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Shockwave are defined as nonlinear pressure waves propagating at supersonic speeds. The first shock wave technology was developed in 1990s, which used explosives and electric current to generate shock. Modern shock generation methods include hypersonic shock tunnels and shock tubes, which are used extensively in the fields of medicine (treatment of kidney stones), biological sciences (bacterial transformation), agriculture (improved processing of crops), oil extraction (increasing yield), food industry (meat tenderization, juice extraction), material processing (property improvement in nano particles), and manufacturing. The objective of this paper is to conduct an overview of the applications of shockwaves specifically in three areas such as materials processing, nanotechnology, medicine, oil extraction and bioscience. Oil, nanoparticles, solid, liquids, grains, meats, agricultural products are few reviewed samples which are exposed to shock waves. Shock waves has an impact in physical, chemical, thermal, mechanical, electrical and thermodynamic properties. Using shock waves as a pre-treatment process was also analyzed. Shock waves also has an impact in varying the structure, texture, color, smell, solubility, morphology and molecular behavior of the organic and inorganic compounds. Techniques like ultrasound, UV spectroscopy, SEM, TEM, XRD, ORAC assay are used to study the Shockwave treated samples for obtaining the results. The results obtained are compared with the results obtained from the sample before shock application. [ABSTRACT FROM AUTHOR]

Using the spin coating approach, n-type TiO2 thin films have been coated using the sol-gel process onto p-type silicon substrates. The film's XRD pattern reveals the existence of the most prominent silicon peak in addition to peaks representing the mixed anatase and rutile phases of TiO2. Debye-Scherrer's formula yielded a crystallization size (CZ) of 48 nm. Using TiO2/Si as the working electrode, the linearity of the electrochemical responses to sucrose at various concentrations was thoroughly examined. [ABSTRACT FROM AUTHOR]

In the context of sustainable materials and technologies, advanced visualization technologies like AR and VR have emerged and integrated in the Retail Jewelry Segment. Can the integration of sustainable immersive technologies in retail jewelry segment contribute to the future of sustainable business goals is to be explored. This research study examines the above study area of analyzing the role of sustainable AR/VR experience on customer perceptions in retail jewelry sector. This paper aims to explore if immersive technologies influences customer perceptions like brand associations and purchase behaviour leading to brand loyalty in the landscape of sustainable jewelry business. Based on a thorough literature review, conceptual framework and hypothesis were formulated to test the interactive, novel and vivid features of AR/VR immersive technologies. To validate this, relational study methodology was adopted, using judgmental sampling 227 customer surveys and insights from jewelry retailers who had adopted AR/VR technologies in their operations. Validated scales was used for collecting the data from customers. SEM analysis was administered to review the model in 2 different stages. Reliability and validity tests followed by various Fit Measures were computed to confirm the results. The results proved that customer perceptions were greatly influenced by AR/VR experiences. By exploring the implications of AR/VR experience in sustainable retail jewelry segment, this paper contributes significant recommendations and marketing strategies that cater to the evolving concept of sustainable materials and technologies. [ABSTRACT FROM AUTHOR]

In recent times, due to the increase in air pollution and its impact on the everyone's health and environment; it has become a very tedious task to monitor and check the air quality. Predicting the air quality is a highly tangled task due to the volatility, particle nature and variables constituting the pollutants. It has become pertinent to check the air quality in rural/urban areas due to the impact it has on people's health and the environment. In this paper, we first do comparative analysis of popular ML(machine learning) method, linear regression, random forest, XGBoost decision tree, k-nearest neighbors (KNN), and L1 and L2 regularization for forecasting pollutant and particulate levels and also the air quality index (AQI) is predicted. Then we apply cross validation and grid search to optimize our random forest model. Finally, we predict the model with Long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

Detecting stammering signals and deducing real words is a new objective of the research. Determine which Wiener and Kalman filters are best for recovering stuttering speech from a 102-person sample. Classifiers might find value in the domain of classification, among others. The measurement is conducted with an 89% G-power. The Wiener Filter model reduced input signal noise by 91.0%, in contrast to the 84.2% reduction achieved by the Kalman Filter model. In terms of the degree of clarity they impart, the Kalman Filter and the Wiener Filter classifiers vary by 0.039 points. [ABSTRACT FROM AUTHOR]

This effort will use the Novel Thermal threshold operator and compare it with Binarization in order to develop a novel edge detection method for gauging the pace of wound healing. Sections and Procedures: We apply two edge detection approaches, namely Binarization and Novel Thermal threshold, to determine the wound healing rate. We used 80% of the pretest power to assess Group 1, which included twelve samples using Novel Thermal threshold, & Group 2, which included twelve samples utilizing binarization. The linearization technique had an accuracy rate of 88.10% and a wound healing rate of 0.38%, while the innovative thermal threshold edge detection method achieved a rate of 88.10%. A 0.006 (p<0.05) result indicated that both methods were statistically significant. Comparing the Novel Thermal threshold approach to the Binarization operator, the findings show that the former achieves much higher accuracy. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle communication (V2V) is a technology that enables vehicles to exchange information with one another in real-time, without the need for human intervention. V2V communication is typically accomplished using wireless technologies such as Dedicated Short-Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X), which allow vehicles to communicate with each other over short distances. Through V2V communication, vehicles can share information about their speed, direction, location, and other relevant data, enabling them to avoid collisions and coordinate their movements on the road. V2V communication can also be used to provide drivers with real-time information about traffic conditions, road hazards, and other important information. V2V communication has the potential to improve road safety, reduce traffic congestion, and improve the overall efficiency of transportation systems. However, there are also significant challenges to implementing V2V communication, including issues related to privacy, security, and interoperability between different communication technologies. As V2V communication continues to evolve, it is likely to play an increasingly important role in the future of transportation. [ABSTRACT FROM AUTHOR]

Battery Technology is an Emerging area of research in the automotive sectors. Various types of battery chemistries such as Li-ion, LifePO4, cells etc. plays a crucial role in developing EV battery. Many cells come with different packaging such as Prismatic, cylindrical and pouch types in order to suit to different field conditions. The study includes the analysis of various battery packages and their effect on the battery performance. Additionally, the study aims to investigate the impact of temperature on the battery's performance and to determine the most suitable temperature range for maximum battery performance. The study will also look into the development of efficient BTM systems to maintain the battery temperature in a desired range. The study will also look into the development of efficient BTM systems to maintain the battery temperature in a desired range. The results of the study can provide valuable insights for the development of reliable and efficient EV batteries, and help to improve the overall performance and safety of these batteries in different field conditions. [ABSTRACT FROM AUTHOR]

Diabetes is a metabolic disease that causes high blood pressure in the body. People diagnosed with diabetes can either produce insulin-a hormone that helps the cells utilize glucose in the blood; or cannot use insulin effectively. This condition, if left untreated, can prove to be fatal. Common symptoms include frequent urination, feeling thirsty, increased hunger, dry skin, unexplained weight loss, blurry vision, numbness, and tingling sensation. Several common conditions like celiac disease and hemochromatosis and thyroid disease. Diabetic patients are further at a risk for various chronic complications, including cardiovascular diseases, hearing impairment, eye damage, skin condition, kidney damage, and Alzheimer's. The prognosis of such an ailment requires frequent visits to diagnostic centers and numerous consultations, prompting the investment of both time and money can be suggested for IoT platforms such as wearable devices. With the advancement in technology and development in the ever so progressing field of machine learning, we can predict illness and help patients take necessary measures to avoid it if possible. We propose a model that focuses on feature analysis and classification using several supervised machine learning techniques. [ABSTRACT FROM AUTHOR]

The exceptional optoelectronic properties of perovskite have attracted significant attention in the field of photovoltaics. By addressing the primary perovskite photovoltaics challenges, one can possibly utilize them for commercial applications. One such requirement is a stable low-cost high-work function (WF) electrode. In this work, carbon electrode was used in the mixed halide perovskite photovoltaics FTO/SnO2/FA(0.85)MA(0.15)Pb(I(0.85)Br(0.15))3/b-PEI/C as an anode. Carbon electrodes were deposited by electron beam evaporation. The expected advantages of C electrodes are (i) leaves reduced interface porosity at electrode/semiconductor (or) electrode/transport layer interface, and (ii) device stability. Carbon paste electrodes leave high surface roughness usually in the range of μm and hence large interface porosity in photovoltaic. Larger porosity leads to enhanced carrier recombination in the device and results in less efficiency. To overcome such issues, the e-beam evaporated carbon electrodes were deposited as anode in PV. Characteristics of stand-along C electrode and PV device were studied and reported. [ABSTRACT FROM AUTHOR]

Wheat is the main growing cereal crop in India. For the country, designing, estimating productivity is the important tool for predicting the production of wheat to determine the circumstances that would be the value of production upcoming year. Fuzzy time series deals with the problems associated with unpredictability ambiguousness and cloudiness. In this study enlargement of fuzzy time series variant model is used in forecasting wheat production. The data of previous years from 2000 to 2021 was taken for analysis and the proposed method on fuzzy time series estimates future events' precision rate. The suggested method used the percentage change as the Universe of Discourse and Average-based Partitioning. [ABSTRACT FROM AUTHOR]

Driver rest is one of the main sources of mishaps and passing for some individuals and the economy. As of late, specialists have started to perceive that lack of sleep is a positive, industry-driving reaction. In this article, we suggest including CNN-based savvy glasses for eye acknowledgment and rest estimation. This new response is contrasted with the customary technique in view of the strategy for comprehension. Both execution, battery utilization, and impression are assessed for execution in incorporated glass. The outcomes show that CNN has over 7% of the precision found at the algorithmic level. Moreover, migraines in memory and battery duration are genuine and make CNN a potential answer for sleep deprivation. [ABSTRACT FROM AUTHOR]

Neural organizations are probably the most intelligent technique for information mining involved by analysts in different fields throughout the last ten years. Securities exchange gauges and examination play had a critical influence in the present economy. The different calculations utilized in arranging can be delegated (AR, MA, ARIMA, ARMA) and nonlinear models. Here we utilize two different financial exchange shutting dates the Indian Stock Exchange (NSE) and the New York Stock Exchange. The pipeline was prepared on the securities exchange of one NSE organization and anticipated five unique organizations in the NSE and NYSE. CNN has shown that it is the best model. The channel was ready with NSE data; however, it was feasible to anticipate the NYSE. This was conceivable in light of the fact that both financial exchanges share inner qualities. Contrasting the outcomes and the ARIMA model, it was observed that the muscle strands were more than typical. [ABSTRACT FROM AUTHOR]

Significant progress has been made in the field of lung cancer over the years (chemical rehabilitation, radiotherapy, individualized treatment). Nevertheless, lung cancer remains a threat to society and a leading cause of death for thousands of people worldwide. The subject of this paper is the diagnosis of lung cancer. Lung cancer is diagnosed using computerized tomography (CT) scans. To accurately diagnose lung cancer, different algorithms are used. The image is filtered through a sharp hidden filter in this case. The edges and areas of the cancer are detected using a flexible canvas method. To identify the features andpredict the risk of lung cancer, using the neural network. The K-Nearest Neighbors method is used to separate cancerand lungs. Lung cancer is diagnosed using computerized tomography (CT) scans. To accurately diagnose lung cancer, different algorithms are used. The image is filtered through a sharp hidden filter in this case. The edges and areas of the cancer are detected using a flexible canvas method. To identify the features and predict the risk of the Using a neural network to study lung cancer. The K-Nearest Neighbors method is used to separate cancer and lungs. Lung cancer has been growing in recent years, so it is very important to know if tumor has turned into cancer. Prognosis occurs early and Accurate predictions can save many lives and assist doctors in starting therapy. CT (computed tomography) is a type of imaging that uses a is important in determining the condition of a plant by determining its size, location, and other factors. [ABSTRACT FROM AUTHOR]

This work presents the structural and the thermoelectric properties of wurtzite ZnO using ab-initio density functional theory (DFT) with GGA exchange-correlation functional. The relaxed lattice parameters are generated by variable cell relaxation method. The thermoelectric properties as a function of chemical potential are studied within constant relaxation-time approximation for different temperatures. The Seebeck co-efficient is found to be more for positive chemical potential where the major contribution is from electrons and it decreases with increase in temperature. The electrical conductivity, power factor and electronic thermal conductivity show enhanced values for p-type doping region and increases with rise in temperature. The figure of merit (ZT) is computed to be 0.98 which is adequate for thermoelectric applications. [ABSTRACT FROM AUTHOR]

Object detection has become more attractive for its wide range of applications. This technology has been driven by an increasing processing power available in software and hardware. It is a computer vision technique that helps us to identify an image or video. In this work we mainly focus on how we detect the object by using SSD algorithm, there are several other algorithms like CNN, R-CNN and etc. But SSD algorithm is simpler compared to other ones. Furthermore, we discuss how the object is detected and where can we can implement this application. Some of its uses are, it can count objects in an image, retrieval of images, crowd counting, surveillance, self-driving cars and inspection of machines. [ABSTRACT FROM AUTHOR]

Legal Analytics is a blooming field of research that draws attraction from various fields such as computational linguistics, natural language processing, machine learning, and data science. Improvement in text analytics has paved the way to a great level in eCommerce, banking, medical and legal sectors after the incoming of deep learning models. The large quantity of digital information in the legal domain and the challenges faced has made this an important area of legal research. Historically, judges and other legal personnel have frequently been required to manually review legal documents to acquire an inclusive judgment justification. This practice is highly challenging also laborious and time-consuming. Most of the existing works predict the legal outcomes based on case facts, but predicting judgment solely on Casefact gives low efficiency. Automation in judgment prediction systems is at the budding stage. Choosing the appropriate word embedding technique in language modeling, Classifier model and loss functions pave way for better results. Selecting the appropriate learning algorithm for text classification on real-time problems is a great challenge as legal documents are lengthy and unstructured. This paper emphasizes the limitations and important aspects of different components of NLP such as tokenization, word embeddings, and classifiers which makes the computer interpret and extract maximum contextual information from documents to provide the best performance for assisting litigants for verdict prediction of countries that use common law system. It also reviews different ideas applicable to legal case facts in a broad manner. [ABSTRACT FROM AUTHOR]

VLSI design focuses on four primary design factors: area, power, speed, and cost. Standard multipliers have a higher number of different operations, which causes an increase in delay. As a result, circuits using conventional multipliers will be more power-hungry and slower. Digital signal processing relies heavily on multipliers. 32-bit enhanced Wallace multiplier design and FPGA implementation are presented in this study. Verilog HDL was used to code the proposed multiplier, and Xilinx Vivado simulated and synthesized it. It was then implemented on a Zynq 7000 series FPGA board. [ABSTRACT FROM AUTHOR]

Retinopathy of Prematurity (ROP) is a major cause of blindness in infants. This disease is a vascular proliferative retinal disease which causes damages to low birth weight children and premature children. The Objective of this proposed system is to develop an efficient automated screening system for diagnosis of ROP by different deep learning network. First, the input color images are captured from the diabetic patients. Then, the acquired images are given as an input to the preprocessing stage for improvement. The Preprocessed noise free color images are applied to the Deep Learning Network for the detection of presence of ROP. Five different deep learning networks such as VGG-16, VGG-19, AlexNet, GoogLeNet and MobileNet are deployed for the classification of normal and abnormal images. The best system out of these five networks will be identified after calculating the evaluation parameters like Accuracy, Precision, Sensitivity, Specificity, ROC AUC. The proposed system is compared with the existing deep learning work and it is found that our proposed system outperforms than all other existing system. This developed Deep Learning CAD system will be a proven affordable technology for the ophthalmologists since it consumes less time in screening for ROP. [ABSTRACT FROM AUTHOR]

The deviations that occur in electrical power supplied by utilities to consumers are termed as power quality disturbances. Due to power quality disturbances, a change is evident for a short duration in voltage, current or frequency. Signal processing techniques can be used to obtain information from the disturbance signals. Wavelet analysis, an important signal processing tool, is used for analysis of power quality disturbance signals which include sag, swell, harmonics, transients and flicker. Discrete wavelet transform is applied for the signals and are decomposed for different levels. Approximations and details represent low and high frequency components of the original signal. Different statistical features are extracted and values are calculated and information obtained can be used as features to describe the signals. [ABSTRACT FROM AUTHOR]

Nanoparticle encapsulated metal-organic framework composites have attracted attention in several fields due to their combined properties. In this paper, a simple method is reported for encapsulating nanoparticles within zeolitic imidazolate framework-67 (ZIF-67). We have discussed the MOF-derived porous structures serving as photo-catalysts for environment-related reactions, including water splitting. Porous structures offer highly accessible surfaces and rich pores that can be exposed to numerous active sites for photocatalytic reactions, which results in an excellent performance. Following the discovery of TiO2 as a photocatalyst for water splitting, a great deal of effort has been devoted to developing highly efficient semiconductor-based photocatalysts, we have developed TiO2@Cobalt-ZIF. The microstructure, and the morphology of ZIF-67 are characterized by PXRD. The bonding interactions are analyzed by FTIR. The porosity and the pore size distribution or surface area of the ZIF-67 were determined by N2 adsorption isotherm data. [ABSTRACT FROM AUTHOR]

Forest fires are one of the awful things happening in the real-time scenario which causes adverse effects on human lives too. The smoke released by the fire leads to accidents due to unclear views of the road. Due to the unawareness of the forest fires and the smoke released by the forest fires the travelers losing their lives by traveling through the road beside the forest. Real-time fire detection and the alerting system detect the presence of fire in a forest through a sensor interfaced with a Raspberry Pi. This system introduces a fire alerting system using a Publish-Subscribe Protocol to detect fire in a forest and alert users. Cloud-based system was maintained and monitored by using android app. The system uses Raspberry Pi which sends tokens to the Firebase console and utilizes Google Map API for location detection. An alert will be sent to the users if the tokens are matched by using Firebase console. Due to awareness of the location, the travelers can move on to the other direction or stop by traveling in the direction of forest fires. [ABSTRACT FROM AUTHOR]

Cancer is abbreviated as a generic term for all the diseases. This disease can be fatal if not detected at theproper time and if the diagnosis is inaccurate. The mortality rate due to cancer can be decreased if it is detected at an early stage. This paper takes into consideration the clinical analysis of the Pap-Smear results of 30 patients and uses deep learning algorithms to segment the overlapping cell with the nucleus. As U-Net architecture requires less images, so the proposed algorithm can work on a smaller number of images. Then the Support Vector Machine classifier is used to detect the stages of the cancer. For segmenting Bio medical images U-Net and Region Proposal Network is used. In the proposed algorithm we leverage the U-Net with the RPN. The training of both the network is done separately rather it is done together. The output of the combined network which are segmented cells and nucleus of cells are put into the SVM classifier for the detection of the stages of the cancer. The false positive rate, false negative rate, true positive rate and true negative rate of the proposed algorithm is compared with another existing algorithm. The comparison results are included in the end of the paper in a separate section. [ABSTRACT FROM AUTHOR]

Deterioration is the major cause faced in today's field of civil engineering. Some of the difficulties are non-engineered construction, improper execution, wear & tear are the major reasons for damages. In such conditions destruction is not advisable. Many new ideas and attempts were subsequently found by many researchers in which usage of FRP has given more preferences to strengthen the member without increasing the self-weight of the member. FRP has more utilization now a days in all types of RC members such as beams, columns, slabs, etc. Here, GFRP strips are used to strengthen the member. The new attempt will be the application of GFRP strips in number of layers. In this paper, the experimental work of Slabs with 3 variation were given. The slab is of size 700x300x80 mm3 in.which 2 variations are slab with 2 and 3 layers of GFRP laminated strip and the conventional slab. Then the experimental results were compared with the analytical results. The results clearly shows that, the application of GFRP laminates in 3 layers in Slab significantly increases the strength than the conventional slab. [ABSTRACT FROM AUTHOR]

One of the popular method for prediction of medical related data is Binary Logistic Regression. This method helps in understanding the relation between the independent variables and the dependent variable and to explore the relationship between them. The aim of this paper is to build a binary logistic model by identifying the various factors that trigger the heart disease of a person.The various factors influencing the heart disease of a person was computed and the performance of this method were evaluated based on standard statistical measures such as MAE, MAPE, RMSE, etc. The results obtained shows that BLR model can be used to monitor the heart disease conditions satisfactorily. [ABSTRACT FROM AUTHOR]

Soret and Dufour effect with hall current of a magneto hydro dynamic flow of a viscous fluid through vertical porous channel is studied here. Also studied the impacts of ramped wall temperature on the incompressible fluid. Using regular perturbation technique with boundary conditions the velocity, temperature and concentration of the fluid are calculated and it shows the velocity of the fluid increases when Soret number increases and velocity decreases with the increasing values of Dufour number. Also the variations in temperature and concentration also studied with the help of graphs. [ABSTRACT FROM AUTHOR]

The solution of the problem obtained using Homotopy Perturbation Method (HPM) in this paper. Considered the boundary layer fluid flow over porous medium with chemical reactions and heat transfer. Using Similarity variables in non-linear PDE it will be reduced to ordinary DE then the solution found using HPM. The fluid flow features and the different values of governing parameters are analysed using graphs. [ABSTRACT FROM AUTHOR]

Coronavirus disease of 2019 (COVID-19) has become widespread within few months and it has lead to a dramatic loss for human life worldwide. This pandemic impacts tens of millions of deaths each day and the number of people were dead by covid-19 is gradually increasing throughout the globe. During this pandemic situation control, we tend to propose a future prediction using Machine Learning algorithms on the death rate, the number of recovered estimates and the number of daily confirmed COVID-19 cases reported within the next ten days. It is based on Machine Learning technique. This forecasting method will predict the upcoming number of COVID-19 cases. Here we use four standard models for forecasting includes linear Regression (LR), The Lowest Absolute and Selective Shrinking Operator (LASSO), Vector Assistance (SVM) and exponential smoothing (ES) will predict the number of COVID-19 cases in future. These four models make three predictions: the mortality rates, the number of newly affected COVID-19 cases and the cummulative number of recovered cases in the next 10 days. These methods are better used in the COVID-19 situation. Based upon the findings, it is a encouraging method to use these standard models in the current situation of COVID-19 spread. The analysis shows that among all the standard forecasting models, ES model performs best, then LR and LASSO which also performs well in predicting the new infected cases of corona, death rate and recovery cases. Whereas the results of SVM were very bad in all the prediction scenarios from the given covid-19 data set. The predictions made by these models relating to the current situation are accurate and will also be useful for future awareness of the future situation. This paper will be enhanced continuously and next we are planning to traverse the prediction methodology using the updated covid-19 data set and we will make use of the most precise and best Machine Learning models for forecasting in future. [ABSTRACT FROM AUTHOR]

This paper reports the synthesis of nanoparticles of ReBa2NbO6 (Re=Y, Sm and Gd) using a modified single-step combustion process, for the first time. Structural conformation is done by means of XRD and vibrational spectroscopic analysis. The studies show that sample transforms from orthorhombic to cubic structure on annealing at 1000°C. The band gap and dielectric properties of the samples are also investigated. The samples are sintered in a temperature range of 1350-1400°C without any sintering aid. [ABSTRACT FROM AUTHOR]