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This study aims to map the rock types in the Um Had region by integrating remote sensing applications of Landsat-8 (OLI) image processing, field studies, and petrographic investigations. The present work involves updating the existing geological map of the Um Had area in the central Eastern Desert, Egypt, due to the lack of a precise and accurate geological map. Several rock types dating to the Neoproterozoic Era, including oceanic crust (ophiolitic and island arc) and continental crust assemblages, originated in the region during two tectonic stages (late to post-orogenic and syn-orogenic). Remote sensing technology is already widely utilized for various geological domains like mineralogy, lithology mapping, geomorphology, and others. In our study, it is specifically used for lithological mapping. We utilized the optimum index factor and correlation coefficient methods to identify the most effective results from False-Color Composite (FCC), Principal Component Analysis (PC), and Band Ratio (BR). These techniques, combined with supervised classification, enabled us to distinguish among different rock units based on their spectral signatures. All results were combined with the previously mentioned techniques that include principal component images (PC1, PC4, and PC3; PC2, PC3, and PC4) and band ratio images (2/4, 5/7, and 5/3 × 2; 4/2, 5/6, and 6/7). Consequently, this supported the geological mapping and confirmed the field and petrographic investigations. This approach enabled the identification of seventeen distinct rock units, namely serpentinite, biotite schist, talc schist, metabasalt, metaandesite, metadacite, metarhyolite, metagabbro, quartz diorite, tonalite, rhyolite, granodiorite, monzogranite, syenogranite, siltstone, graywacke, and conglomerate. A comparative analysis of the newly modified and created lithological maps with previously published maps of the Um Had region significantly enhanced the accuracy and robustness of geological mapping and rock unit identification. [ABSTRACT FROM AUTHOR]

Background: The incidence of fungal urinary tract infections (UTIs) has dramatically increased in the past decades, with Candida arising as the predominant etiological agent. Managing these infections poses a serious challenge to clinicians, especially with the emergence of fluconazole-resistant (FLC-R) Candida species. In this study, we aimed to determine the mechanisms of fluconazole resistance in urinary Candida spp. isolated from hospitalized patients in Alexandria, Egypt, assess the correlation between fluconazole resistance and virulence, and explore potential treatment options for UTIs caused by FLC-R Candida strains. Results: Fluconazole susceptibility testing of 34 urinary Candida isolates indicated that 76.5% were FLC-R, with a higher prevalence of resistance recorded in non-albicans Candida spp. (88.9%) than in Candida albicans (62.5%). The calculated Spearman's correlation coefficients implied significant positive correlations between fluconazole minimum inhibitory concentrations and both biofilm formation and phospholipase production. Real-time PCR results revealed that most FLC-R isolates (60%) significantly overexpressed at least one efflux pump gene, while 42.3% significantly upregulated the ERG11 gene. The most prevalent mutation detected upon ERG11 sequencing was G464S, which is conclusively linked to fluconazole resistance. The five repurposed agents: amikacin, colistin, dexamethasone, ketorolac, and sulfamethoxazole demonstrated variable fluconazole-sensitizing activities in vitro, with amikacin, dexamethasone, and colistin being the most effective. However, the fluconazole/colistin combination produced a notable reduction (49.1%) in bladder bioburden, a 50% decrease in the inflammatory response, and tripled the median survival span relative to the untreated murine models. Conclusions: The fluconazole/colistin combination offers a promising treatment option for UTIs caused by FLC-R Candida, providing an alternative to the high-cost, tedious process of novel antifungal drug discovery in the battle against antifungal resistance. [ABSTRACT FROM AUTHOR]

Four novel lanthanide La (III), Ce (III), Nd (III), and Dy (III) complexes with (Z)‐4‐(benzo[d]thiazol‐2‐ylamino)‐3‐((benzo[d]thiazol‐2‐ylimino)methyl)‐2H‐chromen‐2‐one (L) were prepared and characterized. The novel compounds' structural compositions were elucidated by the application of analytical and spectroscopic methodologies. From physical and chemical measurements, the general formula for the [LnLCl3]2H2O complexes (C1‐C4) was proposed, which was proven by theoretical measurements. Using cisplatin, gentamicin, ampicillin, and ascorbic acid as standards, the compound's anticancer, antibacterial, and antioxidant qualities were assessed, and activities followed the order: Ce (III)L(C2) > Nd (III)L(C3) > Dy (III)L(C4) > La (III)L(C1) > L where the Ce (III) complexes exhibited the highest activity followed by Nd (III) complexes. Molecular docking studies were performed using the MOA2022 software to identify putative binding mechanisms for the methionine adenosyl‐transferases in liver cancer (PDB ID: 5A19) and the most active site of the Bacillus subtilis receptor (PDB ID: 5e6k), where the strength of the interaction increases with the negative binding energy L ˂ [LaLCl3] ˂ [DyLCl3] ˂ [NdLCl3] ˂ [CeLCl3]. [ABSTRACT FROM AUTHOR]

A thin film of 7-oxo-thiazolopyrimidine-3,8-dicarbonitrile derivative [7-ThPyDi]TF was prepared using a spin coating technique. The surface morphology and molecular structure are studied using various techniques such as XRD, FTIR, and scanning electron microscope (SEM). Moreover, the quantum chemical calculations were carried out through time-dependent density functional theory (TD-DFT) to investigate some reactivity descriptors such as softness and electronegativity. Also, Au/[7-ThPyDi]TF/p-Si/Al heterojunction diodes were fabricated. It was revealed that the energy bandgap value of [7-ThPyDi] as an organic thin film is 3.58 eV for direct transitions and 3.94 eV for indirect transitions, respectively, and this value falls within the semiconductor material range. The atomic force microscope demonstrated that the surface roughness of the thin film is approximately 32.2 nm. Because of its high refractive index, this material has the potential application as an antireflection coating for solar cells and as lenses with a wide focal range. We investigated a blue luminescent thiazolopyrimidine compound; the maximum emission in the more aggregated state (higher solution concentration) exhibits a notable blue shift compared to the more diluted solution. This uncommon phenomenon has been understood by structural analyses using density functional theory. The chemical structure of the molecule [planar conjugated cores and strong polar groups (–CO and –CN)] enables it to interact with both itself and the polar solvent. The intermolecular interactions result in the bending of the conjugated plane. As a result, the blue shift happens upon aggregation when the conjugated effect becomes weaker. The studied molecule gave different emission colors (blue, yellow, and reddish green) depending on the molecular packing. [ABSTRACT FROM AUTHOR]

The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues. [ABSTRACT FROM AUTHOR]

Heterocyclic compounds are used as a basic structural tool in medicinal chemistry and are very essential in our daily life. A wide range of heterocyclic compounds were reported to show in the literature with interesting biological and pharmacological properties. In their profound study on a series of several heterocyclic compounds shown to have bio applications, unfortunately, the aromatic π‐conjugated heterocyclic molecules quench the emission intensity due to aggregation caused quenching due to π–π stacking. Owing to the disadvantages, the design and function of aggregation‐induced emission (AIE) active heterocyclic materials were emerging abundantly. On the other note, AIE has often been used to further improve both the emission intensity and stability of compounds, with plenty of potential applications in chemical sensing and bioimaging. In this work, we comprehensively reviewed the recent progress in heterocyclic compounds with AIE characteristics that are effectively used in chemo‐selective and bio‐imaging or as highly selective sensors. [ABSTRACT FROM AUTHOR]

In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human's daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93). [ABSTRACT FROM AUTHOR]

Due to the biological importance of diclofenac derivatives which are included in the composition of the active substance in many medicines used in the treatment of infections. Herein, we present a facile procedure for the synthesis of a new series of diclofenac analogous, in excellent isolated yields starting from the carbohydrazide precursor 1. Acylation and condensation of NH2 group in the starting material with different reagents delivered compounds (2–5). Moreover, the Nucleophilic substitution of chloromethyl derivative 5 with primary and secondary amines gave compounds (6a, 6b, and 7). Furthermore, the hydrazine compound (7) reaction with benzaldehyde and chloroacetylchloride produced derivatives (8, 9). The chemical structures of all newly synthesized compounds have been proved based on elemental and spectral analysis techniques (FTIR, 1HNMR, 13CNMR, and Mass spectroscopy). All synthesized compounds were investigated for their in-vitro antimicrobial activity against different strains of bacteria and fungi in moderate to high activity. A molecular docking approach was utilized to investigate the proposed molecular mechanism of the antibacterial and antifungal activity of the synthesized compounds. [ABSTRACT FROM AUTHOR]

Copyright of Jordanian Journal of Computers & Information Technology is the property of Jordanian Journal of Computers & Information Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital twins are among the most important trends of the fourth industrial revolution. They present a crucial tool for protecting critical mission systems and the development of new services, products, and processes. This paper presents the first digital twin for a data center. The rapid growth of the Internet of things and the areas of modeling and simulation results in high demand for the development of data center digital twins (DCDT) to ensure the safety/protection of critical and costly mission infrastructure and guarantee business continuity, enhance efficiency, and sustain development. This paper presents the design and implementation of a digital twin for a university data center. Different sensory data like temperature, humidity, smoke, and water leakage are analyzed using an intelligent event detection approach, which detects abnormalities using an Extreme Learning Machine (ELM) fed with the minimum ratio between successive real-time data streams. The performance of ELM has outperformed that of both Learning Vector Quantization and Radial Basis Function-based neural network classifiers and proved much faster in abnormal event detection. [ABSTRACT FROM AUTHOR]

Egypt has been fighting the issue of ensuring road safety‚ reducing accidents‚ preserving the lives of citizens since its inception. For these reasons‚ precisely identifying the road condition‚ followed by effective and timely maintenance and rehabilitation measures‚ leads to an increase in the road network's safety level and lifespan. This paper presents a multi-input deep learning framework that combines BiLSTM and Depthwise separable convolution to work in parallel for automatic recognition of road surface quality and different road anomalies. Furthermore, we performed an investigation to compare deep networks approaches against other traditional approaches using real-time data sensed and collected from the Egyptian road network. The proposed deep model has achieved an average accuracy of 93.1%‚ which is superior compared to other evaluated approaches. Finally, we utilized the proposed model to estimate a road quality index in the Egyptian cities. [ABSTRACT FROM AUTHOR]

Antibiotic pollution in the aquatic environment has emerged as one of the most serious concerns raised by Pharmaceuticals and personal care products (PPCPs). Antibiotic levels in wastewater from antibiotics factories and hospitals have been detected at varying levels. Antibiotics may end up in municipal sewage or hospital wastewater in their original form or as metabolites after being consumed by humans and animals. Antibiotics, in fact, have become a type of "pseudo-persistent" pollutant in the aquatic environment due to their widespread and continuous use in daily life, despite the fact that their half-lives are relatively shorter than those of persistent organic pollutants (POPs). Antibiotic residue in the environment is partly to blame for the emergence of drug-resistant bacteria. In this study, a new reliable, and robust developed to estimate the cefotaxime content of pharmaceutical industrial wastewater was developed. This method is based on the accurate detection of this antibiotic using high-performance liquid chromatography (HPLC), ultraviolet (UV), and FT-IR techniques. In addition, the removal of the chelate drug via the formation of a chelate complex with Fe(III). The concentration of cefotaxime-Na in industrial wastewater, temperature, time, and pH factors were observed. The removal percentage of cefotaxime-Na using this technique reached 96% at room temperature at pH 2.8. The study discovered that time limitation is a critical and effective parameter in the complexation reaction behavior when all other parameters are held constant. So, the complexation reaction technique can contribute to the treatment process as a stage for removing the β-lactam antibiotics chemically from industrial wastewater, then preventing the appearance of types of resistant bacteria will be achieved to maintain the effectiveness of β-lactam antibiotics and keep our environment safe. [ABSTRACT FROM AUTHOR]

We present here the FT-IR, DFT computation, XRD, optical, and photophysical characterization of a heterocyclic compound with thienopyrimidine and pyran moieties. TD-DFT/DMOl3 and TD-DFT/CASTEP computations were used to study the geometry of isolated and dimer molecules and their optical behavior. The indirect (3.93 eV) and direct (3.29 eV) optical energy bandgaps, HOMO–LUMO energy gap (3.02 eV), and wavelength of maximum absorption (353 nm) were determined in the gas phase with M062X/6-31+G (d, p). A thin film of the studied molecule was studied using XRD, FT-IR, and UV–Vis spectroscopy. The average crystallite size was found as 74.95 nm. Also, the photoluminescence spectroscopy revealed that the compound exhibited different emission bands at the visible range with different intensities depending on the degree of molecular aggregation. For instance, solutions with different concentrations emitted blue, cyan, and green light. On the other hand, the solid-state material produced a dual emission with comparable intensities at λmax = 455, 505, and 621 nm to cover the entire visible range and produce white emission from a single material with CIE coordinates of (0.34, 0.32) that are very similar to the ideal pure white light. Consequently, these findings could lead to the development of more attractive new luminous materials. [ABSTRACT FROM AUTHOR]

Nowadays heterocyclic compounds containing pyrimidine scaffold attract the interest of many organic chemists due to their strong biological activity, so herein we present the synthesis of new polycyclic compounds starting from 4-chloro-9-phenyl-N-(p-tolyl)pyrimido[5',4':4,5]thieno[3,2-d][1,2,3]triazin-7-amine (1) which allowed to react with hydrazine hydrate to give hydrazinyl triazine compound (2) which subjected to some ring closure reactions using various reagents represented in triethyl orthoformate, acetyl acetone, and carbon disulfide to give compounds (3–5), respectively. Also, when the hydrazinyl compound was allowed to react with acetophenone, ethoxymethylene ethylcyano acetate, and diethyl malonate gave the derivatives (9–11), respectively. The structures of all newly synthesized compounds were confirmed using spectral analysis techniques as well as elemental analysis. Besides, four new compounds were screened for their antimicrobial activity using different strains of bacteria and fungi, and compound 5 displayed the best antimicrobial activity among all the tested compounds. [ABSTRACT FROM AUTHOR]

Incorporating coumarin nuclei into other heterocyclic scaffolds such as thienopyrimidine can significantly increase the photophysical and biological efficiency. Accordingly, we describe in this manuscript the synthesis of new heterocyclic derivatives containing combined coumarin and pyrimidine nuclei to maximize the application benefit of the target compounds. All of the synthesized derivatives were thoroughly characterized using spectral analyses such as FT‐IR, NMR, and mass spectroscopy. The photophysical properties of four compounds in solution were investigated, where the substitutions had a significant effect even when the molecule conjugation was not included. Although having similar luminescent cores, two analog compounds showed different emission colors, blue and green. Uncommonly, one derivative showed a blue shift after aggregation. DFT simulations showed that adding a terminal group can significantly alter the electronic structure or molecular packing which affected directly the luminescence behavior. [ABSTRACT FROM AUTHOR]

Copyright of Annals of Agricultural Science Moshtohor is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Annals of Agricultural Science Moshtohor is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heterocyclic compounds play an important role in the field of pharmaceutical chemistry, especially compounds containing hybrid bioactive moieties. So in continuation of our work, We focused mainly here on design, synthesis, and transformations of novel bio-active molecules bearing thieno[2,3-d]pyrimidine moiety which can be used as antibacterial and antifungal drugs after some modifications. The starting compound 2-chloromethyl-3H-7-p-tolylamino-4-oxo-9-phenyl-pyrimido[4',5':4,5]thieno[2,3-d]pyrimidine (1) which was prepared according to literature, was converted into many other derivatives bearing different functional groups to make a comparable study of their antimicrobial activity. The chemical constructions of all obtained compounds were well elucidated by the means of their analytical and spectral analyses (TLC, IR, 1H NMR, 13C NMR, and MS). The title compounds have been chosen and screened for their in vitro antimicrobial efficiency versus some genera of bacteria and fungi, from the practical results we concluded that most of these compounds were powerful antimicrobial agents. [ABSTRACT FROM AUTHOR]

Diclofenac is considered one of the best drugs used as anti-inflammatory agent, so in the present work new diclofenac derivatives containing bioactive moieties, such as triazole, oxadiazole, and thiadiazol, were synthesized. The structures of the new compounds were established based on spectral and elemental analysis. The new synthesized compounds were tested against anti-inflammatory agents and exhibited high activity as expected. The binding position of new diclofenac derivatives toward Cox-1 and Cox-2 enzymes was proved by molecular docking. Also, some of the new compounds were tested against bacteria and displayed high activity. [ABSTRACT FROM AUTHOR]

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives. [ABSTRACT FROM AUTHOR]

With the development of cognitive computing, machine learning techniques, and big data analytics, cognitive support is crucial for automated industrial production. The real-time automated visual inspection in industrial production is a challenging task. Speed and accuracy are crucial factors for the process of automating the defect detection. Many statistical and spectrum analysis approaches have been introduced; however, they suffer from high computational cost with average performance. This paper proposes a neighborhood-maintaining approach, which is based on the minimum ratio for fast and reliable inspection of industrial products. The minimum ratio between local neighborhood sliding windows is used as a similarity measure for localizing defection. Extreme learning machine is then adapted to classify surfaces to defect or normal. A defect detection accuracy on textile fabrics has achieved 98.07% with 91.29% sensitivity and 99.67% specificity. The minimum ratio shows highly discriminant power to distinguish between normal and abnormal surfaces. A defective region produces a smaller value of minimum ratio than that of a defect-free region. Experimental results show superior speed and accuracy performance over many existing defect detection methods. [ABSTRACT FROM AUTHOR]

This paper presents a proposed approach for the enhancement of Infrared (IR) night vision images. This approach is based on a trilateral contrast enhancement in which the IR night vision images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on thresholding. The second stage, which is the heart of the enhancement approach, depends on additive wavelet transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while plateau histogram equalization is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high-pass filter. Average gradient, Sobel edge magnitude and spectral entropy are used as quality metrics for evaluation of the proposed approach. The used metrics ensure good success of this proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents two proposed approaches for enhancing the visibility of the infrared (IR) night vision images. The first approach is based on merging gamma correction with histogram matching (HM). On the other hand, the second approach depends on merging gamma correction with contrast limited adaptive histogram equalization (CLAHE). The HM depends on a reference visual image for converting IR night vision images into images with better visual quality. Quality metrics such as entropy, average gradient, and Sobel edge magnitude are used for performance evaluation of the proposed approaches. [ABSTRACT FROM AUTHOR]

Pyrimidine and thienopyrimidine derivatives play a very important role in organic chemistry because of their wide applications as bioactive compounds with multiple biological activities. However, a literature survey revealed that the merger of different groups in the thieno[2,3‐d]pyrimidine heterocyclic ring enhances its antibacterial, antifungal and anti‐inflammatory activities. This encouraged us to prepare a new series of thieno[2,3‐d]pyrimidine heterocyclic compounds and to test them as antimicrobial and anti‐inflammatory agents. These compounds have shown remarkable activity toward fungi, bacteria, and inflammation. Thus, these compounds have been prepared by the chloroacylation of 5‐amino‐4‐phenyl‐2‐(p‐tolylamino)thieno[2,3‐d] pyrimidine‐6‐carboxamide (4) using chloroacetyl chloride under neat condition to afford the target compound (6), which was used as precursor for the synthesis of a number of bioactive compounds. Thus reaction of the chloromethylpyrimidine derivative (6) with triphenylphosphine in dry benzene gave the corresponding ((4‐oxo‐9‐phenyl‐7‐(p‐tolylamino)‐3,4‐dihydropyrimido[4′,5′:4,5]thieno[2,3‐d]pyrimidin‐2‐yl)methyl) triphenylphosphonium chloride (7). Compounds 8a–8c and 9a–9c were obtained by the reaction of 7 with some selected aromatic aldehydes and ketones in methanol and sodium methoxide under Wittig reaction condition. The structures of the all new synthesized compounds were established on the basis of their analytical and spectral data (IR, 1H NMR, 13C NMR, and MS). [ABSTRACT FROM AUTHOR]

Outlier detection is critical for many applications such as healthcare, health insurance, medical diagnosis, predictive analytics, pattern recognition, intrusion detection, anomaly or defect detection, video surveillance, credit card fraud detection and text mining. Outlier detection techniques could be statistics, distance- or model based. Techniques, which are based on a single method for outlier detection usually have weaknesses and strengths and are mostly unstable. Outlier detection ensembles harness the strengths of individual detectors and result in stable performance. This paper presents a new parameter based growing self-organizing maps ensemble (GSOME) for outlier detection in multivariate patterns. For outlier detection, the proposed GSOME transforms non-linear relationships between high dimensional patterns into a simple 1D geometric relationship. Whatever the pattern dimensionality is, it is mapped to a single point of a line. The dispersion of mapped points will be used to locate the outliers and measure the degree of outlyingness. Several experiments on both real and synthetic data sets show the promising performance of the proposed GSOME. [ABSTRACT FROM AUTHOR]

5‐Amino‐2‐(p‐tolylamino)‐4‐phenylthieno[2,3‐d]pyrimidine‐6‐carbonitrile 9, which was synthesized by an innovative method, was used as a versatile precursor for synthesizing pyrimido‐thienopyrimidine, triazolopyrimidothienopyrimidine, and pyrimidothienotriazine compounds. Thus, reaction of aminothienopyrimidinecarbonitrile 9 with chloroacetylchloride in dioxane afforded the chloroacetylaminocarbonitrile derivative 10, which underwent nucleophilic substitution reactions with various primary and secondary amines gave the corresponding N‐alkyl‐(aryl)amino acetamides 11a,b. On the other hand, the reaction of aminocarbonitrile 9 with triethyl orthoformate followed by cyclization with hydrazine yielded an aminoiminopyrimidine derivative 13. The latter was used as versatile precursor for synthesis of new heterocyclic compounds. The structures of all the new compounds have been established on the basis of their analytical and spectral data (IR, 1H NMR, 13C NMR, and MS). Some of the synthesized compounds were evaluated in vitro for their anti‐inflammatory activity. All the tested compounds exhibited remarkable anti‐inflammatory activity. [ABSTRACT FROM AUTHOR]

Copyright of Fayoum Journal of Agricultural Research & Development / Maǧallaẗ Al-Fayūm lil Buḥūṯ wa Al-Tanmiyyaẗ Al-Zirāʿiyyaẗ is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. This article presents a robust hybrid combination technique to build a combined classifier that is able to tackle the problem of classification of rotation-invariant 2D textures. Diversity in the components of the combined classifier is enforced through variation of the parameters related to both architecture design and training stages of a neural network classifier. The boosting algorithm is used to make perturbation of the training set using Multi-Layer Perceptron (MLP) as the base classifier. The final decision of the proposed combined classifier is based on the majority voting. Experiments’ results on a standard benchmark database of rotated textures show that the proposed hybrid combination method is very robust, and it presents an excellent texture discrimination for all considered classes, overcoming traditional texture modification methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IOT) is a promising area which will boost the world economy. The constituent components of the IOT are smart objects which generate actuation signals or receive sensory signals which are usually noisy, have trend or has small signal-to-noise ratio. Processing these signals for filtering, detrending and enhancing the signal-to-noise ratio is crucial for embedding intelligence in these smart objects. This research discovers the potential of CEEMD in preparing signals for further intelligent applications such as event detection or pattern recognition in smart objects. Algorithms are presented for signal filtering, detrending and event detection based on a combination of both CEEMD, the autocorrelation function and the learning vector quantization classifier. The performance of the proposed algorithms is compared for both CEEMD and the least squares fit approach. The CEEMD has shown promising results. [ABSTRACT FROM AUTHOR]

The automated visual inspection of homogeneous flat surface products is a challenging task that needs fast and accurate algorithms for defect detection and classification in real time. Multi-directional and Multi-scale approaches, such as Gabor Filter Banks and Wavelets, have high computational cost in addition to their average performance in defect characterization. This paper presents a novel implementation of a neighborhood-preserving approach for the fast and accurate inspection of fine-structured industrial products using a new neighborhood-preserving cross-correlation feature vector. The fast and noise immune Probabilistic Neural Network (PNN) classifier has been found to be very suitable for defect detection in homogeneous non-patterned surfaces with acceptable slight variations, such as textile fabrics. A defect detection accuracy of 99.87% has been achieved with 99.29% recall/sensitivity and 99.91% specificity. The discriminant power shows how well the PNN classifier discriminates between normal and abnormal surfaces. The experimental results show that the proposed system outperforms the Gabor function-based techniques. [ABSTRACT FROM PUBLISHER]

Defect detection of textiles is a necessary requirement for quality control and customer satisfaction. This paper presents a system for decision fusion in order to enhance the accuracy of defect detection in textiles. A multi-classifier decision fusion technique based on majority voting is presented to solve the problems of sensitivity to parameter variation and to make use of the advantages of the individual feature sets for accurate texture characterization. Features based on Gray Level Co-occurrence Matrix (GLCM), Laws Energy (LE) Filter, Local Binary Patterns (LBP), HU Moment invariants, Moment of Inertia (MOI) and Standard Deviation of Gray levels are used to train a set of Learning Vector Quantization (LVQ) classifiers. Detection accuracies of classifiers trained on single-feature sets are compared with those of the majority voting among the individual classifiers. The results obtained from majority voting indicate that the decision fusion technique improves the accuracy and reliability of the detection process. Empirical results indicate the high accuracy of the presented approach. The correct defect detection rate of the proposed approach is 98.64% with an average false acceptance rate of 0.0012. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel method for defect detection in homogeneous flat surface products. The coefficient of variation is used as a homogeneity measure for approximate defect localization and features extracted from the Log – Gabor filter bank response are used to accurately localize and detect the defect while reducing the complexity of Gabor based inspection approaches. The scanning window size and threshold parameter are the two major factors that affect the system performance. An adaptive technique is proposed for selecting the size of the scanning window and automating the selection of the threshold level. Compared to the Log-Gabor filters, the proposed combination resulted in speeding up the defect detection process by about ten times. The experimental results show that the proposed system gives promising results and is applicable for defect detection in homogeneous surfaces like paper, steel plates, ceramic tiles and foils. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for building a committee machine (LVQCM) that is based on learning vector quantization (LVQ) neural networks. The proposed committee machine was then applied to solve the problem of facial gender recognition. Design of individual classifiers is time consuming and results in inaccurate and unstable classifiers. Settling on the right design parameters of a classifier is a non-trivial task. To avoid the abovementioned problems, a committee machine is implemented. Experimental results based on Kuwait University and Stanford University face databases indicate that the performance of the proposed committee machine (99.02%) outperforms that of the best individual classifier used in that combination (93%). Majority voting is used for combining the individual decisions of a group of LVQ weak classifiers generated and trained under different conditions. The experimental results also show that LVQCM outperforms other recently published methods such as: the K-Means, 2^{nd} weight, Mahalanobis, linear discriminant, local linear discriminant, closest match, and the closest diffusion match. The implemented algorithm is not restricted to LVQ neural network and could be applied to other tytpes of neural networks. [ABSTRACT FROM AUTHOR]

A system for invariant face recognition is presented. A combined classifier uses the generalization capabilities of learning vector quantization (LVQ) neural networks to build a representative model of a face from a variety of training patterns with different poses, details, and facial expressions. The combined generalization error of the classifier is found to be lower than that of each individual classifier. The system is tested on an in-house built database and is capable of recognizing a face in about 1 second. The system performance compares favorably with the state-of-the-art systems. While the recognition rates of the individual classifiers ranged from 94% to 96%, a correct recognition rate of 100% is achieved by the combined classifier at 0% rejection. [ABSTRACT FROM AUTHOR]