Experimental Analysis for the Performance Assessment and Characteristics of Enhanced Magnesium Composites Reinforced with Nano-Sized Silicon Carbide Developed Using Powder Metallurgy.

Magnesium, which is lightweight and abundant by nature, was widely used in the 19th century to make parts for automobiles and airplanes. Due to their superior strength-to-weight ratios, magnesium alloys were favored for engineering applications over unadulterated magnesium. These alloys result from the combination of magnesium with various metals, including aluminum (Al), titanium (Ti), zinc (Zn), manganese (Mn), calcium (Ca), lithium (Li), and zirconium (Zr). In this study, an alloy of magnesium was created using the powder metallurgy (PM) technique, and its optimal performance was determined through the Taguchi-Gray (TG) analysis method. To enhance the alloy's mechanical properties, diverse weight fractions of silicon carbide (SiC) were introduced. The study primarily focused on the Mg-Zn-Cu-Mn alloy, achieving the optimal composition of Mg-3Zn-1Cu-0.7Mn (ZC-31). Subsequently, composites of ZC-31/SiC were produced via PM and the hot extrusion (HE) process, followed by the assessment of the mechanical properties under various strain rates. The use of silicon carbide (SiC) resulted in enhanced composite densities as a consequence of the increased density exhibited by SiC particles. In addition, the high-energy postsintering approach resulted in a decrease in porosity levels. By integrating silicon carbide (SiC) to boost the microhardness, as well as the ultimate compressive and tensile strength of the composite material, we can observe significant improvements in these mechanical properties. The experimental findings also demonstrated that an augmentation in the weight fraction of SiC and the strain rate led to enhanced ductility and a shift toward a more transcrystalline fracture behavior inside the composite material.

Convolutional-neural-network-based versus vision-transformer-based SNR estimation for visible light communication networks.

Visible light communication (VLC) has emerged as a promising technology for future sixth-generation (6 G) communications. Estimating and predicting the impairments, such as turbulence and free space signal scattering, can help to construct flexible and adaptive VLC networks. However, the monitoring of impairments of VLC is still in its infancy. In this Letter, we experimentally demonstrate a deep-neural-network-based signal-to-noise ratio (SNR) estimation scheme for VLC networks. A vision transformer (ViT) is first utilized and compared with the conventional scheme based on a convolutional neural network (CNN). Experimental results show that the ViT-based scheme exhibits robust performance in SNR estimation for VLC networks compared to the CNN-based scheme. Specifically, the ViT-based scheme can achieve accuracies of 76%, 63.33%, 45.33%, and 37.67% for 2-quadrature amplitude modulation (2QAM), 4QAM, 8QAM, and 16QAM, respectively, against 65%, 57.67%, 41.67%, and 34.33% for the CNN-based scheme. Additionally, data augmentation has been employed for achieving enhanced SNR estimation accuracies of 95%, 79.67%, 58.33%, and 50.33% for 2QAM, 4QAM, 8QAM, and 16QAM, respectively. The effect of the SNR step size of a contour stellar image dataset on the SNR estimation accuracy is also studied.

Application and comparison of active and transfer learning approaches for modulation format classification in visible light communication systems.

Automatic modulation classification (AMC) is a crucial part of adaptive modulation schemes for visible light communication (VLC) systems. However, most of the deep learning (DL) based AMC methods for VLC systems require a large amount of labeled training data which is quite difficult to obtain in practical systems. In this work, we introduce active learning (AL) and transfer learning (TL) approaches for AMC in VLC systems and experimentally analyze their performances. Experimental results show that the proposed novel AlexNet-AL and AlexNet-TL methods can significantly improve the classification accuracy with small sizes of training data. To be specific, using 60 labeled samples, AlexNet-AL and AlexNet-TL increase the classification accuracy by 6.82% and 14.6% compared to the result without AL and TL, respectively. Moreover, the use of data augmentation (DA) operation along with our proposed methods helps achieve further better performances.

A comprehensive phytochemical, biological, and toxicological studies of roots and aerial parts of Crotalaria burhia Buch.-Ham: An important medicinal plant.

This study was designed to seek the phytochemical analysis, antioxidant, enzyme inhibition, and toxicity potentials of methanol and dichloromethane (DCM) extracts of aerial and root parts of Crotalaria burhia. Total bioactive content, high-performance liquid chromatography-photodiode array detector (HPLC-PDA) polyphenolic quantification, and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) analysis were utilized to evaluate the phytochemical composition. Antioxidant [including 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH)], 2,2'-azino-bis[3-ethylbenzothiazoline-6-sulfonic acid (ABTS), ferric reducing antioxidant power assay (FRAP), cupric reducing antioxidant capacity CUPRAC, phosphomolybdenum, and metal chelation assays] and enzyme inhibition [against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-glucosidase, α-amylase, and tyrosinase] assays were carried out for biological evaluation. The cytotoxicity was tested against MCF-7 and MDA-MB-231 breast cell lines. The root-methanol extract contained the highest levels of phenolics (37.69 mg gallic acid equivalent/g extract) and flavonoids (83.0 mg quercetin equivalent/g extract) contents, and was also the most active for DPPH (50.04 mg Trolox equivalent/g extract) and CUPRAC (139.96 mg Trolox equivalent /g extract) antioxidant assays. Likewise, the aerial-methanol extract exhibited maximum activity for ABTS (94.05 mg Trolox equivalent/g extract) and FRAP (64.23 mg Trolox equivalent/g extract) assays. The aerial-DCM extract was noted to be a convincing cholinesterase (AChE; 4.01 and BChE; 4.28 mg galantamine equivalent/g extract), and α-glucosidase inhibitor (1.92 mmol acarbose equivalent/g extract). All of the extracts exhibited weak to modest toxicity against the tested cell lines. A considerable quantities of gallic acid, catechin, 4-OH benzoic acid, syringic acid, vanillic acid, 3-OH-4-MeO benzaldehyde, epicatechin, p-coumaric acid, rutin, naringenin, and carvacrol were quantified via HPLC-PDA analysis. UHPLC-MS analysis of methanolic extracts from roots and aerial parts revealed the tentative identification of important phytoconstituents such as polyphenols, saponins, flavonoids, and glycoside derivatives. To conclude, this plant could be considered a promising source of origin for bioactive compounds with several therapeutic uses.

Therapeutic efficacy of Honey and Nigella sativa against COVID-19: A multi-center randomized controlled clinical trial (HNS-COVID-PK)

BACKGROUNDNo definitive treatment exists for Coronavirus Disease 2019 (COVID-19). Honey and Nigella sativa (HNS) have established antiviral, antibacterial, anti-inflammatory and immunomodulatory properties. Hence, we investigated efficacy of HNS against COVID-19. wide METHODSWe conducted a multicenter, placebo-controlled, randomized clinical trial at 4 centers in Pakistan. RT-PCR confirmed COVID-19 adults showing moderate or severe disease were enrolled in the study. Patients presenting with multi-organ failure, ventilator support, and chronic diseases (except diabetes mellitus and hypertension) were excluded. Patients were randomly assigned in 1:1 ratio to receive either honey (1 gm/Kg/day) and Nigella sativa seeds (80 mg/Kg/day) or placebo up-to 13 days along with standard care. The outcomes included symptom alleviation, viral clearance, and a 30-day mortality in intention-to-treat population. This trial was registered with ClinicalTrials.gov, NCT04347382. RESULTSThree hundred and thirteen patients - 210 moderate and 103 severe - underwent randomization from April 30 to July 29, 2020. Among these, 107 were assigned to HNS whereas 103 to placebo for moderate cases. For severe cases, 50 were given HNS and 53 were given placebos. HNS resulted in [~]50% reduction in time taken to alleviate symptoms as compared to placebo (Moderate (4 versus 7 days), Hazard Ratio [HR]: 6.11; 95% Confidence Interval [CI]: 4.23-8.84, P<0.0001 and severe (6 versus 13 days) HR: 4.04; 95% CI, 2.46-6.64, P<0.0001). HNS also cleared the virus 4 days earlier than placebo group in moderate (6 versus 10 days, HR: 5.53; 95% CI: 3.76-8.14, P<0.0001) and severe cases (8.5 versus 12 days, HR: 4.32; 95% CI: 2.62-7.13, P<0.0001). HNS further led to a better clinical score on day 6 with normal activity resumption in 63.6% versus 10.9% among moderate cases (OR: 0.07; 95% CI: 0.03-0.13, P<0.0001) and hospital discharge in 50% versus 2.8% in severe cases (OR: 0.03; 95% CI: 0.01-0.09, P<0.0001). In severe cases, mortality rate was four-fold lower in HNS group than placebo (4% versus 18.87%, OR: 0.18; 95% CI: 0.02-0.92, P=0.029). No HNS-related adverse effects were observed. CONCLUSIONHNS significantly improved symptoms, viral clearance and mortality in COVID-19 patients. Thus, HNS represents an affordable over the counter therapy and can either be used alone or in combination with other treatments to achieve potentiating effects against COVID-19. FUNDINGFunded by Smile Welfare Organization, Shaikh Zayed Medical Complex, and Services Institute of Medical Sciences.

An in vitro assessment of anti-SARS-CoV-2 activity of oral preparations of iodine complexes (RENESSANS)

Since the emergence of CoVID-19 pandemic in China in late 2019, scientists are striving hard to explore non-toxic, viable anti-SARS-CoV-2 compounds or medicines. We determined In Vitro anti-SARS-CoV-2 activity of oral formulations (syrup and capsule) of an Iodine-complex (Renessans). A monolayer of vero cells were exposed to SARS-CoV-2 in the presence and absence of different concentrations (equivalent to 50, 05 and 0.5 g/ml of I2) of Renessans. Anti-SARS-CoV-2 activity of each of the formulation was assessed in the form of cell survival, SARS-CoV-2-specific cytopathic effect (CPE) and genome quantization. With varying concentrations of syrup and capsule, a varying rate of inhibition of CPE, cells survival and virus replication was observed. Compared to 0.5 g/ml concentration of Renessans syrup, 5 and 50 g/ml showed comparable results where there was a 100% cell survival, no CPEs and a negligible viral replication ({Delta}CT= 0.11 and 0.13, respectively). This study indicates that Renessans, containing iodine, may have potential activity against SARS-CoV-2 which needs to be further investigated in human clinical trials.

Low-complexity carrier phase recovery based on principal component analysis for square-QAM modulation formats.

We propose, numerically analyze and experimentally demonstrate a low-complexity, modulation-order independent, non-data-aided (NDA), feed-forward carrier phase recovery (CPR) algorithm. The proposed algorithm enables synchronous decoding of arbitrary square-quadrature amplitude modulation (QAM) constellations and it is suitable for a realistic hardware implementation based on block-wise parallel processing. The proposed method is based on principal component analysis (PCA) and it outperforms the well-known and widely used blind phase search (BPS) algorithm at low signal-to-noise ratio (SNR) values, showing much lower cycle slip rate (CSR) both numerically and experimentally. For operation at higher SNR values, a hybrid two-stage implementation combining the proposed method and BPS is also proposed and their performance are investigated benchmarking them against the two-stage BPS (2S-BPS). The complexity of the proposed simple and hybrid methods are evaluated against 2S-BPS and computational complexity savings of 92% and 40% are expected for the simple and hybrid methods, respectively.

Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition.

We propose and experimentally demonstrate the use of principal component analysis (PCA) based pattern recognition to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the fiber under test (FUT) obtained by Brillouin optical time domain analysis (BOTDA) system. The proposed scheme employs a reference database consisting of relevant ideal BGSs with known temperature attributes. PCA is then applied to the BGSs in the reference database as well as to the measured BGSs so as to reduce their size by extracting their most significant features. Now, for each feature vector of the measured BGS, we determine its best match in the reference database comprised of numerous reduced-size feature vectors of the ideal BGSs. The known temperature attribute corresponding to the best-matched BGS in the reference database is then taken as the extracted temperature of the measured BGS. We analyzed the performance of PCA-based pattern recognition algorithm in detail and compared it with that of curve fitting method. The experimental results validate that the proposed technique can provide better accuracy, faster processing speed and larger noise tolerance for the measured BGSs. Therefore, the proposed PCA-based pattern recognition algorithm can be considered as an attractive method for extracting temperature distributions along the fiber in BOTDA sensors.

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling.

We experimentally demonstrate simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in heterogeneous fiber-optic networks by using principal component analysis (PCA) and statistical distance measurement based pattern recognition on scatter plots obtained through asynchronous single channel sampling (ASCS). The proposed technique enables OSNR monitoring for several commonly-used modulation formats with mean OSNR estimation error of 1 dB and without requiring any information about the signal type during the online monitoring process. In addition, it successfully demonstrates the identification of unknown modulation formats of the received signals with an overall accuracy of 98.46%. The effects of chromatic dispersion (CD) on the performance of proposed technique are also analyzed. Due to the use of a single low-speed asynchronous sampling device in the proposed technique, the implementation complexity and cost of the monitoring devices can be significantly reduced.

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks.

We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Results of numerical simulations conducted for six different widely-used modulation formats at various data rates demonstrate that the proposed technique can effectively classify all these modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments. The proposed technique employs extremely simple hardware and digital signal processing (DSP) to enable MFI and can also be applied for the identification of other modulation formats at different data rates without necessitating hardware changes.