Screening of bioactive lipids and radical scavenging potential of some solanaceae plants

Gas-liquid chromatography (GLC) and normal-phase high performance liquid chromatography (HPLC) were used to analyse fatty acids and fat-soluble bioactives of the seeds of four Datura species (D. stramonium, D. metel, D. tatula, D. innoxia) and two Hyoscyamus species (H. muticus, H. niger). The amounts of n-hexane extract were found to be between 5.50% and 12.6%. The major fatty acid was linoleic acid followed by oleic, palmitic and stearic acids. The crude n-hexane extract was characterised by a relatively high amount of phytosterols, wherein the sterol markers were stigmasterol, β-sitosterol, lanosterol, Δ5-avenasterol and sitostanol. In all plant extracts γ-tocopherol was the major component present accounting for more than 80% of total tocopherols detected. When different extracts were compared for their radical scavenging activity (RSA) toward the stable 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, D. Innoxia exhibited a stronger RSA.     

Co-variations of bacterial composition and catabolic genes related to PAH degradation in a produced water treatment system consisting of successive anoxic and aerobic units

This paper reports on the investigation of concentration levels of PAHs, community structure, as well as the abundance of PAH-related catabolic genes including upper-pathway dioxygenase genes (nahAc and phnAc) and down-pathway catechol dioxygenase genes (C12O and C23O) in a successive anoxic and aerobic treatment of produced water from the Jidong Oilfield, China. 93% of total PAHs were removed, almost equally contributed by the anoxic and aerobic units. However, PAHs of more than 3 benzene rings remained almost unchanged. The signals for phnAc and C12O were undetectable in this biological system, whereas the existence of nahAc and C23O was confirmed in the system and the copies of the two genes in the aerobic tank were 2 or 3 orders higher than those in the influent water sample. The different behavior of C23O demonstrated that mineralization of PAHs might mainly occur in the aerobic unit. The existence of nahAc and C23O genes in the influent and the high similarity of genotype between the influent and the two sludge samples suggested that bacteria existing in the influent contributed to PAH removal and bacteria harboring PAH catabolic genes were enriched in the sludge.     

Short and long-term bond performance of prestressed FRP sheet anchorages

This paper discusses experimental results of the short and long-term behavior of the anchorage zones of externally bonded prestressed fiber reinforced polymer (FRP) sheets. An experimental program was conducted to investigate seven beams bonded with prestressed FRP sheets including anchored or unanchored FRP sheet ends. Using different layers of FRP sheet, the prestress level of FRP sheets varied from 20% to 40% of the guaranteed tensile strength. The experimental observation was conducted in an outdoor environment and lasted about twenty months when temperatures were in the 7–30 ^°C range. This study provides significant data on the development of the effective bonding lengths, as well as the initiation and propagation of debonding along the FRP-concrete interface due to creep effect of the adhesive layer. Although adhesive creep leads to debonding propagation at higher shear stress, this creep is favored at low shear stress because it increases the effective bond length which improves the bond capacity of FRP-concrete interface and prevents premature failure of the anchorages. The effective bonding length was found to increase to 50% due to creep of the adhesive layer. The anchored end of the FRP sheets using steel plates and anchor bolts is an effective solution to enhance the bond capacity of FRP-concrete interface for short and long-term loading.     

Experimental and numerical iInvestigations of effects of silica colloids on transport of strontium in saturated sand columns

Transport experiments with strontium were conducted using saturated sand columns in the presence and absence of silica colloids, and numerical modeling was performed with modeling results compared to experimental data. The experiments were aimed at testing the hypothesis that under certain chemical conditions colloids act as movement-retarding agents and yield a larger effective retardation factor for the migrating contaminant. Four individual experiments were conducted to identify conditions where the mobility of silica colloids is increased or decreased, and a similar set was conducted for strontium transport in the absence of colloids. Mobility of colloids was found to increase with decreasing ionic strength and increasing pH, with the ionic strength having the more significant impact. The reverse effect was obtained for strontium. Based on these results, two additional experiments were conducted where both colloids and strontium were injected at the column inlet. Results showed that under certain conditions of ionic strength and pH (I = 3.0 x 10(-2) M and pH = 4-5.4) colloids retarded the movement of strontium. The retardation effect was obtained in two experiments under slightly modified conditions, which confirms the role of colloids as retarding agents. Afinite difference numerical model was used to (a) simulate mobile breakthrough curves and compare to experimental data and (b) estimate the model parameters describing cotransport of strontium and colloids. The model accurately predicted arrival time and the overall shape of the breakthrough curves.     

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.     

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.     

A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications

Ultra-wideband (UWB) is highly preferred for short distance communication. As a result of this significance, this project targets the design of a compact UWB antennas. This paper describes a printed UWB rhombus-shaped antenna with a partial ground plane. To achieve wideband response, two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively. To excite the antenna, a simple microstrip feed line is employed. The suggested antenna is built on a 1.6 mm thick FR4 substrate. The proposed design is very compact with overall electrical size of 0.18λ × 0.25λ (14 × 18 mm²). The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth. The proposed design simulated and measured bandwidths are 83.33% and 80%, respectively. Radiation pattern in terms of E-field and H-field are discussed at 4, 5.5 and 10 GHz respectively. The proposed design has 65% radiation efficiency and 1.5 dBi peak gain. The proposed design is simulated in CST (Computer Simulation Technology) simulator and the simulated design is fabricated for the measured results. The simulated and measured findings are in close resemblance. The obtained results confirm the application of the proposed design for the ultra-wide band applications.