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The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.

Background: When it occurs after a cesarean section or a normal vaginal delivery, postpartum hemorrhage (PPH) is a potentially fatal obstetric emergency. Aims and objectives: The aim of the study was to compare the efficacy and safety of oxytocin against oxytocin plus misoprostol in avoiding PPH during active management of the third stage of labor (AMTSL). Materials and Methods: Using simple randomization, 150 women from the labor ward of the Department of Obstetrics and Gynaecology at the Indore Medical College were recruited and randomly allocated to either test Group A or test Group B. Standard pharmacological treatment, including intramuscular injection of 10 IU of oxytocin and other components of AMTSL criteria, was administered to patients in Group A. In addition to the other components of the AMTSL criteria, Group B patients got the usual pharmacological treatment of 10 IU of oxytocin through the injectable route and 600 g of misoprostol through the oral route. Various characteristics of both groups were compared, including parity, gravida, delivery style, PPH etiology, blood transfusion, and surgical intervention. Results: Mode of delivery was vaginally seen in 85% and 92% and cesarean in 15% and 8%. Etiology was uterine atony in 54% and 64%, retained tissue in 26% and 12%, laceration in 11% and 18%, and coagulopathy in 9% and 6%. Blood transfusion was needed in 27% and 57% and surgical intervention in 82% and 68% in Groups A and B, respectively. A statistically significant difference was observed (P ≤ 0.05). Conclusion: The results of this study support the use of misoprostol in hospital settings as an adjunct to oxytocin since it reduces the incidence of PPH, eliminates the need for intrusive interventions, and ultimately reduces maternal mortality.

Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported over most regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from the regional climate models (RCMs). However, RCMs ultimately rely on the outputs of global circulation models (GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds for the reanalysis data, ERA-Interim, are not well represented in the GCMs. The historical trends in extreme winds from GCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25–100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.

Characterization of climate uncertainty at regional scales over near-term planning horizons (0–30 years) is crucial for climate adaptation. Climate internal variability (CIV) dominates climate uncertainty over decadal prediction horizons at stakeholders’ scales (regional to local). In the literature, CIV has been characterized indirectly using projections of climate change from multi-model ensembles (MME) instead of directly using projections from multiple initial condition ensembles (MICE), primarily because adequate number of initial condition (IC) runs were not available for any climate model. Nevertheless, the recent availability of significant number of IC runs from one climate model allows for the first time to characterize CIV directly from climate model projections and perform a sensitivity analysis to study the dominance of CIV compared to model response variability (MRV). Here, we measure relative agreement (a dimensionless number with values ranging between 0 and 1, inclusive; a high value indicates less variability and vice versa) among MME and MICE and find that CIV is lower than MRV for all projection time horizons and spatial resolutions for precipitation and temperature. However, CIV exhibits greater dominance over MRV for seasonal and annual mean precipitation at higher latitudes where signals of climate change are expected to emerge sooner. Furthermore, precipitation exhibits large uncertainties and a rapid decline in relative agreement from global to continental, regional, or local scales for MICE compared to MME. The fractional contribution of uncertainty due to CIV is invariant for precipitation and decreases for temperature as lead time progresses towards the end of the century.

Thermoelectric power production in the United States primarily relies on wet-cooled plants, which in turn require water below prescribed design temperatures, both for cooling and operational efficiency. Thus, power production in US remains particularly vulnerable to water scarcity and rising stream temperatures under climate change and variability. Previous studies on the climate-water-energy nexus have primarily focused on mid- to end-century horizons and have not considered the full range of uncertainty in climate projections. Technology managers and energy policy makers are increasingly interested in the decadal time scales to understand adaptation challenges and investment strategies. Here we develop a new approach that relies on a novel multivariate water stress index, which considers the joint probability of warmer and scarcer water, and computes uncertainties arising from climate model imperfections and intrinsic variability. Our assessments over contiguous US suggest consistent increase in water stress for power production with about 27% of the production severely impacted by 2030s.

Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.

Over the last several decades, researchers have time and again proposed use of non-chromatographic methods for processing of biotherapeutic products. However, chromatography continues to be the backbone of downstream processing, particularly at process scale. There are many reasons for this, critical ones being the unparalleled scalability, robustness, and selectivity that process chromatography offers over its peers. It is no surprise then that process chromatography has been a topic of major developments in resin matrix, ligand chemistry, modalities, high throughput process development, process modelling, and approaches for control. In this review, we attempt to summarize major developments in the above-mentioned areas. Greater significance has been given to advancements in the last 5 years (2013–2017).

Vehicle automation is one of the main applications of industry 4.0 especially for tourism companies who provide their vehicles to migrants for traveling purposes. It is difficult to maintain trust among the travel agency and the customers. Although there are many existing solutions in the research that addresses the trust issues, but most of the solutions are based on centralized architecture such as cloud computing where the data is prone to various security threats. Motivated by the aforementioned discussion, the authors have proposed a vehicle tracking system based on the integration of blockchain and IoT in this paper. The proposed system will increase the trust among the entities by providing them transparency in tracking details of the vehicle. As the trip details are immutable and decentralized in nature, so, blockchain is used to store the details. Also, the system will generate the trip summary that contains the route detail, the number of kilometres travelled and the total cost of the trip. Tools such as Ethereum, Remix IDE, MetaMask, and RinkeyBy have been used to measure the performance of the system.

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Climate and weather extremes such as tropical cyclones, floods, and heat waves can have potentially devastating societal and economic impacts. Decision-makers responsible for managing critical infrastructures and emergency preparedness are looking for credible projections of climate change over near-term (0-30 year) at regional scales. Private sectors especially insurance industry which are in the business of insuring risk resulting from hurricanes, floods, and tornado hails do not consider climate change in their risk assessment models even though frequency and severity of these extremes have increased due to climate change. There is an immediate need not only for reliable and credible projections of climate change but also for an increased understanding of uncertainty in projected climate change. Further, there is a need to develop framework to do multi-sector impacts assessment in which climate uncertainty from all sources has been incorporated. In this dissertation, I started with evaluation of the performance of latest generation of global climate models, Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating current climatology and multi-model agreement in projected climate change. Subsequently I studied the performance of CMIP5 models in simulating and projecting wind extremes at regional scales. Both of these studies were focused on long-term climatology, the end of the century time horizon. Multi-sector stakeholders are looking for reliable projections of climate change at near-term planning horizons as most of the decisions are made at time scales of one-to-two decades. Consideration of climate uncertainty especially climate internal variability and model response variability becomes more important as they dominate signal of climate change. One of the important contribution of my research has been on enhancing the present understanding of the role of different sources of uncertainty with projection time horizons at multiple spatial scales for precipitation and temperature. Finally, the framework has been applied to study the how much thermoelectric power production will be at risk due to warmer and scarcer water under nonstationary climate change.