Advances in biotechnology continue to introduce new materials for reconstruction of orbital floor fractures. Which material is best fit for orbital floor reconstruction has been a controversial topic. Individual surgeon preferences have been supported by inconsistent inconclusive data. The purpose of this study was to assess and analyze published evidence supporting various materials used for orbital floor reconstruction and to develop a decision-making algorithm for clinical application. A systematic literature review was performed from which 48 studies were selected after primary and secondary screening based on set inclusion and exclusion criteria. This cumulatively included 3475 separate orbital floor reconstructions. Results revealed risk and benefit profiles for all materials. Autologous calvarial bone grafts, porous polyethylene, and polydioxanone (PDS) were most widely used for orbital floor reconstruction. Increased infection rates were reported with polyglactin 910/PDS composites and silastic rubber. Ocular motility was reduced most with lyophilized dura and PDS. Preoperative and postoperative rates for diplopia and enophthalmos varied among the materials. In conclusion, our results revealed continued inadequate evidence to exclusively support the use of any one biomaterial/implant for orbital floor reconstruction. Results have served to create a decision-making algorithm for clinical application. Our authors propose certain parameters for future studies seeking to demonstrate a comparison between 2 or more materials for orbital floor reconstruction.

Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI.

Favorable refractive outcomes were achieved in the majority of patients despite the potential alteration of preoperative measurements and introduction of error into lens selection when using a combined approach. There does not seem to be a difference in the refractive outcome with regard to the type of glaucoma surgery performed. Control patients who had cataract surgery alone had a higher percentage of achieving target refractive goal and less induced cylinder.

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Purpose To evaluate the frequency of ocular surface symptoms and their potential impact on dry eye specific quality of life (QoL) in patients using versus not using glaucoma medications. Material and methods The study was a single-center, cross-sectional survey of patients seen at the Miami Veterans Affairs (VA) ophthalmology and optometry clinics from June to August, 2010. Patients were invited to complete the Dry Eye Questionnaire 5 (DEQ5) and the Impact of Dry Eye on Everyday Life (IDEEL) at their visit. Of 1348 patients seen in the Miami VA eye clinics during this three-month period, 467 patients completed the DEQ5 and 391 responded to both questionnaires. Outcome measures comprised ocular surface symptoms and their impact on dry eye specific QoL in patients using versus not using glaucoma drops. Results An increasing number of glaucoma drops was significantly associated with an increased percentage of severe dry eye symptoms: no medications, 25% (n = 89/353); 1 or 2 medications, 27% (n = 17/62); 3 or more medications, 40% (n = 21/52); p = 0.03 (Armitage’s test for linear-trend in proportions). There was an association between increasing number of drops and decreasing emotional well-being scores (linear p < 0.001; quadratic p = 0.029). Black patients had higher dry eye symptoms and lower emotional QoL scores compared to white patients at every level of medication use. Conclusion An increasing number of glaucoma medications were associated with an increased frequency of severe dry eye symptoms and decreased emotional QoL. Additionally, dry eye specific emotional QoL was more severely affected in black versus white patients.

Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

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