Burden of Hepatitis B and C Infection According to Socioeconomic Status

Objective: To determine the burden of hepatitis B and C infection according to socioeconomic status.Methodology: This cross-sectional study was conducted at medicine department of Peoples University of Medical and Health science study duration was 1 year from March 2016 to February 2017. All the hepatitis B and Hepatitis C infected patients with all age groups either gender were included in the study. All the selected patients were interviewed regarding history of previous surgeries, needle pricking history, tattooing, barber’s shaving and birth history to know the suspected transmitted risk factors. All the patients were also interviewed regarding socioeconomic status. All the data was recorded in predesigned proforma.Results: Total 200 cases were studied, most of the cases 110(55.0%) were found with age group of 31-45 years. Male were most common in this study 120(60.0%). Almost all of the male patients had a history of barber shaving, on other hand extra uses of needles/syringes were most common in both male and female as 125(63.5%) out of total study population. Hepatitis C infection was most common at 71.0%, hepatitis infection was 23.5%, while only 5.5% patients were with co-infection of HCV and HBV. The poor population is mostly infected by hepatitis B and C 48.0%. Patients having poor socioeconomic status were found significantly associated with hepatitis C infection p-value 0.001. No significant difference was in hepatitis B infection according to socioeconomic status p-value 0.282.Conclusion: It is concluded that poor socioeconomic status was significantly associated with hepatitis C infection. Socioeconomic status not a direct risk factor of hepatitis B and C, but it is significantly responsible to develop the other risk factors.&nbsp

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate

Optimisation of Ultra-High-Performance Concrete with Special Quarry Dust

The cement composition of concrete directly affects the CO2 emissions to the environment. UHPC (Ultra High-Performance Concrete) is a new type of concrete rapidly gaining popularity in the building industry due to its superior strength and endurance. In contrast to regular concrete, UHPC requires more than twice as much cement, making it more expensive and leaving a more significant carbon imprint. In this study, waste cement was substituted with 4%, 8%, and 12% special quarry dust from a manufacturer in Kuantan, Malaysia. Maximum compressive strength and quarry dust percentage are determined through experimentation and assessed in Design Expert Software. This investigation tested modified UHPC for strength, durability, and scanning electron microscope (SEM) appearance. Experiments show that substituting 21% quarry dust for cement yields the best outcomes. Since the particle size of quarry dust is finer than that of other matrices, it helps to reduce voids and boosts the UHPC's endurances. The quarry dust adds filler and a minor increase in viscosity to the UHPC, which is a better replacement for anhydrate cement in filler applications

Optimisation of Ultra-High-Performance Concrete with Special Quarry Dust

The cement composition of concrete directly affects the CO2 emissions to the environment. UHPC (Ultra High-Performance Concrete) is a new type of concrete rapidly gaining popularity in the building industry due to its superior strength and endurance. In contrast to regular concrete, UHPC requires more than twice as much cement, making it more expensive and leaving a more significant carbon imprint. In this study, waste cement was substituted with 4%, 8%, and 12% special quarry dust from a manufacturer in Kuantan, Malaysia. Maximum compressive strength and quarry dust percentage are determined through experimentation and assessed in Design Expert Software. This investigation tested modified UHPC for strength, durability, and scanning electron microscope (SEM) appearance. Experiments show that substituting 21% quarry dust for cement yields the best outcomes. Since the particle size of quarry dust is finer than that of other matrices, it helps to reduce voids and boosts the UHPC's endurances. The quarry dust adds filler and a minor increase in viscosity to the UHPC, which is a better replacement for anhydrate cement in filler applications

The association between obesity, mortality and filling pressures in pulmonary hypertension patients; the “obesity paradox”

SummaryBackgroundThe term “obesity paradox”, refers to lower mortality rates in obese patients, and is evident in various chronic cardiovascular disorders. There is however, only scarce data regarding the clinical implication of obesity and pulmonary hypertension (PH). Therefore, in the current study, we evaluated the possible prognostic implications of obesity in PH patients.MethodsWe assessed 105 consecutive PH patients for clinical and hemodynamic parameters, focusing on the possible association between Body Mass Index (BMI) and mortality. Follow-up period was 19 ± 13 months.ResultsSixty-one patients (58%) had pre-capillary PH and 39 patients (37%) out-of-proportion post-capillary PH. During follow-up period, 30 patients (29%) died. Death was associated with reduced functional-class, inverse-relation with BMI, higher pulmonary artery and right atrial pressures, pulmonary vascular resistance and signs of right ventricular failure. In multivariate analysis, obesity (BMI ≥ 30 kg/m²), was the variable most significantly correlated with improved survival [H.R 0.2, 95% C.I 0.1–0.6; p = 0.004], even after adjustment for baseline characteristics. Obese and very-obese (BMI ≥ 35 kg/m²) patients had significantly less mortality rates during follow-up (12% and 8%, respectively) than non-obese patients (41%), p = 0.01. The tendency of survival benefit for the obese vs. non-obese patients was maintained both in the pre-capillary (10% vs. 46% mortality, p = 0.008) and disproportional post-capillary PH patients (11% vs. 40% mortality, p = 0.04).ConclusionsObesity was significantly associated with lower mortality in both pre-capillary and disproportional post-capillary PH patients. It seems that in PH, similarly to other chronic clinical cardiovascular disease states, there may be a protective effect of obesity, compatible with the “obesity paradox”

Cross Talk between Nitric Oxide and Phytohormones Regulate Plant Development during Abiotic Stresses

Plants, being sessile, are concurrently exposed to various biotic and abiotic stresses. The perception of stress signals in plants involves a wide spectrum of signal transduction pathways that interact to induce tolerance against adverse environmental conditions. This functional overlapping among various stress signaling cascades also leads to the expression of genes that regulate biosynthesis or action of other hormones. Phytohormonal signals, activated by both developmental and environmental responses, play a crucial role to develop stress tolerance in plants. Nitric oxide (NO) is one of the major players in plant signaling networks. Emerging evidence supports that NO interplays with signaling pathways of auxins, gibberellins, abscisic acid, ethylene, jasmonic acid, brassinosteroids, and other plant hormones to control metabolism, growth, and development in plants. This chapter focuses on the current state of knowledge of cross talk between signaling pathways of NO and phytohormones in plants exposed to various abiotic stresses

Perovskite LaNiO3/Ag3PO4 heterojunction photocatalyst for the degradation of dyes

Pristine lanthanum nickelate (LaNiO3), silver phosphate (Ag3PO4) and perovskite lanthanum nickelate silver phosphate composites (LaNiO3/Ag3PO4) were prepared using the facile hydrothermal method. Three composites were synthesized by varying the percentage of LaNiO3 in Ag3PO4. The physical properties of as-prepared samples were studied by powder X-ray diffraction (pXRD), Fourier-transform infrared (FT-IR), Scanning electron microscopy (SEM) and Energy-dispersive X-ray (EDX). Among all synthesized photocatalysts, 5%LaNiO3/Ag3PO4 composite has been proved to be an excellent visible light photocatalyst for the degradation of dyes i.e., rhodamine B (RhB) and methyl orange (MO). The photocatalytic activity and stability of Ag3PO4 were also enhanced by introducing LaNiO3 in Ag3PO4 heterojunction formation. Complete photodegradation of 50 mg/L of RhB and MO solutions using 25 mg of 5%LaNiO3/Ag3PO4 photocatalyst was observed in just 20 min. Photodegradation of RhB and MO using 5%LaNiO3/Ag3PO4 catalyst follows first-order kinetics with rate constants of 0.213 and 0.1804 min−1, respectively. Perovskite LaNiO3/Ag3PO4 photocatalyst showed the highest stability up to five cycles. The photodegradation mechanism suggests that the holes (h+) and superoxide anion radicals O2 •− plays a main role in the dye degradation of RhB and MO

Water quality assessment of River Kabul at Peshawar, Pakistan: industrial and urban wastewater impacts

Untreated wastewater discharges may have significant short term and long term effects on the quality of a river system. Present study was undertaken to assess the present status of the water quality of River Kabul near Peshawar in Pakistan. Seven sites were sampled upstream and downstream in River Kabul in 2009. Samples were also taken from waste water channel (Budni Drain) that carries wastewater of Peshawar Industrial Estate as well as the domestic sewers to assess the pollution contribution of these sources to River Kabul. Physico-chemical and microbiological parameters of the samples were analyzed during the study, as well as possible sources of contamination were investigated. The study showed that the pollution level in river is rising from upstream (at city entrance) to downstream (at city exit) due to discharge of domestic waste water effluents, agricultural activities, and solid waste dumping directly into the river

Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks

Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model function

Flow Injection Photosensitized Chemiluminescence of Luminol with Cu(II)-Rose Bengal: Mechanistic Approach and Vitamin A and C Determination

Rose Bengal photosensitized flow injection chemiluminescence method is reported using luminol-Cu(II) for the determination of vitamins A and C in pharmaceutical formulations. The reaction is based on the enhancement effect of analyte in the production of anion radicals of Rose Bengal (RB•−) which rapidly interact with dissolved oxygen and generate superoxide anions radicals (O2•−) and hydrogen peroxide (H2O2). Highly reactive hydroxyl radicals (•OH) were produced via dismutation of H2O2 by catalyst (Cu2+). The generated superoxide anions radicals and hydroxyl radicals thus oxidize luminol in alkaline medium to generate strong chemiluminescence. The limit of detection (3s of the blank, n=6) of vitamins A and C and RB was found to be 0.008, 0.005, and 0.05 μg mL−1, respectively. The sample throughput of 70 h−1 for vitamins A and C and 30 h−1 for RB was found. Calibration curve was linear in the range of 0.05–15, 0.01–20, and 0.1–50 μg mL−1 for vitamins A and C and RB, respectively, with relative standard deviations (RSDs; n=3) in the range 1.6–3.6%. The method was successfully applied to pharmaceutical formulations and the results obtained were in good agreement with the labeled values