Esthetic

Djanssou D.M., Dadjé A., Djongyang N.

Optimal estimation of the intrinsic parameters of photovoltaic cells requires the use of meta-heuristics to increase their efficiency. This paper highlights the estimation of unknown parameters of a PV cell and module. For this purpose, the meta-heuristic optimization algorithm based on the Honey Badger Algorithm (HBA) principle is used. The simulation results via MATLAB prove that this algorithm has a good convergence. Indeed, the root mean square error (RMSE) is 9.8602×10-4, 9.8602×10-4, 2.4251×10-3, 1.7298×10-3 and 1.6783×10-2 for the single diode, dual diode, Photowatt-PWP201, Schutten Solar STM6-40/36 and the STP6-120/36 module respectively. Furthermore, the curves representing the current-voltage and power-voltage characteristics of the calculated unknown parameters versus those of the practical data measured from a PV cell/module datasheet coincide. The proposed algorithm can therefore be classified in the literature as one of the optimal parameter extraction techniques.

Shashi D.M.

Compared to the other classic supply chain, the pharmaceutical supply chain has many critical challenges, such as cold chain transportation, temperature monitoring, long lead time, and counterfeiting prevention. Pharmaceutical products, such as medicines, drugs, vaccines, and specialty treatments, work as intended within a specific specified temperature. Below +25°C (controlled temperature), +2°C to +8°C (temperature-sensitive products), -20 °C to -40 °C (negative temperature), and -70 °C (ultra-low temperature) are some of the precise and regulated storage thresholds for various types of pharmaceutical products. The cold chain includes production, transport, and storage. It requires a reliable infrastructure to maintain a precise temperature when transporting from manufacturers to patients. This multiple qualitative case study aimed to investigate the role of the Internet of Things (IoT) in the pharmaceutical cold chain. The study involved interviewing various pharmaceutical managers with proven digital strategies for implementing IoT-based digital enablers. The theory of constraint was used as the conceptual framework for this qualitative multiple case study. Data from interviews and supporting documents were analyzed using data triangulation to discover themes. Two main themes emerged from data analysis: (a) Known or unknown constraints in the current cold chain system and (b) implementation of IoT-based digital enablers. Six key strategies were developed pertain to these two themes.

Kusumaningrum J., Madenda S., Karmilasari, Nahdalina

Road pavement is a supporting factor for national development, especially in the distribution of trade in goods and services as well as the movement of human mobility. Road maintenance needs to be done regularly so that the road is always in good condition, but the weather and road loads are the things that cause road damage. Road damage is generally categorized into cracks, alligator cracks and potholes. The purpose of this research is to utilize image processing to detect and classify the types of road damage. The steps involved include: image acquisition with a digital camera, conversion of RGB images into grayscale images, image normalization, selection of damage points, counting histogram bins, determining damage bins, calculating noise with image morphology (closing and opening) using a disk element structure of size 5, calculating radial vector and finally classifying road damage using the K-NN (K Nearest Neighbor) method with 3 classes and a K value of 11. The image from the classification results is then calculated the level of damage based on the category according to the SDI (Surface Distress Index) provisions, where the level of crack damage is seen from the width of the crack, the alligator crack is seen from the percentage of damaged area compared to the segment under review and the pathole is seen from many holes. The test used 597 images consisting of 95% training data and 5% test data, the results obtained that the accuracy of this research reached 83%.

Yehia H.M., Elkady O., Elmahdy M.

Tungsten carbide is one of the ceramic materials characterized by high hardness. It has many uses in manufacturing, including cutting tools, die inserts and other parts that need materials with high mechanical wear resistance. In this study, tungsten carbide was reinforced with alumina and different ratios of graphene to improve its mechanical properties. The BSE mode used the electronic imaging device (SEM) to study the powders and manufactured sample's microstructure. The densification, hardness, and toughness of fabricated specimens were evaluated. The results proved that the density of samples was decreased by adding alumina and graphene due to their low density. The samples' toughness was improved due to the addition of nickel, where no cracks were established from the hardness test. The hardness was increased by adding 2.5 wt % Al2O3 and different percentages of graphene up to 0.9 wt %.

Das N.K., Saikia P.M., Buragohain D.M., Saikia N.

A Controller is generally considered as a continuous and discrete mode of execution by a huge sample period that may result in degenerated dynamic performance or system instability. Nowadays, the maximum penetration in thermal, wind, hydropower systems has decreased the power system inertia that leads to rapid frequency response and higher frequency deviation followed by contingencies and requires rapid load frequency control. The goal of load frequency control (LFC) is to achieve zero steady-state errors in frequency deviations and minimize unscheduled tie-line power flows among the interconnected areas. The study of the literature reveals that a lot of research has been carried out in this area to achieve the desired objectives using different approaches. This manuscript proposes an optimization algorithm called Duelist Optimization Algorithm (DOA) in a three area interconnected power system consisting of thermal, wind, and hydro generating systems. The proposed system introduces an adaptive PID fuzzy controller whose parameters are optimized by the DOA algorithm. The Duelist Optimization algorithm is used to optimally tune the parameters of the controller in order to keep the system frequency deviation within the threshold limit, and maintain the power balance among the control areas during load variations. The proposed method is simulated in MATLAB / Simulink environment for the estimation of its performance and then compared with some of the existing techniques such as Artificial Bee Colony (ABC) optimization algorithm, Bacteria Foraging Optimization (BFO), and Particle Swarm Optimization (PSO) algorithm. The simulation result established the suprerioty of the proposed method over the other methods.

Luís A.F., Martins G.M., Caldeira J.M., Soares V.N.

This survey focuses on the benefits of smart lockers and their potential contribution in the last mile problem. It first introduces the related concepts. Then, categorizes existing solutions and identifies the similarities and differences. Further, their strengths and limitations are discussed. Finally, it presents key challenges in the field, and discusses envisioned future research directions that must be factored in by researchers, implementers, and manufacturers to increase the acceptance of smart lockers and to improve their security.

Patel S., Kharawala K.

Surfactants are extensively employed in industrial, agricultural, and food, cosmetics and pharmaceuticals applications. Chemically produced surfactants cause environmental and toxicological hazards. Recently, considerable research has led to environmentally friendly procedures for the synthesis of several forms of biosurfactants from microorganisms. In comparison to chemical surfactants, biosurfactants have several advantages, such as biodegradability, low toxicity and ease of availability of raw materials. This paper offers an in-depth review of the types of surfactants, the need for bio-surfactants, their types and advantages, especially biodegradability. It also examines the biodegradability of selected four surfactants and finds that the biosurfactant is more easily biodegradable than the chemical surfactants.

Khatti J., Grover K.S.

The Gaussian Process Regression (GPR), Decision Tree (DT), Relevance Vector Machine (RVM), and Artificial Neural Network (ANN) AI approaches are constructed in MATLAB R2020a with different hyperparameters namely, kernel function, leaf size, backpropagation algorithms, number of neurons and hidden layers to compute the permeability of soil. The present study is carried out using 158 datasets of soil. The soil dataset consists of fine content (FC), sand content (SC), liquid limit (LL), specific gravity (SG), plasticity index (PI), maximum dry density (MDD) and optimum moisture content (OMC), permeability (K). Excluding the permeability of soil, rest of properties of soil is used as input parameters of the AI models. The best architectural and optimum performance models are identified by comparing the performance of the models. Based on the performance of the AI models, the NISEK_K_GPR, 10LF_K_DT, Poly_K_RVM, and GDANN_K_10H5 models have been identified as the best architectural AI models. The comparison of performance of the best architectural models, it is observed that the NISEK_K_GPR model outperformed the other best architectural AI models. In this study, it is also observed that GPR model is outperformed ANN models because of small dataset. The performance of NISEK_K_GPR model is compared with models available in literature and it is concluded that the GPR model has better performance and least prediction error than models available in literature study.

N S., S K.P.

In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.

Shrivastava P., Singh K., Pancham A.

There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.

Mahajan G.

Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (i) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. This is basically a review paper. The aim of this paper is to study the potential of DT in construction industry, extended it to understand the following issues in better way(i) benefits and impacts of drone in CI, (ii) record disadvantage of drone in CI(iii) integration of BIM with DT at substantial length and volume (iv)extensive description and enumeration on applications and uses of drones in CI(v) use of drone at each stage of construction stage to monitor the progress of construction rightly from the purchase of land to close out the project(vi)lastly appended a note on the impact of COVID-19 on construction. This study (2012-2021) also discusses challenges, opportunities, limitations, and strategies for the adoption of drones in construction. It assists to contractors, building planners, designers, academicians, engineers, and architects to improve the construction activities for greater efficiency and better performance. It also motivates towards inclusion of these technologies in the curriculum in Architecture Engineering

Lakhani S., Jhamb R.

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for the conclusion and assessment of sickness. Be that as it may, this method is vulnerable to doctors and instrument limitations. As a result, the automated investigation and analysis of respiratory sounds has been a field of great research and exploration during the last decades. The classification of respiratory sounds has the potential to distinguish anomalies and diseases in the beginning phases of a respiratory dysfunction and hence improve the accuracy of decision making. In this paper, we explore the publically available respiratory sound database and deploy three different convolutional neural networks (CNN) and combine them to form a dense network to diagnose the respiratory disorders. The results demonstrate that this dense network classifies the sounds accurately and diagnoses the corresponding respiratory disorders associated with them.

Railkar P.N., Mahalle P., Shinde D.G.

IoT is a network of interconnected heterogeneous devices which sense, accumulate the data and forward the same to the cloud platform for analytical purposes. There are various IoT verticals in which huge research is going on. IoT security is the most challenging research area in which researchers are investing a huge number of efforts. The challenges in IoT security include access control, trust management, authentication, authorization, privacy, and secured device to device communication. To overcome these, this paper gives an overview of proposed trust based distributed access control approach in IoT. Some of the challenges and threats can be controlled by blockchain technology. Basically, blockchain is an open and distributed ledger of records that can be verified efficiently and stored permanently. This paper checks the feasibility study of the applicability of blockchain in the IoT ecosystem to apply access control mechanism and privacy-preserving policies. This paper discusses how access control and privacy can be addressed by blockchain without compromising security. This paper consists of rigorous gap analysis which is done on the top of comprehensive literature survey. The paper also addresses the challenges and issues which can be faced while applying access control mechanism using blockchain in the context of IoT.

Jain M., Bhagerathi B., C N D.S.

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.

Almghraby M., Elnady* A.O.

Face mask detection has made considerable progress in the field of computer vision since the start of the Covid-19 epidemic. Many efforts are being made to develop software that can detect whether or not someone is wearing a mask. Many methods and strategies have been used to construct face detection models. A created model for detecting face masks is described in this paper, which uses “deep learning”, “TensorFlow”, “Keras”, and “OpenCV”. The MobilenetV2 architecture is used as a foundation for the classifier to perform real-time mask identification. The present model dedicates 80 percent of the training dataset to training and 20% to testing, and splits the training dataset into 80% training and 20% validation, resulting in a final model with 65 percent of the dataset for training, 15 percent for validation, and 20% for testing. The optimization approach used in this experiment is “stochastic gradient descent” with momentum (“SGD”), with a learning rate of 0.001 and momentum of 0.85. The training and validation accuracy rose until they reached their maximal peak at epoch 12, with 99% training accuracy and 98% validation accuracy. The model's training and validation losses both reduced until they reached their lowest at epoch 12, with a validation loss of 0.050% and a training loss of less than 0.025%. This system allows for real-time detection of someone is missing the appropriate face mask. This model is particularly resource-efficient when it comes to deployment, thus it can be employed for safety. So, this technique can be merged with embedded application systems at public places and public services places as airports, trains stations, workplaces, and schools to ensure subordination to the guidelines for public safety. The current version is compatible with both IP and non-IP cameras. Web and desktop apps can use the live video feed for detection. The program can also be linked to the entrance gates, allowing only those who are wearing masks to enter. It can also be used in shopping malls and universities.