Automated fluvial hydromorphology mapping from airborne remote sensing

Mapping fluvial hydromorphology is an important part of defining river habitat. Mappingvia field sampling or hydraulic modelingis however time consuming, and mappinghydromorphology directly from remote sensing data may offer an efficient solution.Here, we present a system for automated classification of fluvial hydromorphologybased on a deep learning classification scheme applied to aerial orthophotos. Usingselected rivers in Norway, we show how surface flow patterns (smooth or rippled sur-faces vs. standing waves) can be classified in imagery using a trained convolutional neu-ral network (achieving a training and validation accuracy of >95%). We show howintegration of these classified surface flow patterns with information on channel gradi-ent, obtained from airborne topographic LiDAR data, can be used to compartmentalizethe rivers into hydromorphological units(HMUs) that represent the dominant flow fea-tures. Automated classifications were broadly consistent with manual classifications thathad been made in previous ground surveys, with equivalency in automated and manu-ally derived HMU classes ranging from 61.5% to 87.7%, depending on the river stretchconsidered. They were found to be discharge-dependent, showing the temporallydynamic aspect of hydromorphology. The proposed system is quick, flexible, generaliz-able, and provides consistent classifications free from interpretation bias. The deeplearning approach used here can be customized to provide more detailed information onflow features, such as delineating between standing waves and advective diffusion ofair bubbles/foam, to provide a more refined classification of surface flow patterns, andthe classification approach can be furtheradvanced by inclusion of additional remotesensing methods that provide further information on hydromorphological features.publishedVersio

Sensitivity analysis of energy performance and thermal comfort throughout building design process

In a traditional building design process (TDP), design variables are fixed sequentially, as opposed to integrated design process (IDP) which tends to avoid sequential design phases to create more sustainable buildings. First, a reference building is introduced and an energy model based on TRNSYS is presented to determine the energy consumption and comfort in the building. The model is validated based on energy bills, certified simulations and literature. Then, the paper performs an extended sensitivity analysis (SA) of 30 design variables with respect to different performance criteria related to energy consumption and comfort, based on a TRNSYS model. Three SA techniques were used, namely standard regression coefficients (SRC), partial rank correlation coefficients (PRCC) and Sobol indices. Results show that all three techniques yielded a similar ranking of the importance of the variables for most model outputs. Interactions between variables were identified with second-order Sobol indices. In the second part of this paper, a traditional design framework was adopted in which sets of variables were fixed sequentially. A SA was performed at each phase of the process, assuming fixed values for parameters chosen in previous design phases. Results show that fixing variables during the phases of a traditional design process tends to reduce the probabilities of finding low-energy consumption designs. Moreover, the influence of some variables was found to change during the design phases

Performance of a sequential versus holistic building design approach using multi-objective optimization

Integrated design processes are currently pushed forward in order to achieve net-zero energy building designs at affordable cost. Through a case study of a residential building, this paper compares a sequential versus a holistic design approach based on multi-objective optimization. In the holistic approach, 39 design variables related to the architecture and HVAC systems are simultaneously optimized. In the sequential approach, the architecture variables are first optimized; several optimal solutions are then selected for the second phase optimization of the heating system parameters. Carbon footprint, life cycle cost and thermal comfort are optimized by the algorithm NSGA-II. With only 100 computational hours, the holistic approach found 59% of the optimal solutions, whereas it took 765 h to find 41% of the optimal solutions with the sequential approach. This comparison shows the negative effects of making irreversible variable selections in the early phase of a design process, as it reduces the ability to find optimal solutions in the end

A systematic literature review of the quality of evidence for injury and rehabilitation interventions in humanitarian crises.

INTRODUCTION: Humanitarian crises continue to pose a significant threat to health; the United Nations estimates that 144 million people are directly affected by conflict or environmental disasters. During most humanitarian crises, surgical and rehabilitative interventions remain a priority. OBJECTIVES: This review assessed the quality of evidence that informs injury and physical rehabilitation interventions in humanitarian crises. METHODS: Peer-reviewed and grey literature sources were assessed in a systematic manner. Selected papers were evaluated using quality criteria based on a modified version of the STROBE protocol. RESULTS: 46 papers met the inclusion criteria. 63 % of the papers referred to situations of armed conflict, of which the Yugoslav Wars were the most studied crisis context. 59 % of the studies were published since the year 2000. However, only two studies were considered of a high quality. CONCLUSIONS: While there is now a greater emphasis on research in this sector, the volume of evidence remains inadequate given the growing number of humanitarian programmes worldwide. Further research is needed to ensure a greater breadth and depth of understanding of the most appropriate interventions in different settings

Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building

The emergence of building performance optimization is recognized as a way to achieve sustainable building designs. In this paper, the problem consists in minimizing simultaneously the emissions of greenhouse gases (GHG) related to building energy consumption and those related to building materials. This multi-objective optimization problem involves variables with different hierarchical levels, i.e. variables that can become obsolete depending on the value of the other variables. To solve it, NSGA-II is compared with an algorithm designed specifically to deal with hierarchical variables, namely sNSGA. Evaluation metrics such as convergence, diversity and hypervolume show that both algorithms handle hierarchical variables, but the analysis of the Pareto front confirms that in the present case, NSGA-II is better to identify optimal solutions than sNSGA. All the optimal solutions are made of buildings with wooden envelopes and relied either on heat pumps or on electrical heaters for proving heating

Fourfold oscillations and anomalous magnetic irreversibility of magnetoresistance in the non-metallic regime of Pr1.85Ce0.15CuO4

Using magnetoresistance measurements as a function of applied magnetic field and its direction of application, we present sharp angular-dependent magnetoresistance oscillations for the electron-doped cuprates in their low-temperature non-metallic regime. The presence of irreversibility in the magnetoresistance measurements and the related strong anisotropy of the field dependence for different in-plane magnetic field orientations indicate that magnetic domains play an important role for the determination of electronic properties. These domains are likely related to the stripe phase reported previously in hole-doped cuprates.Comment: 11 pages, 5 figure

Comparative Cost-Effectiveness Analysis of Two MSF Surgical Trauma Centers

INTRODUCTION: There is a dearth of data on cost-effectiveness of surgical care in resource-poor countries. Doctors Without Borders (Médecins Sans Frontières; MSF) is a nongovernmental organization (NGO) involved in the many facets of health care for underserved populations, including surgical care. METHODS: A cost-effectiveness analysis (CEA) was attempted at two of their surgical trauma hospitals: Teme Hospital in Nigeria and La Trinité Hospital in Haiti. CONCLUSION: At $172 and$223 per Disability-Adjusted Life-Year (DALY) averted, respectively, they are in line with other reported CEAs for surgical and nonsurgical activities in similar contexts

Challenges of Meeting Surgical Needs in the Developing World

The burden of surgical conditions and diseases is increasing in low-income and middle-income countries, but the capacity to meet the demands they present is not following pace. Ongoing initiatives, such as brief visits by surgeons from advantaged countries, sending surgical residents to spend time in a developing country as part of their training, or ships weighing anchor offshore and offering some limited on-shore or on-board services, have not proven successful. More comprehensive and sustainable solutions include the development of local training programs, better retention of trainees with adequate incentives particularly in rural areas, and engaging government and professional associations, as well as academic institutions, to develop and implement policies to address local training needs