Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach

Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated $M$ -player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.
Systemic Nannizziopsis arthrosporioides in an African Side-neck Turtle (Pelomedusa subrufa)

Abstract A 2-yr-old female African side-neck turtle (Pelomedusa subrufa) housed at a pet store in Illinois, USA presented for evaluation of nasal discharge and an ulcerative skin lesion. Molecular diagnostics for Mycoplasma spp. , herpesviruses, and frog virus 3 (ranavirus) were negative, and cytologic evaluation of the ulceration showed nonseptic inflammation without fungal elements. A presumptive diagnosis of a bacterial upper respiratory infection was treated with ceftazidime (20 mg/kg SC every 72 h) and husbandry improvements were recommended. One month later, the turtle re-presented; physical examination revealed diffuse skin sloughing and severe dehydration, and the animal was euthanized. Necropsy and histopathology revealed multiple fungal granulomas throughout the liver, gastrointestinal tract, and lungs. Heavy growth of a white powdery fungal organism was obtained from culture. Four gene targets (D1/D2 domains of the 28S ribosomal DNA, 18S-28S ribosomal internal transcribed spacer region, actin, and β-tubulin) were sequenced from the fungal isolate and each was >98% identical to Nannizziopsis arthrosporioides. A maximum-likelihood phylogenetic tree constructed from concatenated D1/D2, ITS, actin, and β-tubulin sequences from Nannizziopsis spp. placed the new fungal isolate in a highly supported (99% bootstrap support) monophyletic group with two N. arthrosporioides isolates. This case report describes the first known N. arthrosporioides infection in an aquatic turtle species and adds to the growing literature surrounding N. arthrosporioides infections in nonsquamate reptilian patients.
Exponential H∞ synchronization for discrete‐time chaotic neural networks with time delays and stochastic perturbations via output feedback control

This paper investigates the problem of exponential H∞ synchronization of discrete‐time chaotic neural networks with time delays and stochastic perturbations. First, by using the Lyapunov‐Krasovskii (Lyapunov) functional and output feedback controller, we establish the H∞ performance of exponential synchronization in the mean square of master‐slave systems, which is analyzed using a matrix inequality approach. Second, the parameters of a desired output feedback controller can be achieved by solving a linear matrix inequality. Finally, 2 simulated examples are presented to show the effectiveness of the theoretical results.
Recursive Variational Bayesian Dual Estimation for Nonlinear Dynamics and Non-Gaussian Observations

State space models provide an interpretable framework for complex time series by combining an intuitive dynamical system model with a probabilistic observation model. We developed a flexible online learning framework for latent nonlinear state dynamics and filtered latent states. Our method utilizes the stochastic gradient variational Bayes method to jointly optimize the parameters of the nonlinear dynamics, observation model, and the recognition model. Unlike previous approaches, our framework can incorporate non-trivial observation noise models and infer in real-time. We test our method on point process observations driven by continuous attractor dynamics, demonstrating its ability to recover the phase portrait, filtered trajectory, and produce long-term predictions for neuroscience applications.
Educational Platform Based on a Mechanical Beam With Performance in Real Time

This article presents an educational platform for Mechanics of Materials based in a beam, an Arduino, a smart phone, and Matlab-Simulink with real-time performance. The platform components are given by hardware setup and software setup that these are described. Performance of the platform is assessed by means of real-time experiments using ArduinoIO package for a beam using loading configurations. The platform is easy to implement and to interface using Arduino, electronic circuits, and any computer. The platform is considered portable, reliable, and low-cost, that it can develop for students, opening many new possibilities for practical assignments in Engineering for courses of statics and Mechanics of Materials. Finally, these are included experiments and studied topics during various practices.
An Approach to Global Sensitivity Analysis: FAST on COCOMO

There are various models in software engineering that are used to predict quality-related aspects of the process or artefacts. The use of these models involves elaborate data collection in order to estimate the input parameters. Hence, an interesting question is which of these input factors are most important. More specifically, which factors need to be estimated best and which might be removed from the model? This paper describes an approach based on global sensitivity analysis to answer these questions and shows its applicability in a case study on the COCOMO application at NASA.
Domain-wall brane models of an infinite extra dimension

In this thesis we consider aspects related to the construction and phenomenology of domain-wall brane models of a single, infinite, extra spatial dimension. We discuss how gravity, gauge- and matter-fields become four-dimensional at low energies due to the dynamical formation of a topologicaldefect known as a brane. These ideas are amongst the leading candidates for extensions to the standard models of particle physics and cosmology. We first examine a toy model where a pair of scalar fields, charged under a U(1)⊗U(1) gauge symmetry, form a background domain-wall configuration. This analysis demonstrates the general ideas of domain-wall formation and stability, dimensional reduction and semi-confinement of gauge fields. Gravity is incorporated into this toy model, in the form of a regularised version of the Randall-Sundrum warped metric. For the case of a single real scalar field, we show in detail how a domain wall can be obtained, and determine its relationship to the fundamental brane in the infinitely thin wall limit. Explicit expressions for the modes of such a domain wall are found, as well as the modes associate d with coupled fermions and scalars. The symmetric modified P oschl-Teller potential arises in this context, and the analysis elucidates the reduction of a five-dimensional field to a tower of four-dimensional modes. Adding gravity to the model alters the spectrum of four-dimensional modes trapped to the wall. What were once discrete modes become resonances within a continuum, and low-energy modes are coupled to these continuum bulk modes. We show explicitly how this comes about, and use a toy model to demonstrate that this coupling can be made small enough to avoid experimental constraints. The Dvali-Shifman mechanism for gauge field localisation is introduced, and we use it, along with the previously discussed techniques for constructing domain walls, to write down an SU(5) grand unified, single generation version of the standard model. Fermion mass relations and Higgs induced proton decay are significantly improved from usual SU(5) models due to an inherent split fermion set-up. An extension of the gauge group to E6 is considered, naturally including the clash of symmetries mechanism, and it is shown that the field content can be simplified. Cosmological implications of domain-wall branes are investigated, and we attempt to reproduce an effective four-dimensional Friedmann-Lemaˆitre-Robertson-Walker metric for brane-localised matter. We find that for a domain wall of finite thickness it is not possible to define a common spacetime for all localised species of matter. As a consequence, different species experience a different effective four-dimensional scale factor, an unusual effect that is suppressed when the domain wall is made sufficiently thin. We conclude that domain-wall brane models of an infinite extra dimension are viable extensions of the standard models of particle physics and cosmology. These models provide a rich set of phenomenology, and allow new ways to tackle existing theoretical problems.
Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. After geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.
Effects of Light Intensity and Water Stress on Growth, Photosynthetic Characteristics and Plant Survival of Cistus heterophyllus Desf. Subsp. carthaginensis (Pau) M. B. Crespo & Mateo

The objective of this study was to identify the physiological mechanisms used by Cistus heterophyllus Desf. Subsp. carthaginensis (Pau) M. B. Crespo & Mateo, a species in critical danger of extinction, to cope with two typical abiotic conditions. During the summer of 2021, plants were cultivated under a shade mesh that intercepted 50% of the incident solar radiation (SHADE) and in natural conditions (SUN). Three irrigation treatments were also applied: control, T1, moderate water deficit; T2, 60% of the control, severe deficit; and T3, 30% of the control. Therefore, there were six treatments (SUN-T1, SUN-T2, SUN-T3, SHADE-T1, SHADE-T2, SHADE-T3). Plants were more affected by solar radiation than by deficit irrigation. Although leaf water potential, stomatal conductance and root biomass decreased by up to 45%, 63% and 65%, respectively, as deficit irrigation increased, plants were able to develop a leaf osmotic adjustment and an improved intrinsic water-use efficiency to maintain their growth and survival rate. Shade conditions improved gas exchange, reduced leaf temperature and induced the synthesis of chlorophylls, regardless of the irrigation level imposed. This indicated that radiation was the most limiting factor in our experiment. Applying 50% of the radiation and a moderate water deficit would help to obtain a good plant development and high survival rate in future recovery and conservation programs for the species.
Mycoviruses in the Rust Fungus Uromyces fabae

Uromyces fabae, the causal agent of broad bean rust, is a major cause of yield losses in North and East Africa, China, and Australia. It has also served as an important model species for research on rust fungi. Early EST sequencing in U. fabae showed that viruses might be present in this species; however, no follow-up investigations were conducted. In order to identify these viruses, we performed purification of dsRNA followed by Illumina sequencing. We also used ultracentrifugation followed by negative staining electron microscopy to visualize virus particles. We identified 20 viral sequences, which we termed Ufvss. A phylogenetic analysis was performed that grouped Ufvss into totiviruses, polymycoviruses, and virgaviruse; three sequences could not be included in the phylogeny. We also found isometric particles. Our findings contribute to the knowledge of mycoviral diversity in rust fungi and point to the importance of further investigation of these viruses.
Magnetic ordering induced ferroelectricity in α-Cu2V2O7 studied through non-magnetic Zn doping

We have studied the magnetic and electronic properties of Cu2-xZnxV2O7 by magnetization, specific heat, and dielectric measurements. X-ray structural analysis shows a Zn-mediated phase transition from the α- to the β-phase beyond a critical Zn concentration of xc = 0.15. While Cu2V2O7 exhibits a canted antiferromagnetism with an associated weak ferromagnetism in the α-phase, the β-phase is purely antiferromagnetic. The spin canting arises due to the Dzyaloshinskii-Moriya exchange interaction in the anti-symmetric α-phase. The temperature dependence of the heat capacity for the sample in the α-phase shows a clear lambda like transition at a temperature where the magnetic susceptibility also displays an anomaly and indicates an onset of long range magnetic ordering. Dielectric properties display a clear anomaly around the magnetic transition temperature in α-Cu2V2O7. The anomaly weakens with the increase in the Zn concentration and disappears at the doping level where α to β phase transition occurs. This co.
Non-adaptive velocity tracking controller for a class of vehicles

Manuscript submitted 2016-09-01, revised 2016-12-22, initially accepted for publication 2016-12-27, published in August 2017. Abstract. A non-adaptive controller for a class of vehicles is proposed in this paper. The velocity tracking controller is expressed in terms of the transformed equations of motion in which the obtained inertia matrix is diagonal. The control algorithm takes into account the dynamics of the system, which is included into the velocity gain matrix, and it can be applied for fully actuated vehicles. The considered class of systems includes underwater vehicles, fully actuated hovercrafts, and indoor airship moving with low velocity (below 3 m/s) and under assumption that the external disturbances are weak. The stability of the system under the designed controller is demonstrated by means of a Lyapunov-based argument. Some advantages arising from the use of the controller as well as the robustness to parameters uncertainty are also considered. The performance of the proposed controller is validated via simulation on a 6 DOF robotic indoor airship as well as for underwater vehicle model.
Hardware-in-the-Loop Soft Robotic Testing Framework Using an Actor-Critic Deep Reinforcement Learning Algorithm

Polymer-based soft robots are difficult to characterize due to their non-linear nature. This difficulty is compounded by multiple additional degrees of movement freedom which adds complexity to any control strategy proposed. The following work proposes and demonstrates a modular framework to test, debug and characterize soft robots using the robot operating system (ROS), to enable modeless deep reinforcement learning control strategies through hardware-in-the-loop system training. The framework is demonstrated using an actor-critic algorithm to learn a locomotion policy for a two-actuator pneu-net soft robot with integrated resistive flex sensors. The result of convergent locomotion studies was an 89.5% increase in the likelihood of reaching the end of frame design goal versus random oracle actuation vectors.
Enhanced saturable absorption of MoS 2 black phosphorus composite in 2 μm passively Q-switched Tm: YAP laser

We report the fabrication of an MoS2 black phosphorus (BP) composite saturable absorber by liquid phase exfoliation and the spin-coating method and further exploitation to build a 2 μm passively Q-switched Tm:YAP laser. Such a composite based Q-switched laser with a duration of 488 ns and corresponding peak power of 85.9 W is obtained, which shows an improved saturable absorption effect than that of single MoS2 (616 ns, 68.7 W) and BP (932 ns, 22.4 W). The results indicate that simple and reasonable fabrication of the vertical composite from two-dimensional atomic layer materials opens up the possibility to create an unprecedented saturable absorber with exciting properties.
The flow behaviour study of splitting device for horizontal pipeline

The study of the flow behaviour of splitting device for horizontal pipeline was conducted by using the numerical simulation. The splitting device is functionally to force the roping flow to become homogenous. The study scrutinised the air flow in the pipeline, and also the air flow with particle which is anthracite injected into air in the pipeline. A piping system with 5 different type of splitting device is simulated to find the best splitting device that can make the roping flow become homogenous and improved the roping problem. The simulation was conducted by using the Ansys Fluent. From this study, it can be concluded that the splitting device, does help successfully in reducing the roping problem in the horizontal pipeline.
3D Range Searching Using Chain Decomposition

xi List of Abbreviations and Symbols Used . xii Acknowledgements . xv Chapter
IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

Performing a coronary disease diagnosis remotely is challenging now‐a‐days. COVID‐19 is a worldwide pandemic, and methods for detecting COVID‐19 are hampered by insufficient data and a lack of validation testing. Internet of Things (IoT) applications that rely on cloud computing (CC) are being studied in an effort to improve e‐Healthcare systems, even though CC presents substantial latency, bandwidth, energy consumption, security and privacy issues and so forth. The extension to CC, fog computing (FC), can overcome these said limitations. This study aims to diagnose COVID‐19 patients to fight the outbreak remotely. This study proposes IFCnCov, which enables remote users to diagnose COVID‐19 disease in real‐time using integrated IoT, FC, and CC principles, as well as ensemble learning (EL) and deep learning (DL). The proposed system is a two‐layered architecture, trained with DL approaches to two different datasets: a symptom‐based dataset and a chest x‐rays imaging dataset obtained from the Kaggle repository employing several evaluative measures. From various experiments, this proposed IFCnCov achieves comparatively enhanced accuracies of 97.71% and 98.64%, precision of 97.38% and 98.52%, sensitivity of 98.19% and 98.92%, specificity of 97.21% and 98.34%, and F1‐scores of 97.79% and 98.72% in the first and second stages respectively, which also outperforms some other considered state‐of‐the‐art works. Additionally, this work is validated in terms of several network parameters, including scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.
Comparison of ruminal microbiota, metabolomics, and milk performance between Montbéliarde×Holstein and Holstein cattle

Holstein cattle are well known for their high average milk yield but are more susceptible to disease and have lower fecundity than other breeds of cattle. The purpose of this study was to explore the relationship between ruminal metabolites and both milk performance and ruminal microbiota composition as a means of assessing the benefits of crossbreeding Montbéliarde and Holstein cattle. This experiment crossbred Holstein with Montbéliarde cattle, aimed to act as a reference for producing high-quality dairy products and improving the overall efficiency of dairy cattle breeding. Based on similar age, parity and lactation time, 46 cows were selected and divided into two groups (n = 23 per group) for comparison experiment and fed the same formula: Montbéliarde×Holstein (MH, DIM = 33.23 ± 5.61 d), Holstein (H, DIM = 29.27 ± 4.23 d). Dairy herd improvement (DHI) data is an important basis for evaluating the genetic quality of bulls, understanding the quality level of milk, and improving feeding management. We collected the DHI data of these cows in the early lactation, middle lactation and late lactation period of 10 months. The results showed that the average milk production and protein content in Montbéliarde×Holstein were 1.76 kg (34.41 kg to 32.65 kg, p > 0.05) and 0.1% (3.54 to 3.44%, p < 0.05) higher than in Holstein cattle. Moreover, milk from Montbéliarde×Holstein cattle had lesser somatic cell score (1.66 to 2.02) than Holstein cattle (p < 0.01). A total of 10 experimental cattle in early lactation were randomly selected in the two groups (Lactation time = 92.70 ± 6.81), and ruminal fluid were collected by oral gastric tube. Using 16S rRNA microbial sequencing, we compared the ruminal microbiota composition and found that Montbéliarde×Holstein cattle had a lower abundance of Alphaproteobacteria (p < 0.05) and higher abundance of Selenomonas than Holstein cattle (p < 0.05). These bacteria play roles in protein degradation, nitrogen fixation and lactic acid degradation. The abundance of Succiniclasticum was also greater in Montbéliarde×Holstein cattle (p = 0.053). Through ruminal metabolome analysis, we found that the levels of trans-ferulic acid, pyrrole-2-carboxylic acid, and quinaldic acid were significantly increased in Montbéliarde×Holstein cattle, while that of lathosterol was significantly decreased. The changes in the levels of these metabolites could confer improved antioxidant, anti-inflammatory, and antibacterial activities.
Characteristics Associated with Fear of COVID-19 among Syrian Refugee Parents in Canada

Introduction: The aim was to assess the prevalence and factors associated with fear of COVID-19 among Syrian refugee parents in Ontario, Canada. Methods: A sample of 540 Syrian refugee parents who resettled in Ontario were interviewed between March 2021, and March 2022. The level of fear was measured using the Fear of COVID-19 scale. Multiple linear regression analysis was performed to assess the relationships between socio-demographic, migration, and health-related factors and fear of COVID-19. Results: The mean (SD) score for the Fear of COVID-19 scale was 15.6 (6.02), and 15.4% of the participants were categorized as having high levels of Fear of COVID-19. Results of the multiple linear regression analysis showed that low self-rated English/French language ability was significantly associated with increased fear of COVID-19 (Adjβ=0.65, p=0.047). When compared to participants who do not need an interpreter, those who needed an interpreter, and were always provided with one, were at reduced fear of COVID-19 (Adjβ=-1.56, p=0.061). In addition, findings indicated that low self-perceived socioeconomic status, more years spent in Canada, living in a refugee camp, and poor self-rated mental health contributed significantly to elevated levels of fear of COVID-19. Discussion: Targeted intervention and prevention strategies for reducing the fear of COVID-19 should be considered for the Syrian refugee population in Canada. Language ability is one of the factors related to increased fear of COVID-19, thus, providing information and interventions in different languages is essential for this population.