Status and sustainability challenges of agricultural water usage in Bangladesh

Maintaining sustainability in agricultural water usage is a critical concern particularly when the burgeoning population demands more food while adverse climate change impacts water availability. Despite this, the climate-water-crop nexus is still poorly understood in many regions throughout the globe. This study was conducted to quantify current agricultural water use in Bangladesh, one of the most climate-vulnerable countries, and to assess its sustainability challenges. The number of crops, cropping area, yield, water use, long-term daily rainfall, daily river stage and weekly groundwater level data were collected and statistically analyzed. This study revealed that the two most drought-prone northwest divisions export virtual water embedded in agricultural produce at 14086 Mm3/yr, whereas two urbanized divisions import 18477 m3/yr, to or from the national water-use budget. Only rice production consumed ~88% of the total water used in agriculture, and the dry season rice had higher water demand than the wet season rice. The water use sustainability in the two most water-exporting divisions is at great stake because total rainfall in July is decreasing significantly (2.90 mm/yr) in one division and the number of rainless days in August is significantly increasing (0.033 day/yr) in other division. Irrigated rice production will also face water scarcity because the dry season water level in both rivers (63%) and observation wells (92%) shows a declining trend. The ratio of green (rainfed) to blue (irrigation) water use in the country was estimated at 2.5, which needs to be increased

Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network

In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles fromlow-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasingresolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x usingMsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss toencourage the target detector to be sensitive to the subsequent super-resolution training. The network jointlylearns hierarchical and discriminative features of targets and produces optimal super-resolution results. Weperform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTAdatasets. The experimental results show that our proposed framework achieves better visual quality than thestate-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy ofaerial vehicle detection

In-vitro Relationship between Protein-binding and Free Drug Concentrations of a Water-soluble Selective Beta-adrenoreceptor Antagonist (Atenolol) and Its Interaction with Arsenic

The degree of binding of a drug to plasma proteins has a marked effect on its distribution, elimination, and pharmacological effect since only the unbound fraction is available for distribution into extra-vascular space. The protein-binding of atenolol was measured by equilibrium dialysis in the bovine serum albumin (BSA). Free atenolol concentration was increased due to addition of arsenic which reduced the binding of the compounds to BSA. During concurrent administration, arsenic displaced atenolol from its high-affinity binding Site I, and free concentration of atenolol increased from 4.286±0.629% and 5.953±0.605% to 82.153±1.924% and 85.486±1.158% in absence and presence of Site I probe respectively. Thus, it can be suggested that arsenic displaced atenolol from its binding site resulting in an increase of the free atenolol concentration in plasma

Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems

Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NOx). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment