Search results for: classification of matter

Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Laura Victoria Vigoya Morales, David Rolando Suarez Mora

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The problem of detecting enterococcus faecalis is receiving considerable attention with the new cases of beachgoers infected with the bacteria, which can be found in fecal matter. The process detection of this kind of bacteria would be taking a long time, which waste time and money as a result of closing recreation place, like beach or pools. Hence, new methods for automating the process of detecting and recognition of this bacteria has become in a challenge. This article describes a novel approach to detect the enterococcus faecalis bacteria in water by using an octave algorithm, which embody a network neural. This document shows result of performance, quality and integrity of the algorithm.

Keywords: Enterococcus faecalis, image treatment, octave and network neuronal

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Authors: Hellen L. De Castro e Silva, Ana A. Robles Aguilar, Erik Meers

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Digestates are residual by-products, rich in nutrients and trace elements, which can be used as organic fertilisers on soils. However, due to the non-digestibility of these elements and reduced dry matter during the anaerobic digestion process, metal concentrations are higher in digestates than in feedstocks, which might hamper their use as fertilisers according to the threshold values of some country policies. Furthermore, there is uncertainty regarding the required assimilated amount of these elements by some crops, which might result in their bioaccumulation. Therefore, further processing of the digestate to obtain safe fertilizing products has been recommended. This research aims to analyze the effect of applying the hydrothermal carbonization process to manure-derived digestates as a thermal treatment to reduce the bioavailability of heavy metals in mono and co-digestates derived from pig manure and maize from contaminated land in France. This study examined pig manure collected from a novel stable system (VeDoWs, province of East Flanders, Belgium) that separates the collection of pig urine and feces, resulting in a solid fraction of manure with high up-concentration of heavy metals and nutrients. Mono-digestion and co-digestion processes were conducted in semi-continuous reactors for 45 days at mesophilic conditions, in which the digestates were dried at 105 °C for 24 hours. Then, hydrothermal carbonization was applied to a 1:10 solid/water ratio to guarantee controlled experimental conditions in different temperatures (180, 200, and 220 °C) and residence times (2 h and 4 h). During the process, the pressure was generated autogenously, and the reactor was cooled down after completing the treatments. The solid and liquid phases were separated through vacuum filtration, in which the solid phase of each treatment -hydrochar- was dried and ground for chemical characterization. Different fractions (exchangeable / adsorbed fraction - F1, carbonates-bound fraction - F2, organic matter-bound fraction - F3, and residual fraction – F4) of some heavy metals (Cd, Cr, Ni, and Cr) have been determined in digestates and derived hydrochars using the modified Community Bureau of Reference (BCR) sequential extraction procedure. The main results indicated a difference in the heavy metals fractionation between digestates and their derived hydrochars; however, the hydrothermal carbonization operating conditions didn’t have remarkable effects on heavy metals partitioning between the hydrochars of the proposed treatments. Based on the estimated potential ecological risk assessment, there was one level decrease (considerate to moderate) when comparing the HMs partitioning in digestates and derived hydrochars.

Keywords: heavy metals, bioavailability, hydrothermal treatment, bio-based fertilisers, agriculture

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Authors: P. Paengkoum, S. C. Chen, S. Paengkoum

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Six male growing Thai-indigenous beef cattle with body weight (BW) of 154±13.2 kg were randomly assigned in replicated 3×3 Latin square design, and fed with different levels of crude protein (CP) in total mixed ration (TMR) diets. CP levels in diets were 4.3%, 7.3% and 10.3% base on dry matter (DM). Ruminal ammonia nitrogen (NH3-N) and blood urea nitrogen (BUN) concentrations increased (P<0.01) with increasing CP levels. Moreover, there is a positive relationship between BUN and ruminal NH3-N. Rumen pH, total volatile fatty acid (VFA), molar proportions of acetate, propionate and butyrate were not affected by CP levels (P>0.05).

Keywords: Thai-indigenous beef cattle, crude protein, volatile fatty acid (VFA), total mixed ration (TMR) diets

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: V. A. Vieira, M. P. Sforcini, V. Endo, G. C. Magioni, M. D. S. Oliveira

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The main goal of this paper was evaluate the effect of diets containing different levels of probiotic on performance and milk composition of lactating cows. Eight Holstein cows were distributed in two 4x4 Latin square. The diets were based on corn silage, concentrate and the treatment (0, 3, 6 or 9 grams of probiotic/animal/day). It was evaluated the dry matter intake of nutrients, milk yield and composition. The use of probiotics did not affect the nutrient intake (p>0.05) neither the daily milk production or corrected to 4% fat (p>0.05). However, it was observed that there was a significant fall in milk composition with higher levels of probiotics supplementation. These results emphasize the need of further studies with different experimental designs or improve the number of Latin square with longer periods of adaptation.

Keywords: dairy cow, milk composition, probiotics, daily milk production

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Authors: Pamela R. Jackson, Andrea Hawkins-Daarud, Cassandra R. Rickertsen, Kamala Clark-Swanson, Scott A. Whitmire, Kristin R. Swanson

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Glioblastoma Multiforme (GBM), the most common primary brain tumor, often presents with hyperintensity on T2-weighted or T2-weighted fluid attenuated inversion recovery (T2/FLAIR) magnetic resonance imaging (MRI). This hyperintensity corresponds with vasogenic edema, however there are likely many infiltrating tumor cells within the hyperintensity as well. While MRIs do not directly indicate tumor cells, MRIs do reflect the microenvironmental water abnormalities caused by the presence of tumor cells and edema. The inherent heterogeneity and resulting MRI features of GBMs complicate assessing disease response. To understand how hyperintensity on T2/FLAIR MRI may correlate with edema in the extracellular space (ECS), a multi-compartmental MRI signal equation which takes into account tissue compartments and their associated volumes with input coming from a mathematical model of glioma growth that incorporates edema formation was explored. The reasonableness of two possible extracellular space schema was evaluated by varying the T2 of the edema compartment and calculating the possible resulting T2s in tumor and peripheral edema. In the mathematical model, gliomas were comprised of vasculature and three tumor cellular phenotypes: normoxic, hypoxic, and necrotic. Edema was characterized as fluid leaking from abnormal tumor vessels. Spatial maps of tumor cell density and edema for virtual tumors were simulated with different rates of proliferation and invasion and various ECS expansion schemes. These spatial maps were then passed into a multi-compartmental MRI signal model for generating simulated T2/FLAIR MR images. Individual compartments’ T2 values in the signal equation were either from literature or estimated and the T2 for edema specifically was varied over a wide range (200 ms – 9200 ms). T2 maps were calculated from simulated images. T2 values based on simulated images were evaluated for regions of interest (ROIs) in normal appearing white matter, tumor, and peripheral edema. The ROI T2 values were compared to T2 values reported in literature. The expanding scheme of extracellular space is had T2 values similar to the literature calculated values. The static scheme of extracellular space had a much lower T2 values and no matter what T2 was associated with edema, the intensities did not come close to literature values. Expanding the extracellular space is necessary to achieve simulated edema intensities commiserate with acquired MRIs.

Keywords: extracellular space, glioblastoma multiforme, magnetic resonance imaging, mathematical modeling

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Felipe Haleyson Ribeiro dos Santos, Edson Ronaldo Guarido Filho

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This article aims to review the literature on performance in police organizations. For that, the inOrdinatio method was adopted, which defines the form of selection and classification of articles. The search was carried out in databases, which resulted in a total of 619 documents that were cataloged and classified with the support of the Mendeley software. The theoretical scope intended here is to identify how performance in police organizations has been studied. After deepening the analysis and focusing on management, it was possible to classify the articles into three levels: individual, organizational, and institutional. However, to our best knowledge, no studies were found that addressed the performance relationship between the levels, which can be seen as a suggestion for further research.

Keywords: police management, performance, management, multi-level

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Authors: Molakala Mourya Vardhan Reddy, Kandimala Revanth, Koduru Sumanth, Beena B. M.

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Emergency response and recovery operations are severely hampered by natural catastrophes, especially floods. Understanding post-flood scenarios is essential to disaster management because it facilitates quick evaluation and decision-making. To this end, we introduce FloodNet, a brand-new high-resolution aerial picture collection created especially for comprehending post-flood scenes. A varied collection of excellent aerial photos taken during and after flood occurrences make up FloodNet, which offers comprehensive representations of flooded landscapes, damaged infrastructure, and changed topographies. The dataset provides a thorough resource for training and assessing computer vision models designed to handle the complexity of post-flood scenarios, including a variety of environmental conditions and geographic regions. Pixel-level semantic segmentation masks are used to label the pictures in FloodNet, allowing for a more detailed examination of flood-related characteristics, including debris, water bodies, and damaged structures. Furthermore, temporal and positional metadata improve the dataset's usefulness for longitudinal research and spatiotemporal analysis. For activities like flood extent mapping, damage assessment, and infrastructure recovery projection, we provide baseline standards and evaluation metrics to promote research and development in the field of post-flood scene comprehension. By integrating FloodNet into machine learning pipelines, it will be easier to create reliable algorithms that will help politicians, urban planners, and first responders make choices both before and after floods. The goal of the FloodNet dataset is to support advances in computer vision, remote sensing, and disaster response technologies by providing a useful resource for researchers. FloodNet helps to create creative solutions for boosting communities' resilience in the face of natural catastrophes by tackling the particular problems presented by post-flood situations.

Keywords: image classification, segmentation, computer vision, nature disaster, unmanned arial vehicle(UAV), machine learning.

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Authors: Katalin Bodor, Zsolt Bodor, Robert Szep

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The particulate matter (PM) smaller than 2.5 μm are less studied due to the limited availability of PM₂.₅, and less information is available on the health effects attributable to PM₁₀ in Central-Eastern Europe. The objective of the current study was to assess the human health risk and characterize the spatial and temporal variation of PM₂.₅ and PM₁₀ in eight Romanian regions between the 2009-2018 and. The PM concentrations showed high variability over time and spatial distribution. The highest concentration was detected in the Bucharest region in the winter period, and the lowest was detected in West. The relative risk caused by the PM₁₀ for all-cause mortality varied between 1.017 (B) and 1.025 (W), with an average 1.020. The results demonstrate a positive relative risk of cardiopulmonary and lung cancer disease due to exposure to PM₂.₅ on the national average 1.26 ( ± 0.023) and 1.42 ( ± 0.037), respectively.

Keywords: PM₂.₅, PM₁₀, relative risk, health effect

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Authors: Deepali Lal, Sudha Summerwar, Jyoutsna Pandey

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The resistance of pesticide by the pest is an important matter of concern.The pesticide of plant origin having nontoxic biodegradable and environmentally friendly qualities. The frequent spraying of toxic chemicals is developing resistance to the pesticide. Leaf powder of the plants like Argimone mexicana and Calotropis procera is prepared, Different doses of these plant extracts are given to the Fourth in star stages of Helicoverpa armigera through feeding methods, to find their efficacy the experimental findings will be put under analysis using various parameters. The effect on paritrophic membrane is also studied.

Keywords: distillation plant, acetone, alcohol, pipette, castor leaves, grams pods, larvae of helicoverpa armigera, plant extract, vails, jars, cotton

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Authors: Kayode Julius Oni

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Without mincing words, democracy is by far the most popular form of government in the world today. No matter how we look at it, and regardless of the variant, most leaders in the world today wish to be seen or labeled as Democrats. Perhaps, its attractions in terms of freedom of allocation, accountability, smooth successions of leadership and a lot more, account for its appeal to the ordinary people. The governance style in Nigeria since 1999 cannot be said to be different from the military. Elections are manipulated, judicial processes abused, and the ordinary people do not have access to the dividends of democracy. The paper seeks to address the existing failures experienced under democratic rule in Nigeria which have to transcend into violation of human rights in the conduct of government business. The paper employs the primary and secondary sources of data collection, and it is highly descriptive and critical.

Keywords: democracy, human rights, Nigeria, politics, republic

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Authors: Salwa Aftab, Sehrish Riaz

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The objective of this study was to check the impact of grade sensitivity on learning motivation and academic performance of students and to remove the degree of difference that exists among students regarding the cause of their learning motivation and also to gain knowledge about this matter since it has not been adequately researched. Data collection was primarily done through the academic sector of Pakistan and was depended upon the responses given by students solely. A sample size of 208 university students was selected. Both paper and online surveys were used to collect data from respondents. The results of the study revealed that grade sensitivity has a positive relationship with the learning motivation of students and their academic performance. These findings were carried out through systematic correlation and regression analysis.

Keywords: academic performance, correlation, grade sensitivity, learning motivation, regression

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Authors: Adam Majczak, Grzegorz Barański, Marcin Szlachetka

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Environmental considerations necessitate the search for new energy sources. One of the available solutions is a partial replacement of diesel fuel by compressed natural gas (CNG) in the compression ignition engines. This type of the engines is used mainly in vans and trucks. These units are also gaining more and more popularity in the passenger car market. In Europe, this part of the market share reaches 50%. Diesel engines are also used in industry in such vehicles as ship or locomotives. Diesel engines have higher emissions of nitrogen oxides in comparison to spark ignition engines. This can be currently limited by optimizing the combustion process and the use of additional systems such as exhaust gas recirculation or AdBlue technology. As a result of the combustion process of diesel fuel also particulate matter (PM) that are harmful to the human health are emitted. Their emission is limited by the use of a particulate filter. One of the method for toxic components emission reduction may be the use of liquid gas fuel such as propane and butane (LPG) or compressed natural gas (CNG). In addition to the environmental aspects, there are also economic reasons for the use of gaseous fuels to power diesel engines. A total or partial replacement of diesel gas is possible. Depending on the used technology and the percentage of diesel fuel replacement, it is possible to reduce the content of nitrogen oxides in the exhaust gas even by 30%, particulate matter (PM) by 95 % carbon monoxide and by 20%, in relation to original diesel fuel. The research object is prototype gas injector designed for direct injection of compressed natural gas (CNG) in compression ignition engines. The construction of the injector allows for it positioning in the glow plug socket, so that the gas is injected directly into the combustion chamber. The cycle analysis of the four-cylinder Andoria ADCR engine with a capacity of 2.6 dm3 for different crankshaft rotational speeds allowed to determine the necessary time for fuel injection. Because of that, it was possible to determine the required mass flow rate of the injector, for replacing as much of the original fuel by gaseous fuel. To ensure a high value of flow inside the injector, supply pressure equal to 1 MPa was applied. High gas supply pressure requires high value of valve opening forces. For this purpose, an injector with hydraulic control system, using a liquid under pressure for the opening process was designed. On the basis of air pressure measurements in the flow line after the injector, the analysis of opening and closing of the valve was made. Measurements of outflow mass of the injector were also carried out. The results showed that the designed injector meets the requirements necessary to supply ADCR engine by the CNG fuel.

Keywords: CNG, diesel engine, gas flow, gas injector

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: M. Abd El Hamid Ezzat

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Two field trials were conducted to study the effect of plant densities i.e., 190, 222, 266, 330 and 440 10³ plants ha⁻¹ on morphological characters, physiological and yield attributes of two faba bean types viz. determinate (FLIP-87 -117 strain) and indeterminate (c.v. Giza-461). The results showed that the indeterminate plants significantly surpassed the determinate plants in plant height at 75 and 90 days from sowing, number of leaves at all growth stages and dry matter accumulation at 45 and 90 days from sowing. Determinate plants possessed greater number of side branches than that of the indeterminate plants, but it was only significant at 90 days from sowing. Greater number of flowers were produced by the indeterminate plants than that of the determinate plants at 75 and 90 days from sowing, and although shedding was obvious in both types, it was greater in the determinate plants as compared with the indeterminate one at 90 days from sowing. Increasing plant density resulted in reductions in number of leaves, branches flowers and dry matter accumulation per plant of both faba bean types. However, plant height criteria took a reversible magnitude. Moreover, under all rates of plant densities the indeterminate type plants surpassed the determinate plants in all growth characters studied except for number of branches per plant at 90 days from sowing. The indeterminate plant leaves significantly contained greater concentrations of photosynthetic pigments i.e., chl. a, b and carotenoids than those found in the determinate plant leaves. Also, the data showed significant reduction in photosynthetic pigments concentration as planting density increases. Light extinction coefficient (K) values reached their maximum level at 60 days from sowing, then it declined sharply at 75 days from sowing. The data showed that the illumination inside the determinate faba bean canopies was better than the indeterminate plants. (K) values tended to increase as planting density increases, meanwhile, significant interactions were reported between faba bean type as planting density on (K) at all growth stages. Both of determinate and indeterminate faba bean plant leaves reached their maximum expansion at 75 days from sowing reflecting the highest LAI values, then their declined in the subsequent growth stage. The indeterminate faba bean plants significantly surpassed the determinate plants in LAI up to 75 days from sowing. Growth analysis showed that NAR, RGR and CGR reached their maximum rates at (60-75 days growth stage). Faba bean types did not differ significantly in NAR at the early growth stage. The indeterminate plants were able to grow faster with significant CGR values than the determinate plants. The indeterminate faba bean plants surpassed the determinate ones in number of seeds/pod and per plant, 100-seed weight, seed yield per plant and per hectare at all rates of plant density. Seed yield increased with increasing plant densities of both types. The highest seed yield was attained for both types 440 103 plants ha⁻¹.

Keywords: determinate, indeterminate faba bean, Physiological attributes, yield attributes

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Authors: Ouerchefani Dalel, Mahdhaoui Basma

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Land use change is one of the important factors needed to evaluate later on the impact of human actions on land degradation. This work present the application of a methodology based on remote sensing for evaluation land use change in an arid region of Tunisia. This methodology uses Landsat TM and ETM+ images to produce land use maps by supervised classification based on ground truth region of interests. This study showed that it was possible to rely on radiometric values of the pixels to define each land use class in the field. It was also possible to generate 3 land use classes of the same study area between 1988 and 2011.

Keywords: land use, change, remote sensing, GIS

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

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Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

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Authors: Subhayan Maity

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Complete scenario of cosmic evolution from emergent phase to late time acceleration (i.e. non-singular ever expanding Universe) is a popular preference in the recent cosmology. Yet one can’t exclude the idea that other type of evolution pattern of the Universe may also be possible. Especially, the bouncing scenario is becoming a matter of interest now a days. The present work is an exhibition of such a different pattern of cosmic evolution where the evolution of Universe has been shown as a cyclic thermodynamic process. Under diffusion mechanism (non-equilibrium thermodynamic process), the cosmic evolution has been modelled as [ emergent - accelerated expansion - decelerated expansion - decelerated contraction - accelerated contraction - emergent] .

Keywords: non-equilibrium thermodynamics, non singular evolution of universe, cyclic evolution, diffusive fluid

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Authors: H. Chabalala, A. Grootboom, M. Tang

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The innovative development, production, and clinical utilisation of African natural medicines requires frameworks from systematisation, innovation, registration. Afrika faces challenges when it comes to these sectors. The opposite is the case as is is evident in ancient Asian (Traditional Chinese Medicine and Indian Ayurveda and Siddha) medical systems, which are interfaced into their respective national health and educational systems. Afrikan Natural Medicines (ANMs) are yet to develop systematisation frameworks, i.e. disease characterisation and medicines classification. This paper explores classical medical systems drawn from Afrikan and Chinese experts in natural medicines. An Afrikological research methodology was used to conduct in-depth interviews with 20 key respondents selected through purposeful sampling technique. Data was summarised into systematisation frameworks for classical disease theories, patient categorisation, medicine classification, aetiology and pathogenesis of disease, diagnosis and prognosis techniques and treatment methods. It was discovered that ancient Afrika had systematic medical cosmologies, remnants of which are evident in most Afrikan cultural health practices. Parallels could be drawn from classical medical concepts of antiquity, like Chinese Taoist and Indian tantric health systems. Data revealed that both the ancient and contemporary ANM systems were based on living medical cosmologies. The study showed that African Natural Healing Systems have etiological systems, general pathogenesis knowledge, differential diagnostic techniques, comprehensive prognosis and holistic treatment regimes. Systematisation models were developed out of these frameworks, and this could be used for evaluation of clinical research, medical application including development of curriculum for high-education. It was envisaged that frameworks will pave way towards the development, production and commercialisation of ANMs. This was piloted in inclusive innovation, technology transfer and commercialisation of South African natural medicines, cosmeceuticals, nutraceuticals and health infusions. The central model presented here in will assist in curriculum development and establishment of Afrikan Medicines Hospitals and Pharmaceutical Industries.

Keywords: African Natural Medicines, Indigenous Knowledge Systems, Medical Cosmology, Clinical Application

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Lucas B. Naves, Maria José Abreu

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Reprocessing single-use device has attracted interesting on the medical environment over the last decades. The reprocessing technique was sought in order to reduce the cost of purchasing the new medical device, which can achieve almost double of the price of the reprocessed product. In this manuscript, we have done a literature review, aiming the reuse of medical device that was firstly designed for single use only, but has become, more and more, effective on its reprocessing procedure. We also show the regulation, the countries which allows this procedure, the classification of these device and also the most important issue concerning the re-utilization of medical device, how to minimizing the risk of gram positive and negative bacteria, avoid cross-contamination, hepatitis B (HBV), and C (HCV) virus, and also human immunodeficiency virus (HIV).

Keywords: reusing, reprocessing, single-use medical device, HIV, hepatitis B and C

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Authors: Erico Da Silva Lima, Tiago Neves Pereira Valente, Roberto De Oliveira Roça

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Introduction: Over the last few years the market has increased its demand for high quality meat. Based on this premise some producers have continuously improved their efficiency in breeding beef cattle with the purpose to support this demand. It is well recognized that final quality of beef is intimately linked to animal’s diet. The key objective of this study is to evaluate the influence of feeding animals with cottonseed and its lipids and the final results in terms of pH and shear forces of the meat. Materials and Methods: The study was carried out in the Chapéu de Couro Farm in Aguaí/SP, Brazil. A group of 39 uncastrated Nellore cattle. Mean age of the animals was 36 months and initial mean live weight was 494.1 ± 10.1. Animals were randomly assigned to one of three treatments, based on dry matter: feed with control diet 2.50% cottonseed, feed with 11.50% cottonseed, and feed with 3.13% cottonseed added of 1.77% protected lipid. Forage:concentrate ratio was 50:50 on a dry matter basis. Sugar cane chopped was used as forage. After slaughter, carcasses were identified and divided into two halves that were kept in a cold chamber for 24 h at 2°C. Using pH meter was determined post-mortem pH in Longissimus thoracis muscle between the 12th and 13th rib of the left half carcass. After, part of each animal was removed, and divided in three samples (steaks). Steaks were 2.5 cm thick and were identified and stored individually in plastic bags under vacuum. Samples were frozen in a freezer at -18°C. The same samples cooked were refrigerated by 12 h the 4°C, and then cut into cylinders 1.10 Øcm with the support of a drill press avoiding fats and nerves. Shear force was calculated in these samples cut into cylinders through the Brookfield texture CT3 Texture Analyzer 25 k equipped with a set of blade Warner-Bratzler. Results and Discussion: No differences (P > 0.05) in pH 24 h after slaughter were observed in the meat of Nellore cattle fed different sources of fat, and mean value for this variable was 5.59. However, for the shear force differences (P < 0.05) were founded. For diet with 2,50% cottonseed the lowest value found 5.10 (kg) while for the treatment with 11.50% cottonseed the great value found was 6.30 (kg). High shear force values mean greater texture of meat that indicates less tenderness. The texture of the meat can be influenced by age, weight to the slaughter of animals. For cattle breed Nellore Bos taurus indicus more high value of shear force. Conclusions: The add the cottonseed or protected lipid in diet is not affected pH values in meat. The whole cottonseed does not contribute to the improvement of tenderness of the meat. Acknowledgments: IFGoiano, FAPEG and CNPq (Brazil).

Keywords: beef quality, cottonseed, protected fat, shear force

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Authors: Steven Hidalgo, Patricio Neumann

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The growth of societies has increased the consumption of raw materials and food obtained from nature. This has influenced the services offered by ecosystems to humans, mainly supply and regulation services. One of the indicators used to evaluate these services is Net Primary Productivity (NPP), which is understood as the energy stored in the form of biomass by primary organisms through the process of photosynthesis and respiration. The variation of NPP by defined area produces changes in the properties of terrestrial and aquatic ecosystems, which alter factors such as biodiversity, nutrient cycling, carbon storage and water quality. The analysis of NPP to evaluate variations in ecosystem services includes harvested NPP (understood as provisioning services), which is the raw material from agricultural systems used by humans as a source of energy and food, and the remaining NPP (expressed as a regulating service) or the amount of biomass that remains in ecosystems after the harvesting process, which is mainly related to factors such as biodiversity. Given that agriculture is a fundamental pillar of Chile's integral development, the purpose of this study is to evaluate provisioning and regulating ecosystem services in the agricultural sector, specifically in cereal production, in the communes of the central-southern regions of Chile through a conceptual framework based on the quantification of the fraction of Human Appropriation of Net Primary Productivity (HANPP) and the fraction remaining in the ecosystems (NPP remaining). A total of 161 communes were analyzed in the regions of O'Higgins, Maule, Ñuble, Bio-Bío, La Araucanía and Los Lagos, which are characterized by having the largest areas planted with cereals. It was observed that the region of La Araucanía produces the greatest amount of dry matter, understood as provisioning service, where Victoria is the commune with the highest cereal production in the country. In addition, the maximum value of HANPP was in the O'Higgins region, highlighting the communes of Coltauco, Quinta de Tilcoco, Placilla and Rengo. On the other hand, the communes of Futrono, Pinto, Lago Ranco and Pemuco, whose cereal production was important during the study, had the highest values of remaining NPP as a regulating service. Finally, an inverse correlation was observed between the provisioning and regulating ecosystem services, i.e., the higher the cereal or dry matter production in a defined area, the lower the net primary production remaining in the ecosystems. Based on this study, future research will focus on the evaluation of ecosystem services associated with other crops, such as forestry plantations, whose activity is an important part of the country's productive sector.

Keywords: provisioning services, regulating services, net primary productivity, agriculture

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Authors: Mohsen Rabbani, Trista McLaughlin, Ehsan Vahidi

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the remediation of polluted sites with heavy metals, such as rare earth elements (REEs), has been a primary concern of researchers to decontaminate the soil. Among all developed methods to address this concern, phytoremediation has been established as efficient, cost-effective, easy-to-use, and environmentally friendly way, providing a long-term solution for addressing this global concern. Furthermore, this technology has another great potential application in the metals production sector through returning metals buried in soil via metals cropping. Considering the significant metal concentration in hyper-accumulators, the utilization of bioaccumulated metals to extract metals from plant matter has been proposed as a sub-economic area called phytomining. As a recent, more advanced technology to eliminate such pollutants from the soil and produce critical metals, bioharvesting (phytomining/agromining) has been considered another compromising way to produce metals and meet the global demand for critical/target metals. The bio-ore obtained from phytomining can be safely disposed of or introduced to metal production pathways to obtain the most demanded metals, such as REEs. It is well-known that some hyperaccumulators, e.g., fern Dicranopteris linearis, can be used to absorb REE metals from the polluted soils and accumulate them in plant organs, such as leaves and stems. After soil remediation, the plant species can be harvested and introduced to the downstream steps, namely crushing/grinding, leaching, and purification processes, to extract REEs from plant matter. This novel interdisciplinary field can fill the gap between agriculture, mining, metallurgy, and the environment. Despite the advantages of agromining for the REEs production industry, key issues related to the environmental sustainability of the entire life cycle of this new concept have not been assessed yet. Hence, a comparative life cycle assessment (LCA) study was conducted to quantify the environmental footprints of REEs phytomining. The current LCA study aims to estimate and calculate environmental effects associated with phytomining by considering critical factors, such as climate change, land use, and ozone depletion. The results revealed that phytomining is an easy-to-use and environmentally sustainable approach to either eliminate REEs from polluted sites or produce REEs, offering a new source of such metals production. This LCA research provides guidelines for researchers active in developing a reliable relationship between agriculture, mining, metallurgy, and the environment to encounter soil pollution and keep the earth green and clean.

Keywords: phytoremediation, phytomining, life cycle assessment, environmental impacts, rare earth elements, hyperaccumulator

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Authors: L. Salvati, D. Smiraglia, S. Bajocco, M. Munafò

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This paper focuses on Land Use and Land Cover Changes (LULCC) occurred in the urban coastal regions of the Mediterranean basin in the last thirty years. LULCC were assessed diachronically (1975-2006) in two urban areas, Rome (Italy) and Athens (Greece), by using CORINE land cover maps. In strictly coastal territories a persistent growth of built-up areas at the expenses of both agricultural and forest land uses was found. On the contrary, a different pattern was observed in the surrounding inland areas, where a high conversion rate of the agricultural land uses to both urban and forest land uses was recorded. The impact of city growth on the complex pattern of coastal LULCC is finally discussed.

Keywords: land use changes, coastal region, Rome prefecture, Attica, southern Europe

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Authors: Cláudia Patrocínio, Beatriz Fernandes, Ana Filipa Pires

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Background: Mirror therapy (MT) is used to improve motor function after stroke. During MT, a mirror is placed between the two upper limbs (UL), thus reflecting movements of the non- affected side as if it were the affected side. Objectives: The aim of this review is to analyze the evidence on the effec.tiveness of MT in the recovery of UL function in population with post chronic stroke. Methods: The literature search was carried out in PubMed, ISI Web of Science, and PEDro database. Inclusion criteria: a) studies that include individuals diagnosed with stroke for at least 6 months; b) intervention with MT in UL or comparing it with other interventions; c) articles published until 2023; d) articles published in English or Portuguese; e) randomized controlled studies. Exclusion criteria: a) animal studies; b) studies that do not provide a detailed description of the intervention; c) Studies using central electrical stimulation. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale. Studies with < 4 on PEDro scale were excluded. Eighteen studies met all the inclusion criteria. Main results and conclusions: The quality of the studies varies between 5 and 8. One article compared muscular strength training (MST) with MT vs without MT and four articles compared the use of MT vs conventional therapy (CT), one study compared extracorporeal shock therapy (EST) with and without MT and another study compared functional electrical stimulation (FES), MT and biofeedback, three studies compared MT with Mesh Glove (MG) or Sham Therapy, five articles compared performing bimanual exercises with and without MT and three studies compared MT with virtual reality (VR) or robot training (RT). The assessment of changes in function and structure (International Classification of Functioning, Disability and Health parameter) was carried out, in each article, mainly using the Fugl Meyer Assessment-Upper Limb scale, activity and participation (International Classification of Functioning, Disability and Health parameter) were evaluated using different scales, in each study. The positive results were seen in these parameters, globally. Results suggest that MT is more effective than other therapies in motor recovery and function of the affected UL, than these techniques alone, although the results have been modest in most of the included studies. There is also a more significant improvement in the distal movements of the affected hand than in the rest of the UL.

Keywords: physical therapy, mirror therapy, chronic stroke, upper limb, hemiplegia

Procedia PDF Downloads 51