Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Fertilization on Expression of Bioactive Carotenoid and Chlorophyll Pigments in Clinacanthus nutans Lindau for Potential Use as Functional Natural Colourants

This research aimed to investigate the different effects of fertilization (through the use of vermicompost and chemical fertilizer) on carotenoid composition as well as the chlorophyll content in Clinacanthus nutans Lindau. A field study employing a Randomized Complete Block Design (RCBD) was conducted on four treatment groups; control plants that were not supplied with any fertilizer (NF), plants supplemented with NPK chemical fertilizer (FC), plants supplemented with vermicompost (FV), and plants supplied with the mixture of chemical fertilizer and vermicompost (FM). Vermicompost application was shown to have insignificant effects on the expression of chlorophyll and carotenoid in C. nutans as compared to chemical fertilizer. However, the supplementation of vermicompost (FV and FM) produced plants with greater stability of compounds during storage, in contrast to NF and FC. The major carotenoids in C. nutans methanolic extracts supplied with vermicompost and organic fertilizers were identified as violaxanthin, lutein, α-Carotene and β-Carotene, while control plants lacked in α-Carotene and β-Carotene. This analysis provides a better insight into the application of vermicompost as an alternative source of growth supplements for the sustainable production of pigments which can act as a functional natural colourant and deliver great medicinal benefits to humans.
Development of a rules-based fuzzy algorithm for deviation of obstacles

The objective of this project is to introduce the study of the Fuzzy logic, presenting the development of a navigation system in MATLAB software and its implementation in an autonomous mobile vehicle. The application proposal aims to integrate an Automated Guided Vehicles gifted a navigation system elaborated from Set Theory Fuzzy, in any kind of plan environment with obstacles at random points, so that it is able to identify any obstacles present in the environment in which is inserted and through the developed programming language C based on Fuzzy logic implemented in a ARDUINO MEGA2560 plate, calculating the deviation and executing necessary to avoid a collision with any obstacle encountered in the environment. At the end of deployment tests were conducted to obtain the robot responses and compared with the simulation software, according to the possibilities of reading of the sensors. The responses were satisfactory, following the same trend behavior of the simulations of the Fuzzy controller.
Effective Width Method Based Design for Distortional Buckling

, Abstract This paper presents an Effective Width concept based design method against the distortional buckling of cold-formed steel Z and C-section beams. The distortional buckling may be the predominant buckling mode for many cold-formed steel studs, joists, purlins, or girts, unless the compression flange is fully restrained by attachment to sheathing or paneling. However the distortional buckling remains a largely unaddressed problem in the main body of the current North American Specification for the Design of Cold-Formed Steel Structural Members (NAS, 2001) Edition. Experimental investigations have indicated that NAS provides unconservative predictions for the distortional buckling failures. It was also found that the Direct Strength Method and Australian/New Zealand code work well for distortional buckling, but they demonstrate limited applicability for today’s industry due to the need of advanced computation tools to determine the elastic buckling behavior of sections. The proposed method in this paper was based on the current design procedure in NAS (2001), it enables engineers to predict the distortional buckling strength of cold-formed steel Z or C-section beams using the existing design method, Effective Width Method, with limited modifications. The new method shows good agreements with experimental results as well as the Direct Strength Method predictions.
Pulmonary involvement in Lassa fever: a scoping review

INTRODUCTION: Lassa fever (LF) affects all body systems, however, inadequate knowledge exists on the involvement of the pulmonary system in LF infections. This scoping review, therefore, aimed to describe the pulmonary involvement of LF. MATERIAL AND METHODS: We conducted an extensive search of the literature on two databases, namely PubMed and Google Scholar. Overall, 5,217 articles were retrieved from a database search, out of which 107 duplicates were removed. Overall, 12 articles were included: four review articles, three case reports, three experimental inoculation studies, one retrospective study, and a prospective case-control study. RESULTS: Symptoms experienced included fever, pharyngitis, retrosternal pain, respiratory distress, and proteinuria. Complications included unique pulmonary arteritis, pulmonary embolization, mucosal bleeding, pleural or pericardial effusion, pulmonary edema, and interstitial pneumonitis. Consequences of the effect of Lassa virus infection were impairment of the immune system alongside continual replication of Lassa virus infection in affected tissues and death of affected individuals. LF has varied but serious effects on the pulmonary system. CONCLUSIONS: These symptoms, particularly in areas where LF is known to be endemic, should prompt clinicians to request LF polymerase chain reaction for confirmatory diagnosis. These features should promote the provision of respiratory support for patients in need of such.
Meshfree Methods for Modeling of Material Deformation Using Higher-Order Continuum Theories

In this contribution, meshfree methods are applied for the modeling of gradient elasticity and hyperelasticity using higher-order theories based on only one microstructural parameter [1]. Both the Mixed Meshless Local Petrov-Galerkin (MLPG) approach [2] and the Optimal Transportation Method (OTM) [3] are considered. Firstly, the MLPG collocation method based on the staggered solution strategy [4] is utilized for structures undergoing small strain. Thereafter, the OTM [3] based on the weak-form of gradient continuum for finite strain is considered. Both methods are used for the modeling of homogeneous and heterogeneous structures. In the mixed collocation approach the fourth-order equilibrium equations are solved as an uncoupled sequence of two sets of second-order differential equations, while in the OTM method the weak-form of the original fourth-order equilibrium equations is employed. The independent variables in both approaches are approximated using meshless interpolating functions enabling the satisfaction of the boundary conditions in a simple and straightforward manner without the need for the calculation of additional parameters. The Interpolation Moving Least Squares (IMLS) [5] are utilized in the mixed MLPG methods and the Maximum-Entropy functions [6] are used in the OTM method. The analysis of accuracy and numerical performance of both approaches are demonstrated by a set of suitable numerical examples.
Deep-Reinforcement-Learning-Based Production Scheduling in Industrial Internet of Things

The unprecedented prosperity of the Industrial Internet of Things (IIoT) promotes the traditional industry transforming into intelligent manufacturing so that the whole production process can be comprehensively controlled to achieve flexible production. Intelligent scheduling, as one of the key enabling techniques, is desired to allocate the production of several machines by an efficient solution with minimum makespan. Existing approaches adopt a fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large sized of the practical problems, these methods fail to guarantee the quality of the obtained solution under the real-time requirement. To address this challenge, we formulate the production scheduling problem as a Markov decision process (MDP) and specifically design a job scheduling model made up of a job batching module for the hybrid flow-shop scheduling problem on batch processing machines (HFSP-BPM). Our proposed model consists of an actor network that learns the action under different conditions and a critic network that evaluates the action of the actor. We analyze the convergence of the model under different parameter settings to determine the optimal parameter. Extensive numerical experiments on both publicly available data set and real steel plant production data set demonstrate that the proposed deep reinforcement learning (DRL) approach compared with other baselines, more than 6% average improvements can be observed in many instances.
Influence of measured surface materials on the accuracy of 3D measurement using fringe projection

Optical 3D surface measurement based on structured light illumination has been widely used in machine vision and industrial detection. The structured light 3D measurement is based on the assumption that the surface of the object reflects diffusely. However, most engineering materials do not satisfy this condition, and the influence of the optical properties of the measured surface on the measurement accuracy is often neglected. In this paper, I analyzed and discussed the influence of the optical properties of the surface material on the measurement. The method for calculating and comparing the phase and position of the center of mass of the impulse response is proposed to explain the error that occurs from the use of surfaces with diffuse transmissions and diffuse scattering. I designed a 3D measuring system to obtain the distribution of phase and the position of the impulse response. The results of the comparative experiments show that the optical properties of the measured surface affect the 3D measurement result. It is thus necessary to consider the optical properties of the measured surface in precise 3D structured light measurement.
The Aharonov-Bohm Effect and Transport Properties in Graphene Nanostructures

Abstract In this paper we discuss interplays between the Aharonov-Bohm effect and the transport properties in mesoscopic ring structures based on graphene. The interlayer interaction leads to a change of the electronic structure of bilayer graphene ring such that the electronic energy dispersion law exhibits a gap, either by doping one of the layers or by the application of an external perpendicular electric field. Gap adjustments can be done by varying the external electric field, which provides the possibility of obtaining mesoscopic devices based on the electronic properties of bilayer graphene. This opens the way to controllable manipulations of phase-coherent mesoscopic phenomena, as well as to Aharonov-Bohm oscillations depending on the height of the potential step and on the radius of the ring. For this purpose one resorts to a tight-binding model such as used to the description of conductance.
A CURRENT RETENTION BASED PROTECTION ALGORITHM FOR DC MICROGRIDS

Microgrids have been identified as a step towards goals of global green energy generation as they offer attractive options of renewable resource inclusion in decentralized energy networks, thus providing incentive towards meeting a booming energy demand sustainably. They are however impaired by the characteristic nature of Distributed Renewable Energy Resources (DRERs). While DRERs and microgrids offer the advantage of sustainable energy generation and autonomous operation with respect to the traditional grid, their intermittency and unconventional characteristics due to deviation from the traditional power grid structures causes trepidation while opting for them. A cause of concern while employing microgrids in daily use is the peril to personnel and equipment during the occurrence of a fault. To mitigate severe loss of life and property, it is important to develop and design protection algorithms for microgrids. While there is a comparatively large pool of knowledge on AC microgrid protection, DC microgrid protection is challenging and is being focused on by researchers around the world. The unorthodox nature of these networks cause conventional protection algorithms to be unsuitable and make the protection of these microgrids tasking. The work in this paper aims to contribute to efforts in the protection of hybrid microgrids. While the work in this paper is limited to the DC side of the grid, the proposed algorithm is able to detect and identify the location of various types of DC faults. The algorithm is verified on a secondary radial hybrid microgrid and is further compared with existing DC protection algorithms on various performance parameters.
A Decentralized Bi-Level Decomposition Method for Optimal Operation of Electric Vehicles in Coupled Urban Transportation and Power Distribution Systems

The growing prevalence of electric vehicles (EVs) enhances the coupling of urban transportation networks (UTNs) and power distribution networks (PDNs). First, based on the inverse optimization (IO), this article proposes a holistic pricing framework to manage the power-traffic flows at network equilibrium (NE) toward the least-cost social optimum (SO) state with minimum extra user charges. Second, a coordinated operation model is proposed which assigns the traffic flows and schedules the power generation within grid operation and EV routing and charging constraints. It removes some unrealistic assumptions in existing works. Third, a novel decentralized bi-level decomposition algorithm is designed. It efficiently tackles the computation intractability and preserves the network data privacy by decoupling the UTN and PDN problems in the outer layer and adaptively generating operation results-aware optimal charging paths in the inner layer. Finally, real-world UTN-based case studies validate the effectiveness of the proposed method and specify the economic and operational merits of aligning the costly and congested NE with the economic and efficient SO. The model can reduce the total operation cost by 4.2% and renewable power spillage by 11.7%. Based on the framework, model, and results in this article, an IO-based pricing is proposed in our sequel paper.
Benchmark TPC-C Aplicado em Replicação Máquina de Estados

A Replicação Máquina de Estados (RME) é uma abordagem importante para prover serviços de alta disponibilidade. No entanto, o aumento da vazão em RME é um desafio devido ao seu modelo determinístico. Uma lacuna importante é a falta de cargas de trabalho que permitam avaliar diferentes mecanismos para RME sob os mesmos referenciais, representativos de aplicações de interesse. Identificaram-se aspectos comuns relevantes no contexto de transações online, e propõe-se o uso do benchmark TPC-C do Transaction Processing Performance Comittee para avaliar RMEs. A arquitetura é discutida e implementada em uma plataforma de replicação, no contexto de RME. Os resultados obtidos foram reportados utilizando o modelo clássico de RME. Além disso, foi discutida e implementada uma abordagem para RME paralela com essa carga de trabalho, e os resultados também foram reportados.
Meta-Analytical SEM: Equivalence Between Maximum Likelihood and Generalized Least Squares

Meta-analysis plays a key role in combining studies to obtain more reliable results. In social, behavioral, and health sciences, measurement units are typically not well defined. More meaningful results can be obtained by standardizing the variables and via the analysis of the correlation matrix. Structural equation modeling (SEM) with the combined correlations, called meta-analytical SEM (MASEM), is a powerful tool for examining the relationship among latent constructs as well as those between the latent constructs and the manifest variables. Three classes of methods have been proposed for MASEM: (1) generalized least squares (GLS) in combining correlations and in estimating the structural model, (2) normal-distribution-based maximum likelihood (ML) in combining the correlations and then GLS in estimating the structural model (ML-GLS), and (3) ML in combining correlations and in estimating the structural model (ML). The current article shows that these three methods are equivalent. In particular, (a) the GLS method for combining correlation matrices in meta-analysis is asymptotically equivalent to ML, (b) the three methods (GLS, ML-GLS, ML) for MASEM with correlation matrices are asymptotically equivalent, (c) they also perform equally well empirically, and (d) the GLS method for SEM with the sample correlation matrix in a single study is asymptotically equivalent to ML, which has being discussed extensively in the SEM literature regarding whether the analysis of a correlation matrix yields consistent standard errors and asymptotically valid test statistics. The results and analysis suggest that a sample-size weighted GLS method is preferred for combining correlations and for MASEM.
Starch and Aflatoxin B1 Content of Corn Grain with Different Water Content Added with Synthetic Mold Inhibitor

Penggunaan jagung sebagai pakan harus memenuhi standar kualitas kimia yang dipersyaratkan untuk menjamin keamanan pakan. Penggunaan penghambat jamur sintetis merupakan salah satu strategi untuk menghambat pertumbuhan jamur dan produksi metabolit sekunder yang dapat menurunkan kualitas kimia jagung. Penelitian ini bertujuan untuk menganalisis kandungan amilum (pati) dan aflatoksin B1 (AFB1) jagung kuning pipilan yang terdiri dari tiga level kadar air dan disuplementasi produk anti jamur sintetis. Sejumlah 750 kg jagung kuning pipilan digunakan dalam penelitian ini. Penghambat jamur (PJ) komersial yang digunakan diperoleh dari PT Japfa Comfeed Tbk, Indonesia dengan dosis 450 g/ton jagung. Percobaan dua faktor (Level kadar air: 13, 14 dan 15%; penghambat jamur PJ: -, +) digunakan pada penelitian ini. Hasil uji ANOVA membuktikan bahwa kecuali kandungan pati, interaksi yang signifikan antara LKA dan PJ terhadap kandungan aflatoksin B1 jagung yang disimpan selama 60 hari. Jagung (LKA berbeda dan tanpa PJ) memiliki kandungan AFB1 lebih tinggi dari yang diberi PJ. Simpulannya, penggunaan penghambat jamur efektif menghambat pertumbuhan jamur sehingga produksi aflatoksin B1 dapat ditekan. Penggunaan PJ sintetis tidak mempengaruhi kandungan pati jagung biji selama 60 hari penyimpanan.
Weakly Supervised Hyperspectral Image Classification With Few Samples Based on Intradomain Sample Expansion

Insufficient sample is a common problem in hyperspectral image (HSI) classification and an important factor causing to low accuracy. Adding weak supervision mechanism to model training is an effective way to solve this problem. In this article, we propose a weakly supervised HSI classification method with few samples based on intradomain sample expansion, which is anchored on the spatial correlation between samples. First, to reduce the negative effects of spectral mixing on pseudolabel generation and classifier training, we introduce the continuous side window filter to smooth the original HSIs band-by-band. Second, in order to better balance the correctness and representativeness of the generated pseudolabels, a novel method for sample selection and pseudolabel generation is proposed in this article. The method contains two branches, the segmentation graph based and the neighborhood relationship based. In the branch based on segmentation graphs, the polygon segmentation graph generated by the false color image is used to select the expansion samples. As a complementary branch, the branch based on neighborhood relationship exploits the neighborhood relationship and spectral similarity between samples to further select samples. Finally, in the classification stage, this article uses the broad learning system as a classifier to obtain the classification graph. Experiments on three publicly available datasets show that the method in this article can effectively achieve sample expansion and improve the classification accuracy and stability of HSIs in the case of insufficient labeled samples.
A review on computational modeling of instability and degradation issues of halide perovskite photovoltaic materials

Hybrid halide perovskite solar cells have been recognized as one of the most promising future photovoltaic technologies due to their demonstrated high‐power conversion efficiency, versatile stoichiometry and low cost. However, degradation caused by environmental exposure and structural instability due to ionic defect migration hinders the commercialization of this technology. While the experimental studies try to understand the phenomenology of the degradation mechanisms and devise practical measures to improve the stability of these materials, theoretical studies have attempted to bridge the gaps in our understanding of the fundamental degradation mechanisms at different time and length scales. A deeper understanding of the physical and chemical phenomena at an atomic level through multiscale materials modeling is going to be crucial for the knowledge‐based prognosis and design of future halide perovskites. There have been increased efforts in this direction in the last few years. However, the instability fundamentals explored through atomistic modeling and simulation methods have not been reviewed comprehensively in the literature yet. Therefore, this paper is an attempt to present a critical review, while identifying the existing gaps and opportunities in the investigation of the degradation and instability issues of the halide perovskites using computational methods. The review will primarily focus on the instability caused due to the intrinsic ionic defect migration and degradation due to thermal, moisture and light exposure. The findings from the simulation studies conducted primarily using density functional theory, ab initio molecular dynamics, classical molecular dynamics and machine learning methods will be presented.
Where not to install a reverberation enhancement system

For hundreds (perhaps thousands) of years venues for live performance emphasized communication—the transfer of speech or musical information from performers to listeners—as paramount to success. The Greeks could perform drama to 15,000 listeners without microphones, and halls used by Haydn, Mozart, and Beethoven were small and dry. Wagner built a fan-shaped opera hall with an absorbent stage, a covered pit, and no lateral reflections. It is still considered perhaps the best in the world. Sabine and others made lecture and Vaudeville halls that worked. But in the 20th century suddenly early reflections were essential, and long reverberation times desirable. Wood stages and lateral reflections were mandatory. These ideas linger on. In this talk we share our experiences of often misguided attempts to improve sound in venues of all types. The lessons we learned from a few perceptive musicians and artists have changed our approach to acoustics. Attention is the key, not reverberation. Reverberation can be love.
Application of coupled mode theory on radiative heat transfer between layered Lorentz materials

The coupled mode theory (CMT) provides a simple and clear framework to analyze the radiation energy exchange between reservoirs. We apply CMT to analyze the radiative heat transfer between layered Lorentz materials whose dielectric functions can be approximated by the Lorentz oscillator model. By comparing the transmissivity computed by the exact solution to that computed by CMT, we find that CMT generally gives a good approximation for this class of materials. The biggest advantage of CMT analysis, in our opinion, is that only the (complex) resonant energies are needed to obtain the radiation energy transfer; the knowledge of the spatial profile of resonances is not required. Several issues, including how to choose the resonant modes, what these modes represent, and the limitation of this method, are discussed. Finally, we also apply the CMT method to the electronic systems, demonstrating the generality of this formalism.