Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)

Hyperspectral images (HSIs) are a powerful source of reliable data in various remote sensing applications. But due to the large number of bands, HSI has information redundancy, and methods are often used to reduce the number of spectral bands. Band selection (BS) is used as a preprocessing solution to reduce data volume, increase processing speed, and improve methodology accuracy. However, most conventional BS approaches are unable to fully explain the interaction between spectral bands and evaluate the representation and redundancy of the selected band subset. This study first examines a supervised BS method that allows the selection of the required number of bands. A deep network with 3D-convolutional layers embedded in a genetic algorithm (GA). The GA uses embedded 3D-CNN (CNNeGA) as a fitness function. GA also considers the parent check box. The parent check box (parent subbands) is designed to make genetic operators more effective. In addition, the effectiveness of increasing the attention layer to a 3D-CNN and converting this model to spike neural networks has been investigated in terms of accuracy and complexity over time. The evaluation of the proposed method and the obtained results are satisfactory. The accuracy improved from 6&#x0025; to 21&#x0025;. Accuracy between 90&#x0025; and 99&#x0025; has been obtained in each evaluation mode

Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Land use and land cover (LULC) change is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image in 2001, 2014, and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the support vector machine algorithm and the Markov chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard &#x003D; 0.9160, Kno &#x003D; 0.9379, Klocation &#x003D; 0.9318, and KlocationStrata &#x003D; 0.9320, showing good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001&#x2013;2021, the significant increase of agricultural land (14317 ha) and barren area (9063 ha), and the sharp decline of grassland (26215 ha), and forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that forest cover will continue to decrease from 29.46 &#x0025; (50720.2667 ha) in 2021 to 25.67 &#x0025; of area (44207.78694 ha) in 2040, as well as, unceasing expansion of barren area, agricultural land, and built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

In recent years, national economies are highly affected by crop yield predictions. By early prediction, the market price can be predicted, importing, and exporting plan can be provided, social, and economic effects of waste products can be minimized, and a program can be presented for humanitarian food aid. In addition, agricultural fields are constantly growing to generate products required. The use of machine learning (ML) methods in this sector can lead to the efficient production and high-quality agricultural products. Traditional predictive machine models were unable to find nonlinear relationships between data. Recently, there has been a revolution in prediction systems via the advancement of ML, which can be used to achieve highly accurate decision-making networks. Thus far, many strategies have been used to evaluate agricultural products, such as DeepYield, CNN-LSTM, and ConvLSTM. However, preferable prediction accuracy is required. In this study, two architectures have been proposed. The first model includes 2D-CNN, skip connections, and LSTM-Attentions. The second model comprises 3D-CNN, skip connections, and ConvLSTM Attention. The Input data given from MODIS products such as Land-Cover, Surface-Temperature, and MODIS-Land-surface from 2003 to 2018 on the county level over 1800 counties, where soybean is mainly cultivated in the USA. The proposed methods have been compared with the most recent models. Then, the results showed that the second proposed method notably outperformed the other techniques. In case of MAE, the second proposed method, DeepYield, ConvLSTM, 3DCNN, and CNN-LSTM obtained 4.3, 6.003, 6.05, 6.3, and 7.002, respectively