The location of the study area is surging hills in Bongomene area, Gorontalo, Indonesia. In this study, a geological survey and sampling were taken, and then an analysis of the content of benthic foraminifera was performed in each sample. The study aims to discover the species of benthic foraminifera fossils and to determine the paleobathymetry to the studied regions. The results of the analysis contained seven fossils species, namely Ammomassilina alveoliniformis, Stelligerum astrononion, Haynesia germanica, Nonion fabum, Praeglobobulimina ovata, Rhabdammina discreata and Saccorhiza ramosa. Based on the content of benthic foraminifera fossils, paleobathymetry is determined as Middle Shelf to Outer

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

Nigella sativa has various pharmacological properties and has been used throughout history for a variety of reasons. However, there is limited data about the effects of N. sativa (NS) on human cancer cells. This study aimed at observing the roles of methanolic extract of N. sativa on apoptosis and autophagy pathway in the Human PC3 (prostate cancer) cell line. The cell viability was checked by MTT assay. Clonogenic assay was performed to demonstrate clonogenicity and Western blot was used to check caspase-3, TIGAR, p53, and LC3 protein expression. The results demonstrated that PC3 cell proliferation was inhibited, caspase-3 and p53 protein expression was induced, and LC3 protein expression was modulated. The clonogenic assay showed that PC3

Photonic crystal fiber interferometers (PCFIs) are widely used for sensing applications. This work presented solid core-PCFs based on Mach-Zehnder modal interferometer for sensing refractive index. The general structure of sensor was applied by splicing short lengths of PCF in both sides with conventional single mode fiber (SMF-28).To apply modal interferometer theory collapsing technique based on fusion splicing used to excite higher order modes (LP01 and LP11). A high sensitive optical spectrum analyzer (OSA) was used to monitor and record the transmitted wavelength. This work studied a Mach-Zahnder interferometer refractive index sensor based on splicing point tapered SMF-PCF-SMF. Relation between refractive index sensitivity and tape

Prediction of the formation of pore and fracture pressure before constructing a drilling wells program are a crucial since it helps to prevent several drilling operations issues including lost circulation, kick, pipe sticking, blowout, and other issues. IP (Interactive Petrophysics) software is used to calculate and measure pore and fracture pressure. Eaton method, Matthews and Kelly, Modified Eaton, and Barker and Wood equations are used to calculate fracture pressure, whereas only Eaton method is used to measure pore pressure. These approaches are based on log data obtained from six wells, three from the north dome; BUCN-52, BUCN-51, BUCN-43 and the other from the south dome; BUCS-49, BUCS-48, BUCS-47. Along with the overburden pressur

OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtain

The Atmospheric Infrared Sounder (AIRS) on EOS/Aqua satellite provides diverse measurements of Methane (CH₄) distribution at different pressure levels in the Earth's atmosphere. The focus of this research is to analyze the vertical variations of (CH₄) volume mixing ratio (VMR) time-series data at four Standard pressure levels SPL (925, 850, 600, and 300 hPa) in the troposphere above six cities in Iraq from January 2003 to September 2016. The analysis results of monthly average CH₄VMR time-series data show a significant increase between 2003 and 2016, especially from 2009 to 2016; the minimum values of CH₄ were in 2003 while the maximum values were in 2016. The vertical distribution of CH_4<

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

Manual fruit picking is labor-intensive and can damage fruit. Fully mechanized picking is efficient, but it also risks fruit damage. Therefore, semi-automated tools are needed to improve bitter orange picking. This paper presents a smart manual picker designed to facilitate picking while predicting fruit maturity based on picking force as well as various chemical and physical parameters using machine learning (ML). The study methodology consists of five stages: (1) manufacturing the smart picker, (2) picking 50 bitter orange samples, (3) measuring the characteristics of the bitter oranges in the laboratory, (4) training different ML models, and (5) identifying the most accurate model for predicting fruit maturity. The results indicate that