Inception report on the Technical Assistance study (T.A. No. 1481-PAK): Crop based irrigation operations in the NWFP

Irrigation systems / Irrigation practices / Cropping systems / Water requirements / Pakistan

Effects of proprioceptive neuromuscular facilitation on spine joint position sense in adolescent idiopathic scoliosis: A case report

Introduction. Adolescent idiopathic scoliosis (AIS), described as a complex three-dimensional spinal deformity, is thought to affect neurophysiological processes that result in a loss of proprioceptive input. The main purpose of this case study is to investigate the effect of Proprioceptive Neuromuscular Facilitation (PNF) on spine joint reposition (JR) sense in a 20-year-old with AIS. Methods/ Case Description. The subject was a 20-year-old college student with moderate dextrothoracic and levolumbar scoliosis. She has structural scoliosis-related impaired posture, as evidenced by findings of impaired JR sensation in all directions, postural deviations, and patient-reported deformity perception using the Walter Reed Visual Assessment Scale. She was seen 4 times a week for 3 weeks. Results: The most recent radiographs analyzed by a radiologist revealed that the curvature of the thoracic spine had decreased from 38° to 32° and the curvature of the lumbar spine had decreased from 26° to 24°. There were also improvements noted in JR sensation, postural deviation, and deformity perception. Discussion: Incorporation of PNF in the patient’s plan of care may have positively contributed to improvement in JR sense of the spine, postural symmetry, and deformity perception. Future studies should examine the other components of proprioception, the effect of PNF in subjects with greater or more severe curvature, and information on joint position perception in healthy subjects

Crop-based irrigation operations in the NWFP: Progress report no.2, Kharif 92 on the Technical Assistance Study, T.A. No.1481-PAK

Irrigation operation / Cropping systems / Irrigation canals / Water users' associations / Institutions / Pakistan

Crop-based irrigation operations in the North West Frontier Province of Pakisatn. Vol.I: Synthesis of findings and recommendataions. Final Report

Irrigation management / Crop-based irrigation / Pakistan / North West Frontier Province

The synthesis and characterization of polycarbonates based on 1,1\u27-dihydroxyethyl-2,2\u27-biimidazole

The synthesis of polycarbonates formed by the reaction of 1,1\u27-dihydroxyethyl-2,2\u27-biimidazole and diphenyl or diethyl carbonates was investigated and the products formed were characterized. The molecular weights of the polymers were dependent on catalyst, ratio of starting materials, nature of carbonate (aromatic or aliphatic) and method of polymerization. Isolated polymers exhibit a linear structure. They have no well-defined melting points (melting pt. range of 280- 330⁰C) indicating low degree of crystallization, and undergo less than 9% decomposition below 200⁰C --Abstract, page ii

Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201