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CONTENTS
Volume 15, Number 6, December 2023
 

Abstract
In this research, the impact of micro-silica, nano-silica, and polypropylene fibers on the fracture energy of self-compacting concrete was thoroughly examined. Enhancing the fracture energy is very important to increase the crack propagation resistance. The study focused on evaluating the self-compacting properties of the concrete through various tests, including J-ring, V-funnel, slump flow, and T50 tests. Additionally, the mechanical properties of the concrete, such as compressive and tensile strengths, modulus of elasticity, and fracture parameters were investigated on hardened specimens after 28 days. The results demonstrated that the incorporation of micro-silica and nano-silica not only decreased the rheological aspects of self-compacting concrete but also significantly enhanced its mechanical properties, particularly the compressive strength. On the other hand, the inclusion of polypropylene fibers had a positive impact on fracture parameters, tensile strength, and flexural strength of the specimens. Utilizing the response surface method, the relationship between micro-silica, nano-silica, and fibers was established. The optimal combination for achieving the highest compressive strength was found to be 5% micro-silica, 0.75% nano-silica, and 0.1% fibers. Furthermore, for obtaining the best mixture with superior tensile strength, flexural strength, modulus of elasticity, and fracture energy, the ideal proportion was determined as 5% micro-silica, 0.75% nano-silica, and 0.15% fibers. Compared to the control mixture, the aforementioned parameters showed significant improvements of 26.3%, 30.3%, 34.3%, and 34.3%, respectively. In order to accurately model the tensile cracking of concrete, the authors used softening curves derived from an inverse algorithm proposed by them. This method allowed for a precise and detailed analysis of the concrete under tensile stress. This study explores the effects of micro-silica, nano-silica, and polypropylene fibers on self-compacting concrete and shows their influences on the fracture energy and various mechanical properties of the concrete. The results offer valuable insights for optimizing the concrete mix to achieve desired strength and performance characteristics.

Key Words
fracture energy; micro-silica; nano-silica; self-compacting concrete; softening function

Address
Moosa Mazloom, Amirhosein Abna, Hossein Karimpour and Mohammad Akbari-Jamkarani: Department of Structural and Earthquake Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, I. R. Iran

Abstract
The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

Key Words
cloud computing security; machine learning; malicious; nano-structures

Address
Xu Guo: College of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China

T.T. Murmy: Faculty of Computer Engineering, University of Malaya, Malaysia

Abstract
Quantum-dot cellular automata (QCA) has shown great potential in the nanoscale regime as a replacement for CMOS technology. This work presents a specific approach to static random-access memory (SRAM) cell based on 2:1 multiplexer, 4-bit SRAM array, and 32-bit SRAM array in QCA. By utilizing the proposed SRAM array, a single-layer 16×32-bit SRAM with the read/write capability is presented using an optimized signal distribution network (SDN) crossover technique. In the present study, an extremely-optimized 2:1 multiplexer is proposed, which is used to implement an extremely-optimized SRAM cell. The results of simulation show the superiority of the proposed 2:1 multiplexer and SRAM cell. This study also provides a more efficient and accurate method for calculating QCA costs. The proposed extremely-optimized SRAM cell and SRAM arrays are advantageous in terms of complexity, delay, area, and QCA cost parameters in comparison with previous designs in QCA, CMOS, and FinFET technologies. Moreover, compared to previous designs in QCA and FinFET technologies, the proposed structure saves total energy consisting of overall energy consumption, switching energy dissipation, and leakage energy dissipation. The energy and structural analyses of the proposed scheme are performed in QCAPro and QCADesigner 2.0.3 tools. According to the simulation results and comparison with previous high-quality studies based on QCA and FinFET design approaches, the proposed SRAM reduces the overall energy consumption by 25%, occupies 33% smaller area, and requires 15% fewer cells. Moreover, the QCA cost is reduced by 35% compared to outstanding designs in the literature.

Key Words
cost function; energy consumption; majority gate; nanoscale; quantum-dot cellular automata (QCA); Static Random Access Memory (SRAM)

Address
Moein Kianpour and Behzad Ebrahimi: Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Reza Sabbaghi-Nadooshan: Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Majid Mohammadi: Faculty of Engineering, Shahid Bahonar University, Kerman, Iran

Abstract
This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

Key Words
machine learning; manufactured-sand concrete; stone nano-powder; tensile strength

Address
Zanyu Huang and Qiuyue Han: College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture, Daxing 102600, Beijing, China

Adil Hussein Mohammed: Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq

Arsalan Mahmoodzadeh: IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq

Nejib Ghazouani: Department of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia/ Civil Engineering Laboratory, National Engineers School of Tunis (ENIT), University of Tunis El Manar, Tunis 1002, Tunisia

Shtwai Alsubai and Abed Alanazi: Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia

Abdullah Alqahtani: Software Engineering Department, College of Computer Engineering and Sciences,
Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia

Abstract
The escalating growth of industrial sectors has led to a pervasive global problem—oil pollution, particularly in industrial areas. The release of substantial volumes of oil and its by-products into the environment has resulted in extensive contamination. Multiple factors contribute to the entry of these substances into water bodies and soils, thereby inflicting irreparable consequences on ecosystems, natural resources, and human health. Consequently, it becomes imperative to comprehend the characteristics and behavior of oil pollution, anticipate its impacts, and develop effective mitigation strategies. Understanding this intricate issue requires considering the physicochemical properties of the environment, the interactions between oil and sediments, and biological factors such as evaporation and dissolution. Although the oil industry has brought about remarkable advancements, its activities have raised significant concerns regarding pollution from extraction and production processes. Oil-rich nations face a particularly challenging predicament of soil pollution caused by petroleum compounds. The areas surrounding oil exploration mines and refineries often endure contamination due to oil leakages from storage tanks and transmission lines resulting from deterioration and damage. Investigating the dispersion of such pollutants and devising methods to remediate petroleum-contaminated soil represent crucial and intricate issues within the realm of environmental geotechnics.

Key Words
contamination; environmental geotechnics; oil and derivatives; remediation; soil pollution

Address
Yong Huang: State Key Laboratory of Chemistry and Utilization of Carbon-Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi 830017, Xinjiang, China/ College of Civil Engineering and Architecture, Xinjiang University, Urumqi, 830017 Xinjiang, China/ Xinjiang Communication Construction Co.Ltd. (XCCG), Urumqi 830000, Xinjiang, China/ Chengdu University of Technology, Chengdu 610000, Sichuan, China/ Transpotation Industry Highway Maintenance Collaborative Innovation Platform under Complicated Conditions of Western China, Urumqi 830000, Xinjiang, China/ Western Sub-Alliance of Zhongguancun Zhongke Highway Maintenance Technology Innovation Alliance, Urumqi 830000, Xinjiang, China

Lei Zhang: Xiamen Innovation Research Institute, Xiamen 361000, Fujian, China

Abstract
Coupled porous curved beams, due to their low weight and high flexibility, have many applications in engineering. This study investigates the vibration behavior of coupled porous curved beams in different boundary conditions. The system consists of two curved beams connected by a mid-layer of elastic springs. These beams are made of various materials, such as homogenous steel foam, and composite materials with PMMA (polymethyl methacrylate) and SWCNT (single-walled carbon nanotube) used as the matrix and nanofillers, respectively. To obtain equivalent material properties, the role of mixture (RoM) was employed, followed by the implementation of the porosity function. The system's governing equations were obtained by employing FSDT and Hamilton's law. To investigate thermal vibration, temperature was implemented as a load in the governing equations. The GDQ method was used to solve these equations. To demonstrate the applicability of the GDQ method in calculating the frequencies of the system and the correctness of the developed program, a validation study was conducted. After validation, numerous examples were presented to investigate the behavior of single and coupled curved beams in various material properties and boundary conditions. The results indicate that the frequencies of the curved beams and the system depend highly on the amount of porosity (n) and the distribution pattern. The system frequencies decreased with an increase in the porosity coefficient. The stiffness of the springs had no effect on the first mode frequency but increased frequencies of other modes in a specific range. The frequencies of the system decreased with an increase in environmental temperature.

Key Words
carbon nano tube; coupled curved beams; natural frequency; porous beams; thermal loading

Address
Moein A. Ghandehari and Amir R. Masoodi: Department of Civil Engineering, Ferdowsi University of Mashhad, Iran

Abstract
The killing of bacteria by mechanical forces on nanopatterned surfaces has been defined as a mechano-bactericidal effect. Inspired by nature, this method is a new-generation technology that does not cause toxic effects and antibiotic resistance. This study aimed to simulate the mechano-bactericidal effect of nanopatterned surfaces' geometric parameters and material properties against three implant-derived bacterial species. Here, in silico models were developed to explain the interactions between the bacterial cell and the nanopatterned surface. Numerical solutions were performed based on the finite element method. Elastic and creep deformation models of bacterial cells were created. Maximum deformation, maximum stress, maximum strain, as well as mortality of the cells were calculated. The results showed that increasing the peak sharpness and decreasing the width of the nanopatterns increased the maximum deformation, stress, and strain in the walls of the three bacterial cells. The increase in spacing between nanopatterns increased the maximum deformation, stress, and strain in E. coli and P. aeruginosa cell walls it decreased in S. aureus. The decrease in width with the increase in sharpness and spacing increased the mortality of E. coli and P. aeruginosa cells, the same values did not cause mortality in S. aureus cells. In addition, it was determined that using different materials for nanopatterns did not cause a significant change in stress, strain, and deformation. This study will accelerate and promote the production of more efficient mechano-bactericidal implant surfaces by modeling the geometric structures and material properties of nanopatterned surfaces together.

Key Words
finite element method; implant-derived bacteria; mechano-bactericidal; nanopatterned surface

Address
Ecren Uzun Yaylaci: Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, 53100, Rize, Turkey

Mehmet Emin Özdemir: Department of Civil Engineering, Cankiri Karatekin University, 18100, Çankiri, Turkey

Yilmaz Güvercin: Trabzon Kanuni Training and Research Hospital, Department of Orthopaed & Traumatol, 61000, Trabzon, Turkey

Şevval Öztürk: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey

Murat Yaylaci: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey/ Biomedical Engineering MSc Program, Recep Tayyip Erdogan University, 53100, Rize, Turkey

Abstract
This research delves into the intricate dynamics of the flight response exhibited by a golf ball that incorporates nanoparticles with the goal of enhancing its overall quality. The golf ball is meticulously modeled utilizing beam elements, and the impact of nanoparticles is intricately captured through the application of the Halpin-Tsai theory. Employing a numerical solution, the study thoroughly explores the flight response of the golf ball, taking into account the nuanced effects of the embedded nanoparticles. By scrutinizing the aerodynamic characteristics through advanced simulations, this investigation aims to provide valuable insights that could potentially revolutionize the design and performance of golf equipment, offering a pathway towards superior quality and enhanced functionality in the realm of golf ball technology. Results show that increase in the volume percent of nanoparticles, improves the flight response of the golf ball.

Key Words
dynamic flight; golf ball; model; nanoparticles; numerical solution

Address
Yuwei Du: College of Leisure and Digital Sports, Guangzhou Sport University, Guangzhou 510000 Guangdong, China

Guowen Ai: School of Physical Education, Hainan Normal University, Haikou 570100, Hainan, China

M. Kaffash: School of Mechanical Engineering, Malaya University, Malaysia

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