Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kinds of materials. This sort of molecule fingerprinting has thus led to the widespread application of Raman spectrum in various fields like medical diagnosis, forensics, mineralogy, bacteriology, virology, etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods targeted toward Raman spectra analysis. We examine, experiment, and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both have their perks and pitfalls; therefore, we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to the invariance property in convolutional neural networks (CNNs) and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. This has been achieved by incorporating shifted multi-layer perceptrons (MLP) at the earlier levels of the network to extract significant features across the entire spectrum, which are further refined by the inclusion of triplet loss in the hidden layers. Our experiments on 4 public datasets demonstrate superior performance over the much more complex state-of-the-art methods, and thus, RamanNet has the potential to become the de facto standard in Raman spectra data analysis.
Firms increasingly put financial pressure on their suppliers, also called squeezing. Suppliers react and adapt to financial squeeze as autonomous agents, causing complex ripple effects across the extended supply chain network. To capture intertwined and highly interactive effects among suppliers, we use agent‐based models. We explore the impact of financial squeeze on supply chain network structure and operational outcomes. Results suggest that financial squeeze affects the stability of the supply chain network and the effect varies depending on the location of the suppliers. Firms located at the bottom of the supply chain network suffer most from financial squeeze, and the magnitude of the effect increases as one goes further upstream. In addition, as existing suppliers exit the network and new suppliers enter, three network archetypes (Empty Nest, TransitUp, and StableDown) emerge. We identify the condition and operational consequences associated with these three archetypes. Our findings are informative to managers at buyer firms about the impacts of squeezing strategy on their extended supply chain partners, who often times are out of their immediate purview.
Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysis.
The mobile devices are used to execute the teaching-learning-evaluation process in Mobile Learning (m-learning) methodology. M-learning is a trending field in educational organizations, companies, and for individual study. With the explosion of mobile device ownership among the users aged within 18–29 years who are also the attendees of the higher learning institution (HLI), gives us the opportunity to consider the use of m-learning methodology to be embedded in the HLI beside traditional methodologies. Exceptional circumstances such as the COVID-19 pandemic when traditional face-to-face methodology suddenly changed to online paradigm, is also forcing us to strongly consider the m-learning approach. However, HLI may not have a general policy to implement m-learning into the traditional learning environment. A proper educational outcome needs to be configured to implement a new process into the traditional process. Therefore, a model integrating the m-learning aspects and the education supply chain management factors obtained from this study may benefit the stakeholders of HLI, especially educators and students.
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