Softening of tap water via calcium removal using sphagnum peat moss sorbent by batch and flow-through approaches

ABSTRACT

The Gaza Strip experiences a water crisis of persistent qualitative and quantitative deterioration in regard to groundwater. There is a low recharging rate of the aquifer because of the low seasonal rainfall, the fast expansion of the urban area, and the high seawater intrusion along the narrow coastal Gaza Strip. Thus, the extremely hard water reduces the performance of the reverse osmosis desalination household units and causes membrane fouling. Sphagnum peat for removal of calcium from water was investigated applying batch and flow-through approaches. Fast calcium removal was demonstrated.     

Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

The high incidence of breast cancer in women has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcification. Mammograms—breast X-ray—are considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this paper, two techniques are proposed based on wavelet analysis and fuzzy-neural approaches. These techniques are mammography classifier based on globally processed image and mammography classifier based on locally processed image (region of interest). The system is classified normal from abnormal, mass for microcalcification and abnormal severity (benign or malignant). The evaluation of the system is carried out on Mammography Image Analysis Society (MIAS) dataset. The accuracy achieved is satisfied.     

Green Nanoarchitectonics of Cu/Fe3O4 Nanoparticles Using Helleborus niger Extract Towards an Efficient Nanocatalyst,Antioxidant and Anti-lung Cancer Agent

Biogenic nanoarchitectured magnetic materials have drawn serious attention throughout the last decade. We have attempted the Helleborus niger flower extract functionalized and templated biogenic synthesis of Cu nanoparticles supported Fe₃O₄ as a likewise novel material. The plant phytomolecules were deployed as a non-toxic sustainable reductant and an outstanding capping agent to stabilize the synthesized NPs. The synthesized Cu/H.niger@Fe₃O₄ nanocomposite was undergone comprehensive characterizations through Fourier transformed infrared spectroscopy (FT-IR), electron microscopy (SEM and TEM), energy dispersive X-ray spectroscopy (EDX), elemental mapping, vibrating sample magnetometer (VSM), X-ray diffraction (XRD) and inductively coupled plasma (ICP) techniques. The material was catalytically explored in the synthesis of diverse pyrano[3,2-c]chromene derivatives by coupling 4-hydroxycoumarin, malononitrile and a range of aldehydes in hot water when it afforded excellent yields. Based on its core magnetism, the catalyst was easily recovered using a magnet and reused for 8 successive times without considerable loss in catalytic activity. After the chemical application, the synthesized Cu/H.niger@Fe₃O₄ nanocomposite was engaged in biological assays like study of anti-oxidant properties by DPPH mediated free radical scavenging test using BHT as a reference molecule. Thereafter, on having a significant IC₅₀ value in radical scavenging assay, we extended the bio-application of the desired nanocomposite in anticancer study of A549 and H358 human lung cell lines in-vitro through MTT assay. The cell viability of malignant lung cell line reduced dose-dependently in the presence of desired nanocomposite. So, these results suggest that synthesized Cu/H.niger@Fe₃O₄ as a chemotherapeutic nanomaterial have a suitable anticancer activity against lung cell lines.

     

The effect of heat transfer characteristics of macromolecule fouling on heat exchanger surface: A dynamic simulation study

At the city gate gas pressure reduction stations (CGSs), to prevent natural gas from forming a hydrate in the throttle valve, the natural gas is heated by the heater before reaching the pressure relief valve. Heat exchangers are an essential component of industrial processes that contribute significantly to total system energy. Since the element impacting heat exchanger performance is the fouling process, all fouling processes and models were dynamically simulated in this study. Through coding in the C++ language and simultaneous use of fluent functions, or, in other words, user-defined function (UDF), fouling-related models were defined for this software. The dynamic simulation was performed, and parameters such as fouling strength and layer thickness were calculated. The effects of changing operating conditions, such as gas inlet velocity, surface temperature, and fouling species concentration on fouling growth, were also evaluated. As the concentration of fouling species increased, the fouling rate also increased. The amount of supersaturation and fouling rate increased as the surface temperature increased. Due to the operational limitations of the system, to reduce the fouling rate, the gas inlet velocity should be as high as possible, and the fluid inlet temperature, surface temperature, and concentration of fouling species should be as low as possible. In this study, the required time to reach the efficiency of 70% of the heat exchanger was calculated using the modelling of this chamber, which was equivalent to 190 days. Additionally, the critical thickness of the fouling layer at this time was 3.5 cm.     

Retraction Note to “Melting of nanoparticles-enhanced phase change material (NEPCM) in vertical semicircle enclosure: numerical study”

Journal of Mechanical Science and Technology - The Editor-in-Chief has retracted this article because it contains material that substantially overlaps with the following articles [1, 2]. Mahmoud...     

Effect of Oxidizer on PANI for Producing BaTiO3@PANI Perovskite Composites and Their Electrical and Electrochemical Properties

Journal of Inorganic and Organometallic Polymers and Materials - Polyaniline (PANI) has received significant attention in basic and applied studies because it has electrical and electrochemical...     

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly. To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model. In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs. The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion. Finally, the fused features are passed into bounding box and class prediction networks. For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem. The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.