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Cars have an important role in promoting mobility and economic engagement. However, precisely finding a fair price for a secondhand car remains difficult. In this paper, we apply machine learning to estimate used automobile prices. Using two Kaggle datasets, we examine the performance of three methods: linear regression, random forest, and gradient boosting. Our results show that Random Forest consistently beats the other techniques in both datasets, indicating its usefulness in predicting automobile pricing. This study provides important insights for buyers, sellers, and the automotive industry. [ABSTRACT FROM AUTHOR]

The paper introduces automated focusing and exposure control algorithms tailored for a satellite-mounted camera, essential for observing satellites post-launch and detecting space debris. Integrated with a Ritchey-Chrétien (RC) telescope, the camera faces challenges in orbit due to varying distances and fluctuating light. Automated focusing (AF) and exposure control (AE) are vital for maintaining image clarity and optimal exposure. The Auto Exposure Algorithm features object tracking and skewness-based exposure adjustment, while the Auto Focusing Algorithm includes a Sharpness Score Evaluator and a Focus Point Search Optimizer. These algorithms improve satellite observation reliability, enhancing tracking accuracy and space safety amidst the crowded orbital environment. [ABSTRACT FROM AUTHOR]

In today's digital era, understanding the sentiment of the public towards online content is crucial for various purposes like marketing, public opinion analysis, and content moderation. This research introduces a new method for analyzing sentiment in YouTube comments using natural language processing (NLP) techniques. The technique uses the Natural Language Toolkit (NLTK) Python package to do sentiment analysis after gathering comments from a particular movie using the YouTube API. In order to analyse sentiment in YouTube comments, this research study presents a useful and effective approach that advances the field of sentiment analysis. The proposed approach can be extended to analyze sentiment in other social media platforms and can be integrated into applications for real-time sentiment monitoring and analysis. [ABSTRACT FROM AUTHOR]

As we all know, artificial intelligence (AI) continues to progress and becomes more widespread throughout the world, it has the capability to develop many aspects of society, where cybersecurity is the major part, providing new ways to detect and counter AI threats. Even though the rising use of AI in cyberattacks also gives rise to vital risks present. It also helps identify and govern potential impacts on privacy and civil liberties, guaranteeing the safety and security of individuals or an entity, organizations, and citizens in an AI-driven world. Research in this article suggests that AI threats in cybersecurity are needed to keep up and compete with current trends and threats to develop powerful security methods. AI is the most effective way to recognize and manage the impacts that can occur on privacy and public rights. This can improve technical reliability and security. Investing in research and development to bypass the AI threat breach in cybersecurity and anticipate the latest threats is crucial. This includes applying AI-driven mitigation strategies and the best security and data privacy approaches as well as methods. Association between education, industry, and government can help identify and manage emerging threats and develop or create effective countermeasures. By addressing some major issues, research can provide valuable understanding into powerful mitigation strategies and support a more secure, resilient, and strong cybernated economy or society. This abstract will explore and inspect the feasibility and provide an extensive overview and outline of the threats that AI uses in cybersecurity, including the challenges of safeguarding against AI threats with the risks of AI-powered attacks and including security measures. [ABSTRACT FROM AUTHOR]

In the current study, we have reported the dependence of structural properties, surface morphology, and compositions of Cr-substituted Mn-Ni-Sn Heusler alloy films on thickness. Fabrication of thin films is done by Ultra High Vacuum (UHV) Magnetron Sputtering system, and their thickness varied from 8 nm to 120 nm by changing the deposition time of films on MgO (100) substrates. All the films were deposited at ∼300℃ and annealed at 800℃ for 4 hrs in situ. Film thickness (d) was estimated from the XRR data, and the typical growth rate was found to be ∼4nm/min. All films have a cubic structure with a small admixture of a possible orthorhombic phase at room temperature. The crystallinity of the films improves with increasing thickness, and it could be due to the accommodation of the film–substrate interfacial strain resulting in the preferred oriented growth of the films. Films with d∼8nm have a continuous surface morphology with fine grain-like entities having average chemical composition Mn1.4Ni1.8Cr0.002Sn0.46. The d∼20nm film shows continuous and smoother surface morphology, and its average composition is Mn1.6Ni1.58Cr0.17Sn0.63. The film with a thickness of d∼120nm has a continuous surface with larger and densely packed grains. The average composition is Mn1.51Ni1.75Cr0.21Sn0.51 which is quite close to the target stoichiometry. This shows that as the film thickness increases, the overall chemical composition approaches that of the target, with a concomitant improvement in the surface morphology. [ABSTRACT FROM AUTHOR]

Asthmatics often endure severe recurrent attacks, which repeatedly interfere with daily life. To meet this challenge, the Asthma Attack Prediction System (AAPS) was developed with a unique U-shaped design. The system comes with an attack prediction (AP) algorithm, using a combination of biosensors to predict attacks by monitoring cough sounds, throat movements, and heart rate – an alternative way to detect asthma severity an integrated smartphone is a remarkable feat. In emergencies, AAPS can mechanically alert hospitals through SMS, ensuring well-timed intervention. Calibration and validation against business gadgets revealed superb accuracy, with coronary heart charge measurements hitting 99.03% accuracy and cough sounds with throat movement sensors recording 93.75% and 95.83% accuracy, respectively. This gave an impressive 96.2% overall detection precisely. These consequences pass beyond previous studies and highlight advances in assault prediction, portability-making plans, and implementation. [ABSTRACT FROM AUTHOR]

The proposed system automates fish separation in coastal regions using electro-mechanical components: conveyor belt, camera, Deep Convolutional Neural Networks (CNNs), and separating bins. Mechanical features prevent fish overlap under the camera, while bins collect desired fish types. The effectiveness and accuracy of the proposed model are verified by considering different environmental conditions, such as illumination and background effects. In real-time condition, the proposed fish classifier model classifies five distinct types of fish available on the Mangaluru coast. Currently, no dedicated electro-mechanical system for fish classification in harbor is available. Hence, the developed model is customized for the harbor environment. The fish which comes from mechanical system is captured with a camera, features are extracted using image processing algorithms and classification is performed using Deep Learning algorithm. A Deep Learning Algorithm, MobileNet V2 CNN model is used for the classification of fishes. Both image processing and Deep Learning algorithm are implemented using Raspberry Pi and Computer. The major attributes of the developed system is economical, energy efficient, and reduction of labour costs. The developed system also works in real-time and invariant to background conditions such as illumination, overlapping of fishes etc. with an accuracy of 97%. The developed model works in real-time and able to classify more than 10000 fishes in an hour. [ABSTRACT FROM AUTHOR]

Breast cancer has lately been the leading cause of death among women. Early detection of breast cancer is crucial because it allows for the possibility of treatment options that may enhance a woman's prognosis and quality of life. Early detection has the potential to increase survival rates for breast cancer, despite the lack of a cure at this time. The purpose of this research is to assess several approaches to breast cancer detection, such as ML, DL with optimisation and preprocessing, and DL with transfer learning. In this research, we look at the pros and cons of each method. [ABSTRACT FROM AUTHOR]

Children safety is a concern for every parent. When the child start going to school for the first time and when he/she is small to understand the situation to act upon it, the mechanism is required to track the same. The framework proposed in this paper is evolved after studying many incidents related to the children safety during travelling to and from school. Human oversight or supervisor's absence may lead to a heartbreaking incident. Manual system may overlook some threats associated. Proposed work in the paper is a system to report the child boarding and drop with the time stamp and location to the parents and the school authority daily. The system helps the concerned parents and school authorities to take immediate action against any mishap or unfortunate incident. The proposed system consists of RFID card, RFID reader, Arduino Controller enabled with GPS, GSM, and clock. The proposed system in the paper provides an economic solution for the above-mentioned problem and that can be affordable to all economic classes easily. This work has taken place for the school students Jaipur city, Rajasthan stat, India. [ABSTRACT FROM AUTHOR]

Rotary machines is a very significant part of the industry and one of the majorly used mechanical device is the centrifugal pump. Bearings are the key element in the pumps and hence, early identification of the defects of bearing can help in lowering the maintenance cost as well as downtime. In the field of machine fault detection, time and frequency-based methods are well known for their capability to handle complex signals. In this research rolling element bearing with defects in the roller, outer race, and inner race, and defects in a impeller of a pump are considered that are compared against the healthy condition of the pump. Maximum Overlap Discrete Wavelet Transform (MODWT) and Wavelet Packet Transform (WPT) are applied as signal processing techniques for the dataset. Generated final results are then taken to the mother wavelet selection and both techniques are compared for the same. After extraction of coefficients, Statistical time domain features are calculated from it and fed to Support Vector Machine, Ensemble Bagging Tree and K-Nearest Neighbour algorithms for training. A comparison of all these three algorithms is presented in this study and a conclusion has been made based on the 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

This paper reviewed the Hand Sign Recognition system from 2018 to 2022. Based on the review, much research is done on Deep learning-based hand sign recognition. Many of these studies are published annually in article, journals, and conference proceedings. Additionally, we also examined the performance of the HGR system in terms of recognition accuracy. In this paper we concentrate on the data environment, the data acquisition, and the hand gesture presentation— three crucial components of the vision-based HGR system. We also looked at how well the vision-based HGR system performed regarding recognition accuracy. [ABSTRACT FROM AUTHOR]

Phase locked loop (PLL), it is one of the control systems that produces an output, which is synchronous with input fed signal and it is a closed loop system. VCO is a Voltage controlled oscillator and it is known as the heart of the PLL. The output of VCO is the frequency signal which was decided on input voltage that is given to the VCO. Phase frequency detector (PFD), and this is one of the important components of PLL, designing of PLL starts with this PFD. The main function of PFD is to compare the frequency of an input feedback signal and reference signal. The basic operation is negative addition of both signals of the error components from the feedback signal and produce output from PFD. Feedback loop acts as the path to transfer the fractional part of the output from the last stage of PLL and it has two types of system : positive and negative feedback systems. [ABSTRACT FROM AUTHOR]

India, the land of art and culture, has always been the center of tourism since ages and people welcome and serve their guest as a god as they believed in "Atithi Devo Bhawah" which means "Guest is like God". Also, the government provides many subsidies and incentives on making people entrepreneurs and encouraging the hotel and tourism industry to come up with the best ambience and hospitality for their guests. Hospitality industry provides lots of opportunity in terms of jobs as well as revenue for development of states like Sikkim, Himachal Pradesh, Uttarakhand etc. Tourist places are mostly situated in the extreme interiors of rural areas led by local entrepreneurs, where life is itself a challenge to deal with the extreme climatic conditions, not well-structured accommodations, poor safety guards and lack of medical facilities that are all intrinsic to meet the basic living standards. In Uttarakhand, such rural areas include religious sites Yamunotri, Gangotri, Kedarnath and Badrinath, Chopta, Chakarata, etc along with some semi and well-developed cities like Haridwar, Rishikesh, Dehradun, Nainital, etc which all remain flooded with tourists in the season. In this COVID-19 phase, the whole industry has suffered badly and left everyone bewildered to make any decision for the future. Central and the state governments had updated some and will update several guidelines and policies for the tourism industry in the coming days. Hotels with good financial conditions and market values can easily adopt new SOP's and technologies to deal with the situations after the pandemic, but it will not be the same for the small or local entrepreneurs. This study covers the current and post COVID-19 problems faced by small and local entrepreneurs of Uttarakhand and proposals and plans needed by them to boost their business and tourism with the same theme of Atithi Devo Bhawah. [ABSTRACT FROM AUTHOR]

Farmer worldwide face huge problems in identifying pest and pathogens which causes tremendous losses thereby reducing yield loss. The main sources of worry in agriculture were drought, fertilizers, herbicides, and quality of yield. The majority of the time, knowledge was necessary to examine the issues, which might be a time consuming and expensive issue in developing nations. To accurately and economically monitor agronomic data one of the approaches that may be employed was image processing. In recent years, optical sensors, including hyper spectral and consumer-grade cameras, have been rapidly developed these days in order to provide the best possible picture quality. Instead of using visual or other forms of sensor input, several researches have concentrated on band characteristics for measuring plant health via vegetation indices (VIs). Image processing have numerous applications in weed control, detection of nutrients and minerals etc. [ABSTRACT FROM AUTHOR]

In our society, we have been going through two main problems i.e. environmental pollution and energy crisis. Fabrication of the materials which can help to generate energy from the sustainable and greener energy sources, and also capable of diminishing the pollutants from the environment is very essential. Titanium dioxide (TiO2) is one the perfect examples for such a material in the form of different nano-structures. But problem associated with TiO2 is its bandgap of ca. 3.2 eV, which only can be activated under the UV light illumination. In addition, high charge carrier recombination is also another issue. Therefore, modification of TiO2 nanostructures as well as its bandgap is essential to increase its light absorption toward the visible region of the sunlight. To fulfil the above purposes, pure TiO2 and Ni-modified TiO2 nanorods were synthesized and characterized using FTIR, PXRD, UV-Visible in DRS method and photoluminescence techniques. UV-Visible spectra showed enhanced visible light harvesting upon Ni modification of the TiO2 nanorods as compared to the same for the pure one. On the other hand, lower intensity of the photoluminescence spectrum for the Ni-modified TiO2 convincingly proves minimized recombinations of the photo-generated electron-hole pairs. [ABSTRACT FROM AUTHOR]

This work aims at a thorough understanding of the structural and Magnetic properties of Ni2-xCoxMnSn (x=0,1) Heusler alloys synthesized by a solid-state processing route. Structural analysis was performed by employing techniques like X-ray Diffraction (XRD) and scanning electron microscopy (SEM) together with energy-dispersive X-ray spectroscopy (EDX) to show the formation of alloys of desired stoichiometry. The SQUID magnetometry result shows that the studied compositions are soft ferromagnetic. The saturation moment for x=0 (Ni2MnSn) is close to the 3µB, value predicted by the Slater-Pauling rule. However, the moment for x=1 (NiCoMnSn) is much below the anticipated value of 6μB. The Ni-Mn-Sn ternary alloy's curie point and saturation magnetization are raised by the addition of Co. [ABSTRACT FROM AUTHOR]

Single crystals of undoped and Co-doped β-Ga2O3 with different doping concentration have been grown using optical floating zone technique. The powder X-ray diffraction analysis shows no significant variation in lattice parameters on Co doping in the crystal. The absorption spectra shows a decrease in the absorption edge with Co-doping and a broad absorption band in the range 950-1700 nmdue to transitions in Co ions. The absorption analysis shows that both undoped and Co-doped β-Ga2O3 crystal possess indirect bandgap with band gap energy decreasing on increasing the Co-doping. Further, the Urbach energy has also been estimated which is found to increase with the increase in Co-doping. The observed absorption band can be utilized as a saturable to achieve passive Q-switching of near IR lasers. [ABSTRACT FROM AUTHOR]

The most common problem faced in robots nowadays is their inability to stick to the ground continuously, i.e., even small elevations result in the toppling of the robot, which can be avoided using a legged mechanism. Theo Jansen, a famous Dutch artist, invented a mechanism, namely Theo Jansen Mechanism, which is used in strandbeest, inspired by observing the movements of animals. This mechanism effectively adapts to the surface, which leads to multiple new opportunities for robots. The following review article's primary aim is to study Jansen linkage and its application in various fields. The article also explores many approaches for optimizing leg mechanisms and detailed research to find the most effective optimization method, including using an actuator in place of a link to optimize link length for Theo Jansen's leg mechanism. This article also contains a computational and experimental analysis of the geometrical advancements in Jansen's Linkage. [ABSTRACT FROM AUTHOR]

Existing case study deals with turning operation of aluminum 6082. Cutting speed, feed and depth of cut are selected as input parameter, while material removal rate and surface roughness are response variable. Taguchi's orthogonal array is used to select permutation and combinations. Experiments are repeated twice to minimize the effect of noise factors. Integrated fuzzy-TOPSIS method is used to convert multiple responses into single response. Analysis of variance is performed to check significance level of input parameters using Minitab statistical software. Regression analysis shows the high level of correlation for MRR [R-sq (96.3%), R-sq (adj) (95.4%)] and moderate correlation for Ra [R-sq (82.8%), R-sq(adj) (78.5%)]. [ABSTRACT FROM AUTHOR]

In the current pandemic situation, safety and surveillance are required. As Covid19 has come out to be such a huge pandemic it results in the loss of many lives due to people not following Covid19 guidelines created by the government. Surveillance is difficult and time-consuming all of the time, therefore it is required to build a system that observed Covid19 guidelines for humans automatically. This work primarily helps to solve the problem of Covid19 observation and to protect people themselves against Covid19. The proposed method is used to detect the mask on the face of humans using deep learning models. The system is able to classify the different types of face masks worn by individuals, either surgical masks or N-95. The system can also identify whether the person has Covid19 symptoms or not this way it could reduce the spread of the virus. The experimental outcomes suggest that the proposed system has been able to reduce the transmission risk of the Covid19 virus. [ABSTRACT FROM AUTHOR]

Optimization of share price in order to evaluate whether to invest in specific share or not is subject of study paper. Optimized value has been utilized to enhance the performance and accuracy when dataset is trained using CNN. Review has been given on big cap, mid cap & small cap fund which are accessible in large, medium and small amount for the purpose of investing. It has been found that share in big cap are more dependable but offers limited return. However share in case of mid cap are considered comparatively more hazardous than big cap yet return may be substantial. The suggested study has given mechanism that will make use of optimization mechanism to filter the data set considering PSO and MVO mechanism. The suggested CNN based training model is claimed high performance system with improved accuracy. [ABSTRACT FROM AUTHOR]

e-Governance is more about good governance rather than the utilization of ICT tools and technology in a particular domain. The strategic view of e-Governance is for simplifying the governance process and bringing transparency to government business functions. e-Governance is considered as a simulation of good governance. This paper will examine the status of ICT infrastructure (Critical infrastructure, hardware, software, and ICT services) at the Lumbini province government. The study also aims to identify the impact of e-governance on public service delivery. To explore the status of ICT infrastructure adopted by local government and benchmarking with Government Enterprise Architecture (GEA). The research has been carried out by survey design. The study area will be the Local government (LGs) of Lumbini Province, Nepal. This research will involve different LGs and groups of stakeholders involved and affected by ICT implementation in Nepal. The total population of the study was 109 out of 753 LGs. The study has explored the status of ICT infrastructure, product, and service delivery identified at LGs. [ABSTRACT FROM AUTHOR]

The process of amalgamating multicultural, global or international dimensions according to a purpose, functions, or conveyance of education at a higher level is known as internationalization. Internationalization has always been the result of students visiting other countries for completing higher education but with the emergence of information and communication technology (ICT) in the system of education, technological advancement gave rise to internationalization at home. The international officers must help make sure the policies regarding this are properly implied and there is the continuous development of the teachers and learners. This review paper analyses the utilization of ICT tools and techniques by the international officers in India for implementing the concept of internationalization at home. [ABSTRACT FROM AUTHOR]

Cloud computing has become a popular approach for processing data and running computationally expensive services on a pay-as-you-go basis. Due to the ever-increasing requirement for cloud-based apps, appropriately allocating resources according to user requests while meeting service-level agreements between customers and service providers has become increasingly complex. An efficient and versatile resource allocation method is required to properly deploy these assets and meet user needs. The technique of distributing resources has become more arduous as user demand has increased. One of the key areas of research experts is how to design optimal solutions for this approach. In this paper, a literature review on proposed dynamic resource allocation approaches is introduced. [ABSTRACT FROM AUTHOR]

Malware assaults against Android phones, which account for 72.2 percent of the total market share, are on the rise as mobile devices become more popular. Numerous hacking techniques, such as credential theft, surveillance, and deceptive marketing are used by cybercriminals to get access to cellphones. It has been found to be an effective method of detecting these attacks because of the ability for (ml)-based devices to learn from training examples, eliminating the need to define specific signatures while developing malware-detectors, which is an important consideration when designing these systems. Here, we provide an overview of Android malware detection methods based on machine learning (ML). It takes a close look at 30 carefully chosen articles and analyses both their positive and negative aspects, as well as potential ways to improve them. The ml-based approaches for identifying ASCII text record vulnerabilities are noticed sooner or later since it might be more difficult to operate properly after launching the program. That's why our goal with this publication is to help scientists get a deeper understanding of the topic and identify potential directions for future research. [ABSTRACT FROM AUTHOR]

Brain oscillations are essential to math and spatial skills. Cognition, attention, memory processing and language are influenced by these oscillations. Neurological explanations proposed to account for the fluctuations in brain activity help us understand how important the relation of rhythmicity is within a neural network. Desynchronization refers to the sharp and irregular changes of the oscillations in brain waves called rapid-eye movement sleep spindle activity during sleep. It is the disruption of interactions between resting oscillations of brain regions typically producing rhythmic neuronal firing that occur simultaneously in different frequency bands like alpha or beta. The beta band is associated with attention and self-focused activities like perceiving, thinking, judging, and problem solving. In this paper, the authors develop a neural model of nonlinear oscillators that transforms stimuli into characteristic oscillatory activity of the beta band. [ABSTRACT FROM AUTHOR]

The scope of the paper is to leverage the power of Machine Learning and Deep Learning to detect diseases in crops and to provide solutions for the diseases detected. Farmers usually detect diseases in the crops using their naked eyes, eventually leading them to make wrong predictions regarding diseases in the crops. They might use the wrong fertilizers or more than the usual dose, which could damage the plants, soil, and fields. Thus, this paper focuses on providing solutions for detecting diseases in crops and solutions for the diseases identified. Convolutional Neural Networks are used to identify various diseases in crops since it limits the number of parameters and thereby make the model more efficient. The said model is implemented by making a cross-platform code using Flutter with Dart as the programming language. [ABSTRACT FROM AUTHOR]

Every Industry, today, is either harnessing the power of 5G or at least planning to do so and the Marketing Industry is no exception. This paper highlights different aspects of 5G that can be leveraged by marketers to promote their brands in a better or more effective way. This paper explores the future growth of 5G and the opportunities that it can create for Digital Marketers around the world. The paper also conducts a detailed discussion on various implications of 5G on Different Channels of digital marketing. Different 5G-enabled technologies can revolutionize how brands interact with customers. Customer Experience is of utmost importance for a modern-day marketer and 5G-backed technologies can help marketers to provide an unprecedented customer experience. The paper also highlights different use cases where 5G and 5G-driven technologies corroborated marketers to market their brands in a better and smarter way. [ABSTRACT FROM AUTHOR]

The fight during pandemic has been very difficult, and the only effective prevention was to maintain Social Distancing. Maintaining social distancing allowed the disease to spread at a slower rate. Social Distancingis basically meant for people to avoid contact and interaction in public. Even in the past Artificial Intelligence proved to give some promising results to avoid common life problems. The desired prototype will be provided with some detailed information on using python, CV, deep learning to keep a check on people and their distancing. In order to maintain social distancing on streets this tool will be subjected, thus it will allow a safe distance between people. Observing People at work environments, processing plants, shops we can coordinate this device to theirsurveillance camera frameworks and can screen whether individuals are maintaining a protected separation fromone another or not. [ABSTRACT FROM AUTHOR]

Millions of children are travelling every day to attend their school through school bus, but the parents of children and school management are not focusing on their safety and security. According to the study, at every 40 seconds, one child goes missing in the world. When children start going to school for the first time and when they are small to understand the situation to act upon it, every parent is concerned about their children's safety. Looking at children abuse, kidnapping in urban areas, children's safety has become a major concern for every nation. Parents of children are unaware about the information like children status and location of children, whether their children reach the school or not. We did the last 10-years literature study on RFID-based children safety management systems, and we found that many researchers worked on this problem, and they proposed RFID, GSM, and IoT-based system for children safety management. Using RFID and GSM-based system, it is possible to track the location of the student. This paper presents a detailed review of RFID based children safety management system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has made exceptional impacts internationally that has changed the lives of people all around the world as per the new normal social and office norms. In the new normal conditions, there is a paradigm shift of activities on virtual platforms in all the sectors. The health emergency caused by the corona pandemic has stopped the world, but information technology has allowed people to maintain active virtual connections. Stress caused by a pandemic raises the likelihood of cyberattacks. This article analyses the COVID-19 pandemic from a cybersecurity perspective and sheds light on the various cyberattacks that have occurred around the world during the pandemic. The present study provides review and insights of reports on cyber security issues and the way forward to overcome the IT crimes, risks and scam. [ABSTRACT FROM AUTHOR]

Type 2 Diabetes Mellitus (T2DM) is a long-term metabolic syndrome. Metformin, an allopathic medicine, is the most commonly prescribed treatment for T2DM. Alternative therapy such as physical activity and natural sources like Olive Leaf Extract (OLE), Oleuropein, Aloe vera, turmeric, Pongamia pinnata can help to manage insulin resistance. Although these alternative therapies have remarkable effects in T2DM, there is no recognized optimum dose that describes the best combination of metformin, Oleuropein and exercise together. The objective of this study is to optimize doses of metformin and oleuropein in combination, as well as the suggested exercise intensity, to overcome insulin resistance in people with different levels of T2DM. In this paper, we have used our recently published in silico dynamic model to solve a control engineering optimization problem. An optimization problem was formulated with metformin, oleuropein, and exercise as control inputs. The objective was to see what combined dosages resulted in the highest surface GLUT4 concentration in the presence of very low, low, moderate, high, and very high types of T2DM. Inequality constraints were defined for the problem based on the prescribed dosages. An optimization solver was then utilized to address the problem. As a result, the optimal dosages of metformin, oleuropein, and exercise for improving T2DM were successfully determined. The proposed optimization technique proved to be useful in selecting the best medication for improving insulin resistance at various stages of diabetes. In silico study thus reduces the experimental efforts used to explore the effects of different combined drugs and diseases with appropriate regimens. [ABSTRACT FROM AUTHOR]

Tunable band gap in different nanostructure forms is an interesting feature of transition metal dichalcogenides (TMDCs) like WS2, WSe2, MoS2, and MoSe2 etc. We, herein, report the synthesis of Nb doped WS2/WO2 hybrid quantum dots (QDs) by a facile hydrothermal method followed by solution-based probe sonication and centrifugation. The structural characteristics, surface morphology and elemental composition of the hydrothermally synthesized powder samples were investigated. The UV-Vis absorbance spectra of Nb doped WS2/WO2 QDs suggested a red shift in the absorption edge with increased doping percentage. [ABSTRACT FROM AUTHOR]

The community#x2019;s views and inputs have always been the main and most beneficial source for varied range of enterprises. With more widespread community media, it provides a spectacular study and assessment of many fields in which companies used to have faith in peculiar, exhausting and inaccurate ways. This form of analysis is subclass of #x2019;sentence analysis#x2019; area. Sentiment analysis is a broad term that refers to the process of effectively classifying user-generated content into specific polarities. To perform sentiment identification and analysis, a variety of tools and techniques are available, includes supervised techniques for machine-learning that classify the target group after training in data. Hybrid instruments are a blend of machine learning and lexicon-based algorithms, which classify according to annotated dictionary. We employed the SVM with Weka for analyzing sentiments in this paper. Two pre-categorized datasets of tweets are utilized. The performance of SVM is analyzed with the help of analytical metrics. [ABSTRACT FROM AUTHOR]

This paper describes the development of porous carbons synthesized from direct carbonization followed by chemical activation at suitable parameters for environmental sustainablity. Adsorbents were developed using synthesized zeolite and low-cost precursors. Carbonization and chemical activation at 600 °C were carried out that resulted in the generation of porous adsorbents. Surface characterization result reveals the effect of optimized chemical activation parameters, confirmed from direct carbonized and activated chemical samples. CO2 uptake of the developed adsorbents obtained from direct carbonization shows low CO2 uptake, and the carbon material developed from Chemical activation shows high CO2 uptake. [ABSTRACT FROM AUTHOR]