The  lossy-FDNR  based  aclive  fil ter has an  important   property among  many  design  realizations. 'This includes  a significant reduction in component count particularly in the number  of OP-AMP which consumes   power.  However  the·  problem  of  this   type  is the  large component spreads  which affect the fdter performance.

In  this  paper   Genetic   Algorithm   is  applied   to  minimize   the component  spread   (capacitance  and  resistance  p,read). The minimization of these spreads allow the fil

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

Twenty three samples of granular chemical fertilizers and organic fertilizers commonly utilized in Iraqi ranches were collected. The samples were prepaid and stored in a Marinelli beaker to measure; dose rate, general count rate and surface contamination of the samples using the RadEye B20 detector, firstly with shield, secondly without the shield to estimate the effect of shielding on the measurements. The results showed that using shield made a significant decrease in the radiation measurements reached about 25%. However the mean value of surface contamination, dose rate and general count rate with shield were 0.54Bq/cm2, 0.65µsv/h, and 0.28Cps respectively, and without shield being 0.34Bq/cm2, 1.33µsv/h, and 1.52Cps respectively

A new imidazolidine 4-one derivative, of namly 2-[2-(4-Bromo-phenyl)-imidazo [1,2-a] pyridine-3-yl]-3-(4-nitro-phenyl)-imidazolidine-4-one (BPIPNP) was investigated as corrosion inhibitor for carbon steel in salty (3.5% NaCl) and acidic (0.5M HCl) solutions using potentiometric polarization measurements. The results revealed that the percentage inhibition efficiencies (%IE) in the salty solution (90.67%) are greater than that in the acidic solution (83.52%). Experimentally, the thermodynamic parameters obtained have supported a physical adsorption mechanism and which followed Langmuir adsorption isotherm. Density Functional Theory (DFT) of quantum mechanical method with B3LYP 6-311++G (2d, 2p) level was used to calculate geometrical stru

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

Abstract: The international community now places significant emphasis on achieving zero carbon emissions, requiring both new researchers and experienced policymakers to prioritise this goal. This article examines the effects of carbon taxes, carbon cap and trade, renewable energy (RE) production and consumption, and economic growth (EG) on carbon emission reduction in the United States, Japan, Canada, and Australia. The study collected secondary data from the World Development Indicators (WDI) secondary source spanning the years 1991 to 2022. The study examines the relationship between variables using the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) approach. The findings indicate that carbon taxes, carbon cap and tr