Analysis of climate extreme indices over the Komadugu-Yobe basin, Lake Chad region: Past and future occurrences

This study investigates trends of climate extreme indices in the Komadugu-Yobe Basin (KYB) based on observed data of the period 1971–2017 as well as regional climate model (RCM) simulations for the historical period (1979–2005), the near future (2020–2050), and the far future (2060–2090). In order to correct change points in the time historical series, the Adapted Caussinus Mestre Algorithm for homogenising Networks of Temperature series homogeneity test is used. The magnitude of the linear trends is estimated using the Sen\u27s slope estimator and Mann-Kendall\u27s test is performed to check the statistical significance of the trends. Future trends are assessed using the ensemble mean of eight regional climate model data under two emission scenarios, provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX). Therefore, the projected rainfall and temperature have been corrected for biases by using empirical Quantile Mapping. In the observations, warm spell duration, warm day-, and warm night frequencies exhibit statistically significant positive trends. Although there is a positive trend in the annual total rainfall, the number of consecutive wet (dry) days decreases (increases). The future climate also shows a continuing positive trend in the temperature extreme indices as well as more frequent extreme rainfall events. Therefore, it is pertinent for decision-makers to develop suitable adaptation and mitigating measures to combat climate change in the Basin

Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa

Study Area: The discharge of the transboundary Komadugu-Yobe Basin, Lake Chad Area, West Africa is calibrated using multi-objective optimization techniques. Study focus: The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multiobjective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a timefrequency wavelet transform. New hydrological insights for the region: For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin

JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds

Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D semantic segmentation methods, little attention has been drawn to the learning of 3D semantic edge detectors, even less to a joint learning method for the two tasks. In this paper, we tackle the 3D semantic edge detection task for the first time and present a new two-stream fully-convolutional network that jointly performs the two tasks. In particular, we design a joint refinement module that explicitly wires region information and edge information to improve the performances of both tasks. Further, we propose a novel loss function that encourages the network to produce semantic segmentation results with better boundaries. Extensive evaluations on S3DIS and ScanNet datasets show that our method achieves on par or better performance than the state-of-the-art methods for semantic segmentation and outperforms the baseline methods for semantic edge detection. Code release: https://github.com/hzykent/JSENetComment: Accepted to ECCV 2020, supplementary materials include

Field demonstration of a tunable WDM-PON system with novel SFP+ modules and centralized wavelength control

We report on a demonstration of a novel tunable WDM-PON system over 25km of field deployed fiber. We show error-free operation at 1 GbE with sensitivity better than -30dBm and centralized control of the ONU wavelengths.This work has been supported by the EU ERA-NET+ projects PIANO+ IMPACT and PIANO+ TUCAN co-funded by the German BMBF, the UK Technology Strategy Board and the Austrian FFG. Also support from the UK EPSRC INTERNET project is acknowledged.This is the author accepted manuscript. The final version is available from OAS Publishing via http://dx.doi.org/10.1364/OFC.2015.M2A.

Essential Medicines at the National Level : The Global Asthma Network's Essential Asthma Medicines Survey 2014

Patients with asthma need uninterrupted supplies of affordable, quality-assured essential medicines. However, access in many low- and middle-income countries (LMICs) is limited. The World Health Organization (WHO) Non-Communicable Disease (NCD) Global Action Plan 2013-2020 sets an 80% target for essential NCD medicines' availability. Poor access is partly due to medicines not being included on the national Essential Medicines Lists (EML) and/or National Reimbursement Lists (NRL) which guide the provision of free/subsidised medicines. We aimed to determine how many countries have essential asthma medicines on their EML and NRL, which essential asthma medicines, and whether surveys might monitor progress. A cross-sectional survey in 2013-2015 of Global Asthma Network principal investigators generated 111/120 (93%) responses41 high-income countries and territories (HICs); 70 LMICs. Patients in HICs with NRL are best served (91% HICs included ICS (inhaled corticosteroids) and salbutamol). Patients in the 24 (34%) LMICs with no NRL and the 14 (30%) LMICs with an NRL, however no ICS are likely to have very poor access to affordable, quality-assured ICS. Many LMICs do not have essential asthma medicines on their EML or NRL. Technical guidance and advocacy for policy change is required. Improving access to these medicines will improve the health system's capacity to address NCDs.Peer reviewe

Association of respiratory symptoms and lung function with occupation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Background Chronic obstructive pulmonary disease has been associated with exposures in the workplace. We aimed to assess the association of respiratory symptoms and lung function with occupation in the Burden of Obstructive Lung Disease study. Methods We analysed cross-sectional data from 28 823 adults (≥40 years) in 34 countries. We considered 11 occupations and grouped them by likelihood of exposure to organic dusts, inorganic dusts and fumes. The association of chronic cough, chronic phlegm, wheeze, dyspnoea, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC with occupation was assessed, per study site, using multivariable regression. These estimates were then meta-analysed. Sensitivity analyses explored differences between sexes and gross national income. Results Overall, working in settings with potentially high exposure to dusts or fumes was associated with respiratory symptoms but not lung function differences. The most common occupation was farming. Compared to people not working in any of the 11 considered occupations, those who were farmers for ≥20 years were more likely to have chronic cough (OR 1.52, 95% CI 1.19–1.94), wheeze (OR 1.37, 95% CI 1.16–1.63) and dyspnoea (OR 1.83, 95% CI 1.53–2.20), but not lower FVC (β=0.02 L, 95% CI −0.02–0.06 L) or lower FEV1/FVC (β=0.04%, 95% CI −0.49–0.58%). Some findings differed by sex and gross national income. Conclusion At a population level, the occupational exposures considered in this study do not appear to be major determinants of differences in lung function, although they are associated with more respiratory symptoms. Because not all work settings were included in this study, respiratory surveillance should still be encouraged among high-risk dusty and fume job workers, especially in low- and middle-income countries.publishedVersio

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio