Miracle-dz/UniFAR-FTUs huggingface dataset:
Clastogenic and Cytotoxic Effects of Four Pesticides Used to Control Insect Pests of Stored Products on Root Meristems of Allium cepa

While the use of pesticides of stored product has increased productivity in agriculture, there is concern about their use because some are mutagens and/or carcinogens, harm non-target organisms or cause pest resistance. In this study Grain Treat (GT) (Mercaptothion, 10 g kg, Permethrin 1.5 g kg); QuickPhos (QP) (Aluminium Phosphide, 560 g kg); Nuvan Profi (NP) (Dichlorvos, 124 g kg) and Eriocephalus punctulatus plant smoke condensate (EPSC) were evaluated for cytotoxicity and the induction of genotoxicity in the onion (Allium cepa) test. Onion seeds were germinated and exposed to pesticides (mg ml), GT (12.5, 25, 50); QP (0.75, 1.5, 3.0); NP (0.064, 0.128, 0.256); EPSC (0.0025, 0.0049, 0.0098) for 24 hours. For each concentration, three root tips were transferred to three microscope slides, stained with aceto-carmine, covered with cover slip, squashed and observed microscopically. The cytotoxicity and genotoxicity induced by each pesticide concentration was compared with the value for the concomitant negative control using t-test. Only QP (3.0 mg mL) and NP (0.256 mg mL) significantly depressed the MI, i.e. cytotoxic (P < 0.05). Genotoxicity was determined by examining, 100 anaphase and telophase cells on each of three slides per concentration for chromosome aberration (CA). The three concentrations of each pesticide induced CAs (P < 0.05) in the following general order of frequency; stickiness > multipolarity > c-mitosis > anaphase and telophase bridges > chromosome Largards. The induction of sticky chromosomes indicated that the pesticides caused abnormal DNA condensation, abnormal chromosome coiling and inactivated the spindles. Because abnormalities of the cell division process results from the genotoxic effects of environmental chemicals, the four pesticides have the potential to cause aneuploidy in exposed organisms and adverse human health and environmental effects.
A Comprehensive Self-Interference Model for Single-Antenna Full-Duplex Communication Systems

Single-antenna full-duplex communication technology has the potential to substantially increase spectral efficiency. However, limited propagation domain cancellation of single-antenna system results in a higher impact of receiver chain nonlinearities on the residual self-interference (SI) signal. In this paper, we offer a comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase/quadrature (IQ) imbalances, phase noise effect, and receiver noise figure. To validate our model, we also propose a more appropriate digital SI cancellation approach considering receiver chain RF and baseband nonlinearities. The proposed technique employs orthogonalization of the design matrix using QR decomposition to alleviate the estimation and cancellation error. Finally, through circuit-level waveform simulation, the performance of the digital cancellation strategy is investigated, which achieves 20 dB more cancellation compared to existing methods.
Modeling and inverting radio occultation signals in the moist troposphere

Accurate modeling of radio occultation signals is performed by solving the Helmholtz equation with the use of a multiple‐phase‐screen technique. Refractivity is assumed spherically symmetric, and vertical profiles are reproduced from high‐resolution tropical radiosondes. As a result, the characteristics of the signals, which are important for their tracking in low Earth orbit, are evaluated: the spectral bandwidth, ∼50 Hz, and the random phase acceleration, ∼1000 Hz/s. The complex signals are inverted with the use of two radio holographic methods: back propagation and sliding spectral (radio optics). For the back propagation method, finding the position of the auxiliary trajectory which provides an unambiguous bending angle function of impact parameter appears to be a problem. For the sliding spectral method a simple technique, which takes into account the whole spectral content of the signal without identification and selection of local spectral maxima, is introduced and tested. The sliding spectral method allows for the stable reconstruction of bending angles and refractivity with vertical resolution of ∼0.5 km. The small‐scale laminated structure of refractivity results in propagation of radio occultation signals down to significantly lower observation altitudes than in the case of smooth refractivity. Information content of radio occultation signals at those low altitudes is important for the radio holographic inversions.
Roadmap to a Cosmic Model that Supports Nonphysical and Nonlocal Phenomena

Dark matter and dark energy have characteristics in common with other enigmatic phenomena, such as quantum waves, entanglement and mental activity. They all seem to have nonphysical or nonlocal qualities. Basic assumptions of string theory are discussed to find a new understanding of the theory that is capable of modelling nonphysical and nonlocal phenomena. The six extra spatial dimensions of string theory are proposed to be open and capable of modelling two additional parallel, nonphysical universes/branes that can be observed by introspection and referred to as mental. The predicted extra particles of string theory are understood to be nonphysical, populating the nonphysical universes, unable to collide with physical matter, but having a mutual influence by means of gravity, such as required by the string theory. Dark matter and dark energy could exist in these two parallel universes and could be the storage medium of our memories and personality. The negative solution of the energy equation could point to a real phenomenon in this model, - negative energy could be the seat of consciousness in a separate fourth parallel universe/brane.
Extraction, purification and characterization of hyaluronic acid from Rooster comb

Hyaluronic acid, is extract by different procedures from various sources like pig, rabbit, oxes and human are available, but these processes have certain imitations like low yield, and also it requires the killing of these animals which is against the experimental ethics. In the present study, we have carried out the extraction of hyaluronic acid from cockâ€™s comb which was further analyzed with qualitative test, viscosity, UV absorption, endotoxin detection assay. Also, the protein contamination of extracted hyaluronic acid was determined by using SDS-PAGE of hyaluronic acid was studied for checking the protein contaminants and it was noted that there were no bands observed in the well loaded with extracted hyaluronic acid sample indicating that the final extract of hyaluronic acid is not contaminated with the protein. The extraction and purification of hyaluronic acid by using the method reported here give pure hyaluronic acid. The viscosity of extracted hyaluronic acid was found to be 2.55 poise which is economical and can be used for industrial production of hyaluronic acid having clinical applications
Recovering low-rank structure from multiple networks with unknown edge distributions

In increasingly many settings, particularly in neuroimaging, data sets consist of multiple samples from a population of networks, with vertices aligned across networks. For example, fMRI studies yield graphs whose vertices correspond to brain regions, which are the same across subjects. We consider the setting where we observe a sample of networks whose adjacency matrices have a shared low-rank expectation, but edge-level noise distributions may vary from one network to another. We show that so long as edge noise is sub-gamma distributed in each network, the shared low-rank structure can be recovered accurately using an eigenvalue truncation of a weighted network average. We also explore the extent to which edge-level errors influence estimation and downstream inference tasks. The proposed approach is illustrated on synthetic networks and on an fMRI study of schizophrenia.
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.
The male-female wage gap in France: an analysis using non-parametric methods

Using the French data set A¢â‚¬A“2002 Employment SurveyA¢â‚¬? , this paper aims to shed light on the nature of the gender wage differential in France, exploring the added-value of a non-parametric analysis over previous knowledge based on parametric estimates. The parametric decomposition of the gender wage gap is run following various assumptions on the A¢â‚¬A“referenceA¢â‚¬? wage structure and on sample selection. It is applied to different sub-populations (wage earners, full time workers only, private or public sector workers only). Furthermore, we analyze the contribution of each productivity characteristic in explaining the wage gap. One can highlight two main limitations in this standard OaxacaA¢â‚¬â„¢s decomposition methodology. First, it is based on parametric assumptions about the form of the conditional expected earnings function, which can induce specification errors. Secondly, the gender earnings gap is measured at the mean, thereby ignoring the differences in the form of the entire earnings distribution. Hence, we implement a non-parametric procedure to analyze the influence of workersA¢â‚¬â„¢ productive characteristics on gender differences in the distribution of wages. The main estimation problem is thus to construct a counterfactual wage density that would prevail for men (women) if they had women (men) A¢â‚¬â„¢s distribution of characteristics. This counterfactual density can be estimated by applying standard nonparametric Kernel density estimation techniques to a re-weighted sample of men (women). We are then able to bring out the role of male-female variations in each productive characteristic in explaining the differences in densities.
The Entrepreneurial Network of Muslim Women Entrepreneurs: The Study of Multiple-Cases of Small Businesses in the Southern Region of Malaysia

This paper seeks to empirically explore the dynamics roles of strong and weak ties within Muslim female entrepreneurs’ networks during critical problems (“tipping points”), and how this affects the subsequent performance of the small businesses. The literature review shows that many studies have explored the mix of strong and weak ties in small businesses linking them to stages of business development. More recent literature, however, has conceptualized firm growth through the notion of critical problems or “tipping points” that must be successfully tackled in order to continue on a development path. While the importance of network ties has been well documented in relation to growth stages, limited work has investigated the contribution that strong and weak ties make as a firm faces specific “tipping points,” thus showing a call for further investigation. This is a qualitative study involving four cases of small-sized food manufacturing firms in the Southern region of Malaysia. The main data collection method is an in-depth interview with the owner-managers using critical incident interview technique. The triangulation of data has been carried out by interviewing individuals who are seen as strong and weak ties of the network. They suggest that the dynamics roles of strong and weak ties are essential during the “tipping points. ” The findings have implications for firms, networks and the government. It also provides a detailed understanding on the nature of entrepreneurial network of entrepreneurs in Malaysia. Recommendations are presented that address the need to support small businesses in dealing with critical problems in relation to their networking practices. Keywords: Social Network; Network Ties; Growth; Tipping Points; Small Businesses JEL Classifications: M00
Analysis of Exhaust Manifold using Computational Fluid Dynamics

Overall engine performance of an engine can be obtained from the proper design of engine exhaust systems. With regard to stringent emission legislation in the automotive sector, there is a need design and develop suitable combustion chambers, inlet, and outlet manifold. Exhaust manifold is one of the important components which affect the engine performance. Flow through an exhaust manifold is time dependent with respect to crank angle position. In the present research work, numerical study on four-cylinder petrol engine with two exhaust manifold running at constant speed of 2800 rpm was studied. Flow through an exhaust manifold is dependent on the time since crank angle positions vary with respect to time. Unsteady state simulation can predict how an intake manifold work under real conditions. The boundary conditions are no longer constant but vary with time. The main objectives that to be studied in this work is: • To prepare the cad model in the CATIA software by using the actual parametric dimensions. • To prepare finite element model in the Computer aided analysis software by specifying the approximate element size for meshing. • To find and calculate the actual theoretical values for the input boundary conditions. • To study the flow patterns generated due to the flow of the exhaust gases from the manifold. • To study the velocity and pressure distribution in ports at maximum flow rate. • To study the static pressure drop, total pressure drop, and energy loss in the flow pattern generated in the exhaust manifold.
Design and Characterization of the Dynamic Robotic Actuator Dyrac

A new variable stiffness actuator (VSA) is presented, designed for reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular. Existing VSAs show good performance in terms of either quick stiffness changing time, high output velocity, or variable damping, but not in the combination required for highly dynamic tasks. Goal of the presented design is to reach with one system a stiffness changing time of 50 ms, a peak output velocity of 20 rad/s and variable damping, based on the requirements of literature and our previous research. A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper. Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper. Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator particularly suitable for highly dynamic tasks, while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Disociación enzimática de músculos largos de ratón: un modelo para el estudio de los tipos de fibras del músculo esquelético

La disociación enzimática de músculos cortos de ratón, como el flexor digitorum brevis, ha permitido la acumulación de gran cantidad de información fisiológica, farmacológica y bioquímica sobre el músculo esquelético, la cual, sin embargo, ha estado limitada a los tipos de fibras presentes en estos músculos. La acumulación de información sobre los demás tipos de fibras ha sido escasa y se ha logrado mediante el aislamiento manual de fibras en otras especies, clásicamente en ratas: un procedimiento difícil y que toma mucho tiempo. En este trabajo se presenta una descripción de la técnica que permite la disociación enzimática de músculos largos de ratón como el soleus o el extensor digitorum longus (EDL), lo cual aumenta el espectro de tipos de fibras y el número de ellas disponibles para el estudio de los fenómenos biológicos en el músculo esquelético. Además se compara la cinética de los transitorios de Ca2+ en fibras de soleus y EDL y se prueba la utilidad del modelo en otros experimentos que utilizan técnicas de fisiología, bioquímica y biología celular. Esto abre la posibilidad de utilizar todo el espectro de fibras musculares para estudiar la biología muscular y la fisiopatología de las enfermedades musculares.
Augmenting Visual Feedback Using Sensory Substitution

Direct interaction in virtual environments can be realized using relatively simple hardware, such as standard webcams and monitors. The result is a large gap between the stimuli existing in real-world interactions and those provided in the virtual environment. This leads to reduced efficiency and effectiveness when performing tasks. Conceivably these missing stimuli might be supplied through a visual modality, using sensory substitution. This work suggests a display technique that attempts to usefully and non-detrimentally employ sensory substitution to display proximity, tactile, and force information. We solve three problems with existing feedback mechanisms. Attempting to add information to existing visuals, we need to balance: • not occluding the existing visual output; • not causing the user to look away from the existing visual output, or otherwise distracting the user; and • displaying as much new information as possible. We assume the user interacts with a virtual environment consisting of a manually controlled probe and a set of surfaces. Our solution is a pseudo-shadow: a shadow-like projection of the user’s probe onto the surface being explored or manipulated. Instead of drawing the probe, we only draw the pseudo-shadow, and use it as a canvas on which to add other information. Static information is displayed by varying the parameters of a procedural texture rendered in the pseudo-shadow. The probe velocity and probe-surface distance modify this texture to convey dynamic information. Much of the computation occurs on the GPU, so the pseudo-shadow renders quickly enough for real-time interaction. As a result, this work contains three contributions: • a simple collision detection and handling mechanism that can generalize to distancebased force fields; • a way to display content during probe-surface interaction that reduces occlusion and spatial distraction; and • a way to visually convey small-scale tactile texture.
A Novel Feature Representation for Single-Channel Heartbeat Classification based on Adaptive Fourier Decomposition

This paper proposes a novel approach for heartbeat classification from single-lead electrocardiogram (ECG) signals based on the novel adaptive Fourier decomposition (AFD). AFD is a recently developed signal processing tool that provides useful morphological features, referred to as AFD-derived instantaneous frequency (IF) features, that are different from those provided by traditional tools. A support vector machine (SVM) classifier is trained with the AFD-derived IF features, ECG landmark features, and RR interval features. To evaluate the performance of the trained classifier, the Association for the Advancement of Medical Instrumentation (AAMI) standard is applied to the publicly available benchmark databases, including MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database, to classify heartbeats from single-lead ECG. The overall performance in terms of sensitivities and positive predictive values is comparable to the state-of-the-art automatic heartbeat classification algorithms based on two-leads ECG.
A hybrid algorithm based on binary chemical reaction optimization and tabu search for feature selection of high-dimensional biomedical data

In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance assessment of at-risk patients and improve disease diagnosis, treatment, and prevention. However, these datasets usually have many features, which contain many irrelevant or redundant information. Feature selection is a solution that involves finding the optimal subset, which is known to be an NP problem because of the large search space. Considering this, a new feature selection approach based on Binary Chemical Reaction Optimization algorithm (BCRO) and k-Nearest Neighbors (KNN) classifier is presented in this paper. Tabu search is integrated with CRO framework to enhance local search capacity. KNN is adopted to evaluate the quality of selected candidate subset. The results for an experiment conducted on nine standard medical datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.
Euclid: Nonparametric point spread function field recovery through interpolation on a graph Laplacian

Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error, and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy. Aims. The aims of this paper are twofold. Firstly, we took steps toward a nonparametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Secondly, we studied the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in an Euclid scenario. Methods. We extended the recently proposed resolved components analysis approach, which performs super-resolution on a field of under-sampled observations of a spatially varying, image-valued function. We added a spatial interpolation component to the method, making it a true 2-dimensional PSF model. We compared our approach to PSFEx, then quantified the impact of PSF recovery errors on galaxy shape measurements through image simulations. Results. Our approach yields an improvement over PSFEx in terms of the PSF model and on observed galaxy shape errors, though it is at present far from reaching the required Euclid accuracy. We also find that the usual formalism used for the propagation of PSF model errors to weak lensing quantities no longer holds in the case of an Euclid-like PSF. In particular, different shape measurement approaches can react differently to the same PSF modeling errors.