 23 (up to 69%), carbonylated prot eins (up to 76%) and 8-OHdG (up to 16%): hs-CRP increase reached 72% despite the levels were still within the normal range. Any reduction of soluble antioxidants activity in plasma was detected, whereas in urine and saliva a reduction of 45% and 38% respectively was shown. In conclusion, the antioxidant surplus determined by a single antioxidants pool administration seems to protect DNA and proteins from oxidation. On the contrary the shortage of antioxidant intake increases all the markers of OS, particularly those related to lipids and proteins, whereas the DNA seems to be protected more efficiently. The AC in plasma tends to be constant, and the limitation of antioxidants intake is followed by reduction of AC in urine and saliva.
The changing landscape of religious affiliation in Brazil between 1980 and 2010: age, period, and cohort perspectives

There has been a remarkable decline in the number of Catholics in Brazil over the last few decades, a fact that is attributed to the growth of Pentecostal churches and to an increase in the number of people with no religious affiliation. We analyzed the age, period, and cohort effects associated with religious affiliation in Brazil from 1980 to 2010, applying the Hierarchical Age-Period-Cohort and Cross-Classified Random Effects Model. We observed that age effects were significant but low for Catholics and Pentecostals, and were substantial for those with no religious affiliation, with a negative relationship. For these first two religious affiliations, period effects were of greater magnitude with clear trends: negative for Catholics and positive for Pentecostals. Cohort effects were significant for all three affiliations, but magnitudes were lower than the other two effects. We also verified that the likelihood of being a Pentecostal decreased with formal education, and the opposite occurred for persons with no religious affiliation. These effects, however, were smaller for younger cohorts, possibly due to the reduced social selectivity of recent students pursuing higher education and the increase in overall levels of education.
Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

Despite the tremendous progress that has been made in data assimilation (DA) methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS). Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction) centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction) Gridpoint Statistical Interpolation (GSI) DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS), which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues. Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled. We have proposed four different calibration schemes to compensate for the missing trailing eigenvectors. Results show that the method with calibration for a small number of eigenvectors cannot pick up the observation impacts over the regions with fewer observations as well as a benchmark with a large number of eigenvectors, but proper calibrations do enhance and improve the impact signals over regions with more data. When compared with the observation locations, the method generally captures the OIS over regions with more observation data, including satellite data over the southern oceans. Over the tropics, some observation impacts may be missed due to the smaller background errors specified in the GSI, which is not related to the method. It is found that a large number of eigenvectors are needed to retrieve impact signals that resemble the banded structures from satellite observations, particularly over the tropics. Another benefit from the Lanczos method is that the dominant eigenvectors can be used in preconditioning the conjugate gradient algorithm in the GSI to speed up the convergence.
Drought severity–duration–frequency curves: a foundation for risk assessment and planning tool for ecosystem establishment in post-mining landscapes

Eastern Australia has considerable mineral and energy resources, with areas of high biodiversity value co-occurring over a broad range of agro-climatic environments. Lack of water is the primary abiotic stressor for (agro)ecosystems in many parts of eastern Australia. In the context of mined land rehabilitation quantifying the severity–duration–frequency (SDF) of droughts is crucial for successful ecosystem rehabilitation to overcome challenges of early vegetation establishment and long-term ecosystem resilience. The objective of this study was to quantify the SDF of short-term and long-term drought events of 11 selected locations across a broad range of agro-climatic environments in eastern Australia by using three drought indices at different timescales: the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), and the Standardized Precipitation-Evapotranspiration Index (SPEI). Based on the indices we derived bivariate distribution functions of drought severity and duration, and estimated the recurrence intervals of drought events at different timescales. The correlation between the simple SPI and the more complex SPEI or RDI was stronger for the tropical and temperate locations than for the arid locations, indicating that SPEI or RDI can be replaced by SPI if evaporation plays a minor role for plant available water (tropics). Both short-term and long-term droughts were most severe and prolonged, and recurred most frequently in arid regions, but were relatively rare in tropical and temperate regions. Our approach is similar to intensity–duration–frequency (IDF) analyses of rainfall, which are crucial for the design of hydraulic infrastructure. In this regard, we propose to apply SDF analyses of droughts to design ecosystem components in post-mining landscapes. Together with design rainfalls, design droughts should be used to assess rehabilitation strategies and ecological management using drought recurrence intervals, thereby minimising the risk of failure of initial ecosystem establishment due to ignorance of fundamental abiotic and site-specific environmental barriers, such as flood and drought events.
AUDITORY SCENE CLASSIFICATION USING ENSEMBLE LEARNING WITH SMALL AUDIO FEATURE SPACE Technical Report

The report presents the results of an analysis of audio feature space for auditory scene classification. The final small feature set was determined by the selection of the attributes from various representations. Feature importance was calculated exploiting the Gradient Boosting Machine. A number of classifiers were employed to build the ensemble classification scheme, and majority voting was performed to obtain the final decision. In the result, the proposed solution uses 223 attributes and outperforms the baseline system by over 6 per cent.
An Efficient Strategy for Online Performance Monitoring of Datacenters via Adaptive Sampling

Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, and capacity planning decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, this paper develops and validates a strategy based on adaptive-rate compressive sampling. It exploits the fact that the signals of interest often can be sparsified under an appropriate representation basis and that the sampling rate can be tuned as a function of sparsity. We use the Trade6 application as our experimental platform and measure the signals of interest—in our case, signals pertaining to memory and disk I/O activity—using adaptive sampling. We then evaluate whether the reconstructed signals can be used for trend detection to track the gradual deterioration of system performance associated with software aging. Our experiments show that the signals recovered by our methods can be used to detect, with high confidence, the existence of trends within the original signal. We also evaluate the reconstructed signals for threshold-violation detection wherein the magnitude of the signal exceeds a preset value. Our experiments show that performance bottlenecks and anomalies that manifest themselves in portions of the signal where its magnitude exceeds a threshold value can also be detected using the reconstructed signals. Most importantly, detection of these anomalies is achieved using a substantially reduced sample size—a reduction of more than 70 percent when compared to the standard fixed-rate sampling method.
A Observação Participante enquanto metodologia de investigação qualitativa

Enquanto metodo de investigacao qualitativa, a Observacao Participante possibilita obter uma perspetiva holistica e natural das materias a serem estudadas. No presente artigo descrevem-se e discutem-se alguns aspetos inerentes a esta tecnica. Apos a fundamentacao metodologica, sumariam-se as diversas praticas de Observacao Participante, distinguindo-se o papel do observador participante do mero observador. Apos discussao das vantagens e limites, sugere-se complementar esta metodologia de investigacao com outras, no sentido de se apurar o entendimento da problematica em analise sob diversas perspetivas.
DCASE 2018 Challenge baseline with convolutional neural networks

The Detection and Classification of Acoustic Scenes and Events (DCASE) is a well-known IEEE AASP challenge consisting of a number of audio classification and sound event detection tasks. DCASE 2018 has five tasks: 1) Acoustic scene classification, 2) General-purpose audio tagging, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio tagging. In this paper, we describe our Python implementation of all of these tasks, which we have released under an open-source license. The baseline source code contains the implementation of convolutional neural networks (CNNs), including AlexNetish and VGGish -- networks originating from computer vision. We researched how the performance varies from task to task with the same configuration of neural networks. Experiments show that the deeper VGGish network performs better than AlexNetish on all tasks except Task 1, where VGGish and AlexNetish perform similarly. With the VGGish network, we achieve an accuracy of 0.680 on Task 1, a mean average precision (mAP) of 0.928 on Task 2, an area under the curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8\% on Task 4 and an F1 score of 87.75\% on Task 5.
Double rank-ordering technique of ROMA (Rank-Ordered Multifractal Analysis) for multifractal fluctuations featuring multiple regimes of scales

Abstract. Rank-Ordered Multifractal Analysis (ROMA), a technique capable of deciphering the multifractal characteristics of intermittent fluctuations, was originally applied to the results of a magnetohydrodynamic (MHD) simulation. Application of ROMA to measured fluctuations in the auroral zone, due to the dominant physical effects changing from kinetic to MHD as the scale increases, requires an additional level of rank-ordering in order to divide the domain of scales into regimes. An algorithm for the additional step in this double rank-ordering technique is discussed, and is demonstrated in the application to the electric field fluctuations in the auroral zone as an example. As a result of the double rank-ordering, ROMA is able to take into account the nonlinear crossover behavior characterized by the multiple regimes of time scales by providing a scaling variable and a scaling function that are global to all the time scales.
Energy-Weighted Multi-Band Novelty Functions for Onset Detection in Piano Music

Onset detection refers to the estimation of the timing of events in a music signal. It is an important sub-task in music information retrieval and forms the basis of high-level tasks such as beat tracking and tempo estimation. Typically, the onsets of new events in the audio such as melodic notes and percussive strikes are marked by short-time energy rises and changes in spectral distribution. However, each musical instrument is characterized by its own peculiarities and challenges. In this work, we consider the accurate detection of onsets in piano music. An annotated dataset is presented. The operations in a typical onset detection system are considered and modified based on specific observations on the piano music data. In particular, the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold are shown to improve the detection of soft onsets in the vicinity of loud notes. We further present a grouping algorithm which reduces spurious onset detections.
Synchronization crossover of polariton condensates in weakly disordered lattices

We demonstrate that the synchronization of a lattice of solid-state condensates when inter-site tunnelling is switched on, depends strongly on the weak local disorder. This finding is vital for implementation of condensate arrays as computation devices. The condensates here are nonlinear bosonic fluids of exciton-polaritons trapped in a weakly disordered Bose-Hubbard potential, where the nearest neighboring tunneling rate (Josephson coupling) can be dynamically tuned. The system can thus be tuned from a localized to a delocalized fluid as the number density, or the Josephson coupling between nearest neighbors increases. The localized fluid is observed as a lattice of unsynchronized condensates emitting at different energies set by the disorder potential. In the delocalized phase the condensates synchronize, and long-range order appears, evidenced by narrowing of momentum and energy distributions, new diffraction peaks in momentum space, and spatial coherence between condensates. Our work identifies similarities and differences of this nonequilibrium crossover to the traditional Bose-glass to superfluid transition in atomic condensates.
Proposal of Channel Estimation Method for ITS systems by using STBC MIMO-OFDM

This paper proposes a new road-to-vehicle communication system for the future ITS by using the STBC MIMO-OFDM technique which can provide the safety and comfortable driving, and collection of variable information from the network in the realtime to the users on the vehicle. To realize the proposed STBC MIMO-OFDM system, it is required to estimate the channel frequency response at every symbol precisely in the time varying fading channel which is the typical operation conditions for the roadto-vehicle communications system. In this paper, we propose a novel channel estimation method by using the scattered pilots and null sub-carriers inserted into the data sub-carriers both in the frequency and time axes which enables the accurate channel estimation even in the higher time varying fading channel. From the computer simulation results, this paper demonstrates the effectiveness of proposed system which can achieve the higher transmission data rate with keeping the higher signal quality even under the higher mobile ITS environments.
Time Analysis of Pulse-Based Face Anti-Spoofing in Visible and NIR

In this paper we study Presentation Attack Detection (PAD) in face recognition systems against realistic artifacts such as 3D masks or good quality of photo attacks. In recent works, pulse detection based on remote photoplethysmography (rPPG) has shown to be a effective countermeasure in concrete setups, but still there is a need for a deeper understanding of when and how this kind of PAD works in various practical conditions. Related works analyze full video sequences (usually over 60 seconds) to distinguish between attacks and legitimate accesses. However, existing approaches may not be as effective as it has been claimed in the literature in time variable scenarios. In this paper we evaluate the performance of an existent state-of-the-art PAD scheme based on rPPG when analyzing short-time video sequences extracted from a longer video. Results are reported using the 3D Mask Attack Database (3DMAD), and a self-collected dataset called Heart Rate Database (HR), including different video durations, spectrum bands, resolutions and frame rates. Several conclusions can be drawn from this work: a) PAD performance based on rPPG varies significantly with the length of the analyzed video, b) rPPG information extracted from short-time sequences (over 5 seconds) can be discriminant enough for performing the PAD task, c) in general, videos using the NIR band perform better than those using the RGB band, and d) the temporal resolution is more valuable for rPPG signal extraction than the spatial resolution.
Solid State Fermentation Process for Polygalacturonase Production Using Cashew Apple

The aim of this work was the optimization of solid state fermentation for polygalacturonases production using cashew apple dry bagasse as substrate and Aspergillus niger CCT0916. For this, it was observed the influence of moisture content, spore concentration, ammonium sulfate concentration and fermentation temperature on polygalacturonase activity. It was observed that moisture is the limiting factor in the process. Maximum polygalacturonase activity (33 U/g) was obtained with 50 %w.b initial moisture, 10 6 spores/g, 1.5 %(w/w) ammonium sulfate and temperature of 35°C. The models for 21, 29 and 54-hours of fermentation were statistically significant at 95% confidence level.