Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGENDALIAN BIOLOGI NEMATODA Meloidogyne spp. DENGAN JAMUR Paecilomyces fumosoroseus DAN BAKTERI Pasteuria penetrans SERTA PENGARUHNYA TERHADAP TANAMAN BUNCIS (Phaseolus vulgaris L.)

Produksi buncis di Indonesia masih rendah dibandingkan dengan negara-negara lain. Hal ini diakibatkan oleh serangan nematoda bengkak akar (Meloidogyne spp.). Usaha pengendalian Meloidogyne spp. dapat dilakukan dengan beberapa cara, salah satunya dengan pemanfaatan musuh alami Meloidogyne spp. yaitu Paecilomyces fumosoroseus dan Pasteuria penetrans. Penelitian ini bertujuan untuk memperoleh efektifitas P. fumosoroseus, dan P. penetrans, atau campuran keduanya terhadap indeks gall akar, jumlah telur, berat segar bagian atas tanaman, jumlah larva II Meloidogyne spp. dalam 100 ml tanah, dan hasil tanaman buncis. Penelitian menggunakan metode percobaan dengan Rancangan Acak Kelompok, terdiri atas 8 perlakuan dan 4 ulangan. Perlakuan tersebut adalah tanaman buncis diinokulasi dengan: 1). Meloidogyne spp. + P. fumosoroseus, 2). Meloidogyne spp. + Pasteuria penetrans, 3). Meloidogyne spp. + P. fumosoroseus + P. penetrans, 4). Hanya Meloidogyne spp. , 5). Hanya P. fumosoroseus, 6). Hanya P. penetrans, 7). P. fumosoroseus + P. penetrans. 8). Kontrol tanpa perlakuan. Data hasil percobaan dianalisis dengan menggunakan analisis ragam. Hasil percobaan menunjukkan, bahwa perlakuan yang mengandung Meloidogyne spp. dan P. fumosoroseus, atau Meloidogyne spp. dan P. penetrans, atau Meloidogyne spp. ditambah P. fumosoroseus dan P. penetrans mampu menurunkan indeks gall akar, jumlah telur, mampu meningkatkan berat segar bagian atas tanaman, menurunkan jumlah larva II Meloidogyne spp. dalam 100 ml tanah, dan meningkatkan hasil tanaman buncis. P. fumosoroseus dan P. penetrans yang diaplikasikan bersama mampu menurunkan jumlah telur, dan jumlah larva II Meloidogyne spp. dalam 100 ml tanah, dan hasil buncis lebih tinggi dibandingkan jika diaplikasikan secara sendiri-sendiri. Kata kunci : Pengendalian secara biologi, Meloidogyne spp. , Paecilomyces fumosoroseus, Pasteuria penetrans, buncis
Renewable Power Application based Switched Boost Inverter

Switched Boost Inverter (SBI) is a type of power converter. It converter the power from DC-AC with boosting in a single stage. This can be applied for micro grid and nano grid applicationusing renewable power sources , as this converter supplies the power for DC loads and AC loads in the same time. The operation of SBI is shoot through of inverter legs without damaging the converter operation. Control signals for SBI is given through the sinusoidal pulse width modulation (SPWM), also it has good EMI noise immunity. The proposed Switched boost inverter is explained theoretically & simulated using MTALAB / simulink model. This paper also represent the laboratory prototype of SBI , with its results.
The impact of severe drought on survival, fecundity, and population persistence in an endangered amphibian

Climate is globally changing. In Europe, studies have highlighted an increasing trend in both the frequency and magnitude of droughts. Abrupt changes in the frequency, location, or intensity of extreme heatwaves and droughts can have direct and severe effects on wild populations. Amphibians are the planet's most threatened group of vertebrates, with over 40% of known species considered in decline. To date, researchers have mainly focused on the influence of repeated droughts on species occurrence and community composition; however, evidence of the direct impact of climatic factors on the demographic parameters of amphibians is currently not well documented. Further investigation of this issue is therefore of critical importance in order to optimize local and global wildlife conservation policies in the context of a changing climate. This study used capture–recapture data to investigate the impact of severe drought on the survival and fecundity of a threatened amphibian, the yellow-bellied toad (Bombina variegata, L.), as well as to predict how potential changes in the frequency of droughts might influence the population growth rate. By developing multievent capture-recapture models, we showed that severe drought has a negative impact on fecundity and postmetamorphic survival at different ontogenetic stages. Then, using stochastic matrix population models, we predicted that changes in drought frequency negatively influence the population growth rate, which is a warning sign for population persistence. Direct conservation actions are then proposed to mitigate the detrimental effects of drought on population dynamics.
MELATONIN ELICITED GROWTH, PHOTOSYNTHESIS AND ANTIOXIDANT RESPONSES IN PEA PLANTS: A CONCENTRATION AND MODE DEPENDENT STUDY

The aim of this study was to dissect the response of melatonin under different mode of application with series of concentrations in pea plants for growth, photosynthesis and antioxidant system. Surface sterilized seeds of Pisum sativum were soaked in deionized water (control), 50, 100, and 200 M of melatonin for 4, 8, and 12 h (shotgun approach). In another mode of application, same concentrations of ML applied through foliage of plants and were allowed to grow for 30 days. At this stage of growth, plants were sampled to assess the various growths and photosynthetic traits as well as selected biochemical characteristics. The results indicated that growth characteristics, net photosynthetic rate, antioxidants system, and proline content showed diverse response for different concentrations and mode of application. Foliar application excelled over the seed soaking and 100 M of ML proved best in enhancing activity of antioxidant system and proline accumulation in comparison to control plants. The up-regulation of antioxidant enzymes as well as proline triggered by melatonin could have great potential for improving crop yield and act as an eco-friendly plant growth regulator for sustainable agriculture practices.
The Precision of Metaphor for Information Retrieval

We present a theoretical model for an information retrieval filter based on metaphors. The justification for this proposal is the current information boom, especially due to new technologies. The theoretical basis for our proposal is an association of the Cooperation Principle proposed by Grice (1975), the Indolence Principle by Berrendonner (1989), Lakoff and Johnson's (1980) concept of metaphorical representation, and Ranganathan's (1967) faceted system. A case study was done with a group of researchers in the field of Transport Engineering, as well as an analysis of manifestos written by a group of contemporary artists with the purpose of broadening the scope of the proposed model. We conclude that the model is feasible, although it would benefit from further studies involving different fields of knowledge.
Adequate treatment of nutrition and exercise for elderly people associated with sarcopenia and nutritional disorder

The importance of exercise has been advocated for years. Exercise is roughly divided into aerobic exercise and resistance exercise [1]. In young people, irregular lifestyle is one of the problems due to various schedules. For the actual efficacy of exercise, the continuation of aerobic exercise for 30 min would be recommended. On the other hand, what about the elderly? Healthy elderly people with good diet and exercise habits can continue their lifestyle. However, if the subject has sarcopenia or frailty, what is the adequate treatment? [2] In such a case, resistance exercise would be more important than aerobics in order to make strength for their trunk and legs. The authors have continued various management in the areas of rehabilitation and sports medicine until now [3]. Among them, the target subjects include professional baseball and soccer players, candidates for the Tokyo Paralympics, Masters athletes, high school and college students, healthy elderly people and frail elderly people [4]. As lectures and workshops, we have provided fitness, strength training, masters athletics, pole exercises, stretching, baseball-specific exercises and so on [5]. In this way, we have advised and treated various kinds of exercise therapy so far [6]. In this article, some topics concerning sarcopenia in elderly people would be described. Among the elderly, there are many subjects probably with sarcopenia and nutritional problems [7]. For treating them, there are four crucial points in the followings. They include I) Strengthen antigravity muscles, II) Be aware of the total workload, III) Continue the exercise plan and IV) Plan to increase protein intake [8]. Along with these outlines, the detail would be described below. The first is strengthening the antigravity muscles [9]. Muscle atrophy and weakness have been observed along the aging process. This is likely to occur in especially antigravity muscles. The deterioration of these functions leads to poor posture, gait restriction, Activities Daily Living (ADL) restriction and others. Among the antigravity muscles, the most susceptible ones to aging would be large muscle groups near the body surface that contains much amount of type II (fast muscle) fibers. Listed below are included such as the cervical muscle group, trapezius, latissimus dorsi, abdominal muscle group, gluteus medius, gluteus maximus, psoas major and quadriceps femoris muscles.
$4$-Colouring of Generalized Signed Planar Graphs

Assume $G$ is a graph and $S$ is a set of permutations of positive integers. An $S$-signature of $G$ is a pair $(D, \sigma)$, where $D$ is an orientation of $G$ and $\sigma: E(D) \to S$ is a mapping which assigns to each arc $e=(u,v)$ a permutation $\sigma(e)$ in $S$. We say $G$ is $S$-$k$-colourable if for any $S$-signature $(D, \sigma)$ of $G$, there is a mapping $f: V(G) \to [k]$ such that for each arc $e=(u,v)$ of $G$, $\sigma(e)(f(u)) \ne f(v)$. The concept of $S$-$k$-colourable is a common generalization of many colouring concepts. This paper studies the problem as to which subsets $S$ of $S_4$, every planar graph is $S$-$4$-colourable. We call such a subset $S$ of $S_4$ a good subset. The Four Colour Theorem is equivalent to saying that $S=\{id\}$ is good. It was proved by Jin, Wong and Zhu (arXiv:1811.08584) that a subset $S$ containing $id$ is good if and only if $S=\{id\}$. In this paper, we prove that, up to conjugation, every good subset of $S_4$ not containing $id$ is a subset of $\{(12),(34),(12)(34)\}$.
Coulomb interactions and effective quantum inertia of charge carriers in a macroscopic conductor

We study the low frequency admittance of a quantum Hall bar of size much larger than the electronic coherence length. We find that this macroscopic conductor behaves as an ideal quantum conductor with vanishing longitudinal resistance and purely inductive behavior up to f<1MHz. Using several measurement configurations, we study the dependence of this inductance on the length of the edge channel and on the integer quantum Hall filling fraction. The experimental data are well described by a scattering model for edge magnetoplasmons taking into account effective long range Coulomb interactions within the sample. This demonstrates that the inductance's dependence on the filling fraction arises from the effective quantum inertia of charge carriers induced by Coulomb interactions within an ungated macroscopic quantum Hall bar.
Ultrafast modification of the electronic structure of a correlated insulator

Electronic materials properties are determined by the interplay of many competing factors. Electro-magnetic fields strong enough to rival atomic interactions can disturb the balance between kinematic effects due to electrons hopping between lattice sites and the Coulomb repulsion between electrons that limits the band formation. This allows for new insights into quantum phases, as well as the time-scales and energies involved in using quantum effects for possible applications. Here we show that 0.2 V/A ultrashort optical fields in the high harmonic generation regime lead to a pronounced transient inter-site charge transfer in NiO, a prototypical correlated electron insulator. Element-specific transient x-ray absorption spectroscopy detects a negligible change in electron correlations of Ni 3d-states. This behaviour is captured by time-dependent density functional theory and points to a speed limit for the dynamical screening of the Coulomb interaction taking place above our experimental 6.9 femtoseconds optical cycle.
An Economic Perspective on Predictive Maintenance of Filtration Units

This paper provides an economic perspective on the predictive maintenance of filtration units. The rise of predictive maintenance is possible due to the growing trend of industry 4.0 and the availability of inexpensive sensors. However, the adoption rate for predictive maintenance by companies remains low. The majority of companies are sticking to corrective and preventive maintenance. This is not due to a lack of information on the technical implementation of predictive maintenance, with an abundance of research papers on state-of-the-art machine learning algorithms that can be used effectively. The main issue is that most upper management has not yet been fully convinced of the idea of predictive maintenance. The economic value of the implementation has to be linked to the predictive maintenance program for better justification by the management. In this study, three machine learning models were trained to demonstrate the economic value of predictive maintenance. Data was collected from a testbed located at the Singapore University of Technology and Design. The testbed closely resembles a real-world water treatment plant. A cost-benefit analysis coupled with Monte Carlo simulation was proposed. It provided a structured approach to document potential costs and savings by implementing a predictive maintenance program. The simulation incorporated real-world risk into a financial model. Financial figures were adapted from CITIC Envirotech Ltd, a leading membrane-based integrated environmental solutions provider. Two scenarios were used to elaborate on the economic values of predictive maintenance. Overall, this study seeks to bridge the gap between technical and business domains of predictive maintenance.
Lectures on Naturalness, String Landscape and Multiverse

The cosmological constant and electroweak hierarchy problem have been a great inspiration for research. Nevertheless, the resolution of these two naturalness problems remains mysterious from the perspective of a low-energy effective field theorist. The string theory landscape and a possible string-based multiverse offer partial answers, but they are also controversial for both technical and conceptual reasons. The present lecture notes, suitable for a one-semester course or for self-study, attempt to provide a technical introduction to these subjects. They are aimed at graduate students and researchers with a solid background in quantum field theory and general relativity who would like to understand the string landscape and its relation to hierarchy problems and naturalness at a reasonably technical level. Necessary basics of string theory are introduced as part of the course. This text will also benefit graduate students who are in the process of studying string theory at a deeper level. In this case, the present notes may serve as additional reading beyond a formal string theory course.
Patching as Translation: the Data and the Metaphor

Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that software patching is like language translation. We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as “proof-of-concept” tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy; it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at https://github.com/ARiSE-Lab/Patch-as-translation.
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning

Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks' target vectors are all close to a common meta-parameter vector. However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks' distribution cannot be captured by a single meta-parameter vector. We address this limitation by conditional meta-learning, inferring a conditioning function mapping task's side information into a meta-parameter vector that is appropriate for that task at hand. We characterize properties of the environment under which the conditional approach brings a substantial advantage over standard meta-learning and we highlight examples of environments, such as those with multiple clusters, satisfying these properties. We then propose a convex meta-algorithm providing a comparable advantage also in practice. Numerical experiments confirm our theoretical findings.
Fault slip in hydraulic stimulation of geothermal reservoirs: Governing mechanisms and process-structure interaction

Hydraulic stimulation of geothermal reservoirs in basement or crystalline igneous rock can enhance permeability by reactivation and shear-dilation of existing fractures. The process is characterized by interaction between fluid flow and the fractured structure of the formation. The flow is highly affected by the fracture network, which in turn is deformed because of hydromechanical stress changes caused by the fluid injection. This process-structure interaction is decisive for the outcome of a stimulation and, in analysis of governing mechanisms, physics-based modeling has potential to complement field and experimental data. Here, we show how recently developed simulation technology is a valuable tool to understand governing mechanisms of hydro-mechanical coupled processes and the reactivation and deformation of faults. The methodology fully couples flow in faults and matrix with poroelastic matrix deformation and a contact mechanics model for the faults, including dilation because of slip. Key elements are high aspect ratios of faults and strong nonlinearities in highly coupled governing equations. Example simulations illustrate direct and indirect hydraulic fault reactivation and corresponding permeability enhancement.
Efficient Detection Of Infected Individuals using Two Stage Testing

Group testing is an efficient method for testing a large population to detect infected individuals. In this paper, we consider an efficient adaptive two stage group testing scheme. Using a straightforward analysis, we characterize the efficiency of several two stage group testing algorithms. We determine how to pick the parameters of the tests optimally for three schemes with different types of randomization, and show that the performance of two stage testing depends on the type of randomization employed. Seemingly similar randomization procedures lead to different expected number of tests to detect all infected individuals, we determine what kinds of randomization are necessary to achieve optimal performance. We further show that in the optimal setting, our testing scheme is robust to errors in the input parameters.
Diverse response of atmospheric ozone to COVID-19 lockdown in China

Ozone (O$_{3}$) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O$_{3}$ have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O$_{3}$ across China for periods before and during the lockdown. We find that O$_{3}$ decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the observed O$_{3}$ changes. The larger reduction in NO$_{x}$ than volatile organic compounds induced O$_{3}$ increases in the NO$_{x}$-saturated north, whereas the comparably large decreases in both precursors resulted in O$_{3}$ declines in the south. Our study highlights the importance of meteorology in the short-term O$_{3}$ variability and the complex dependence of O$_{3}$ on its precursors. Our findings are relevant to elucidating the underlying factors driving O$_{3}$ changes during the COVID-19 lockdown in other parts of the world.
Entanglement spreading in non-equilibrium integrable systems

These are lecture notes for a short course given at the Les Houches Summer School on ``Integrability in Atomic and Condensed Matter Physics'', in summer 2018. Here, I pedagogically discuss recent advances in the study of the entanglement spreading during the non-equilibrium dynamics of isolated integrable quantum systems. I first introduce the idea that the stationary thermodynamic entropy is the entanglement accumulated during the non-equilibrium dynamics and then join such an idea with the quasiparticle picture for the entanglement spreading to provide quantitive predictions for the time evolution of the entanglement entropy in arbitrary integrable models, regardless of the interaction strength.
Theory of Deep Q-Learning: A Dynamical Systems Perspective

Deep Q-Learning is an important algorithm, used to solve sequential decision making problems. It involves training a Deep Neural Network, called a Deep Q-Network (DQN), to approximate a function associated with optimal decision making, the Q-function. Although wildly successful in laboratory conditions, serious gaps between theory and practice prevent its use in the real-world. In this paper, we present a comprehensive analysis of the popular and practical version of the algorithm, under realistic verifiable assumptions. An important contribution is the characterization of its performance as a function of training. To do this, we view the algorithm as an evolving dynamical system. This facilitates associating a closely-related measure process with training. Then, the long-term behavior of Deep Q-Learning is determined by the limit of the aforementioned measure process. Empirical inferences, such as the qualitative advantage of using experience replay, and performance inconsistencies even after training, are explained using our analysis. Also, our theory is general and accommodates state Markov processes with multiple stationary distributions.