Analysing Supply Chain Resilience: Integrating the Constructs in a Concept Mapping Framework Via a Systematic Literature Review

Purpose: The purpose of this paper is to analyse the concept of supply chain resilience (SCRES) using a concept mapping framework to seek conceptual clarity, with an emphasis on SCRES definitions, essential capabilities, elements and managerial practices. Design/methodology/approach: A systematic literature review was conducted of 103 peer-reviewed journal articles covering the period from 2000 to 2015, with the aim to identify supply chain resilience concept. Findings: Through analysis and synthesis of the literature, the study revealed three major constructs used to define resilience in supply chain: SCRES phases, strategies, and capabilities. The study has addressed five core resilience capabilities: the ability to anticipate, to adapt, to respond, to recover, and to learn. The study has also identified 13 essential elements and several managerial practices that support firms to acquire the five capabilities. The studied capabilities are then linked with supply chain resilient phases and strategies in order to establish an integrated view of the concept. Research limitations/implications: The explorative nature of this study and the role of the concept mapping framework, which does not empirically test the relationships in the model, are considered as limitations, to be addressed by the authors in future research. Originality/value: The originality of this paper lies in the classification of different features of SCRES through a comprehensive concept mapping framework that establishes relationships and interactions between them. This study, therefore, lays a foundation for testing these connections in future empirical studies. The article brings together fragmented literature from multiple studies to create a solid body of knowledge that addresses the need for conceptual clarity in SCRES literature

Sensitive and rapid spectrophotometric methods for sertraline monitoring in pharmaceutical formulations

Purpose: To develop simple, rapid, and selective spectrophotometric methods for the assay of sertraline in a pharmaceutical formulation. Method: These methods depend on the formation of colored ion-pair complexes between the drug and five different reagents; methyl blue (MB), bromophenol red (BPR), methyl green (MG), phenol red (PR), and methyl orange (MO) in B-R buffer solutions of pH ranging from 2.0 – 8.0. The colored products were measured at 668, 747, 647, 717, and 553 nm, respectively. Results: The calibration graphs were linear over the concentration range of 2 – 18 μg/mL for MB and BPR, and 2 – 16 μg/mL for MG, PR, and MO. In all cases, the reaction stoichiometry was 1:1. The proposed methods were successfully applied to solid-dose pharmaceutical preparations (tablets). Excipients in the commercial formulation did not interfere with the analysis. Conclusion: The investigated methods can be recommended for routine analysis and quality control where cost-effectiveness, high specificity of the analytical technique, and time are of great importance

A Fuzzy Logic Based Approach to Support Users Self Control of Their Private Contextual Data Retrieval

In context aware applications such as location based, m-health care, and m-business applications, it is expected that a huge amount of users context information will be collected, which threatens their privacy concerns. Users consent is a mandatory requirement of users privacy support. Increasingly, it seems clear that user consent decision implies the consideration of a number of factors. These factors vary in their impact on the user consent from one situation to another and from a user to another. In this paper, we propose a consent provider model that considers a number of factors influencing users consent, modeling their impact and evaluating their roles in the consent decision process based on fuzzy logic reasoning. Increasingly, we define a new data set called “contextual privacy attributes” which is associated with each user contextual data, and corresponds to influencing factors. We have prototyped the proposed consent provider model and integrated it into the real time UMTS mobile information and entertainment services (MIES) platform hosted at the university campus

Superdiffusive heat conduction in semiconductor alloys -- II. Truncated L\'evy formalism for experimental analysis

Nearly all experimental observations of quasi-ballistic heat flow are interpreted using Fourier theory with modified thermal conductivity. Detailed Boltzmann transport equation (BTE) analysis, however, reveals that the quasi-ballistic motion of thermal energy in semiconductor alloys is no longer Brownian but instead exhibits L\'evy dynamics with fractal dimension $\alpha < 2$. Here, we present a framework that enables full 3D experimental analysis by retaining all essential physics of the quasi-ballistic BTE dynamics phenomenologically. A stochastic process with just two fitting parameters describes the transition from pure L\'evy superdiffusion as short length and time scales to regular Fourier diffusion. The model provides accurate fits to time domain thermoreflectance raw experimental data over the full modulation frequency range without requiring any `effective' thermal parameters and without any a priori knowledge of microscopic phonon scattering mechanisms. Identified $\alpha$ values for InGaAs and SiGe match ab initio BTE predictions within a few percent. Our results provide experimental evidence of fractal L\'evy heat conduction in semiconductor alloys. The formalism additionally indicates that the transient temperature inside the material differs significantly from Fourier theory and can lead to improved thermal characterization of nanoscale devices and material interfaces

ECG Signal Transmissions Performance over Wearable Wireless Sensor Networks

AbstractSudden death is the most common disease of heart diseases and usually caused by cardiac arrest. The electrocardiograph (ECG) system is the most direct way to observe and monitor health status of the heart. In this kind of disease, the patient may need continuous monitoring, and sometimes is kept in Intensive Care Unit (ICU), which needs more utilities and manpower that will eventually leads to increase the cost and demands for qualified medical staff. In this paper we introduce a comprehensive real time monitoring ECG system for continuous monitoring patients inside/outside home. Wearable Wireless Sensor Network with a cluster head is connected to patient body for monitoring. Measured data by WWSS are transmitted via cluster head to an internet connection to the monitoring system located on the hospital. In case of emergency, the measured data is sent to the physician's cell phone for any necessary actions. We discuss the inside/outside system structure. Additionally, we analyze the proposed system in terms of power consumptions and optimum distance between WSN sensors

Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning

Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. Results: The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue

1D Phonon BTE Solver (Small Scale Heat Transport Simulation)

In current technology, electronic devices shrink to the size of nanometers. The ability to accurately model heat transport to understand the thermal behavior of these small electronic devices becomes increasingly important. Since heat transport is very difficult to measure directly in small electronic devices, simulation becomes an effective means to model heat transport. A user-interactive simulation tool is created to model heat transport in small electronic devices of different lengths. Heat transport is modeled by solving one-dimensional Boltzmann transport equation (BTE) to obtain the transient temperature profile of a multi-length and multi-timescale thin film under constant temperature boundary condition or under hotspot cooling process. Unlike Fourier Heat equation, BTE can capture the effect of ballistic phonon transport expected at short lengh/time scale. Rapid Application Infrastructure (Rappture), a toolkit used to create user-interactive graphical user interface, is used to create the user interface for this simulation. The inputs to the simulation tool are thin film length and simulation time specified by user. BTE is then solved by using Lattice Boltzmann method (LBM) in MATLAB to obtain the temperature profile plot. The temperature profile plot explains how the temperature changes throughout the entire length of the material. This simulation tool allows users to accurately simulate heat transport in small electronic devices of different lengths that will help them in the thermal design and thermal management of small electronic devices