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

Development of hybrid coconut shell-peek adsorbent for methane adsorption: optimization using response surface methodology

Adsorbed natural gas (ANG) provides efficient and clean combustion, with minimal emissions compared to diesel and gasoline. This article was designed to develop techniques of ANG for transportation application by apply RSM and CCD to identify the optimum preparation conditions for preparation of stable adsorbent for methane adsorption. Coconut shell and poly ether ketone (PEEK) was selected for synthesis of activated carbon (AC). The effectiveness of the parameters was determined using response surface method (RSM) couple with central composite design (CCD). The analysis of variance (ANOVA) was applied to identify the significant parameters. The quadratic model was adopted, as it has the highest F-value of 21.62 and P-value of less than 0.05, which relate the parameters and response. Microwave power has the highest F-value of 62.36. The maximum methane uptake of 5.12mmol g-1 was achieved. Overall, the hybrid coconut-PEEK adsorbent was found to be suitable for CH4 adsorption