Sources of Data for Micro Level Planning from Village Level Institutions: An Overview

A study was conducted to compile the databases related to agricultural development available at the local level that could be used for micro level planning. For this purpose, the details of legacy databases in offices of the department of agriculture and local self government institutions were collected to find out the frequency of updating information and completeness of data. Further investigation was done to find out the static and dynamic nature of legacy registers and how best they could be used in building up a comprehensive database for facilitating micro level planning in agriculture

Case study in six sigma methadology : manufacturing quality improvement and guidence for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities

Delivery of chondroitinase by canine mucosal olfactory ensheathing cells alongside rehabilitation enhances recovery after spinal cord injury

Spinal cord injury (SCI) can cause chronic paralysis and incontinence and remains a major worldwide healthcare burden, with no regenerative treatment clinically available. Intraspinal transplantation of olfactory ensheathing cells (OECs) and injection of chondroitinase ABC (chABC) are both promising therapies but limited and unpredictable responses are seen, particularly in canine clinical trials. Sustained delivery of chABC presents a challenge due to its thermal instability; we hypothesised that transplantation of canine olfactory mucosal OECs genetically modified ex vivo by lentiviral transduction to express chABC (cOEC-chABC) would provide novel delivery of chABC and synergistic therapy. Rats were randomly divided into cOEC-chABC, cOEC, or vehicle transplanted groups and received transplant immediately after dorsal column crush corticospinal tract (CST) injury. Rehabilitation for forepaw reaching and blinded behavioural testing was conducted for 8 weeks. We show that cOEC-chABC transplanted animals recover greater forepaw reaching accuracy on Whishaw testing and more normal gait than cOEC transplanted or vehicle control rats. Increased CST axon sprouting cranial to the injury and serotonergic fibres caudal to the injury suggest a mechanism for recovery. We therefore demonstrate that cOECs can deliver sufficient chABC to drive modest functional improvement, and that this genetically engineered cellular and molecular approach is a feasible combination therapy for SCI

Accomplishing Food Security through Community Action

ABSTRAC

Sources of Data for Micro Level Planning from Village Level Institutions: An Overview

A study was conducted to compile the databases related to agricultural development available at the local level that could be used for micro level planning. For this purpose, the details of legacy databases in offices of the department of agriculture and local self government institutions were collected to find out the frequency of updating information and completeness of data. Further investigation was done to find out the static and dynamic nature of legacy registers and how best they could be used in building up a comprehensive database for facilitating micro level planning in agriculture

A Study on Significance of Backwater Tourism and Safe Houseboat Operation in Kerala

The Backwaters of Kerala are historically important. The backwaters and interconnecting navigable canals have made a number of rural tourism destinations with matchless beauty.These backwater systems were once Kerala’s own trade highways.The major component of backwater tourism is houseboat cruising.The State has sensed the potential of backwater tourism in nowadays.Mass tourism movement in this sector caused for the multidimensional impacts on the economic,socio-cultural and bio-physical environment.Being an Eco-tourism product, backwater tourism needs sustainable and responsible tourism practice. Considering the need for the sustenance of the houseboat operation as a unique tourismproduct,it is mandatory to ensure the quality of facilities and services. In this paper, the researcher is trying to focus on the importance of backwater tourismin Kerala. Also giving special attention to identify various aspects of safe houseboat operations and the issues related. The major issues related to houseboat operation are lack of infrastructure,issues of licensing,issues of safety, environmental issues, and lack of quality services.In this study, the researcher is highlighting the need for the sustenance of the house boating by giving effective recommendations to reduce the negative aspects

Knowledge Management for Participatory Planning at the Grassroots: Dimensions, Prospects and Issues

ABSTRAC

MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network

Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2∼32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F1-score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net