Accurate recognition of facial expression is a challenging problem especially from multi-scale and multi orientation face images. In this article, we propose a novel technique called Weber Local ...Binary Image Cosine Transform (WLBI-CT). WLBI-CT extracts and integrates the frequency components of images obtained through Weber local descriptor and local binary descriptor. These frequency components help in accurate classification of various facial expressions in the challenging domain of multi-scale and multi-orientation facial images. Identification of significant feature set plays a vital role in the success of any facial expression recognition system. Effect of multiple feature sets with varying block sizes has been investigated using different multi-scale images taken from well-known JAFEE, MMI and CK+ datasets. Extensive experimentation has been performed to demonstrate that the proposed technique outperforms the contemporary techniques in terms of recognition rate and computational time.
The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check the performance of a single threshold for distinguishing population-wise bimodal-distributed test ...results. However, for population-wise three-modal distributed test results, a single threshold ROC (stROC) analysis showed poor discriminative performance. The purpose of this study is to use a double-threshold ROC analysis for the three-modal distributed test results to provide better discriminative performance than the stROC analysis. A double-threshold receiver operating characteristic plot (dtROC) is constructed by replacing the single threshold with a double threshold. The sensitivity and specificity coordinates are chosen to maximize sensitivity for a given specificity value. Besides a simulation study assuming a mixture of lognormal, Poisson, and Weibull distributions, a clinical application is examined by a secondary data analysis of palpation test results of the C7 spinous process using the modified thorax-rib static technique. For the assumed mixture models, the discrimination performance of dtROC analysis outperforms the stROC analysis (area under ROC (AUROC) increased from 0.436 to 0.983 for lognormal distributed test results, 0.676 to 0.752 for the Poisson distribution, and 0.674 to 0.804 for Weibull distribution).
Melatonin was discovered in plants in the late nineties, but its role, signaling, and crosstalk with other phytohormones remain unknown. Research on melatonin in plants has risen dramatically in ...recent years and the role of this putative plant hormone under biotic and abiotic stress conditions has been reported. In the present review, we discuss the main functions of melatonin in the growth and development of plants, its role under abiotic stresses, such as water stress (waterlogging and drought), extreme temperature (low and high), salinity, heavy metal, and light-induced stress. Similarly, we also discuss the role of melatonin under biotic stresses (antiviral, antibacterial, and antifungal effects). Moreover, the present review meticulously discusses the crosstalk of melatonin with other phytohormones such as auxins, gibberellic acids, cytokinins, ethylene, and salicylic acid under normal and stressful conditions and reports melatonin receptors and signaling in plants. All these aspects of melatonin suggest that phytomelatonin is a key player in crop improvement and biotic and abiotic stress regulation.
In numerical investigations of supersymmetric Yang–Mills theory on a lattice, the supersymmetric Ward identities are valuable for finding the critical value of the hopping parameter and for examining ...the size of supersymmetry breaking by the lattice discretisation. In this article we present an improved method for the numerical analysis of supersymmetric Ward identities, which takes into account the correlations between the various observables involved. We present the first complete analysis of supersymmetric Ward identities in
N
=
1
supersymmetric Yang–Mills theory with gauge group SU(3). The results indicate that lattice artefacts scale to zero as
O
(
a
2
)
towards the continuum limit in agreement with theoretical expectations.
An important issue in competitive energy markets is the accurate and efficient wind speed forecasting for wind power production. However, wind speed forecasting models developed for one location ...usually do not match the other site for various reasons like changes in terrain, different wind speed patterns, and atmospheric factors such as temperature, pressure, humidity, etc. Thus, introducing a flexible model that captures all the features is a challenging task. This paper proposes a functional data analysis (FDA) approach to forecast the site variant wind daily profiles with higher accuracy. Unlike the traditional methods, the FDA is more attractive as it forecasts a complete daily profile, and thus, forecasts can be obtained in the ultra-short period. To this end, the wind speed data is first filtered for extreme values. The filtered series is then divided into deterministic (Component-I) and stochastic (Component-II) components. Component-I is modeled and forecasted based on the generalized additive modeling technique. On the other hand, Component-II is modeled and forecasted using functional models such as functional autoregressive (FAR) and FAR with explanatory variables (FARX). For comparison purposes, forecasts from the traditional univariate autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), SARIMA with exogenous information (SARIMAX), and neural network autoregressive (NNAR) models are also obtained. For empirical analysis, the wind speed data are obtained from the NASA power project for the site Canada located in Durham, England, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting performance of different models is assessed through different accuracy measures, namely mean error, root mean squared error, mean absolute error, and mean absolute standard error. The results indicate that the functional models outperform the classical ARIMA, SARIMA, SARIMAX, and a deep learning model, NNAR. Within the functional models, the forecasting ability of the FARX is superior to FAR.
Reactive oxygen species (ROS) and reactive nitrogen species (RNS) play a pivotal role in the dynamic cell signaling systems in plants, even under biotic and abiotic stress conditions. Over the past ...two decades, various studies have endorsed the notion that these molecules can act as intracellular and intercellular signaling molecules at a very low concentration to control plant growth and development, symbiotic association, and defense mechanisms in response to biotic and abiotic stress conditions. However, the upsurge of ROS and RNS under stressful conditions can lead to cell damage, retarded growth, and delayed development of plants. As signaling molecules, ROS and RNS have gained great attention from plant scientists and have been studied under different developmental stages of plants. However, the role of RNS and RNS signaling in plant-microbe interactions is still unknown. Different organelles of plant cells contain the enzymes necessary for the formation of ROS and RNS as well as their scavengers, and the spatial and temporal positions of these enzymes determine the signaling pathways. In the present review, we aimed to report the production of ROS and RNS, their role as signaling molecules during plant-microbe interactions, and the antioxidant system as a balancing system in the synthesis and elimination of these species.
Some soil microbes, with their diverse inhabitance, biologically active metabolites, and endospore formation, gave them characteristic predominance and recognition among other microbial communities. ...The present study collected ten soil samples from green land, agricultural and marshy soil sites of Khyber Pakhtunkhwa, Pakistan. After culturing on described media, the bacterial isolates were identified through phenotypic, biochemical and phylogenetic analysis. Our phylogenetic analysis revealed three bacterial isolates, A6S7, A1S6, and A1S10, showing 99% nucleotides sequence similarity with Brevibacillus formosus, Bacillus Subtilis and Paenibacillus dendritiformis. The crude extract was prepared from bacterial isolates to assess the anti-bacterial potential against various targeted multidrug-resistant strains (MDRS), including Acinetobacter baumannii (ATCC 19606), Methicillin-resistant Staphylococcus aureus (MRSA) (BAA-1683), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (BAA-2108), Staphylococcus aureus (ATCC 292013), Escherichia coli (ATCC25922) and Salmonella typhi (ATCC 14028). Our analysis revealed that all bacterial extracts possess activity against Gram-negative and Gram-positive bacteria at a concentration of 5 mg/mL, efficiently restricting the growth of E. coli compared with positive control ciprofloxacin. The study concluded that the identified species have the potential to produce antimicrobial compounds which can be used to control different microbial infections, especially MDRS. Moreover, the analysis of the bacterial extracts through GC-MS indicated the presence of different antimicrobial compounds such as propanoic acid, oxalic acid, phenol and hexadecanoic acid.
A plant’s response to osmotic stress is a complex phenomenon that causes many abnormal symptoms due to limitations in growth and development or even the loss of yield. The current research aimed to ...analyze the agronomical, physiological, and biochemical mechanisms accompanying the acquisition of salt resistance in the Vigna radiata L. variety ‘Ramzan’ using seed osmo- and thermopriming in the presence of PEG-4000 and 4 °C under induced salinity stresses of 100 and 150 mM NaCl. Seeds were collected from CCRI, Nowshera, and sowing was undertaken in triplicate at the Department of Botany, Peshawar University, during the 2018–2019 growing season. Rhizospheric soil pH (6.0), E.C (2.41 ds/m), field capacity, and moisture content level were estimated in the present study. We observed from the estimated results that the agronomic characteristics, i.e., shoot fresh weight and shoot dry weight in T9 (4oC + 150 mM NaCl), root fresh weight and root dry weight in T4 (PEG + 100 mM NaCl), shoot moisture content in T5 (PEG + 100 mM NaCl), and root moisture content in T6 (PEG + 150 mM NaCl) were the highest, followed by the lowest in T1 (both shoot and root fresh weights) and T2 (shoot and root dry weights). Similarly, the shoot moisture content was the maximum in T5 and the minimum in T6, and root moisture was the highest in T6. We observed from the estimated results that agronomical parameters including dry masses (T4, T6, T4), leaf area index, germination index, leaf area, total biomass, seed vigor index under treatment T9, and relative water content and water use efficiency during T5 and T6 were the highest. Plant physiological traits such as proline, SOD enhanced by T1, carotenoids in treatment T2, and chlorophyll and protein levels were the highest under treatment T4, whereas sugar and POD were highest under treatments T7 and T8. The principal component analysis enclosed 63.75% of the total variation among all biological components. These estimated results confirmed the positive resistance by Vigna radiata during osmopriming (PEG) and thermopriming (4 °C) on most of the features with great tolerance under a low-saline treatment such as T4 (PEG), T5 (PEG + 100 mM NaCl), T7 (4 °C), and T8 (4 °C + 100 mM NaCl), while it was susceptible in the case of T6 (PEG + 150 mM NaCl) and T9 (4 °C + 150 mM NaCl) to high salt application. We found that the constraining impact of several priming techniques improved low salinity, which was regarded as economically inexpensive and initiated numerous metabolic processes in plants, hence decreasing germination time. The current study will have major applications for combatting the salinity problem induced by climate change in Pakistan.
This research intends to evaluate the asymmetric relationship between pandemic uncertainty and public health expenditures in selected European Union nations (Germany, France, Sweden, Belgium, ...Austria, Netherlands, Denmark, Spain, Finland, and Portugal). Earlier studies used panel data methodologies to get consistent results about the pandemic-health expenditures nexus, irrespective of the reality that numerous economies did not identify such a link independently. By contrast, the present research utilizes a unique technique, quantile-on-quantile, that explores time-series dependency in every nation by offering worldwide yet country-related insight into the linkage between the variables. Estimations reveal that pandemic uncertainty increases public health expenditures in most of the selected economies at specified quantiles of data. Additionally, the data indicate that the level of asymmetries among our variables varies by country, stressing the significance of policymakers paying special attention while executing policies concerning health expenditures and pandemic uncertainty.
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