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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manisha Bhardwaj ◽  
Rajat Agrawal

PurposeThe purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.Design/methodology/approachInitially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.FindingsThe findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.Originality/valueThis research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.


BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Wouter Deelder ◽  
Gary Napier ◽  
Susana Campino ◽  
Luigi Palla ◽  
Jody Phelan ◽  
...  

Abstract Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.


Author(s):  
Ashfaque H. Bokhari ◽  
Muhammad Farhan ◽  
Tahir Hussain

In this paper, we have studied Noether symmetries of the general Bianchi type I spacetimes. The Lagrangian associated with the most general Bianchi type I metric is used to find the set of Noether symmetry equations. These equations are analyzed using an algorithm, developed in Maple, to get all possible Bianchi type I metrics admitting different Noether symmetries. The set of Noether symmetry equations is then solved for each metric to obtain the Noether symmetry algebras of dimensions 4, 5, 6 and 9.


2021 ◽  
pp. 875529302110552
Author(s):  
Mario Ordaz ◽  
Danny Arroyo

The current practice of Probabilistic Seismic Hazard Analysis (PSHA) considers the inclusion of epistemic uncertainties involved in different parts of the analysis via the logic-tree approach. Given the complexity of modern PSHA models, numerous branches are needed, which in some cases leads to concerns regarding performance issues. We introduce the use of a magnitude exceedance rate which, following Bayesian conventions, we call predictive exceedance rate. This rate is the original Gutenberg–Richter relation after having included the effect of the epistemic uncertainty in parameter β. The predictive exceedance rate was first proposed by Campbell but to our best knowledge is seldom used in current PSHA. We show that the predictive exceedance rate is as accurate as the typical logic-tree approach but allows for much faster computations, a very useful property given the complexity of some modern PSHA models.


2021 ◽  
Author(s):  
Rafael Guerrero ◽  
Navin Dookeram ◽  
Patrick Hosein
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