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This research examines the structural stability of the mutant FTO protein, which is associated to obesity and mass, by employing molecular docking and the Autodock Vina tool. The Ingredients and Procedures: I have downloaded the protein structure from RCSB and UniProt PDB for the fat mass and obesity linked protein (FTO). We compiled a list of missense mutations that were found to have negative effects on fat mass and the obesity-associated protein (FTO) from the several SNP database systems. Following this, the SPDBV service was used to mutagenize the missense mutations. We performed molecular docking using PyRx, which operates on the autodock vina approach, after collecting the ligand's 3D structure from PubChem. Results: R316Q, S319F, and R322Q showed alterations and destructive effects on fat mass and obesity-associated protein structure, according to the expectations given by all the methodologies investigated for a disease. Conclusion: Using the PyRx online tools, we were able to find the best ligand that complements the protein's active areas and minimises the negative effects of FTO mutations on stability. Future molecular simulation and in vivo research aiming to create a therapy for poly malformation syndrome can take advantage of this. [ABSTRACT FROM AUTHOR]

The purpose of this research is to use the Autodock Vina method for molecular docking in order to identify the ligand that is most effective in establishing structural stability of mutant proteins that are connected with obesity and fat mass (FTO). Instruments and Methods for Research: Through the work that we have done, we have been able to effectively rebuild the RCSB PDB, UniProt structure of the FTO protein, which is associated with fat mass and obesity. With the use of the SNP database, we were able to build a list of missense mutations that were anticipated to have a negative effect on the FTO protein. The next step was to produce the mutations by utilising the SPDBV server. Following the retrieval of the three-dimensional structure of the novel ligand from PubChem, we utilised the autodock vina approach in order to conduct out molecular docking in Pyrx. According to the findings, the FTO structure was destabilised by the replacements 316Q, S319F, and R322Q, which was consistent with the predictions made by all of the methodologies that were utilised in the research. In conclusion, the online tools provided by PyRx assisted us in locating the most effective ligand that interacts with the active areas of the protein as well as the mutations that have a negative influence on the stability of FTO. This lays the path for further in vivo investigations and molecular dynamic simulations, both of which are necessary in order to identify a cure for poly malformation syndrome. [ABSTRACT FROM AUTHOR]

Innovative solutions that enable people to make educated dietary decisions are critically needed, as evidenced by the rise in lifestyle-related diseases and the increased focus on personalized health management. Consequently, we present Nourish-Meter, an all-inclusive nutrition estimator created to tackle the fundamental components of a healthful diet: calorie computation, prediction of Body Mass Index (BMI), and calorie computation based on medical background. To provide a comprehensive approach to nutrition assessment, Nourish Meter makes use of cutting-edge technology like dietary analysis and machine learning. Users may quickly and precisely ascertain the calorie composition of their meals with its help as a calorie estimator. In addition, BMI prediction features of Nourish Meter let users determine their current BMI and create customized eating plans. Nourish Meter provides information about the user's medical history in addition to BMI prediction and calorie estimation, taking nutrition assessment a step further. By entering their medical history, users can receive personalized suggestions from the system that highlight foods that could worsen or trigger particular health concerns. We think that Nourish Meter's comprehensive method of nutrition estimate and management can be very helpful in managing and preventing diseases linked to an unhealthy lifestyle. [ABSTRACT FROM AUTHOR]

As we saw that the development of new technology and communication in digital media is increasing day by day, as the internet is showing tremendous growth for communication and in another sector also, here the information represented in the form of multimedia, image data is one of them. Communication for the image data is very vital nowadays because data in the form of an image is very important for the various section, and medical imaging or the healthcare sector is one of them. In medical imaging for communication, we used the data compression and decompression techniques for transmission of data. In this work, we present the comparative literature study with medical image compression and find the performance parameters value for different techniques i.e. wavelet transformation techniques and machine learning techniques. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) have provided us a promising solution to address spectrum scarcity by enabling opportunistic access to underutilized frequency bands. Neighbour discovery plays a vital role in establishing efficient communication among secondary users (SUs) in CRNs. However, traditional neighbour discovery methods face challenges in terms of reliability, security, and scalability. This paper proposes a novel approach that integrates traditional beaconing with blockchain technology to enhance neighbour discovery in CRNs. By including the decentralized and transparent nature of blockchain, the proposed approach improves trust, security, and efficiency in discovering nearby SUs. The combination of traditional beaconing and blockchain provides a robust and reliable solution for efficient neighbour discovery in CRNs, addressing the limitations of existing approaches. The suggested approach opens up new possibilities for effective spectrum utilization and seamless connectivity in cognitive radio networks. [ABSTRACT FROM AUTHOR]

Maintaining discipline and security is of immense importance in any institution, whether a school, college, or workplace. Wearing uniforms in educational institutions and workplaces creates a sense of unity and pride. It increases people's confidence, saving time, money, and energy that would otherwise be spent deciding what to wear daily. Also, recognition of authorized and unauthorized people entering an institution is significant from a security point of view. In this paper, a system has been proposed for uniform identification and authentication of people at the entrance gate of an educational institution. After the camera at the institution's entrance gate takes the picture, a deep learning-based segmentation algorithm will identify the logo on the uniform. The data will match the photograph of the person/student in the database, and attendance will also be recorded at the entrance gate. If the person is authorized, then he/she will be allowed to enter the university, else, the person/student will be fined with an amount of Rs 500. The paper presents the fine collected in a university for a working week in the month of August 2023. [ABSTRACT FROM AUTHOR]

Kidney diseases can severely impact the health of a person. The early diagnosis and medication play an import role in the control of disease and to avoid any undue effects. Convolution neural networks have been proven to be effective in the identification of diseases from medical images. In the present study, transfer learning techniques are used to fine tune four different pre-trained CNN models: VGG16, InceptionV3, ResNet50 and ResNet101 to classify the kidney CT-scan images into different categories. The pre-trained models with transfer learning shows effectiveness in classifying CT images into the category of: Normal, Cyst, Stone and Tumor images. The results demonstrate that the proficiency of ResNet101 model for classification of CT images with overall model accuracy of 0.99. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) specifies simulation of human intelligence through machines to acquire the ability to act rationally like humans for solving problems. The design of an artificially intelligent system is based on various factors such as natural language processing (NLP), representation of knowledge, automated reasoning, machine and deep learning, computer vision, robotics etc. The area of computer vision (CV) focuses on reproducing parts of the complex human visual system and enables the computer systems to identify, analyze, understand, and process the objects present in an image like the human brain. There are various sub-domains of computer vision, such as object/event detection, object/activity identification, pose/motion estimation, and scene/image reconstruction etc. Object detection is a computer vision task employed to detect the instances of visual objects of a particular class in an image. There are different platforms available for achieving real-time object detection in different applications of intelligent systems. This paper surveys the computer vision implementation tasked across object detection using Machine Learning (ML) and Deep Learning (DL) algorithms for design of intelligent systems. The computer vision toolbox available in MATLAB is also very popular among researchers for its high utilization of advanced features for object detection and its tracking. This study provides a comprehensive summary of state-of-the-art technologies for implementation of computer vision and discusses the perspectives of applications in intelligent systems achieving real-time object detection in different domains. [ABSTRACT FROM AUTHOR]

Image processing techniques are widely used in all domains of application, including digital imaging, precision agriculture, computer vision, remote sensing, medical imaging, and many more. The aforementioned applications utilize various types of images, such as RGB, Infrared, Multispectral, and so forth. The image generated by a single source, sensor, or modality is insufficient for precisely realizing the item in applications such as medical imaging and remote sensing. Image fusion provides a more effective and efficient way to produce highly useful data for human perception when used with individual input source data. Numerous image fusion techniques exist, including Laplacian pyramids, Gradient Pyramids, SF, IHS, PCA, DCT, and DWT. This study examines several spatial domain Image Fusion techniques to assess the efficacy of distinct techniques based on noise content, spectral degradation, and color distortion. Comparing the outcomes of various spatial domain methods, it is found that PCA is a good choice as its PSNR value is the largest of all spatial domain methods. [ABSTRACT FROM AUTHOR]

The global demand for cost-effective and environmentally sustainable solutions in water infrastructure and wastewater management has been growing, aiming to treat and repurpose wastewater for various purposes. The concept of wetlands provides a sustainable approach to wastewater treatment. Wetlands are employed in treating municipal wastewater, industrial wastewater, and stormwater, effectively eliminating organic contaminants and improving nutrient removal efficiency. Among the various types of wetlands available, constructed wetlands (CWs) and floating wetland systems (FWS) are commonly utilized. However, selecting the most suitable wetland type that considers all relevant factors remains a challenge. To address this gap, an investigation was conducted, incorporating case studies. The comparison analysis conducted in this study focuses on evaluating the effectiveness of both constructed wetlands and floating wetlands. The study provides a comprehensive assessment of treatment efficiency and highlights the benefits of the proposed approach. The findings clearly demonstrate that the proposed study significantly reduces organic pollutants by 70-80%. The study confirms that floating wetlands are particularly effective in treating contaminated water bodies and are considered the top choice for eco-friendly wastewater treatment technology. [ABSTRACT FROM AUTHOR]

This study utilizes molecular docking techniques to identify optimal ligands for stabilizing the FTO protein mutant using the Autodock Vina algorithm. The mutant R316Q 3D structure of human FTO was obtained from RCSB PDB and UniProt. Missense mutations with predicted the impact of FTO in deleterious structural effects were sourced from the SNP database and respective mutagenesis of these mutations was conducted by SPDBV server. Ligand structures were retrieved from PubChem, and molecular docking was performed using Pyrx with Autodock Vina. The R316Q mutant was found to exhibit significant effects associated with poly malformation syndrome. The study identified promising ligands that bind to the protein's active sites and deleterious mutations in FTO. Future directions include molecular simulations and in vivo studies to potentially develop a drug for poly malformation syndrome. [ABSTRACT FROM AUTHOR]

The aim of this research is to detect speed bumps from a given image using Otsu's threshold for better detection accuracy. The total sample size of 120 has been split into two different groups. The first group holds 60 samples and was tested on Adaptive Gaussian Threshold whereas the next group holds 60 samples and was tested with Otsu's Threshold. The sample size for each group is 10. The research's dataset was downloaded from Kaggle.com and consists of 6000 photos, of which 4800 are utilised for training and the remaining ones for testing. The G power for this project is approximately around 80%. The resultant accuracy is higher for the Otsu's Threshold (88.40%) than that of Adaptive Gaussian Threshold (85.60%). The achieved significance value is 0.002 (p<0.05) which shows that there is statistical significance between the two algorithms considered for the speed bump detection. Various approaches for detecting speed bumps are discussed in this paper and compared with the existing models. The research work concludes that the accuracy of Otsu's Threshold is higher than Adaptive Gaussian Threshold with an accuracy percentage of 88% and 85% respectively. [ABSTRACT FROM AUTHOR]

Using multi-walled carbon nanotubes (MWCNTs) that were purchased commercially, porosity growth was developed following chemical activation by KOH. Chemical activation was used to create activated multi-walled carbon nanotubes (A-MWCNTs), which have well-developed pore architectures and can be used as energy storage materials. The A-MWCNTs' microstructure and crystallinity were assessed using Raman spectroscopy. A-MWCNTs' specific surface area and pore size distribution were examined using nitrogen gas sorption analysis at 77 K. The outcomes showed that, at an activation temperature of 800°C, the MWCNTs' specific surface area increased from 251.6 m2/g (for Pristine MWCNTs) to 371.5 m2/g (for A-MWCNTs). In this instance, the specific capacitance of the nanotubular electrode material increased from 114 F/g to 193 F/g in the potential range of 0-1 V (as from GCD data at a current density of 0.5 Ag−1), and microporosity was significantly improved. Suspension in water was verified by using ultrasonication. It's probable attributed microporosity was increased which is strongly related to the energy storage capacity. This demonstrates how pore diameter affects energy storage behavior. Even though a large pore diameter is desirable for storing energy. The precursor reacts with KOH, destroying the nanotubular shape. With KOH, which produced a significant number of flaws in the nanotube walls, activation effects are visible. The well-conducting nanotubular material's open mesoporous network enables simple ion access to the electrode/electrolyte interface. The A-MWCNT-based solid-state supercapacitor electrodes have an extended voltage window with high specific capacitance and exhibit excellent cyclic stability even after 10,000 cycles. [ABSTRACT FROM AUTHOR]

We consider a neuron modelled by a map on a random fractal namely a diffusion-limited aggregate. This excitable and chaotic map shows transition to the synchronized state with increasing coupling strengths ϵ. We also study randomness in coupling connection p over and above underlying fractional geometry. For purely local connections p = 0, the chaotic map stabilizes for all ϵ > ϵfixed. For weaker coupling, the system loses its stability and one obtains chaos. The presence of randomness in coupling p ≠ 0 leads to change in spatiotemporal fixed point ϵfixed. ϵfixed decreases with an increase in p. The system remains chaotic in the range ϵ < ϵfixed. We study average synchronization error with a change in coupling strengths ϵ for p = 0, 0.4, 0.6, 1. [ABSTRACT FROM AUTHOR]

The classification of the acoustic events happening around us, is important for monitoring and recognizing them individually to know every minute things. Previous works on audio event classification utilized some supervised pre-training and meta-learning systems that depend significantly on labelled data. In this work, audio event classification is performed using an unsupervised hybrid Recurrent Neural Network and Bidirectional Gated Recurrent Units (RNN-BiGRU) learning framework which is used in classifying the audio sequences in vector format. A vast variety of unlabeled data has been trained for building the Audio Event Classification (AEC) model. Long Short Term Memory (LSTM), a type of RNN architecture has been used frequently and its feedback connections has the capability to process a single data point, aside from that it can also progress the whole data sequences which is why LSTM is utilized in the AEC. However, GRUs are very similar to LSTM which uses gates to control the flow of information. This approach reduces the classification errors for any acoustic condition. Furthermore, detecting and classifying noises other than speech using GRU might help improve the robustness of speech technologies like automatic speech recognition. When compared to other feature sets such as the BoAW, eGe-MAPS, AVEC 11, and ComparE 13, our proposed work achieves a higher accuracy of 90.9%. [ABSTRACT FROM AUTHOR]

In this paper, a SIRI(Susceptible-Infected-Recovered-Infected) model is framed using the concept of retrial queues. The proposed model is analysed through queueing theory techniques. The SIRI epidemic model is formulated on an individual level. The stability of the proposed epidemic model is checked and the stationary distribution, generating function, explicit and recursive formulae of the proposed model is derived. The stability condition of the proposed model and the modified model are derived. The proposed SIRI(Susceptible-Infected-Recovered-Infected) model is modified with quarantine technique and analysed. The proposed SIRI model and a traditional SIRI model are simulated using Simulink and MATLAB respectively. The numerical simulations suggest that the proposed SIRI model is efficient in predicting the disease spread. [ABSTRACT FROM AUTHOR]

Software defined networks are an emerging technology of the future Internet where the control plane and data plane are separated unlike IP networks. The creation of complex network functions like load balancing is made easier by the consolidation of the control logic in a controller with comprehensive network knowledge which shows the centralization of control logic. Traditional networks have dedicated load balancers, which become a single point of failureleading to decrease in network performance. To address this problem, we propose a load balancing application that controls the data plane and ensures that the network is not overloaded with the incoming data packets. The SDN controller used is Open Network Operating System (ONOS), and Mininet emulator for creating a virtual network for demonstration purposes. The load balancing algorithm is implemented on the given topology. Experimental results prove that the proposed algorithm selects the best path from source to destination and balances traffic. [ABSTRACT FROM AUTHOR]

This system utilizes GPS and GSM technology to track the health and position of soldiers in the field. The system includes wearable devices that soldiers wear on their bodies, which monitor vital signs such as heart rate, oxygen levels, and body temperature. These devices also have GPS capabilities, allowing for real-time tracking of the soldier's location. In case of an emergency, the soldier can press a button on the device to send an alert to their superiors, who will then be able to locate them using the GPS data. The system also allows for communication between soldiers and their commanders through the GSM network, providing an efficient way to keep track of the soldiers' movements and overall well-being. The system can also be integrated with other technologies, such as drones, for monitoring and surveillance purposes. The data collected from the wearable devices can also be analyzed to identify patterns and trends in the soldiers' health,which can be used to improve overall troop health and readiness. Overall, this system provides a valuable tool for military operations and can help ensure the safety and well-being of soldiers in the field. [ABSTRACT FROM AUTHOR]

Prediction analysis is a data mining method used to predict future possibilities based on current information. The traffic prediction techniques have 3 steps which are preprocessing, feature extraction & classification. In pre-processing data set is assembled which is used to distant missing and unwanted values. In the feature extraction phase, the relationship between attribute and target set is settled. This study has been influenced by the different possibilities. The proposed method is a combination of Logistic Regression and KNN classifier compiled laidback by means of a voting classifier intended to predict traffic. The hybrid model gives good results as compared to linear regression model in terms of MSE. When the MSE value get reduced then accuracy get improved for the Traffic prediction. [ABSTRACT FROM AUTHOR]

In measurable and civilian applications, multibiometric arrays with facial and ear sensors are increasingly being implemented to overcome facial expression and occlusion posture issues. Various ear and collision detection methods have been proposed and much work has been done, but the areas are 3D data localization and 3D ear detection. This paper captures 3D ears under an unusual type of yaw. This yaw can be performed naked or in the right ear without prior knowledge of ear area strategies, identifiable ear signs, and prior posture. Displays posture classification based on. [ABSTRACT FROM AUTHOR]

We examined the I-V characteristics of a NiFe2O4 nanoparticle plate in our experiment at room temperature in both the presence and absence of a magnetic field. We also measured the impedance spectroscopy using an impedance analyzer. According to the results of our experiment, our sample has positive magnetoresistance and magnetic field-dependent electrical irreversibility or hysteresis. Electrical memory effect in the sample can be seen by the "hysteretic" quality of the I-V curve. With a rise in magnetic field, it is discovered that the memory window of our sample shrinks. Significantly, it has been discovered that the magnetic field can regulate this type of hysteresis. We put up a phenomenological explanation based on the presence of induced polarisation in the sample to account for the observed electrical irreversibility. [ABSTRACT FROM AUTHOR]

In the digital era each and every thing are digitally worked and the machine algorithm are used to predict the healthcare data and the machine are understand the thing by the previous data. In this paper proposed initially, take the dataset from the healthcare department, into the CSV format and load the dataset and divide the dataset into train and test split use the SciPy tool. Secondly, apply the k nearest neighbor method for classification and finally shows the simulation results model accuracy,F-measure, precision & recall of the algorithm. [ABSTRACT FROM AUTHOR]

Aim: The objective of this research is to diagnose epileptic seizures in Electroencephalogram signals using a hybrid framework novel classification Approach based on a decision tree classifier. The identification of epileptic discharges in the Electroencephalogram is a key component of the epilepsy diagnosis. Materials & Methods : The total of 1372 samples are collected from the UCI repository. The epilepsy detection is carried out with the help of two groups where group 1 is Decision Tree and group 2 is Artificial Neural Networks. Result: Decision Tree Achieved 97.9% respectively compared to 88% by Artificial Neural Networks. The obtained significant value is (P<0.05). Conclusion: It is concluded that Decision Tree has significantly greater accuracy when compared with the Artificial Neural Networks. [ABSTRACT FROM AUTHOR]

The fibre-reinforced composite structural members play an essential role in enhancing the strength-to-weight ratio of the structure. Smart reinforcing fibres like Shape Memory Alloy (SMA) provide an additional feature of adaptive stiffness to the structure. In this paper, SMA wire-reinforced epoxy resin composite is analyzed for change in natural frequency with respect to change in transformation temperature of SMA wires to reveal the difference in stiffness of composite beam, which is a function of change in transformation temperature from martensite to the austenite phase. The numerical study is carried out using FEA software, and the experimental validation of the same is completed. The natural frequency shift is predicted by measuring the natural frequencies of the composite for free-free boundary conditions at different transformation temperatures. The significant change in the natural frequency of composite is observed during the shift in transformation temperature, and the FEA results are close to experimental results with reasonable accuracy; thus, the proposed composite provides adaptive stiffness. [ABSTRACT FROM AUTHOR]

Currently, a lot of effort has been carried for the development of an alternate fuel to meet with the energy requirement and transportation vehicle system of the World. In this regard, one of the most promising alternates, that have been used for small scale production is "Biodiesel". Biodiesel is broadly defined as the monoalkyl ester of long chain fatty acids that can be derived from renewable sources like lipid feed stocks, vegetable oils like rape seed, coal seed, waste cooking oil, algal oil and/or any waste oil. The use of algal oil for biodiesel production produces some interesting properties due to its easy availability all around the world. [ABSTRACT FROM AUTHOR]

Conceptual Sentimental analysis assumes a significant part in many fields including education, where understudy input is fundamental to assess the viability of learning advancements. There are a few stages taken to lead this supposition investigation, which is to gather information utilizing libraries in python, text preparing, information, and text grouping utilizing the Naïve Bayes technique. The Naïve Bayes strategy is utilized for characterizing classes. This multi-disciplinary examination looks at the etymological attributes which impact correspondence and social connection on PC. This paper uses the feedback of MOOC courses collected as the dataset to present the results. This paper starts by directing a subjective information investigation on gathering of graduate understudies taking on the web courses. Project considers singular understandable notes and examines their phonetic qualities. It takes a gander at the enthusiastic prompts, the utilization of verifiable, target language and other phonetic highlights. It proposes a technique for reasoning note of objectivity and has registered dependability testing of this strategy. It shows that there is a high connection between the utilization of target language in a note and the worth that understudies put on that note. [ABSTRACT FROM AUTHOR]

A novel design of low observable microstrip antenna array using complementary split ring resonators (CSRR) as ground plane is presented in this paper for stealth application. By employing complementary split ring resonator on the ground plane of the proposed antenna array, RCS reduction is achieved. Conventional antenna array is designed and simulated for a frequency of 5.4GHz. As compared to the conventional microstrip antenna array, themonostatic RCS can be reduced as much as 26 dBsmm while preserving almost the same radiation characteristics. Finally, a prototype antenna array with CSRR is fabricated. Measured results acceptably agree with the simulated results. [ABSTRACT FROM AUTHOR]

In recent years, Laser based underwater communication is used as a best tool for mining the mysterious ocean atmosphere for gathering different types of information. Unmanned underwater vehicles are deployed underwater for this purpose. Laser based data transmission and reception gives a better way of communication utilizing the very interesting properties of it. Real time videos of Deep Ocean are captured using sensors and cameras and may be transferred to another UUV or equipment placed at land. This real time video transfer requires a feasible high speed with high data rate communication technique. This paper tries to implement a high speed underwater laser communication link by utilizing the USB based data transfer with the aid of lab-VIEW programming. The major objectives of the research are to study, compare the various underwater wireless technologies, design and development of UW laser communication link using USB based data transfer, to evaluate the communication link performance in varying simulated water conditions like pure, coastal and turbid waters, evaluate the link with data like text, images, audio and video files, bit Error rate analysis. Another objective is to introduce an IoUT concept with high data rate and low bit error rate. Internet of Underwater Things (IoUT) is defined as a network of smart interconnected underwater objects for monitoring/carrying out underwater operations. IoUT enables a system of autonomous underwater vehicles (AUV) communicating with each other, sensing, collecting and transmitting data to control centers above the surface at regular Internet speeds. [ABSTRACT FROM AUTHOR]

Due to complex environmental conditions and the significant resemblance between the raw tomatoes and the plant, along with the occlusion of the tomatoes by branches and leaves, it generally results in poor identification. Detecting specific objects under various environmental conditions is the first step towards the mechanization of the agricultural industry. To improve the detection of small targets in tomato fields, we applied the YOLOv4 neural network algorithm to obtain the features of tomato fruits, which helps in accurately predicting the class each tomato belongs to. The experimental results manifest that the YOLOv4 algorithm helps to improve the detection accuracy and real-time performance under various illumination and occlusion conditions. The proposed method also provides information on the count of ripe tomatoes ready to be harvested. Counting accuracy of 97.57% for ripe tomatoes and 91.19% for raw tomatoes is obtained. Moreover, the results are compared with YOLOv4-tiny, YOLOv3, and YOLOv3-tiny. [ABSTRACT FROM AUTHOR]

Using density functional theory, the effect of layer number and stacking pattern on the electronic and optical properties of PdSe2 is explored. The size of the band gap and optical properties are very sensitive to the stacking pattern. The monolayer PdSe2 (m-PdSe2) has band gap of about 1.29 eV and its value drops with rise in layer thickness. The band gap of bilayer PdSe2 (b-PdSe2) changes from 1.13 eV to 0.79 eV with stacking pattern AA and AB, respectively. The optical properties like dielectric function, absorption coefficient etc. are also calculated. The bilayer PdSe2 with stacking AB is found to have the most prominent optical properties. A red shift in optical properties is also observed as the number of layers increases. [ABSTRACT FROM AUTHOR]

In our work, we have done the fabrication of Si(P-type)/NiFe2O4/ZnO-rGO heterostructure device. Photo response has been measured by I-V characteristics in absence and presence of red light with variable intensities. Furthermore, we have also done the I-V measurement under various magnetic field with different illumination conditions. Hence magnetic field dependent photocurrent variation has been recorded. Therefore, our device can act as a magnetically tunable photodetector. [ABSTRACT FROM AUTHOR]

Screening of bacteria, potentially producing biosurfactants from different aquatic habitats was performed. A total of thirteen bacterial isolates were obtained in 2% (v/v) crude oil supplemented Mineral Salt Medium (MSM). Efficient biosurfactant bacteria were selected based on two tests for example drop collapse and emulsification index (E 24%). The bacterial isolates S2B1, S2B2, S4B1, S4B2, S5B3, and S6B2 tested positive for both tests. Isolate S2B1 exhibited the greatest Colony Forming Unit (CFU) per ml, the highest E 24%, and the maximum weight for biosurfactant, so it was identified as a potent producer of biosurfactant. 16S rRNA sequencing revealed S2B1 was Micrococcus luteus. Thin Layer Chromatography (TLC) revealed that M. lutues S2B1 was producing the lipopeptide biosurfactant. The environmental and nutritional culture conditions were optimized for M. luteus S2B1 to produce biosurfactant at the highest levels. The maximum CFU per ml and the highest E 24% were obtained after 72 h of incubation, at pH of 7, and temperature 37 ±4 ° C, when the Carbon (C) source was glucose and ammonium nitrate was a source of Nitrogen (N), both were added in the respective media at 2% (w/v) separately by maintaining Single-factor optimization methodology. [ABSTRACT FROM AUTHOR]

This research work deals with the study, strategy analysis, of battery charge controller for Islanded Micro Grid topology. Proposed system is verified with MATLAB Simulink and same system can be executed in hardware using the results. The projected scheme of Micro Grid consists of solar PV, Horizontal Axis Wind Turbine, Batteries, Buck-Boost Converter, Inverter for AC loads, Charge Controller for batteries and various types of active and passive loads. All AC types of loads are connected through a Voltage Source Inverter. Micro grid is able to operate either in Islanding or in Grid connected manner. In this proposed strategy, Charge Controller is designed for Islanded mode of operation and utility connections are possible with Static Transfer Switch and an appropriate Transformer. Planned network topology of Micro grid is based on Distributed Energy Resources (DERs) such as Solar Energy, Wind Energy, Fuel Cells, and Diesel Generators etc. Further this scheme is individually separated into design of Buck Converter, suitable Charge Controller and Inverter as per load Requirement. The designed charge controller for Micro grid lab test bed will be undergone the different analysis about Total Harmonic Distortion (THD), transient behavior and other analysis regarding power system such as improvement of Power Factor on load side. By the use of DERs in the Micro grid topology it is very beneficial to the environment as there is enormous use of Renewable Energy Resources. [ABSTRACT FROM AUTHOR]