Various aspects of soil and tree layer vegetation analysis in tropical dry deciduous forest of Hastinapur

Different parameters of Soil and vegetation analysis were carried out in Tropical dry deciduous forest of Hastinapur region of Uttar Pradesh. Vegetation of present study sites showed effects of various anthropogenic disturbances. The highly disturbed stand I showed low tree density while less disturbed stand III showed high tree density and good regeneration pattern. D-D curve were also drawn on the basis of the IVI of different species. Population structure of different tree species was drawn to understand the regeneration pattern. The most characteristic feature of the forest is dominance of xerophytic species and open forest canopy due to disturbances. Overgrazing and other biotic factors are making the area poor both in nutrient and top soil, which will eventually result in desertification of the Hastinapur in long run

Evolution of superconductivity in PrFe1-xCoxAsO with x = 0.0 to 1.0

We report the synthesis and physical property characterization of PrFe1-xCoxAsO with x = 0.0 to 1.0. The studied samples are synthesized by solid state reaction route via vacuum encapsulation method. The pristine compound PrFeAsO does not show superconductivity, but rather exhibits a metallic step like transition due to spin density wave ordering of Fe moments below 150 K, followed by another upward step due to anomalous ordering of Pr moments at 12 K. Both the Fe-SDW and Pr-TN temperatures decrease monotonically with Co substitution at Fe site. Superconductivity appears in a narrow range of x from 0.07 to 0.25 with maximum Tc at 11.12 K for x = 0.15. Samples, with x = 0.25 exhibit metallic behavior right from 300 K down to 2 K, without any Fe-SDW or Pr-TN steps in resistivity. In fact, though Fe-SDW decreases monotonically, the Pr-TN is disappeared even with x = 0.02. The magneto transport measurements below 14 Tesla on superconducting polycrystalline Co doped PrFeAsO lead to extrapolated values of the upper critical fields [Hc2(0)] of up to 60 Tesla.Comment: 15 pages Text+Fig

AC susceptibility study of superconducting YBa2Cu3O7:Agx bulk composites (x = 0.0-0.20): The role of intra and inter granular coupling

We report the effect of silver addition on superconducting performance of bulk YBCO (YBa2Cu3O7) superconductor. All the studied samples are prepared by conventional solid-state reaction method. Rietveld fitted X-ray diffraction data confirmed the single phase formation for all the studied samples. Detailed AC susceptibility measurements as a function of driven AC amplitude (1Oe-17Oe) of these samples revealed the enhancement of grains coupling with increasing Ag content in YBCO+Agx composite system. 10wt% Ag added YBCO superconductors exhibited the optimum inter granular coupling. The Scanning Electron Microscopy (SEM) observations indicate an increase in the grains connectivity in terms of narrow grain boundaries for doped samples. The average grain size is found to increase with Ag doping. It is concluded that limited addition of Ag in bulk YBCO superconductor significantly improves the grains coupling and as result optimum superconducting performance. YBCO+Ag composites could prove to be potential candidates for bulk superconducting applications of the studied high Tc system.Comment: 15 pages of text + Fig

A Comprehensive Survey of Artificial Intelligence (AI): Principles, Techniques, and Applications

AI has emerged as a transformational technology with enormous potential to change a wide range of sectors. Its foundations are founded on robots' capacity to learn and do jobs that would normally need human intellect. AI techniques such as machine learning and deep learning have grown in sophistication, enabling for the development of strong AI applications in fields such as healthcare, finance, and transportation. Yet, the fast development and implementation of AI raises a slew of issues that must be addressed. Ethical issues, data privacy and security, transparency and explainability, legislation and policy, technological hurdles, adoption and acceptability, accessibility, and interaction with current systems are among these challenges. To address these issues, industry, government, and academia must work together to create ethical frameworks, invest in research and development, and encourage openness and accessibility. Notwithstanding these obstacles, the potential advantages of AI are enormous. AI has the ability to improve efficiency, production, and decision-making in a variety of industries. It also has the ability to enhance people's lives and find answers to some of the world's most urgent problems. Overall, the ideas, methodologies, and applications of AI provide great prospects for good change; nevertheless, addressing the issues is critical to ensuring that AI is created and utilised in an ethical and responsible manner

PyCaret based URL Detection of Phishing Websites

The primary objective of the research project is to employ machine learning algorithms to conduct studies and identify instances of phished URLs that might direct people to fraudulent websites. The Kaggle repository, which contains more than 11,000 URLs, is where the authors received a phished dataset for this application. Examples of both genuine and phished URL links can be found in the collection. Also, the dataset contains 31 features that must be obtained using feature engineering stages and methodologies. Nevertheless, this dataset is also available as a csv file and has been further pre-processed to remove redundant and pointless data. This is followed by the feature extraction process, which extracts URL properties including domain-based, content-based, and address-based attributes. The implementation of PyCaret follows, with each line of code being in charge of the entire execution. Nonetheless, the testing at this level consists of three parts. In order to create accuracy, the initial stage of PyCaret's implementation includes running 14 built-in algorithms. The top three accuracy-generating algorithms are combined to build a stacking model in the last stage of the system model's implementation, which is divided into two stages. The second stage of the system model implementation entails taking random forest into account. In the conclusion, the accuracy of each algorithm is assessed together with its performance. After comparison, the technique with the highest generating accuracy is considered to be the optimised model

Study of deep level defects of N+-CdS/P-CdTe solar cells

Among various photovoltaic materials, polycrystalline cadmium telluride thin film is now the most promising material, due to its low production cost excellent stability and reliability. Current-voltage and capacitance-voltage measurements of CdTe photovoltaic devices at different temperatures can provide valuable information about non-idealities in the n-p semiconductor junction. There are certain limitations which limit the efficiency of CdTe solar cells. There is no real distinction between defects and impurities in CdTe solar cells as both act as beneficial dopants or detrimental traps unlike Si where intentional shallow dopants and traps are distinctly different. Therefore, the role of defect states on CdTe solar cell performance, the effect of processing on defect states, and simple and effective characterization techniques must be investigated and identified. In this research the thin film n+-CdS/p-CdTe solar cells made with evaporated Cu as a primary back contact, are characterized by using the temperature dependence of the reverse bias diode current (J-V-T) to determine the energy levels of deep defects. The results of the J-V-T measurements on solar cells made at NJIT show that while modest amounts of Cu enhance cell performance, an excessive high temperature annealing step degrades device quality and reduces efficiency. This work addresses the error that can be introduced during defect energy level estimation if the temperature dependence of the carrier capture cross-section is neglected. Therefore, the location of traps is derived using a Shockley-Read-Hall recombination model with modified assumptions. A Cu-related deep level defect with activation energy of 0.57eV is observed for Cu evaporated back contact cells and an intrinsic defect with activation energy 0.89eV is found. Frequency dispersion in Capacitance-Voltage measurements confirms the presence of Cu-related deep level traps for cells with a Cu evaporated back contact, whereas no such defects are observed in carbon paste contact. The behavior is believed to be due to diffusion of excess Cu from the contact. It is further observed that majority carrier deep level traps (Cu-related or intrinsic) contribute differently to the degradation of electronic properties of the CdTe solar cells. A simple and effective characterization technique based on temperature dependent capacitance spectroscopy (TDCS) is used to identify majority carrier trapping defects in thin film n+-CdS/p-CdTe solar cell, made with evaporated Cu as a primary back contact. The distinct deep level traps, observed by TDCS seem to be due to the ionization of impurity centers located in the depletion region of n+-CdS/p-CdTe junctio