Effect of sowing dates and varieties on soybean performance in Vidarbha region of Maharashtra, India

oybean production is widely fluctuating in response to agro-environmental conditions year to year in Vidarbha region. Weather variations are the major determinants of soybean growth and yield. It is also important to study the response of suitable soybean varieties to varying weather parameters. So a field investigation was carried out to study the crop weather relationship of soybean and to optimize the sowing date with different soybean varie-ties. The results revealed that soybean crop sown up to 27th MW accumulated higher growing degree days (1640.5 0C day), photothermal units (20498.1 0C day hour) and recorded significantly higher seed yield (839 kg ha-1) and biological yield (2773 kg ha-1) with maximum heat use efficiency (0.51 kg ha-1°C day-1) and water productivity (2.49 kg ha-mm-1). Later sowings i.e. 30th MW sowing caused decreased amount of rainfall and increased maximum temperature regime across the total growing period with consequently lower seed yield (530 kg ha-1), GDD (1539.2 0C day), PTU (18689.9 0C day hour), heat use efficiency (0.34kg ha-1 °Cday-1) and water productivity (2.05kg ha-mm-1). Soybean variety TAMS 98-21 recorded significantly higher seed yield (734 kg ha-1) and highest biological yield (2649 kg ha-1) with maximum heat use efficiency (0.44 kg ha-1 °C day-1), GDD (1650.5 0C day ) and water productivity (2.41 kg ha-mm-1). Thus, the results of this study illustrated the importance of early sowing with suitable variety of soybean and indicates that sowing upto 27th MW with variety TAMS 98-21 is optimum for maximizing the yield in the Akola region of Vidarbha

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Tidal modelling in the Gulf of Khambhat based on a numerical and analytical approach

1253-1259In this study, a two-dimensional numerical model of the Gulf of Khambat has been implemented based on the Delft3D code to study the tidal propagation in the basin. The model has been calibrated and validated versus tidal measurements. Moreover, an analytical model for tidal propagation in converging estuary has been implemented based on the analytical work of Van Rijn (2011a). The two models show a good agreement, also with respect to observations. Moreover, the use of an analytical model with very low computational time, in support to a more sophisticated but computational time consuming numerical model, allows for an easy understanding of the different geometrical parameters (e.g. convergence length, water depth) and physical processes (e.g. damping due to bottom friction, tidal shoaling due to funnelling shape, tidal reflection) affecting the system and for an interpretation of the results of the more complex Delft3D numerical model. The effects of external changes on the physical system (e.g. impact of sea level rise) can also be easily accessed by means of a simple analytical model.</span

A Review of Alzheimers Diseases

Alzheimer’s disease, recognized just like an accelerated multifaceted neurological condition, is the primary reason for Alzheimer’s disorder in early adulthood. Over the last 20 years, progress in the pathogenesis field has influenced the study’s authors for the investigative process of novel medicinal therapeutics focused on the pathogenetic occurrences of such disorders. Such substances slow down the progression of the disease and focus on providing indicative relief even though they fail to produce a significant indication remedy. Whereas the neurodegenerative characteristics of Alzheimer’s disorder have been identified, the intricate details of such a process have still not been firmly established. The above lack of comprehension considering the pathogenic method could be the probable explanation for such non-availability after all successful treatments, which can protect against incidence and development after all disorders. Due mainly to the growth opportunity in the ground of pathogenic mechanisms in the last several years, new therapy goals can be found should deliver the underlying medical condition of being addressed directly. Throughout this summary, publishers may analyze the different elements of pathogenetic methods underneath Alzheimer’s disorder and its control via conventional drug treatment and advanced investigational treatment approaches, recently concluded and continuing

Effect of sowing dates and varieties on soybean performance in Vidarbha region of Maharashtra, India

oybean production is widely fluctuating in response to agro-environmental conditions year to year in Vidarbha region. Weather variations are the major determinants of soybean growth and yield. It is also important to study the response of suitable soybean varieties to varying weather parameters. So a field investigation was carried out to study the crop weather relationship of soybean and to optimize the sowing date with different soybean varie-ties. The results revealed that soybean crop sown up to 27th MW accumulated higher growing degree days (1640.5 0C day), photothermal units (20498.1 0C day hour) and recorded significantly higher seed yield (839 kg ha-1) and biological yield (2773 kg ha-1) with maximum heat use efficiency (0.51 kg ha-1°C day-1) and water productivity (2.49 kg ha-mm-1). Later sowings i.e. 30th MW sowing caused decreased amount of rainfall and increased maximum temperature regime across the total growing period with consequently lower seed yield (530 kg ha-1), GDD (1539.2 0C day), PTU (18689.9 0C day hour), heat use efficiency (0.34kg ha-1 °Cday-1) and water productivity (2.05kg ha-mm-1). Soybean variety TAMS 98-21 recorded significantly higher seed yield (734 kg ha-1) and highest biological yield (2649 kg ha-1) with maximum heat use efficiency (0.44 kg ha-1 °C day-1), GDD (1650.5 0C day ) and water productivity (2.41 kg ha-mm-1). Thus, the results of this study illustrated the importance of early sowing with suitable variety of soybean and indicates that sowing upto 27th MW with variety TAMS 98-21 is optimum for maximizing the yield in the Akola region of Vidarbha

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms