Supply Chain Performance Measurement Practices of Indian Industries

In any Industry the Supply chain performance plays a crucial role and it is vital in growth of the industry. Through this study an attempt is made to find some insight to the supply chain performance measurement practices of Indian industries through an exploratory survey. The study reveals almost all the respondents (84%) felt that supply chain performance measurement system employed in their organisation has a clear purpose. Also the study reveals that most supply chain performance measurement system provides high importance to quality measurements and includes both financial and non-financial indicators. The study gave clarity in understanding the objectives of implementing supply chain performance measurement systems and metrics (measures) used in supply chain performance measurement systems The Multivariate analysis revealed three factors emerged from this study are ‘Strategic Orientation’ followed by ‘Internal Focus’ and ‘Motivation and Control’

Agricultural bio-waste recycling through efficient microbial consortia

In India and other countries, rice straw, a byproduct of rice production, is burned in enormous amounts, which contributes to environmental pollution and climate change by releasing greenhouse gases viz., CO2, N2O, CH4, into the atmosphere. This study aimed to accelerate the degradation of this enormous amount of agricultural biomass via microbial inoculants. Four treatments—rice straw (RS), rice straw plus water (RSW), rice straw plus water plus Pusa decomposer (RSWF), and rice straw plus water plus Tamil Nadu Agricultural University (TNAU) biomineralizer (RSWB) were used in the current investigation. The study's findings demonstrated that rice straw treated with microorganisms decomposed more quickly than RS and RSW treatments. According to EDAX spectra of elemental composition, the carbon content of rice straw in the RS, RSW, RSWF, and RSWB treatments was 33.66%, 29.75%, 13.33%, and 20.65% w/w, respectively. The RSWF treatment of rice straw was found to have the highest nitrogen concentration (0.64% w/w), followed by RSWB (0.61% w/w), RSW (0.45%) w/w, and RS (0.43% w/w). Treatments RSWF and RSWB had lower C/N ratios 20.83, and 33.85, respectively, than that RSW (66.11) and RS (78.28). The RSWF and RSWB treatments' porous, distorted, and rough surface structures provided further evidence that both microbial consortia could decompose rice straw more quickly than the RSW and RS treatments. Therefore, the results of this study imply that rice straw could be added to the soil to improve soil fertility for sustainable crop production rather than being burned

Comparative Study of Surrogate Techniques for CNN Hyperparameter Optimization

Optimizing hyper parameters in Convolutional Neural networks is a tedious process for many researchers and practitioners. It requires a high degree of expertise or experience to optimise the hyper parameters, and manual optimisation is likely to be biased. To date, methods or approaches to automate hyper parameter optimization include grid search, random search, and Genetic Algorithms (GAs). However, evaluating large number of sample points in the hyperparameter configuration space, as is typically required by these methods, is computationally expensive process. Hence, the objective of this paper is to explore regression as a surrogate technique in CNN hyperparameter optimisation. Performance in terms of accuracy, error rate, training time and coefficient of determination (R2) are evaluated and recorded. Although there is no significant performance difference between the resulting optimized Deep Learning and state-of-the-art on CIFAR-10 datasets, using regression as a surrogate technique for CNN hyperparameter optimization contributes to minimising the time taken for the optimization process, a benefit which has not been fully explored in the literature to the best of the author’s knowledge

Empowering the people: Development of an HIV peer education model for low literacy rural communities in India

<p>Abstract</p> <p>Background</p> <p>Despite ample evidence that HIV has entered the general population, most HIV awareness programs in India continue to neglect rural areas. Low HIV awareness and high stigma, fueled by low literacy, seasonal migration, gender inequity, spatial dispersion, and cultural taboos pose extra challenges to implement much-needed HIV education programs in rural areas. This paper describes a peer education model developed to educate and empower low-literacy communities in the rural district of Perambalur (Tamil Nadu, India).</p> <p>Methods</p> <p>From January to December 2005, six non-governmental organizations (NGO's) with good community rapport collaborated to build and pilot-test an HIV peer education model for rural communities. The program used participatory methods to train 20 NGO field staff (Outreach Workers), 102 women's self-help group (SHG) leaders, and 52 barbers to become peer educators. Cartoon-based educational materials were developed for low-literacy populations to convey simple, comprehensive messages on HIV transmission, prevention, support and care. In addition, street theatre cultural programs highlighted issues related to HIV and stigma in the community.</p> <p>Results</p> <p>The program is estimated to have reached over 30 000 villagers in the district through 2051 interactive HIV awareness programs and one-on-one communication. Outreach workers (OWs) and peer educators distributed approximately 62 000 educational materials and 69 000 condoms, and also referred approximately 2844 people for services including voluntary counselling and testing (VCT), care and support for HIV, and diagnosis and treatment of sexually-transmitted infections (STI). At least 118 individuals were newly diagnosed as persons living with HIV (PLHIV); 129 PLHIV were referred to the Government Hospital for Thoracic Medicine (in Tambaram) for extra medical support. Focus group discussions indicate that the program was well received in the communities, led to improved health awareness, and also provided the peer educators with increased social status.</p> <p>Conclusion</p> <p>Using established networks (such as community-based organizations already working on empowerment of women) and training women's SHG leaders and barbers as peer educators is an effective and culturally appropriate way to disseminate comprehensive information on HIV/AIDS to low-literacy communities. Similar models for reaching and empowering vulnerable populations should be expanded to other rural areas.</p

Impact of structurally constraining loss functions in contour delineation for adaptive radiotherapy

Image processing techniques are critically important to adaptive radiotherapy (ART) as they can improve the treatment and help minimise radiation toxicity. Contour delineation is one of the essential processes in ART based cancer treatment as it can tailor the treatment to the anatomy on any particular day, thus providing a better treatment. The aim of this research is to study how a structurally constrained loss function influences contour delineation when it estimates a new treatment plan by establishing a geometrical relationship between the planning scan acquired prior to treatment and a time-of-treatment scan. A block matching based registration method with two-pass regularisation was developed to automate contour delineation. Using the planning scan and on-the-day-of-treatment scan, this registration effectively restrains unnatural and unrealistic tissue deformations. These scans were axial CT scans acquired from abdominal region for prostate cancer treatment. A twopass distance and neighbourhood-orientation based regularisation applied during the motion vector estimation in the block matching based non-rigid registration was able to perform contour delineation better than parametric (demons), non-parametric (b-spline) and state-ofthe- art (pyramidal block matching) methods. The Dice scores of the b-spline’s, demons’ and pyramidal block matching’s average Dice scores are respectively 7.9%, 10.66% and 3.49% lower than the Dice score of the proposed method. The averaged computational time of disparity-regularised block matching is respectively 2.7, 1.15 and 1.8 times less than the averaged computational time of the b-spline, demons, and pyramidal block matching. The averaged normalised mean square error of the disparity-regularised block matching is respectively 0.69, 1.06 and 3.75 times less than the averaged normalised mean square error of the b-spline, demons, and pyramidal block matching. The disparity-regularised block matching was modified to the Lung Computed Tomography (CT) image analysis. The Lung CT usually has large coarser regions and disparity-regularised block matching uses mean absolute error as the image similarity metric which does not consider image texture. In order to include the texture information in the block matching criteria, texture feature maps were used as a part of the block matching process along with the voxel intensities and the displacement vector field estimation was evaluated using the image quality metrics namely, structural similarity index (SSIM) and normalised mean squared error. The averaged structural similarity index and the averaged normalised mean squared error between the scans were 0.9960 and 0.4 respectively, showing that the estimated motion vectors and displacement vector fields were closer to the ground-truth geometrical differences between the Lung CT scans. To facilitate the contour delineation process, deep learning based generative adversarial networks were developed to generate synthetic CT and cone beam CT (CBCT) images. The generative adversarial networks were trained as a part of the CycleGAN with a structural constraint loss to preserve the anatomical structures in the synthetic images. The variants of the structural similarity index metric (SSIM) were included as a training loss in the 2D CycleGAN and the performance of the image synthesis was evaluated using the image quality metrics such as structural similarity index, mean square error, and peak signal-to-noise ratio. The other variants of the SSIM are multi-scale SSIM, 4-components weighted, 4-components gradient weighted, 4-components weighted multi-scale and 4-components gradient weighted multi-scale SSIM. From the metrics, it was observed that the 2D CycleGAN with 4-gradient SSIM has generated synthetic images with two times better SSIM metrics than the other 2D CycleGAN variants. On the other hand, the 2D CycleGAN with 4-components gradient weighted SSIM generated synthetic image whose mean squared error was 14% lower than the mean squared error of other 2D CycleGAN variants. Also, both these variants generated synthetic images with better peak signal-to-noise ratio than the rest. Similar to the 2D CycleGAN variants, the performance metrics of the 3D variants showed that the CycleGAN with 4-components gradient weighted SSIM generated better synthetic CT and CBCT images. Finally, to understand the influence of the structural constraint loss in the contour propagation, the 2D and 3D generative adversarial networks were merged with the two-pass regularised block matching rigid registration. The contour propagation by this framework was then assessed using clinical validation metrics and image quality metrics such as Dice score, mean squared error and Hausdorff distance. Finally, in the block matching based non-rigid registration, two-pass distance and orientation regularisation is a type of structural constraint applied in the block matching process. In Image-to-Image synthesis by generative adversarial networks, the inclusion of structural similarity index metrics in the training loss functions is also a type of structural constraint applied in the pseudo image generation process. Overall, this research contributes in understanding the positive impact of structural constraints in traditional and state-of-the-art medical image processing techniques for Adaptive Radiotherapy

Comparative Performance of LSTM and ARIMA for the Short-Term Prediction of Bitcoin Prices

This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM\u27s persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA

Ru Nanoparticles Supported on Mesoporous Al-SBA-15 Catalysts for Highly Selective Hydrogenation of Furfural to Furfuryl Alcohol

This is an open access article under the terms of the Creative Commons Attribution Non-Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Furfuryl alcohol, which is the hydrogenated product of furfural, has been identified as a very promising platform chemical with high potential for applications in the manufacture of key chemicals, lubricants, fragrances, and pharmaceuticals. In this work, bare SB, and x % Ru/Al-SB (x=1.5, 2.5, 3.5, and 4.5 wt. %) samples were fabricated by a hydrothermal method. Bare and most active catalysts were characterized by different techniques, such as BET, FE-SEM, TEM, FT-IR, and XRD, to understand their physical and chemical properties. An evaluation of the effects of various reaction parameters, such as catalyst loading, reaction temperature, and reaction time, on the catalytic performance, showed higher catalytic conversion of furfural and selectivity for the desired products. The most active RuS3 catalyst showed 100 % conversion of furfural and 99 % selectivity for furfuryl alcohol. It could be reused for five consecutive reaction cycles without significant loss of performance. In addition, Ru leaching and loss of conversion or selectivity were not noticed during the five-run recycling test. The EDS elemental mapping analysis of the used catalyst established the preservation of the mesoporous structure, suggesting a strong interaction between the hexagonal porous silicate and the Ru nanoparticles

Standardization of the Optimum Effects of Indole 3-Butyric Acid (IBA) to Control Root Knot Nematode, <i>Meloidogyne enterolobii</i>, in Guava (<i>Psidium guajava</i> L.)

Guava is an important revenue generating crop for small, medium, and commercial guava cultivators all over the world. Nematode infestation is one of the factors that cause declines in fruit production. Researches have proven that the application of plant growth regulators induces the synthesis of defense-related proteins in Guava. IBA is one such plant growth regulator, and its effects on guava plants has not yet been elucidated. Thus, this research is focused on the optimization of IBA concentrations, which results in the induction and production of maximum defense-related proteins to defend against root knot nematode. The present study includes the application of IBA on M. enterolobii-infested experimental guava plants at different concentrations ranging from 100 ppm to 2000 ppm. The synthesis of defense-related proteins is identified with 1000 ppm of IBA. At this concentration, IBA influences the morphological, physiological, and biochemical characteristics and enhances the induction of defense-related proteins in M. enterolobii-infested experimental guava plants. Thus, 1000 ppm of IBA prevents nematode infestation in Lucknow-49 guava plants