Anaerobic digestion (AD) is the most common process for dealing with primary and secondary wastewater sludge. In the present work, four pre-treatment methods (ultrasonic, chemical, thermal, and thermo-chemical) are investigated in Al-Rustumya Wastewater Treatment plant in order to find their effect on biogas production and volatile solid removal efficiency during anaerobic digestion.
Two frequencies of ultrasonic wave were used 30 KHz and 50 KHz during the pre-treatment. Sodium hydroxide was added in different amounts to give three pH values of 9, 10 and 11 in chemical pre-treating processes. The sludge was heated at 60oC and 80oC through thermal pre-treatment experiment. Also, the sludge was treated thermo-chemically at 80 oC and pH

   The economical and highly performed anode material is the critical factor affecting the efficiency of electro-oxidation toward organics. The present study aimed to detect the best conditions to prepare Mn-Co oxide composite anode for the electro-oxidation of phenol. Deposition of Mn-Co oxide onto graphite substrate was investigated at 25, 30, and 35 mA/cm² to detect the best conditions for deposition.  The structure and the crystal size of the Mn-Co oxide composite electrode were examined by using an X-Ray diffractometer (XRD), the morphological properties of the prepared electrode were studied by scanning electron microscopy (SEM) and Atomic force microscopy (AFM) techniques, and the chemical composition of the various

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Water saturation is the most significant characteristic for reservoir characterization in order to assess oil reserves; this paper reviewed the concepts and applications of both classic and new approaches to determine water saturation. so, this work guides the reader to realize and distinguish between various strategies to obtain an appropriate water saturation value from electrical logging in both resistivity and dielectric has been studied, and the most well-known models in clean and shaly formation have been demonstrated. The Nuclear Magnetic Resonance in conventional and nonconventional reservoirs has been reviewed and understood as the major feature of this approach to estimate Water Saturation based on T2 distribution. Artific

A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

Deep Learning Techniques For Skull Stripping of Brain MR Images

Biodiesel is popular as an eco-friendly fossil fuel alternative. Neem oil is manufactured from Azadirachta indica seeds. Transesterifying triglycerides with alcohol in the presence of a synthesized CaO nanocatalyst is the most common biodiesel synthesis procedure—recent advances, difficulties, and prospects in laser feedstock pretreatment to boost reactivity and reduce energy use. Oil pre-treatment with a 540-nm green laser is cost-effective and environmentally friendly. This study examined neem oil with and without laser pretreatment for biodiesel synthesis. CaO nanocatalysts were synthesised using Sol–gel and characterised using XRD and SEM. According to catalyst recyclability, the CaO nanocatalyst did not lose activity after five re

Due to the advantages over other metallic materials, such as superior corrosion resistance, excellent biocompatibility, and favorable mechanical properties, titanium, its alloys and related composites, are frequently utilized in biomedical applications, particularly in orthopedics and dentistry. This work focuses on developing novel titanium-titanium diboride (TiB2; ceramic material) composites for dental implants where TiB2 additions were estimated to be 9 wt.%. In a steel mold, Ti-TiB2 composites were fabricated using a powder metallurgy technique and sintered for five hours at 1200 °C. Microstructural and chemical properties were analyzed by energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and X-ra

In this research, Zinc oxide (ZnO)/epoxy nanocomposite was synthesized by simple casting method with 2wt. % ZnO concentration. The aim of this work was to study the effect of pH and composite dosage on the photocatalytic activity of ZnO/ epoxy nanocomposite. Scanning electron microscopy (SEM) technique images proof the homogeneous distribution of ZnO nanoparticles in epoxy. A synthesized nanocomposite samples were characterized by Fourier Transform Infrared spectrometer (FTIR) measurements. Two spectra for epoxy and 2wt.% ZnO/epoxy nanocomposites were similar and there are no new bonds formed from the incorporation of ZnO nanoparticles. Using HCl and NaOH were added to Methylene blue (MB) dye (5ppm) to gat pH values 3 and 8. The degradat

Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,