Measurement of Ethnic Knowledge Associated with Semecarpus anacardium L. f. -- A Rare and Endangered Ethnobotanical Plant of Jharkhand, India

During the observation on ethnobotanical wealth used by the tribal people in their day to day needs, healthcare and socio-religious ceremonies of Jharkhand state of India, it was observed that due to the depletion of plants, the ethnic culture and associated knowledge system related with the plant species is also being lost. It has also seriously affected the oral traditions and knowledge of the tribal communities. To assess the above problem several indices like ‘Knowledge frequency’, ‘Plant-lore Index’ and percentage of ‘Community knowledge loss’ were developed to measure the knowledge status of tribes associated with Semecarpus anacardium L.f

Data-driven modeling and parameter estimation of Nonlinear systems

Nonlinear systems are prevalent in many fields of science and engineering, and understanding their behavior is essential for developing effective control and prediction strategies. In this paper, we present a novel data-driven approach for accurately modeling and estimating parameters of nonlinear systems using trust region optimization. Our method is applied to three classic systems: the Van der Pol oscillator, the Damped oscillator, and the Lorenz system, which have broad applications in various fields, including engineering, physics, and biology. Our results demonstrate that our approach can accurately identify the parameters of these nonlinear systems, providing a reliable characterization of their behavior. We show that the ability to capture the dynamics on the attractor is crucial for these systems, especially in chaotic systems like the Lorenz system. Overall, this article presents a robust data-driven approach for parameter estimation of nonlinear dynamical systems, with promising potential for real-world applications.Comment: 17 pages, 6 figure

Automotive Driveline Backlash State and Size Estimator Design for Anti-Jerk Control

Vehicle drivability is an important factor which more and more customers have started assessing before buying a vehicle. Customers carry out this assessment based on both vehicle reviews/ratings and based on the test drives. One of common maneuver which a customers perform during the test drive is sudden accelerator pedal tip-in or tip-out to accelerate or coast the vehicle. Clunk and shuffle are the phenomena that usually occur during this scenario causing driver discomfort. The clunk and shuffle are caused by the backlash and compliance physical properties of the driveline. Consequently, control strategy needs to be developed which can provide a fast driveline response without clunk and shuffle. One major input to develop a control strategy is the knowledge of the vehicle states and parameters based on the available measurements, which is the major focus of this work. This work begins with a discussion of various existing estimation strategies that have been used to estimate the states of vehicle along with their merits and demerits. Then a full order model, developed in the previous works, is validated for a locked torque converter case along with its reduced order model which is used for estimator development. The error in the simulated shuffle frequency for the full order model and reduced order model is less than 1%. The reduced order model is then used to develop an observable state space model to estimate the backlash state and size of the model. The estimators developed are validated and the robustness analysis is done for different scenarios of torque inputs, delays and sampling times. It is found that the sampling time of the estimators and measurement inputs significantly effect the estimates of lash traversal time and backlash size with a mean error of 9% in lash traversal time estimate and 2% error in lash size for 10 ms sampling time. Furthermore, the estimators are found to be more robust to the variations in the wheel speed measurements as compared to variations in the engine speed measurements

Entomological surveillance for rodent and their ectoparasites with special reference to potential of Scrub Typhus at Kolkata Port Trust (KPT), Kolkata (India)

     Rodents are well established at port areas and their ectoparasite can be vectors of many diseases. Ships at seaport play an important role in spreading disease by transporting infected vectors. Plague, murine typhus, salmonellosis, trichinosis, leptospirosis and rat bite fever are known to be spread by rats. In view of the seriousness of the problem present study was undertaken to assess the prevalence of rodent – ectoparasite association at Kolkata Port Trust, Kolkata (India). A total of 442 rodent traps were laid and 17 rodents including two species of rodents Bandicota indica and Rattus rattus were collected with 3.8 trap positivity rate. Infestation rate of ectoprasites was 76.5%. Mites with 86.6% were the predominant ectoparisite retrieved from rodents followed by fleas and lice. The rodent ectoparasite index was 13.6 per rat. A total 78 vector larval trombiculide mite chigger (Leptotrombidium deliense) and 123 mesostigmatid mites (Laelaps sp.) were collected. Chigger infestation rate was found to be 11.1 per rat.  Passenger terminal was the only site where no mite could be retrieved.  A total 26 fleas were retrieved giving an overall flea index as 1.53. The flea species collected from rodents were Xenopsylla cheopis and Ctenocephalides felis. Serological examination of rodent serum was found non- reactive for Orentia (Scrub typhus) and plague antibodies. Bacteriological investigation of organs impression smear and tissue of rodents showed no plague bacilli activity and isolation of Yersinia pestis. Result of the study suggests routine surveillance for rodent and their arthropods ectoparasite to apply appropriate control methods for controlling transmission and spreading of rodent borne diseases

Stability and electronic properties of amorphous silicon p-i-n devices fabricated using ECR plasma enhanced chemical deposition

The prime objective of the research was to investigate the fabrication, stability and electronic properties of a-Si:H devices deposited using electron cyclotron resonance (ECR) plasma deposition technique. Both reactive (hydrogen) and non-reactive (helium) gases were used as the primary plasma gas. The reactants generated by the ECR plasma from these gases were allowed to flow towards the substrate, where they reacted with silane to give rise to growth of a-Si:H. In general, we used a high dilution ratio of plasma gas (H2 or He) to silane, about 15:1 to 20:1; for growing a-Si:H. Very high quality a-Si:H films could be deposited using H-ECR plasma at relatively higher temperatures (325°C-375°C). The use of He in the discharge does not lead to any etching during growth (as with H-plasma), but does lead to significant ion bombardment. We found that by using a highly diluted He-ECR growth technique, we can reduce the Tauc bandgap of a-Si:H to 1.67eV, which is significantly lower than the 1.75eV obtained with H as the plasma gas. We further found that the hydrogen content in the films grown using He dilution was much lower than the H content in the films made using H-dilution. The microstructure of these films were further probed using TEM. We succeeded in making reasonable quality devices from the ECR-deposited a-Si:H materials prepared at high temperatures. The devices were deposited both on tin oxide (superstrate) and stainless steel (substrate) substrates. To deposit the superstrate type devices on tin oxide substrate we had to overcome various problems, such as reduction in tin oxide at high temperature and diffusion of B across the p-i interface at high temperature. The reduction in tin oxide was avoided by using a He-diluted p-layer deposited at low temperature. The diffusion of \u27B\u27 was avoided by using an a-Si:H buffer layer with a high C content between the p and i-layer. We found that the design of the buffer layer was critical to achieving good performance. While we had successfully solved these problems a lower temperatures (up to 360°C), it became progressively more difficult to solve the diffusion problem as the growth temperature was increased. These problems were overcome by the use of a substrate cell geometry. We could finally achieve fill factors of about 72% using such geometries. We also investigated the stability of the cells that we made using ECR plasma and compared it with the cells grown using glow discharge growth technique. We found that the solar cells made using H-ECR growth technique were more stable than those made using glow discharge technique. The hole mobility lifetime product ([mu][tau]) has not been previously measured in a-Si:H based devices grown using EGR growth technique. We measured the hole [mu][tau] product in devices, by making a systematic series of devices, all with same p and n Bayers, but with i-layers grown using hydrogen or helium dilutions and at different growth temperatures. Using quantum efficiency spectroscopy, we could deduce the hole [mu][tau] product. The quantum efficiency values were accurately modeled by adjusting just two parameters: the p-layer absorption and the hole [mu][tau] product. The results were quantified by modifying the earlier model, developed by Greg Baldwin

A. Eye Detection Using Varients of Hough Transform B. Off-Line Signature Verification

PART (A): EYE DETECTION USING VARIANTS OF HOUGH TRANSFORM: Broadly eye detection is the process of tracking the location of human eye in a face image. Previous approaches use complex techniques like neural network, Radial Basis Function networks, Multi-Layer Perceptrons etc. In the developed project human eye is modeled as a circle (iris; the black circular region of eye) enclosed inside an ellipse (eye-lashes). Due to the sudden intensity variations in the iris with respect the inner region of eye-lashes the probability of false acceptance is very less. Since the image taken is a face image the probability of false acceptance further reduces. Hough transform is used for circle (iris) and ellipse (eye-lash) detection. Hough transform was the obvious choice because of its resistance towards the holes in the boundary and noise present in the image. Image smoothing is done to reduce the presence of noise in the image further it makes the image better for further processing like edge detection (Prewitt method). Compared to the aforementioned models the proposed model is simple and efficient. The proposed model can further be improved by including various features like orientation angle of eye-lashes (which is assumed constant in the proposed model), and by making the parameters adaptive. PART (B): OFF-LINE SIGNATURE VERIFICATION: Hand-written signature is widely used for authentication and identification of individual. It has been the target for fraudulence ever since. A novel off-line signature verification algorithm has been developed and tested successfully. Since the hand-written signature can be random, because of presence of various curves and features, techniques like character recognition cannot be applied for signature verification. The proposed algorithm incorporates a soft-computing technique “CLUSTERING” for extraction of feature points from the image of the signature. These feature points or centers are updated using the clustering update equations for required number of times, then these acts as extracted feature points of the signature image. To avoid interpersonal variation 6 to 8 signature images of the same person are taken and feature points are trained. These trained feature points are compared with the test signature images and based on a specific threshold, the signature is declared original or forgery. This approach works well if there is a high variation in the original signature, but for signatures with low variation, it produces incorrect results

‘\u3cem\u3eAgia Vaital\u3c/em\u3e’: The People’s Belief About Natural Fire in Forests and its Link with \u3cem\u3ePygmaeopremna herbacea\u3c/em\u3e (Roxb.) Mold. in Chhotanagpur Plateau, India

Tribal, forest dwellers and rural people of Chhotanagpur plateau in India, call abrupt fire in forest as ‘Agia Vaital’. This may be anthropogenic, but in traditional myths, it is believed as natural. Pygmaeopremna herbacea (Roxb.) Mold. is a potential ethnomedicinal plant and every year it suffers with periodic forests fire in summers. The rosettes of two to three pairs of leaves of this stem less herb lies flat on the ground. In hot temperature during summer the blaze of fire, without any human cause has been observed by the people of the region in the forests of the plateau and due to this fact the plant is known as ‘Agia Vaital’. The people’s observation and information based on the plant related with forest fire would be useful for forest management and preservation of the valuable plant species in the wild

Attention-Based Data Analytic Models for Traffic Flow Predictions

Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration