Combined application of macro and micro nutrients and Rhizobium inoculation to nodulation and yield response of chickpea (Cicer Arietinum L.) at Halaba Woreda, Southern Ethiopia

The effects of combining the application of recently introduced blended fertilizer with Rhizobium inoculation on chickpea nodulation and productivity are not being studied in Halaba special woreda. Hence, the application of blended fertilizer and rhizobium inoculation for chickpea production under rain-fed conditions at Halaba Special District, Southern Ethiopia is crucial. Factorial combination of eight fertilizers (Control, NP, NPS, NPSB, NPSB+K, NPS+Zn, NPSB+Zn and NPSB+K+Zn) and inoculation and without inoculation of rhizobium biofertilizer was used as an experimental factor laid out in a randomized complete block design with three replications. Fertilizer application significantly influenced crop phenology, nodulation, growth parameters, yield and yield components, except number of seeds pod-1 and harvest index. Maximum days to 50% flowering (48.33) and 95% physiological maturity (112.3) were obtained on NPSB+K and without fertilizer treatment respectively. The Highest number of nodules (23.25), nodules dry weight (0.13 g), number of branches plant-1 (17.64) and plant height (43.34 cm) were recorded on NPSB, NPSB+K+Zn, NPSB+Zn and NPS+Zn respectively. Similarly, the higher number of pods plant-1(61.6), and hundred seed weight (28.0 g) were observed for blended fertilizer treatments of NPSB+K and NPS+Zn respectively. Maximum grain yield (1.85 ton ha-1) was obtained for blended fertilizer of NPSB+K application with an increment 57.9% over control treatment. Rhizobium inoculation increased the number of nodules plant-1 (23.29), nodules dry weight (0.11 g), number of branches plant-1 (17.70), number of pods plant-1 (59), number of seeds pod-1 (1.17) and hundred seed weight (27.7 g). Maximum grain yield (1.84 ton ha-1) was recorded on rhizobium inoculated and it increased chickpea grain yield by 33.3% over uninoculated. Regarding the economic feasibility of fertilizers greater net benefits with acceptable MRR 1802, 866 and 257 were recorded for blended fertilizers of NPS, NPS+Zn and NPSB, respectively. Given the fact that the three fertilizers had statistically similar grain yields, the blended fertilizer NPS is a better choice among the three alternatives. Similarly, a higher net benefit with acceptable MRR (4189%) was recorded for Rhizobium inoculation. Therefore, blended fertilizer; NPS and Rhizobium inoculation were found to be relevant and recommended for chickpea production in the study area

Blue collar laborers’ travel pattern recognition: Machine learning classifier approach

This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State of Qatar. The activity-based data from the travel diary of 1051 blue-collar workers collected by the Ministry of Transportation and Communication (MoTC) in Qatar was used for analysis. A pattern recognition model is applied to a revealed preference (RP) survey obtained from the Ministry of Transportation and Communication (MoTC) in Qatar for the travel diary for blue-collar workers. Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. The research methodology undertaken in this paper comprises a combination of different machine learning techniques, predominantly by applying clustering and classification methods. A bagged Clustering algorithm was employed to identify the number of clusters, then the C-Means algorithm and the Pamk algorithm were implemented to validate the results. Meanwhile, the interdependencies between the resulted clusters and the socio-demographic attributes for the households were examined using crosstabulation analysis. The study results show significant diversity amongst the clusters in terms of trip purpose, modal split, destination choice, and occupation. Furthermore, whilst the Bagged Clusters and Pamk Clusters techniques on the three attributes yielded similar results, the Cmeans Clusters differed significantly in a number of the clusters. Applying such pattern recognition models on big and complex activity datasets could assist transport planners to understand the travel needs of segments of the population well and formulating better-informed strategies

Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers

Based on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and optimization of charging vehicles. Designing the charging infrastructure for EVs has many impacts such as stress on the power network, increase in traffic flow, and change in driving behaviors. Therefore, the optimal placement of charging stations is one of the most important issues to address to increase the use of electric vehicles. In this regard, the purpose of this study is to present an optimization method for choosing optimal locations for electric car charging stations for Campus charging over long-term planning. The charger placement problem is formulated as a complex Multi-Criteria Decision Making (MCDM) which combines spatial analysis techniques, power network load flow, traffic flow models, and constrained procedures. The Analytic Hierarchy Process (AHP) approach is used to determine the optimal weights of the criteria, while the mean is used to determine the distinct weights for each criterion using the AHP in terms of accessibility, environmental effect, power network indices, and traffic flow impacts. To evaluate the effectiveness of the proposed method, it is applied to a real case study of Qatar University with collected certain attributes data and relevant decision makers as the inputs to the linguistic assessments and MCDM model. The Ranking of the optimal locations is done by aggregating four techniques: Simple Additive Weighting Method (SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II). A long-term impact analysis is a secondary output of this study that allows decision-makers to evaluate their policy impacts. The findings demonstrate that the proposed framework can locate optimal charging station sites. These findings could also help administrators and policymakers make effective choices for future planning and strategy

Planning and Optimizing Electric-Vehicle Charging Infrastructure Through System Dynamics

One of the key solutions to address the issue of energy efficiency and sustainable mobility is to integrate plug-in electric vehicle (EV) infrastructure and photovoltaic (PV) systems. The research proposes a comprehensive EV infrastructure planning and analysis tool (EVI-PAT) with solar power generation for micro-scale projects for the deployment of EV Charging Stations (EVCS). For the evaluation of the proposed infrastructure, a case study of Qatar University (QU) campus is chosen for the integration of the EV charging infrastructure and PV power generation to evaluate the performance of the presented framework. The model estimates the EV adoption and the number of vehicles based on the inputs related to the country's EV adoption, campus vehicle count, and driving behavior. Economic and environmental indicators are used for evaluating policy choices. The findings in the paper show that the proposed planning framework can find the optimum staging plan for EV and PV infrastructure based on the policy choices. The staging plan optimizes the sizes and times of installing EVCSs combined with solar PV keeping the EV-PV project at maximum economic and environmental targets. The optimum policy can affect the optimum power infrastructure limit to maximize the economic benefit by the solar tariff.10.13039/100019779-Qatar National Librar

Exposure to household air pollution from solid cookfuels and childhood stunting: a population-based, cross-sectional study of half a million children in low- and middle-income countries

BACKGROUND: Household air pollution from the incomplete combustion of solid cookfuels in low- and middle-income countries (LMICs) has been largely ignored as a potentially important correlate of stunting. Our objective was to examine the association between solid cookfuel use and stunting in children aged <5 y. METHODS: We used data from 59 LMICs' population-based cross-sectional demographic and health surveys; 557 098 children aged <5 y were included in our analytical sample. Multilevel logistic regression was used to examine the association between exposure to solid cookfuel use and childhood stunting, adjusting for child sex, age, maternal education and number of children living in the household. We explored the association across key subgroups. RESULTS: Solid cookfuel use was associated with child stunting (adjusted OR 1.58, 95% CI 1.55 to 1.61). Children living in households using solid cookfuels were more likely to be stunted if they lived in rural areas, the poorest households, had a mother who smoked tobacco or were from the Americas. CONCLUSIONS: Focused strategies to reduce solid cookfuel exposure might contribute to reductions in childhood stunting in LMICs. Trial evidence to assess the effect of reducing solid cookfuel exposure on childhood stunting is urgently needed

Insomnia and common mental disorder among patients with pre-existing chronic non-communicable diseases in southern Ethiopia: a survey during COVID-19 pandemic

BackgroundCOVID-19 has been causing significant mental health problems and other health-related issues. Despite the fact that COVID-19 has a significant impact on chronic disease patients, there is scant research on insomnia, common mental health disorders (CMD), and their associated factors among chronic disease patients.ObjectiveThe purpose of this study was to assess the prevalence of insomnia and common mental disorders (CMD) and their associated factors among patients with pre-existing chronic NCDs in Sidama, southern Ethiopia.MethodsA multicenter cross-sectional study was undertaken between June 1 and September 1, 2021. The study included 633 participants. CMD and insomnia were assessed using a 20-item Self-Reported Questionnaire (SRQ-20) and a 7—item Insomnia Severity Index (ISI) scale, respectively. To describe the various variables, descriptive statistics were used. We performed multivariable logistic regression analysis to identify independent factors associated with CMD and insomnia. A value of p &lt; 0.05 was considered statistically significant at a 95% confidence interval.ResultsThe prevalence of insomnia and CMD was found to be 39.3% and 46.8%, respectively. Being merchant (AOR = 0.33; 95% CI = 0.13, 0.82), having a diagnosis of diabetes mellitus (AOR = 1.89; 95% CI = 1.04, 3.46), comorbid diagnosis (AOR = 3.96; 95% CI = 2.27, 6.89), low social support (poor (AOR = 3.37; 95% CI = 1.51, 7.57) and moderate (AOR = 3.13; 95% CI = 1.46, 6.69)), symptoms of insomnia (AOR = 12.08; 95% CI = 7.41, 19.72) and poor quality of life (QOL) (AOR = 1.67; 95% CI = 1.04, 2.72) were independent predictors of CMD. We also found out that, having cardiovascular disorders (CVDs) (AOR = 2.48; 95% CI = 1.18, 5.19), CMD (AOR = 12.09; 95% CI = 7.46, 19.61), and poor QOL (AOR = 2.04; 95% CI = 1.27, 3.26) were significantly associated with insomnia symptoms.ConclusionOur study suggests that substantially high prevalence of CMD and insomnia. Significant association between CMD and occupation, diagnosis, comorbidity, social support, insomnia, and QOL were found. We also revealed that having CVDs, CMD, and poor QOL were significantly associated with insomnia symptoms. Therefore, dealing with the mental health problems of patients with chronic NCDs is an essential component of public health intervention during the COVID-19 pandemic

Aberrant driving behaviors as mediators in the relationship between driving anger patterns and crashes among taxi drivers: An investigation in a complex cultural context

Objective: Taxis have become an integrated component of Qatar’s urban transportation network due to their convenience, comfort, and flexibility. Qatar has seen an uptick in the demand for professional taxi drivers. Most Qatari taxi drivers come from developing countries with poor awareness of road safety; therefore, they regularly engage in aberrant driving behavior, leading to traffic violations and crashes. For taxi rides to be safer, it is essential to determine the association between driving aberration and road traffic crashes (RTCs), with an emphasis on the underlying factors that trigger these behaviors. Methods: To this end, we collected the data from taxi drivers relying on standard questionnaires, namely the Driving Anger Scale (DAS) and the Driver Behavior Questionnaire (DBQ), together with the real crash data of the same taxi drivers obtained from the police department. We relied on factor analysis to identify the main factors of these tools and then structural equation modeling to predict their causal relationship with RTCs. Results: The results indicated that the component of DAS, namely “illegal driving”, triggered all dimensions of aberrant driving behaviors, whereas hostile gestures had a positive correlation with lapses. In addition, the factor “error” was identified as a significant direct predictor, while the factor “illegal driving” was identified as a significant indirect predictor for RTCs. Regarding demographic characteristics, professional driving experience was found to be negatively associated with RTCs. Conclusion: Driving aberration mediated the impact of driving anger on RTCs. The findings from this study could help road safety practitioners and researchers better understand these relations. In addition, these results could also be very helpful for driving instructors to train taxi drivers in a way to cope with provoking situations.Open Access funding provided by the Qatar National Library. This study was made possible by the internal grant award [QUCG-CENG-21/22-2] from Qatar University

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Intelligent Transportation System: Application of Telematics Data for Road Safety

Road safety is a growing concern as the number of vehicles on road increase yearly. Road related accidents is within the top ten cause of death globally and number one cause of death for youth between the ages of 19-29 years old. With the growth of "Internet of Things"and artificial intelligence, there is a huge potential of using smart technologies to support the Intelligence Transportation System (ITS) framework through the usage of smartphone telematics devices. The aim of this study is two folds. The first is to investigate the usage of feedback to drivers through push notifications on app that will monitor the driving of each participant. The second is to propose a telematics derived Usage Based Insurance (UBI) framework. For the first objective, 60 participants took part in the approved intervention study where each participant had to install an app called SafeDriver that monitors their driving for 12 weeks. It was found that the app had made some positive changes to the driving behavior and from an exit survey, many participants were happy with this app and are willing to use it if some financial incentives were provides such as reduced vehicle insurance premiums. For the UBI, after closely examining the literature, a conceptual model was pro-posed. In summary, telematics coupled with artificial intelligence (data mining) hold a promising future in promoting a more improved road safet