Search results for: high conservation value forest
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 21586

Search results for: high conservation value forest

20776 Evaluation of Random Forest and Support Vector Machine Classification Performance for the Prediction of Early Multiple Sclerosis from Resting State FMRI Connectivity Data

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

Abstract:

The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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20775 Examination of Public Hospital Unions Technical Efficiencies Using Data Envelopment Analysis and Machine Learning Techniques

Authors: Songul Cinaroglu

Abstract:

Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

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20774 Spatial Structure of First-Order Voronoi for the Future of Roundabout Cairo Since 1867

Authors: Ali Essam El Shazly

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The Haussmannization plan of Cairo in 1867 formed a regular network of roundabout spaces, though deteriorated at present. The method of identifying the spatial structure of roundabout Cairo for conservation matches the voronoi diagram with the space syntax through their geometrical property of spatial convexity. In this initiative, the primary convex hull of first-order voronoi adopts the integral and control measurements of space syntax on Cairo’s roundabout generators. The functional essence of royal palaces optimizes the roundabout structure in terms of spatial measurements and the symbolic voronoi projection of 'Tahrir Roundabout' over the Giza Nile and Pyramids. Some roundabouts of major public and commercial landmarks surround the pole of 'Ezbekia Garden' with a higher control than integral measurements, which filter the new spatial structure from the adjacent traditional town. Nevertheless, the least integral and control measures correspond to the voronoi contents of pollutant workshops and the plateau of old Cairo Citadel with the visual compensation of new royal landmarks on top. Meanwhile, the extended suburbs of infinite voronoi polygons arrange high control generators of chateaux housing in 'garden city' environs. The point pattern of roundabouts determines the geometrical characteristics of voronoi polygons. The measured lengths of voronoi edges alternate between the zoned short range at the new poles of Cairo and the distributed structure of longer range. Nevertheless, the shortest range of generator-vertex geometry concentrates at 'Ezbekia Garden' where the crossways of vast Cairo intersect, which maximizes the variety of choice at different spatial resolutions. However, the symbolic 'Hippodrome' which is the largest public landmark forms exclusive geometrical measurements, while structuring a most integrative roundabout to parallel the royal syntax. Overview of the symbolic convex hull of voronoi with space syntax interconnects Parisian Cairo with the spatial chronology of scattered monuments to conceive one universal Cairo structure. Accordingly, the approached methodology of 'voronoi-syntax' prospects the future conservation of roundabout Cairo at the inferred city-level concept.

Keywords: roundabout Cairo, first-order Voronoi, space syntax, spatial structure

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20773 Treatment and Conservation of an Antique Stone Stela by Nano Calcium Hydroxide with Nano Silica in Egyptian Museum of Cairo

Authors: Elhussein Ahmed Elsayed

Abstract:

An ancient limestone stela dating back to the epoch of the middle kingdom and displayed in the exhibition hall of the middle kingdom, it was discovered in Lisht in Giza, registered with No. 3045 and as a result of its display in an inappropriate display as a result of the use of natural lighting in the display, Represented in sunlight through windows opened day and night. The alternation of these daily changes between the temperature degrees of night and day, both daily and seasonally, causes the expansion and contraction of the rocks and then weakens their cohesion, causing fragmentation. This is indeed the current situation of this stela displayed in the hall, in addition to the damage and fading of colors, as well as the use of a high-viscosity restoration material in the consolidation that led to the attraction of dust and dirt and its adhesion to the surface. The color faded as a result of the lack of lighting control inside the exhibition hall, the remnants of the existing colors were blurred as a result of applying a consolidation material with a high viscosity, which led to the attraction of dust and dirt, and then blurring the colors on the inscription. Examinations and analyzes were carried out on the block, and the results of the examination with a polarized microscope showed that it is of primitive limestone, which contains fossils and microorganisms, which helps to damage. The analysis using the Raman device also showed that the high-viscosity material used in restoration in the past is Paralloid B72. The stone stela was consolidated by using two materials; Nano calcium hydroxide with Nano silica in the form of (Core-shell) at a concentration of 10% and it was applied using the brush.

Keywords: Egyptian museum, stone stela, treatment, nano materials, nano silica

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20772 Sense of Place in Historic City

Authors: Hiba Alkhalaf

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Historic cities and places of cultural significance is continuously under the pressure of economic development and social change that threaten its natural and cultural environment. The challenge here is to find a balance between preserving the cultural character while ensuring the socio-economic gains and continuity of its uniqueness. That is by sustaining the use, character, meaning and social interaction associated with the place, in other words the sense of place. The main argument here is what we attempt to conserve is the cultural physical and non-physical dimensions of the historic city. It is based on the proposition that what give the historic city its character is its strong sense of place- whether it is historic or current. When properly identified, its various dimensions (use, meaning and form) would help determine what to sustain and what not by making the development meaningfully related to the uniqueness of the historic place. Accordingly, those socio-economic features within the context of a changing historic environment needed to be clarified. This paper, thus, explores the various perspectives of the role of sense of place within the historic city and its connection to cultural heritage. It also reviews urban conservation practice as it is currently understood in the context of historic city development. It concludes that sense of place lies in complex interrelated relationships between various users of the place and the physical, economic, cultural, political, and environmental contexts in which they interact. This calls for the need to sustain the sense of place as part of the overall urban development and conservation strategies.

Keywords: cultural heritage, historic urban areas, urban development, sense of place

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20771 Pineapple Patriarch: Local Agency in Sustainability Initiatives despite Community Reliance on Pineapple Monoculture

Authors: Afshan Golriz

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This paper addresses the nuances in the relationship between the rural community of Volcan, Costa Rica, and the presence of multinational pineapple giant Pineapple Development Corporation (PINDECO). The paper analyzes the continuous negotiation between the need for environmental protection in the face of pineapple monoculture and the socioeconomic dependencies of the community on the company. Drawing on eight years of ethnographic work in Volcan de Buenos Aires and relying on intergenerational interviews that document oral histories, this article provides a socio-historical account of the economic and environmental impact of the presence of PINDECO in the southern zone of the country. The paper draws on interviews and in-depth participant observation, conducted by the author in intermittent periods over eight years. The research sheds light on the tensions between the village and PINDECO, as simultaneous acceptance of and opposition to the company persist by different stakeholders in the region. In doing so, this paper examines the strikingly powerful affinity toward the company and the community's regard for PINDECO as the town patriarch despite social and environmental injustices. In demonstrating these tensions, the author problematizes the practice of conducting foreign environmental research in developing countries, and more importantly, proposing changes to environmental conservation and socioeconomic structures without understanding community reliance on the presence of corporations such as PINDECO and the threats that changes to existing structures could pose to community members' livelihoods. In complicating these common western academic practices, the author takes an anti-colonial approach to environmental research, refusing the assumption that the affinity toward the company by the community of Volcan is rooted in ignorance, lack of education, or lack of interest in environmental conservation. The author instead highlights local knowledge and agency, demonstrating the many ways in which the community itself is producing knowledge and taking action. Through this paper, common assumptions regarding the agency of such communities are contested, and the grassroots environmental initiatives of Volcan, Costa Rica are brought to life.

Keywords: environmental conservation, grassroots movements, local knowledge, agricultural multinational

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20770 Using Greywolf Optimized Machine Learning Algorithms to Improve Accuracy for Predicting Hospital Readmission for Diabetes

Authors: Vincent Liu

Abstract:

Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

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20769 Forensic Necropsy-Importance in Wildlife Conservation

Authors: G. V. Sai Soumya, Kalpesh Solanki, Sumit K. Choudhary

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Necropsy is another term used for an autopsy, which is known as death examination in the case of animals. It is a complete standardized procedure involving dissection, observation, interpretation, and documentation. Government Bodies like National Tiger Conservation Authority (NTCA) have given standard operating procedures for commencing the necropsies. Necropsies are rarely performed as compared to autopsies performed on human bodies. There are no databases which maintain the count of autopsies in wildlife, but the research in this area has shown a very small number of necropsies. Long back, wildlife forensics came into existence but is coming into light nowadays as there is an increase in wildlife crime cases, including the smuggling of trophies, pooching, and many more. Physical examination in cases of animals is not sufficient to yield fruitful information, and thus postmortem examination plays an important role. Postmortem examination helps in the determination of time since death, cause of death, manner of death, factors affecting the case under investigation, and thus decreases the amount of time required in solving cases. Increasing the rate of necropsies will help forensic veterinary pathologists to build standardized provision and confidence within them, which will ultimately yield a higher success rate in solving wildlife crime cases.

Keywords: necropsy, wildlife crime, postmortem examination, forensic application

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20768 Water Efficiency: Greywater Recycling

Authors: Melissa Lubitz

Abstract:

Water scarcity is one of the crucial challenges of our time. There needs to be a focus on creating a society where people and nature flourish, regardless of climatic conditions. One of the solutions we can look to is decentralized greywater recycling. The vision is simple. Every building has its own water source being greywater from the bath, shower, sink and washing machine. By treating this in the home, you can save 25-45% of potable water use and wastewater production, a reduction in energy consumption and CO2 emissions. This reusable water is clean, and safe to be used for toilet flushing, washing machine, and outdoor irrigation. Companies like Hydraloop have been committed to the greywater recycle-ready building concept for years. This means that drinking water conservation and water reuse are included as standards in the design of all new buildings. Sustainability and renewal go hand in hand. This vision includes not only optimizing water savings and waste reduction but also forging strong partnerships that bring this ambition to life. Together with regulators, municipalities and builders, a sustainable and water-conscious future is pursued. This is an opportunity to be part of a movement that is making a difference. By pushing this initiative forward, we become part of a growing community that resists dehydration, believes in sustainability, and is committed to a living environment at the forefront of change: sustainable living, where saving water is the norm and where we shape the future together.

Keywords: greywater, wastewater treatment, water conservation, circular water society

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20767 A Study to Assess the Energy Saving Potential and Economic Analysis of an Agro Based Industry in Karnataka, India

Authors: Sangamesh G. Sakri, Akash N. Patil, Sadashivappa M. Kotli

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Agro based industries in India are considered as the micro, small and medium enterprises (MSME). In India, MSMEs contribute approximately 8 percent of the country’s GDP, 42 percent of the manufacturing output and 40 percent of exports. The toor dal (scientific name Cajanus cajan, commonly known as yellow gram, pigeon pea) is the second largest pulse crop in India accounting for about 20% of total pulse production. The toor dal milling industry in India is one of the major agro-processing industries in the country. Most of the dal mills are concentrated in pulse producing areas, which are spread all over the country. In Karnataka state, Gulbarga is a district, where toor dal is the main crop and is grown extensively. There are more than 500 dal mills in and around the Gulbarga district to process dal. However, the majority of these dal milling units use traditional methods of processing which are energy and capital intensive. There exists a huge energy saving potential in these mills. An energy audit is conducted on a dal mill in Gulbarga to understand the energy consumption pattern to assess the energy saving potential, and an economic analysis is conducted to identify energy conservation opportunities.

Keywords: conservation, demand side management, load curve, toor dal

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20766 The Effect of Relocating a Red Deer Stag on the Size of Its Home Range and Activity

Authors: Erika Csanyi, Gyula Sandor

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In the course of the examination, we sought to answer the question of how and to what extent the home range and daily activity of a deer stag relocated from its habitual surroundings changes. We conducted the examination in two hunting areas in Hungary, about 50 km from one another. The control area was in the north of Somogy County, while the sample area was an area of similar features in terms of forest cover, tree stock, agricultural structure, altitude above sea level, climate, etc. in the south of Somogy County. Three middle-aged red deer stags were captured with rocket nets, immobilized and marked with GPS-Plus Collars manufactured by Vectronic Aerospace Gesellschaft mit beschränkter Haftung. One captured species was relocated. We monitored deer movements over 24-hour periods at 3 months. In the course of the examination, we analysed the behaviour of the relocated species and those that remained in their original habitat, as well as the temporal evolution of their behaviour. We examined the characteristics of the marked species’ daily activities and the hourly distance they covered. We intended to find out the difference between the behaviour of the species remaining in their original habitat and of those relocated to a more distant, but similar habitat. In summary, based on our findings, it can be established that such enforced relocations to a different habitat (e.g., game relocation) significantly increases the home range of the species in the months following relocation. Home ranges were calculated using the full data set and the minimum convex polygon (MCP) method. Relocation did not increase the nocturnal and diurnal movement activity of the animal in question. Our research found that the home range of the relocated species proved to be significantly higher than that of those species that were not relocated. The results have been presented in tabular form and have also been displayed on a map. Based on the results, it can be established that relocation inherently includes the risk of falling victim to poaching, vehicle collision. It was only in the third month following relocation that the home range of the relocated species subsided to the level of those species that were not relocated. It is advisable to take these observations into consideration in relocating red deer for nature conservation or game management purposes.

Keywords: Cervus elaphus, home range, relocation, red deer stag

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20765 Real Estate Trend Prediction with Artificial Intelligence Techniques

Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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20764 Assessment of Fermentative Activity in Heavy Metal Polluted Soils in Alaverdi Region, Armenia

Authors: V. M. Varagyan, G. A. Gevorgyan, K. V. Grigoryan, A. L. Varagyan

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Alaverdi region is situated in the northern part of the Republic of Armenia. Previous studies (1989) in Alaverdi region showed that due to soil irrigation with the highly polluted waters of the Debed and Shnogh rivers, the content of heavy metals in the brown forest steppe soils was significantly higher than the maximum permissible concentration as a result of which the fermentative activity in all the layers of the soils was stressed. Compared to the non-polluted soils, the activity of ferments in the plough layers of the highly polluted soils decreased by 44 - 68% (invertase – 60%, phosphatase – 44%, urease – 66%, catalase – 68%). In case of the soil irrigation with the polluted waters, a decrease in the intensity of fermentative reactions was conditioned by the high content of heavy metals in the soils and changes in chemical composition, physical and physicochemical properties. 20-year changes in the fermentative activity in the brown forest steppe soils in Alaverdi region were investigated. The activity of extracellular ferments in the soils was determined by the unification methods. The study has confirmed that self-recovery process occurs in soils previously polluted with heavy metals which can be revealed by fermentative activity. The investigations revealed that during 1989 – 2009, the activity of ferments in the plough layers of the medium and highly polluted soils increased by 31.2 – 52.6% (invertase – 31.2%, urease – 52.6%, phosphatase – 33.3%, catalase – 41.8%) and 24.1 – 87.0% (invertase – 40.4%, urease – 76.9%, phosphatase – 24.1%, catalase – 87.0%) respectively which indicated that the dynamic properties of the soils, which had been broken due to heavy metal pollution, were improved. In 1989, the activity of the Alaverdi copper smelting plant was temporarily stopped due to financial problems caused by the economic crisis and the absence of market, and the factory again started operation in 1997 and isn’t currently running at full capacity. As a result, the Debed river water has obtained a new chemical composition and comparatively good irrigation properties. Due to irrigation with this water, the gradually recovery of the soil dynamic properties, which had been broken due to irrigation with the waters polluted with heavy metals, was occurred. This is also explained by the fact that in case of irrigation with the partially cleaned water, the soil protective function against pollutants rose due to a content increase in humus and silt fractions. It is supposed that in case of the soil irrigation with the partially cleaned water, the intensity of fermentative reactions wasn’t directly affected by heavy metals.

Keywords: alaverdi region, heavy metal pollution, self-recovery, soil fermentative activity

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20763 State of Conservation of the British Colonial Architectural Heritage of Karachi: Case Study of Damage Mapping of Empress Market Building

Authors: Tania Ali Soomro

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In 1839, the British, after the annexation of the port city of Karachi, established a new urban centre consisting of various quarters and introduced new settlements there. These quarters were out of the boundaries of fortified native old area and now contain much of the oldest parts of the city and signify the colonial history of Karachi, in particular the Saddar Bazaar and the neighboring areas of Kharadar and Mithadar. These quarters bestow a mix of functional typology built in a hybrid form of construction - an adaptation of the western architectural attributes to regional requirements and characteristics. This approach is referred to as the Anglo Vernacular, Colonial or the Domestic Gothic architectural form. This research paper investigates the historical and architectural value of one such property: the Empress Market designed by then Municipal Architect, Ar. James Strachan in 1889 as a commemorative monument for the jubilee of Her Majesty the Queen Victoria; Empress of British India, at that time. This paper presents information on the present conservation status of the market building and highlights its role as a catalyst to the community interconnection. This building has survived to present day and functioned well, despite undergoing numerous transformations. A detailed analysis of the bio-degradation (Natural-Chemical dissolution of material) and the bio-deterioration (Manmade-Negative state change of the material) of the building, based on the examination of the prevailing causes of these bio-alterations is carried out, and is presented in form of a damage atlas containing both the categories of bio-alteration/ changes occurred to the building over the time. The research methodology followed in this paper starts with the available archival analysis, physical observation, photographic documentation, the statistics review and the interviews with the direct and indirect stakeholders. The results and findings of this research portray that these bio-alterations and changes are the essential part of the life cycle of Empress Market building which illustrate the historic development of the premise and therefore ought to be given due importance (depending upon their condition) while developing the conservation plan for the building.

Keywords: British colonial architecture, bio-alteration, bio-degradation, bio-deterioration, domestic gothic architectural form

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20762 Using Machine Learning Techniques for Autism Spectrum Disorder Analysis and Detection in Children

Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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20761 Ichthyofauna and Population Status at Indus River Downstream, Sindh-Pakistan

Authors: M. K. Sheikh, Y. M. Laghari., P. K. Lashari., N. T. Narejo

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The Indus River is one of the longest important rivers of the world in Asia that flows southward through Pakistan, merges into the Arabian Sea near the port city of Karachi in Sindh Province, and forms the Indus Delta. Fish are an important resource for humans worldwide, especially as food. In fish, healthy nutriments are present which are not found in any other meat source because it have a huge quantity of omega- 3 fatty acids, which are very essential for the human body. Ichthyologic surveys were conducted to explore the diversity of freshwater fishes, distribution, abundance and current status of the fishes at different spatial scale of the downstream, Indus River. Total eight stations were selected namely Railo Miyan (RM), Karokho (Kk), Khanpur (Kp), Mullakatiyar (Mk), Wasi Malook Shah (WMS), Branch Morie (BM), Sujawal (Sj) and Jangseer (JS). The study was carried in the period of January 2016 to December 2019 to identify River and biodiversity threats and to suggest recommendations for conservation. The data were analysed by different population diversity index. Altogether, 124 species were recorded belonging to 12 Orders and 43 Families from the downstream of Indus River. Among 124 species, 29% belong to high commercial value and 35% were trash fishes. 31% of fishes were identified as marine/estuarine origin (migratory) and 05% were exotic fish species. Perciformes is the most predominated order, contributing to 41% of families. Among 43 families, the family Cyprinidae was the richest family from all localities of downstream, represented by 24% of fish species demonstrating a significant dominance in the number of species. A significant difference was observed for species abundance in between all sites, the maximum abundance species were found at first location RM having 115 species and minimum observed at the last station JS 56 genera. In the recorded Ichthyofauna, seven groups were found according to the International Union for Conservation of Nature status (IUCN), where a high species ratio was collected, in Least Concern (LC) having 94 species, 11 were found as not evaluated (NE), whereas 8 was identified as near threatened (NT), 1 was recorded as critically endangered (CR), 11 were collected as data deficient (DD), and while 8 was observed as vulnerable (VU) and 3 endangered (EN) species. Different diversity index has been used extensively in environmental studies to estimate the species richness and abundance of ecosystems outputs of their wellness; a positive environment (biodiversity rich) with species at RM had an environmental wellness and biodiversity levels of 4.566% while a negative control environment (biodiversity poor) on last station JS had an environmental wellness and biodiversity levels of 3.931%. The status of fish biodiversity and river has been found under serious threat. Due to the lower diversity of fishes, it became not only venerable for fish but also risky for fishermen. Necessary steps are recommended to protect the biodiversity by conducting further conservative research in this area.

Keywords: ichthyofaunal biodiversity, threatened species, diversity index, Indus River downstream

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20760 Object-Based Image Analysis for Gully-Affected Area Detection in the Hilly Loess Plateau Region of China Using Unmanned Aerial Vehicle

Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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20759 Intrusion Detection in Cloud Computing Using Machine Learning

Authors: Faiza Babur Khan, Sohail Asghar

Abstract:

With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

Procedia PDF Downloads 320
20758 Cysteine Proteases of Plants That Act on the Coagulation Cascade: Approach from Bioinformatics

Authors: Tapiwa Brine Mutsauri

Abstract:

The MEROPS system is an information resource for proteases that classifies them into clans according to their catalytic type. Within the Plant kingdom, cysteine ​​proteases are one of the best known, as they are the catalytic type on which the first studies on plant proteases were focused. Plant cysteine ​​proteases have a similar mechanism of action to serine proteases, and some are known to have activity on factors of the blood coagulation cascade, such as a potent antithrombotic effect, and also cause increased fibrinolysis. Of a few plant cysteine ​​proteases, the three-dimensional structure is known, so a method of interest to be able to predict their potential activity on the factors of the coagulation cascade would be to know their structure. Phylogenetics is the study of the evolutionary relationships between biological entities, often species, individuals, or genes (which can be called taxa). It is essential to identify the evolutionary position and the possible distribution of these enzymes in the plant kingdom, particularly those that act on coagulation factors. Bioinformatic tools, such as Clustal Omega / Jalview and Mega6, can be used to create phylogenetic trees. From the results of the alignment, it can be seen that although there is a certain degree of conservation (Conservation) and consensus (Consensus) among the eleven sequences, the functionally important motifs (those corresponding to the active site), the degree of conservation and consensus is very low. We could then infer that although activity on coagulation is reported for these enzymes, linked to their structural and mechanistic similarity with serine proteases, this activity may not have a direct or primary relationship with the proteolytic activity associated with their common, poorly conserved active site in this case. This ultimately could be related to modifications in the reaction mechanism of several of the enzymes studied, which would require more detailed study. Also, below, we will deal with factors that may have a greater influence on this result. The results of this work enrich the understanding of how species (and molecular sequences in general) evolve. Through phylogenetics, we learn not only how sequences came to be the way they are today but also the general principles that allow us to predict how they will change in the future. For pharmaceutical sciences, phylogenetic selection of biologically related species can help identify closely related members of a species with compounds of pharmacological importance, such as plant cysteine ​​proteases, in addition to identifying structural features that may influence their pharmacological activity and which can be valuable for drug design.

Keywords: computational simulation, proteases, coagulation, bioinformatics

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20757 Customer Churn Prediction by Using Four Machine Learning Algorithms Integrating Features Selection and Normalization in the Telecom Sector

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 134
20756 Fish Diversity and Conservation of Two Lacustrine Wetlands of the Upper Benue Basin, Nigeria

Authors: D. L. David, J. A. Wahedi, Q. T. Zaku

Abstract:

A study was conducted at River Mayo Ranewo and River Lau, Taraba State Nigeria. The two rivers empty into the Upper Benue Basin. A visual encounter survey was conducted within the two wetlands from June to August, 2014. The fish record was based entirely on landings of fishermen, number of canoes that land fish was counted, types of nets and baits used on each sampling day. Fish were sorted into taxonomic groups, identified to family/species level, counted and weighed in groups. The relative species abundance was determined by dividing the number of species from a site by the total number of species from all tributaries/sites. Fish was preserved in 2% formaldehyde solution and taken to the laboratory, where they were identified. Shannon-Weiner index of species diversity indicated that the diversity was highest at River Mayo Ranewo than River Lau. In the result showed at River Mayo Ranewo, the family Mochokidae recorded the highest (23.15%), followed by Mormyridae (2.64%) and the least was the family Lepidosirenidae (0.04%). While at River Lau the family Mochokidae recorded the highest occurrence of (24.1%), followed by Bagridae (20.20%), and then Mormyridae, which also was the second highest in River Lau, with 18.46% occurrence. There was no occurrence of Malapteruridae and Osteoglossidae (0%) in River Lau, but the least occurrence was the family Gymnarchidae (0.04%). These results indicated that the fish composition were not significantly (p ≤ 0.05) different based on t-test.

Keywords: conservation, diversity index, Lau, Mayo Ranewo, wetlands

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20755 Model-Driven and Data-Driven Approaches for Crop Yield Prediction: Analysis and Comparison

Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 231
20754 The State of Herb Medicine in Oriental Morocco: Cases of Debdou, Taourirt and Guerssif Districts

Authors: Himer Khalid, Alami Ilyass, Kharchoufa Loubna, Elachouri Mostafa

Abstract:

It has been estimated by the World Health Organization that 80% of the world's population relies on traditional medicine to meet their daily health requirements. In Morocco reliance on such medicine is partly owing to the high cost of conventional medicine and the inaccessibility of modern health care facilities. There was high agreement in the use of plants as medicine in Oriental Morocco. Our objective is to evaluate the informant’s knowledge on medicinal plants by the local population and to document the uses of medicinal plants by this community, for the treatment of different illnesses. Using an ethnopharmacological approach, we collected information concerning the traditional medicinal knowledge and the medicinal plants used, by interviewing successfully 458 informants living in oriental Morocco (from Debdou, Taourirt, Guersif a,d Laayoune districts). The data were analyzed by statistical methods (Component Analysis “CA”, Factorial Analysis “FA”) and other methods such as through Informant’s Consensus Factor (ICF) and Use Value (UV). Our results indicate that, more than 60% of the population in these regions relies on medicinal plants for the treatment of different ailments with predominance of women consumers. 135 plant species belonging to 61 families were documented. These plants were used by the population for the treatment of a group of illness (about 14 principal ailments). We conclude that, in oriental Morocco, till now, the population has some traditional knowledge commonly used as medical tradition. These wealthy heritage needs conservation and evaluation.

Keywords: Morocco, medicinal plants, traditional knowledge, wealthy heritage

Procedia PDF Downloads 289
20753 Comparison of Different Machine Learning Algorithms for Solubility Prediction

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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20752 Use of Lactic Strains Isolated from Algerian Ewe's Milk in the Manufacture of a Natural Yogurt

Authors: Chougrani Fadela, Cheriguene Abderrahim

Abstract:

Fifty three strains of thermophilic and mesophilic lactic acid bacteria were isolated from the ewe’s milk. Identification reveals the presence of nineteen strains (36%) of Lactobacillus sp., seventeen strains (32%) of Lactococcus sp., nine strains (17%) of Streptococcus thermophilus and eight strains (15%) of Leuconostoc sp. The strains were characterized for their technological properties. A high diversity of properties among the studied strains was demonstrated. On the basis of technological characteristics, two strains (Lactobacillus bulgaricus and Streptococcus thermophilus) were screened with respect to their acid and flavour production for the preparation of a natural yogurt and compared to a commercial starter cultures. Sensorial analyses revealed that the product manufactured on the basis of the isolated strains have a cohesiveness and adhesiveness corresponding to standard products. The pH and the acidity recorded are also within accepted levels during all the period of conservation.

Keywords: Lactobacillus bulgaricus, Streptococcus thermophilus, yoghurt, cohesiveness, adhesiveness, Algerian ewe’s milk

Procedia PDF Downloads 344
20751 A Flexible Bayesian State-Space Modelling for Population Dynamics of Wildlife and Livestock Populations

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Hans-Peter Piepho

Abstract:

We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions.

Keywords: bayesian state-space model, Markov Chain Monte Carlo, population dynamics, conservation

Procedia PDF Downloads 208
20750 Integration of Educational Data Mining Models to a Web-Based Support System for Predicting High School Student Performance

Authors: Sokkhey Phauk, Takeo Okazaki

Abstract:

The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

Procedia PDF Downloads 106
20749 The Temporal Pattern of Bumble Bees in Plant Visiting

Authors: Zahra Shakoori, Farid Salmanpour

Abstract:

Pollination services are a vital service for the ecosystem to maintain environmental stability. The decline of pollinators can disrupt the ecological balance by affecting components of biodiversity. Bumble bees are crucial pollinators, playing a vital role in maintaining plant diversity. This study investigated the temporal patterns of their visitation to flowers in Kiasar National Park, Iran. Observations were conducted in Jun 2024, totaling 442 person-minutes of observation. Five species of bumble bees were identified. The study revealed that they consistently visited an average of 12-15 flowers per minute, regardless of species. The findings highlight the importance of protecting natural habitats, where their populations are thriving in the absence of human-induced stressors. This study was conducted in Kiasar National Park, located in the southeast of Mazandaran, northern Iran. The surveyed area, at an altitude of 1800-2200 meters, includes both forest and pasture. Bumble bee surveys were carried out on sunny days from June 2024, starting at dawn and ending at sunset. To avoid double-counting, we systematically searched for foraging habitats on low-sloping ridges with high mud density, frequently moving between patches. We recorded bumble bee visits to flowers and plant species per minute using direct observation, a stopwatch, and a pre-prepared form. We used statistical analysis of variance (ANOVA) with a confidence level of 95% to examine potential differences in foraging rates across different bumble bee species, flowers, plant bases, and plant species visited. Bumble bee identification relied on morphological indicators. A total of 442 person-minutes of bumble bee observations were recorded. Five species of bumble bees (Bombus fragrans, Bombus haematurus, Bombus lucorum, Bombus melanurus, Bombus terrestris) were identified during the study. The results of this study showed that the visits of bumble bees to flower sources were not different from each other. In general, bumble bees visit an average of 12-15 flowers every 60 seconds. In addition, at the same time they visit between 3-5 plant bases. Finally, they visit an average of 1 to 3 plant species per minute. While many taxa contribute to pollination, insects—especially bees—are crucial for maintaining plant diversity and ecosystem functions. As plant diversity increases, the stopping rate of pollinating insects rises, which reduces their foraging activity. Bumble bees, therefore, stop more frequently in natural areas than in agricultural fields due to higher plant diversity. Our findings emphasize the need to protect natural habitats like Kiasar National Park, where bumble bees thrive without human-induced stressors like pesticides, livestock grazing, and pollution. With bumble bee populations declining globally, further research is essential to understand their behavior in different environments and develop effective conservation strategies to protect them.

Keywords: bumble bees, pollination, pollinator, plant diversity, Iran

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20748 Coprophagus Beetles (Scarabaeidae: Coleoptera) of Buxa Tiger Reserve, West Bengal, India

Authors: Subhankar Kumar Sarkar

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Scarab beetles composing the family Scarabaeidae is one of the largest families in the order Coleoptera. The family is comprised of 11 subfamilies. Of these, the subfamily Scarabaeinae includes 13 tribes globally. Indian species are however considered within 2 tribes Scarabaeini and Coprini. Scarab beetles under this subfamily also known as Coprophagus beetles play an indispensable role in forestry and agriculture. Both adults and larvae of these beetles do a remarkable job of carrying excrement into the soil thus enriching the soil to a great extent. Eastern and North Eastern states of India are heavily rich in diversity of organisms as this region exhibits the tropical rain forests of the eastern Himalayas, which exhibits one of the 18 biodiversity hotspots of the world and one of the three of India. Buxa Tiger Reserve located in Dooars between latitudes 26°30” to 26°55” North & longitudes 89°20” to 89°35” East is one such fine example of rain forests of the eastern Himalayas. Despite this, the subfamily is poorly known, particularly from this part of the globe and demands serious revisionary studies. It is with this background; the attempt is being made to assess the Scarabaeinae fauna of the forest. Both extensive and intensive surveys were conducted in different beats under different ranges of Buxa Tiger Reserve. For collection sweep nets, bush beating and collection in inverted umbrella, hand picking techniques were used. Several pit fall traps were laid in the collection localities of the Reserve to trap ground dwelling scarabs. Dung of various animals was also examined to make collections. In the evening hours UV light, trap was used to collect nocturnal beetles. The collected samples were studied under Stereozoom Binocular Microscopes Zeiss SV6, SV11 and Olympus SZ 30. The faunistic investigation of the forest revealed in the recognition of 19 species under 6 genera distributed over 2 tribes. Of these Heliocopris tyrannus Thomson, 1859 was recorded new from the Country, while Catharsius javanus Lansberge, 1886, Copris corpulentus Gillet, 1910, C. doriae Harold, 1877 and C. sarpedon Harold, 1868 from the state. 4 species are recorded as endemic to India. The forest is dominated by the members of the Genus Onthophagus, of which Onthophagus (Colobonthophagus) dama (Fabricius, 1798) is represented by highest number of individuals. Their seasonal distribution is most during Premonsoon followed by Monsoon and Postmonsoon. Zoogeographically all the recorded species are of oriental distribution.

Keywords: buxa tiger reserve, diversity, India, new records, scarabaeinae, scarabaeidae

Procedia PDF Downloads 241
20747 Evolution of Bioactive Components of Prickly Pear Juice (Opuntia ficus indica) and Cocktails with Orange Juice

Authors: T. Hadj Sadok, R. Hattab Bey, K. Rebiha

Abstract:

The valuation of juice from prickly pear of Opuntia ficus indica inermis as cocktails appears an attractive alternative because of their nutritional intake and functional compound has anti-radical activity (polyphenols, vitamin C, carotenoids, Betalaines, fiber and minerals). The juice from the fruit pulp is characterized by a high pH 5.85 which makes it difficult for its conservation and preservation requires a thermal treatment at high temperatures (over 100 °C) harmful for bioactive constituents compared to juice orange more acidic and processed at temperatures < 100 °C. The valuation as fig cocktails-orange is particularly interesting thanks to the contribution of polyph2nols, fiber, vitamin C, reducing sugar (sweetener) and betalaine, minerals while allowing lower temperature processing to decrease pH. The heat treatment of these juices: orange alone or in cocktails showed that the antioxidant power decreases by 12% in presence of 30% of juice treated by the heat and of 28 and 32% in the presence of 10 and 20% juice which shows the effect prickly pear juice of Opuntia. During storage for 4 weeks the loss of vitamin C is 40 and 38% in the presence of 10 and 20% juice and 33% in the presence of 30% pear juice parallel, a treatment of stabilization by heat affects relatively the polyphenols rate which decreases from 10.5% to 30% in the cocktail, and 6.11-6.71pour cocktails at 10% and 20%. Vitamin C decreases to 12 to 24 % after a heat treatment at 85°C for 30 minutes respectively for the orange juice and pear juice; this reduction is higher when the juice is in the form of cocktails composed of 10 to 30 % pear juice.

Keywords: prickly pear juice, orange cocktail, polyphenol, Opuntia ficus indica, vitamin

Procedia PDF Downloads 380