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Bee propolis is a heterogeneous mixture of phytoactive metabolites with reported antioxidant, anti-inflammatory, and neuroprotective properties. The chemical composition is highly variable, complex and presents the greatest challenge for standardization. So, the study was aimed at standardization of selected phytochemicals in propolis collected from a region in India (IP). A thin-layer chromatography (TLC) method was developed for five markers with prior evidence of neuroprotective activity using orthogonal detection and subsequently validated as per the International Council for Harmonisation ICH Q2 (R1) guidelines. The methodology was assessed for greenness using different metrices and then applied to marketed samples. Furthermore, antioxidant and anticholinesterase activity were evaluated directly using TLC‒bioautography technique. The method was developed using toluene‒ethyl acetate‒formic acid (7.4:2.6:0.5, V/V) as the mobile phase, with RF values of 0.235, 0.353, 0.552, 0.606, and 0.655 for luteolin, p-coumaric acid, chrysin, galangin, and pinocembrin, respectively. The optimized method was found to be precise (%RSD ≤ 2.0%), accurate (90‒110%), linear over the concentration ranges (R2 ≥ 0.995), sensitive and robust. Pinocembrin (2.30 ± 0.12% w/w) and galangin (5.78 ± 0.30% w/w) were found in the highest concentration. TLC‒bioautography analysis confirmed the contribution of the selected phenolics in anticholinesterase and antioxidant activity. The analytical eco-score value of 76, analytical greenness calculator (AGREE) score of 0.62 and other assessed analytical pictograms indicated the methodology developed had low environmental impact. Overall, a simple, rapid, precise, and green TLC analytical methodology has been developed for IP for quality control and preliminary screening of neuroprotective activity. [ABSTRACT FROM AUTHOR]

Metamaterial (MM) is an artificial constituent, which as a distinct properties i.e., negative permittivity and refractive-index, which doesn't subsists naturally in the environment. MM is widely used in the antenna application owing to their abundant advantages. Split Ring-Resonator configuration in MM antenna can enhance the performance of antenna. Therefore, the present study aims to develop an improved regression model, which evaluates the performance of MM antennas effectively. In that context, initially, the study performs, pre-processing by removing the unnecessary data and missing values. Then, the feature extraction is employed with Bi-LSTM, which extracts the efficient features. Followed by this, the training and testing split is performed as 80% training data and 20% of testing data. The modified regression model is constructed with empirical loss function and XG-Boost algorithms. By implementing the proposed model, the prediction phase is enhanced efficiently. The experimental evaluation of the proposed model is accomplished with Metamaterial antenna dataset (MM antenna dataset), which is openly available and the analysis is done in terms of error rate and accuracy. The proposed system attains 99.23% accuracy rate. [ABSTRACT FROM AUTHOR]

A contact-free people walk identification has numerous applications in surveillance and suspicious activity detection to take the precautionary actions. This paper presents a millimeter-wave radar-based automated system for walk type identification in which the received complex radar signals are decomposed using flexible analytic wavelet transform. The Correntropy and centered temporal correntropy features are computed for the decomposed components of radar signals and followed by students t-test based feature ranking. The classification is done using ensemble subspace discriminant classifier with classifier fusion. Six different types of walk namely, slow walk (SW), fast walk (FW), slow walk with hand in pocket (SWHP), slow walk with swinging hands (SWSH), walk with a limp (WL), and walk hiding bottle (WHB) are considered for walk identification. Six different combinations of different types of walk are formed to develop a robust system for accurate identification in different scenarios. The proposed method achieved 85.5% accuracy to classify all six classes and 100% accuracy to classify SW and FW. In terms of activity focused identification, 100% accuracy is achieved using the proposed system to classify SWHP, SWSH, WL, and WHB classes. The classification performance is better than the compared methods for the considered walk activity combinations in terms of accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

A 3D-supramolecular nickel integrated Ni-SDZ complex was synthesized using sodium salt of sulfadiazine as the ligand and nickel(II) acetate as the metal salt using a condensation process and slow evaporation approach to growing the single crystal. The metal complex was characterized for its composition, functional groups, surface morphology as well as complex 3D structure, by resorting to various analytical techniques. The interacting surface and stability as well as reactivity of the complex were carried out using the DFT platform. From ADMET parameters, human Intestinal Absorbance data revealed that the compound has the potential to be well absorbed, and also Ni-SDZ complex cannot cross the blood–brain barrier (BBB). Additionally, the complex's DNA binding affinity and in-vivo and in-vitro cytotoxic studies were explored utilizing UV–Vis absorbance titration, viscosity measurements, and S. pombe cells and brine shrimp lethality tests. In visible light radiation, the Ni-SDZ complex displayed exceptional photo-degradation characteristics of approximately 70.19% within 70 min against methylene blue (MB). [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "A Modified Regression Model for Analyzing the Performance of Metamaterial Antenna Using Machine Learning and Deep Learning" published in the journal Wireless Personal Communications. The correction addresses an error in the author biography section, where the text for Raghavendra Sharma was mistakenly duplicated from the text for Rahul Dubey. The correct biography for Raghavendra Sharma is provided, detailing his educational background, research career, and current position as a Professor & Head (ECE) in Amity University Madhya Pradesh, Gwalior. The original article has been corrected. [Extracted from the article]

People all over the world are affected by the chronic, disabling autoimmune illness known as rheumatoid arthritis (RA). Herbal products are more commonly used in treating RA as of their safety and efficacy. The selected formulation for the present study is ANTARTH capsules, which is a polyherbal formulation containing different herbs effective in the treatment of RA. The purpose of this study was to develop and validate a precise, accurate, novel high-performance thin-layer chromatography (HPTLC) method for the simultaneous estimation of quercetin, berberine, rutin, and curcumin and to determine the levels in a polyherbal formulation. A good chromatographic separation was accomplished using a mobile phase consisting of toluene‒ethyl acetate‒methanol‒formic acid (5:3:2:0.5, V/V) with wavelengths of 366 nm and 426 nm using an ultraviolet‒visible (UV‒VIS) detector. The retention factors for quercetin, berberine, rutin, and curcumin were found to be 0.57, 0.316, 0.093, and 0.663, respectively. A good linear regression relationship between peak areas and concentrations was obtained with correlation coefficients of 0.9988, 0.9987, 0.9977, and 0.9984 for quercetin, rutin, berberine, and curcumin, respectively. The method developed was found to be accurate, precise, and robust. The method was then applied in a marketed formulation (ANTARTH capsule), and the percent content was found to be 5.26 ± 1.09, 4.76 ± 0.83, 3.80 ± 0.94 and 7.62 ± 1.58 for quercetin, rutin, berberine, and curcumin, respectively. [ABSTRACT FROM AUTHOR]

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning-based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning-based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique. [ABSTRACT FROM AUTHOR]

This paper reports a systematic X-ray crystallographic investigation of cadmium complex of sulfathiazole dihydrate with secondary ligand pyridine. The cadmium complex of sulfathiazole [Cd(C9H9N3O2S2)(C5H5N)2·2H2O] crystallizes in monoclinic space group C2/c with lattice parameter a = 14.1145(3) Å, b = 9.7656 (3) Å, c = 32.3573 (8) Å, β = 91.959(1)° and Z = 8. The experimental studies have been complemented by DFT studies in terms of optimization. HOMO–LUMO energy, natural bond orbital analysis and charge on the atoms have been analyzed using B3LYP method to understand molecular properties. Hirshfeld surface analysis has been introduced to visualize the conformity of the crystal structure. This method permitted for the identification of individual types of intermolecular contacts and their influence on the crystal packing. [ABSTRACT FROM AUTHOR]

The cadmium complex of picoline and sulfadiazine, a constituent of the triple sulfa drug, was synthesized and characterized by IR, NMR, and PL. It crystallizes in the orthorhombic space group Pca21 with the lattice parameters a = 13.73(10) Å, b = 17.0800(13) Å and c = 14.0558(11) Å. The experimental geometric data of the crystalline compound were compared with theoretical data in gaseous phase (B3LYP data). The HOMO and LUMO energy of the title molecule were calculated to get better knowledge of the reactivity and charge transfer properties. In addition, charge distributions from a Mullikan population, natural population, and NBO analysis were performed using the B3LYP method. Cadmium complex of sulfadiazine has been synthesized and characterized by X-ray crystallography with secondary ligand β-picoline [ABSTRACT FROM AUTHOR]