Search results for: search algorithms

Authors: Renata Markiewicz-Żukowska, Patryk Nowakowski, Sylwia K. Naliwajko, Jakub M. Bołtryk, Katarzyna Socha, Anna Puścion-Jakubik, Jolanta Soroczyńska, Maria H. Borawska

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Selenium (Se) is an essential element for human health. As an integral part of glutathione peroxidase, it has antioxidant, anti-inflammatory and anticancer activities. Unfortunately, Se dietary intake is often insufficient, especially in regions where the soil is low in Se. Therefore, in search for good sources of Se, the content of this element in food products should be monitored. Food product can be considered as a source of Se when its standard portion covers above 15% of recommended daily allowance. In the case of nuts, 42g is recognized as the standard portion. The aim of this study was to determine the Se content in nuts and to answer the question of whether the studied nuts can be considered as a source of Se in the diet. The material for the study consisted of 10 types of nuts (12 samples of each one): almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts. The nuts were mineralized using microwave technique (Berghof, Germany). The content of Se was determined by atomic absorption spectrometry method with electrothermal atomization in a graphite tube with Zeeman background correction (Hitachi, Japan). The accuracy of the method was verified on certified reference material: Simulated Diet D. The statistical analysis was performed using Statistica v. 13.0 software. Statistical significance was determined at p < 0.05 level. The highest content of Se was found in Brazil nuts (4566.21 ± 3393.9 µg/kg) and the lowest in almonds (36.07 ± 18.8 µg/kg). A standard portion (42g) of almonds, brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts covers the recommended daily allowance for Se respectively in: 2, 192, 28, 2, 16, 7, 4, 3, 12, 6%. Brazil nuts, cashews and macadamia nuts can be considered as a good source of Se in diet.

Keywords: atomic absorption spectrometry, diet, nuts, selenium

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Authors: Jamal Alasiri, Naif Hakeem, Saoud Almaslmani

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Background: Patients with humeral shaft fractures have two different treatment options. Surgical therapy has lesser risks of non-union, mal-union, and re-intervention than non-surgical therapy. These positive clinical outcomes of the surgical approach make it a preferable treatment option despite the risks of radial nerve palsy and additional surgery-related risk. We aimed to evaluate patients’ outcomes and return to work after surgical vs. non-surgical management of shaft humeral fracture. Methods: We used databases, including PubMed, Medline, and Cochrane Register of Controlled Trials, from 2010 to January 2022 to search for potential randomised controlled trials (RCTs) and cohort studies comparing the patients’ related outcome measures and return to work between surgical and non-surgical management of humerus fracture. Results: After carefully evaluating 1352 articles, we included three RCTs (232 patients) and one cohort study (39 patients). The surgical intervention used plate/nail fixation, while the non-surgical intervention used a splint or brace procedure to manage shaft humeral fracture. The pooled DASH effects of all three RCTs at six (M.D: -7.5 [-13.20, -1.89], P: 0.009) I2:44%) and 12 months (M.D: -1.32 [-3.82, 1.17], p:0.29, I2: 0%) were higher in patients treated surgically than in non-surgical procedures. The pooled constant Murley score at six (M.D: 7.945[2.77,13.10], P: 0.003) I2: 0%) and 12 months (M.D: 1.78 [-1.52, 5.09], P: 0.29, I2: 0%) were higher in patients who received non-surgical than surgical therapy. However, pooled analysis for patients returning to work for both groups remained inconclusive. Conclusion: Altogether, we found no significant evidence supporting the clinical benefits of surgical over non-surgical therapy. Thus, the non-surgical approach remains the preferred therapeutic choice for managing shaft humeral fractures due to its lesser side effects.

Keywords: shaft humeral fracture, surgical treatment, Patient-related outcomes, return to work, DASH

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Odunayo Christy Atewolara-Odule, Olapeju O. Aiyelaagbe

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The search for new natural anti-oxidants has grown tremendously over the years because reactive oxygen species (ROS) production and oxidative stress have been linked to a large number of human degenerative diseases, such as cancer, cardiovascular diseases, inflammation, and diabetes. Tapinanthus bangwensis, a parasitic plant commonly known as mistletoe belonging to the Loranthaceae family, is mostly employed traditionally to treat inflammation, cancer, diabetes, and hypertension to mention a few. In this study, air-dried pulverized leaves and stem of Tapinanthus bangwensis were successively extracted with n-hexane, ethyl acetate, and methanol to give the corresponding crude extracts. The extracts were purified by column chromatography and high-performance liquid chromatography to give the isolated compounds. Structural elucidation was done using mass spectrometry, Fourier transform infra-red, 1D and 2D NMR spectroscopy. The antioxidant activity of the compounds was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ascorbic acid as standard. Three compounds; Friedelin, Eudesmic acid (3,4,5-trimethoxybenzoic) and Methyl-3,4,5-trimethoxybenzoate were isolated from the extracts of Tapinanthus bangwensis. Friedelin was isolated from the ethyl acetate extract of the stem while the two other compounds were isolated from the methanol extract of the leaves. The percentages of free radical scavenging activities of the compounds are as follows: Friedelin, 73.69%, methyl-3,4,5-trimethoxybenzoate, 79.33% and eudesmic, 87.68% anti-oxidant activity which were quite comparable to 93.96% given by ascorbic acid. We are reporting, to our best knowledge, for the first time the occurrence of friedelin and eudesmic acid in Tapinanthus bangwensis. The high anti-oxidant activity of these compounds supports the use of this plant in the management of diabetes and hypertension as they will be useful in combating complications arising from the disease.

Keywords: column chromatography, eudesmic acid, friedelin, Tapinanthus bangwensis

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Authors: Malik Abid Hussain Khokhar, Muhammad Adnan Tahir, Hisham Bin Hafeez Awan

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Continuous environmental determinism and climatic change in the entire globe due to increasing land surface temperature (LST) has become a vital phenomenon nowadays. LST is accelerating because of increasing greenhouse gases in the environment which results of melting down ice caps, ice sheets and glaciers. It has not only worse effects on vegetation and water bodies of the region but has also severe impacts on monsoon areas in the form of capricious rainfall and monsoon failure extensive precipitation. Environment can be monitored with the help of various geographic information systems (GIS) based algorithms i.e. SC (Single), DA (Dual Angle), Mao, Sobrino and SW (Split Window). Estimation of LST is very much possible from digital image processing of satellite imagery. This paper will encompass extraction of LST of Abbottabad using SW technique of GIS and Remote Sensing over last ten years by means of Landsat 7 ETM+ (Environmental Thematic Mapper) and Landsat 8 vide their Thermal Infrared (TIR Sensor) and Optical Land Imager (OLI sensor less Landsat 7 ETM+) having 100 m TIR resolution and 30 m Spectral Resolutions. These sensors have two TIR bands each; their emissivity and spectral radiance will be used as input statistics in SW algorithm for LST extraction. Emissivity will be derived from Normalized Difference Vegetation Index (NDVI) threshold methods using 2-5 bands of OLI with the help of e-cognition software, and spectral radiance will be extracted TIR Bands (Band 10-11 and Band 6 of Landsat 7 ETM+). Accuracy of results will be evaluated by weather data as well. The successive research will have a significant role for all tires of governing bodies related to climate change departments.

Keywords: environment, Landsat 8, SW Algorithm, TIR

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Authors: Xiaoyuan Suo

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3D object reconstruction is a broad research area within the computer vision field involving many stages and still open problems. One of the existing challenges in this field lies with micromotion, such as the facial expressions on the appearance of the human or animal face. Similar literatures in this field focuses on 3D reconstruction in stable conditions such as an existing image or photos taken in a rather static environment, while the purpose of this work is to discuss a flexible scan system using multiple cameras that can correctly reconstruct 3D stable and moving objects -- human face with expression in particular. Further, a mathematical model is proposed at the end of this literature to automate the 3D object reconstruction process. The reconstruction process takes several stages. Firstly, a set of simple 2D lines would be projected onto the object and hence a set of uneven curvy lines can be obtained, which represents the 3D numerical data of the surface. The lines and their shapes will help to identify object’s 3D construction in pixels. With the two-recorded angles and their distance from the camera, a simple mathematical calculation would give the resulting coordinate of each projected line in an absolute 3D space. This proposed research will benefit many practical areas, including but not limited to biometric identification, authentications, cybersecurity, preservation of cultural heritage, drama acting especially those with rapid and complex facial gestures, and many others. Specifically, this will (I) provide a brief survey of comparable techniques existing in this field. (II) discuss a set of specialized methodologies or algorithms for effective reconstruction of 3D objects. (III)implement, and testing the developed methodologies. (IV) verify findings with data collected from experiments. (V) conclude with lessons learned and final thoughts.

Keywords: 3D photogrammetry, 3D object reconstruction, facial expression recognition, facial recognition

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Sandra Cecilia Muhirirwe, Bart Van Der Bruggen, Violet Kisakye

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Economic analysis of Rainwater harvesting (RWH) systems is vital in search of a cost-effective solution to water unreliability, especially in low-income countries. There is little literature focusing on the financial aspects of RWH for dairy farmers. The main purpose was to assess the economic viability of rainwater harvesting for diary framers in the Rwenzori region. The study focused on the use of rainwater harvesting systems from the rooftop and collection in above surface tanks. Daily rainfall time series for 12 years was obtained across nine gauging stations. The daily water balance equation was used for optimal sizing of the tank. Economic analysis of the investment was carried out based on the life cycle costs and the accruing benefits for the period of 15 years. Roof areas were varied from 75m2 as the minimum required area to 500m2 while maintaining the same number of cattle and keeping the daily water demand constant. The results show that the required rainwater tank sizes are very large and may be impractical to install due to the strongly varying terrain and the initial cost of investment. In all districts, there is a significant reduction of the volume of the required tank with an increasing collection area. The results further show that increasing the collection area has a minor effect on reducing the required tank size. Generally, for all rainfall areas, the reliability increases with an increase in the roof area. The results indicate that 100% reliability can only be realized with very large collection areas that are impractical to install. The estimated benefits outweigh the cost of investment. The Present Net Value shows that the investment is economically viable and investment with a short payback of a maximum of 3 years for all the time series in the study area.

Keywords: dairy cattle, optimisation, rainwater harvesting, economic analysis

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Sayyed Benyamin Alavi, Giovanni Cerri, Leila Chennaoui, Ambra Giovannelli, Stefano Mazzoni

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Primary energy consumption and emissions of pollutants (including CO2) sustainability call to search methodologies to lower power absorption for unit of a given product. Cool production plants based on vapour compression are widely used for many applications: air conditioning, food conservation, domestic refrigerators and freezers, special industrial processes, etc. In the field of cool production, the amount of Yearly Consumed Primary Energy is enormous, thus, saving some percentage of it, leads to big worldwide impact in the energy consumption and related energy sustainability. Among various techniques to reduce power required by a Vapour Compression Cool Production Plant (VCCPP), the technique based on Power Regeneration by means of Internal Direct Cycle (IDC) will be considered in this paper. Power produced by IDC reduces power need for unit of produced Cool Power by the VCCPP. The paper contains basic concepts that lead to develop IDCs and the proposed options to use the IDC Power. Among various selections for using turbo machines, Best Economically Available Technologies (BEATs) have been explored. Based on vehicle engine turbochargers, they have been taken into consideration for this application. According to BEAT Database and similarity rules, the best turbo machine selection leads to the minimum nominal power required by VCCPP Main Compressor. Results obtained installing the prototype in “ad hoc” designed test bench will be discussed and compared with the expected performance. Forecasts for the upgrading VCCPP, various applications will be given and discussed. 4-6% saving is expected for air conditioning cooling plants and 15-22% is expected for cryogenic plants.

Keywords: Refrigeration Plant, Vapour Pressure Amplifier, Compressor, Expander, Turbine, Turbomachinery Selection, Power Saving

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Authors: Lijuan Zhou, Mengqi Wu, Changyong Niu

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Attributed graph clustering can utilize the graph topology and node attributes to uncover hidden community structures and patterns in complex networks, aiding in the understanding and analysis of complex systems. Utilizing contrastive learning for attributed graph clustering can effectively exploit meaningful implicit relationships between data. However, existing attributed graph clustering methods based on contrastive learning suffer from the following drawbacks: 1) Complex data augmentation increases computational cost, and inappropriate data augmentation may lead to semantic drift. 2) The selection of positive and negative samples neglects the intrinsic cluster structure learned from graph topology and node attributes. Therefore, this paper proposes a method called self-supervised Attributed Graph Clustering with Dual Contrastive Loss constraints (AGC-DCL). Firstly, Siamese Multilayer Perceptron (MLP) encoders are employed to generate two views separately to avoid complex data augmentation. Secondly, the neighborhood contrastive loss is introduced to constrain node representation using local topological structure while effectively embedding attribute information through attribute reconstruction. Additionally, clustering-oriented contrastive loss is applied to fully utilize clustering information in global semantics for discriminative node representations, regarding the cluster centers from two views as negative samples to fully leverage effective clustering information from different views. Comparative clustering results with existing attributed graph clustering algorithms on six datasets demonstrate the superiority of the proposed method.

Keywords: attributed graph clustering, contrastive learning, clustering-oriented, self-supervised learning

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Authors: Elzo Alves Aranha, Marcio M. Araki

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Business model (BM) is a term that has been receiving the attention of scholars and practitioners and has been consolidating itself as a field of study and research. Although there is no agreement in the academic literature on the definition of BM, at least there is an explicit agreement: BM defines a logical structure of how an organization creates value, capture value and delivers value for the customers and stakeholders. The lack of understanding about connections and elements among BM and higher education, university, and entrepreneurship education opens a gap in the academic literature. Thus, it is interesting to analyze how BM has been approached by the literature and applied in higher education, university, and entrepreneurship education aimed to know the main streams of research. This is because higher education institutions are characterized by innovation, leading to a greater acceptance of new and modern concepts such as BM. Our research has the main motivation to fill the gap in the academic literature, making it possible to increase the power of understanding about connections and aspects among BM and higher education, university, and entrepreneurship education. The objective of the research is to analyze the main aspects among BM and higher education, university, and entrepreneurship education in academic literature. The research followed the systematic literature review (SLR). The SLR is based on three main factors: clarity, validity, and auditability. 82 academic papers were found in the past 10 years, from 2009-2019. The search was carried out in Science Direct and Periodicos Capes databases. The main findings indicate that there are links between BM and higher education, BM and university, BM, and entrepreneurship education. The main findings are inserted within seven aspects. The findings are innovative and contribute to increase the power of understanding about the connection among BM and higher education, university, and entrepreneurship education in academic literature. The research findings addressed to the gap exposed in academic literature. The research findings have several practical implications, and we highlight only two main ones. First, researchers will be able to use the research findings to mitigate a BM research agenda involving connections between BM and higher education, BM and university, and BM and entrepreneurship education. Second, directors, deans, and university leaders will be able to carry out BM awareness programs, BM professors training programs, and makers planning for the inclusion of BM, as one of the components of the curricula of the undergraduate and graduate courses.

Keywords: business model, entrepreneurship education, higher education, university

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Authors: A. A. Sadia, A. Emdad, E. Hossain

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The increasing importance of mushrooms as a source of nutrition, health benefits, and even potential cancer treatment has raised awareness of the impact of climate-sensitive variables on their cultivation. Factors like temperature, relative humidity, air quality, and substrate composition play pivotal roles in shaping mushroom growth, especially in Bangladesh. Oyster mushrooms, a commonly cultivated variety in this region, are particularly vulnerable to climate fluctuations. This research explores the climatic dynamics affecting oyster mushroom cultivation and, presents an approach to address these challenges and provides tangible solutions to fortify the agro-economy, ensure food security, and promote the sustainability of this crucial food source. Using climate and production data, this study evaluates the performance of three clustering algorithms -KMeans, OPTICS, and BIRCH- based on various quality metrics. While each algorithm demonstrates specific strengths, the findings provide insights into their effectiveness for this specific dataset. The results yield essential information, pinpointing the optimal temperature range of 13°C-22°C, the unfavorable temperature threshold of 28°C and above, and the ideal relative humidity range of 75-85% with the suitable production regions in three different seasons: Kharif-1, 2, and Robi. Additionally, a user-friendly web application is developed to support mushroom farmers in making well-informed decisions about their cultivation practices. This platform offers valuable insights into the most advantageous periods for oyster mushroom farming, with the overarching goal of enhancing the efficiency and profitability of mushroom farming.

Keywords: climate variability, mushroom cultivation, clustering techniques, food security, sustainability, web-application

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: D. Aydinoglu, N. Karaca, O. Ceylan

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Limited water resources on the earth come first among the most alarming issues. In this respect, several solutions from treatment of waste water to water management have been proposed. Recently, use of hydrogels as soil additive, which is one of the water management ways in agriculture, has gained increasing interest. In traditional agriculture applications, water used with irrigation aim, rapidly flows down between the pore structures in soil, without enough useful for soil. To overcome this fact and increase the abovementioned limit values, recently, several natural based hydrogels have been suggested and tested to find out their efficiency in soil. However, most of these researches have dealt with grafting of synthetic acrylate based monomers on natural gelling agents, most probably due to reinforced of the natural gels. These results motivated us to search a natural based hydrogel formulations, not including any synthetic component, and strengthened with montmorillonite clay instead of any grafting polymerization with synthetic monomer and examine their potential in this field, as well as characterize of them. With this purpose, carrageenan-psyllium/ montmorillonite hybrid hydrogels have been successively prepared. Their swelling capacities were determined both in deionized and tap water and were found to be dependent on the carrageenan, psyllium and montmorillonite ratios, as well as the water type. On the other hand, mechanical tests revealed that especially carrageenan and montmorillonite contents have a great effect on gel strength, which is one of the essential features, preventing the gels from cracking resulted in readily outflow of all the water in the gel without beneficial for soil. They found to reach 0.23 MPa. The experiments carried out with soil indicated that hydrogels significantly improved the water uptake capacities and water retention degrees of the soil from 49 g to 85 g per g of soil and from 32 to 67%, respectively, depending on the ingredient ratios. Also, biodegradation tests demonstrated that all the hydrogels undergo biodegradation, as expected from their natural origin. The overall results suggested that these hybrid hydrogels have a potential for use as soil additive and can be safely used owing to their totally natural structure.

Keywords: carrageenan, hydrogel, montmorillonite, psyllium

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Authors: Hushyar Azari

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Aim and background: Deep brain stimulation (DBS) is regarded as an important therapeutic choice for Parkinson's disease (PD). The two most common targets for DBS are the subthalamic nucleus (STN) and globus pallidus (GPi). This review was conducted to compare the clinical effectiveness of these two targets. Methods: A systematic literature search in electronic databases: Embase, Cochrane Library and PubMed were restricted to English language publications 2010 to 2021. Specified MeSH terms were searched in all databases. Studies which evaluated the Unified Parkinson's Disease Rating Scale (UPDRS) III were selected by meeting the following criteria: (1) compared both GPi and STN DBS; (2) had at least three months follow-up period; (3)at least five participants in each group; (4)conducted after 2010. Study quality assessment was performed using the Modified Jadad Scale. Results: 3577 potentially relevant articles were identified, of these, 3569 were excluded based on title and abstract, duplicate and unsuitable article removal. Eight articles satisfied the inclusion criteria and were scrutinized (458 PD patients). According to Modified Jadad Scale, the majority of included studies had low evidence quality which was a limitation of this review. 5 studies reported no statistically significant between-group difference for improvements in UPDRS ш scores. At the same time, there were some results in terms of pain, action tremor, rigidity, and urinary symptoms, which indicated that STN DBS might be a better choice. Regarding the adverse effects, GPi was superior. Conclusion: It is clear that other larger randomized clinical trials with longer follow-up periods and control groups are needed to decide which target is more efficient for deep brain stimulation in Parkinson’s disease and imposes fewer adverse effects on the patients. Meanwhile, STN seems more reasonable according to the results of this systematic review.

Keywords: brain stimulation, globus pallidus, Parkinson's disease, subthalamic nucleus

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Authors: Lukas Ded

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Studying the tissue structure and histogenesis in the natural, 3D context is challenging but highly beneficial process. Contrary to classical approach of the physical tissue sectioning and subsequent imaging, it enables to study the relationships of individual cellular and histological structures in their native context. Recent developments in the tissue clearing approaches and microscopic volume imaging/data processing enable the application of these methods also in the areas of developmental and reproductive biology. Here, using the CLARITY tissue procedure and 3D confocal volume imaging we optimized the protocol for clearing, staining and imaging of the mice seminiferous tubules isolated from the testes without cardiac perfusion procedure. Our approach enables the high magnification and fine resolution axial imaging of the whole diameter of the seminiferous tubules with possible unlimited lateral length imaging. Hence, the large continuous pieces of the seminiferous tubule can be scanned and digitally reconstructed for the study of the single tubule seminiferous stages using nuclear dyes. Furthermore, the application of the antibodies and various molecular dyes can be used for molecular labeling of individual cellular and subcellular structures and resulting 3D images can highly increase our understanding of the spatiotemporal aspects of the seminiferous tubules development and sperm ultrastructure formation. Finally, our newly developed algorithms for 3D data processing enable the massive parallel processing of the large amount of individual cell and tissue fluorescent signatures and building the robust spermatogenic models under physiological and pathological conditions.

Keywords: CLARITY, spermatogenesis, testis, tissue clearing, volume imaging

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Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson

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The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.

Keywords: PCR, optimisation, microfluidics, COMSOL

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Authors: F. M. Pisano, M. Ciminello

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Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: interactive dashboards, optical fibers, structural health monitoring, visual analytics

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Authors: Nuur Insan Tangkelangi

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Needs on academic writing competence as the primary focus in higher education encourage the university institutions around the world to provide academic writing courses to support their students dealing with their tasks pertaining to this competence. However, a pilot study conducted previously in one of the universities in Palopo, a city in South Sulawesi, revealed that even though the institution has provided academic writing courses, supported by some workshops related to academic writing and some supporting facilities at campus, the students still face difficulties in completing their assignments related to academic writing, particularly in writing their theses. The present study focuses on investigating the specific needs of the students in the same institution in terms of competences required in academic writing. It is also carried out to examine whether the syllabus exists and accommodates the students’ needs or not. Questionnaire and interview were used to collect data from sixty students of sixth semester and two lecturers of the academic courses. The results reveal that the students need to learn all aspects of linguistic competence (language features, lexical phrases, academic language and vocabulary, and proper language) and some aspects in discourse competence (how to write introduction, search for appropriate literature, design research method, write coherent paragraphs, refer to sources, summarize and display data, and link sentences smoothly). Regarding the syllabus, it is found that the academic writing courses provided in the institution, where this study takes place, do not have syllabus. This condition is different from other institutions which provide syllabi for all courses. However, at the commencement of the course, the students and the lecturers have negotiated their learning goals, topics discussed, learning activities, and assessment criteria for the course. Therefore, even though the syllabus does not exist, but the elements of the syllabus are there. The negotiation between the students and the lecturers contributes to the students’ attitude toward the courses. The students are contented with the course and they feel that their needs in academic writing have been accommodated. However, some suggestions for the next academic writing courses are stated by the students. Considering the results of this study, a syllabus is then proposed which is expected to accommodate the specific needs of students in that institution.

Keywords: Students' needs, academic writing, syllabus design for instruction, case study

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: María del Carmen Recio-Ruiz, Ramiro Ruiz-Rosas, Juana María Rosas, José Rodríguez-Mirasol, Tomás Cordero

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Nowadays, fossil resources are main precursors for fuel production. Due to their contribution to the greenhouse effect and their future depletion, there is a constant search for environmentally friendly feedstock alternatives. Biomass residues constitute an interesting replacement for fossil resources because of their zero net CO₂ emissions. One of the main routes to convert biomass into energy and chemicals is pyrolysis. In this work, conventional pyrolysis of different biomass residues highly available such as almond shells, hemp hurds, olive stones, and Kraft lignin, was studied. In a typical experiment, the biomass was crushed and loaded into a fixed bed reactor under continuous nitrogen flow. The influence of temperature (400-800 ºC) and heating rate (10 and 20 ºC/min) on the pyrolysis yield and composition of the different fractions has been studied. In every case, the mass yields revealed that the solid fraction decreased with temperature, while liquid and gas fractions increased due to depolymerization and cracking reactions at high temperatures. The composition of every pyrolysis fraction was studied in detail. The results showed that the composition of the gas fraction was mainly CO, CO₂ when working at low temperatures, and mostly CH₄ and H₂at high temperatures. The solid fraction developed an incipient microporosity, with narrow micropore volume of 0.21 cm³/g. Regarding the liquid fraction, pyrolysis of almond shell, hemp hurds, and olive stones led mainly to a high content in aliphatic acids and furans, due to the high volatile matter content of these biomass (>74 %wt.), and phenols to a lesser degree, which were formed due to the degradation of lignin at higher temperatures. However, when Kraft lignin was used as bio-oil precursor, the presence of phenols was very prominent, and aliphatic compounds were also detected in a lesser extent.

Keywords: Bio-oil, biomass, conventional pyrolysis, lignocellulosic

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Authors: El Montassir Dahmane, Nadia Eladlani, Aziz Ouahrouch, Mohammed Rhazi, Moha Taourirte

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The present study was aimed to approximate the optimal conditions to chromium recovery from wastewater by nanoparticles and whiskers of chitosan. Chitosan with an average molecular weight of 63 kDa and a 96% deacetylation degree was prepared according to our previous study. Chromium recovery is influenced by different parameters. In our search, we determined the appropriate range of pH to form chitosan–Cr(III), nanoparticles Cr(III), and whiskers– Cr(III) complex. We studied also the influence of chromium concentration and the nature of chitosan-based materials on the complexation process. Our main aim is to approximate the optimal conditions to remove chromium(III) from the tanning bath, recuperated from tannery wastewater of Marrakech in Morocco. A Perkin Elmer optima 2000 Inductively Coupled Plasma- Optical Emission Spectrometer (ICP-OES), was used to determine the quantity of chromium persistent in tannery wastewater after complexation phenomenon. To the best of our knowledge, this is the first report interested in the optimal conditions for chromium recovery from wastewater by nanoparticles and whiskers of chitosan. From our research, we found that in chromium solution, the appropriate range of pH to form complex is between 5.6 and 6.7. Also, the complexation of Cr(III) is depending on the nature of complexing ligand and chromium concentration. The obtained results reveal that nanoparticles present an excellent adsorption capacity regardless of chromium concentration. In addition, after a critical chromium concentration (250 mg/l), our ligand becomes saturated, that requires an increase of ligand mass for increasing chromium concentration in order to have a better adsorption capacity. Hence, in the same conditions, we used chitosan, its nanoparticles, whiskers, and chitosan based films to remove Cr(III) from tannery wastewater. The pH of this effluent was around 6, and its chromium concentration was 300 mg/l. The results expose that the sequence of complexing ligand in the effluent is the same in chromium solution, determined via our previous study. However, the adsorbed quantity is less due to the presence of other metallic ions in tannery wastewater. We conclude that the best complexing ligand-based chitosan is chitosan nanoaprticles whether it’s in chromium solution or in tannery wastewater. Nanoparticles are the best complexing ligand after 24 h of contact nanoparticles can remove 70% of chromium from this tannery wastewater.

Keywords: nanoparticles, whiskers, chitosan, chromium

Procedia PDF Downloads 137

Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: W. Stawiński, A. Wegrzyn, O. M. Freitas, S. A. Figueiredo

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Water is a life supporting resource, crucial for humanity and essential for natural ecosystems, which have been endangered by developing industry and increasing human population. Dyes are common in effluents discharged by various industries such as paper, plastics, food, cosmetics, and textile. They produce toxic effects on animals and disturb natural biological processes in receiving waters. Having complex molecular structure and resistance to biological decomposition they are problematic and difficult to be treated by conventional methods. In the search of efficient and sustainable method, sorption has been getting more interest in application to wastewaters treatment. Clays are minerals that have a layer structure based on phyllosilicate sheets that may carry a charge, which is balanced by ions located between the sheets. These charge-balancing ions can be exchanged resulting in very good ion-exchange properties of the material. Modifications of clays enhance their properties, producing a good and inexpensive sorbent for the removal of pollutants from wastewaters. The presented work proves that the treatment of a clay, vermiculite, with nitric acid followed by washing in citric acid strongly increases the sorption of two cationic dyes, methylene blue (C.I. 52015) and astrazon red (C.I. 110825). Desorption studies showed that the best eluent for regeneration is a solution of NaCl in ethanol. Cycles of sorption and desorption in column system showed no significant deterioration of sorption capacity and proved that the material shows a very good performance as sorbent, which can be recycled and reused. The results obtained open new possibilities of further modifications on vermiculite and modifications of other materials in order to get very efficient sorbents useful for wastewater treatment.

Keywords: cationic dyestuffs, sorption and regeneration, vermiculite, wastewater treatment

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Authors: Gülcan Taşkıran, Ayla Bayık Temel

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Background: Florence Nightingale, the modern founder of the nursing, is most famous for her role as a nurse. But not so much known about her contributions as a mathematician and statistician. Aim: In this conceptual article it is aimed to examine Florence Nightingale's statistics education, how she used her passion for statistics and applied statistical data in nursing care and her scientific contributions to statistical science. Design: Literature review method was used in the study. The databases of Istanbul University Library Search Engine, Turkish Medical Directory, Thesis Scanning Center of Higher Education Council, PubMed, Google Scholar, EBSCO Host, Web of Science were scanned to reach the studies. The keywords 'statistics' and 'Florence Nightingale' have been used in Turkish and English while being screened. As a result of the screening, totally 41 studies were examined from the national and international literature. Results: Florence Nightingale has interested in mathematics and statistics at her early ages and has received various training in these subjects. Lessons learned by Nightingale in a cultured family environment, her talent in mathematics and numbers, and her religious beliefs played a crucial role in the direction of the statistics. She was influenced by Quetelet's ideas in the formation of the statistical philosophy and received support from William Farr in her statistical studies. During the Crimean War, she applied statistical knowledge to nursing care, developed many statistical methods and graphics, so that she made revolutionary reforms in the health field. Conclusions: Nightingale's interest in statistics, her broad vision, the statistical ideas fused with religious beliefs, the innovative graphics she has developed and the extraordinary statistical projects that she carried out has been influential on the basis of her professional achievements. Florence Nightingale has also become a model for women in statistics. Today, using and teaching of statistics and research in nursing care practices and education programs continues with the light she gave.

Keywords: Crimean war, Florence Nightingale, nursing, statistics

Procedia PDF Downloads 293

Authors: Krish Jhurani

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The research paper presents an in-depth analysis of symmetry properties in linear algebraic systems under the operation of non-canonical scalar multiplication structures, specifically semirings, and near-rings. The objective is to unveil the profound alterations that occur in traditional linear algebraic structures when we replace conventional field multiplication with these non-canonical operations. In the methodology, we first establish the theoretical foundations of non-canonical scalar multiplication, followed by a meticulous investigation into the resulting symmetry properties, focusing on eigenvectors, eigenspaces, and invariant subspaces. The methodology involves a combination of rigorous mathematical proofs and derivations, supplemented by illustrative examples that exhibit these discovered symmetry properties in tangible mathematical scenarios. The core findings uncover unique symmetry attributes. For linear algebraic systems with semiring scalar multiplication, we reveal eigenvectors and eigenvalues. Systems operating under near-ring scalar multiplication disclose unique invariant subspaces. These discoveries drastically broaden the traditional landscape of symmetry properties in linear algebraic systems. With the application of these findings, potential practical implications span across various fields such as physics, coding theory, and cryptography. They could enhance error detection and correction codes, devise more secure cryptographic algorithms, and even influence theoretical physics. This expansion of applicability accentuates the significance of the presented research. The research paper thus contributes to the mathematical community by bringing forth perspectives on linear algebraic systems and their symmetry properties through the lens of non-canonical scalar multiplication, coupled with an exploration of practical applications.

Keywords: eigenspaces, eigenvectors, invariant subspaces, near-rings, non-canonical scalar multiplication, semirings, symmetry properties

Procedia PDF Downloads 123

Authors: Siyanbola Mojisola Funmilayo, Amao Emmanuel Ayodele

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The poultry industry in Nigeria has been played by a variety of problems, which include the search for feed ingredients that are not competed for by man. This has resulted in a reduced interest of farmers in the industry leading to a reduction in animal protein availability for human consumption as a consequence of a high cost of production. The incorporation of wild sunflower meal (Tithonia diversfolia, Hemsl A. Gray) (WSF Meal) and some others in poultry diets have been reported to result in compounded feed with nutrient profiles that compare favourable with feeds of conventional feedstuff and reduce feed cost as they reduce competition with humans. A 98-day feeding trial was used to evaluate the effect of Wild sunflower leaf (WSL) at varying levels on the hematology and biochemistry of cockerels. A total of one hundred and twenty(120) cockerel birds were randomly allotted into four experimental diets with three replicates per experimental diet (ten birds per replicate). Wild sunflower leaf was included in four graded levels ; 0, 5, 10, and 15%. Packed cell volume, Red blood cell count, White blood cell count, Hemoglobin count, Lymphocyte count, Neutrophil count, Platelets, Mean Corpuscular Hemoglobin Concentration (MCHC), Mean Corpuscular Hemoglobin (MCH), Aspartate aminotransferase (AST), Glucose, Urea, Chloride, Sodium, and Potassium ion values were significantly different (p<0.05) among the treatments. Mean values obtained for Creatinine, Total Protein, Alanine aminotransferase (ALT), Albumin, and Mean Corpuscular Volume (MCV) were not significantly different (p>0.05) in all the treatment. WSL could be included up to 15% in the diet of cockerel without any deleterious effect on the birds. Based on the results, up to 15% Wild sunflower meal (WSL) can be included in the diet of cockerel without any adverse effect on the hematology and biochemical indices of birds.

Keywords: biochemical changes, cockerels, hematology, wild sunflower leaf

Procedia PDF Downloads 447

Authors: A. Adetutu, T. M. Awodugba, O. A. Owoade

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The development of resistance to currently known conventional anti-typhoid drugs has necessitated search into cheap, more potent and less toxic anti-typhoid drugs of plant origin. Therefore, this study investigated the anti-typhoid activity of fractions of A. indica in Salmonella typhi infected rats. Leaves of A. indica were extracted in methanol and fractionated into n-hexane, chloroform, ethyl-acetate, and aqueous fractions. The anti-salmonella potentials of fractions of A. indica were assessed via in-vitro inhibition of S. typhi using agar well diffusion, Minimum Inhibitory Concentration (MIC), Minimum Bactericidal Concentration (MBC) and biofilm assays. The biochemical and haematological parameters were determined by spectrophotometric methods. The histological analysis was performed using Haematoxylin and Eosin staining methods. Data analysis was performed by one-way ANOVA. Results of this study showed that S. typhi was sensitive to aqueous and chloroform fractions of A. indica, and the fractions showed biofilm inhibition at concentrations of 12.50, 1.562, and 0.39 mg/mL. In the in-vivo study, the extract and chloroform fraction had significant (p < 0.05) effects on the number of viable S. typhi recovered from the blood and stopped salmonellosis after 6 days of treatment of rats at 500 mg/kg b.w. Treatments of infected rats with chloroform and aqueous fractions of A. indica normalized the haematological parameters in the animals. Similarly, treatment with fractions of the plants sustained a normal antioxidant status when compared with the normal control group. Chloroform and ethyl-acetate fractions of A. indica reversed the liver and intestinal degeneration induced by S. typhi infection in rats. The present investigation indicated that the aqueous and chloroform fractions of A. indica showed the potential to provide an effective treatment for salmonellosis, including typhoid fever. The results of the study may justify the ethno-medicinal use of the extract in traditional medicine for the treatment of typhoid and salmonella infections.

Keywords: Azadirachta indica L, salmonella, typhoid, leave fractions

Procedia PDF Downloads 132

Authors: Chen Wang, Qiuxiao Chen

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With the advent of the information age and the era of big data, urban planning research is no longer satisfied with the analysis and application of traditional data. Because of the limitations of traditional urban commercial center system research, big data provides new opportunities for urban research. Therefore, based on the quantitative evaluation method of big data, the commercial center system of the main city of Hangzhou is analyzed and evaluated, and the scale and hierarchical structure characteristics of the urban commercial center system are studied. In order to make up for the shortcomings of the existing POI extraction method, it proposes a POI extraction method based on adaptive adjustment of search window, which can accurately and efficiently extract the POI data of commercial business in the main city of Hangzhou. Through the visualization and nuclear density analysis of the extracted Point of Interest (POI) data, the current situation of the commercial center system in the main city of Hangzhou is evaluated. Then it compares with the commercial center system structure of 'Hangzhou City Master Plan (2001-2020)', analyzes the problems existing in the planned urban commercial center system, and provides corresponding suggestions and optimization strategy for the optimization of the planning of Hangzhou commercial center system. Then get the following conclusions: The status quo of the commercial center system in the main city of Hangzhou presents a first-level main center, a two-level main center, three third-level sub-centers, and multiple community-level business centers. Generally speaking, the construction of the main center in the commercial center system is basically up to standard, and there is still a big gap in the construction of the sub-center and the regional-level commercial center, further construction is needed. Therefore, it proposes an optimized hierarchical functional system, organizes commercial centers in an orderly manner; strengthens the central radiation to drive surrounding areas; implements the construction guidance of the center, effectively promotes the development of group formation and further improves the commercial center system structure of the main city of Hangzhou.

Keywords: business center system, business format, main city of Hangzhou, POI extraction method

Procedia PDF Downloads 140