Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Structures on Neptune Observed with Keck Telescope Adaptive Optics

We report on observations obtained with the adaptive optics system at the 10 m Keck II Telescope during engineering validation and early science observing time for the adaptive optics system. We observed Neptune at near-infrared wavelengths. Angular resolution was 0. ″05–0. ″06, corresponding to a spatial scale of approximately 1000 km at Neptune. We discuss the latitudinal structure of circumferential cloud bands and of compact infrared-bright features seen in the southern hemisphere, as well as their variation with wavelength. We determine the values of I/F (proportional to the ratio of reflected intensity to incident solar flux) in the J and H infrared-wavelength bands, including narrowband filters where there is strong methane absorption. We use the I/F values inside and outside of methane bands to estimate the altitude of clouds responsible for the brightest compact features in the infrared. Our data show that, on two of our four observing dates, the brightest region on Neptune contained highly reflective haze layers located below the tropopause but not deeper than a few bars.
Light propagation in layered photonic crystals with admixture layers

Abstract The virtual-crystal approximation is used for numerical simulation of a polariton spectrum transformation in composite materials, consisting of alternating silicon and liquid crystal layers and randomly included admixture layers. The character of dependence of the bandgap width and the refractive index upon the concentra-tion of admixture layers is discussed. It is shown that the energy structure of this imperfect superlattice can be significantly altered by implantation of appropriate defect layers. Keywords: photonic crystal, Si - liquid crystal system, admixture layer, bandgap width. PACS: 78.66. -w UDC: 535.3 1. Introduction Propagation of electromagnetic waves in thin films and layered crystalline media is cur-rently drawing a close attention of researchers. The works [1–4] give the account of the related researches carried out for photonic crystals and [5] – for some composite materials based on silicon and liquid crystals. On the one hand, the interest towards these objects is due to their significance for electronics. On the other hand, this is because a notable ad-vance of technology that allows growing of ultra-thin films and periodic structures with controlled characteristics. There have been numerous theoretical and experimental studies (see, e.g., [6] and references therein) on exciton-like excitations in ideal dielectric superlattices. A general theory of optical waves in anisotropic crystals, including those formed of macroscopic layers, has been discussed in Ref. [7]. The authors [8] have investigated dispersion of po-laritons in a superlattice with two admixture layers. At the same time, a considerable in-terest has been focused on non-ideal superlattices with an arbitrary number of admixture layers, as well as on dependence of polariton spectrum on the concentration of corre-sponding defects. Further development of the theory of layered structures evidently re-quires considering more complex models like superlattices with randomly included ad-mixture layers of variable composition. A better understanding of how the optical proper-
Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes

Nowadays, sensor-equipped mobile devices allow us to detect basic daily activities accurately. However, the accuracy of the existing activity recognition methods decreases rapidly if the set of activities is extended and includes training routines, such as squats, jumps, or arm swings. Thus, this paper proposes a model of a personal area network with a smartphone (as a main node) and supporting sensor nodes that deliver additional data to increase activity-recognition accuracy. The introduced personal area sensor network takes advantage of the information from multiple sensor nodes attached to different parts of the human body. In this scheme, nodes process their sensor readings locally with the use of recurrent neural networks (RNNs) to categorize the activities. Then, the main node collects results from supporting sensor nodes and performs a final activity recognition run based on a weighted voting procedure. In order to save energy and extend the network’s lifetime, sensor nodes report their local results only for specific types of recognized activity. The presented method was evaluated during experiments with sensor nodes attached to the waist, chest, leg, and arm. The results obtained for a set of eight activities show that the proposed approach achieves higher recognition accuracy when compared with the existing methods. Based on the experimental results, the optimal configuration of the sensor nodes was determined to maximize the activity-recognition accuracy and reduce the number of transmissions from supporting sensor nodes.
Relationship Study of Meltblowning Variables Based on Machine Learning Algorithm

: For the missing intercalation rate data, we first filled it in, and then split it according to the variable pairs to observe the changes of six indexes before and after intercalation. Then, we made grey correlation analysis between the changes and intercalation rate, and got the influence of intercalation rate on each index. In order to further explore the relationship between process parameters and structural variables, nine models, KNN, linear regression, ridge regression, lasso regression, decision tree, support vector machine, robust model, XGBoost and random forest, are used to train the data, and finally XGBoost regression model has the highest accuracy. Then, using the prediction results of structural variables obtained by XGBoost, we firstly make factor analysis on three indexes of structural variables and product performance, and the index with the largest factor load represents structural variables and product performance, and then make Pearson correlation analysis on these two indexes to get the relationship between structural variables and product performance. Through Pearson analysis of the three internal variables of structural variables and product performance, the internal correlation is obtained. For three indexes in the structural variables, respectively, they are linearly fitted with two variables of process parameters to obtain three fitting equations, and then they are fitted with filtration efficiency to obtain the fitting equation between filtration efficiency and structural variables. Finally, the linear regression equation between filtration efficiency and process parameters is sorted out. However, the effect of changing the linear regression model is not good in model testing, and we think it has a complex nonlinear relationship. Therefore, we use machine learning to carry out regression training on variables. The results show that when the result variables are used to regress the product
Pleochaeta indica, a new record of powdery mildew from Pakistan

A comprehensive study of a powdery mildew observed on leaves of Celtis tetrandra Roxb. was carried out by the authors. The symptoms appeared as white mycelium on leaves with embedded small black to brown spherical ascomata. Infected plants were collected from Thandiani (District Abbottabad, Division Hazara) of Khyber Pakhtunkhwa province, Pakistan, during a phytopathogenic survey in 2019. The causal agent was observed and identified on the basis of morphological and molecular analyses, which reveals that this fungus belongs to genus Pleochaeta. Further investigation identified it as Pleochaeta indica. This is the first report of a powdery mildew infection caused by this pathogen in Pakistan. A complete description and illustrations of the fungus are presented.
Neural Signatures of Actively Controlled Self-Motion and the Subjective Encoding of Distance

Abstract Navigating through an environment requires knowledge about one’s direction of self-motion (heading) and traveled distance. Behavioral studies showed that human participants can actively reproduce a previously observed travel distance purely based on visual information. Here, we employed electroencephalography (EEG) to investigate the underlying neural processes. We measured, in human observers, event-related potentials (ERPs) during visually simulated straight-forward self-motion across a ground plane. The participants’ task was to reproduce (active condition) double the distance of a previously seen self-displacement (passive condition) using a gamepad. We recorded the trajectories of self-motion during the active condition and played it back to the participants in a third set of trials (replay condition). We analyzed EEG activity separately for four electrode clusters: frontal (F), central (C), parietal (P), and occipital (O). When aligned to self-motion onset or offset, response modulation of the ERPs was stronger, and several ERP components had different latencies in the passive as compared with the active condition. This result is in line with the concept of predictive coding, which implies modified neural activation for self-induced versus externally induced sensory stimulation. We aligned our data also to the times when subjects passed the (objective) single distance d_obj and the (subjective) single distance d_sub. Remarkably, wavelet-based temporal-frequency analyses revealed enhanced theta-band activation for F, P, and O-clusters shortly before passing d_sub. This enhanced activation could be indicative of a navigation related representation of subjective distance. More generally, our study design allows to investigate subjective perception without interfering neural activation because of the required response action.
Bacterial Community Structure and Function Shift in Rhizosphere Soil of Tobacco Plants Infected by Meloidogyne incognita

Root-knot nematode disease is a widespread and catastrophic disease of tobacco. However, little is known about the relationship between rhizosphere bacterial community and root-knot nematode disease. This study used 16S rRNA gene sequencing and PICRUSt to assess bacterial community structure and function changes in rhizosphere soil from Meloidogyne incognita-infected tobacco plants. We studied the rhizosphere bacterial community structure of M. incognita-infected and uninfected tobacco plants through a paired comparison design in two regions of tobacco planting area, Yuxi and Jiuxiang of Yunnan Province, southwest China. According to the findings, M. incognita infection can alter the bacterial population in the soil. Uninfested soil has more operational taxonomic unit numbers and richness than infested soil. Principal Coordinate Analysis revealed clear separations between bacterial communities from infested and uninfested soil, indicating that different infection conditions resulted in significantly different bacterial community structures in soils. Firmicutes was prevalent in infested soil, but Chloroflexi and Acidobacteria were prevalent in uninfested soil. Sphingomonas, Streptomyces, and Bradyrhizobium were the dominant bacteria genera, and their abundance were higher in infested soil. By PICRUSt analysis, some metabolism-related functions and signal transduction functions of the rhizosphere bacterial community in the M. incognita infection-tobacco plants had a higher relative abundance than those uninfected. As a result, rhizosphere soils from tobacco plants infected with M. incognita showed considerable bacterial community structure and function alterations.
Testing the goodness of fit of the binomial distribution

SUMMARY Using the C(o) procedure of Neyman (1959), we derive tests for the goodness of fit of the binomial distribution which are asymptotically optimal against generalized binomial alternatives proposed by Altham (1978) and Kupper & Haseman (1978). The C(Q) tests optimal against correlated binomial alternatives, beta-binomial alternatives and a general class of mixture alternatives suggested by Wisniewski (1968) are shown to be closely related to the binomial variance test. A numerical example is given and the small sample behaviour of the variance test and the C(oa) tests is investigated in a Monte Carlo sampling experiment. The asymptotic relative efficiencies of the goodness of fit statistics are examined.
GREEN SYNTHESIS AND CHARACTERISATION OF ZnO NANOPARTICLES FROM MANIHOT ESCULENTA (CASSAVA) PEEL AND THEIR ANTIBACTERIAL STUDY

A technique for green manufacturing of zinc oxide based nanoparticles utilising cassava peel has been approached in this research for antibacterial investigation. Especially, the cassava peel is an agro waste from starch and food processing industries and the extract of Cassava peel is used for synthesising the zinc oxide nanoparticles (ZnO-NPs). The peel based zinc oxide nanoparticles were synthesized using bioreduction method. The plasmon peak from UV–Visible spectroscopy is observed at 380 nm, while the XRD shows an average crystallite size of 67.31nm with hexagonal structure and also the stability of bio synthesised nanoparticles are confirmed by FTIR study. Additionally, FESEM confirms the structure is found to be spherical whereas Zeta potential analysis has confirmed the nanoparticles have a stable and well-dispersed form. Furthermore, the anti bacterial study against bio synthesized ZnO-NPs is carried out for both gram positive and gram negative bacteria. Therefore, the results proved that the bacteria are found to be more active against the synthesized nanoparticles.
PEG Reinforced Scaffold Promotes Uniform Distribution of Human MSC-Created Cartilage Matrix

Previously, we used a gelatin/hyaluronic acid (GH)-based scaffold to induce chondrogenic differentiation of human bone marrow-derived mesenchymal stromal cells (hBMSC). The results showed that hBMSCs underwent robust chondrogenesis and facilitated in vivo cartilage regeneration. However, it was noticed that the GH scaffolds display a compressive modulus that is markedly lower than native cartilage. In this study, we aimed to enhance the mechanical strength of GH scaffolds without significantly impairing their chondrosupportive property. Specifically, polyethylene glycol diacrylate (PEGDA) and photoinitiators were infiltrated into pre-formed hBMSC-laden GH scaffolds and then photo-crosslinked. Results showed that infiltration of PEG at the beginning of chondrogenesis significantly increased the deposition of glycosaminoglycans (GAGs) in the central area of the scaffold. To explore the mechanism, we compared the cell migration and proliferation in the margin and central areas of GH and PEG-infiltrated GH scaffolds (GH+PEG). Limited cell migration was noticed in both groups, but more proliferating cells were observed in GH than in GH+PEG. Lastly, the in vitro repairing study with bovine cartilage explants showed that PEG- impregnated scaffolds integrated well with host tissues. These results indicate that PEG-GH hybrid scaffolds, created through infiltrating PEG into pre-formed GH scaffolds, display good integration capacity and represent a new tool for the repair of chondral injury.
DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition

Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals.
Winds over Japan.

Abstract Before World War II, weather forecasters had little knowledge of upper-air wind patterns above 20000 feet. Data were seldom avai able at these heights, and the need was not great because commercial aircraft seldom flew at these altitudes. The war in the Pacific changed all that. Wind forecasts for 30000 feet plus became urgent to support the XXI Bomber Command in its bombing mission over Japan. The U.S. Army Air Force Pacific Ocean Area (AAFPOA) placed a Weather Central in the Marianas Islands in 1944 (Saipan in 1944 and Guam in 1945) to provide forecasting support for this mission. A forecasting procedure was put into operation that combined the elements known as “single-station forecasting” and an advanced procedure that used “altirmeter corrections” to analyze upper-airdata and make prognoses. Upper-air charts were drawn for constant pressure surfaces rather than constant height surfaces. The constant pressure surfaces were tied together by means of the atmospheric temperature field represente.
MIXED-CONVECTIVE COOLING OF AN ISOTHERMAL HOT SURFACE BY CONFINED SLOT JET IMPINGEMENT

The flow and heat transfer characteristics in the cooling of a heated surface by impinging slot jets have been investigated numerically. Computations are done for vertically downward-directed two-dimensional slot jets impinging on a hot isothermal surface at the bottom and confined by a parallel adiabatic surface on top. Some computations are also performed where the jet is vertically upward, with an impingement plate at the top. The principal objective of this study is to investigate the associated heat transfer process in the mixed-convective regime. The computed flow patterns and isotherms for various domain aspect ratios (4–10) and for a range of jet exit Reynolds numbers (100–500) and Richardson numbers (0–10) are analyzed to understand the mixed-convection heat transfer phenomena. The local and average Nusselt numbers and skin friction coefficients at the hot surface for various conditions are presented. It is observed that for a given domain aspect ratio and Richardson number, the average Nusselt number at the hot surface increases with increasing jet exit Reynolds number. On the other hand, for a given aspect ratio and Reynolds number, the average Nusselt number does not change significantly with Richardson number, indicating that the buoyancy effects are not very significant in the overall heat transfer process for the range of jet Reynolds number considered in this study. Also, for the same problem configuration, the average Nusselt number does not change significantly when the jet is moving upward or downward.
COP1 Mediates Dark-Induced Stomatal Closure by Suppressing FT, TSF and SOC1 Expression to Promote NO Accumulation in Arabidopsis Guard Cells

RING-finger-type ubiquitin E3 ligase Constitutively Photomorphogenic 1 (COP1) and floral integrators such as FLOWERING LOCUS T (FT), TWIN SISTER OF FT (TSF) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) have been identified as regulators of stomatal movement. However, little is known about their roles and relationship in dark-induced stomatal closure. Here, we demonstrated that COP1 is required for dark-induced stomatal closure using cop1 mutant. The cop1 mutant closed stomata in response to exogenous nitric oxide (NO) but not hydrogen peroxide (H2O2), and H2O2 but not NO accumulated in cop1 in darkness, further indicating that COP1 acts downstream of H2O2 and upstream of NO in dark-induced stomatal closure. Expression of FT, TSF and SOC1 in wild-type (WT) plants decreased significantly with dark duration time, but this process was blocked in cop1. Furthermore, ft, tsf, and soc1 mutants accumulated NO and closed stomata faster than WT plants in response to darkness. Altogether, our results indicate that COP1 transduces H2O2 signaling, promotes NO accumulation in guard cells by suppressing FT, TSF and SOC1 expression, and consequently leads to stomatal closure in darkness. These findings add new insights into the mechanisms of dark-induced stomatal closure.
Modeling and Measurement of an Ultrasound Power Delivery System for Charging Implantable Devices Using an AlN-Based pMUT as Receiver

Ultrasound power delivery can be considered a convenient technique for charging implantable medical devices. In this work, an intra-body system has been modeled to characterize the phenomenon of ultrasound power transmission. The proposed system comprises a Langevin transducer as transmitter and an AlN-based square piezoelectric micro-machined ultrasonic transducer as receiver. The medium layers, in which elastic waves propagate, were made by polydimethylsiloxane to mimic human tissue and stainless steel to replace the case of the implantable device. To characterize the behavior of the transducers, measurements of impedance and phase, velocity and displacement, and acoustic pressure field were carried out in the experimental activity. Then, voltage and power output were measured to analyze the performance of the ultrasound power delivery system. For a root mean square voltage input of approximately 35 V, the power density resulted in 21.6 µW cm−2. Such a result corresponds to the data obtained with simulation through a one-dimensional lumped parameter transmission line model. The methodology proposed to develop the ultrasound power delivery (UPD) system, as well as the use of non-toxic materials for the fabrication of the intra-body elements, are a valid design approach to raise awareness of using wireless power transfer techniques for charging implantable devices.
Optimization of Cloud-Based Multi-Agent System for Trade-Off Between Trustworthiness of Data and Cost of Data Usage

This paper considers the cloud-based multi-agent system (MAS) in which for any agent to obtain the most trustworthy data (MTD) that best matches the agents’ personalized demands on the trusted attributes of data, the agents’ data usage often comes with cost, and the better the trustworthiness of data (TOD), the higher the cost of data usage (CDU). Therefore, under the environments where the budgets for data usage are constrained, there may exist a trade-off between the TOD and CDU for the cloud-based MAS, and how to optimally make such trade-off arises as an interesting issue in practice. To address this issue, this paper first formulates the trade-off as a mathematical constrained optimization problem, then explores out the theoretical foundations, both using the fuzzy similarity computing, and finally proposes an algorithm to guarantee the agents to obtain the MTD within the acceptable level of CDU. The experiment results illustrate its effectiveness. Note to Practitioners— This paper addresses the interesting trade-off between TOD and CDU for the cloud-based MAS that operates in the cloud environments where the TOD and CDU are positively correlated with each other. By establishing the mathematical constrained optimization model and designing the algorithm for the MTD recommendation within the acceptable CDU based on the theoretical foundations using fuzzy similarity computing, it helps the MAS with cloud settings to achieve the MTD that best matches their personalized demands on the trusted attributes within the acceptable CDU, which could push the development of cloud-based MAS by benefiting from the optimal trade-off between TOD and CDU. The experiments show that the solution proposed here outperforms existing solutions.