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Climate change is a global problem, which affects the various geographical regions at different levels. It is also associated with a wide range of human health problems, which pose a burden to health systems, especially in regions such as Africa. Indeed, across the African continent public health systems are under severe pressure, partly due to their fragile socioeconomic conditions. This paper reports on a cross-sectional study in six African countries (Ghana, Nigeria, South Africa, Namibia, Ethiopia, and Kenya) aimed at assessing their vulnerabilities to climate change, focusing on its impacts on human health. The study evaluated the levels of information, knowledge, and perceptions of public health professionals. It also examined the health systems’ preparedness to cope with these health hazards, the available resources, and those needed to build resilience to the country’s vulnerable population, as perceived by health professionals. The results revealed that 63.1% of the total respondents reported that climate change had been extensively experienced in the past years, while 32% claimed that the sampled countries had experienced them to some extent. Nigerian respondents recorded the highest levels (67.7%), followed by Kenya with 66.6%. South Africa had the lowest level of impact as perceived by the respondents (50.0%) when compared with the other sampled countries. All respondents from Ghana and Namibia reported that health problems caused by climate change are common in the two countries. As perceived by the health professionals, the inadequate resources reiterate the need for infrastructural resources, medical equipment, emergency response resources, and technical support. The study’s recommendations include the need to improve current policies at all levels (i.e., national, regional, and local) on climate change and public health and to strengthen health professionals’ skills. Improving the basic knowledge of health institutions to better respond to a changing climate is also recommended. The study provides valuable insights which may be helpful to other nations in Sub-Saharan Africa.

The study of neutron-induced reactions is of high relevance in a wide variety of fields, ranging from stellar nucleosynthesis and fundamental nuclear physics to applications of nuclear technology. In nuclear energy, high accuracy neutron data are needed for the development of Generation IV fast reactors and accelerator driven systems, these last aimed specifically at nuclear waste incineration, as well as for research on innovative fuel cycles. In this context, a high luminosity Neutron Time Of Flight facility, n_TOF, is operating at CERN since more than a decade, with the aim of providing new, high accuracy and high resolution neutron cross-sections. Thanks to the features of the neutron beam, a rich experimental program relevant to nuclear technology has been carried out so far. The program will be further expanded in the near future, thanks in particular to new highflux experimental area, now under construction.

Abstract In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a transmission power system non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner© and is the property of Tractebel Engineering (Gaz de France – Suez) company. It has been here applied to an academic test system: the Roy Billinton Test System (RBTS).

In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.

Deltas are among the most productive and diverse global ecosystems. However, these regions are highly vulnerable to natural disasters and climate change. Nature-based solutions (Nbs) have been increasingly adopted in many deltas to improve their resilience. Among decision support tools, assessment of ecosystem services (ES) through spatially explicit modelling plays an important role in advocating for Nbs. This study explores the use of the Land Utilisation and Capability Indicator (LUCI) model, a high-resolution model originally developed in temperate hill country regions, to map changes in multiple ecosystem services (ES), along with their synergies and trade-offs, between 2010 and 2018 in the Vietnamese Mekong Delta (VMD). In so doing, this study contributes to the current knowledge in at least two aspects: high-resolution ES modelling in the VMD, and the combination of ES biophysical and economic values within the VMD to support Nbs implementation. To date, this is the highest resolution (5 by 5 m) ES modelling study ever conducted in the VMD, with ~1500 million elements generated per ES. In the process of trialling implementations of LUCI within the VMD’s unique environmental conditions and data contexts, we identify and suggest potential model enhancements to make the LUCI model more applicable to the VMD as well as other tropical deltaic regions. LUCI generated informative results in much of the VMD for the selected ES (flood mitigation, agriculture/aquaculture productivity, and climate regulation), but challenges arose around its application to a new agro-hydrological regime. To address these challenges, parameterising LUCI and reconceptualising some of the model’s mechanisms to specifically account for the productivity and flood mitigation capability of water-tolerant crops as well as flooding processes of deltaic regions will improve future ES modelling in tropical deltaic areas. The ES maps showed the spatial heterogeneity of ES across the VMD. Next, to at least somewhat account for the economic drivers which need to be considered alongside biophysical valuations for practical implementations of ES maps for nature-based solutions (Nbs) in the upstream VMD, economic values were assigned to different parcels using a benefit transfer approach. The spatially explicit ES economic value maps can inform the design of financing incentives for Nbs. The results and related work can be used to support the establishment of Nbs that ultimately contribute to the security of local farmers’ livelihoods and the sustainability of the VMD.

This paper presents roadmaps for each of the 50 United States to convert their energy infrastructures to 100% wind, water, and sunlight (WWS) for all purposes by 2050.

Abstract Bioenergy systems are expected to expand over the coming decades due to their potential to address energy security and environmental pollution challenges. Nevertheless, any renewable energy project can only survive if approved environmentally superior to its conventional counterparts. Life cycle assessment (LCA) is an internationally standardized and validated methodology to evaluate and quantify the environmental impacts of bioenergy systems. However, due to its methodological scope, the LCA method measures only the environmental consequences of the target products of energy systems. The LCA approach can neither allocate the environmental impacts at the component level nor measure the environmental impacts of intermediate products. These challenges can be substantially resolved by systematically integrating the LCA approach with the thermodynamically-rooted exergy, offering a powerful environmental sustainability assessment tool known as “exergoenvironmental analysis“. Due to the unique methodological and conceptual characteristics of exergoenvironmental analysis in revealing the possibilities and trends for improvement, it has recently received increasing attention to mitigate the environmental impacts of bioenergy systems. Therefore, this review is aimed to thoroughly summarize and critically discuss the evaluation of sustainability aspects of bioenergy systems based on exergoenvironmental analysis. The pros and cons of using exergoenvironmental analysis in bioenergy research are also outlined to identify possible future directions for the field. Overall, exergoenvironmental analysis can offer more detailed information on the environmental consequences of each flow and component of bioenergy production plants, thereby diagnosing the breakthrough points for additional environmental improvements.

In order to improve the startup flexibility of steam turbines, it becomes relevant to analyze their dynamic thermal behavior. In this work, the relative expansion between rotor and casing was studied during cold-start conditions. This is an important property to monitor during startup given that clearances between rotating and stationary components must be controlled in order to avoid rubbing. The investigation was performed using a turbine thermal simplified model from previous work by the authors. The first step during the investigation was to extend and refine the modeling tool in order to include thermomechanical properties. Then, the range of applicability of the model was validated by a twofold comparison with a higher order finite element (FE) numerical model and measured data of a cold start from an installed turbine. Finally, sensitivity studies were conducted with the aim of identifying the modeling assumptions that have the largest influence in capturing the correct thermal behavior of the turbine. It was found that the assumptions for the bearing oil and intercasing cavity temperatures have a large influence ranging between ±25% from the measured values. In addition, the sensitivity studies also involved increasing the initial temperature of the casing in order to reduce the peak of differential expansion. Improvements of up to 30% were accounted to this measure. The studies performed serve as a base toward further understanding the differential expansion during start and establishing future clearance control strategies during turbine transient operation.