Miracle-dz/UniFAR-FTUs huggingface dataset:
SYNERGISTIC EFFECT OF SALTS AND CELLULOSE BASED COATING ON SHELF LIFE OF KINNOW ( C ITRUS RETICULATA BLANCO )

Kinnow (Citrus reticulata Blanco) is achieving core consideration globally owing to its nutrition and high juice content. In Pakistan, kinnow export is limited due to inadequate postharvest handling and lack of appropriate storage practices. Considering these issues, the present study was designed to evaluate the effect of hydroxy-propyl-methylcellulose (HPMC) coating in combination with CaCl2 and MgSO4 on shelf life extension of kinnow. Fruits were treated with different levels of CaCl2 and MgSO4 followed by HPMC coating (3 and 5%) and stored at 10°C with 80% relative humidity for 6 weeks. Fruits were analyzed for various physico-chemical parameters on weekly basis. During this study lower fruit firmness (0.24Nm-2), loss in weight (0.64 %) and ethylene production (0.039 μL·kg-1·hr-1) was observed in fruits treated with 1% CaCl2 + 1% MgSO4 + 5% HPMC (T6) during storage of 42 days. Minimum chilling injury indices 0.22% and 0.61% were recorded in treatments T4 and T6, respectively. T6 showed higher values of titerable acidity (0.29%) and ascorbic acid contents (39.82mg/100g). Minimum TSS (9.62°Brix) were found in fruits of T6. Overall, T6 showed significantly better results for various parameters as compared to all other treated and control fruits.
Exact closed-form expressions for the performance of the split-symbol moments estimator of signal-to-noise ratio

Previously, the performance of the split-symbol moments estimator (SSME) of signal-to-noise ratio (SNR) has been evaluated by means of approximate expressions for the estimator mean and variance. These are asymptotic formulas in the sense that they become accurate as the number of estimator samples gets large. Here, exact closed-form expressions are obtained for the same quantities. These expressions confirm the accuracy of the previously derived asymptotic results, and, unlike the asymptotic formulas, they are useful even when the number of samples is small. It is also shown that the conventional split-symbol estimator can be trivially scaled to form a signal-to-noise ratio estimator which is precisely unbiased (as long as the estimate is based on more than two split-symbols).
Ketogenic diet‐induced weight loss occurs independent of housing temperature and is followed by hyperphagia and weight regain after cessation in mice

Ketogenic diets (KDs) are a popular tool used for weight management. Studies in mice have demonstrated that KDs reduce food intake, increase energy expenditure and cause weight loss. These studies were completed at room temperature, a condition below the animal's thermal neutral zone which induces thermal stress. As energy intake and expenditure are sensitive to environmental temperature it is not clear if a KD would exert the same beneficial effects under thermal neutral conditions. Adherence to restrictive diets is poor and consequently it is important to examine the effects, and underlying mechanisms, of cycling from a ketogenic to an obesogenic diet. The purpose of the current study was to determine if housing temperature impacted the effects of a KD in obese mice and to determine if the mechanisms driving KD‐induced weight loss reverse when mice are switched to an obesogenic high fat diet. We demonstrate that KD‐induced reductions in food intake, increases in energy expenditure, weight loss and improvements in glucose homeostasis are not dependent upon housing temperature. KD‐induced weight loss seems to be largely explained by reductions in caloric intake while cycling mice back to an obesogenic diet following a period of KD feeding leads to hyperphagia‐induced weight gain. Collectively, our results suggest that prior findings with mice fed a KD at room temperature are likely not an artifact of how mice were housed and that initial changes in weight when transitioning from an obesogenic to a ketogenic diet or back are largely dependent on food intake.
Learning All-In Collaborative Multiview Binary Representation for Clustering

Multiview clustering via binary representation has attracted intensive attention due to its effectiveness in handling large-scale multiple view data. However, these kind of clustering approaches usually ignore a very important potential high-order correlation in discrete representation learning. In this article, we propose a novel all-in collaborative multiview binary representation for clustering (AC-MVBC) framework, where multiview collaborative binary representation and clustering structure are learned in a joint manner. Specifically, using a new type of tensor low-rank constraint, the high-order collaborations, i.e., cross-view and inner view collaborations, can be effectively captured in our model. Moreover, by incorporating the Bregman discrepancy, the projective consistency among different views can be guaranteed to achieve a more powerful binary representation. An efficient optimization algorithm is also proposed to solve the objective function with fast convergence empirically. Experimental results on several challenge datasets demonstrate that the proposed method has achieved highly competent performance compared with the state-of-the-art multiview clustering (MVC) methods while maintaining low computational and memory requirements.
Improving Precipitation Forecasts by Generating Ensembles through Postprocessing

AbstractThis paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach.Calibrated QPFs are produced for 10 Australian catchments that cover a wide .
Instrumental Uncertainties in Z–R Relationships and Raindrop Fall Velocities

Abstract This study demonstrates the sensitivity of reflectivity–rainfall rate (Z–R) relationships, which were derived from disdrometer-based drop size distribution measurements, to the fall velocity of the drops. The dataset used comes from the simultaneous observation of a series of five moderate rainfall events with a Joss and Waldvogel disdrometer and an optical spectropluviometer. The signal-processing software of the latter was able to measure the residence time of the drops in its sampling volume, enabling computation of the volumetric drop concentration without any assumption on the fall velocity of the drops. The Z–R relationships derived from the two instruments are significantly different. This difference is shown to essentially arise from the drop fall velocities in a twofold manner. First, it comes from the use of a theoretical terminal fall velocity in still air to compute the drop concentration from the drop flux. Second, it comes from the principle of drop size measurement by the impact-ty.
Analytic solution of nonlinear fractional Burgers-type equation by invariant subspace method

In this paper we study the analytic solutions of Burgers-type nonlinear fractional equations by means of the Invariant Subspace Method. We first study a class of nonlinear equations directly related to the time-fractional Burgers equation. Some generalizations linked to the forced time-fractional Burgers equations and variable-coefficient diffusion are also considered. Finally we study a Burgers-type equation involving both space and time-fractional derivatives.
Analysis of the influence of engine torque excitation on clutch judder

A simplified three-degree-of-freedom dynamic model with nonlinear friction torque and engine torque excitation, capable of identifying the effect of the engine excitation on clutch judder, is presented. The analysis of harmonic order is performed and a sinusoidal contact pressure between friction surfaces is considered, along with an analytical solution for the relative angular velocity of the clutch plates. The average fluctuation amplitude of the clutch relative angular velocity is used to evaluate the judder. Numerical calculations indicate that the clutch judder increases significantly when the angular velocity of the crankshaft, corresponding to the harmonic orders of the engine, is equal or close to the natural frequency of the driveline. An identical frequency of the engine excitation and the oil pressure fluctuation contributes little to the clutch judder, unless the excitation is at or near the resonance frequency. The amplitudes of oscillations due to the engine excitation grow when the pulsating torque of the engine increases. The mean torque of the engine has little influence on the judder, although it governs the clutch engagement time. The results further show that clutch judder attenuates as the torsional stiffness of the system increases.
A Whitehead–Ganea approach for proper Lusternik–Schnirelmann category†

Abstract We establish Whitehead and Ganea characterizations for proper LS-category. We use the embedding of the proper category into the exterior category, and construct in the latter a suitable closed model structure of Strøm type. Then, from the axiomatic LS-category arising from the exterior homotopy category we can recover the corresponding proper LS invariants. Some applications are given.
Development of a 3D Avatar to improve the User Experience of a Movement-based Rehabilitation System

In the past years, rehabilitation systems have been proposed to allow patients to perform physical therapy from their own homes. To assist them in this task, 3D avatars can be used to guide them through the different exercises. In this paper, we have transformed and improved a movement-based physical rehabilitation system, called Rehabilita, to enhance the user experience of the patients. By adding a 3D avatar to guide users through physical therapy, and updating the UI elements of the system, we have improved the user experience of the patients. These modifications have followed the proposals of a heuristic evaluation proposed in a previous work. An evaluation with real users was later carried out to measure the effectiveness and efficiency of the modifications performed over the original system. The assessment results show that users can follow the steps of an exercise more easily, although some limitations must be discussed.
LevPet: A Magnetic Levitating Spherical Pet with Affective Reactions

LevPet combines affective computing and magnetic levitation to create an artificial levitating pet with affective responses and novel ways of moving to express emotions. Our interactive pet can recognise the user’s emotional status using computer vision, and respond to it with a low-level empathy system based on mirroring behaviour. For example, if you approach it with a happy face, the pet will greet you and move in a nimble way. A repulsive magnetic levitator is attached to a mechanical stage controlled by a computer system. On top of it, there is the pet playground, where a house, a ping-pong ball,a xylophone and other accessories are placed. Two cameras allow to capture the user’s face and the objects placed on the playground, so that the pet can interact with them. LevPet is an exploration of how to communicate internal state with only a levitating sphere; it is a platform for experimentation and an interactive demo that brings together an outer-worldly levitating metallic sphere with familiar things like emotions and a playground made of traditional items.
A Practical Incremental Device Scanning Start-up Time Prediction Scheme for IoT

With the development of IoT, IoT devices are deployed to numerous critical infrastructures, and their security has received wide attention. Using a device fingerprint to identify the device is the key step to improve security. Device fingerprint can be obtained via network scanning, which has a huge cost and may lead to network interference. To improve it, we propose an incremental device scanning start time prediction scheme. We first define the problems of the device scanning and introduce the realization of the scheme in detail. Finally, experimental results show the effectiveness of the scheme.
Use of Intelligent Techniques for Throughput Estimation of Unreliable two-machine Production Lines with Random Processing Times: Preliminary Results

In this paper, an application of intelligent techniques for modeling production systems is proposed. Such problems are usually highly complex so for most types of production systems, no accurate general formulas exist in the literature for the calculation of throughput. The specific problem studied in this paper is that of an unreliable two-machine production line with random processing times. Training data corresponding to specific configurations of the production line are produced from solving the system analytically with the use of stationary probabilities using the Markovian model. These training data are used as input for training (a) a neural network and (b) a genetic programming approach, trying to induce a generalized model of high accuracy (i.e., a network structure), or high comprehensibility (i.e., closed-form formula), respectively. Results show that the applied intelligent techniques succeed to obtain both, highly accurate approximation, and highly comprehensible outcomes, for out-of-sample test data, taken from the same range of production parameters as the training data range. The paper contains only encouraging preliminary results. Experiments are still in progress, regarding either results concerning out-of-range data for testing the generalization ability of the methods used, or results regarding the performance of intelligent approaches for larger production lines of similar type.
DRL based Joint Affective Services Computing and Resource Allocation in ISTN

Affective services will become a research hotspot in artificial intelligence (AI) in the next decade. In this paper, a novel service paradigm combined with wireless communication in integrated satellite-terrestrial network (ISTN) is proposed. On this basis, an affective services computing offloading and transmission network (ASCTN) with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on dueling deep Q-network (Dueling-DQN) is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.
Traffic Data Prediction with Geometric Algebra Spatial-Temporal Attention Neural Network

Traffic data prediction is complicated due to the changeable and complex traffic conditions and is an important part of Intelligent Transportation System (ITS). With the development of cities, the demand for traffic management is exceeding. Traffic data, such as traffic speed, can support in traffic decision-making like traffic congestion prediction. Existing approaches mainly capture the spatial dependency relying on the traffic map structure and catch temporal dependency by using recursive neural networks, convolutional neural networks or other variants. But these methods can only handle one-dimensional information and hardly ever explore the relevance among multidimensional input information. Geometric algebra is an extension of linear algebra, which can effectively represent the correlation among different dimensions of information. By combining graph attention, temporal convolution network and geometric algebra, we propose Geometric Algebraic Spatial-Temporal Attention Neural Networks (GASTANN). This network can integrate multidimensional features and capture dynamic spatial-temporal dependency. We use data of the past 1h as inputs and the experimental results on two public traffic prediction datasets (METR-LA, PEMS-BAY) show that proposed approach has better performance than state-of-the-art methods, especially in the 1h ahead prediction.
Compressing Audio Visual Speech Recognition Models With Parameterized Hypercomplex Layers

Audio visual speech recognition has seen remarkable progress in the last few years. This progress is a result, on the one hand, of advances in deep learning-based architectures, such as convolutional and recurrent neural networks, and, on the other hand, due to large-scale public datasets have been introduced that provide a great variety of speakers. Both factors have led authors to develop deep architectures that achieve impressive results that surpass humans in the areas of speech recognition, especially in cases where only the video is present. Nevertheless, these architectures involve millions of parameters that increase their storage and memory demands and also limit their deployment in resource constrained scenarios. An additional issue is the energy expenditure due to the amount of calculations required for training, fine-tuning and testing. In this work, we attempt to mitigate some of these shortcomings in speech recognition models by incorporating parameterized hypercomplex layers that reduce the number of required resources. We present models that are competitive with the state-of-the-art while operating with fewer parameters.
The distributions of stellar remnants in disk galaxies

This thesis presents an account of work on the distribution of supernova remnants in galaxies. It consists of 3 parts, the identification of runaway stars in galaxies and then the modelling of the expected distribution of these stars around a galaxy and their expected dispersion over time. The first part of the thesis is a thorough survey of supernovae in the Asiago database and identification of those that are located away from the plane of the galaxy. He investigated their height and radial distribution relative to the galaxy and confirmed previous work with a larger sample size. The rest of the work involved computer modelling of the distribution of star remnants and runaway objects in galaxies and the time evolution of their distribution. It concluded that the radial distribution of remnants would be similar to thar of the stars in the galaxy but their height distribution would be larger indicating sufficient velocity to escape from the disk. Despite their larger velocity relative to surrounding stars, the time evolution of the HMXB spatial distribution shows a similar pattern to that of the stars in the galaxy.
Case study on technological acceptance of autonomous vehicles and the influence of situational awareness: Lane detection in winter conditions

The new technological era and digital transformation have brought different advances to our lives, among which autonomous vehicles (AVs) stand out. The increasing visibility of these has given way to several investigations on the acceptance and perception of this technology, however, in many of these studies the participants are based solely on a preconceived belief since most have not driven or had contact with these types of vehicles. This issue needs to be explored further, however, test studies with real AVs are expensive, dangerous and unethical. In response to this, virtual reality emerges as a possible solution as it provides a realistic, safe and controlled environment, where different road and weather situations can be implemented. This paper presents a preliminary research design, based on the Technology Acceptance Model (TAM), which seeks to assess and infer the level of acceptance of AVs and user behaviour when using and interacting with them, based on an immersive virtual simulation of an AV, where the user is exposed to a risky situation where the vehicle is unable to recognize the lane due to weather difficulties.
Numerical simulation to the effect of applying rationing system on the stability of the Earth canal: Birmana canal in Iraq as a case study

Abstract Extreme conditions will cause the water level of high fill canal segment to change suddenly, which will affect the velocity and pore pressure of the slope. A 9 km irrigation earth canal in the city of Alsyahy, 15 km away from Al-Hilla city, and branching off from the left side of Shatt Al-Hilla at 57 km, was studied. The aim of this work is to study and analyze the effect of rationing system on the Birmana earthen canal during rapid drawdown case. Finite element modeling with Geo-Studio software was used in the present study to analyze the combined seepage and slope stability for three cycles. The resulting minimum safety factor obtained from the analysis using the saturated and unsaturated soil model was found to be 1.161, 1.142, 1.159, and 1.2 from the Janbu, Bishop, Morgenstern-Price, and Spencer methods, respectively. The factor of safety (FOS) values in three cycles are less than the required value. Finally, the FOC decreased by 66% from its value before the rationing system was applied, the area of canal and slip surface increased by 77 and 14%, respectively, due to the applied rationing system. These changes led to an irregular water distribution along the canal, in addition to a reduction in road width.
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection

Face recognition has evolved as a prominent biometric authentication modality. However, vulnerability to presentation attacks curtails its reliable deployment. Automatic detection of presentation attacks is essential for secure use of face recognition technology in unattended scenarios. In this work, we introduce a Convolutional Neural Network (CNN) based framework for presentation attack detection, with deep pixel-wise supervision. The framework uses only frame level information making it suitable for deployment in smart devices with minimal computational and time overhead. We demonstrate the effectiveness of the proposed approach in public datasets for both intra as well as cross-dataset experiments. The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0.42% in Protocol-1 of OULU dataset outperforming state of the art methods.
Energy Consumption, Carbon Dioxide Emissions and Economic Growth in Ethiopia: a VAR Analysis Approach

This study has attempted to investigate the relationship between, energy consumption, CO2 emissions and economic growth in Ethiopia, using time series data from 1970/71 to 2010/11. The finding indicates variables of interests are integrated of the same order I (1). Cointegration test approves existence of one co-integrating equation among the variables. The causality test result shows energy consumption causes Economic Growth in Ethiopia. Based on the outcome shocks to energy consumption have a negative impact on economic growth. The contributions of energy consumption to CO2 emissions were insignificant and economic growth is positively related to CO2 emissions. To secure the sustenance of CO2 emissions free economic growth in Ethiopia, cost effective, carbon free, and efficient utilization of renewable energy consumption based on the country comparative advantage that consider alternative use of resources are advisable like Hydro and Geothermal.