Search results for: fruit recognition

Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Madihah Shukri

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Research examining whether eating behaviour is related to unhealthy or healthy eating pattern is required to explain the mechanisms underlying obesity, and to inform health intervention aim to prevent and treat obesity. The purpose of this study was to investigate the relationship between eating behaviours and nature of food consumption. Methods: This was a cross-sectional study of 588 adults (males = 231 and females = 357). The Dutch Eating Behaviour Questionnaire (DEBQ) was used to measure restrained, emotional and external eating. Nature of food consumption was assessed by self-reported consumption of fruit and vegetables, sweet food, junk food and snacking. Results: Results revealed that emotional eating was found to be the principal predictor of the consumption of less healthy food (sweet food, junk food and snacking), while external eating predicted sweet food intake. Intake of fruit and vegetable was associated with restrained eating. In light of the significant associations between eating behaviour and nature of food consumption, acknowledging individuals eating styles can have implications for tailoring effective nutritional programs in the context of obesity and chronic disease epidemic.

Keywords: eating behaviour, food consumption, adult, Malaysia

Procedia PDF Downloads 348

Authors: Violina R. Angelova, Sava G. Tabakov, Aleksander B. Peltekov, Krasimir I. Ivanov

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A field study was conducted to evaluate the chemical composition and quality of the Aronia fruits, as well as the possibilities of Aronia cultivation on soils contaminated with heavy metals. The experiment was performed on an agricultural field contaminated by the Non-Ferrous-Metal Works (NFMW) near Plovdiv, Bulgaria. The study included four varieties of Aronia; Aron variety, Hugin variety, Viking variety and Nero variety. The Aronia was cultivated according to the conventional technology on areas at a different distance from the source of pollution NFMW- Plovdiv (1 km, 3.5 km, and 15 km). The concentrations of macroelements, microelements, and heavy metals in Aronia fruits were determined. The dry matter content, ash, sugars, proteins, and fats were also determined. Aronia is a crop that is tolerant to heavy metals and can successfully be grown on soils contaminated with heavy metals. The increased content of heavy metals in the soil leads to less absorption of the nutrients (Ca, Mg and P) in the fruit of the Aronia. Soil pollution with heavy metals does not affect the quality of the Aronia fruit varieties.

Keywords: aronia, chemical composition, fruits, quality

Procedia PDF Downloads 181

Authors: Ajmal Khan Kassi, Humayun Javed, Syed Abdul Qadeem

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The study was conducted to find out the best management practices against American bollworm on Okra variety Arka Anamika during 2016. The three different management practices viz. Release of Trichogramma chilonis, hoeing and weeding, clipping and lufenuron insect growth regulator (IGR) which were tested individually and with all possible combinations for the controlling of American bollworm at 3 diverse areas viz. University Research Farm Koont, NARC and Farmer Field Taxila. All the treatment combinations regarding damage of fruit showed significant results. The minimum fruit infestation i.e. 3.20% and 3.58% was recorded with combined treatment (i.e. T. chilonis + hoeing + weeding + lufenuron) in two different localities. This combined treatment also resulted in maximum yield at NARC and Taxila i.e. 57.67 and 62.66 q/ha respectively. This treatment gave the best results to manage H. armigera. On the basis of different integrated pest management techniques, Arka Anamika variety proved to be comparatively resistant against H. armigera in different localities. So this variety is recommended for the cultivation in Pothwar region to get maximum yield.

Keywords: management, american bollworm, arka anamika, okra

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Authors: Amin Pashaye Amiri

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Freedom of information, according to which the public has a right to have access to government-held information, is largely considered as a tool for improving transparency and accountability in governments, and as a requirement of self-governance and good governance. So far, more than ninety countries have recognized citizens’ right to have access to public information. This recognition often took place through the adoption of an act referred to as “freedom of information act”, “access to public records act”, and so on. A freedom of information act typically imposes a positive obligation on a government to initially and regularly release certain public information, and also obliges it to provide individuals with information they request. Such an act usually allows governmental bodies to withhold information only when it falls within a limited number of exemptions enumerated in the act such as exemptions for protecting privacy of individuals and protecting national security. Some steps have been taken at the national and international level towards the recognition of freedom of information as a human right. Freedom of information was recognized in a few countries as a part of freedom of expression, and therefore, as a human right. Freedom of information was also recognized by some international bodies as a human right. The Inter-American Court of Human Rights ruled in 2006 that Article 13 of the American Convention on Human Rights, which concerns the human right to freedom of expression, protects the right of all people to request access to government information. The European Court of Human Rights has recently taken a considerable step towards recognizing freedom of information as a human right. However, in spite of the measures that have been taken, public access to government information is not yet widely accepted as an international human right. The paper will consider the degree to which freedom of information has been recognized as a human right, and study the possibility of widespread recognition of such a human right in the future. It will also examine the possible benefits of such recognition for the development of the human right to free expression.

Keywords: freedom of information, freedom of expression, human rights, government information

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Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

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Authors: Sanja Seljan, Ivan Dunđer

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The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

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Authors: Kamla Malik

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For ethanol production from paddy straw firstly pretreatment was done by using sodium hydroxide solution (2.0%) at 15 psi for 1 hr. The maximum lignin removal was achieved with 0.5 mm mesh size of paddy straw. It contained 72.4 % cellulose, 15.9% hemicelluloses and 2.0 % lignin after pretreatment. Paddy straw hydrolysate (PSH) with fruits wastes (5%), such as sweet lime, apple, sapota, grapes, kinnow, banana, papaya, mango, and watermelon were subjected to simultaneous saccharification and co-fermentation (SSCF) for 72 hrs by co-culture of Saccharomyces cerevisiae HAU-1 and Candida sp. with 0.3 % urea as a cheap nitrogen source. Fermentation was carried out at 35°C and determined ethanol yield at 24 hours interval. The maximum production of ethanol was produced within 72 hrs of fermentation in PSH + sapota peels (3.9% v/v) followed by PSH + kinnow peels (3.6%) and PSH+ papaya peels extract (3.1 %). In case of PSH+ banana peels and mango peel extract the ethanol produced were 2.8 % and 2.2 % (v/v). The results of this study suggest that wastes from fruits that contain fermentable sugar should not be discarded into our environment, but should be supplemented in paddy straw which converted to useful products like bio-ethanol that can serve as an alternative energy source.

Keywords: ethanol, fermentation, fruit wastes, paddy straw

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Rishi Richa, N. C. Shahi, J. P. Pandey, S. S. Kautkar

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Citrus fruits rank third in area and production after banana and mango in India. Sweet oranges are the second largest citrus fruits cultivated in the country. Andhra Pradesh, Maharashtra, Karnataka, Punjab, Haryana, Rajasthan, and Uttarakhand are the main sweet orange-growing states. Citrus fruits occupy a leading position in the fruit trade of Uttarakhand, is casing about 14.38% of the total area under fruits and contributing nearly 17.75 % to the total fruit production. Malta is grown in most of the hill districts of the Uttarakhand. Malta common is having high acceptability due to its attractive colour, distinctive flavour, and taste. The excellent quality fruits are generally available for only one or two months. However due to its less shelf-life, Malta can not be stored for longer time under ambient conditions and cannot be transported to distant places. Continuous loss of water adversely affects the quality of Malta during storage and transportation. Method of picking, packaging, and cold storage has detrimental effects on moisture loss. The climatic condition such as ambient temperature, relative humidity, wind condition (aeration) and microbial attack greatly influences the rate of moisture loss and quality. Therefore, different agro-climatic zone will have different moisture loss pattern. The rate of moisture loss can be taken as one of the quality parameters in combination of one or more parameter such as RH, and aeration. The moisture contents of the fruits and vegetables determine their freshness. Hence, it is important to maintain initial moisture status of fruits and vegetable for prolonged period after the harvest. Keeping all points in views, effort was made to store Malta at ambient condition. In this study, the response surface method and experimental design were applied for optimization of independent variables to enhance the shelf life of four months stored malta. Box-Benkhen design, with, 12 factorial points and 5 replicates at the centre point were used to build a model for predicting and optimizing storage process parameters. The independent parameters, viz., scavenger (3, 4 and 5g), polythene thickness (75, 100 and 125 gauge) and fungicide concentration (100, 150 and 200ppm) were selected and analyzed. 5g scavenger, 125 gauge and 200ppm solution of fungicide are the optimized value for storage which may enhance life up to 4months.

Keywords: Malta fruit, scavenger, packaging, shelf life

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Authors: Amna Jalal, Muhammad Dildar Gogi, Muhammad Jalal Arif, Anum Tariq, Waleed Afzal Naveed, Talha Farooq, Mubashir Iqbal, Muhammad Junaid Nisar

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Melon fruit flies (Diptera: Tephritidae: Dacinae) are economically important pests of the cucurbits and are geographically distributed throughout the tropics and subtropics of the world. It causes heavy quantitative and qualitative losses in bitter gourd. The present experiment was carried out to evaluate the performance of different biodegradable waxes based SPLAT-MAT-CL (Specialized Pheromone and Lure Application Technology-Male Anhelation Technique- Cue Lure) formulations in bitter gourd field. Fourteen SPLAT-MAT emulsions/formulations were prepared by admixing different SPLAT matrices with toxicant (spinosad) and sex pheromone cuelure (attractant) in different proportionate percentage by weight. The results revealed that attraction and trapping of fruit flies of B. cucurbitae varied significantly for different SPLAT-MAT-CL formulations (p < 0.05). The maximum B. cucurbitae males were trapped in SPLAT-MAT-CL-7 (60 flies/trap/day) followed by SPLAT-MAT-CL-9 (40 flies/trap/day). The performance of all other formulations of SPLAT-MAT-CL was found in the order of SPLAT-MAT-CL-8 (30 flies/trap/day) > SPLAT-MAT-CL-3 (28 flies/trap/day) > SPLAT-MAT-CL-5 (25 flies/trap/day) > SPLAT-MAT-CL-4 (22 flies/trap/day) > SPLAT-MAT-CL-12 (20 flies/trap/day) SPLAT-MAT-CL-2 (19 flies/trap/day) > SPLAT-MAT-CL-14 (17 flies/trap/day) > SPLAT-MAT-CL-13 (15 flies/trap/day) > SPLAT-MAT-CL-11 (10 flies/trap/day) > SPLAT-MAT-CL-1 (8 flies/trap/day) > SPLAT-MAT-CL-10 (02 flies/trap/day). Overall, all the SPLAT-MAT-CL formulations, except SPLAT-MAT-CL-10, demonstrated higher density of captures of B. cucurbitae males as compared to standard (06 flies/trap/day). The results also demonstrate that SPLAT-MAT-CL-7, SPLAT-MAT-CL-9, SPLAT-MAT-CL-8, SPLAT-MAT-CL-3, SPLAT-MAT-CL-5, SPLAT-MAT-CL-4, SPLAT-MAT-CL-12, SPLAT-MAT-CL-2, SPLAT-MAT-CL-14, SPLAT-MAT-CL-13, SPLAT-MAT-CL-11 and SPLAT-MAT-CL-1 explained approximately 5, 4.6, 4.1, 3.6, 3.3, 3.1,2.8,2.5 and 1.6 times higher captures of B. cucurbitae males over standards. However, SPLAT-MAT-CL-10 demonstrated 3 times fewer captures of B. cucurbitae males over standards. In conclusion, SPLAT-MAT-CL-7, SPLAT-MAT-CL-9 can be exploited for the monitoring and trapping of B. cucurbitae in its IPM of program.

Keywords: attractancy, field conditions, melon fruit fly, SPLAT-MAT-CL

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Authors: Albandary Alamer, Noura Alaskar, Sana Bukhamseen, Jawaher Alkhamis, Enas Alghamdi, Almaha Almulhim, Hina Gull, Rachid Zagrouba, Madeeha Saqib

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A good and healthy seed will always produce a nice fruit, whereas an infected seed will produce an infected fruit. The same concept applies to the children, and the healthier the environment in which the kids grow, the more likely they become valuable members of society. Kiddo project introduces us to a mobile application that focuses on enhancing the sense of responsibility from a young age and makes raising kids fun and easy. The application aims to enhance the communication between parents and their children and to enrich the good habits of the kid. Kiddo Application enables kids to share their accomplishments with their peers in an interactive environment full of enjoyment, followed by parental monitoring to handle what their kids are posting and friends following. Kiddo provides the kids' and parents’ society with a safe platform free of cyberbullying and inappropriate content with parents' fun engagement.

Keywords: kids social media, educational app, child-raising, parental control, cyberbullying, parent-child relationship, good habits

Procedia PDF Downloads 129

Authors: Tuiana A. Aiusheeva, Sergey S. Tamozhnikov, Alexander E. Saprygin, Arina A. Antonenko, Valentina V. Stepanova, Natalia N. Tolstykh, Alexander N. Savostyanov

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Situation of choice is an important condition for the formation of essential character qualities of a child, such as being initiative, responsible, hard-working. We have studied the behavioral and EEG reactions in Russian schoolchildren during recognition of syntactic errors in emotionally colored sentences that describe the choice situation. Twenty healthy children (mean age 9,0±0,3 years, 12 boys, 8 girls) were examined. Forty sentences were selected for the experiment; the half of them contained a syntactic error. The experiment additionally had the hidden condition: 50% of the sentences described the children's own choice and were emotionally colored (positive or negative). The other 50% of the sentences described the forced-choice situation, also with positive or negative coloring. EEG were recorded during execution of error-recognition task. Reaction time and quality of syntactic error detection were chosen as behavioral measures. Event-related spectral perturbation (ERSP) was applied to characterize the oscillatory brain activity of children. There were two time-frequency intervals in EEG reactions: (1) 500-800 ms in the 3-7 Hz frequency range (theta synchronization) and (2) 500-1000 ms in the 8-12 Hz range (alpha desynchronization). We found out that behavioral and brain reactions in child brain during recognition of positive and negative sentences describing forced-choice situation did not have significant differences. Theta synchronization and alpha desynchronization were stronger during recognition of sentences with children's own choice, especially with negative coloring. Also, the quality and execution time of the task were higher for this types of sentences. The results of our study will be useful for improvement of teaching methods and diagnostics of children affective disorders.

Keywords: choice situation, electroencephalogram (EEG), emotionally colored sentences, schoolchildren

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Ria Ghosh, Soumendra Singh, Dipanjan Mukherjee, Susmita Mondal, Monojit Das, Uttam Pal, Aniruddha Adhikari, Aman Bhushan, Surajit Bose, Siddharth Sankar Bhattacharyya, Debasish Pal, Tanusri Saha-Dasgupta, Maitree Bhattacharyya, Debasis Bhattacharyya, Asim Kumar Mallick, Ranjan Das, Samir Kumar Pal

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Drug delivery to a target without adverse effects is one of the major criteria for clinical use. Herein, we have made an attempt to explore the delivery efficacy of SDS surfactant in a monomer and micellar stage during the delivery of the model drug, Toluidine Blue (TB) from the micellar cavity to DNA. Molecular recognition of pre-micellar SDS encapsulated TB with DNA occurs at a rate constant of k1 ~652 s 1. However, no significant release of encapsulated TB at micellar concentration was observed within the experimental time frame. This originated from the higher binding affinity of TB towards the nano-cavity of SDS at micellar concentration which does not allow the delivery of TB from the nano-cavity of SDS micelles to DNA. Thus, molecular recognition controls the extent of DNA recognition by TB which in turn modulates the rate of delivery of TB from SDS in a concentration-dependent manner.

Keywords: DNA, drug delivery, micelle, pre-micelle, SDS, toluidine blue

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Authors: Hong Li, Tingxian Li, Xudong Wang, Fengliang Zhao

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Unreliable flowering of chilling-required longan (Dimocarpus longan) due to increased air-temperatures have been the common concerns in the tropical areas. Our objectives were to assess the efficiency of chemicals in longan tree flowering and bearing using Growing Degree Days (GDD). The 2-year study was contacted in the tropical Haihan Island during 2012-2013. At pruning (August) the GDD values were started to count. The KClO3 treatments were applied to the root zones under the canopies at GDD 1300ºC while KH2PO4 rates were applied to the leaves at fruit setting at GDD 3000ºC and GDD 4000ºC. The results showed that total cumulative GDD was 6050ºC for longan. The GDD-guided KClO3 applications induced significant tree budding and flowering. The GDD-guided KH2PO4 applications stimulated higher leaf photosynthesis, carbonxylation efficiency, marketable fruit yield and quality (K+ and sugar) (P<0.05). It was concluded that the GDD-based model could efficiently support longan reliable flowering and bearing.

Keywords: canopy nutrition, flowering induction, growing degree days, longan, oxidant KClO3, tree physiology

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Authors: Yan Li, Qingxiang Meng, Bo Zhou, Zhenming Zhou

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The objective of this study was to compare the effects of ensiled mulberry leaves (EML), and sun-dried mulberry fruit pomace (SMFP) on fecal bacterial communities in Simmental crossbred finishing steers fed the following 3 diets: a standard TMR diet, standard diet containing EML and standard diet containing SMFP, and the diets had similar protein and energy levels. Bacterial communities in the fecal content were analyzed using Illumina Miseq sequencing of the V4 region of the 16S rRNA gene amplification. Quantitative real-time PCR was used to detect the selected bacterial species in the feces. Most of the sequences were assigned to phyla Firmicutes (56.67%) and Bacteroidetes(35.90%), followed by Proteobacteria(1.86%), Verrucomicrobia(1.80%) and Tenericutes(1.37%). And the predominant genera included the 5-7N15 (5.91%), CF231 (2.49%), Oscillospira (2.33%), Paludibacter (1.23%) and Akkermansia(1.11%). As for the treatments, no significant differences were observed in Firmicutes (p = 0.28), Bacteroidetes (p = 0.63), Proteobacteria (p = 0.46), Verrucomicrobia (p = 0.17) and Tenericutes (p = 0.75). On the genus level, classified genera with high abundance (more than 0.1%) mainly came from two phyla: Bacteroidetes and Firmicutes. Also no differences were observed in most genera level, 5-7N15 (p = 0.21), CF231 (p = 0.62), Oscillospira (p = 0.9), Paludibacter (p = 0.33) and Akkermansia (p = 0.37), except that rc4-4 were lower in the CON and SMFP groups compared to the EML animals (p = 0.02). Additionally, there were no differences in richness estimate and diversity indices (p > 0.16), and treatments had no significant effect on most selected bacterial species in the fecal (p > 0.06), except that Ruminococcus albus were higher in the EML group (p < 0.01) and Streptococcus bovis were lower in the CON group (p < 0.01). In conclusion, diets supplemented with EML and SMFP have little influence on fecal bacterial community composition in finishing steers.

Keywords: fecal bacteria community composition, sequencing, ensiled mulberry leaves (EML), sun-dried mulberry fruit pomace (SMFP)

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: A. M. Attalla, A. F. lbrahim, Laila Y. Mostaffa

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This study was conducted during three seasons 2010, 2011 and 2012 on Zahhloul date palm cultivar grown in calcareous soil, Alexandria governorate, Egypt. The palms received recommended dose of mineral N only or plus different rates of organic N with or without bio fertilizer to study the effect of such treatments on date palm yield and fruit nitrate and nitrite content due to its negative influence on human, animal and environment. The obtained results clarified that all used treatments of organic and bio fertilizers were effective in improving date palm yield and decreased fruit content of NO2 and NO3 in comparison with 100 % mineral N. It was also noticed that combined treatments of 50 % mineral N + 50 % organic manure with bio fertilizer is the superior treatments for increasing the values of yield and decreasing its content of NO2 and NO3. Hence, it could be concluded that, minimizing the use of chemical nitrogen fertilizer to half of recommended dose through addition of 50 % mineral N + 50 % organic manure with bio fertilizer and also, the utilization of organic and bio fertilizers is considered as a promising alternative for chemical fertilizers to avoid pollution and reduce the costs of mineral fertilizers.

Keywords: organic and bio fertilizers, mineral fertilizer, nitrate, nitrite, zaghloul date palm cv

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Authors: Amandeep Kaur, Sangeeta Sharma

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Keeping in view the serious entanglement of heavy metals ( Pb+2 and Ni+2) ions in an aqueous environment, a rapid search for efficient adsorbents for the adsorption of heavy metals has become highly desirable. In this quest, green synthesized Fe np’s have gathered attention because of their excellent adsorption capability of heavy metals from aqueous solution. This research report aims at the fabrication of Fe np’s using the fruit Cucumis melo and leaves of Ficus virens via a biogenic synthesis route. Further, synthesized CM-Fe-np’s and FV-Fe-np’s have been tested as potential bio-adsorbents for the removal of Pb+2 and Ni+2 by carrying out adsorption batch experiments. The influence of myriad parameters like initial concentration of Pb/Ni (5,10,15,20,25 mg/L), contact time (10 to 200 min.), adsorbent dosage (0.5, 0.10, 0.15 mg/L), shaking speed (120 to 350 rpm) and pH value (6,7,8,9) has been investigated. The maximum removal with CM-Fe-np’s and FV-Fe-np’s has been achieved at pH 7, metal conc. 5 mg/L, dosage 0.9 g/L, shaking speed 200 rpm and reaction contact time 200 min during the adsorption experiment. The results obtained are found to be in accordance with Freundlich and Langmuir's adsorption models; consequently, they could be highly applicable to the wastewater treatment plant.

Keywords: adsorption, biogenic synthesis, nanoparticles, nickel, lead

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Authors: Arinze Ezieke, Antonio Serrano, William Clarke, Denys Villa-Gomez

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Berry cultivation results in discharge of high organic strength putrescible solid waste which potentially contributes to environmental degradation, making it imperative to assess options for its complete management. Anaerobic digestion (AD) could be an ideal option when the target is energy generation; however, due to berry fruit characteristics high carbohydrate composition, the technology could be limited by its high alkalinity requirement which suggests dosing of additives such as buffers and trace elements supplement. Overcoming this limitation in an economically viable way could entail replacement of synthetic additives with recycled by-product waste. Consequently, ash from co-smouldering of high COD characteristic AD digestate and coco-coir could be a promising material to be used to enhance the AD of berry fruit waste, given its characteristic high pH, alkalinity and metal concentrations which is typical of synthetic additives. Therefore, the aim of the research was to evaluate the stability and process performance from the AD of BFW when ash from co-smoldered digestate and coir are supplemented as alkalinity and trace elements (TEs) source. Series of batch experiments were performed to ascertain the necessity for alkalinity addition and to see whether the alkalinity and metals in the co-smouldered digestate ash can provide the necessary buffer and TEs for AD of berry fruit waste. Triplicate assays were performed in batch systems following I/S of 2 (in VS), using serum bottles (160 mL) sealed and placed in a heated room (35±0.5 °C), after creating anaerobic conditions. Control experiment contained inoculum and substrates only, and inoculum, substrate and NaHCO3 for optimal total alkalinity concentration and TEs assays, respectively. Total alkalinity concentration refers to alkalinity of inoculum and the additives. The alkalinity and TE potential of the ash were evaluated by supplementing ash (22.574 g/kg) of equivalent total alkalinity concentration to that of the pre-determined optimal from NaHCO3, and by dosing ash (0.012 – 7.574 g/kg) of varying concentrations of specific essential TEs (Co, Fe, Ni, Se), respectively. The result showed a stable process at all examined conditions. Supplementation of 745 mg/L CaCO3 NaHCO3 resulted to an optimum TAC of 2000 mg/L CaCO3. Equivalent ash supplementation of 22.574 g/kg allowed the achievement of this pre-determined optimum total alkalinity concentration, resulting to a stable process with a 92% increase in the methane production rate (323 versus 168 mL CH4/ (gVS.d)), but a 36% reduction in the cumulative methane production (103 versus 161 mL CH4/gVS). Addition of ashes at incremental dosage as TEs source resulted to a reduction in the Cumulative methane production, with the highest dosage of 7.574 g/kg having the highest effect of -23.5%; however, the seemingly immediate bioavailability of TE at this high dosage allowed for a +15% increase in the methane production rate. With an increased methane production rate, the results demonstrated that the ash at high dosages could be an effective supplementary material for either a buffered or none buffered berry fruit waste AD system.

Keywords: anaerobic digestion, alkalinity, co-smoldered digestate ash, trace elements

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Authors: E. K. Mendez, N. A. Salazar, C. E. Orrego

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There is undoubted proof that increasing the intake of fruit lessens the risk of hypertension, coronary heart disease, stroke, and probable evidence that lowers the risk of cancer. Proper fruit drying is an excellent alternative to make their shelf-life longer, commercialization easier, and ready-to-eat healthy products or ingredients. The conventional way of drying is by hot air forced convection. However, this process step often requires a very long residence time; furthermore, it is highly energy consuming and detrimental to the product quality. Nowadays, power ultrasound (US) technic has been considered as an emerging and promising technology for industrial food processing. Most of published works dealing with drying food assisted by US have studied the effect of ultrasonic pre-treatment prior to air-drying on food and the airborne US conditions during dehydration. In this work a new approach was tested taking in to account drying time and two quality parameters of mango slices dehydrated by convection assisted by 20 KHz power US applied directly using a holed plate as product support and sound transmitting surface. During the drying of mango (Mangifera indica L.) slices (ca. 6.5 g, 0.006 m height and 0.040 m diameter), their weight was recorded every hour until final moisture content (10.0±1.0 % wet basis) was reached. After previous tests, optimization of three drying parameters - frequencies (2, 5 and 8 minutes each half-hour), air temperature (50-55-60⁰C) and power (45-70-95W)- was attempted by using a Box–Behnken design under the response surface methodology for the optimal drying time, color parameters and rehydration rate of dried samples. Assays involved 17 experiments, including a quintuplicate of the central point. Dried samples with and without US application were packed in individual high barrier plastic bags under vacuum, and then stored in the dark at 8⁰C until their analysis. All drying assays and sample analysis were performed in triplicate. US drying experimental data were fitted with nine models, among which the Verna model resulted in the best fit with R2 > 0.9999 and reduced χ2 ≤ 0.000001. Significant reductions in drying time were observed for the assays that used lower frequency and high US power. At 55⁰C, 95 watts and 2 min/30 min of sonication, 10% moisture content was reached in 211 min, as compared with 320 min for the same test without the use of US (blank). Rehydration rates (RR), defined as the ratio of rehydrated sample weight to that of dry sample and measured, was also larger than those of blanks and, in general, the higher the US power, the greater the RR. The direct contact and intermittent US treatment of mango slices used in this work improve drying rates and dried fruit rehydration ability. This technique can thus be used to reduce energy processing costs and the greenhouse gas emissions of fruit dehydration.

Keywords: ultrasonic assisted drying, fruit drying, mango slices, contact ultrasonic drying

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Authors: Megan Weisbart

Abstract:

Background/Significance: The Intensive Care Unit (ICU) environment contributes to nurse burnout. Burnout costs include decreased employee compassion, missed workdays, worse patient outcomes, diminished job performance, high turnover, and higher organizational cost. Meaningful recognition, nurturing of interpersonal connections, and mindfulness-based interventions are associated with decreased burnout. The purpose of this quality improvement project was to decrease Neonatal ICU (NICU) nurse burnout using a Wellness Bundle that fosters meaningful recognition, interpersonal connections and includes mindfulness-based interventions. Methods: The Professional Quality of Life Scale Version 5 (ProQOL5) was used to measure burnout before Wellness Bundle implementation, after six months, and will be given yearly for three years. Meaningful recognition bundle items include Online submission and posting of staff shoutouts, recognition events, Nurses Week and Unit Practice Council member gifts, and an employee recognition program. Fostering of interpersonal connections bundle items include: Monthly staff games with prizes, social events, raffle fundraisers, unit blog, unit wellness basket, and a wellness resource sheet. Quick coherence techniques were implemented at staff meetings and huddles as a mindfulness-based intervention. Findings: The mean baseline burnout score of 14 NICU nurses was 20.71 (low burnout). The baseline range was 13-28, with 11 nurses experiencing low burnout, three nurses experiencing moderate burnout, and zero nurses experiencing high burnout. After six months of the Wellness Bundle Implementation, the mean burnout score of 39 NICU nurses was 22.28 (low burnout). The range was 14-31, with 22 nurses experiencing low burnout, 17 nurses experiencing moderate burnout, and zero nurses experiencing high burnout. Conclusion: A NICU Wellness Bundle that incorporated meaningful recognition, fostering of interpersonal connections, and mindfulness-based activities was implemented to improve work environments and decrease nurse burnout. Participation bias and low baseline response rate may have affected the reliability of the data and necessitate another comparative measure of burnout in one year.

Keywords: burnout, NICU, nurse, wellness

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Authors: Mohammed Auwal Ibrahim, Isa Yunusa, Nafisa Kabir, Shazali Ali Baba, Amina Muhammad Yushau, Suraj Suraj Ibrahim, Zaharaddeen Idris Bello, Suleiman Haruna Suleiman, Murtala Bindawa Isah

Abstract:

Background: Inhibition of intestinal maltase and sucrase prevents postprandial blood glucose excursions which are beneficial in ameliorating diabetes-associated complications. Objective: In this study, the inhibitory effects of fruit extracts of Parinari macrophylla, Detarium microcarpum, Ziziphus spina-christi, Z. mairei and Parkia biglobosa were investigated against intestinal maltase and sucrase. Methods: Rats were given co-administration of the fruit extracts with maltose or sucrose and blood glucose levels were measured at 0, 30, 90 and 120 min. Results: The glucose-time curves indicated that all the fruits had the most potent inhibitory effects on both maltase and sucrase within the first 30 min. The computed Area Under the Curves (AUC0-120)for all the fruits indicated more potent inhibitory effects against intestinal maltase than sucrase.The ED50 range for the fruits extract against maltase and sucrase were 647.15-1118.35 and 942.44-1851.94 mg/kg bw respectively. Conclusion: The data suggests that the fruits could prevent postprandial hyperglycemia via inhibition of intestinal maltase and sucrase.

Keywords: diabetes mellitus, fruits, α-glucosidases, maltase, sucrase

Procedia PDF Downloads 359