Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity and specificity of orchid mycorrhizal fungi in a leafless epiphytic orchid, Dendrophylax lindenii and the potential role of fungi in shaping its fine-scale distribution

Orchids grow in diverse habitats worldwide with most (approximately 69%) growing on trees as epiphytes. Although orchid mycorrhizal fungi have been identified as potential drivers for terrestrial orchid distribution, the influence of these fungi on the fine-scale distribution of epiphytic orchids is poorly understood. In this study, we investigated the mycorrhizal fungal community and fine-scale distribution of Dendrophylax lindenii, a rare and endangered epiphytic orchid that is leafless when mature. We used amplicon sequencing to investigate the composition of orchid mycorrhizal fungi in the roots of 39 D. lindenii individuals in their natural habitat, the swamps of Florida. We compared the orchid mycorrhizal fungi of D. lindenii to those of co-occurring epiphytic orchids, as well as to the orchid mycorrhizal fungal communities of bark from potential host trees, with and without D. lindenii. Our results show that D. lindenii has a high specificity for a single Ceratobasidium species, which is widely distributed on phorophytes and detected in both wet and dry periods in the orchid’s habitat. This Ceratobasidium species was mostly absent or only recorded in low frequency in the roots of co-occurring epiphytic orchids. Phylogenetic analysis documented that this Ceratobasidium was conspecific with the strain that is used to germinate D. lindenii ex-situ. However, our findings suggest that laboratory germinated adult D. lindenii transplanted into the field had lower read abundances of this Ceratobasidium compared to naturally occurring plants. These findings suggest that this orchid mycorrhizal fungus may play a significant role in the fine-scale distribution of naturally occurring D. lindenii.
Community-Based Knowledge Translation Strategies for Maternal, Neonatal, and Perinatal Outcomes: A Systematic Review of Quantitative and Qualitative Data

Objective: To identify and assess the effect of community-based Knowledge Translation Strategies (KTS) on maternal, neonatal, and perinatal outcomes. Methods: We conducted systematic searches in Medline, Embase, CENTRAL, CINAHL, PsycInfo, LILACS, Wholis, Web of Science, ERIC, Jstor, and Epistemonikos. We assessed the certainty of the evidence of the studies using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. Results: We identified seven quantitative and seven qualitative studies. Quantitative findings suggest that there is a possible effect on reducing maternal mortality (RR 0.65; 95% CI, 0.48–0.87; moderate evidence certainty); neonatal mortality (RR 0.79; 95% CI 0.70–0.90; moderate evidence certainty); and perinatal mortality (RR 0.84; 95% CI 0.77–0.91; moderate evidence certainty) in women exposed to KTS compared to those who received conventional interventions or no intervention at all. Analysis of qualitative studies identified elements that allowed to generate benefit effects in improving maternal, neonatal, and perinatal outcomes. Conclusion: The KTS in maternal, neonatal, and perinatal outcomes might encourage the autonomy of communities despite that the certainty of evidence was moderate.
Impacts of microphysical parameterizations on banded convective system in convection-permitting simulation: a case study

The representation of cloud microphysical processes in models has always been a challenge leading to uncertainty in convective simulations. This paper evaluates the effect of cloud microphysical parameterizations on the simulation of mesoscale convective systems (MCSs) through a realistic banded convection process. A series of numerical simulation experiments are performed using the Weather Research and Forecasting (WRF) model at a convection-permitting scale with a 3 km horizontal grid spacing. Specifically, four experiments considering different hydrometeor species within the WRF single-moment-microphysics schemes (WSMMPs) are conducted, and three additional sensitivity tests change the graupel particle properties. The results indicate that the significant differences in the times of convection initiation across the experiments reach 120 min, and more hydrometeor species may lead to later convection initiation. Moreover, the frozen graupel hydrometeor characteristics can appreciably alter the simulated convective morphology, even more than other hydrometeor species. When the graupel becomes smaller (such as Graupel-like), the fall speed of the graupel particles decreases. Therefore, more numerous graupel particles reside in clouds for a longer time, and experience more atmospheric diabatic heating and cooling effects. As a result, the simulated convective systems exhibit strong banded convective echo characteristics, the surface 10-m wind gust increases, and the cold pools associated with additional melting and evaporation become stronger, accelerating the propagation of the system. In contrast, larger and less abundant graupels (e.g., Hail-like) have a faster fall speed, and the atmospheric diabatic heating and cooling decrease by shortening the duration of their residence time within the clouds, resulting in a weak quasi-linear convective system, weak surface 10-m wind gust, unobvious cold pool, and slower propagation. Comparisons of the experiments further demonstrate that the fall speeds of frozen graupel particles largely impact the vertical distribution of the hydrometeors and the related microphysical processes.
B-1 cells in immunotoxicology: Mechanisms underlying their response to chemicals and particles

Since their discovery nearly 40 years ago, B-1 cells have continued to challenge the boundaries between innate and adaptive immunity, as well as myeloid and lymphoid functions. This B-cell subset ensures early immunity in neonates before the development of conventional B (B-2) cells and respond to immune injuries throughout life. B-1 cells are multifaceted and serve as natural- and induced-antibody-producing cells, phagocytic cells, antigen-presenting cells, and anti-/pro-inflammatory cytokine-releasing cells. This review retraces the origin of B-1 cells and their different roles in homeostatic and infectious conditions before focusing on pollutants comprising contact-sensitivity-inducing chemicals, endocrine disruptors, aryl hydrocarbon receptor (AHR) ligands, and reactive particles.
A Numerical Scheme for Time-Domain FE Analysis of Viscoelastic Structures with Fractional Derivative Constitutive Equations

A numerical scheme is presented for time-domain simulations of structural dynamic problems with viscoelastic materials described by fractional derivative constitutive equations. The proposed approach combines a classical Newmark time-integration method used to solve second-order mechanical systems (obtained for example after finite element discretization), with a diffusive representation based on the transformation of the fractional operator into a system of linear differential equations. The focus is given on the general formulation of the problem, the algorithm implementation into a finite element framework, and the developpement of a closed-form solution for a fractionnally damped single degree-of-freedom equation.
Discovery and Characterization of a Higgs boson using four-lepton events from the CMS experiment

A new particle decaying to a pair of vector bosons was discovered in 2012 by the ATLAS and CMS experiments at the Large Hadron Collider. In the wake of this discovery a rush of measurements was made to characterize this particle. The fourlepton final state has been instrumental in both the discovery and characterization of this new particle. With only about 20 events seen in the resonance peak at 125 GeV the CMS experiment has been able to make considerable progress in characterizing the Higgs-like boson using the wealth of information in this final state in concert with other decay modes. In addition to the search for this new boson we present three recent results in the study of the Higgs-like boson properties: studies of the production mode, total width, and spin-parity quantum numbers. First we present the search for this new resonance using the H → ZZ → 4l decay channel. Then we discuss the production mode measurement using this final state. Next, we present two results that provided breakthroughs in the study of the Higgslike resonance. One is the measurement of the width from an interplay between the off-shell and on-shell production, setting a limit three orders of magnitude tighter ii
Smoothing of tensile signals of yarns

A smoothing algorithm for the processing of tensile signals of yarns is proposed. Our algorithm consists of three-stage smoothing B-spline interpolations and broken-point rectification (BPR) which corrects the time delay of broken-points of tensile signals. Smoothing B-spline interpolation transforms discrete signals into continuous B-spline space, in which discrete B-spline coefficients represent continuous signals so that correct tensile results can be calculated based on the continuous signals. The proposed smoothing algorithm raises the SNR of a sample tensile signal from 21 dB to 45 dB and the BPR algorithm reduces the time delay of broken-points on the smoothed tensile signals from 14 ms to 2.5 ms.
Serverledge: Decentralized Function-as-a-Service for the Edge-Cloud Continuum

As the Function-as-a-Service (FaaS) paradigm enjoys growing popularity within Cloud-based systems, there is increasing interest in moving serverless functions towards the Edge, to better support geo-distributed and pervasive applications. However, enjoying both the reduced latency of Edge and the scalability of FaaS requires new architectures and implementations to cope with typical Edge challenges (e.g., nodes with limited computational capacity). While first solutions have been proposed for Edge-based FaaS, including light function sandboxing techniques, we lack a platform with the ability to span both Edge and Cloud and adaptively exploit both. In this paper, we present Serverledge, a FaaS platform designed for the Edge-to-Cloud continuum. Serverledge adopts a decentralized architecture, where function invocation requests can be fully served within Edge nodes. To cope with load peaks, Serverledge also supports vertical (i.e., from Edge to Cloud) and horizontal (i.e., among Edge nodes) computation offloading. Our evaluation shows that Serverledge outperforms Apache OpenWhisk in an Edge-like scenario and has competitive performance with state-of-the-art frameworks optimized for the Edge, with the advantage of built-in support for vertical and horizontal offloading.
Bi-LSTM based Interdependent Prediction of Physiological Signals

Two of the most essential physiological signals obtained during patient care are the photoplethysmogram (PPG) and electrocardiogram (ECG). Due to recent technological advancements the correlation between the two has come to light. The significance of each signal warrants a solution to predict one when the other is absent. Also, the inexpensive and non-invasive approach of PPG provides a cheaper and comfortable way of monitoring instead of installing ECG. Thus, This study proposes a Bi-LSTM model to predict the two physiological signals. The model was successful in predicting long term data after filtering and aligning the signals efficiently with an MSE value of 0.092 and 0.065 for ECG and PPG respectively.
VID2XML: Automatic Extraction of a Complete XML Data From Mobile Programming Screencasts

Developers often refer to video-hosting online platforms to find screencasts that provide a step-by-step guide to help them solve a programming task at hand or learn a new concept. More specifically, developers search for resources that help them design and implement effective mobile graphical user interfaces (GUI) using <monospace>XML</monospace>. Although mobile programming screencasts contain a vast amount of <monospace>XML</monospace> data at developers’ disposal, they cannot be easily found and copied-pasted due to the image nature of videos. Given that the most common task developers perform online is copy-pasting, mobile programming screencasts must support that and be complemented with <monospace>XML</monospace> data in a textual format. To overcome this challenge and aid developers, this paper presents <monospace>vid2XML</monospace>, which is a three-phase approach that leverages both visual and textual information of video frames to locate <monospace>XML</monospace> region in video frames, locate the currently opened file, and extract <monospace>XML</monospace> data for each file presented in video frames. We evaluated each phase of <monospace>vid2XML</monospace> in a comprehensive empirical evaluation on videos collected from YouTube. The results reveal that <monospace>vid2XML</monospace> is able to accurately (i) locate <monospace>XML</monospace> regions, outperforming four previous work, (ii) locate the bounding box of the selected file, and (iii) extract, fix, and merge <monospace>XML</monospace> data for each file opened/created in a video.
Bayesian Analysis of Binary Data Using Skewed Logit Models

The logistic regression is one of the most widely used models for binary response data in medical and epidemiologic studies. However, in some applications, the overall fit can be improved significantly by the use of a noncanonical link and in particular by an asymmetric link. In this paper, we consider a skewed logit model for analyzing binary response data with presence of covariates. Using a Bayesian approach, we propose informative priors using historical data from a similar previous study. We use various Bayesian methods for model comparison and model adequacy. More specifically, we use conditional predictive ordinates (CPO) to develop the pseudo-Bayes factor and Bayesian standardized residuals. In addition, we propose Bayesian latent residuals for assessing choices of link functions. Data sets from a prostate cancer study are used to demonstrate the methodology as well as the role of informative priors in model comparison and model adequacy.
Speech emotion recognition for psychotherapy: an analysis of traditional machine learning and deep learning techniques

Human being most often shows their emotions through their speech, and detecting emotions from the speech is a crucial task where machine learning plays a significant role. In this paper, comparison and application of traditional machine learning models and deep learning models were conducted using spectral features like Mel-frequency cepstral coefficients on combined dataset of multiple audio files resources such as RAVDESS, TESS, and SAVEE. Using Random Forest Classifiers, the overall accuracy for predicting emotion classes is 86.3 percent and using Boosting Ensemble, we achieve 85.8 percent. However, deep learning techniques like LSTM and CNN are also applied and compared with traditional machine learning techniques, they achieve around 75 percent overall accuracy.
Load Frequency Analysis Using Optimal Virtual Inertia and SMES Considering A Random Load

Renewable energy sources (RESs) are getting prominence due to the scarcity of fossil fuels. On the other hand, most RESs are connected to the grid through inverters or converters, which are power electronic interfaces. Utilizing converters and inverters will significantly decrease the power system's inertia due to the absence of rotating mass. As a result, the use of advanced technologies to achieve and maintain frequency stability is essential in power systems. This work proposes two new control coordination i) virtual inertia and ii) superconducting magnetic energy storage (SMES) of load frequency control (LFC) along with a PI controller. Since SMES technology and virtual inertia are both effective energy storage systems and frequency stabilizers, respectively, this research develops two controllers to emulate inertia utilizing SMES. The mathematical model is developed to facilitate control design and implementation in simulation platform. The developed techniques show better performance in reducing the frequency deviation of highly RESs integrated system. Three loads are considered in this study to check the stability and effectiveness of the proposed control coordination. Determining the best approach by analyzing the two controllers individual outputs is one of this work's main purposes.
Optical properties of two-dimensional quasicrystalline plasmonic arrays

Simulation models for the optical properties of 2D quasiperiodic plasmonic structures often fail due to their lack of periodicity. Therefore, it is necessary to find an appropriate model to describe the optical properties of such structures. In this paper we present a model which is able to describe the optical spectra of 2D metallic photonic quasicrystals on top of a waveguide. We take the 2D Fourier transform of the structure and consider all possible waveguide modes in the specific energy range. By utilizing the dispersion relations, the optical spectra can be calculated. The presented model is verified by measurements of a quasiperiodic lattice as well as a rectangular lattice as reference. We find distinct differences in the behavior of quasicrystalline vs rectangular lattices, in particular when investigating rotated and elongated plasmonic particles.
Techno-Economical Comparison of MVAC and MVDC Micro-Grids with High PV Penetration

Recent advances in DC hybrid circuit breakers (HCBs) and wide bandgap (WBG) devices have created an opportunity to deploy new DC and hybrid AC/DC grid options. With the prevailing distributed energy resources (DERs) and DC loads, Medium Voltage Direct Current (MVDC) grids are considered a promising solution for highly reliable, efficient, and safe power distribution. This paper proposes a techno-economical assessment framework and conducts a comparative analysis between MVAC and MVDC micro-grids with high PV penetration. The performance is evaluated through a two-step probabilistic optimal power flow (OPF) and reflected in a set of economic value streams. Numerical results show that the MVDC study case leads its AC counterpart in the key economic metrics.
Effective Microgrid Optimal Dispatch Settings

Active power control is one of the crucial functions run by the microgrid controller to schedule distributed energy resources (DER) in a microgrid. This schedule can be obtained through solving an optimization problem, so-called optimal dispatch, to minimize the cost of microgrid operation. To match all possible cases that can occur during real-time operation as well as to ensure that the optimal dispatch always reaches a feasible solution, some of the constraints associated with optimal dispatch problem are defined in terms of soft constraints, which introduces penalty factors into the problem. The user needs to tune these penalty factors, which might be confusing and cumbersome. In this regard, there is a considerable value in easing the hassle of tuning penalty factors. This paper first proposes a method to reduce the number of penalty factors used in the microgrid optimal dispatch problem. Additionally, the penalty factors are introduced in such a form that users can easily tune them all together with minimum effort.
A Review on Traditional and Deep Learning based Object Detection Methods

Fast and accurate object detection systems are in high demand due to the advent of autonomous vehicles, smart video surveillance, facial detection, and numerous people counting applications. These systems not only detect and classify every object in an image or video, but also locate each one by creating a bounding box around it. This paper analyses the traditional and recent deep learning-based object detection methods from different perspectives, incorporating features recognition on many scales, data expansion, training approach, and perspective detection, in order to make it easier to deeply understand object detection. Some commonly used standard datasets for object detection are discussed. It also addressed the challenges and possible research scope in the future from the perspective of evolving object detection datasets and the framework for object detection tasks. From the analysis, it is observed that the performance of the methods in use for object detection is moderate and requires improvement, especially in difficult environments such as large object scale variance, obstructed object view, and horrific mild prerequisites. Therefore, the possible research scope for inventions and implementation of more novel deep learning methods to enhance object detection and classification accuracy is discussed.
Comparative Study of the Open-loop Boost Converter and the Closed-loop PID Controlled Boost Converters

This research compares the output voltage and harmonics of open-loop and closed-loop PID-controlled boost converters. Open-loop systems produce output based on their input voltage. The output voltage's THD & the capacitor's and inductor's input and output currents' THD don't change much with voltage. Using the same circuit and setting the reference voltage at half the open loop output voltage, the closed-loop system's output voltage remains almost constant for input voltages below the reference value. If the input voltage crosses the reference voltage, the output voltage jumps up somewhat. The closed-loop method is less effective than the open-loop method since the output voltage THD and the input and output current THD of the inductor and capacitor are higher and vary more with voltage. Open loop output voltage THD was 375.06% and closed loop THD was 1742.84% for a 17-volt input.
DC Capacitor Voltage Balance Control of Delta- Connected Cascaded H-Bridge STATCOM

In this paper, the DC capacitor voltage balance of delta connected cascaded H-bridge is deeply studied. From the point of view of currents and average active power, a three-layer controller of capacitor voltage at the DC side of delta-connected H-bridge is established. The first layer of control is the total DC side voltage control by generating active current. The second layer of control is the voltage control among three phases. Due to the delta configuration, there is a circulating current flowing through it. The cluster voltage balancing is realized by circulating current control even under unbalanced load. The third layer is the capacitor voltage control in each cluster, and the capacitor voltage balance in each cluster is completed by adjusting convert’s output voltage of each bridge-cell. For the capacitor-voltage balance control, the three-layer control strategy is analyzed theoretically in detail. Finally, the effectiveness of the control method is verified by simulation.
Data Anomaly Detection in Wireless Sensor Networks Using $\beta$ -Variational Autoencoder

Anomaly detection is the process of identifying data instances that drastically deviate from the majority of data instances. Anomaly detection is a key challenge to ensure the security in Wireless Sensor Networks. The detection of such anomalous data is required to reduce false alarms. The data that is generated from wireless sensor networks have several imbalances. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers. Data imbalance is the major challenge in machine learning models that is resolved in the proposed model using deep learning technique. Deep learning technique proposed is beta variational autoencoders in which a parameter $\boldsymbol{\beta}$ is included to the KL divergence term of the Variational Autoencoder (VAE)'s loss function. The introduction of the parameter beta provides disentangled representation of data. The proposed VAE model uses multivariate normal distribution instead of normal distribution.