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In the realm of Human-Computer Interaction (HCI), the importance of hands cannot be overstated. Hands serve as a fundamental means of communication, expression, and interaction in the physical world. In recent years, Augmented Reality (AR) has emerged as a next-generation technology that seamlessly merges the digital and physical worlds, providing transformative experiences across various domains. In this context, accurate hand pose and shape estimation plays a crucial role in enabling natural and intuitive interactions within AR environments. Augmented Reality, with its ability to overlay digital information onto the real world, has the potential to revolutionize how we interact with technology. From gaming and education to healthcare and industrial training, AR has opened up new possibilities for enhancing user experiences. This study proposes an innovative approach for hand pose and shape estimation in AR applications. The methodology commences with the utilization of a pretrained Single Shot Multi-Box (SSD) model for hand detection and cropping. The cropped hand image is then transformed into the HSV color model, followed by applying histogram equalization on the value band. To precisely isolate the hand, specific bounds are set for each band of the HSV color space, generating a mask. To refine the mask and diminish noise, contouring techniques are applied to the mask, and gap-filling methods are employed. The resultant refined mask is then combined with the original cropped image through logical AND operations to accurately delineate the hand boundaries. This meticulous approach ensures robust hand detection even in complex scenes. To extract pertinent features, the detected hand undergoes two concurrent processes. Firstly, the Scale-Invariant Feature Transform (SIFT) algorithm identifies distinctive keypoints on the hand's outer surface. Simultaneously, a pre-trained lightweight Convolutional Neural Network (CNN), namely MobileNet, is employed to extract 3D hand landmarks, the hand's center (middle finger metacarpophalangeal joint), and handedness information. These extracted features, encompassing hand keypoints, landmarks, center, and handedness, are aggregated and compiled into a CSV file for further analysis. A Gated Recurrent Unit (GRU) is then employed to process the features, capturing intricate dependencies between them. The GRU model successfully predicts the 3D hand pose, achieving high accuracy even in dynamic scenarios. The evaluation results for the proposed model are very promising that the Mean Per Joint Position Error in 3D (MPJPE) is 0.0596 between the predicted pose and the ground truth hand landmarks, while the Percentage of Correct Keypoints (PCK) is 95%. Upon predicting the hand pose, a mesh representation is employed to reconstruct the 3D shape of the hand. This mesh provides a tangible representation of the hand's structure and orientation, enhancing the realism and usability of the AR application. By integrating sophisticated detection, feature extraction, and predictive modeling techniques, this method contributes to creating more immersive and intuitive AR experiences, thereby fostering the seamless fusion of the digital and physical worlds. [ABSTRACT FROM AUTHOR]

In this study, human-safe lactic acid microorganisms (LAM) were isolated from food samples to be used as potential additives for human food. Samples from various food sources (artisanal cheeses, fermented chickpeas, fermented rice, natural yogurt, pickles, and raw milk) were used to inoculate de Man, Rogosa, and Sharpe (MRS) and malt agar plates, which were incubated at 30°C for 48 h or 37°C for 72 h to isolate lactic acid bacteria and yeast, respectively. Out of 120 isolates (85 bacteria and 35 yeast), 75 isolates showed γ-hemolytic activity and were considered “Generally Recognized As Safe” (GRAS) isolates. After testing their growth rate under the gastrointestinal tract (GIT) conditions, including acidic pH and specific bile salt concentrations, we selected 30 isolates. Then, we evaluated the fermentative abilities of these 30 isolates on nine types of carbohydrates, their total acidity, and their antagonistic activity against five human pathogens. Based on the results of these tests, four isolates were selected for identification using the Biolog program and 16S rRNA sequencing for bacteria and 18S rRNA sequencing for yeast and found to be Bacillus bingmayongesis (FJAT-13831), Lacticaseibacillus paracasei (R094), Pediococcus pentosaceus (DSM 20336), and one yeast isolate as Saccharomyces cerevisiae (SaCe1 26S). [ABSTRACT FROM AUTHOR]

Background: Hepatitis C virus (HCV) infection is one of the main causes of chronic liver disease worldwide. It has been shown that up to 50% of patients with chronic HCV infection experience neuropsychiatric issues. Objective: This study aimed to evaluate the cognitive impairment, extrapyramidal signs and their effects on patients' quality of life. Patients and Methods: The study included 60 untreated patients (20 chronic hepatitis, 40 patients with liver cirrhosis) and 20 healthy controls matched for age, sex, and educational level. Cirrhotic patients were graded according to the Child-Pugh classification. Patients underwent a thorough clinical and neurological evaluation, liver function tests, hepatitis markers, abdominal ultrasonography, psychometric tests and auditory p300 evoked potentials. Unified Parkinson's Disease Rating Scale (UPDRS) and chronic liver disease questionnaire (CLDQ) were used. Results: Patients had significant cognitive impairment that became greater with increased severity of liver disease. The extrapyramidal manifestations were absent in chronic hepatitis while present in cirrhotics and their frequencies were increased with cirrhotic severity (P~0.001). P300 latency was delayed in patients with and without extrapyramidal signs. Quality of life measured by CLDQ was significantly lower in all patients than controls (P~ 0.001), and became worse with increased severity of liver disease except worry increased in chronic hepatitis. Conclusion: Patients with HCV infection had cognitive impairment, which increased with chronic liver disease (CLD) severity. Extrapyramidal signs were absent in chronic hepatitis patients, while, present in cirrhotics and increased with increasing severity of cirrhosis. Cognitive impairment was not attributed to the extrapyramidal affection. The CLD and cognitive impairment had negative impact on the quality of life. [ABSTRACT FROM AUTHOR]

Four agro-wastes were evaluated as substrates for bioethanol production. Seven of the pioneer isolates in the production of cellulase, xylanase and laccase enzymes were selected for soluble sugar and bioethanol production. The highest level of soluble sugar was produced on sugar beet pulp followed by corn cobs. The experimental design included as well using soybean okara and sesame husk as nitrogen sources add to the production medium. Extraordinary soluble sugar yields were obtained on sugar beet pulp or corn cobs in combination with soybean okara or sesame husk at the concentration of (0.3%) of either. Different concentrations of corn cobs or sugar beet (5, 10, 15, g/100 m medium) were mixed with optimal concentrations of nitrogen sources. Results showed that 10 g of sugar beet or corn cobs achieved the highest soluble sugar yield. The efficiency of four strains of S. cerevisiae for bioethanol production was investigated, S.cerevisiae (AUMC 14720) recorded the highest level of bioethanol on fermentable fungal broth after four days at 30°C. [ABSTRACT FROM AUTHOR]

Remote sensing techniques used in many studies for classfying and measuring of wildfires. Satellite Landsat8(OLI) imagery is used in the presented work. The satellite is considered as a near-polar orbit, with a high multispectral resolution for covering Wollemi National Park in Australia. The work aims to study and measure wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia for a year (October, 2019), as well as analyzing the harm resulting from such wildfires and their effects on earth and environment through recognizing satellite images for studied region prior to and throughout wildfires. A discussion of methods for computing the affecred area is covered regarding each one of the classes and lessening or limiting the quickly-spreading wildfires damage. This paper propose a 2-phases techniques: training and classifying. In the training phase, the number of clustering is computed by using C# Programming Language and feature extracted and clustered as a group and stored in the dataset. The classification used the moments with (K-Means) classification approach in RS (Remote Sensing) for classified image. The results of classification showed 5 distinctive classes (trees, rivers, bare earth, buildings with no trees, and buildings with trees) in which it might be indicates that the region is secured via each one of the classes prior to and throughout wildfires as well as the changed pixels with regard to all the classes. Also, the classification experimental methods results indicate an excellent performance recision with a good classifying and result analysis about the harms caused by fires in the study area. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is one of the most common neurological disorders, which required long term therapy or lifelong treatment in some cases. Chronic use of antiepileptic drugs (AEDs) therapy might modify some vascular risk factors, Since atherosclerotic vascular alterations may start early in life, this study focuses on dyslipidemia which is a major atherogenic risk factor among epileptic children. The aim of this study: Was to evaluate the effect of epilepsy and antiepileptic drugs (AEDs) on lipid profile of children with idiopathic epilepsy. Method: This study was carried out on 80 children, their age rage (7-16) divided into 20 epileptic children treated with new AED therapy,20 epileptic children treated with old AED therapy (VAP & CBZ namely),20 epileptic children newly diagnosed epileptic (according to ILAE2014 diagnostic criteria of epilepsy) they didn’t receive medication till the time of study and 20 healthy children their age and sex matched served as a control . Measurement received for all participant include detailed history, clinical examination,neurological examination and serumfasting lipid profile, while the analysis of seizure (onset, type, frequency and classification of epilepsy according to (ILAE2017)), inter-ictal EEG, history of AEDs administration; daily dose and compliance to medication)were for epileptic patient only. Result: Serum TC, LDL were significantly higher in epileptic children treated with (old and new generation) AEDs than other groups, TG and VLDL were higher in epileptic children treated with old AEDs than other groups, while newly diagnosed epileptic children have higher LDL than control group, on the other hand there is a significant higher HDL in epileptic children treated with new antiepileptic drugs than newly diagnosed epileptic children and epileptic children treated with old antiepileptic drugs. Conclusion: Epilepsy and antiepileptic drugs especially old generation antiepileptic drugs are considered risk factors of dyslipidemias & atherosclerosis. We recommend routine monitoring of the serum lipid profile in epileptic children especially they treated with old generation AEDs (VAP and CBZ namely). [ABSTRACT FROM AUTHOR]

A total of 89 endophytic bacterial cultures were isolated by two techniques from seven medicinal plants; Coriandrum sativum, Anethum graveolens, Pelargonium graveolens, Ocimum basilicum, Rosmarinus officinalis, Saliva officinalis and Origanum majorana. To address the biological activity of these isolates as endophytic bacteria, preliminary screening showed 44, 36, 28, 22, 82 isolates out of total one gave positive results for cellulase, pectinase, amylase activities, indole acetic acid (IAA) and gibberellic acid (GA3) production, respectively. The secondary screening twenty endophytic bacterial isolates were selected and examined for their antagonistic effect against four pathogenic fungi, antioxidants activity, total phenols, indole acetic acid, ammonia (NH3), siderophores production and nitrogenase activity. According to the last screening; six isolates were chosen because of their multi-plant growth promoting (PGP) traits. They recorded a wide range of total flavonoids concentration in endophytic bacterial supernatant. The values of total flavonoids varied from 1.43 to 31.14 ppm. While the alkaloids production was detected by all tested isolates except isolate S14. Isolate B3 gave the maximum alkaloids concentration in its supernatant being 0.34 ppm. Four isolates are able to solubilize phosphate, low variations between tested isolates in phosphate solubilization ranged from 5.65 ppm by isolate RO14 to 3.85 ppm by isolate C8. All isolates showed negative result for hydrogen cyanide (HCN) production. Furthermore, the colonization of the six most efficient isolates inside the tissue of two medicinal plant Ocimum basilicum and Coriandrum sativum were performed by two techniques; 2,3,5-triphenyl tetrazolium chloride (TTC) stain and Transmission electron microscope (TEM). Based on some morphological and biochemical characteristics of the most efficient isolates (RO10, RO14, S14, D6, C8 and B3); the six isolates were similar to three genera (Enterobacter sp., Aeromonas sp. and Bacillus sp.). Hypersensitivity test proved that all the six endophytic bacterial isolates are nonpathogenic bacteria. Where, these isolates gave negative symptoms of hypersensitivity reaction (HR) test on the pepper plants (Capsicum annuum) used as indicator plant. [ABSTRACT FROM AUTHOR]