Synergistic effect of antagonistic microflora and farmyard manure (FYM) to reduce wilt disease in chickpea caused by Fusarium oxysporum f sp ciceri

Wilt disease of chickpea caused by Fusarium oxysporum f sp ciceri (Pudwick) Synd. and Hans. is serious problems across the country. The experiment was conducted at the farm College of Agriculture, Rajasthan Agricultural University, Bikaner in for two crop seasons 2007-08 and 2008-09 to manage the wilt disease of chickpea. Chickpea variety RSG- 44 was used for this experiment. For the purpose, two antagonist, i.e. Trichoderma harzianum and Pseudomonas fluorescens were applied as seed treatment (alone @ 8 g/ kg seeds; in combination of both bioagents @ 4 g + 4 g/ kg seeds) and soil application (alone @ 10 kg/ ha; in combination of both bioagents @ 5 kg + 5 kg/ ha) along with FYM @ 5, 10, and 15 tonnes/ ha in the field. Minimum wilt disease incidence 23.65% and 25.35% was found in the treatment T12: T. harzianum+P. fluorescens seed treatment (4+4) kg/seed) and soil application (5+5) kg/ha along with FYM @15 tonnes/ h followed by T11:T. harzianum +P. fluorescens seed treatment (4+4) kg/seed) and soil application (5+5) kg/ha along with FYM @10 tonnes/ ha (27.67 and 28.61%) and T8: P. fluorescens ST (8 g/ kg seed + SA (10 kg/ h + FYM 15 tonnes/ha (28.51 and 29.48%) in 2007-08 and 2008-09 under field conditions respectively. The plant growth, i.e. root and shoot lengths, dry weight and seed yield were found higher when T. harzianum and P. fluorescens were used along with higher dose of FYM, i.e. 15 tonnes/ha. A significant variation was recorded among the treatments. The organic carbon content of the soil was increased by increasing the dose of FYM from 5 to 15 tonnes/h irrespective of bioagents. The Fusarium population was suppressed by the two bioagents used alone or in combinations. The population of T. harzianum and P. fluorescens was higher in rhizosphere soil when FYM was applied at higher dose (15 tonnes/ha). Our result findings indicate that microbial bioagents and FYM have synergistic effect on reducing the wilt incidence in chickpea and promote the plant growth significantly

Effect of Uncertainty, Supplier Involvement, Supplier Performance and Partnership Quality on Buyer-Supplier Relationship

The research is undertaken to evaluate the aspects of Uncertainty, Earlier Supplier Involvement, Supplier Performance and Partnership quality on Buyer dependence on the Supplier. In this explanatory research, a model is developed to validate the premise that by drafting value creating supplier relationship in order to help and implement strategies in supply chain management in manufacturing and non-manufacturing companies which will increase the buyer’s dependency of the suppliers. The study used data from atleast 228 procurement personnel from various industries. The results show that early supplier involvement, partnership quality, supplier performance have a significant and positive impact on buyer’s dependence. Whereas, uncertainty has also a positive but insignificant relationship with buyer dependence. To increase buyer dependency suppliers should be involved in early stage of the design and production of the product. Partnership quality and supplier performance also have significant impact on buyer dependence so management should consider the factors

Salat Postures Detection Using a Hybrid Deep Learning Architecture

Salat, a fundamental act of worship in Islam, is performed five times daily. It entails a specific set of postures and has both spiritual and bodily advantages. Many people, notably novices and the elderly, may trouble with maintaining proper posture and remembering the sequence. Resources, instruction, and practice assist in addressing these issues, emphasizing the need of prayer sincerity. Our contribution in the research is two-fold as we have developed a new dataset for Salat posture detection and further a hybrid model Media Pipe+3DCNN. Dataset is developed of 46 individuals performing each of the three compulsory Salat postures of Qayyam, Rukku and Sajdah and model was trained and tested with 14019 images. Our current research is a solution for correct posture detection which can be used for all ages. We examined the Media Pipe library design as a methodology, which leverages a multistep detector machine learning pipeline that has been proven to work in our research. Using a detector, the pipeline first locates the person's region-of-interest (ROI) within the frame. The tracker then forecasts the pose landmarks and division mask in between the ROIs using the ROI cropped frame as input. A 3D convolutional neural network (3DCNN) was also utilized to extract features and classification from key-points retrieved from the Media Pipe architecture. With real-time evaluation, the newly built model provided 100% accuracy and a promising result. We analyzed different evaluation matrices such as Loss, Precision, Recall, F1-Score, and area under the curve (AUC) to give validation process authenticity; the results are 0.03, 1.00, 0.01, 0.99, 1.00 and 0.95.  accordingly

Salat Postures Detection Using a Hybrid Deep Learning Architecture

Salat, a fundamental act of worship in Islam, is performed five times daily. It entails a specific set of postures and has both spiritual and bodily advantages. Many people, notably novices and the elderly, may trouble with maintaining proper posture and remembering the sequence. Resources, instruction, and practice assist in addressing these issues, emphasizing the need of prayer sincerity. Our contribution in the research is two-fold as we have developed a new dataset for Salat posture detection and further a hybrid model Media Pipe+3DCNN. Dataset is developed of 46 individuals performing each of the three compulsory Salat postures of Qayyam, Rukku and Sajdah and model was trained and tested with 14019 images. Our current research is a solution for correct posture detection which can be used for all ages. We examined the Media Pipe library design as a methodology, which leverages a multistep detector machine learning pipeline that has been proven to work in our research. Using a detector, the pipeline first locates the person's region-of-interest (ROI) within the frame. The tracker then forecasts the pose landmarks and division mask in between the ROIs using the ROI cropped frame as input. A 3D convolutional neural network (3DCNN) was also utilized to extract features and classification from key-points retrieved from the Media Pipe architecture. With real-time evaluation, the newly built model provided 100% accuracy and a promising result. We analyzed different evaluation matrices such as Loss, Precision, Recall, F1-Score, and area under the curve (AUC) to give validation process authenticity; the results are 0.03, 1.00, 0.01, 0.99, 1.00 and 0.95.  accordingly

Molecular docking of taraxerol acetate as a new COX inhibitor

Cycloxygenase inhibitors are one of the main class of therapeutic agents for management of inflammation. New COX inhibitors are discovered from natural and synthetic sources. In the current investigation taraxerol acetate have been discovered as a new COX inhibitor. Taraxerol acetate showed considerable inhibitory activity against both COX-1 (IC50: 116.3 ± 0.03 ?M) and COX-2 (IC50: 94.7 ± 0.02 ?M) enzymes using in-vitro enzyme inhibition assay. Molecular docking revealed significant interactions of taraxerol acetate with the important amino acid residues surrounding the inhibitor in binding pocket of COX-2 enzyme. This study indicate potential of taraxerol acetate to be further explored and modified as a new lead compound for better management of inflammatory conditions via targeting COX enzymes.  

The Effect of Fines on Hydraulic Conductivity of Lawrencepur, Chenab and Ravi Sand

The amount of fines in sand greatly influence the permeability of sandy soils. Thus, this research was conducted to study the effect of plastic and non-plastic fines on the permeability of three types of sands (Lawrencepur sand, Chenab sand and Ravi sand). For this purpose, plastic and non-plastic fines were collected from different location of Lahore. Samples were prepared by mixing plastic and non-plastic fines into each type of sand separately, in amounts ranging from 0% to 50% with increments of five percent. Overall 63 samples were prepared. Sieve analysis and hydrometric analysis were performed to obtain particle size distribution for each sample. Atterberg’s limits were also determined and each sample was classified according to the Unified Soil Classification System (USCS). Compaction tests were performed on all samples as per the procedure in a standard Proctor test. The test samples were compacted in permeability molds with optimum moisture contents to obtain the density, as per a standard Proctor test. Hydraulic conductivity tests were performed on all sixty-three samples using a constant head permeameter and a falling head permeameter. Permeability results were plotted against the percentage of fines added. It was noted from the curves that the permeability of sand-fine mixtures shows a decreasing trend with the addition of fine contents. A few trials were performed to formulate a correlation. Validation of the correlation was performed with the results of 52 data sets from the field. Finally, the devised correlation was compared with three empirical equations proposed by Mujtaba, Kozeny–Carman and Hazen

Computational Study of Elastic, Structural, Electronic, and Optical Properties of GaMF3 (M = Be and Ge) Fluoroperovskites, Based on Density Functional Theory

This paper explains our first-principle computational investigation regarding the structural, optical, elastic, and electrical characteristics of gallium-based GaMF3 (M = Be and Ge) perovskite-type (halide-perovskite) compounds. Our current computation is based on density functional theory (DFT) and is achieved with the help of the WIEN2k code. We used the Birch–Murnaghan equation for optimization; in both compounds, we found that both GaBeF3 and GaGeF3 compounds are structurally stable. For the computation of elastic characteristics, the IRelast package for calculating elastic constants (ECs) is utilized. These compounds are mechanically ductile, scratch-resistant, anisotropic, and mechanically stable, showing huge opposition to plastic strain. The modified Becke–Johnson (TB-mBJ) potential approximation method is used to calculate different physical characteristics and shows that GaGeF3 behaves as a metal, whereas the GaBeF3 compound is insulating in nature. The involvement of various electronic states in band structures is calculated using the theory of the density of states. The different optical properties of these compounds can be studied easily using their band gap energy. At high energy ranges, these substances demonstrate strong absorption. At low energies, the GaGeF3 compound is transparent, while the GaBeF3 compound is opaque to incoming photons. Investigation of the optical characteristics has led us to the conclusion that both GaGeF3 and GaBeF3 compounds can be used for high-frequency ultraviolet device applications. This computational work is considered to be the first time that we can study these compounds, which to our knowledge have not previously been experimentally validated

Insight into the Structural, Electronic, Elastic, Optical, and Magnetic Properties of Cubic Fluoroperovskites ABF3 (A = Tl, B = Nb, V) Compounds: Probed by DFT

This work displays the structural, electronic, elastic, optical, and magnetic properties in spin-polarized configurations for cubic fluoroperovskite ABF3 (A = Tl, B = Nb, V) compounds studied by density functional theory (DFT) by means of the Tran-Blaha-modified Becke-Johnson (TB-mBJ) approach. The ground state characteristics of these compounds, i.e., the lattice parameters a0, bulk modulus (B), and its pressure derivative B′ are investigated. The structural properties depict that the selected compounds retain a cubic crystalline structure and have stable ground state energy. Electronic-band structures and DOS (density of states) in spin-polarized cases are studied which reports the semiconducting nature of both materials. The TDOS (total density of states) and PDOS (partial density of states) studies in both spin configurations show that the maximum contributions of states to the different bands is due to the B-site (p-states) atoms as well as F (p-states) atoms. Elastic properties including anisotropy factor (A), elastic constants, i.e., C11, C12, and C44, Poisson’s ratio (υ), shear modulus and (G), Young’s modulus (E) are computed. In terms of elastic properties, the higher (bulk modulus) “B” and ratio of “B/G” yield that these materials exhibit a ductile character. Magnetic properties indicate that both the compounds are ferromagnetic. In addition, investigations of the optical spectra including the real (ε1ω) and imaginary (ε2ω) component of the dielectric function, refractive index nω, optical reflectivity Rω, optical conductivity σω, absorption coefficient αω, energy loss function Lω, and electron extinction coefficient kω are carried out which shows the transparent nature of TlVF3 and TlNbF3. Based on the reported research work on these selected materials, their applications can be predicted in many modern electronic gadgets

Sustainable Brick Masonry Bond Design and Analysis: An Application of a Decision-Making Technique

This research intends to explore the sustainable masonry bond formation and interface behaviour of brick masonry bonds with different cement mortar ratios. To test the sustainable behaviour of different brick bonds, different tests were applied to evaluate the performance of the developed five brick masonry structures with the help of four mortar ratios. Following that pattern, the methodologies of a prism triplet test, a bond wrench test, a shear bond test and strength tests for brick masonry were applied. The prism triplet test explained the bonding behaviour of mortar by producing a maximum strength (0.21 MPa) with a 1:3 mix ratio, and the minimum strength (0.095 MPa) with a 1:8 mix ratio. The bond wrench test showed a bond strength of maximum 0.0685 MPa with a mortar ratio of 1:3 and a minimum of 0.035 MPa with a mortar ratio of 1:8. The strength tests for masonry structures expressed that compressive strength (0.786 MPa) and flexural strength (0.352 MPa) were found to be at maximum level with a mortar ratio (1:3) with an English bond formation. For predictions of compressive and flexural strength, artificial neural networks (ANNs) were deployed, and successful predictions of these values along with the relationships between different properties of the material, mortar combinations and bond combinations are presented to complete the exploration of the relationship. This pattern can be helpful for the selection of sustainable brick masonry formations for housing development