Drivers and barriers to implement sustainable manufacturing concepts in Sri Lankan manufacturing sector

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 171–176.Sri Lanka promotes manufacturing sector without much concern on environmental and social problems as one of the driving forces for the economic prosperity. This has aggravated number of issues and this has lead to adapting of some sustainability related initiatives in local manufacturing sector. However, majority of them function independently with fix boundaries. Conversely, there are some hindrances to implement the sustainable manufacturing concepts as a one comprehensive solution. This research intends to investigate the motivators and barriers to adapt sustainable manufacturing concept to overcome current issues faced by the manufacturing industry. An evaluation criterion was developed based on some of the popularly used guidelines and sustainability options available in number of sub domains. Results highlights the main motivators plus common and cluster specific barriers to implement sustainable manufacturing in local industry. These outcomes can be easily considered for the policy development purposes in developing countries in future

Reflection High-Energy Electron-Diffraction Study of Melting and Solidification of Pb on Graphite

The melting and solidification of Pb thin films on pyrolytic graphite are investigated in situ by reflection high-energy electron diffraction. Thin films with thicknesses of 4-150 monolayers are investigated. The surface morphology of the thin films were studied by scanning electron microscopy. Superheating of the Pb thin films by 4±2 to 12±2 K is observed from diffraction intensity measurements. Upon cooling the substrate, the Pb on graphite is seen to supercool by ∼69±4 K

Spectral Dependence of Coherent Backscattering of Light in a Narrow-Resonance Atomic System

We report a combined theoretical and experimental study of the spectral and polarization dependence of near resonant radiation coherently backscattered from an ultracold gas of 85Rb atoms. Measurements in an approximately 6 MHz range about the 5s^{2}S_{1/2}- 5p^{2}P_{3/2}, F=3 - F'=4 hyperfine transition are compared with simulations based on a realistic model of the experimental atomic density distribution. In the simulations, the influence of heating of the atoms in the vapor, magnetization of the vapor, finite spectral bandwidth, and other nonresonant hyperfine transitions are considered. Good agreement is found between the simulations and measurements.Comment: 10 pages, 12 figur

Fast Ground State Manipulation of Neutral Atoms in Microscopic Optical Traps

We demonstrate Rabi flopping at MHz rates between ground hyperfine states of neutral $^{87}$Rb atoms that are trapped in two micron sized optical traps. Using tightly focused laser beams we demonstrate high fidelity, site specific Rabi rotations with crosstalk on neighboring sites separated by $8 \mu\rm m$ at the level of $10^{-3}$. Ramsey spectroscopy is used to measure a dephasing time of $870 \mu\rm s$ which is $\approx$ 5000 times longer than the time for a $\pi/2$ pulse.Comment: 4 pages, 4 figure

Coherent Backscattering of Light in Atomic Systems: Application to Weak Localization in an Ensemble of Cold Alkali-Metal Atoms

Development of a theoretical treatment of multiple coherent light scattering in an ultracold atomic gas is reported. Specific application is made to coherent backscattering of a weak-radiation field from realistically modeled samples of ultracold atomic 85Rb. Comprehensive Monte Carlo simulations of the spatial, spectral, and polarization dependence of the backscattering line shape are made and compared with available experimental results

Learning in the compressed data domain: Application to milk quality prediction

Smart dairy farming has become one of the most exciting and challenging area in cloud-based data analytics. Transfer of raw data from all farms to a central cloud is currently not feasible as applications are generating more data while internet connectivity is lacking in rural farms. As a solution, Fog computing has become a key factor to process data near the farm and derive farm insights by exchanging data between on-farm applications and transferring some data to the cloud. In this context, learning in the compressed data domain, where decompression is not necessary, is highly desirable as it minimizes the energy used for communication/computation, reduces required memory/storage, and improves application latency. Mid-infrared spectroscopy (MIRS) is used globally to predict several milk quality parameters as well as deriving many animal-level phenotypes. Therefore, compressed learning on MIRS data is beneficial both in terms of data processing in the Fog, as well as storing large data sets in the cloud. In this paper, we used principal component analysis and wavelet transform as two techniques for compressed learning to convert MIRS data into a compressed data domain. The study derives near lossless compression parameters for both techniques to transform MIRS data without impacting the prediction accuracy for a selection of milk quality traits

Measurement of Correlated Multiple Light Scattering in Utracold Atomic \u3csup\u3e85\u3c/sup\u3eRb

We report an experimental study of correlated multiple light scattering in an ultracold gas of 85Rb confined in a magneto-optic trap. Measurements are made of the polarization dependence of the spatial and spectral profile of light backscattered from the sample. The results show an interferometric enhancement sensitive to coherent multiple scattering in the atomic gas, and strong variations with the polarization of the incident and detected light. The spatial width and peak value of the enhancement are found to be dependent on the sample size. Comparison of all the measurements with realistic quantum Monte Carlo simulations yields a very good agreeme

A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture

Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (ICT) infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML methodology, which enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there need to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with a large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequentially and parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques is still at an early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance