Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature-Weighting-Based Prediction of Drought Occurrence via Two-Stage Particle Swarm Optimization

Drought directly affects environmental sustainability. Predicting the drought at the earliest opportunity will help to execute drought mitigation plans. Several drought indices are used to predict the severity of drought across different geographical regions. The two main drought indices used in India for meteorological drought are the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). This work is a study to find the ability of above mentioned indices to predict meteorological drought for the state of Tamil Nadu using 62 years of data. The prediction results are evaluated using the performance metrics of precision, recall, f1 score, Matthews correlation coefficient, and accuracy. The dataset is severely imbalanced due to the low number of drought incidence years. Hence there exists a tug of war between precision and recall, so for improving it without affecting one another, a multi-objective optimization process is applied. The prediction performance is improved by using the filter-global-supervised feature weighting and wrapper-global-supervised feature weighting techniques. In the filter-based feature weighting approach, the information gain measure and Pearson correlation coefficient are used as feature weights. For the wrapper-based feature weighting approach, two-stage particle swarm optimization (PSO) is designed to calculate the weights of the features, and the random forest is used as the classifier. This two-stage PSO constructs the best population set for individual objectives and then searches around it to find the best particle so that the multiple contradicting objectives will converge into the best solution easier. When compared to classification without feature weighting, two-stage PSO feature weighting achieves a 45% improvement in precision. However, only a moderate improvement in recall is obtained. According to the findings, SPI3 and SPEI12 should be given more weightage in metrological drought prediction.
State‐space model for proxy‐based millennial reconstruction

It is important to study historical temperature time series prior to the industrial revolution so that one can view the current global warming trend from a long‐term historical perspective. Because there are no instrumental records of such historical temperature data, climatologists have been interested in reconstructing historical temperatures using various proxy time series. In this paper, the authors examine a state‐space model approach for historical temperature reconstruction which not only makes use of the proxy data but also information on external forcings. A challenge in the implementation of this approach is the estimation of the parameters in the state‐space model. The authors developed two maximum likelihood methods for parameter estimation and studied the efficiency and asymptotic properties of the associated estimators through a combination of theoretical and numerical investigations. The Canadian Journal of Statistics 38: 488–505; 2010 © 2010 Crown in the right of Canada
Millimeter-wave spectrum of the CCO radical

The pure rotational spectrum of the CCO radical in the /sup 3/. sigma. /sup -/ ground electronic state has been observed in the laboratory in the region 45-185 GHz. The radical was generated in a 3.5 m long free space absorption cell by a DC glow discharge in carbon suboxide (C/sub 3/O/sub 2/). Seven rotational transitions of Nn = 2reverse arrow1 to 8reverse arrow7 in each triplet sublevels were precisely measured and the observed frequencies were least-squares analyzed to obtain molecular constants.
Effect of Fluid Viscoelasticity on Turbulence and Large-Scale Vortices behind Wall-Mounted Plates

Direct numerical simulations of turbulent viscoelastic fluid flows in a channel with wall-mounted plates were performed to investigate the influence of viscoelasticity on turbulent structures and the mean flow around the plate. The constitutive equation follows the Giesekus model, valid for polymer or surfactant solutions, which are generally capable of reducing the turbulent frictional drag in a smooth channel. We found that turbulent eddies just behind the plates in viscoelastic fluid decreased in number and in magnitude, but their size increased. Three pairs of organized longitudinal vortices were observed downstream of the plates in both Newtonian and viscoelastic fluids: two vortex pairs were behind the plates and the other one with the longest length was in a plate-free area. In the viscoelastic fluid, the latter vortex pair in the plate-free area was maintained and reached the downstream rib, but its swirling strength was weakened and the local skin-friction drag near the vortex was much weaker than those in the Newtonian flow. The mean flow and small spanwise eddies were influenced by the additional fluid force due to the viscoelasticity and, moreover, the spanwise component of the fluid elastic force may also play a role in the suppression of fluid vortical motions behind the plates.
Analysis of microstrip antennas on circular-cylindrical substrates with a dielectric overlay

The use of dyadic Green's functions and the moment method is explored for the solution of microstrip antenna problems on circular cylindrical substrates. The dyadic Green's functions of the electric type are obtained for a medium consisting of three cylindrical dielectric layers concentric with a perfectly conducting cylinder, and integral equations are developed for the evaluation of the electromagnetic fields. The effect of a dielectric overlay on the resonant frequency of a cylindrical-rectangular microstrip antenna is analyzed. The patch is directly fed by means of a microstripline printed along the cylinder axial direction. The results show that the effect of the dielectric overlay is substantial when its relative permittivity and thickness are increased, such that this effect has to be very carefully considered in the design of microstrip antennas. >
Association between Use of Spironolactone and Risk of Stroke in Hypertensive Patients: A Cohort Study

Objective: to investigate the relationship between the use of spironolactone and the risk of stroke in hypertensive patients. Methods: a total of 2464 spironolactone users and 12,928 non-users were identified (unmatched original cohort), and 1:1 matched pairs of 2461 spironolactone users and 2461 non-users based on propensity scores were created (propensity-score-matched cohort). Results: In the unmatched original cohort, the unadjusted analysis showed that the use of spironolactone was associated with a lower risk of total stroke (HR, 0.71; 95% CI, 0.61–0.84; p < 0.001), which was sustained in the adjusted analysis. According to stroke type, the association was with ischemic strokes (propensity-score-adjusted HR, 0.71; 95% CI, 0.59–0.85; p < 0.001) and hemorrhagic ones (propensity-score-adjusted HR, 0.63; 95% CI, 0.45–0.88; p = 0.008). Similar results were shown in the propensity-score-matched cohort. The results of the subgroup and sensitivity analyses were consistent with those of the primary analysis. The dose–response analysis demonstrated a dose-dependent association of spironolactone with a lower risk of stroke in hypertensive patients. Conclusions: The use of spironolactone was associated with a significantly lower risk of stroke events in hypertensive patients. Further research, including prospective randomized clinical trials, is needed to validate our findings.
TESTS OF SIGNIFICANCE IN TWO-WAY DESIGNS (MIXED MODEL) WITH DICHOTOMOUS DATA

Computer simulation experiments are performed to investigate the applicability of different tests of treatment and interaction effects in mixed two-way designs with dichotomous data. The main results are that the F test can be used with good approximation as a test of equal treatment effects, except for strongly asymmetric populations, and that the F test may be used with some caution as a test of no interaction.
Multi-Task Reinforcement Learning for Distribution System Voltage Control With Topology Changes

This letter proposes a multi-task deep reinforcement learning (DRL) approach for distribution system voltage regulation considering topology changes via PV smart inverter control. The key idea is to encode the topology as an additional state for the DRL and leverage the multi-task learning scheme for joint learning of all task control policies. Unlike other DRL-based methods, our approach is robust to different topologies. Comparison results on the modified IEEE 123-node system demonstrate the enhanced robustness of the proposed method.
Ring structure in the Monoceros R1 molecular clouds

CO observations of the Mon R1 region show a partial ring structure of molecular clouds which is kinematically distinct from the larger complex of clouds associated with Mon OB1. This morphology suggests that a previous energetic event was responsible for ordering the currently observed ring. There is also some evidence for an expanding H I feature in this direction. If the molecular and atomic features have a common origin, an expansion age approx.1--3 x 10/sup 6/ years is derived by considering models for the three most likely precursor events: a supernova blast wave, a shock driven by an expanding H II region, and a stellar wind from an early-type star. The relationship of star formation in this region to these possible events is discussed.
Accurate dynamic scene model with double-layer Gaussian mixture

Gaussian mixture model is a popular method to model dynamic scenes viewed by a fixed camera. However, we found that it is not a trivial problem for each component of Gaussian mixture to learn the accurate parameters for complex pixels. Furthermore, traditional method of Gaussian mixture has to make a tradeoff between system stability and convergence rate. We developed a mechanism of double-layer Gaussian mixture model for moving object detection from dynamic scenes, which can improve the convergence rate without compromising the system stability. Additionally, temporal consistency of variances was taken into account to alleviate camouflage problems in the process of detection.
Optimization of Enzyme-Assisted Mechanical Extraction Process of Hodgsonia heteroclita Oilseeds and Physical, Chemical, and Nutritional Properties of the Oils

Hodgsonia heteroclita subsp. Indochinensis W.J.de Wlide & Duyfjes (or Making in Thai) is a neglected and underutilized crop (NUC) with high fat containing nuts. In this study, the enzyme-assisted mechanical extraction of H. heteroclita seed oil was investigated using response surface methodology (RSM) to predict the optimal fat extraction conditions. The most efficient enzyme used in the experiment was a mixture of Flavourzyme® and Viscozyme® (1:1, w/w). The predicted maximum oil yield was 46.44%, using the following extraction conditions: 2.98% (w/w) enzyme loading, 48 °C incubation temperature and 76 min of incubation time. H. heteroclita seed oil obtained from heat and enzymatic pretreatments exhibited the highest lightness and viscosity. The chemical properties of this seed oil, including water and volatile compounds (≤0.2% w/w), acid value (≤4.0 mg KOH/g), peroxide value (≤15 mEq of active oxygen/kg) and soap content (≤0.005% w/w), were within the acceptable levels specified by the Codex Alimentarius (2019). H. heteroclita seed oil obtained from heat and enzymatic pretreatments contained the highest content of δ-tocopherol (88.29 mg/100 g) and omega-6 fatty acids (48.19 g/100 g). This study is the first to report on the enzyme-assisted mechanical extraction of H. heteroclita oilseeds as a promising plant material for vegetable oil production.
Genistein Promotes Anti-Heat Stress and Antioxidant Effects via the Coordinated Regulation of IIS, HSP, MAPK, DR, and Mitochondrial Pathways in Caenorhabditis elegans

To determine the anti-heat stress and antioxidant effects of genistein and the underlying mechanisms, lipofuscin, reactive oxygen species (ROS), and survival under stress were first detected in Caenorhabditis elegans (C. elegans); then the localization and quantification of the fluorescent protein was determined by detecting the fluorescently labeled protein mutant strain; in addition, the aging-related mRNAs were detected by applying real-time fluorescent quantitative PCR in C. elegans. The results indicate that genistein substantially extended the lifespan of C. elegans under oxidative stress and heat conditions; and remarkably reduced the accumulation of lipofuscin in C. elegans under hydrogen peroxide (H2O2) and 35 °C stress conditions; in addition, it reduced the generation of ROS caused by H2O2 and upregulated the expression of daf-16, ctl-1, hsf-1, hsp-16.2, sip-1, sek-1, pmk-1, and eat-2, whereas it downregulated the expression of age-1 and daf-2 in C. elegans; similarly, it upregulated the expression of daf-16, sod-3, ctl-1, hsf-1, hsp-16.2, sip-1, sek-1, pmk-1, jnk-1 skn-1, and eat-2, whereas it downregulated the expression of age-1, daf-2, gst-4, and hsp-12.6 in C. elegans at 35 °C; moreover, it increased the accumulation of HSP-16.2 and SKN-1 proteins in nematodes under 35 °C and H2O2 conditions; however, it failed to prolong the survival time in the deleted mutant MQ130 nematodes under 35 °C and H2O2 conditions. These results suggest that genistein promote anti-heat stress and antioxidant effects in C. elegans via insulin/-insulin-like growth factor signaling (IIS), heat shock protein (HSP), mitogen-activated protein kinase (MAPK), dietary restriction (DR), and mitochondrial pathways.
Large-Scale Effects of Deep Convection on the GATE Tropical Boundary Layer

Abstract The large-scale response of the atmospheric boundary layer to the passage of tropical wave disturbances is investigated. Observations from GATE indicate that during periods of extensive deep convective activity rather shallow mixed layers frequently are found, primarily in association with mesoscale precipitation systems. Convective-scale precipitation downdrafts accompanying such systems contribute to the formation of large trailing “wakes” wherein there exist considerably enhanced fluxes of sensible heat from the mean surface. Observations in the convectively active trough portion of tropical waves indicate that a considerable fraction of the total area is covered by wakes. However, there remain regions between rain systems in the wave trough covering ∼15–30% of the total area, depending on how these regions are defined, which have mixed layers more characteristic of those observed in the undisturbed ridge portion of the wave than those in wakes. A simple model is developed to study the mainten.
Wideband MIMO Antennas With Enhanced Isolation Using Coupled CPW Transmission Lines

This article presents a wideband printed-monopole antenna pair with enhanced isolation. A clear two-step method based on even-mode (EM) and odd-mode (OM) analyses is developed for antenna decoupling and matching: first, the OM input impedance of the antenna pair is matched by adjusting the element spacing; next, the EM input impedance is matched by adding a coupled TEM transmission line. Those two steps are independent and easy to implement. Notably, the use of a nondispersive coupled transmission line facilitates wideband decoupling and intrinsically allows an extremely small feed-point spacing. The proposed antenna pair is experimentally tested: it offers isolation higher than 20 dB and total efficiency higher than 87% from 3.7 to 7.3 GHz (a fractional bandwidth of 65.5%). The separation between the two feed points is only 5.6 mm ( $0.069~\lambda $ at 3.7 GHz). Furthermore, we construct a four-element multiple-input–multiple-output (MIMO) array by intersecting orthogonally two of the proposed antenna pairs at their central line, which offer an isolation level better than 20 dB from 3.8 to 7.4 GHz for every two ports. With a straightforward design process, compact feeding structures, and wideband performances, the proposed dual- and four-element MIMO antennas are promising for the fifth-generation wireless communication.
Finite-Time Observer-Based $H_{\infty }$ Fault-Tolerant Output Formation Tracking Control for Heterogeneous Nonlinear Multi-Agent Systems

This paper investigates the robust <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> fault-tolerant output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multi-agent system, whose object is that the outputs of followers need to track leader's output and achieve the desired formation under the influences of actuator faults and external disturbances. In order to derive the leader's information including state, system matrices, and the upper bound of the unknown input, two kinds of distributed finite-time observers are presented for each follower, respectively, which removes the assumption that this information is derived by all agents directly in previous studies. Then, a novel adaptive <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> fault-tolerant output TVFT controller is developed to make up for the parameter uncertainties, actuator faults and leader's unknown input. Moreover, with the help of Lyapunov theory and the linear matrix inequality technique, the robust <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> output TVFT criterion for the considered multi-agent system subject to the leader's unknown input and actuator faults is obtained based on the presented distributed adaptive <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> output TVFT controller. Finally, a simulation example is given to demonstrate these derived theoretical results.
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. This optimization leads to reduced operational costs, improved customer demand fulfillment, and enhanced production efficiency. However, machine scheduling remains a challenging combinatorial problem due to its NP-hard nature. Deep Reinforcement Learning (DRL), a key component of artificial general intelligence, has shown promise in various domains like gaming and robotics. Researchers have explored applying DRL to machine scheduling problems since 1995. This paper offers a comprehensive review and comparison of DRL-based approaches, highlighting their methodology, applications, advantages, and limitations. It categorizes these approaches based on computational components: conventional neural networks, encoder-decoder architectures, graph neural networks, and metaheuristic algorithms. Our review concludes that DRL-based methods outperform exact solvers, heuristics, and tabular reinforcement learning algorithms in terms of computation speed and generating near-global optimal solutions. These DRL-based approaches have been successfully applied to static and dynamic scheduling across diverse machine environments and job characteristics. However, DRL-based schedulers face limitations in handling complex operational constraints, configurable multi-objective optimization, generalization, scalability, interpretability, and robustness. Addressing these challenges will be a crucial focus for future research in this field. This paper serves as a valuable resource for researchers to assess the current state of DRL-based machine scheduling and identify research gaps. It also aids experts and practitioners in selecting the appropriate DRL approach for production scheduling.
The kinematics of the Seyfert 2 galaxy NGC 5728 circumnuclear region

We secured spectral images of the circumnuclear, 12 x 10 arcsec 2 region of the Seyfert 2 galaxy NGC 5728 with the Optically Adaptive System for Imaging Spectroscopy at the Canada-France-Hawaii Telescope 3.6-m telescope. The radial velocity distribution of the two bright zones, main central (C) core and north-west (NW) core, and strategically important points along the position angles, PA 33°, 90°, 123° and 143°, are analysed. The large-scale reconstructed monochromatic images of the Ha, Hβ, [OIII] and [NII] lines show a bipolar morphology along PA ∼ 123°. The prominent elliptical ring is seen in the Ha and Hβ monochromatic images. Our analysis implies that this ring is rotating in the same clockwise direction as that of the galactic disc; this elliptical ring is in fact a projected result of a circular ring of radius ∼5.4 arcsec, with i ∼ 50° (nearly aligned with the host galactic plane). We used the [O III]5007 line profiles for a detailed analysis of kinematics in the circumnuclear region of the NGC 5728. The [O III]5007 line profiles reveal distinct kinematical structures. (1) The strongest component of Doppler peaks: two core components, consisting of the receding main C-core and the approaching NW-core with the radial velocities, V r ≃ +250 and -240 (or -250) km s -1 , respectively. These two cores consist of a roughly spherically symmetric distribution of the gas with radii of r ∼ 1.0 arcsec (at V r ≃ +240 km s -1 ) and 0.9 arcsec (at V r ≃ -240 km s -1 ), respectively. Hints of the presence of inflows around these cores are observed. (2) The next strong line profile component: this component corresponds to the bipolar cone aligned PA ∼ 123°. (3) The reverse S-shaped kinematical boundary which runs through the double-peaked zone of 6-7 arcsec long in the galactic major axis direction is likely to be caused by a combination of the disc rotation of the host galaxy and the revolution of the two central cores.
On the constitutive relations for dispersed particles in nonuniform flows. I: Dispersion in a simple shear flow

The work presented follows on from a previous study in which a kinetic approach was used to generate the continuum equations for a dilute dispersed phase of noncolliding particles in a nonuniform turbulent flow. Here this approach is used to derive forms for the Reynolds stresses and the fluctuating interphase momentum transfer for particles at equilibrium in a bounded simple shear flow; the combination of the two also gives the total stress and long‐term particle diffusion coefficients. These forms are based on equilibrium solutions of a kinetic equation describing the transport of the particle phase space probability density <W(v,x,t)≳ for a particle with velocity v and position x at time t. The crucial constituent of the equation is the form of the phase space diffusion current given here by −(∂/∂v⋅μ+∂/∂x⋅λ+γ)<W(v,x,t)≳, where μ, λ, and γ are tensors whose components depend upon gradients of the local mean carrier flow, as well as particle and carrier flow time scales. While some of the results quoted .
A multiscale feature fusion method for cursive text detection in natural scene images

ABSTRACT Text detection in natural images is a challenging problem due to variations in text size, aspect ratio, alignment and background complexity. This paper proposes a multiscale feature fusion convolutional neural network method to detect cursive and multi-language text in natural images. The proposed method combines VGG-16 features at multi-scales and multi-layers and creates a new convolutional feature map of shallow and deep layers. On top of convolutional feature map, a vertical text proposal generation method is used that generates fixed-size text proposals. A recurrent layer is implemented which takes the convolutional feature maps of window as sequential input and updates the recurrent state internally in the hidden layers. The output of recurrent layer is mapped to the two fully connected layers to predict the text/non-text region proposals and bounding boxes regression. The model is evaluated on a custom-developed Urdu scene text dataset and the ICDAR-MLT17 Arabic text image dataset.