2-(Prop-2-en­yl)-1,2-benzisothia­zol-3(2H)-one 1,1-dioxide

In the title compound, C10H9NO3S, the benzisothia­zole group is almost planar (with a maximum deviation of 1.61 Å). The crystal structure is stabilized by weak inter­molecular C—H⋯O hydrogen bonds, forming a chain of mol­ecules along b

2-[(E)-3-Phenyl­prop-2-en­yl]-1,2-benzisothia­zol-3(2H)-one 1,1-dioxide

In the crystal structure of the title compound, C16H13NO3S, the benzisothia­zole group is almost planar (r.m.s. deviation for all non-H atoms excluding the two O atoms bonded to S = 0.009 Å). The dihedral angle between the fused ring and the terminal ring is 13.8 (1)°. In the crystal, mol­ecules are linked through inter­molecular C—H⋯O contacts forming a chain of mol­ecules along b

Energy efficient parallel configuration based six degree of freedom machining bed

The process of material removal from a workpiece to obtain the desired shape is termed machining. Present-day material removal technologies have high spindle speeds and thus allow quick material removal. These high-speed spindles are highly exposed to vibrations and, as a result, the accuracy of the final workpiece’s dimensions is compromised. To overcome this problem, the motion of the tool is restricted, and multiple degrees of freedom are given through the motion of the workpiece in different axes. A machining bed configured as a parallel manipulator capable of giving six degrees of freedom (DOF) to the workpiece is proposed in this regard. However, the proposed six DOF machining bed should be energy efficient to avoid an increase in machining cost. The benefit of using the proposed configuration is a reduction in dimensional error and computational time which, as a result, reduces the energy utilization, vibrations, and machining time in practice. This paper presents kinematics, dynamics and energy efficiency models, and the development of the proposed configuration of the machining bed. The energy efficiency model is derived from the dynamics model. The models are verified in simulation and experimentally. To minimize error and computation time, a PID controller is also designed and tested in simulation as well as experimentally. The resulting energy efficiency is also analyzed. The results verify the efficacy of the proposed configuration of the machining bed, minimizing position error to 2% and reducing computation time by 27%, hence reducing the energy consumption and enhancing the energy efficiency by 60%

Phytochemical profiling of Costus (Saussurea lappa Clarke) root essential oil, and its antimicrobial and toxicological effects

Purpose: To carry out gas chromatography-mass spectrometry (GC-MS) analysis of the phytochemical content of the root essential oil of Saussurea lappa Clarke Asteraceae (Costus, SLEO), and to evaluate its physicochemical, antimicrobial and cytoxic properties. Methods: The oil was extracted from the plant’s roots by steam distillation using a Clevenger system. Various physicochemical parameters for the oil including refractive index, color, acid value, saponification number, ester and peroxide values were measured. Flavonoid content was assessed using thin layer chromatography (TLC). Thermoscientific trace ultra gas chromatograph equipped with a Thermoscientific capillary TR-5MS column was utilized to determine the volatile components of SLEO. Antimicrobial activity of SLEO was performed against various Gram (+ve) and Gram (-ve) microorganisms, viz, Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Candida albicans, while cytotoxic effect was monitored using Artemia salina (brine shrimp) lethality assay. Results: Essential oil yield was good (3 %). Concentration-dependent antimicrobial effects were observed on all test microorganisms and no marked difference in lethality levels was observed among the tested SLEO concentrations on brine shrimp (p < 0.05). The main component of SLEO was costunolide or eudesma-5,11(13)-dien-8,12-olide (52.01 %). Conclusion: The results indicate the promising therapeutic properties of S. lappa. However, further phytochemical and biological investigations are required to establish the mechanism of action and toxicological the extract

Insect Pest Complex of Wheat Crop

Wheat Triticum aestivum L. is grown on broad range of climatic conditions because of edible grains, cereal crop and stable food of about 2 Billion peoples worldwide. Additionally, it is the rich source of carbohydrates (55–60%), vegetable proteins and contributed 50–60% daily dietary requirement in Pakistan. Globally, wheat crops is grown over 90% area of total cultivated area; facing devastating biotic and abiotic factors. The estimated economic losses in wheat quantity and quality are about 4 thousands per tonne per year including physical crop losses and handling. Economic losses of about 80–90 million USD in Pakistan are recorded due to inadequate production and handling losses. Wheat agro-ecosystem of the world colonizes many herbivore insects which are abundant and causing significant losses. The feeding style of the insects made them dispersive from one habitat to another imposing significant crop loss. Areas of maximum wheat production are encountered with either insect which chew the vegetative as well as reproductive part or stem and root feeders. This chapter provides the pest’s taxonomic rank, distribution across the globe, biology and damage of chewing and sucking insect pest of wheat. It is very important to study biology of the pest in accordance with crop cycle to forecast which insect stage is economically important, what the proper time to manage pest is and what type of control is necessary to manage crop pest. The chapter will provide management strategies well suited to pest stage and environment

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T1-weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06)

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms

Political Rights of Minorities in the Light of Quran and Sunnah: اقلیتوں کے سیاسی حقوق قران و سنت کی روشنی میں

 When we talk about human rights in Islam we mean to say that these rights have been bestowed by Allah Almighty; they have not been granted by any emperor or any legislative assembly. The rights given by the rulers or by the legislative assemblies, can be taken back in the same way in which they are conferred. The rights given or accepted by any powerful king he can withdraw when he is unhappy; and he can violate them when he likes. In Islam human rights have been given by Allah, no worldly legislative parliament, or any government in the world has any amendment or change in the rights granted by God. Nobody can abolish them or withdraw them. Islamic human rights are not like those rights conferred on paper just for pump and show and denied in actual life when the show is over. It is very clear that the concept of Islam in regard to the human rights is based upon equality, dignity, respect and justice for all human beings.  The western concept of basic human rights is a manmade philosophy of law, in some ways it may right or wrong because it is not God gifted. Western people have done a long struggle to attain basic human rights since Magna Carta to present age but Islamic law is bestowed by Almighty Allah. In this study effort are made to compare fundamental human rights in the light of Islamic teachings and French laws.  Comparative and analytical research methodology is adopted in this study with qualitative approach.  This study perceives that Islamic teachings has all kind of rights and duties, liberties and duties. However, it binds the rights with duties and liberties with responsibilities, which make it distinguish to any other man mad laws including French laws

3D U-Net for Skull Stripping in Brain MRI

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953