The North-West (NW) region of Bangladesh is pivotal for the country's agricultural development, mainly in producing irrigated Boro rice. However, increasing cost of irrigation water, fertilizers, ...labour and other inputs, and the spatio-temporal variation in actual yield, market price and profitability of rice, have added uncertainty to the sustainability of Boro rice cultivation. In this study, we evaluated the productivity, profitability, and prospect of Boro rice production using comprehensive field data collected directly from 420 farmers' fields over two consecutive seasons (2015-16 and 2016-17), across seven geographically distributed locations in the NW region. We also analyzed the risk and return trade of popular Boro rice cultivars using Monte-Carlo simulation. The results show that there were significant (p≤0.05) variations in rice yield between sites, irrigation pump-types, and rice varieties, with Hybrid rice and BRRI dhan29 producing highest yields (6.0-7.5 t/ha). Due to different pricing systems, the cost of irrigation water varied from site to site and from year to year, but always comprised the highest input cost (20-25% of total production). The total paid-out cost, gross benefit, and gross income of rice significantly (p≤0.05) differed between sites, type of irrigation pumps, rice varieties, transplanting dates, and two cropping years. The variations in observed yield and profitability reveal considerable scope to improve rice production systems. Market variation in the price of rice affected overall profitability significantly. Probability and risk analysis results show that Minikit and BRRI dhan29 are the most stable varieties for yield and profitability. Hybrid rice, which has the maximum attainable yield among the cultivated rice varieties, also has the risk of negative net income. Based on the analysis, we discussed ways to improve yield and profitability and the prospect of Boro rice cultivation in the region. The study provides valuable information for policy-makers to sustain irrigated rice cultivation in both the NW region and nationally.
•Water productivity of Boro rice based on 420 farmers’ field data over 2 seasons.•Water used by the farmers are several times lower than general perception.•Reducing water supply to Boro rice may not ...have impacts on groundwater decline.•Inform policy makers to identify options for sustainable groundwater irrigation.
Groundwater-irrigated Boro rice is produced on 83 % of the net cultivable area (NCA) in North-West (NW) region of Bangladesh. Falling groundwater levels in many parts of the region raised concerns about the sustainability of groundwater irrigation. It is generally accepted that, in the absence of a comprehensive scientific study, uncontrolled groundwater use for Boro rice cultivation leads to water wastage and low water productivity. Therefore, it is crucial to know the actual field-level water usage and, irrigation water productivity, which will help identifying options to sustain groundwater irrigation. This study provides a comprehensive account of these aspects of Boro rice cultivation based on field observations at 420 farmers’ plots in 2015–16 and 2016–17 seasons across seven sites in the NW region. Necessary data, including land type, soil type, rice varieties, seeding and transplanting times, grain and biomass yields pump discharge, and irrigation amount were recorded. Average water productivity was 0.67 kg m−3 and 0.64 kg m−3 based on total available water (rainfall plus irrigation) in the fields, 0.80 kg m−3 and 0.95 kg m−3 based on supplied irrigation, and 1.60 kg m−3 and 1.78 kg m−3 based on estimated actual crop evapotranspiration (ETc) during 2015–16 and 2016–17, respectively. These water productivities are the highest among the major rice-growing Asian countries indicating limited scope for improving farmers’ water management practices. Comparison of the actual water supplied to the field and the estimated requirements shows that farmers are, in general, very efficient in supplying water to rice. In shallow tube well (STW) sites, water supplied by the farmers was very close to actual requirements, but rice plots in DTW sites had some over application. The average total amount of water available in the field to grow one kilogram of rice was 1,606 L (L) in 2015−16 and 1605 L in 2016−17. The Average irrigation water supplied to the field was 1402 L kg−1 in 2015−16 and 1086 L kg−1 in 2016−17. However, not all water supplied to the rice plots are consumed by the plants. Actual crop evapotranspiration is the real water use and based on that only 661 L in 2015−16 and 584 L in 2016−17 were required to grow one kilogram of rice. Percolation and seepage water return to the underlying aquifer as return flow. So, the current government policy of so called ‘water savings’ by reducing pumping of groundwater is unlikely to have any major impacts on the sustainable groundwater irrigation in the NW region.
Until now, no vaccine or effective drug is available for the control, prevention, and treatment of COVID-19. Preventive measures are the only ways to be protected from the disease and knowledge of ...the people about the preventive measures is a vital matter.
The aim of the study was to assess the knowledge of the general people in Rajshahi district, Bangladesh regarding the COVID-19 preventive measures.
This cross sectional study was conducted from March 10 to April 25, 2020. Data were collected with a semi-structured questionnaire from 436 adult respondents selected by using a mixed sampling technique. Frequency analysis, chi-square test, and logistic regression model were utilized in this study. SPSS (IBM, Version 22) was used for data analysis. 95% confidence interval and p-value = 0.05 were accepted for statistical significance.
Only 21.6% of the respondents had good knowledge of the COVID-19 preventive measures. The highest 67.2% of them knew that washing hands with soap could prevent the disease, but contrarily, the highest 72.5% did not know that avoidance of touching mouth, nose, and eyes without washing hands was a preventive measure. Only 28.4% and 36.9% of the respondents knew that maintaining physical distancing and avoiding mass gatherings were measures of prevention of COVID-19 respectively. The younger age (≤25 years), low family income (≤15,000 Bangladeshi Taka (BDT), occupation others than business and service, and nuclear family had the lower odds of having no/less knowledge about the preventive measures.
The knowledge level of the general people regarding prevention of COVID-19 was alarmingly low in Bangladesh. The government of Bangladesh, health policy makers and donor agencies should consider the findings and take immediate steps for improving knowledge of the public about prevention of the disease.
The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum ...mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method.
The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious ...disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.
The utilization of recycled fibers from textile waste has greatly increased in recent times due to growing environmental consciousness, legislative sustainability mandates, and escalating raw ...material expenses. This study seeks to utilize dyed brush fiber waste from a textile finishing machine called sueding, which is also known as emerizing, sanding, or brush machine, in order to create environmentally-friendly mélange yarn. Due to the brush fibers' limited length, the primary obstacle was utilizing them as a raw material for yarn production. In order to surmount this obstacle, brush fibers were blended with raw cotton and subsequently introduced into the carding machine to generate carded slivers. Subsequently, three variants of ring-spun mélange yarn samples were generated, namely: one derived from dyed brush fiber, one obtained from post-consumer waste fiber, and another produced from virgin viscose fiber. In order to verify the existence of the short brush fibers in the yarn, this research conducted optical microscopy on both the sliver and yarn, as well as scanning electron microscopy specifically on the yarn. Next, the quality parameters of ring-spun mélange yarn manufactured from brush fiber are contrasted with those of ring-spun mélange yarn made from viscose fiber and ring-spun mélange yarn generated from post-consumer fiber. The study revealed that ring-spun mélange yarn made from brush fiber exhibited a 6.42% and 4.52% rise in unevenness, an 8.48% and 4.53% increase in hairiness, a 69.02% and 7.80% enhance in imperfection index, and a 16.51% and 13.98% decrease in strength compared to ring-spun mélange yarn made from viscose fiber and post-consumer fiber, respectively. Subsequently, Minitab 2023 was employed to conduct a statistical analysis to determine the significance of various quality criteria. The results revealed that all criteria, with the exception of elongation, were significant. The ring-spun mélange yarn made from brush fiber is of lower quality compared to both grey mélange yarn and post-consumer mélange yarn. This is due to the significantly shorter length of the brush fibers in comparison to viscose and post-consumer waste fiber. Using dyed brush fiber ensures the utilization of waste materials, decreases dyeing expenses, minimizes water and energy consumption, has a lesser environmental impact, and is cost-effective. By incorporating 1 kg of brush fiber into the fiber mixing process, it is possible to decrease water usage by 200–400L, energy consumption by 50–64 MJ, and CO2 emissions by 2.8–6 kg. The utilization of brush fiber in the manufacturing of ring-spun mélange yarn results in a cost reduction of around 10–15 cents per kilogram, in comparison to mélange yarn produced from viscose and post-consumer waste fiber. Additionally, fabrics were manufactured to showcase the visual aspect, demonstrating the versatility of this yarn in creating various clothing such as t-shirts, underwear, and more. The findings of this study suggest that the utilization of brush fiber for the production of mélange yarn may be a feasible option.
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Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large ...variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.
The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts ...for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation.
In low to middle-income countries where home births are common and neonatal postnatal care is limited, community health worker (CHW) home visits can extend the capability of health systems to reach ...vulnerable newborns in the postnatal period. CHW assessment of newborn danger signs supported by mHealth have the potential to improve the quality of danger sign assessments and reduce CHW training requirements. We aim to estimate the validity (sensitivity, specificity, positive and negative predictive value) of CHW assessment of newborn infants aided by mHealth compared to physician assessment.
In this prospective study, ten CHWs received five days of theoretical and hands-on training on the physical assessment of newborns including ten danger signs. CHWs assessed 273 newborn infants for danger signs within 48 h of birth and then consecutively for three days. A physician repeated 20% (n = 148) of the assessments conducted by CHWs. Both CHWs and the physician evaluated newborns for ten danger signs and decided on referral. We used the physician's danger sign identification and referral decision as the gold standard to validate CHWs' identification of danger signs and referral decisions.
The referrals made by the CHWs had high sensitivity (93.3%), specificity (96.2%), and almost perfect agreement (K = 0.80) with the referrals made by the physician. CHW identification of all the danger signs except hypothermia showed moderate to high sensitivity (66.7-100%) compared to physician assessments. All the danger signs assessments except hypothermia showed moderate to high positive predictive value (PPV) (50-100%) and excellent negative predictive value (NPV) (99-100%). Specificity was high (99-100%) for all ten danger signs.
CHW's identification of neonatal danger signs aided by mHealth showed moderate to high validity in comparison to physician assessments. mHealth platforms may reduce CHW training requirements and while maintaining quality CHW physical assessment performance extending the ability of health systems to provide neonatal postnatal care in low-resource communities.
clinicaltrials.gov NCT03933423 , January 05, 2019.
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