| 1. |
- Sbarra, AN, et al.
(author)
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Mapping routine measles vaccination in low- and middle-income countries
- 2021
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 589:7842, s. 415-
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Journal article (peer-reviewed)abstract
- The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.
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| 2. |
- Graetz, N, et al.
(author)
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Mapping disparities in education across low- and middle-income countries
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 577:77917789, s. 235-238
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Journal article (peer-reviewed)abstract
- Educational attainment is an important social determinant of maternal, newborn, and child health1–3. As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting4–6. The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness7,8; however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health9–11. Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but—to our knowledge—no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries12–14. By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations.
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| 3. |
- Ahmed, Waqas, et al.
(author)
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A Comparison and Introduction of Novel Solar Panel’s Fault Diagnosis Technique Using Deep-Features Shallow-Classifier through Infrared Thermography
- 2023
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In: Energies. - : MDPI. - 1996-1073. ; 16:3
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Journal article (peer-reviewed)abstract
- Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The proposed approach trains the shallow classifier in approximately 13 s with 95.5% testing accuracy. The approach is validated by manually extracting thermograph features and through the transfer of learned deep neural network approaches in terms of accuracy and speed. The proposed method is also compared with other existing methods.
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| 4. |
- Niaz, R., et al.
(author)
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Proposing a new framework for analyzing the severity of meteorological drought
- 2023
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In: Geocarto International. - : Taylor & Francis. - 1010-6049 .- 1752-0762. ; 38:1
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Journal article (peer-reviewed)abstract
- The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
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| 5. |
- Ullah, I., et al.
(author)
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Prevalence of depression and anxiety among general population in Pakistan during COVID-19 lockdown : An online-survey
- 2022
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In: Current Psychology. - : Springer. - 1046-1310 .- 1936-4733.
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Journal article (peer-reviewed)abstract
- The present study's aim is to find the prevalence of two of the common indicators of mental health - depression and anxiety – and any correlation with socio-demographic indicators in the Pakistani population during the lockdown from 5 May to 25 July 2020. A cross-sectional survey was conducted using an online questionnaire sent to volunteer participants. A total of 1047 participants over 18 were recruited through convenience sampling. The survey targeted depression and anxiety levels, which were measured using a 14 item self-reporting Hospital Anxiety and Depression Scale (HADS). Out of the total sample population (N=354), 39.9% suffered from depression and 57.7% from anxiety. Binary logistical regressions indicated significant predictive associations of gender (OR=1.410), education (OR=9.311), residence (OR=0.370), household income (OR=0.579), previous psychiatric problems (OR=1.671), and previous psychiatric medication (OR=2.641). These were the key factors e associated with a significant increase in depression. Increases in anxiety levels were significantly linked to gender (OR=2.427), residence (OR=0.619), previous psychiatric problems (OR=1.166), and previous psychiatric medication (OR=7.330). These results suggest depression and anxiety were prevalent among the Pakistani population during the lockdown. Along with other measures to contain the spread of COVID-19, citizens' mental health needs the Pakistani government's urgent attention as well as that of mental health experts. Further large-scale, such as healthcare practitioners, should be undertaken to identify other mental health indicators that need to be monitored.
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| 6. |
- Ali, A., et al.
(author)
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Diversity-productivity dependent resistance of an alpine plant community to different climate change scenarios
- 2016
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In: Ecological Research. - : Wiley. - 0912-3814 .- 1440-1703. ; 31:6, s. 935-945
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Journal article (peer-reviewed)abstract
- Here we report from a experiment imposing different warming scenarios [control with ambient temperature, constant level of moderate warming for 3 years, stepwise increase in warming for 3 years, and one season of high level warming (pulse) simulating an extreme summer event] on an alpine ecosystem to study the impact on species diversity-biomass relationship, and community resistance in terms of biomass production. Multiple linear mixed models indicate that experimental years had stronger influence on biomass than warming scenarios and species diversity. Species diversity and biomass had almost humpback relationships under different warming scenarios over different experimental years. There was generally a negative diversity-biomass relationship, implying that a positive diversity-biomass relationship was not the case. The application of different warming scenarios did not change this tendency. The change in community resistance to all warming scenarios was generally negatively correlated with increasing species diversity, the strength of the correlation varying both between treatments and between years within treatments. The strong effect of experimental years was consistent with the notion that niche complementarity effects increase over time, and hence, higher biomass productivity over experimental years. The strongest negative relationship was found in the first year of the pulse treatment, indicating that the community had weak resistance to an extreme event of one season of abnormally warm climate. Biomass production started recovering during the two subsequent years. Contrasting biomass-related resistance emerged in the different treatments, indicating that micro sites within the same plant community may differ in their resistance to different warming scenarios.
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| 7. |
- Almalki, Yassir Edrees, et al.
(author)
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Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis
- 2022
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In: Life. - : MDPI. - 2075-1729. ; 12:7
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Journal article (peer-reviewed)abstract
- Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.
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| 8. |
- Asif, M., et al.
(author)
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A dataset about anthropometric measurements of the Pakistani children and adolescents using a cross-sectional multi-ethnic anthropometric survey
- 2021
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In: Data in Brief. - : Elsevier. - 2352-3409. ; 34
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Journal article (peer-reviewed)abstract
- Evaluation of nutritional status is necessary during childhood and the juvenile years when the level of hydration and the adipose tissues experience significant changes. Anthropometric measurements and their derived indices are valid proxies to predict body fat, obesity (general or central) and their associated cardiovascular risks. The dataset under consideration also provides the socio-demographic related information and anthropometric measurement values related to height, weight, body mass index (BMI), waist circumference (WC), hip circumference (HpC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), mid-upper arm circumference (MUAC), neck circumference (NC), and wrist circumference (WrC). Standard procedure was adopted for quantifying the body measurements. The data were consisting of 10,782 children and adolescents aged 2–19 years, belonging four major cities of Pakistan viz. Multan, Lahore, Rawalpindi and Islamabad. This dataset is beneficial to develop anthropometric growth charts which will provide the essential knowledge of growth and nutritional disorders (e.g., stunted, overweight and obesity) of Pakistani children and adolescents. The dataset can also be used by researchers to calculate body surface area (BSA), body frame size (BFS), body shape index (BSI), and tri-ponderal mass index (TMI) of children and adolescents that are also some other reliable indicators of obesity and insulin resistance as well as cardiometabolic risk in children and adults.
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| 9. |
- Homod, Raad Z., et al.
(author)
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Deep clustering of cooperative multi-agent reinforcement learning to optimize multi chiller HVAC systems for smart buildings energy management
- 2023
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In: Journal of Building Engineering. - : Elsevier. - 2352-7102. ; 65
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Journal article (peer-reviewed)abstract
- Chillers are responsible for almost half of the total energy demand in buildings. Hence, the obligation of control systems of multi-chiller due to changes indoor environments is one of the most significant parts of a smart building. Such a controller is described as a nonlinear and multi-objective algorithm, and its fabrication is crucial to achieving the optimal balance between indoor thermal comfort and running a minimum number of chillers. This work proposes deep clustering of cooperative multi-agent reinforcement learning (DCCMARL) as well-suited to such system control, which supports centralized control by learning of agents. In MARL, since the learning of agents is based on discrete sets of actions and stats, this drawback significantly affects the model of agents for representing their actions with efficient performance. This drawback becomes considerably worse when increasing the number of agents, due to the increased complexity of solving MARL, which makes modeling policy very challenging. Therefore, the DCCMARL of multi-objective reinforcement learning is leveraging powerful frameworks of a hybrid clustering algorithm to deal with complexity and uncertainty, which is a critical factor that influences to the achievement of high levels of a performance action. The results showed that the ability of agents to manipulate the behavior of the smart building could improve indoor thermal conditions, as well as save energy up to 44.5% compared to conventional methods. It seems reasonable to conclude that agents' performance is influenced by what type of model structure.
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| 10. |
- Li, Li, et al.
(author)
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A network analysis of the Internet Disorder Scale-Short Form (IDS9-SF) : A large-scale cross-cultural study in Iran, Pakistan, and Bangladesh
- 2023
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In: Current Psychology. - : Springer. - 1046-1310 .- 1936-4733. ; 42, s. 21994-22003
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Journal article (peer-reviewed)abstract
- The Internet Disorder Scale-Short Form (IDS9-SF) is a validated instrument assessing internet disorder which modified the internet gaming disorder criteria proposed in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). However, the relationships between the nine items in the IDS9-SF are rarely investigated. The present study used network analysis to investigate the features of the IDS9-SF among three populations in Bangladesh, Iran, and Pakistan. Data were collected (N = 1901; 957 [50.3%] females; 666 [35.0%] Pakistani, 533 [28.1%] Bangladesh, and 702 [36.9%] Iranians) using an online survey platform (e.g., Google Forms). All the participants completed the IDS9-SF. The central-stability-coefficients of the nine IDS9-SF items were 0.71, 0.89, 0.96, 0.98, 0.98, 1.00, 0.67, 0.79, and 0.91, respectively. The node centrality was stable and interpretable in the network. The Network Comparison Test (NCT) showed that the network structure had no significant differences among Pakistani, Bangladeshi, and Iranian participants (p-values = 0.172 to 0.371). Researchers may also use the IDS9-SF to estimate underlying internet addiction for their target participants and further explore and investigate the phenomenon related to internet addiction.
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