Periodicals of Engineering and Natural Sciences
Periodicals of Engineering and Natural Sciences (ISSN: 2303-4521) is an international open access single-blind review journal published online. Indexed: Scopus, Google Scholar... Digital Object Identifier DOI: 10.21533/pen NOTES *The status of an uploaded article can be one of the following: Unassigned - awaiting assignment to an Editor. Archived - no longer "active", rejected as unsuitable and archived without peer review. In Review - under peer review and awaiting comments before a decision can be made. In Editing - only for accepted papers. **If your published paper does not appear in Scopus within six weeks, you may request its addition by completing the Scopus web form, and selecting the option "Add Missing Document".
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Improving face recognition by elman neural network using curvelet transform and HSI color space
In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition. The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University. First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network. after applying proposed algorithm, the rate of face recognition 94%.
Copyright (c) 2019 https://creativecommons.org/licenses/by/4.0
The impact of curriculum on student’s learning research
This study investigates the impact of curriculum design on student learning outcomes in secondary education in Jordan, an area that has seen limited research amidst ongoing curriculum reforms. Involving 158 teachers from both public and private schools in Amman, the research assessed five curriculum components, objectives, content, teaching methods, assessment, and flexibility, alongside four indicators of student learning: knowledge acquisition, motivation, critical thinking, and application skills. The findings revealed a strong reliability of the survey instrument and indicated a significant positive correlation between curriculum dimensions and student outcomes, with correlations ranging from r = 0.49 to r = 0.65 (p < 0.01). Notably, instructional methods showed the highest correlation with student engagement. The analysis demonstrated that curriculum components accounted for 69% of the variance in student learning outcomes, with effective instructional strategies having the most substantial impact. The results suggest that successful curriculum implementation in Jordan hinges on integrated and flexible designs that align goals, delivery, and assessment. The study has aims for prioritizing active learning strategies in future curriculum reform efforts for enhancing meaningful educational achievements at the secondary level.
Copyright (c) 2025 Mousa Khaleel Abunawas, Ahmad Fares Albadareen, Yazan Khaled Daifallah AlZawahreh, Mansour Ali Falah Alzyod, Mousa Hamed Mohammed Abu Suailik https://creativecommons.org/licenses/by/4.0
A review paper: Blockchain security with IoT devices and deep-learning methods
Internet of Things (IoT) is changing the face of modern world by interconnecting billions of devices, transforming human life and revolutionizing innovation in various domains such as healthcare, transportation, and smart cities. However, the widespread deployment of IoT applications is dramatically hindered by the aforementioned remaining challenges, which mostly relate to the security, privacy, and trustworthiness of data. This work also discusses their limitations including: small computing power, use of insecure communication protocols, weak authentication, lack of encryption and susceptibility to Distributed Denial of Services (DDoS) attacks. Blockchains A blockchain is a decentralized, tamper-resistant authority which exhibits high levels of security, which could help solve the mentioned security problems of the IoT ecosystems. Blockchain can provide trust through data immutability, transparency and consensus and hence can be employed to provide security and reliability to IoT communication and storage. But integrating blockchain directly in low-resource IoT systems has certain issues like added latency, increased bandwidth usage and additional computational overhead. The objective of this paper is to present a detailed review of the synergy between the architecture of blockchain, the IoT systems and deep learning techniques, and the way that they can be collaboratively utilized for improving data security, trustworthiness, and decision-making in IoT networks. Specifically, we examine existing blockchain-based deep learning frameworks by categorizing them based on four major criteria: blockchain type (public, private, consortium), deep learning approach (e.g., CNN, GAN, DRL), consensus protocols, and datasets used for model training and validation. Furthermore, the paper analyzes the strengths and limitations of these integrated frameworks in addressing real-world IoT challenges. The review highlights how blockchain enhances the trustworthiness and traceability of deep learning outputs, while deep learning models can contribute to intelligent threat detection, adaptive control, and data-driven automation in IoT networks. Finally, we outline current research gaps and propose future research directions that focus on optimizing blockchain-deep learning frameworks for scalability, energy efficiency, and real-time performance in large-scale IoT deployments.
Copyright (c) 2025 Laith F. Jumma, Leila Sharifi, Parviz Rashidi https://creativecommons.org/licenses/by/4.0
The Effectiveness of Cooperative Learning in Enhancing Student Participation among Grade 10 English Students in Jordanian Public Schools
This study responded to the problem of classroom participation among Jordanian public school students, whose education has been mainly teacher-centered and has constrained students' learning autonomy. This study seeks to explore the impact of cooperative learning methods for increasing classroom participation among Grade 10 students. A quasi-experimental pretest-posttest design was applied with 80 participants labeled as an experimental group (40 participants) and a control group (40 participants). The treatment group received cooperative learning approaches, including Think-Pair-Share (TPS), Numbered Heads Together (NHT), and Teams Games Tournament (TGT), while the control received regular instruction. Measures that were used to collect data included a 10-item observation checklist questionnaire, a 15-item student self-report questionnaire, and teacher reflection logs. The finding suggested that the experimental group had a marked improvement in mean participation, from 1.9 to 3.2 (+1.3 or +68.4%), while the control group had a minimal increase from 1.8 to 2.1 (+0.3 or +16.7%). A separate-samples t-test likewise determined that the improvement in the experimental group was significant (p < 0.05), and the students demonstrated more confidence and responsibility. In general, the application of cooperative learning techniques greatly increased student participation, which was a welcome change in the Jordanian academic setting, especially English lessons. However, study limitations include overreliance on subjective rating, single-site design, and self-reporting by teachers, with suggestions for future research including the incorporation of objective performance measures and exploration of digital substitutes to increase ease of deployment.
Demographic and occupational factors predicting personality types among healthcare practitioners: An analysis using structural equation modeling (SEM)
Personality significantly influences healthcare professionals' performance, interpersonal communication, and stress management. While existing literature suggests that individual personality traits may be shaped by demographic and occupational variables, there is a scarcity of studies employing robust statistical modeling in healthcare settings. This study aimed to investigate the structural impact of demographic factors (such as gender, academic qualification, and subspecialty) and professional variables (including workplace, work shifts, weekly working hours, and experience) on the Big Five personality traits among healthcare practitioners using Structural Equation Modeling (SEM). A total of 364 healthcare workers from hospitals, primary health care centers, and emergency clinics participated in the study, with data collected through a structured questionnaire utilizing the Big Five Inventory–2 Short Form (BFI-2-S). The analysis revealed that demographic variables accounted for 11% of the variance in the latent personality construct (R² = 0.11, β = 0.326, p < .001), with a statistically significant structural effect observed (β = 0.326, p < .001), particularly affecting Agreeableness and Open-Mindedness. In contrast, professional variables did not demonstrate a meaningful impact (β = -0.079, p = 0.693). Further regression analyses indicated that gender and academic qualification were significant predictors of Extraversion, Agreeableness, and Neuroticism. These findings suggest that personality is more strongly influenced by demographic background than by occupational conditions among healthcare professionals, emphasizing the importance of incorporating personality-based considerations into recruitment, supervision, and training programs within healthcare institutions. Future research should adopt longitudinal and culturally sensitive approaches to further explore these relationships in diverse clinical contexts. Personality significantly influences healthcare professionals' performance, interpersonal communication, and stress management. While existing literature suggests that individual personality traits may be shaped by demographic and occupational variables, there is a scarcity of studies employing robust statistical modeling in healthcare settings. This study aimed to investigate the structural impact of demographic factors (such as gender, academic qualification, and subspecialty) and professional variables (including workplace, work shifts, weekly working hours, and experience) on the Big Five personality traits among healthcare practitioners using Structural Equation Modeling (SEM). A total of 364 healthcare workers from hospitals, primary health care centers, and emergency clinics participated in the study, with data collected through a structured questionnaire utilizing the Big Five Inventory–2 Short Form (BFI-2-S). The analysis revealed that demographic variables accounted for 11% of the variance in the latent personality construct (R² = 0.11, β = 0.326, p < .001), with a statistically significant structural effect observed (β = 0.326, p < .001), particularly affecting Agreeableness and Open-Mindedness. In contrast, professional variables did not demonstrate a meaningful impact (β = -0.079, p = 0.693). Further regression analyses indicated that gender and academic qualification were significant predictors of Extraversion, Agreeableness, and Neuroticism. These findings suggest that personality is more strongly influenced by demographic background than by occupational conditions among healthcare professionals, emphasizing the importance of incorporating personality-based considerations into recruitment, supervision, and training programs within healthcare institutions. Future research should adopt longitudinal and culturally sensitive approaches to further explore these relationships in diverse clinical contexts.
Copyright (c) 2025 Yahya Khatatbeh https://creativecommons.org/licenses/by/4.0
Transformation of teaching strategies in higher education in the context of the development of artificial intelligence
The work examines the transformation of teaching strategies in post-secondary education and the background of the growth of artificial intelligence (AI). The purpose of the study is to review how AI technologies influence educational approaches and to identify efficient strategies for their integration into university teaching. A qualitative research method, namely case study, was applied, including literature analysis of Ukrainian and international scientists and case studies of AI implementation in educational modes. The main selection criteria were professional teaching experience and AI usage. Purposive sampling was applied to recruit participants who met these criteria. This study has focused on the perspective of educators, but more research is needed on the student side. Future studies should explore how students use AI in their learning and how it affects their critical thinking and academic performance. The goal is to explore personal experiences and views. The findings of the study reveal a significant shift towards individualized learning, adaptive assessment, and increased use of AI teaching strategies. The research concludes that successful integration of AI demands not only technological preparedness, but also pedagogical innovation and educational support
Copyright (c) 2025 Svitlana Vitvytska, Anna Khudaverdova, Volha Hurskaya, Vladyslava Artiukhova, Krystyna Yandola https://creativecommons.org/licenses/by/4.0
Digital content management of Heet Sib Sorng custom for semantic search
This research presents the results of the alteration knowledgeable following the instruction of the digital content management by semantic technology development. The boundary of this research consists of the integration of the opportunities provided by the existing ontology and two datasets with the resources having different contents, including the datasets from the central library, physical and electronic data, and the development of semantical web approaches in ontology. In this research, research and development methodology is used and the data were acquired through literature review. This research shows that the ontology and applications evaluated are high level, and that there is an inclination for integrated systems oriented towards digital content management in the Thai custom semantic search system, which finally bring to the convergence of linked data and knowledge-based system.
Copyright (c) 2020 https://creativecommons.org/licenses/by/4.0
Single objective optimization of surface water coagulation process using inorganic/organic aid formulation by taguchi method
The combined inorganic/organic coagulant has received much attention in turbidity removal for its positive influence on the environment and economy when introduced to the classic coagulation-flocculation clarification process. The present study investigates the effect of different parameters on turbidity removal. These parameters are initial turbidity, coagulant dosage, organic load, and pH. The Taguchi method was utilized in designing the experiment. the Analysis of the variable indicates that the organic load, initial turbidity, coagulant dose, and pH contributions to the turbidity removal was 58.92%, 20.03%, 11.67%, and 6.73% respectively. Results show that the predicted quadratic model can represent the turbidity removal with an R2 value of 99.24%. Optimum operation conditions were: an organic load of 25% of the total dose, total coagulants dose of 10 ppm, pH of 6 with an estimation of high turbidity value of 100 NTU. Confirmation experiments were conducted and the experimental results have a slight deviation from theoretical values representing model strength. The results confirm the role of an organic aid to provide superior turbidity removal at reasonable economic and operational benefits.
Decentralized security and data integrity of blockchain using deep learning techniques
Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment. With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well. With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series. In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security. In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains. We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification. We evaluate LSTM based networks. As data for training we use historical security data in one-minute intervals from August 2019 to August 2020. We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model’s predictions. We analyze that performance and compare it with the buy and hold strategy. The simulation is carried out on bullish, bearish and stagnating time periods. In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task. We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better. The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy.
Designing a dynamic fenestration to improve visual performance in educational spaces using daylight
This research studies the daylighting performance of diverse design prototypes of a classroom unit. The prototypes are suggested for high-performance approach that reflects a dynamic design, through the incorporation of daylight that will boost up the learning experience. Simulation is carried out using Radiance and Daysim for daylighting measurements and Evalglare for glare analysis. Window to Wall Ratio (WWR) and window-sill height are designed to provide the optimum daylight levels for students performing different educational tasks. These two variables are evaluated according to certain goals and within certain constraints to refine the optimum solution. The result of this research is a design of dynamic façade that opens and shuts in response to the illumination levels required by the task performed; such as traditional learning, discussion or computer activities. The function of the dynamic façade is to achieve uniform and sufficient daylight in the classroom to perform visual tasks with minimal discomfort caused by the glare.
The effect of changing the exchange rate on some economic variables by using the autoregressive distributed Lag (ARDL) in Iraq
The issue of the exchange rate has emerged as one of the most important problems facing countries in drawing the parameters of their foreign exchange, as dealing in the foreign currency market has become one of the most dangerous transactions, and studies have shown the existence of several mutual effects between exchange rate fluctuations and macroeconomic variables. The study focuses on analyzing the impact of fluctuations The exchange rate on macroeconomic variables. This comes in the wake of the fundamental changes in the macroeconomic indicators in Iraq, which included inflation and oil prices, which had a major impact on the value of the Iraqi dinar against the US dollar. The Autoregressive Distributed Lag (ARDL) was used to analyze time-series data from 1990-2017. This model can distinguish between dependent and explanatory variables and eliminate the problems that may arise due to the presence of autocorrelation and internal growth. The ARDEL model can also estimate the short-term and long-term relationship, simultaneously. The results showed that there is a long-term relationship between the exchange rate with inflation, GDP, expanded the money supply, and foreign reserves. This is consistent with the reality of economic theory, but in the short term, there is a relationship between the exchange rate and inflation. The reason is that there is no relationship between the price Exchange and other variables in the short term due to the inability to demonstrate the impact of these variables in a relatively short period, from which we conclude that reducing volatility in macroeconomic variables due to the stability the exchange rate in the Iraqi economy to help decision-makers draw correct economic policies.
Algorithm for receiving the recommended bandwidth of a wireless self-organizing network channel
The article presents the development and research results of a support decision-making algorithm forobtaining the recommended channel bandwidth to achieve the required probability values of satisfactory requests service for audio communication sessions inamobilead - hocnetwork. Therecommended channel bandwidth calculation is based on theservicemodelforrequestingaudio communication sessions in amobileadhoc network.The analytical dependenciesof the satisfactory service are probability values of voice streaming requests on channel characteristics anddynamism of the network topology which have taken into account when executing the algorithm. The resultsof computationalexperimentsarepresented, which are confirming the correctness of the proposed algorithm.The implementation of the algorithm, allows justifying the recommended bandwidth when designing a mobile ad hoc network.
Content marketing and website users engagement: The impact of relevant content on the web on user engagement behaviors
The marketing landscape is changing; we are switching from traditional methods to digitalized methods to attract consumers/users. The new trend of marketing tends to gain loyal consumers. Nowadays, content marketing is used to create this trusting consumer relationship. It consists in providing valuable information in the form of content, this content is in both sides giving information to the user and strengthening bonds with the brand offering the content. This article is a study of Content Marketing linked to Website users’ engagement. Its aim is to understand how the relevance of the content and the quality of a website can influence the users’ engagement. A study of existing concepts and an exploratory research were developed in the article to find the relationship between content marketing and engagement behavior of Internet users. The article tries to understand how consumers/users perceive the content online and based on which standards they choose to engage or not. The challenge today for the enterprises is not only creating content for the users, but creating a valuable and relevant content for their consumers.
A critical analysis of absorptive capacity research
The ability to absorb new external knowledge (ACAP) becomes a major source of innovation and competitive advantage. Despite the abundance of literature devoted to this concept, it remains shrouded in conceptual and empirical confusion. In this paper, we will attempt to shed light on this construct. Based on a thematic analysis of this literature, we identify five main themes covered by absorptive capacity research, such as: conceptualizations of ACAP, dimensions, measurement scales, main antecedents and organizational outputs of ACAP. The results show that, despite the efforts made in this field, the variety of conceptualizations, the diversity of measures used, the focus on “Content” of knowledge at the expense of “Processes”, “IT Systems” and “Context” of their integration, restrict our understanding of this concept. The paper then addresses some limitations of that works, and discusses avenues for future researches.
Optimum horizontal well length considering reservoir properties and drainage area
The length of a horizontal well increases and so does its drainage area. The efficiency of the long horizontal well is no longer proportional to the length of the well, since the rise in the length of horizontal well output segment also tends to causes frictional pressure losses in the well. Nevertheless, there are currently no reliable standards which take into account quantitatively the parameters necessary to determine the optimum well length of horizontal drilling. A new strategy to the basic productivity index is introduced, taking into account the friction losses under influx conditions in a long manufacturing segment. The consequence of this special productivity index is the constant state flow in an anisotropic structure of a very compressible fluid. This paper presents a technique developed to achieve an optimum length of horizontal pool based on the shift in the overall economics and productivity index (PI) in the long horizontal wellbore with frictional loosing results. In order to achieve optimal overall efficiency in a horizontal well project, an integrated method is proposed for numerical analysis of the parameters that affect profitability using Computer Modelling Group reservoir simulation (CMG).
A proposed hybrid cryptography algorithm based on GOST and salsa (20)
Security concepts are frequently used interchangeably. These concepts are interrelated and share similar objectives for the protection of privacy, credibility, and access to information; however, there are some slight differences between them. Such variations lie mostly in the subject matter approach, the approaches used, and the focus fields. With the intention of protecting data in contradiction of unauthorized or unintentional disclosure, cryptography is used during transit (electronic or physical) and when data is stored. In the course of the past few years, some block ciphers and stream ciphers have been proposed. These block ciphers take encryption method that uses Substitution-Permutation and Feistel network structure while stream ciphers choose a onetime method. GOST encryption is based on the confidentiality of the secret key. However, it leads to the same ciphertext being generated when the encryption program is used with the same key for the plain text. Reproduction of messages can thus easily be identified by an opponent that is a weak link in any communication. In this paper, proposed a hybrid encryption method based on GOST block cipher and Salsa stream cipher to provide proper security with as high hardness randomly enhances the five standard tests and modifies key schedule as secure operations. The downside of the GOST algorithm is a simple key schedule so that in certain circumstances be the weak point of the method of cryptanalysis as related-key cryptanalysis. However, this resolved by the proposed method by passing the keys of GOST to Salsa stream to have the right combination and more robustness security. Its need for 2256 probable keys to breaking keys that, because of its uncomfortable procedure in this situation, is to be not used brute force attack. Correspondingly, five standard tests successfully surpassed the randomness of a proposed method.
Study and improvement the effect of internal sulphate on properties of normal and light weight concrete
Sulphate attack is a major difficulty which has a local effect on the constructional materials, specially the concrete. Internal sulphate attack takes place due to the source of sulphate being incorporated into the concrete at the period of mixing, while adding gypsum in the cement etc. This project aims to investigate the influence of internal Sulphate Attack (ISA) on some characteristics of light weight (LWC) and normal concrete (NC). In this study, two types of concretes have been prepared, light weight and normal concrete. Two levels of sulphates (3% and 6%) are added as a part of sand to determine the influence of sulphates on NC and LWC that produced a local material and compared it with control normal mix. These added ratios of sulphate are highly convergence of the actual reality of internal sulphate. To decrease the harmful effect of internal sulphate coming from 3 and 6% by weight of sand, two ways have been utilized in this study. First one, using type I cement with 20% of silica – fume as replacement with a part of ordinary cement, second one by SRPC instead of type I is preferred to resist internal effect of sulphate. Light weight concrete has been prepared by total replacement for gravels by crushed bricks. As a result of this research, it concluded that the increment of SO3 amount caused a decrement in the compressive strength by 43.81% at 90 days and by 33.5 % at 60 days but there is a slight effect appear at 28 for samples consist of OPC with presence of 3% SO3. When sulphates increased to 6%, compressive strength decreased by 49.3% at 90 day, and by 38.3% at age of 60 days with very slight effect at age of 28 days. Tensile strength of NC follows same the behaviour as that of the compressive strength. For light weight concrete, the reduction in compressive and tensile strength will result from combined effect of the ettringite formation and high porosity due to introducing crushed bricks as total replacement for gravels. LWC was highly effected by sulphate attack even at early ages and it concluded that the increment in the SO3 amount by (0-3) % by weight of sand, the compressive strength of LWC, decreased by 20.11 % at age of 28 days, 29% at 60 days, while they decreased by 44 % was at 90 days. Using of 20% silica fume with type I cement heals the effect of 3% and 6% calcium sulphate in the A4 and A5 samples and produced a compressive and tensile strength approximately equal to that of control samples. Using of SRPC with 3% and 6% calcium sulphate in the A7, A8, L7 and L8 samples prevented the effect of internal sulphate in both NC and LWC and gave compressive and tensile strength higher than that of standard samples (without sulphate).
Projects evaluation in construction industry
The economic activity support in the construction industry in numerous developed countries is a percentage of about 5-11 of their GDP consumed yearly on different kinds of construction projects and associated activities. This paper attempts to evaluate the construction project under different circumstances and different periods. This paper was divided into several stages to accomplish its aim, the first stage, the researchers have gathered information about 30 projects in a different period starting from 2001 to 2017. The second stage included a questionnaire survey to get an indication of the importance of these factors. The third stage comprised analyzing the project's evolution using an artificial intelligence technique, which is a K-nearest neighbor classifier. The fourth stage included using the run chart technique to show the change in the evaluation of the project. It can conclude that the project's evaluation is continuously fluctuated in different periods, the risks that occur have a direct relationship with cost and time overruns and indirect connection with project evaluation.
The methods for choosing appropriate bandwidth (smoothing parameter) are important to approximate the regression function to the original function so that the error is as small as possible. It is always a good starting point, and that the smoothing accuracy depends only on the bandwidth, which in turn affects the degree of smoothing of the estimated curve and its proximity to the true curve. The equilibrium between bias and variance is called the smoothing parameter or the bandwidth parameter. Its value is greater than zero, which reduces the function and its value corresponds to the smallest standard, and that large values of (h) produce smoothed results, because it increases the bias and reduces the variance to estimate the original regression function. It is one of the methods to reduce the mean squares of error. This article explains the structured methods of bandwidth choice for estimating the rth derivative of a univariate kernel employing techniques methods of cross-validation. We used kedd package proposed by Arsalane Chouaib Guidoum based on R programming with different kernel functions for computing bandwidth choice for density derivative. Simulation approaches are used to construct the method of bandwidth along with real-life data sets.
Bayesian and non-Bayesian estimation of the Lomax model based on upper record values under weighted LINEX loss function
In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions.
Strain analysis at flat surfaces of loaded members using digital image correlation technique
This research examines the applicability of the planned Digital Image Correlation (DIC) system to measure the strains in tensile experiments. DIC is a low-cost optical technique, and is an appropriate measurement used to measure surface displacement, strain and stress map distribution without any contact with the tested surfaces. In the present research, the tensile test is conducted on two different flat samples, which are painted in a speckle pattern on the tested surface to use DIC features in stain measurements. To guarantee the efficiency of the planned DIC system, the DIC code has been built using MATLAB programming language. The obtained results from DIC technique is compared with the results from open-source software (Ncorr), the finite element analysis (ANSYS) as well as the exact and analytical solutions. The comparison results showed that there was A quite acceptable and agreement achieved between them. According to the exact solution, the percentage of accuracy of the obtained results for the Aluminum without hole plate was around (89-93) % whereas the accuracy with the NCORR was about 96 %. For the second copper plate with a central hole, the accuracy has been obtained to be (80.7-99) % with the analytical solution wherein its value has reached (81-97) % with Ncorr software.
Kernel estimation of returns of retirement funds of employers based on monetary earnings (subscriptions and compensation) via regression discontinuity in Iraq
Regression Discontinuity (RD) means a study that exposes a definite group to the effect of a treatment. The uniqueness of this design lies in classifying the study population into two groups based on a specific threshold limit or regression point, and this point is determined in advance according to the terms of the study and its requirements. Thus, thinking was focused on finding a solution to the issue of workers retirement and trying to propose a scenario to attract the idea of ​​granting an end-of-service reward to fill the gap (discontinuity point) if it had not been granted. The regression discontinuity method has been used to study and to estimate the effect of the end -service reward on the cutoff of insured workers as well as the increase in revenues resulting from that. The research has showed that this reward has a clear effect on increasing revenues due to the regularity of workers in their work and their work continuity. It has also found that using Local Linear Smother (LLS) by using three models of bandwidth selection. Its results after the analysis in the Regression program have been as follows: The CCT (Calonico, Cattaneo & Titiunik) beamwidth gives the best performance followed by the local linear regression using the LK (Lembens and kalyanman) beamwidth. The real data has been used in sample size 71 represented in compensation as a variable of effectiveness (illustrative) X and the revenue as a result or an approved variable Y, while the results of the traditional OLS estimation method have not been good enough.
Robust video data security using hybrid cryptography-steganography technique
The interest in the digital videos confidentiality in the current electronic and interrelated world has increased. Thus, this paper aimed at making a video steganography scheme for an acceptable security with high speed of calculation by embedding data (video frames) in other video frames. The techniques of embedding and encrypting video frames in a cover video file were done through two ways. Firstly, two keys and XOR bit operation were employed to create a large range of different keys for encryption. Secondly, a modified method of Least Significant Bit (LSB) technique was used for hiding high resolution video frames (bitmap color) in selective cover video frames, offering two security layers. The procedures of encrypting and hiding video data were successfully tested on many secret videos such ad Eye Video, Secret Medicine Video and Traffic Video. All experiments were conducted using MATLAB-GUI software, representing an efficient and easy tool for video management supported by powerful testing tools as histograms and mathematics for video quality. Experimental result demonstrated a good performance with low correlation and very high PSNR of the Stego video frames.
Enhanced thresholding-based wavelet noise filtering in optical fiber communications
Nowadays, the growing requirement of higher data transmission rates for real-time applications of communication systems. The capacity of data transmission increased with the higher carrier frequency. Optical Fiber Communication (OFC) systems gained a significant interest of researchers due to its capability of enhancing the data-carrying capacity. The optical waves in OFC systems operate in the range of THz those results in the increased capacity of data-carrying. The OFC systems achieved a high data rate, however, suffered from the challenges of various noises. The presence of noises in OFC may degrade the transmitting signal quality & increases the error rates. The OFC systems design by considering the noise in optical communication links recently received great interest from researchers. In this paper, we first presented the OFC design with noises such as white Gaussian noise, shot noise, & thermal noise. Secondly, the impact of noises in OFC analyzed through simulation results by performing optical communications with & without noises. Third, to suppress the noise effects on optical communications, we propose the enhanced thresholding-based wavelet Denoising approach called Wavelet Denoising using Enhanced Thresholding (WDET). The aim of WDET for optical communications is to improve the signal quality & minimize the signal errors effectively in the presence of various noises. The design of WDET is based on the properties of hard & soft thresholding of wavelet Denoising. The simulation results show that the proposed Denoising approach improves the signal quality factor with reduced Bit Error Rate (BER) & Mean Square Error (MSE) compared to existing filtering methods.
Behavior of reinforced geopolymer concrete flat slab exposed to high temperature
In this study, fifteen 700x500 mm concrete flat slabs made of geopolymer concrete were examined under concentrated to identify the maximum punching shear resistance under the influence of various factors such as steel fiber content, slab thickness, flexural reinforcement ratio, in addition to replacing 10% of metakaolin by silica fume under the influence of temperature increasing up to 300 oC in addition to testing ninty control specimens to investigate the mechanical properties. It was observed there was a beginning to change in the structural behavior when the temperature reached 100 oC due to an increasing in the pressure by the liquid vapor, then a important drop in the capacity and behavior when reaching the temperature 300 oC. The presence of iron fibers led to a decreasing in the distance between the edge of the column and the critical section, which reduced the punching shear values. Decreasing of the maximum and cracking of the punching shear were recorded at 37% and 42%, respectively, when the temperature increased to 300 ° C.
Effect of irrigation methods and tillage system, seed level on water use efficiency and wheat (Triticum aestivum L.) growth
Afield trial was conducted at Al- Hashimya area, Babylon district, Iraq during the growing seasons 2016-2017 and 2017-2018 to investigate the effect of some agronomic practices viz. irrigation system ( sprinkler irrigation (I1) and surface irrigation (I2) which occupied the main plots; tillage system (Zero tillage (T0), minimum tillage (T1) and deep tillage (T2) which occupied the subplot ); and seeding rates (120 Kg ha-1( S1), 180 Kg ha-1(S2),240 Kg ha-1 (S3)and 300 Kg ha-1 (S4) which occupied the sub _ subplots on biological yield, harvest index, water _use efficiency, water consumptive use, crop coefficient of bread wheat (Triticum aestivum L.). Results indicated that biological yield was increased in both seasons by 9 and 10%, respectively, following the application of sprinkler irrigation. This may be due to the increased water _use efficiency by 29 and 32% in both seasons, respectively, as influenced by sprinkler irrigation. However, sprinkler irrigation did not affect the harvest index in both seasons. Similarly, minimum tillage increased the biological yield by 10.6,10.2,11.8 and 3.4 % over zero _ tillage and deep tillage in both seasons, respectively. However, the tillage system had no significant effect on the harvest index in both seasons. The seeding rate (240 Kg ha-1) increased the biological yield by 15.1 and 16.0 % over the lowest seeding rate (S1 120 Kg ha-1) with no significant effect on the harvest index in both seasons, respectively. The exciting result that the application of sprinkler irrigation (I1) decreased the consumptive use mm. Season in both seasons and consequently the crop coefficient (Kc) was decreased at tillering, elongation, flowering and maturity stages in both seasons. It can be concluded that the application of sprinkler irrigation under the minimum tillage and using the seeding rate 240 Kg ha-1 has increased the biological yield via increased water – use efficiency and decreased both consumptive use and crop coefficient.
Animate systems can organize their complexities to stay alive. They run in easiest ways within specific boundaries to keep their existence and to maintain highest levels of interaction with their surrounding environments. They are (living) systems of emergent (adaptive) and evolved (survived) complexities. The focus here will be on adaptive complexities of their flexible structures. Man-made systems, like cities, are constructed and shaped by instant and accumulative human decisions. Metaphorical questions about the possibility of these systems to behave alike are re-raised. It is argued that their emergent (generative) processes according to optimal combinations of physical and visual connections would enhance their adaptivity. A different method, derived from space syntax, provides a new tool for detecting and estimating these adaptive complexities. It provides measurable dimensions, as sensitive indicators, of adaptive complexities and explains how their continual and generative (size-dependence) processes emerge. In (2D) systems, it is found that organized complexities have adaptive dimensions of fractal values approach to (DA≈1-2). Also, from results on the grounds, each existing urban fabric has a structure with a specific and comparable local and global adaptive dimension. More supportive researches and applications in various (2D) and (3D) systems are needed to develop the concept.
Investigation thermal comfort in urban environmental centres by combining traditional and modern principles
Traditional cities have been able to provide thermal comfort to their residents through the use of simple physical principles related to the movement of air from cold areas high-pressure represented by a cold air gathering focal like the basements and the middle courtyard and shaded alleys to hot areas low-pressure such as large urban open courtyards that exposed to sunlight. However, due to the industrial revolution, The Urban development has deteriorated the traditional urban fabric, and it’s environmental components, which provided urban fabric with thermal comfort reasons, have disappeared, and a modern urban fabric has emerged that does not take environmental considerations and thermal comfort into account. Here, the research tries to suggest a planning urban style that combines the Principles of architecture and traditional planning with a modern ideas that are appropriate for the times, achieving new urban centers within the city capable of providing thermal comfort to its occupants and creating an urban environmental vision to revitalize traditional urban areas by using vertical green walls.
Improving the concrete sections after removing intermediate support of RC continuous non-prismatic beam
An experimental study was conducted to investigate the performance of the 2-span continuous reinforced concrete beams of different section depth after removing the middle support by adding steel fibers or steel plates. The beams were loaded monotonically with two-point loads. One continuous beam and six simply supported beams of non-prismatic section were tested using two different content of steel fibers and three different locations of steel plates welded to the reinforcement. The test results reveal that using inclined steel plates at the region of changing of cross section thickness at the middle support increase the load capacity of the beam significantly up to 75% of the continuous beam capacity, and a significant warning before failure is shown. Adding steel fibers to the concrete has less influence on the capacity of the beam. The failure mode of the beams with no middle support is the same, but with different values of deflection. The inclined steel plate again is the most effective way to decrease the deflection because of the increased stiffness of the cross section. To achieve the same capacity of the continuous beam after removing the middle support, it is recommended to use horizontal steel plates welded to the reinforcement at the region of the middle support extended within one fourth the length of each span of different thickness to avoid the stress concentration resulted from the large deflection at that region.
A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network
Series Arc Fault (SAF) is the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common malfunctions that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures, such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In typical cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new part will add to the signal; therefore, CNN used to discriminate against the additional component to detect the SAF accurately. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.