Biodiesel production from Cannabis sativa oil from Pakistan

The present study was appraised using response surface methodology for process optimization owing to strong interaction of reaction variables: NaOCH3 catalyst concentration (0.25–1.50%), methanol/oil molar ratio (3:1–9:1), reaction time (30–90 min), and reaction temperature (45–65°C). The quadratic polynomial equation was determined using response surface methodology for predicting optimum methyl esters yield from Cannabis sativa oil. The analysis of variance results indicated that molar ratio and reaction temperature were the key factors that appreciably influence the yield of Cannabis sativa oil methyl esters. The significant (p < 0.0001) variable interaction between molar ratio × catalyst concentration and reaction time × molar ratio was observed, which mostly affect the Cannabis sativa oil methyl esters yield. The optimum Cannabis sativa oil methyl esters yield, i.e., 86.01% was gained at 53°C reaction temperature, 7.5:1 methanol/oil molar ratio, 65 min reaction time, and 0.80% catalyst concentration. The results depicted a linear relationship between observed and predicted values. The residual analysis predicted the appropriateness of the central composite design. The Cannabis sativa oil methyl esters, analyzed by gas chromatography, elucidated six fatty acid methyl esters (linoleic, α-linolenic, oleic, palmitic, stearic, and γ-linolenic acids). In addition, the fuel properties, such as kinematic viscosity at 40°C; cetane number; acid value; flash point; cloud, pour, and cold filter plugging points; ash content; density; and sulphur content, of Cannabis sativa oil methyl esters were evaluated and discussed with reference to ASTM D 6751 and EU 14214 biodiesel specifications

Optimization of callus induction and regeneration system for Pakistani wheat cultivars Kohsar and Khyber-87

Wheat is a member of family Poaceae. It is the major staple food of Pakistan. The present study was done to improve the regeneration of two commercially grown wheat varieties Kohsar and Khyber-87.Mature embryos were used as explants. Five different concentrations of 2,4-D; 2, 2.5, 3, 3.5 and 4 mg/L were used for callus induction. For regeneration, initially different concentrations (0.1 to 0.2) of IAA(indole-3-acetic acid) and BAP (6-benzylaminopurine) were experimented. The best combination of these hormones that is, 0.1 mg/L IAA and 0.5 mg/L BAP were further subjected to experimentation along with different concentrations of kinetin; 0.1, 0.2, 0.3, 0.4, 0.5 and 1 mg/L. Maximum calli of Kohsar (83.3%) was obtained at 3 mg/L 2,4-D whereas for Khyber-87 maximum callus induction (71.70%) was obtained at 3.5 mg/l 2,4-D. The maximum regeneration of both Kohsar and Khyber-87 (80.5 and 62.2%, respectively) were obtained at the combinations of 0.1 mg/L IAA, 0.5 mg/L BAP and 0.5 mg/L kinetin

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

An Ultra-Wideband Antenna with Triple Band-Notched Characteristics for Wearable Application

This work presents a compact UWB antenna with triple band-notched at WiMAX (3.2 GHz to 3.7 GHz), C-band (3.7 GHz to 4.2 GHz) and WLAN (5.15 GHz to 5.35 GHz) for wearable applications. The UWB antenna is fabricated on a semi-flexible thin FR-4 substrate. In order to reduce the complexity, only two slots are introduced on the radiating patch instead of three slots to reject each narrowband frequency. In this case, one slot rejects a combination of WiMAX and C-band and the other slot rejects the WLAN frequency band. The UWB antenna on the thin FR-4 material has an overall size of 21×16 mm2, which is very compact and thus, suitable for wearable applications without causing discomfort when worn on-body. Although the antenna is small in size, the performance is not compromised. The UWB antenna has the frequency range from 2.51 GHz to 12.09 GHz, maximum radiation efficiency of 100% and maximum gain of 4 dBi. Nevertheless, the antenna is able to reject the WiMAX and C-band as well as the WLAN band. The simulated Specific Absorption Rate (SAR) results show that the antenna complies with the SAR limit Federal Communication Commission (FCC) and International Commission of Non-Ionizing Radiation Protections (ICNIRP) standards for 1 mW input power. Bending investigations performed on different diameters of Styrofoam cylinders shows that the frequency range and the notch bands are not very much affected