“Nanodentistry”- The Next Big Thing Is Small

Nanotechnology has revolutionized the field of dentistry with tremendous potential to provide the comprehensive oral health care using the nanomaterials, advanced clinical tools and devices. The new era of dentistry will encompass precisely regulated analgesia, tooth renaturalization, complete cure for hypersensitivity and rapid orthodontic treatment. Many novel nanotechnology products are on the way and new treatment modalities are also proposed. Nanotechnology has increased the hope for better oral health care delivery and improved maintenance through the ongoing research in diagnosis, cure and prevention of oral diseases. This review article provides an insight about the importance and possible applications of nanotechnology in the field of dentistry

The use of wind pumps for greenhouse microirrigation: a case study for tomato in Cuba.

Crop irrigation is a major consumer of energy. Only a few countries are self-sufficient in conventional non-renewable energy sources. Fortunately, there are renewable ones, such as wind, which has experienced recent developments in the area of power generation. Wind pumps can play a vital role in irrigation projects in remote farms. A methodology based on daily estimation balance between water needs and water availability was used to evaluate the feasibility of the most economic windmill irrigation system. For this purpose, several factors were included: three-hourly wind velocity (W3 h, m/s), flow supplied by the wind pump as a function of the elevation height (H, m) and daily greenhouse evapotranspiration as a function of crop planting date. Monthly volumes of water required for irrigation (Dr, m3/ha) and in the water tank (Vd, m3), as well as the monthly irrigable area (Ar, ha), were estimated by cumulative deficit water budgeting taking in account these factors. An example is given illustrating the use of this methodology on tomato crop (Lycopersicon esculentum Mill.) under greenhouse at Ciego de Ávila, Cuba. In this case two different W3 h series (average and low wind year), three different H values and five tomato crop planting dates were considered. The results show that the optimum period of wind-pump driven irrigation is with crop plating in November, recommending a 5 m3 volume tank for cultivated areas around 0.2 ha when using wind pumps operating at 15 m of height elevation

Helio-aero-gravity effect

Preliminary results are presented to establish the feasibility of solar-electric energy conversion through the helio-aero-gravity effect. A micro-scale model was constructed for the study. The outputs of the model at different insolation levels were compared with theoretical predictions. Although the technology was found promising, further research and development are required to optimise plant parameters to increase the overall conversion efficiency.

Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications

Recognition of Sign Language Based on Hand Gestures

The target of SLR or sign language recognition is to interpret the sign language into text, respectively. So the deaf and mute people can communicate with ordinary people easily. Sign language recognition has a tremendous social impact; however, it is challenging due to the significant variations and complexity in the hand actions. There are many existing methods for recognizing sign language that uses handcraft features for describing the motion of sign language and then, based on the features it makes the classification models. To approach the problem, we have discussed considering the KNN that can conveniently extract the features. The proposed model can be validated on a real data set