Prediction of fretting wear in spline couplings

The original contribution of this work is modeling of fretting wear in aero-engine spline couplings widely used in aero-industry to transfer power and torque. Their safe operation is very critical with respect to flight safety. They consist of two components namely hub and shaft. As they are of light weight, usually it is difficult to realize a perfect alignment. To allow for misalignment, their teeth are designed to be of crowned shape. The crowing allows a degree of misalignment without concentration of stresses which is otherwise inevitable if a misalignment is introduced in case of straight teeth. However, crowing results in another problem of fretting wear and fretting fatigue owing to kinematic constraints imposed as a result of misalignment. The focus of this work is development of mathematical models for prediction of fretting wear and not fretting fatigue. The spline couplings under consideration are industrial scale and made up of nitrogen hardened 42CrMo4. The aero industry requires a reliable method to model and predict fretting wear to be able to optimize the design of spline coupling and reduce the maintenance costs. Wear tests on crowned spline couplings on a dedicated test bench have been conducted and analyzed. Empirical, artificial neural network based and analytical models have been de- veloped to analyse, predict and formulate fretting wear in spline couplings. The empirical and artificial neural netwrok based models are specific to the given case of spline couplings and tribological conditions. However, the analytical model developed has been found to be quite general. Incremental fretting wear formulation both in terms of wear volume and wear depth has been realized. Some novel findings regarding effect of roughness parameters in conjunction with applied torque and misalignment angles with respect to fretting wear are also reported. It has been observed that the evolution of wear depth accelerates with increased applied torque or misalignment angle. Changes in roughness parameters are also found to be increasing with torque and misalignment angle in most of the cases. Preliminary tests for frequency effects on fretting wear have also been conducted

Trade Liberalisation Policies, Intra-regional Trade and Opportunities for Sustainable Agricultural Development

Many of the Near East (NE) countries are currently opening their agricultural markets at three distinct but interacting levels: unilateral liberalisation, regional integration schemes and multilateral trade liberalisation. These changes hold important implications for intra- and extra-regional trade, use of agricultural resources and sustainability of agricultural development in the NE countries. Unilaterally, and since the late 1980s, most countries of the region have liberalised their agriculture sectors by eliminating or reducing input subsidies, removing or reducing guaranteed producer prices, reducing the number of subsidised commodities and liberalising the exchange rate and the trade regime. Most of the implicit and explicit subsidies for agricultural inputs and outputs were withdrawn. However, some of the NE countries were able to continue supporting agriculture mainly for food security reasons. Experiences showed that domestic reform is necessary but not sufficient condition for economic growth.

Ecological Study of Vanellus indicus in District Narowal, Pakistan

Red wattled lapwing is commonly found and endemic species of Asian agricultural lands. Its preferred habitat is airy lands, rural and even urban areas. The main aims of current investigatory effort were the observation of different parameters of breeding ecology of Vanellus indicus including breeding sites, incubation period, the clutch size, survival rate of chicks and possible reasons of their mortality which are localized in Narowal district. Red wattled lapwings forage on several types of insects, snails, seeds, and of invertebrates. Experimental observations highlight that the annual breeding season is of average 26-30 days of V. Indicus begins from the March and till the end of August. Moreover, the weight of their eggs falls between 22.0 to 28.0g. These findings about local species may serve for next experimental designs based of on V. indicus and further focus should be on those factors which may improve its breeding. Similarly, the estimation of feeding preferences of this species can directly help in low cost biological control management for crops

Trade Liberalisation Policies, Intra-regional Trade and Opportunities for Sustainable Agricultural Development

Many of the Near East (NE) countries are currently opening their agricultural markets at three distinct but interacting levels: unilateral liberalisation, regional integration schemes and multilateral trade liberalisation. These changes hold important implications for intra- and extra-regional trade, use of agricultural resources and sustainability of agricultural development in the NE countries. Unilaterally, and since the late 1980s, most countries of the region have liberalised their agriculture sectors by eliminating or reducing input subsidies, removing or reducing guaranteed producer prices, reducing the number of subsidised commodities and liberalising the exchange rate and the trade regime. Most of the implicit and explicit subsidies for agricultural inputs and outputs were withdrawn. However, some of the NE countries were able to continue supporting agriculture mainly for food security reasons. Experiences showed that domestic reform is necessary but not sufficient condition for economic growth

Tribological Characterization of Electrical Discharge Machined Surfaces for AISI 304L

Surface treatments are normally carried out after machining. Surface treatment is a costly and time-consuming process. Hence, it makes sense to reduce the requirement of surface treatment as much as possible. Electrical Discharge Machining (EDM) is a frequently used machining process. EDM produces a recast layer on the surface of machined components. The tribological performance of this recast layer is not very well understood. The properties of the recast layer formed as a result of EDM depend upon the discharge current, electrodes and dielectrics. This work aims to study the effects of each on the tribological performance – in terms of the wear depth, friction coefficient, friction force and contact surface temperature of recast layers. Subsequent improvement in the quality of surfaces will significantly reduce the cost and time required to treat surfaces after machining. Hence, various combinations of discharge current, dielectrics and electrodes have been used to characterize and deduce their effects. The tribo-tests are performed in the boundary lubrication regime under pin-on-disc configuration to analyze sliding friction, contact surface temperature and the wear of the recast layers formed on AISI 304L. The surface morphology of the test pins has been performed by Scanning Electron Microscopy (SEM) before and after the tests. The results show that indeed it is possible to control the tribological performance of the recast layers by varying EDM parameters. This approach promises to be a useful methodology to improve the tribological performance of the layers formed after EDM and reduce the time and costs required for surface treatments post machining

Analysis of rule-based and shallow statistical models for COVID-19 cough detection for a preliminary diagnosis

Coronavirus pandemic that has spread all over the world, is one of its kind in the recent past, that has mobilized researchers in areas such as (not limited to) pre-screening solutions, contact tracing, vaccine developments, and crowd estimation. Pre-screening using symptoms identification, cough classification, and contact tracing mobile applications gained significant popularity during the initial outbreak of the pandemic. Audio recordings of coughing individuals are one of the sources that can help in the pre-screening of COVID-19 patients. This research focuses on quantitative analysis of covid cough classification using audio recordings of coughing individuals. For analysis, we used three different publicly available datasets i.e., COUGHVID, NoCoCoDa, and a self-collected dataset through a web application. We observed that wet cough has more correlation with covid cough as opposed to dry cough. However, the classification model trained with wet and dry coughs, both, has similar test performance as that of the model trained with wet cough samples only. We conclude that audio-signal recordings of coughing individuals have the potential as a pre-screening test for COVID-19

Detection of Grape Clusters in Images using Convolutional Neural Network

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple Computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don’t address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes

Diagnostic Efficacy of Pleural Fluid Adenosine Deaminase Level in diagnosing TB Pleural Effusion is Excellent in a High Prevalence Area.

Objectives: To evaluate the efficacy of Pleural Fluid ADA level in diagnosing tuberculous pleural effusions.Place and Duration: The study was conducted at the Gulab Devi Chest Hospital Lahore from 03-01-2017 to 30-09-2017.Methodology: 456 cases having age > 14 years and radiological evidence of pleural effusion were included. Patients having Age > 65 years, minimal inaspirable pleural effusion, negative consent for ADA estimation and with obvious radiological signs of malignancy were excluded. Detailed history, physical examination, radiological, haematological and biochemical findings were recorded. Pleural fluid analysis including ADA levels were recorded. Statistics was applied to reach the conclusion.Results: 422/456(92.54%)cases were exudates while 34/456(7.45%) were transudate. 352 cases were diagnosed as TB pleuritis. 330(93.75%)cases showed PFADA levels 40 IU/L and above, while 22 cases showed ADA level < 40IU/L. Mean ADA level for TB peural effusion is 74.43IU/L. Using a cut off value 40 IU/L, we got a sensitivity of 93.75%, specificity 91.42 % and Positive predictive value 98.21% for tuberculosis.Conclusion: Pleural fluid ADA level is a valuable bio-marker for TB diagnosis in an area of high prevalence. It successfully differentiates between tuberculous and non-tuberculous pleural effusion

Learning fruit class from short wave near infrared spectral features, an AI approach towards determining fruit type

This paper analyzes the potential of using shortwave NIRS (near-infrared spectroscopy) for fruit classification problems. The research focuses on O-H and C-H overtone features of fruit and its correlation with NIRS and therefore opens a new dimension of fruit classification problems using NIRS. Eleven fruits, which include apple, cherry, hass, kiwi, grapes, mango, melon, orange, loquat, plum, and apricot, were used in this study to cover physical characteristics such as peel thinness, pulp, seed thickness, and size. NIR spectral data is collected using the industry-standard F-750 fruit quality meter (wavelength range 300-1100nm) for all fruit mentioned above. Different shallow machine learning architectures were trained to classify fruits using spectral feature vectors. At first, using 83 features vectors within the range of 725-975nm (3nm-resolution) and then using only four features of wavelength 770nm, 840nm, 910nm, and 960nm (corresponding to O-H and C-H overtone features). For the 83 spectral features range as an input, the QDA classifier achieved a cross-validation accuracy of 100% and a test data accuracy of 93.02%. For the four features vector as an input, the QDA classifier achieved a cross-validation accuracy of 97.1% and test data accuracy of 90.38%. The results demonstrate that fruit classification is mainly a function of absorptivity of short wave NIR radiation primarily with respect to O-H and C-H overtones features. An LED-based device mainly having 770nm, 840nm, 910nm, and 960nm range LEDs can be used in applications where automation in fruit classification is required