Table of Contents--Current issue--Volume 3, Number 1

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Advances in Engineering: an International Journal (ADEIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Engineering. The journal focuses on all technical and practical aspects of Engineering. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.

Education

Advances in Engineering: an International Journal (ADEIJ)
https://airccse.com/adeij/index.html
Current Issue: Volume 3, Number 1
Influence Mechanism of Thickness and Viscosity of Flat Energy Director to the Joints
Quality by Ultrasonic Welding
Jiale Lu1
, Wei Dai2
, Jun Wang2,3
, Xianghui Gan4
, Zhuang Wu4
, Hang Wang4
, Xujun Tian4
,
1
Princeton International School of Mathematics and Science, USA, 2
School of Materials Science
and Engineering, Wuhan University of Technology, China, 3
Institute of Advanced Material
Manufacturing Equipment and Technology, Wuhan University of Technology, China, 4
China
Ship Development and Design Center, China
https://airccse.com/adeij/vol3.html

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The relationship between technology and application of civil engineering is not a new concept. Over the years civil engineering has encountered a slew of issues, most of them have been solved with the aid of technology. Prediction of punching shear strength is one such problem statement which could be solved using a metaheuristic approach of optimization. Numerous experiments on the punching shear resistance of reinforced concrete slabs have been conducted by researchers, with positive findings. The actual service life will be shortened due to steel bars’ propensity for corrosion. The main goals of all organizations are to make civil engineering applications more valuable intrinsically so that people can use them to construct faster so that resources are used more effectively, and to ultimately improve people’s lives. Using evolutionary artificial neural networks, internal flat slabs of reinforced concrete can be predicted for their punching shear strength. It is a hybrid model of an artificial neural network (ANN) and a Genetic algorithm, a metaheuristic based on natural selection that is a subset of the larger category of evolutionary algorithms (EA). The experimental findings from 519 flat slabs tested by various authors starting in 1938 were used in this research. The model tries to predict the dependent feature, Punching shear resistance, using independent features such as Shape of the column cross section, Column side or smaller side, Larger side of the column, Average effective depth in X and Y directions, Average reinforcement ratio in X and Y directions, Column effective width, Effective width / Effective depth, Concrete compressive strength, and Steel yield strength. Sometimes, signals are altered at the receiving synapses, and the processing element adds the weighted inputs. Input from one neuron is sent to another (or output is sent to the outside world) if it reaches the threshold, and the cycle continues. The algorithm builds the subsequent population at each stage using members of the current generation. By using Selection, Crossover and Mutation, we can obtain a set of optimal parameters that aid in producing effective results. We also contrasted the accuracy attained using GA with other popularly employed optimizer types like SGD, ADAM and RMSProp. We have also made use of the benefits of the GA algorithm, such as its adaptability, understanding ability, and lack of computational complexity.

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Each year number of students enrolling in higher education is increasing significantly. Students from diverse backgrounds can be found in a class. These changing circumstances are making it necessary to develop Innovative teaching and Learning methodologies. Active Learning methodology is an innovative strategy and Lightweight Team comes under this Active Learning methodology. Lightweight Team approach is one such low-stake activity and it has very little or no direct impact on a student's grade whereas it makes the learning process fun and interesting. A student’s Emotion towards a class plays a major role in their class performance. In this work we use the feedback from the Student Survey Data which aims to evaluate student emotions and overall satisfaction with Course Teaching methods and Group Work experience. We use Actionable Pattern Discovery methodology to provide suggestions in the form of Action Rules to enhance student Emotions thereby achieving a Positive Learning and Teaching experience.

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In this work we have find out the antioxidants activity of the ethyl acetate, methanolic and aquaous extract of leaves and bark of Baccaurea ramiflora (Lour.). The antioxidant activity of the extracts was measured by in vitro chemical analyses involving the assays of (1) Au nanoparticle formation potential (2) 1,1-diphenyl2-picrylhydrazyl (DPPH) radical scavenging activity (3) ferric ion reducing power and (4) ferrous ion chelating activity. A simpler method has been created based on Au nanoparticles formation to assess the antioxidant activity of any plant extract. It was for the assessment of the antioxidant activity of all the extract of leaves and bark of Baccaurea ramiflora (Lour.). In all the assays, methanolic extract of leaves of Baccaurea ramiflora (MEBRL) and methanolic extract of bark of Baccaurea ramiflora (MEBRB) showed significantly greater activity over other extracts. This work provides a scientific support for the high antioxidant activity of this plant and thus it may find potential applications in the treatment of the diseases caused by ROS.

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Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner)

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Cloud storage services are a lot of well-liked today . To secure information from those that don't have access, several encoding schemes are projected. Most of the projected schemes assume cloud storage service suppliers or trustworthy third parties handling key management are trustworthy and can't be hacked; but, in follow, some entities could intercept communications between users and cloud storage suppliers and so compel storage suppliers to unleash user secrets by victimisation government power or alternative means that. During this case, encrypted information are assumed to be identified and storage suppliers are requested to unleash user secrets. Since it's tough to fight against outside coercion, we tend to aimed to create Associate in Nursing encoding theme that might facilitate cloud storage suppliers avoid this plight. We provide cloud storage suppliers means that to make pretend user secrets. Given such pretend user secrets, outside coercers will solely obtained solid information from a user’s keep cipher text. Once coercers suppose the received secrets are real, they'll be happy and a lot of significantly cloud storage suppliers won't have discovered any real secrets. Therefore, user privacy continues to be protected.

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In recent days, skin cancer is seen as one of the most Hazardous form of the cancer found in Humans. Skin Cancer is a malignant tumor that grows in the skin cells. It can be affected mostly by the reason of skin burn caused by sunlight. Early detection and treatment of Skin cancer can significantly improve patient outcome. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. A person’s in which they have inadequate amount of melanoma will be exposed to the risk of sun burns and the ultra violet rays from the sun will be affected that body. Malignant melanomas is a type of melanoma that has irregular borders, color variations so analyze the shape, color and texture of the skin lesion is important for the early detection. It can have the components of an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction and finally classification. Finally the result show that the system is efficient achieving classification of the lesion as either melanoma or Non melanoma causes.

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The research in this paper is an essential part of a bigger effort to develop ultrasonic welding using flat energy directors (FED) in thermoplastic composites. It mainly focused on assessing the effect of the changes in thickness and viscosity of FED on welding strength and welding strength stability. Furthermore, the welding and failure mechanisms were investigated by monitoring the real-time temperature evolution at the interface and analyzing the welding area (WA) and the macro and microfracture surface. From the experimental results, the use of FED with thinner thickness (0.1-0.2 mm) and slightly higher viscosity greatly improved weld strength and weld strength stability. Due to the effect of glass fibers forming a mechanical interlocking effect between the adherend and FED, joints with 20% SGF/PP FED also had a surprisingly excellent weld quality. Welding area analysis indicated a significantly positive correlation between the lap shear strengthⅠand the welded area for the joints with FED. Based on real-time temperature curves, thinner FED had an improvement on the initial temperature rate and the most temperature curves leveled off at the onset melting temperature (Tonset) of the FED after a time-lapse of about 0.4 s. Morphology analysis showed that the melting and flow of matrix at the interface started at the edges of the overlap; the melting area and morphology of the interfaces with 1% EG/PP FED and 20% SGF/PP are much bigger and coarse. And failure mechanisms of the joints with FED are a combination of adhesive failure, cohesive failure, and fiber-matrix debonding failure.

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Generally PV generators are considered reliable compared to other systems, but like all processes, a PV system can be exposed to several failures causing the PV system to malfunction. Several studies have found that the reliability of PV systems is highly dependent on the equipment used for the construction of PV panels, temperature, humidity and solar radiation. A PV system can have several defects, be it defects of construction types, or material and electrical defects caused by climatic conditions. As such, we can cite the fault most commonly encountered in a PV generator which is the partial shading defect.

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Segmentation of objects with various sizes is relatively less explored in medical imaging, and has been very challenging in computer vision tasks in general. We hypothesize that the receptive field of a deep model corresponds closely to the size of object to be segmented, which could critically influence the segmentation accuracy of objects with varied sizes. In this study, we employed “AmygNet”, a dual-branch fully convolutional neural network (FCNN) with two different sizes of receptive fields, to investigate the effects of receptive field on segmenting four major subnuclei of bilateral amygdalae. The experiment was conducted on 14 subjects, which are all 3-dimensional MRI human brain images. Since the scale of different subnuclear groups are different, by investigating the accuracy of each subnuclear group while using receptive fields of various sizes, we may find which kind of receptive field is suitable for object of which scale respectively. In the given condition, AmygNet with multiple receptive fields presents great potential in segmenting objects of different sizes.

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In this paper, the modeling, optimization and simulation results of the piezoelectric energy harvesting using bond graph approach are presented. Firstly, a lightweight equivalent model derived from the bond graph is proposed. It’s a comprehensive model, which is suitable for piezoelectric seismic energy harvester investigation and power optimization. The optimal charge impedance for both the resistive load and complex load are given and analysed. Finally a bond graph approach is proposed to allow optimization of the extracted energy while keeping simplicity and standalone capability. The proposed model does not rely on any inductor and is constructed with a simple switch. The power harvested is more than twice the conventional technique one on a wide band of resistive load. The bond graph model is valid close to the analysed mode centre frequency and delivers results compared to experimental and analytical data. Furthermore, we also show that the harvester can be electrically tuned to match the excitation frequency. This makes it possible to maximize the power output for both linear and non-linear loads.

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The piezoelectricity is a reversible phenomenon, which corresponds to the direct piezoelectric effect. So that, the battery loads arrive at the pump, an electronic module must be established in the circuit. A flow pump must be used and its effectiveness must be evaluated according to the desired need. The system is conceived around 24 hour old clock which can be programmed to provide fluid in variable quantity on a regular basis. The piezo-hydraulic pump presented in this work is composed mainly of shareholder piezoelectric with two valves and a diaphragm of the room reinforced by a hard disk. This one is used to improve the performance of the micro-pump in forecast of pump realization. Modeling, analysis and simulation are carried out under SYMBOLS software.

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