Extraction of Lycopene from Tomato Paste and its Immobilization for Controlled Release

Lycopene is one of the 600 carotenoids found in nature and can be easily identified in tomatoes. Several epidemiological studies report that lycopene rich diets have beneficial effects on human health, showing strong correlations between the intake of carotenoids and a reduced risk of cancer, coronary and cardiovascular diseases. The purpose of this study was to develop a simple and effective method for solvent extraction of lycopene from tomato paste, and to stabilise and encapsulate the lycopene in the form of sodium-alginate beads. The Soxhlet method was used for the extraction of lycopene from commercially available tomato paste. Several solvents were tested, and ethyl acetate was found to be the best solvent for extraction, resulting in the separation of more lycopene than other solvents. Due to the presence of unsaturated bonds in its molecular structure, lycopene is susceptible to oxidation and degrades easily when exposed to light and heat. In the food processing field, microencapsulation techniques have been widely used to protect food ingredients against deterioration, volatile losses, or interaction with other ingredients and factors. Lycopene was encapsulated in 4% alginate (4g/100 mL), 1% agar-agar and chitosan. The stability of the resulting beads was tested under conditions to simulate those in the human intestine. The lycopene beads showed good survivability when exposed to the acidic conditions such as those in the gastric environment (pH 2.0–3.0). The lycopene release rate was best at higher pH levels (pH 6.6) such as in the intestine, which is where nutrient absorption occurs. The release rate of chitosan-coated alginate-lycopene was found to be much faster at body temperature (37°C) than at 24°C. The experimental results show that Ca-alginate chitosan coated lycopene beads have a potential application as pH/temperature-controlled drug release carriers in the biomedical field

Inter and Intra Class Correlation Analysis (IIcCA) for Human Action Recognition in Realistic Scenarios

This paper has been presented at : 8th International Conference of Pattern Recognition Systems (ICPRS 2017)Human action recognition in realistic scenarios is an important yet challenging task. In this paper we propose a new method, Inter and Intra class correlation analysis (IICCA), to handle inter and intra class variations observed in realistic scenarios. Our contribution includes learning a class specific visual representation that efficiently represents a particular action class and has a high discriminative power with respect to other action classes. We use statistical measures to extract visual words that are highly intra correlated and less inter correlated. We evaluated and compared our approach with state-of-the-art work using a realistic benchmark human action recognition dataset.S.A. Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, the Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Feature Similarity and Frequency-Based Weighted Visual Words Codebook Learning Scheme for Human Action Recognition

This paper has been presented at : 8th Pacific-Rim Symposium, PSIVT 2017.Human action recognition has become a popular field for computer vision researchers in the recent decade. This paper presents a human action recognition scheme based on a textual information concept inspired by document retrieval systems. Videos are represented using a commonly used local feature representation. In addition, we formulate a new weighted class specific dictionary learning scheme to reflect the importance of visual words for a particular action class. Weighted class specific dictionary learning enriches the scheme to learn a sparse representation for a particular action class. To evaluate our scheme on realistic and complex scenarios, we have tested it on UCF Sports and UCF11 benchmark datasets. This paper reports experimental results that outperform recent state-of-the-art methods for the UCF Sports and the UCF11 dataset i.e. 98.93% and 93.88% in terms of average accuracy respectively. To the best of our knowledge, this contribution is first to apply a weighted class specific dictionary learning method on realistic human action recognition datasets.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. Authors also acknowledges support from the Directorate of ASR and TD, University of Engineering and Technology Taxila, Pakistan

Active slip control of a vehicle using fuzzy control and active suspension

This paper presents an active slip control system (ASCS) for a four-wheel drive electric vehicle (EV) using an active suspension of the vehicle. The integrated control mechanism is designed using a combination of a fuzzy controller and a nonlinear back-stepping controller to control the slip of the individual wheels with the help of the active suspension of the vehicle. In this research, the presented control mechanism is implemented in two steps. In the first step, based on the friction coefficient calculated from a nonlinear tire model, the fuzzy controller will generate the vehicle roll and pitch angles required to reduce the slipping of the individual wheels by changing the vertical load of the individual wheel. In the second step, a nonlinear back-stepping controller is used to track the required roll and pitch angles using the active suspension of the vehicle. A linear seven degree of freedom (DOF) vertical mathematical model is used for the design of the nonlinear back-stepping controller, while the rules of the fuzzy controller are interpreted from the friction coefficients of the tyre model. The performance of the presented control mechanism is verified using a 14-DOF nonlinear model with nonlinear tyre dynamics. The simulations using a nonlinear vehicle model show that the presented controller can successfully improve vehicle stability by reducing the slipping of the individual wheel

Comparing the effect of Hypoalbuminemia on Sodium measured by Indirect versus Direct Ion Selective Electrode Method

Objective: To evaluate the effect of low serum Albumin levels on serum sodium measurement when analyzed by the indirect Ion Selective Electrode (ISE) method and to compare the results with the direct Ion selective electrode (ISE) method. Study Design: Cross-sectional study Place and Duration of Study: Department of Chemical Pathology, Armed Forces Institute of Pathology, Rawalpindi Pakistan, from Jan to Mar 2021. Methodology: Patients of either gender, aged 18 to 70 years, who were admitted to the Intensive Care Unit of Combined Military Hospital, Rawalpindi, were selected. A total of 200 blood samples were collected in a gel tube. Serum samples were analyzed for albumin and sodium within two hours of sample collection. Sodium levels were measured concurrently by both direct and indirect ISE methods. The difference in results between these two techniques was studied. Results: Hypoalbuminemia was detected in 176(88%) patients, while 24(12%) had normal albumin levels. In Hypoalbuminemic patients, serum sodium measurements were higher using the indirect ISE method(134.07±5.55) compared to the direct ISE method (130.95±6.04); the difference between the two techniques was statistically significant (p-value <0.001).Pearson correlation coefficient (r-value = -0.86, p-value <0.001) revealed a symmetrical increase in differences between the two methods as the albumin level decreased. Conclusion: In Hypoalbuminemic patients, the indirect ISE method gave falsely raised results of serum sodium. In such patients, serum sodium measurement by the Direct ISE method offers more accurate and consistent electrolyte results

A Bag of Expression framework for improved human action recognition

The Bag of Words (BoW) approach has been widely used for human action recognition in recent state-of-the-art methods. In this paper, we introduce what we call a Bag of Expression (BoE) framework, based on the bag of words method, for recognizing human action in simple and realistic scenarios. The proposed approach includes space time neighborhood information in addition to visual words. The main focus is to enhance the existing strengths of the BoW approach like view independence, scale invariance and occlusion handling. BOE includes independent pairs of neighbors for building expressions, therefore it is tolerant to occlusion and capable of handling view independence up to some extent in realistic scenarios. Our main contribution includes learning a class specific visual words extraction approach for establishing a relationship between these extracted visual words in both space and time dimension. Finally, we have carried out a set of experiments to optimize different parameters and compare its performance with recent state-of-the-art-methods. Our approach outperforms existing Bag of Words based approaches, when evaluated using the same performance evaluation methods. We tested our approach on four publicly available datasets for human action recognition i.e. UCF-Sports, KTH, UCF11 and UCF50 and achieve significant results i.e. 97.3%, 99.5%, 96.7% and 93.42% respectively in terms of average accuracy.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander

The impact of culture and sociological and psychological issues on Muslim patients with breast cancer in Pakistan

This is a non-final version of an article published in final form in Cancer Nursing, 32(4), 2009. The final published article is available from the link below.Breast cancer is the most common form of cancer in Muslim women in Pakistan. The impact of the initial diagnosis, culture, religion, and psychosocial and psychological aspects of the disease is not well established. This qualitative study examined the experience and coping strategies used by patients with breast cancer in relation to its impact on their physical, mental health, religious, and family issues. Thirty patients with breast cancer were interviewed. Data were analyzed using thematic analysis. The patient's experience of breast cancer focused on the range of emotions felt throughout the illness trajectory, the importance of religion and family support on coping strategies used to manage the adverse effects of chemotherapy, and also the financial concerns. This is the first study to examine Pakistani Muslim women's views on the lived experience of breast cancer. This article provides clarification of the voiced experiences of women with breast cancer. The data not only highlight the role of religion and family support as essential coping strategies but also emphasize the issues of isolation, aggression, and anger as common responses to chemotherapy. Unique features of this study are women's need to seek spiritual support for their illness and the overriding innate characteristic of maternal responsibility. These cultural features require further analysis and research

Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition

This article belongs to the Section Intelligent SensorsHuman action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N° 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander. Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR&TD, University of Engineering and Technology Taxila, Pakistan

Utilization of malted barley flour as replacement of wheat flour to improve technological, rheological, physicochemical, and organoleptic parameters of fortified breads

Introduction: Flours from cereal grains have the potential to be used in the production of bakery products, especially breads, and the addition of other non–wheat plant materials in the form of flours, extracts and malts has always been the area of interest for food producers. Methods: In this research work, barley grains were converted into barley malt flour (BMF), by adopting a series of processes, including steeping, germination, kilning, drying and milling. With the aim of compensating the role of commercial bread improvers, wheat flour was replaced at 0, 2.5, 5, 7.5, and 10% levels with BMF, to study the effect of BMF on physicochemical and sensory characteristics of bread. Results and discussion: Chemical analysis of flours revealed that ash, fat, moisture, protein and fibers were found greater in BMF and BMF–incorporated composite flours, as compared to wheat flour. Significant increases in water absorption and decrease in dough stability, dough development time and falling number were noticed, as a result of an increase in the replacement level of BMF. Water absorption of control dough was 58.03%, which increased to 58.77% in composite flour having 10% BMF, whereas dough development time, dough stability and α–amylase activity of control, were 6.97 min, 12 min, and 736 s, respectively, which were decreased to 3.83 min, 4.73 min, and 360 s, respectively in composite flour having 10% BMF. The internal and external characteristics of breads obtained the best sensorial score at 5% replacement level of BMF, and deterioration in the quality of breads was noticed, as the level of BMF was further increased to 7.5 and 10%. Hence, breads developed with 5% BMF and 95% wheat flour, were not only nutritionally rich, but were also with optimum physical and sensory features. BMF could prove a useful alternate ingredient of wheat flour, and a cost-effective replacement of commercially available bread improvers, in the breads manufacturing process in replacement of synthetic bread improvers.info:eu-repo/semantics/publishedVersio