Miracle-dz/UniFAR-FTUs huggingface dataset:
A graphical null model for scaling biodiversity–ecosystem functioning relationships

Global biodiversity is declining at rates faster than at any other point in human history. Experimental manipulations at small spatial scales have demonstrated that communities with fewer species consistently produce less biomass than higher diversity communities. Understanding the consequences of the global extinction crisis for ecosystem functioning requires understanding how local experimental results are likely to change with increasing spatial and temporal scales and from experiments to naturally assembled systems. Scaling across time and space in a changing world requires baseline predictions. Here, we provide a graphical null model for area scaling of biodiversity–ecosystem functioning relationships using observed macroecological patterns: the species–area curve and the biomass–area curve. We use species–area and biomass–area curves to predict how species richness–biomass relationships are likely to change with increasing sampling extent. We then validate these predictions with data from two naturally assembled ecosystems: a Minnesota savanna and a Panamanian tropical dry forest. Our graphical null model predicts that biodiversity–ecosystem functioning relationships are scale‐dependent. However, we note two important caveats. First, our results indicate an apparent contradiction between predictions based on measurements in biodiversity–ecosystem functioning experiments and from scaling theory. When ecosystem functioning is measured as per unit area (e.g. biomass per m2), as is common in biodiversity–ecosystem functioning experiments, the slope of the biodiversity ecosystem functioning relationship should decrease with increasing scale. Alternatively, when ecosystem functioning is not measured per unit area (e.g. summed total biomass), as is common in scaling studies, the slope of the biodiversity–ecosystem functioning relationship should increase with increasing spatial scale. Second, the underlying macroecological patterns of biodiversity experiments are predictably different from some naturally assembled systems. These differences between the underlying patterns of experiments and naturally assembled systems may enable us to better understand when patterns from biodiversity–ecosystem functioning experiments will be valid in naturally assembled systems. Synthesis. This paper provides a simple graphical null model that can be extended to any relationship between biodiversity and any ecosystem functioning across space or time. Furthermore, these predictions provide crucial insights into how and when we may be able to extend results from small‐scale biodiversity experiments to naturally assembled regional and global ecosystems where biodiversity is changing.
A Study on Sentiment Analysis of Mental Illness Using Machine Learning Techniques

In the digital age, social media plays a crucial role in society. Social media provides a platform to youth for exchanging their views on public issues and express their personal issues. Hence online media can be used for studying the behavior of people. Applying sentiment analysis on the data obtained timely from social networking sites (here Twitter), depression, anorexia, and other similar mental illness can be predicted among youth. The importance of detecting depression is that it is the root cause of a plethora of diseases. Early prediction can also mitigate the number of suicides. This work is to detect depression and PTSD (Post Traumatic Stress Disorder) among the Twitter users. Analysing the tweets, how likely a person is to suffer from any of the aforementioned diseases can be discovered.
RF and microwave photonic, fractional differentiation, integration, and Hilbert transforms based on Kerr micro-combs

Integrated Kerr micro-combs are a powerful source of multiple wavelength channels for photonic radio frequency (RF) and microwave signal processing, particularly for transversal filter systems. They offer significant advantages featuring a compact device footprint, high versatility, large numbers of wavelengths, and wide Nyquist bands. We review progress photonic RF and microwave high bandwidth temporal signal processing based on Kerr micro-combs with comb spacings from 49GHz to 200GHz. We focus on integral and fractional Hilbert transforms, differentiators as well as integrators. The future potential of optical micro-combs for RF photonic applications in terms of functionality and ability to realize integrated solutions is also discussed.
Grid Current Feedback Active Damping Control Based on Disturbance Observer for Battery Energy Storage Power Conversion System with LCL Filter

Adopting the battery energy storage system is an effective way to compensate the continuously growing fluctuating power generated by renewable sources. The power conversion system is considered as one of the core equipment used for interfacing battery packs to the grid in a battery energy storage system. This paper aims to apply an improved active damping control to a grid-tied power conversion system with LCL filter to attenuate its inherent resonance characteristics. The anti-interference ability is enhanced by estimating the second-order derivation of grid-injected current based on a modified disturbance observer. Meanwhile, the negative effects of parameter mismatch are equivalent to unmeasurable disturbances, which are possible to be compensated by subtracting the estimated values from the modulated voltage references. Moreover, the design method and robustness issue of the disturbance observer are discussed in detail. The presented control algorithm is implemented based on Simulink and dSpace. Detailed simulation results are provided, which can verify the feasibility and correctness of control strategy. Furthermore, an experimental prototype rated at 2.3 kW/110 V is constructed. The experimental results confirm that the presented control method is effective to be applied in the power conversion system (PCS).
Advanced Glaucoma Detection Using Hybrid Approach and Singular Value Decomposition from Fundus Images

Glaucoma is a known to be an of eye disease that is accountable for impaired vision worldwide. For medical images, image decomposition has high impact for diagnosis of diseases. This work proposes an intelligent hybrid approach-based image decomposition method for advanced glaucoma detection. The proposed approach employs Discrete Wavelet Transform (DWT) hybridisation with Empirical Wavelet Transform (EWT). Texture features are used and extracted from decomposed image components as these captures subtle variation. To discover effective features, extracted features are added to Singular Value Decomposition (SVD). In addition, efficient features are added to the Support Vector Machine (SVM) algorithm and 95.83 percent accuracy, 91.67 percent sensitivity and 100 percent specificity has been achieved for twelve-fold cross validation. Hence, the approach proposed demonstrates superior efficiency of decomposition.
A fast and robust reference current generation algorithm for three-phase shunt active power filter

The identification of the reference currents constitutes an important part of the control of the active power filter. This part requires an accurate estimation of the frequency, phase, and proper extraction of the load current harmonics. This makes the modeling more difficult and requests a rigorous selection of techniques to be used. For the sake of simplicity, the direct method is motivated by the need for the simplicity and flexibility than the existing techniques such as the instantaneous power theory and diphase currents method. However, this method requires a robust phase-locked loop to extract the unity voltages and a robust controller to estimate the magnitude of the source current. To this end, this paper proposes the hybrid phase-locked loop (HPLL) as a good option mainly because 1) it achieves zero phase error under frequency drifts, 2) Fast dynamic response, 3) totally block the DC offset, 4) From the control point of view, it is a type 1 control system which results in high stability margin. To the best of authors’ knowledge, the HPLL has not been used in active power filter yet. Furthermore, a neural PI regulator is used to estimate the magnitude of the source current. Simulation results show the efficiency of the proposed technique and illustrate all its interesting features. For the sake of comparison, the proposed method is compared to other advanced techniques.
Determination of the Temperature-Dependent Thermal Material Properties in the Cooling Process of Steel Plates

Accelerated cooling (ACC) is a key technology in producing thermomechanically controlled processed (TMCP) steel plates. In a TMCP process, hot plates are subjected to a strong cooling resulting in a complex microstructure leading to increased strength and fracture toughness. The microstructure, residual stresses, and flatness deformations are strongly affected by the temperature evolution during the cooling process. Therefore, the full control (quantification) of the temperature evolution is essential regarding plate design and processing. It can only be achieved by a thermophysical characterization of the material and the cooling system. In this paper, the focus is on the thermophysical characterization of the material properties which govern the heat conduction behavior inside of the plates. Mathematically, this work considers a specific inverse heat conduction problem (IHCP) utilizing inner temperature measurements. The temperature evolution of a heated steel plate passing through the cooling device is modeled by a 1D nonlinear partial differential equation with temperature-dependent material parameters which describe the characteristics of the underlying material. Usually, the material parameters considered in IHCPs are often defined as functions of the space and/or time variables only. Since the measured data (the effect) and the unknown material properties (the cause) depend on temperature, the cause-to-effect relationship cannot be decoupled. Hence, the parameter-to-solution operator can only be defined implicitly. By proposing a parametrization approach via piecewise interpolation, this problem can be resolved. Lastly, using simulated measurement data, the presentation of the numerical procedure shows the ability to identify the material parameters (up to some canonical ambiguity) without any a priori information.
Design and Analysis of a 28 GHz T/R Front-End Module in 22-nm FD-SOI CMOS Technology

This article presents practical design considerations and methodologies for a 28-GHz front-end module (FEM) in 22-nm fully depleted silicon on insulator (FD-SOI) CMOS technology for the fifth generation (5G) wireless communication. The design adopts a gain-boosting technique that is comprehensively analyzed with a transformer-based stacked-FET power amplifier (PA). Then, the co-design of the transmit/receive (T/R) switch with the PA and low-noise amplifier (LNA) is investigated, and an electrostatic-discharge (ESD)-aware T/R switch incorporating PA circuitry is proposed to leverage the Tx- and Rx-mode performance. Moreover, the robustness of standalone PA and Tx-mode FEM is simulated and experimentally verified. Furthermore, the ground return paths and supply parasitic paths of the adopted single-ended LNA together with the proposed T/R switch are studied, properly simulated, and assessed. Finally, the proposed PA topology is first verified standalone, exhibiting 32-dB power gain ( $G_{p}$ ), an 18.2-dBm output 1-dB compression point (OP1 dB), and a 31.1% power-added efficiency (PAE) at OP1 dB (PAE1 dB). Using this PA in the FEM yields a Tx-mode OP1 dB/PAE1 dB of 16 dBm/19.4%, and an average output power (Poutavg)/PAE of 10.1 dBm/8.3% for a 100-MHz bandwidth 256-QAM single-carrier signal at an error-vector magnitude (EVM) of −30 dB. In the Rx mode, noise figure (NF) and input-referred third-order intercept point (IIP3) are 3.2 and −5.4 dBm, respectively. A 2-kV human-body model (HBM) ESD protection of the FEM is predicted in transient simulations and measured with transmission line pulse (TLP) tests.
Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm

Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.
Chemical Solution Deposition of La-Substituted BiFe0.5Sc0.5O3 Perovskite Thin Films on Different Substrates

In the present work, polycrystalline Bi0.67La0. 33Fe0.5Sc0. 5O3 thin films were synthesized using a simple and cost-effective chemical solution deposition process employing the spin coating technique. In order to check the feasibility of the fabrication of thin films on various types of substrates, the films were deposited on Pt-coated silicon, silicon, sapphire, corundum, fused silica and glass. Based on the results of thermogravimetric analysis of precursor and thermal stability study, it was determined that the optimal annealing temperature for the formation of perovskite structure is 600 °C. It was observed that the relative intensity of the pseudocubic peaks (001)p and (011)p in the XRD patterns is influenced by the nature of substrates, suggesting that the formed crystallites have some preferred orientation. Roughness of the films was determined to be dependent on the nature of the substrate.
Antioxidant Content of Palm Fruit (Borassus flabellifer L.) Seed Coat

Background: Sunlight, cigarette smoke, and pollution substances triggered free radicals to get into the body. Antioxidant has functions to against free radicals. Borassus flabellifer ( B. flabellifer ) contains antioxidants however there is no study about the content of their wasted seed coat. Aim: This study aims to determine antioxidant content of B.flabellifer seed coat ethanol extract using DPPH ( 2,2-diphenyl-1-picrylhydrazyl ) assay. Methods: This in vitro experimental study was conducted by three main stages. First stage was B.flabellifer seed coat ethanol extract preparation. Second stage was the quantitative phytochemical tests. At the third stage, antioxidant activity was tested with DPPH assay using the DPPH powder as the free radical toward the seven groups which were negative control of the blank solution, ethanol extract of B.flabellifer seed coat concentration of 3 ppm, 6 ppm, 9 ppm, 12 ppm, 15 ppm, and positive control of gallic acid. Results: B.flabellifer seed coat ethanol extract contains antioxidant groups of flavonoid, phenol, and tannins. B.flabellifer seed coat ethanol extract and control positive of gallic acid has average IC 50 of 12,29 ppm and 0,96ppm respectively. coat ethanol extract and gallic acid showed a strong antioxidant characteristic. Conclusion: seed coat ethanol extracts contain antioxidant groups of flavonoid, phenol, and tannins and has a very strong antioxidant characteristic. However, have not surpassed the gallic acid as positive control.
Time Series Clustering of Online Gambling Activities for Addicted Users’ Detection

Ever since the worldwide demand for gambling services started to spread, its expansion has continued steadily. To wit, online gambling is a major industry in every European country, generating billions of Euros in revenue for commercial actors and governments alike. Despite such evidently beneficial effects, online gambling is ultimately a vast social experiment with potentially disastrous social and personal consequences that could result in an overall deterioration of social and familial relationships. Despite the relevance of this problem in society, there is a lack of tools for characterizing the behavior of online gamblers based on the data that are collected daily by betting platforms. This paper uses a time series clustering algorithm that can help decision-makers in identifying behaviors associated with potential pathological gamblers. In particular, experimental results obtained by analyzing sports event bets and black jack data demonstrate the suitability of the proposed method in detecting critical (i.e., pathological) players. This algorithm is the first component of a system developed in collaboration with the Portuguese authority for the control of betting activities.
Biogenic selenium nanoparticles produced by Lactobacillus casei ATCC 393 inhibit colon cancer cell growth in vitro and in vivo

Selenium compounds exhibit excellent anticancer properties but have a narrow therapeutic window. Selenium nanoparticles, however, are less toxic compared to other selenium forms, and their biogenic production leads to improved bioavailability. Herein, we used the probiotic strain Lactobacillus casei ATCC 393, previously shown to inhibit colon cancer cell growth, to synthesize biogenic selenium nanoparticles. We examined the anticancer activity of orally administered L. casei, L. casei-derived selenium nanoparticles and selenium nanoparticle-enriched L. casei, and investigated their antitumor potential in the CT26 syngeneic colorectal cancer model in BALB/c mice. Our results indicate that L. casei-derived selenium nanoparticles and selenium nanoparticle-enriched L. casei exert cancer-specific antiproliferative activity in vitro. Moreover, the nanoparticles were found to induce apoptosis and elevate reactive oxygen species levels in cancer cells. It is noteworthy that, when administered orally, selenium nanoparticle-enriched L. casei attenuated the growth of colon carcinoma in mice more effectively than the isolated nanoparticles or L. casei, suggesting a potential additive effect of the nanoparticles and the probiotic. To the best of our knowledge this is the first comparative study examining the anticancer effects of selenium nanoparticles synthesized by a microorganism, the selenium nanoparticle-enriched microorganism and the sole microorganism.
Corruption and Tax Burden: What Is the Joint Effect on Total Factor Productivity?

A common conclusion in the literature is that both corruption and taxation hamper economic growth. It is also plausible that both affect total factor productivity, which, by the famous Solow residual, is a vital driver of economic progress. Moreover, corruption and tax burden are supposed to be intertwined. This paper focuses on the supposedly linked effects of corruption and tax burden on total factor productivity. The empirical study uses panel data from 90 countries for the time span of 1996–2014. The results show that both corruption and tax burden deteriorate total factor productivity, but that an increase in tax burden mitigates the negative effect of corruption.
Lesson of Drama in Language Education: Why do We Have to Learn English Through Drama Performance?

The primary goal of this research is intended to report how the implementation of Drama Performance as a project based in learning English foreign language and whether the lessons, the learning activity can motivate students interest also encouraging students to use their English language skills. Further, the discussion also takes into the issues of the problem that may exist during drama performance such as some students believe they cannot perform in English and they may choose to withdraw from the activity. The researcher is employing qualitative methodology to investigate the research questions. Observation and interview use as the instrument of this present study. The participants are the students who take Drama in Language Education course at the fifth semester of English Education Department at School of Teacher Training and Education (STKIP) Muhammadiyah Bogor. The students have performed drama, and have experience in preparing the session. The students are under the researcher supervision. The participants from other Department to be interviewed became the source of the data who watched drama performance. The students are under the researcher supervision. Activities are designed in accordance with the course objectives to share information about the students, the learning process and the phenomenon. The implementation of drama performance as a project based learning has myriad benefits for students and drama activities are a unique way of enhancing students’ motivation and participation. Language skills can be exposed to students not only through conventional technique but also through drama performance. Project based learning provides students with the opportunity to explore the contents of Drama in Language Education. To get into the outcome, the core activities focus on the elements of drama, creative writing (composing a play script), characterization, make-up character, providing students with the information about drama in language education, and also hope to give students the opportunity to perform a drama in English. The lecturer’s role in the teaching learning process is crucial. It can influence the students’ response towards project based learning. Keywords—drama performance, project based learning, learning skills
Main and Secondary Controller Assignment With Optimal Priority Policy Against Multiple Failures

This paper proposes a main and secondary controller assignment model against multiple controller failures in software defined networks considering latency between switches and controllers. The survivability guarantee of each switch is satisfied by assigning a set of controllers, where one of them works as the main controller to control the switch. Given assigned controllers, we introduce a policy-based approach to automatically specify the main controllers in each failure pattern, which leads to a lightweight configuration on a switch. We define the average-case expected latency, the worst-case expected latency, and the expected number of switches within a latency bound, as three objectives to be optimized in three different problems. We prove that a low latency first policy achieves the optimal objective for each considered problem. We formulate the proposed controller assignment model with different goals as three mixed integer linear programming problems. We prove the NP-completeness for all the three problems. A greedy algorithm with polynomial time complexity is developed; we show that it provides a 1/2-approximation for the case without the survivability guarantee constraint. The numerical results observe that the proposed model obtains the optimal objective value with the computation time about $10^{2}$ times shorter than that of a baseline that introduces decision variables to determine the main controllers.