Purpose: This paper examines supply chain risk management (SCRM) from a holistic systems thinking perspective by considering the different typologies that have evolved as a result of earlier research. The purpose of this paper is to identify important strategic changes in the field and to outline future requirements and research opportunities in SCRM.Design/methodology/approach: The systematic literature review (SLR) methodology employed by this research was used to evaluate and categorise a literature survey of quality articles published over a period of ten years (2000‐2010). Additionally, the findings from the SLR have been strengthened through cross validation against results obtained from an associated text mining activity.Findings: The SLR methodology has provided a rich, unbiased and holistic picture of the advances in the field of SCRM. Consequently, important new research areas have been identified based on a multi‐perspective descriptive and thematic data analysis. In addition, the analysis, based on evolved typologies, indicates a growth of SCRM from a nascent to a fairly established activity over the past decade.Practical implications: The systematic approach undertaken for the literature review will provide future researchers and managers with an insightful understanding of the scope of the SCRM field. Also, the literature review provides important clues on new research directions for SCRM through identification of gaps in current knowledge.Originality/value: The holistic approach to SCRM was found to be an important missing link in earlier literature surveys. The outcome of the SLR reported in this paper has provided critical insights into the present and future scope of the SCRM field. The identified research insights, gaps and future directions will encourage new research techniques, with a view to managing the risks in the globalized supply chain environment.

Purpose: With increasing exposure to disruptions, it is vital for supply chains to manage risks proactively. Prediction of potential failure points and overall impact of these risks is challenging. In this paper, systems thinking concepts are applied for modelling supply chain risks. The purpose of this paper is to develop a holistic, systematic and quantitative risk assessment process for measuring the overall risk behaviour.Design/methodology/approach: A framework for supply chain risk management (SCRM) is developed and tested using an industrial case study. A systematically developed research design is employed to capture the dynamic behaviour of risks. Additionally, a system‐based supply chain risk model is conceptualized for risk modelling. Sensitivity modelling results are combined for validating the supply chain risk model.Findings: The systems approach for modelling supply chain risks predicts the failure points along with their overall risk impact in the supply chain network. System‐based risk modelling provides a holistic picture of risk behavioural performance, which is difficult to realise through other research methodologies commonly preferred in SCRM research.Practical implications: The developed framework for SCRM is tested in an industry setting for its viability. The framework for SCRM along with the supply chain risk model is expected to benefit practitioners in understanding the intricacies of supply chain risks. The system model for risk assessment is a working tool which could provide a perspective of future disruptive events.Originality/value: A holistic, systematic and quantitative risk modelling mechanism for capturing overall behaviour of risks is a valuable contribution of this research. The paper presents a new perspective towards using systems thinking for modelling supply chain risks.

Noncontact imaging photoplethysmography (PPG) can provide physiological assessment at various anatomical locations with no discomfort to the patient. However, most previous imaging PPG (iPPG) systems have been limited by a low sample frequency, which restricts their use clinically, for instance, in the assessment of pulse rate variability (PRV). In the present study, plethysmographic signals are remotely captured via an iPPG system at a rate of 200 fps. The physiological parameters (i.e., heart and respiration rate and PRV) derived from the iPPG datasets yield statistically comparable results to those acquired using a contact PPG sensor, the gold standard. More importantly, we present evidence that the negative influence of initial low sample frequency could be compensated via interpolation to improve the time domain resolution. We thereby provide further strong support for the low-cost webcam-based iPPG technique and, importantly, open up a new avenue for effective noncontact assessment of multiple physiological parameters, with potential applications in the evaluation of cardiac autonomic activity and remote sensing of vital physiological signs.

Imaging photoplethysmography (PPG) is able to capture useful physiological data remotely from a wide range of anatomical locations. Recent imaging PPG studies have concentrated on two broad research directions involving either high-performance cameras and or webcam-based systems. However, little has been reported about the difference between these two techniques, particularly in terms of their performance under illumination with ambient light. We explore these two imaging PPG approaches through the simultaneous measurement of the cardiac pulse acquired from the face of 10 male subjects and the spectral characteristics of ambient light. Measurements are made before and after a period of cycling exercise. The physiological pulse waves extracted from both imaging PPG systems using the smoothed pseudo-Wigner-Ville distribution yield functional characteristics comparable to those acquired using gold standard contact PPG sensors. The influence of ambient light intensity on the physiological information is considered, where results reveal an independent relationship between the ambient light intensity and the normalized plethysmographic signals. This provides further support for imaging PPG as a means for practical noncontact physiological assessment with clear applications in several domains, including telemedicine and homecare.

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.