Miracle-dz/UniFAR-FTUs huggingface dataset:
Process Development Studies on Recovery of Clean Coal From Ultra Fine Hardcoal Tailings Using Enhanced Gravity Separator

In this study, we report the experimental results with respect to the beneﬁciation of ﬁne hardcoal from the C¸atala˘gzi Coal Preparation site tailingscontaining about 90% (by weight) of −600 m size particles. These tailings contain 28.41% ash. The coal sample was subjected to a two-stageconcentration process. The underﬂow product of hydrocyclone under optimized conditions was further processed in Mozley Multi-GravitySeparator to reduce the ash content of the coal. The results showed that a coal product containing 6.98% ash was obtainable with recovery of61.73% by this two-stage concentration process.Dans cette etude, nous pr´ esentons un compte rendu des r´ ´esultats exp ´erimentaux relatifs a l’enrichissement de ﬁnes de houille provenant des`traˆin´ees du lieu de pr eparation du charbon de C´ ¸atala˘gzi comportant environ 90% (par poids) de particules d’une dimension de −600 m. Cestraˆin´ees renferment 28,41% de cendres et 0,48% de soufre total. L’ echantillon de charbon a´ ´et e soumis´ a un processus de concentration` `a deux´etages. Le produit du courant de fond de l’hydrocyclone, sous conditions optimis ´ees, a egalement´ ´et ´e soumis au traitement d’un s eparateur´multigravit´e de Mozley aﬁn de r eduire la teneur en cendres du charbon. Les r´ esultats ont montr´ e qu’un produit du charbon renfermant 5,26%´de cendres et 0,52% de soufre combustible pouvait ˆetre obtenu avec un rendement de 62,84% gr aceˆ `a ce processus de concentration `a deux´etapes.Keywords: ﬁne hardcoal tailings, waste processing, hydrocyclone, Multi-Gravity Separator (MGS)
Area and Power-Efficient Variable-Sized DCT Architecture for HEVC Using Muxed-MCM Problem

This paper presents an area and power-efficient variable-size DCT architecture for HEVC application. We develop a reconfigurable and scalable shift-and-add unit (SAU) embedded in our 1D-DCT architecture by leveraging Muxed-MCM problem with the aim of increasing the hardware reusability in the arithmetic units, while reducing the hardware cost. The key idea behind the proposed architecture is the fact that in most of the times (≈90%) the lower point DCTs are performed when the higher point SAUs remain unused. Accordingly, we focus on merging the SAUs of lower point DCTs into the higher point DCTs to compute multiple lower point DCTs in parallel as well as processing any combination of transform sizes. The experimental results show that the proposed folded and fully-parallel 2D-DCT architectures achieve the best hardware cost by 45% and 30% reduction in gate count, respectively, amongst the existing architectures. Moreover, power saving of 55% and 32% can be achieved for the proposed folded and fully-parallel architectures, respectively, where they can process 60 fps of 4K and 30 fps of 8K UHD video sequences in 300 MHz operating frequency.
Influence of Dietary Fiber and Sodium Bicarbonate on Digestibility, Rumen Fermentation, Blood Metabolites and Performance of Dairy Cows Fed Pineapple Peel-Concentrate Mixed Diets

Three mid-lactation primiparous crossbred Holstein dairy cows averaging 440.0 +7.3 kgBW were assigned in a 3x3 Latin square design. Each cow was fed one of three experimental diets including: T1) control, pineapple peel (PL)-concentrate pellet (CT) mixed diet at ratio of 70:30, T2) PL-CT mixed diet at ratio of 50:30 plus 20 % rice straw (RS) as dietary fiber (DF) supplementation and T3) PL-CT mixed diet at ratio of 70:30 with 1.2 % sodium bicarbonate (NaHCO3). The results revealed that feed intake and nutrient digestibility were unaffected by DF or NaHCO3 supplement (P>0.05). Body weight was slightly increased by DF and NaHCO3 inclusion (P>0.05). Differences in rumen pH and concentration of volatile fatty acids were small (P>0.05). Supplemental DF and NaHCO3 did not influence on blood metabolites, blood electrolytes, milk yield and milk composition (P>0.05). Results suggest the PL-CT mixed diet with or without both DF and NaHCO3 can be effectively used by dairy cows without adversely affecting digestive efficiency, metabolites and performance. Further research should be conducted to examine these effects with larger number of animals over a longer period of time. Keywords: Pineapple peel; rice straw; sodium bicarbonate; dairy cow.
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database

Diabetic retinopathy (DR) is a primary cause of blindness in which damage occurs to the retina due to an accretion of sugar levels in the blood. Therefore, prior detection, classification, and diagnosis of DR can prevent vision loss in diabetic patients. We proposed a novel and hybrid approach for prior DR detection and classification. We combined distinctive models to make the DR detection process robust or less error-prone while determining the classification based on the majority voting method. The proposed work follows preprocessing feature extraction and classification steps. The preprocessing step enhances abnormality presence as well as segmentation; the extraction step acquires merely relevant features; and the classification step uses classifiers such as support vector machine (SVM), K-nearest neighbor (KNN), and binary trees (BT). To accomplish this work, multiple severities of disease grading databases were used and achieved an accuracy of 98.06%, sensitivity of 83.67%, and 100% specificity.
Delta-Sigma FDC Enhancements for FDC-Based Digital Fractional-N PLLs

This paper describes all-digital enhancements for digital fractional-<inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> phase-locked loops (PLLs) based on delta-sigma (<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>) frequency-to-digital converters (FDCs). The enhancements include an improved dual-mode ring oscillator (DMRO)-based <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> FDC architecture and a digital background calibration technique that compensates for the <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> FDC’s forward path gain error. The improved <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> FDC has significantly relaxed timing constraints and a <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> smaller phase-frequency detector output pulse-width span relative to the prior art, which make it simpler to implement and amenable to higher-frequency reference signals. The calibration technique compensates for non-ideal DMRO frequencies in the digital domain. It eliminates the need to tune the DMRO instantaneous frequencies as a function of the PLL output frequency, thereby simplifying the DMRO implementation, and it also improves the phase noise performance of PLLs with high loop bandwidths.
Blockchain-based Verifiable Credential Sharing with Selective Disclosure

Sharing credentials could raise privacy concerns. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides (i) secure verification of the participant identities and credentials to increase trust, and (ii) a data minimisation mechanism to reduce the risk of oversharing the credential data. This paper proposes CredChain, a blockchain-based Self-Sovereign Identity (SSI) platform architecture that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, a flexible selective disclosure solution is proposed using redactable signatures. The credentials are managed through a decentralised application/wallet which allows users to store their credential data privately under their full control and re-use as necessary. Our evaluation results show that CredChain architecture is feasible, secure and exhibits the level of performance that is within the expected benchmarks of the well-known blockchain platform, Parity Ethereum.
Low Cost Design of Non-Contact Thermometry for Diagnosis and Monitoring

The novel coronavirus pandemic has created the need for employee health monitoring and diagnosis in the workplace as one of the measures. The authors have attempted to design a non-contact thermometry device to create an open-source design that is simultaneously inexpensive. Such a device can be manufactured and deployed in a wide variety of economically challenged areas. The first iteration of a working design is summarized in this paper. Future work will include design customizations for cost-savings, accuracy, and precision improvements and considerations such as design for manufacturability and repairability.
A percolation cluster model of the temperature dependent dielectric properties of hydrated proteins

This study investigates the temperature dependence of the low frequency dielectric properties (0.1 Hz–1 MHz) of hydrated globular proteins (namely, ovalbumin, lysozyme and pepsin). The study aims to reveal the mechanisms of water–protein interaction from the dielectric response of these model proteins. Two principle dielectric responses were observed for each hydrated protein, namely, an anomalous low frequency dispersion and a dielectric loss peak at higher frequency (called the e3 dispersion). The low frequency response conformed to a fractional power low of frequency, while the higher frequency response conformed to a Davidson–Cole model. The strength of both processes reached a maximum at a certain temperature within the experimental temperature range. This temperature is referred to as the percolation threshold (PT) and is thought to be associated with the percolation of protons between hydrogen-bonded water molecules. The relaxation times of the e3 dispersion conformed to Arrhenius behaviour at temperatures below the PT, from which an activation energy (ΔH) could be calculated. This activation energy is thought to be a measure of the concentration of available charged sites through which proton transport is facilitated. The structural fractal dimension in the hydrated protein system was also calculated, and enabled the approximation of the pathway for charge percolation in the protein matrix.
Wheel Loader Scooping Controller Using Deep Reinforcement Learning

This article presents a deep reinforcement learning-based controller for an unmanned ground vehicle with a custom-built scooping mechanism. The robot’s aim is to autonomously perform earth scooping cycles with three degrees of freedom: lift, tilt and the robot’s velocity. While the majority of previous studies on automated scooping processes are based on data recorded by expert operators, we present a method to autonomously control a wheel loader to perform the scooping cycle using deep reinforcement learning methods without any user-provided demonstrations. The controller’s learning approach is based on the actor-critic, Deep Deterministic Policy Gradient algorithm which we use to map online sensor data as input to continuously update the actuator commands. The training of the scooping policy network is done solely in a simplified simulation environment using a virtual physics engine, which converges to an average of a 65% fill factor from the full bucket capacity and a <inline-formula> <tex-math notation="LaTeX">$5\left [{ sec }\right]$ </tex-math></inline-formula> average cycle time. We illustrate the performance of the trained policy in simulations and in real-world experiments with 3 different inclination angles of the earth. An additional scooping experiment compared the performance of our controller to remote manual human control. Overall, the deep reinforcement learning-based controller exhibited good performance in terms of both achieved visually bucket fill with varying scooped earth weights of <inline-formula> <tex-math notation="LaTeX">$4.1-7.2[kg]$ </tex-math></inline-formula>, and a <inline-formula> <tex-math notation="LaTeX">$5.1-7.1[sec]$ </tex-math></inline-formula> cycle time. The experimental results confirm the ability of our planner to fill bucket as required, indicating that our controller can be used for excavation purposes.
Development of a Running Hexapod Robot with Differentiated Front and Hind Leg Morphology and Functionality

This article introduces an innovative model-based strategy for designing a legged robot to generate animal-like running dynamics with differentiated leg braking and thrusting force patterns. Linear springs were utilized as legs, but instead of having one end of each spring connected directly to the hip joint, one extra bar was added to offset the spring’s direction. The robot’s front and hind legs were offset with the same magnitudes but in different directions. Therefore, the legs produced different ground braking and thrusting force patterns. The robot’s running motion was planned based on its reduced-order model. The model’s fixed-point and passive-dynamics motion served as the robot’s reference motion. The proposed strategy was experimentally validated, and the results confirmed that the robot could successfully perform stable running in a differentiated leg force pattern.
Real-time Spatio-Temporal based Outlier Detection Framework for Wireless Body Sensor Networks

Wireless body sensor networks (WBSNs) are threatened by many issues like anomalies in collected data and failure in their hardware components. An outlier detection approach applied on online monitoring of vital signs can both prevent collection of outlier data and detect emergent health degradation. In this paper, we propose an outlier detection framework for real time sensed data by WBSNs. Our proposed solution is twofold: Robust z score algorithm is executed at first step on the sensor nodes level to detect abnormal values and send them to the coordinator. After that, Isolation Forest is executed at the coordinator to distinguish between a faulty measurement and a critical health state. Correlation among vital signs are exploited to differentiate between an emergent healthy event and an anomaly in the measured data. Experiments conducted on real physiological datasets show that our proposed method is able to achieve a good detection accuracy with a low false alarm rate. Complexity and energy efficiency studies demonstrate the low complexity and lightness of our proposed solution.
Friction Identification in a Pneumatic Gripper

Mechanical systems are typically composed of a number of contacting surfaces that move against each other. Such surfaces are subject to friction forces. These dissipate part of the actuation energy and cause an undesired effect on the overall system functioning. Therefore, a suitable model of friction is needed to elide its action. The choice of such a model is not always straightforward, as it is influenced by the system properties and dynamics. In this paper, we show the identification of different friction models and evaluate their prediction capability on an experimental dataset. Despite being state-of-the-art models, some modifications were introduced to improve their performance. A pneumatic gripper was used to collect the data for the models evaluation. Two experimental setups were mounted to execute the experiments: information from two pressure sensors, a load cell and a position sensor was employed for the identification. During the experiments, the gripper was actuated at different constant velocities. Results indicate that all the identified models offer a proper prediction of the real friction force.
Health Indicator of Bearing Constructed by rms-CUMSUM and GRRMD-CUMSUM With Multifeatures of Envelope Spectrum

Because a fault of bearing may cause a serious accident of the running machine, it is very important for monitoring condition and identifying the performance degradation of bearing. Two novel health monitoring indicators are proposed by root mean square with cumulative sum (rms-CUMSUM) and growth rate of real-time Mahalanobis distance with cumulative sum (GRRMD-CUMSUM) based on multifeatures of envelope spectrum for health condition monitoring of bearings. Multifeature parameters of envelope spectrum are integrated with rms and RMD based on the vibrational signal of bearings, and the GRRMD is calculated. The rms and GRRMD are processed by the CUMSUM method. The rms-CUMSUM and GRRMD-CUMSUM can represent the tendency and rate of bearings’ performance degradation, respectively. The different stages of bearings’ performance degradation can be classified using the initial fault time and the special turning point of GRRMD-CUMSUM reference to rms-CUMSUM. The initial fault time is roughly estimated by the abrupt warning point of RMD, and further accurately determined by the backtracking strategy with the interval-halving principle by fast independent component analysis based on empirical mode decomposition (EMD-Fast ICA). The proposed approach was verified by two different experimental data sets. The experimental results show that the proposed approach is simple and efficient for determining the initial fault, assessing, and classifying the performance degradation of bearings.
Empirical Investigation About the Factors Affecting the Cost Estimation in Global Software Development Context

Software organization always aims at developing a quality software product using the estimated development resources, effort, and time. Global Software Development (GSD) has emerged as an essential tool to ensure optimal utilization of resources, which is performed in globally distributed settings in various geographical locations. Global software engineering focuses on reducing the cost, increasing the development speed, and accessing skilled developers worldwide. Estimating the required amount of resources and effort in the distributed development environment remains a challenging task. Thus, there is a need to focus on cost estimation models in the GSD context. We nevertheless acknowledge that several cost estimation techniques have been reported. However, to the best of our knowledge, the existing cost estimation techniques/models lack considering the additional cost drivers required to compute the accurate cost estimation in the GSD context. Motivated by this, the current work aims at identifying the other cost drivers that affect the cost estimation in the context of GSD. To achieve the targeted objectives, current state-of-the-art related to existing cost estimation techniques of GSD is reported. We adopted SLR and Empirical approach to address the formulated research questions. The current study also identifies the missing factors that would help the practitioners improve the cost estimation models. The results indicate that previously conducted work ignores the additional elements necessary for the cost estimation in the GSD context. Moreover, the current work proposes a conceptual cost estimation model tailored to fit the GSD context.
Prediction and validation of gold nanoparticles (GNPs) on plant growth promoting rhizobacteria (PGPR): a step toward development of nano-biofertilizers

Abstract Several soil microbes are present in the rhizosphere zone, especially plant growth promoting rhizobacteria (PGPR), which are best known for their plant growth promoting activities. The present study reflects the effect of gold nanoparticles (GNPs) at various concentrations on the growth of PGPR. GNPs were synthesized chemically, by reduction of HAuCl4, and further characterized by UV-Vis spectroscopy, X-ray diffraction technique (XRD), and transmission electron microscopy (TEM), etc. The impact of GNPs on PGPR was investigated by Clinical Laboratory Standards Institute (CLSI) recommended Broth-Microdilution technique against four selected PGPR viz., Pseudomonas fluorescens, Bacillus subtilis, Paenibacillus elgii, and Pseudomonas putida. Neither accelerating nor reducing impact was observed in P. putida due to GNPs. On the contrary, significant increase was observed in the case of P. fluorescens, P. elgii, and B. subtilis, and hence, GNPs can be exploited as nano-biofertilizers.
Interest Sustainability-Aware Recommender System

The key to successful recommendations is to provide users with items likely to be consumed in the future. From realworld data, we observe that users' consumption patterns for items change over time. For example, users may no longer like some items they liked in the past. However, existing recommender systems model user's preference to items without considering how much users' interests in each item will sustain in the future. Thus, they often recommend less interesting items in the deployment time (i.e., test time). In this work, we propose a novel recommender system, called CRIS, that considers the change of users' interest in each item over time. More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future. Then, our goal is to make users closer to the items with high interest sustainability scores in the representation space than those with low interest sustainability scores. We perform experiments on 11 real-world datasets to show the effectiveness of CRIS. We also show that considering the interest sustainability is indeed crucial for boosting the accuracy of recommendations.
Heuristic Based Routing Algorithms for Vehicular Network Using Tabu Search and ANN

Efficient routing to guide the vehicles to reach their destinations is a challenging problem in vehicular networks. Many solutions have been proposed in the literature to address the routing problem in vehicular networks. However, most of these solutions are graph-based and do not properly address the dynamic characteristics of vehicular networks. This paper proposes two novel heuristic routing algorithms based on Tabu search and Neural Networks. The proposed algorithms are evaluated and their findings are presented using the UK RTA based roadside dataset. Experimental results along with the comparative analysis made with other related studies are provided to prove the efficiency of the proposed algorithms. The findings highlight the superior performance achieved by the suggested routing algorithms.