• Advances in materials Research
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• 제12권2호
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• pp.161-177
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• 2023

This paper proposes an analytical method to investigate the free vibration behaviour of new functionally graded (FG) carbon nanotubes reinforced composite beams based on a higher-order shear deformation theory. Cosine functions represent the material gradation and material properties via the thickness. The kinematic relations of the beam are proposed according to trigonometric functions. The equilibrium equations are obtained using the virtual work principle and solved using Navier's method. A comparative evaluation of results against predictions from literature demonstrates the accuracy of the proposed analytical model. Moreover, a detailed parametric analysis checks for the sensitivity of the vibration response of FG nanobeams to nonlocal length scale, strain gradient microstructure-scale, material distribution and geometry.

• Computers and Concrete
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• 제32권3호
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• 국제초고층학회논문집
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• 제11권2호
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• pp.115-124
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• 2022

Welding is a significant part of the construction industry. Since most high-rise building construction structures rely on a robust metal frame welded together, welding defect can damage welded structures and is critical to safety and quality. Despite its importance and heavy usage in construction, the labor shortage of welders has been a continuous challenge to the construction industry. To deal with the labor shortage, the ultimate goal of this study is to design and develop an automated robotic welding system composed of a welding machine, unmanned ground vehicle (UGV), robotic arm, and visual sensors. This paper proposes and focuses on automated weaving using the robotic arm. For automated welding operation, a microcontroller is used to control the switch and is added to a welding torch by physically modifying the hardware. Varying weave patterns are mathematically programmed. The automated weaving is tested using a brush pen and a ballpoint pen to clearly see the patterns and detect any changes in vertical forces by the arm during weaving. The results show that the weave patterns have sufficiently high consistency and precision to be used in the actual welding. Lastly, actual welding was performed, and the results are presented.

• Computers and Concrete
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• 제32권4호
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Steel and Composite Structures
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• 제49권5호
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• pp.547-570
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• 2023

In the present work, a comparative study has been carried out between power, exponential, and sigmoidal sandwich FGM plates for free vibration conditions under hygro-thermal conditions. Rules of mixture is used to determine effective material properties across the thickness for power-law and sigmoid sandwich FGM plates. Exponential law is used to plot effective material properties for exponentially graded sandwich FGM plates. Temperature and moisture dependent material properties were used during the analysis. Free vibration analysis is carried out using recently proposed finite element based HOZT. Present formulation satisfies interlayer transverse stress continuity conditions at interfaces and transverse shear stress-free conditions at the plate's top and bottom surfaces. The present model is free from any penalty or post-processing requirements. Several new results are reported in the present work, especially for unsymmetric sandwich FGM plates and exponential and sigmoidal sandwich FGM plates.

• Journal of Information Science Theory and Practice
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• 제12권1호
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.

• Computers and Concrete
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• 제33권2호
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Journal of Periodontal and Implant Science
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• 제43권5호
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• pp.215-220
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• 2013

Purpose: Cigarette smoking is a major risk factor in periodontal diseases. The pathogenesis of periodontal diseases may be affected by alterations of the inflammatory response by smoke. Nitric oxide (NO) is a gaseous, colorless, highly reactive, short-lived free radical with a pivotal role in the regulation of various physiological and pathological mechanisms in the body. It is important in host defense and homeostasis, on the one hand, whereas, on the other hand, it modulates the inflammatory response in periodontitis, leading to harmful effects. The aim of this study was to assess the levels of NO in both the serum and saliva of smokers and nonsmokers having chronic periodontitis and to compare them with periodontally healthy controls. Methods: Sixty subjects participated in the study and were divided into three groups: group I, healthy nonsmoking subjects; group II, nonsmoking patients with chronic periodontitis; group III, smoking patients with chronic periodontitis. Each group consisted of twenty subjects. The biochemical estimation of NO in the collected serum and in the saliva was performed using the Griess colorimetric reaction. Results: The results showed that the mean value of the salivary and serum NO was greater in group II than in group I, and also greater in group III than in group II. Conclusions: NO appears to play an important and rather complex role in the immuno-inflammatory process and in the remodeling and maintenance of osseous structures. It is therefore logical that modulation of this mediator has potential for the treatment of a number of inflammatory conditions including periodontal disease.

• Annals of Clinical Neurophysiology
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• 제2권2호
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• pp.81-88
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• 2000

한 정신병원에 장기입원한 정신분열증환자에서 계속 발생한 8명의 급성 축삭성 GBS로 추정되는 환자들의 평균연령은 38세였으며 7명이 남자였다. 모든 환자들은 급성 상행성 양쪽하지 마비나 사지마비를 보이면서 심부 건반사가 소실되었다. 이 병은 주로 여름철에 많이 발생 하였으며 전기생리학적 검사상 축삭이 주로 손상된 소견을 보였다. IVIG치료를 한 1명을 제외한 나머지 환자들은 경제적 사정상 대증요법으로 치료하였다. AMAN형태의 환자 3명 중 1명에서 임상적 호전을 보였고, AMSAN형태의 환자 5명 중 2명에서 임상적 호전을 보였다. AMSAN형태의 환자중 1명에선 10개월 뒤 같은 증상이 재발하였다.

• 전기학회논문지
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• 제65권10호
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• pp.1755-1760
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• 2016

Automation of greenhouses has proved to be extremely helpful in maximizing crop yields and minimizing labor costs. The optimum conditions for cultivating plants are regularly maintained by the use of programmed sensors and actuators with constant monitoring of the system. In this paper, we have designed a prototype of a smart greenhouse using Arduino microcontroller, simple yet improved in feedbacks and algorithms. Only three important microclimatic parameters namely moisture level, temperature and light are taken into consideration for the design of the system. Signals acquired from the sensors are first isolated and filtered to reduce noise before it is processed by Arduino. With the help of LabVIEW program, Time domain analysis and Fast Fourier Transform (FFT) of the acquired signals are done to analyze the waveform. Especially, for smoothing the outlying data digitally, Moving average algorithm is designed. With the implement of this algorithm, variations in the sensed data which could occur from rapidly changing environment or imprecise sensors, could be largely smoothed and stable output could be created. Also, actuators are controlled with constant feedbacks to ensure desired conditions are always met. Lastly, data is constantly acquired by the use of Data Acquisition Hardware and can be viewed through PC or Smart devices for monitoring purposes.