Search results for: analyzing initial efficacy

Authors: Sneha Singh, Abhimanyu Dev, Vinod Nigam

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The major issue which decides the impending use of gold nanoparticles (AuNPs) in nanobiotechnological applications is their particle size and stability. Often the AuNPs obtained biomimetically are considered useless owing to their instability in the aqueous medium and thereby limiting the widespread acceptance of this facile green synthesis procedure. So, the use of nontoxic surfactants is warranted to stabilize the biogenic nanoparticles (NPs). But does the surfactant only play a role in stabilizing by being adsorbed to the NPs surface or can it have any other significant contribution in synthesis process and controlling their size as well as shape? Keeping this idea in mind, AuNPs were synthesized by using surfactant treated (lechate) and untreated (cell lysate supernatant) Bacillus licheniformis cell extract. The cell extracts mediated reduction of chloroauric acid (HAuCl 4) in the presence of non-ionic surfactant, Tween 20 (TW20), and its effect on the AuNPs stability was studied. Interestingly, the surfactant used in the study served as potential alternative to harvest cellular enzymes involved in bioreduction process in a hassle free condition. The surfactants ability to solubilize/leach membrane proteins and simultaneously stabilizing the AuNPs could have advantage from process point of view as it will reduce the time and economics involve in the nanofabrication of biogenic NPs. The synthesis was substantiated with UV-Vis spectroscopy, Dynamic light scattering study, FTIR spectroscopy, and Transmission electron microscopy. The Zeta potential of AuNPs solutions was measured routinely to corroborate the stability observations recorded visually. Highly stable, ultra-small AuNPs of 2.6 nm size were obtained from the study. Further, the biological efficacy of the obtained AuNPs as potential antibacterial agent was evaluated against Bacilllus subtilis, Pseudomonas aeruginosa, and Escherichia coli by observing the zone of inhibition. This potential of AuNPs of size < 3 nm as antibacterial agent could pave way for development of new antimicrobials and overcoming the problems of antibiotics resistance

Keywords: antibacterial, bioreduction, nanoparticles, surfactant

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Tarek Abdelkader, Suresh Selvaraj, Prasad Iyer, Yong Mun Hin, Hajmath Begum, P. Gopalakrishnakone

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Nowadays, more and more educational institutes, as well as students, rely on 3D anatomy programs as an important tool that helps students correlate the actual locations of anatomical structures in a 3D dimension. Lately, virtual reality (VR) is gaining more favor from the younger generations due to its higher interactive mode. As a result, using virtual reality as a gamified learning platform for anatomy became the current goal. We present a model where a Virtual Human Anatomy Program (VHAP) was developed to assist with the anatomy learning experience of students. The anatomy module has been built, mostly, from real patient CT scans. Segmentation and surface rendering were used to create the 3D model by direct segmentation of CT scans for each organ individually and exporting that model as a 3D file. After acquiring the 3D files for all needed organs, all the files were introduced into a Virtual Reality environment as a complete body anatomy model. In this ongoing experiment, students from different Allied Health orientations are testing the VHAP. Specifically, the cardiovascular system has been selected as the focus system of study since all of our students finished learning about it in the 1st trimester. The initial results suggest that the VHAP system is adding value to the learning process of our students, encouraging them to get more involved and to ask more questions. Involved students comments show that they are excited about the VHAP system with comments about its interactivity as well as the ability to use it solo as a self-learning aid in combination with the lectures. Some students also experienced minor side effects like dizziness.

Keywords: 3D construction, health sciences, teaching pedagogy, virtual reality

Procedia PDF Downloads 154

Authors: Aria Teimourzadeh, Marc Favier, Samaneh Kakavand

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The emergence of social networks has revolutionized the exchange of information. Every behavior on these platforms contributes to the generation of data known as social network data that are processed, stored and published by the social network service providers. Hence, it is vital to investigate the role of these platforms in user data by considering the privacy measures, especially when we observe the increased number of individuals and organizations engaging with the current virtual platforms without being aware that the data related to their positioning, connections and behavior is uncovered and used by third parties. Performing analytics on social network datasets may result in the disclosure of confidential information about the individuals or organizations which are the members of these virtual environments. Analyzing separate datasets can reveal private information about relationships, interests and more, especially when the datasets are analyzed jointly. Intentional breaches of privacy is the result of such analysis. Addressing these privacy concerns requires an understanding of the nature of data being accumulated and relevant data privacy regulations, as well as motivations for disclosure of personal information on social network platforms. Some significant points about how user's online information is controlled by the influence of social factors and to what extent the users are concerned about future use of their personal information by the organizations, are highlighted in this paper. Firstly, this research presents a short literature review about the structure of a network and concept of privacy in Online Social Networks. Secondly, the factors of user behavior related to privacy protection and self-disclosure on these virtual communities are presented. In other words, we seek to demonstrates the impact of identified variables on user information disclosure that could be taken into account to explain the privacy preservation of individuals on social networking platforms. Thirdly, a few research directions are discussed to address this topic for new researchers.

Keywords: information disclosure, privacy measures, privacy preservation, social network analysis, user experience

Procedia PDF Downloads 281

Authors: Wenfeng Xie

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Bulk/hull cavitation evolution induced by an underwater explosion (UNDEX) near a free surface (bulk) or a deformable structure (hull) is numerically investigated using a multiphase compressible fluid solver coupled with a one-fluid cavitation model. A series of two-dimensional computations is conducted with varying material elasticity and surface curvature. Results suggest that material elasticity and surface curvature influence the peak pressures generated from UNDEX shock and cavitation collapse, as well as the bulk/hull cavitation regions near the surface. Results also show that such effects can be different for bulk cavitation generated from UNDEX-free surface interaction and for hull cavitation generated from UNDEX-structure interaction. More importantly, results demonstrate that shock wave focusing caused by a concave solid surface can lead to a larger cavitation region and thus intensify the cavitation reload. The findings can be linked to the strength and the direction of reflected waves from the structural surface and reflected waves from the expanding bubble surface, which are functions of material elasticity and surface curvature. Shockwave focusing effects are also observed for axisymmetric simulations, but the strength of the pressure contours for the axisymmetric simulations is less than those for the 2D simulations due to the difference between the initial shock energy. The current method is limited to two-dimensional or axisymmetric applications. Moreover, the thermal effects are neglected and the liquid is not allowed to sustain tension in the cavitation model.

Keywords: cavitation, UNDEX, fluid-structure interaction, multiphase

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Authors: Katie Emerson, Sara Perez-Ojalvo, Jim Komorowski, Danielle Greenberg

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The purpose of this study was to evaluate arginase activity levels in response to combinations of an inositol-stabilized arginine silicate (ASI; Nitrosigine®), L-arginine, and Inositol. Arginine acts as a vasodilator that promotes increased blood flow resulting in enhanced delivery of oxygen and nutrients to the brain and other tissues. ASI alone has been shown to improve performance on cognitive tasks. Arginase, found in human serum, catalyzes the conversion of arginine to ornithine and urea, completing the last step in the urea cycle. Decreasing arginase levels maintains arginine and results in increased nitric oxide production. This study aimed to determine the most effective combination of ASI, L-arginine and inositol for minimizing arginase levels and therefore maximize ASI’s effect on cognition. Serum was taken from untreated healthy donors by separation from clotted factors. Arginase activity of serum in the presence or absence of test products was determined (QuantiChrom™, DARG-100, Bioassay Systems, Hayward CA). The remaining ultra-filtrated serum units were harvested and used as the source for the arginase enzyme. ASI alone or combined with varied levels of Inositol were tested as follows: ASI + inositol at 0.25 g, 0.5 g, 0.75 g, or 1.00 g. L-arginine was also tested as a positive control. All tests elicited changes in arginase activity demonstrating the efficacy of the method used. Adding L-arginine to serum from untreated subjects, with or without inositol only had a mild effect. Adding inositol at all levels reduced arginase activity. Adding 0.5 g to the standardized amount of ASI led to the lowest amount of arginase activity as compared to the 0.25g 0.75g or 1.00g doses of inositol or to L-arginine alone. The outcome of this study demonstrates an interaction of the pairing of inositol with ASI on the activity of the enzyme arginase. We found that neither the maximum nor minimum amount of inositol tested in this study led to maximal arginase inhibition. Since the inhibition of arginase activity is desirable for product formulations looking to maintain arginine levels, the most effective amount of inositol was deemed preferred. Subsequent studies suggest this moderate level of inositol in combination with ASI leads to cognitive improvements including reaction time, executive function, and concentration.

Keywords: arginine, inositol, arginase, cognitive benefits

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Authors: Zahid Rehman, TorOve Leiknes

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Quorum sensing (QS) is the process by which bacteria communicate with each other through small signaling molecules, such as N-acylhomoserine lactones (AHLs). Also, certain bacteria have the ability to degrade AHL molecules by a process referred to as quorum quenching (QQ); therefore, QQ can be used to control bacterial infections and biofilm formation. In this study, we aimed to identify new species of bacteria with QQ activities. To achieve this, sediments from Red Sea were collected either in the close vicinity of Sea grass or from area with no vegetation. From these samples, we isolated 72 bacterial strains and tested their ability to degrade/inactivate AHL molecules. Chromobacterium violaceum based bioassay was used in initial screening of isolates for QQ activity. The QQ activity of the positive isolates was further confirmed and quantified by employing liquid chromatography and mass spectrometry. These analyses showed that isolated bacterial strain could degrade AHL molecules with different acyl chain length and modifications. Sequencing of 16S-rRNA genes of positive isolates revealed that they belong to three different genera. Specifically, two isolates belong to genus Erythrobacter, four to Labrenzia and one isolate belongs to Bacterioplanes. Time course experiment showed that isolate belonging to genus Erythrobacter could degrade AHLs faster than other isolates. Furthermore, these isolates were tested for their ability to inhibit formation of biofilm and degradation of 3OXO-C12 AHLs produced by P. aeruginosa PAO1. Our results showed that isolate VG12 is better at controlling biofilm formation. This aligns with the ability of VG12 to cause at least 10-fold reduction in the amount of different AHLs tested.

Keywords: quorum sensing, biofilm, quorum quenching, anti-biofouling

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Authors: Sophia Francesca D. Lu

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This research tried to produce evidence that formal institutions, such as electoral and internal party quotas, can advance women’s active roles in the public sphere using the cases of four European countries: Belgium, Germany, Italy, and the Netherlands. The quantitative dataset was provided by the University of Chicago and the Inter-University Consortium of Political and Social Research based on a two-year study (2008-2010) of political parties. Belgium engages in constitutionally mandated electoral quotas. Germany, Italy and the Netherlands, on the other hand, have internal party quotas, which are voluntarily adopted by political parties. In analyzing each country’s chi-square and Pearson’s r correlation, Belgium, having an electoral quota, is the only country that was analyzed for electoral quotas. Germany, Italy and the Netherlands’ internal voluntary party quotas were correlated with women’s descriptive representations. Using chi-square analysis, this study showed that the presence of electoral quotas is correlated with an increase in the percentage of women in decision-making bodies as well as with an increase in the percentage of women in decision-making bodies. Likewise, using correlational analysis, a higher number of political parties employing internal party voluntary quotas is correlated with an increase in the percentage of women occupying seats in parliament as well as an increase in the percentage of women nominees in electoral lists of political parties. In conclusion, gender quotas, such as electoral quotas or internal party quotas, are an effective policy tool for greater women’s representation in political bodies. Political parties and governments should opt to have gender quotas, whether electoral or internal party quotas, to address the underrepresentation of women in parliament, decision-making bodies, and policy-formulation.

Keywords: electoral quota, Europe, formal institutions, institutional feminism, internal party quota, women’s electoral participation

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Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Kayode Oshinubi, Brice Kammegne, Jacques Demongeot

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The use of mathematical tools like Partial Differential Equations and Ordinary Differential Equations have become very important to predict the evolution of a viral disease in a population in order to take preventive and curative measures. In December 2019, a novel variety of Coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China causing a severe and potentially fatal respiratory syndrome, i.e., COVID-19. Since then, it has become a pandemic declared by World Health Organization (WHO) on March 11, 2020 which has spread around the globe. A reaction-diffusion system is a mathematical model that describes the evolution of a phenomenon subjected to two processes: a reaction process in which different substances are transformed, and a diffusion process that causes a distribution in space. This article provides a mathematical study of the Susceptible, Exposed, Infected, Recovered, and Vaccinated population model of the COVID-19 pandemic by the bias of reaction-diffusion equations. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined using the Lyapunov function are considered and the endemic equilibrium point exists and is stable if it satisfies Routh–Hurwitz criteria. Also, adequate conditions for the existence and uniqueness of the solution of the model have been proved. We showed the spatial distribution of the model compartments when the basic reproduction rate $\mathcal{R}_0 < 1$ and $\mathcal{R}_0 > 1$ and sensitivity analysis is performed in order to determine the most sensitive parameters in the proposed model. We demonstrate the model's effectiveness by performing numerical simulations. We investigate the impact of vaccination and the significance of spatial distribution parameters in the spread of COVID-19. The findings indicate that reducing contact with an infected person and increasing the proportion of susceptible people who receive high-efficacy vaccination will lessen the burden of COVID-19 in the population. To the public health policymakers, we offered a better understanding of the COVID-19 management.

Keywords: COVID-19, SEIRV epidemic model, reaction-diffusion equation, basic reproduction number, vaccination, spatial distribution

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Authors: A. Idawaty, O. Mohamed, A. Z. Zuriati

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This paper presents the development of an event based Discrete Event Simulation (DES) for a recovery algorithm known Backward Recovery Global Preemptive Utility Accrual Scheduling (BR_GPUAS). This algorithm implements the Backward Recovery (BR) mechanism as a fault recovery solution under the existing Time/Utility Function/ Utility Accrual (TUF/UA) scheduling domain for multiprocessor environment. The BR mechanism attempts to take the faulty tasks back to its initial safe state and then proceeds to re-execute the affected section of the faulty tasks to enable recovery. Considering that faults may occur in the components of any system; a fault tolerance system that can nullify the erroneous effect is necessary to be developed. Current TUF/UA scheduling algorithm uses the abortion recovery mechanism and it simply aborts the erroneous task as their fault recovery solution. None of the existing algorithm in TUF/UA scheduling domain in multiprocessor scheduling environment have considered the transient fault and implement the BR mechanism as a fault recovery mechanism to nullify the erroneous effect and solve the recovery problem in this domain. The developed BR_GPUAS simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. Simulation results revealed that BR_GPUAS algorithm can saved almost 20-30% of the accumulated utilities making it reliable and efficient for the real-time application in the multiprocessor scheduling environment.

Keywords: real-time system (RTS), time utility function/ utility accrual (TUF/UA) scheduling, backward recovery mechanism, multiprocessor, discrete event simulation (DES)

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Authors: Mohamed Ghoz, Hala Alsabeh

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Context: Macroscopic Nose Macroneedling (MNM) is a new non-surgical procedure for lifting and tightening the nose. It is a tissue-non-invasive technique that uses a needle to create micro-injuries in the skin. These injuries stimulate the production of collagen and elastin, which results in the tightening and lifting of the skin. Research Aim: The research aim of this study was to investigate the efficacy and safety of MNM for the treatment of nasal deformities. Methodology A total of 100 patients with nasal deformities were included in this study. The patients were randomly assigned to either the MNM group or the control group. The MNM group received a single treatment of MNM, while the control group received no treatment. The patients were evaluated at baseline, 6 months, and 12 months after treatment. Findings: The results of this study showed that MNM was effective in improving the appearance of the nose in patients with nasal deformities. At 6 months after treatment, the patients in the MNM group had significantly improved nasal tip projection, nasal bridge height, and nasal width compared to the patients in the control group. The improvements in nasal appearance were maintained at 12 months after treatment. Theoretical Importance: The findings of this study provide support for the use of MNM as a safe and effective treatment for nasal deformities. MNM is a non-surgical procedure that is associated with minimal downtime and no risk of scarring. This makes it an attractive option for patients who are looking for a minimally invasive treatment for their nasal deformities. Data Collection: Data was collected from the patients using a variety of methods, including clinical assessments, photographic assessments, and patient-reported outcome measures. Analysis Procedures: The data was analyzed using a variety of statistical methods, including descriptive statistics, inferential statistics, and meta-analysis. Question Addressed: The research question addressed in this study was whether MNM is an effective and safe treatment for nasal deformities. Conclusion: The findings of this study suggest that MNM is an effective and safe treatment for nasal deformities. MNM is a non-surgical procedure that is associated with minimal downtime and no risk of scarring. This makes it an attractive option for patients who are looking for a minimally invasive treatment for their nasal deformities.

Keywords: nose, surgery, tie, suture

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Authors: Hao-Yuan Tseng, Chuan-Yang Chang, Ying-Hui Chen, Sheng-Che Chen, Chih-Han Chang

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Orthodontic tooth treatment has demonstrated a high success rate in clinical studies. It has been agreed upon that orthodontic tooth movement is based on the ability of surrounding bone and periodontal ligament (PDL) to react to a mechanical stimulus with remodeling processes. However, the mechanism of the tooth movement is still unclear. Recent studies focus on the simple principle compression-tension theory while rare studies directly measure tooth movement. Therefore, tracking tooth movement information during orthodontic treatment is very important in clinical practice. The aim of this study is to investigate the mechanism responses of the tooth movement during the orthodontic treatments. A stereovision system applied to track the tooth movement of the patient with the stamp brackets. The system was established by two cameras with their relative position calibrate. And the orthodontic force measured by 3D printing model with the six-axis load cell to determine the initial force application. The result shows that the stereovision system accuracy revealed the measurement presents a maximum error less than 2%. For the study on patient tracking, the incisor moved about 0.9 mm during 60 days tracking, and half of movement occurred in the first few hours. After removing the orthodontic force in 100 hours, the distance between before and after position incisor tooth decrease 0.5 mm consisted with the release of the phenomenon. Using the stereovision system can accurately locate the three-dimensional position of the teeth and superposition of 3D coordinate system for all the data to integrate the complex tooth movement.

Keywords: orthodontic treatment, tooth movement, stereovision system, long-term tracking

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Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: E. Al-Essa, R. Bello-Mendoza, D. G. Wareham

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Anaerobic batch experiments were conducted to investigate the effect of magnetite-supplementation (7 mM) on methane production from digested sludge undergoing two different microbial growth phases, namely fresh sludge (exponential growth phase) and degassed sludge (endogenous decay phase). Three different particle sizes were assessed: small (50 - 150 nm), medium (168 – 490 nm) and large (800 nm - 4.5 µm) particles. Results show that, in the case of the fresh sludge, magnetite significantly enhanced the methane production rate (up to 32%) and reduced the lag phase (by 15% - 41%) as compared to the control, regardless of the particle size used. However, the cumulative methane produced at the end of the incubation was comparable in all treatment and control bottles. In the case of the degassed sludge, only the medium-sized magnetite particles increased significantly the methane production rate (12% higher) as compared to the control. Small and large particles had little effect on the methane production rate but did result in an extended lag phase which led to significantly lower cumulative methane production at the end of the incubation period. These results suggest that magnetite produces a clear and positive effect on methane production only when an active and balanced microbial community is present in the anaerobic digester. It is concluded that, (i) the effect of magnetite particle size on increasing the methane production rate and reducing lag phase duration is strongly influenced by the initial metabolic state of the microbial consortium, and (ii) the particle size would positively affect the methane production if it is provided within the nanometer size range.

Keywords: anaerobic digestion, iron oxide, methanogenesis, nanoparticle

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Authors: Jogi Prayogo, Dwi Qaqa Prasetyatama, Rahmad Dwi Afandi, Kunto Arief Prasetyo, Viorel Herniza Leksono

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PT. Pertamina Trans Kontinental managed a fleet of 364 ships in 2018 - 2020. In that period, there were 8 incidents of ship damage, causing breakdown maintenance on 6 ships belonging to PT Pertamina Trans Kontinental throughout Indonesia's operational areas due to ropes entangled in propellers. The company's losses that were caused by the fouled propellers amounted to 306.35 Million Rupiah. Of the 8 incidents, Mooring Boat PSC I was taken as a pilot project for further analysis considering the location of the ship which is in Surabaya and Mooring Boat PSC I has experienced 2 incidents of rope entanglement that caused the company's losses due to the largest Breakdown Maintenance amounted to 200.99 Million Rupiah. After analyzing the rope entanglement in the ship's propeller based on the data of Mooring Boat PSC I related to the location of propellers that are often fouled in the conventional propulsion system, it was found that there is a suitable location for the implementation of SI KUMAN tool that serves to cut ropes to prevent the occurrence of rope entangled in ship propellers. The determination of SI KUMAN tool is based on the strength of the ship's material to be installed and a suitable design to prevent the occurrence of ropes being entangled in propellers. After the installation of the "SI KUMAN" tool and monitoring carried out for 1 year period (August 2020 - August 2021), it was found that SI KUMAN tool can eliminate the risk of fouled propeller incidents which previously occurred twice in one year so that the company's loss amounted to 200.99 Million Rupiah can be eliminated and SI KUMAN tool can still operate optimally.

Keywords: breakdown maintenance, mooring boat, fleet, fouled propeller, rope entangled, cut

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Authors: Nikita Pasricha, Karen Smith, Simone Marshall, Vincent DePaul, Jessica Trier

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Understanding that physical activity in rehabilitation programs shapes outcomes in acquired brain injury (ABI) populations is not a new concept. However, there is a void in understanding the physical activity patterns of inpatients in ABI rehabilitation, the trajectory of physical activity recovery, and factors that contribute to the recovery of physical activity over the initial months post-ABI. The purpose of this study was to determine if physical activity patterns vary in people with recent ABI in inpatient rehabilitation. The study also investigated differences in physical activity patterns in ABI patients compared to age-related healthy participants. Results revealed that ABI patients spent approximately 6.7 times longer per day in sedentary postures than in active positions. In comparison, the control group spent only 2.8 times longer in sedentary postures compared to active positions. Patients with ABI took significantly fewer steps than age-matched health control participants. Within the ABI population, patients took 0.78 times fewer steps on weekends compared to weekdays. Participants with greater mobility limitations had a greater difference in WD to WE steps taken. Potential reasons could be from no structured weekend rehabilitation programs, lower availability of staff, or varying schedules. Given that the rehabilitation program is only structured on weekdays, further research to investigate the benefits of structured physical activities like group walking programs on weekends for ABI patients in inpatient rehabilitation programs is warranted.

Keywords: brain, ABI, TBI, rehabilitation

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Authors: Min Wei

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The green hotel provides the products/services consistent with the full utilization of resources, protecting the ecological environment conducive to customers’ requirements and health. In order to better develop the green hospitality industry, this paper applies the game theory to analyze the intrinsic relationship and balanced interests among the stakeholders including government, hotels, and tourists during green hospitality development. Based on the hypothesis in game theory, this paper tries to construct a linkage mechanism in stakeholders, by which a theoretical basis for the interests’ balance can be realized. By using game theory and constructing a game model including tourists, hotels and government, this paper analyzes the relationship of the various stakeholders involved in the green hospitality development, and subsequently proposes the development model of green hospitality industry. On the one hand, this paper applies game theory to construct a green hotel development model and provides a theoretical basis for the interest balance of stakeholders based on theoretical perspective. On the other hand, the current development of green hospitality industry is still in initial phase, and the outcome of this research tries to guide tourists to form a green awareness and to establish the concept of green consumption for hotel development, so that green hotel products/services are provided. In addition, this paper provides a basis for decision making in the relevant government departments so that the interests of all stakeholders are promoted and cooperative game between stakeholders is established, for which the sustainable development of green hotels is achieved. The findings indicate that the process of achieving green hospitality industry development is to maximize the whole interests of stakeholders.

Keywords: green hospitality, game theory, stakeholders, development model

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Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Mohammed H. Abu-Dieyeh, Mohammad M. Zalloum

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Plant growth-promoting rhizobacteria (PGPR) are known to enhance plant growth and/or reduce plant damage due to soil-borne pathogens. Tomato is the highest consumable vegetable world-wide including Jordan. Fusarium oxysporum is a pathogen that causes well-known damages and losses to many vegetable crops including tomato. In this study, purification of 112 isolates of PGPR strains from rhizosphere environment of different regions in Jordan was accomplished. All bacterial isolates were In-vitro screened for antagonistic effects against F. oxysporum. The eleven most effective isolates that caused 30%-50% in-vitro growth reduction of F. oxysporum were selected. 8 out of 11 of these isolates were collected from Al-Halabat (arid-land). 7 isolates of Al-Halabat exerted 40-54% In-vitro growth reduction of F. oxysporum. Four-week-old seedlings of tomato cultivar (Anjara, the most susceptible indigenous cultivar to F. oxysporum) treated with PGPR5 (Bacillus amyloliquefaciens), and exposed to F. oxysporum, showed no disease symptoms and no significant changes in biomasses or chlorophyll contents indicating a non-direct mechanism of action of PGPR on tomato plants. However PGPR3 (Bacillus sp.), PGPR4 (Bacillus cereus), and PGPR38 (Paenibacillus sp.) treated plants or PGPR treated and exposed to F. oxysporum showed a significant increasing growth of shoot and root biomasses as well as chlorophyll contents of leaves compared to control untreated plants or plants exposed to the fungus without PGPR treatment. A significant increase in number of flowers per plant was also recorded in all PGPR treated plants. The characterization of rhizobacterial strains were accomplished using 16S rRNA gene sequence analysis in addition to microscopic characterization. Further research is necessary to explore the potentiality of other collected PGPR isolates on tomato plants in addition to investigate the efficacy of the identified isolates on other plant pathogens and then finding a proper and effective methods of formulation and application of the successful isolates on selected crops.

Keywords: antagonism, arid land, growth promoting, rhizobacteria, tomato

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Authors: Vaibhav D. Bhatt, Anju P. Kunjadia, D. S. Nauriyal, Bhumika J. Joshi, Chaitanya G. Joshi

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Metagenomic analysis of milk samples collected from local cattle breed, kankrej (Bos indicus), Gir (Bos indicus) and Crossbred (Bos indicus X Bos taurus) cattle harbouring subclinical mastitis was carried out by next-generation sequencing (NGS) 454 GS-FLX technology. Around 56 different species including members of Enterobacteriales, Pseudomonadales, Bacillales and Lactobacillales with varying abundance were detected in infected milk. The interesting presence of bacteriophages against Staphylococcus aureus, Escherichia coli, Enterobacter and Yersinia species were observed, especially Enterobacteria and E. coli phages (0∙32%) in Kankrej, Enterobacteria and Staphylococcus phages (1∙05%) in Gir and Staphylococcus phages (2∙32%) in crossbred cattle. NGS findings suggest that phages may be involved in imparting natural resistance of the cattle against pathogens. Further infected milk samples were subjected for bacterial isolation. Fourteen different isolates were identified, and DNA was extracted. Genes (Tet-K, Msr-A, and Mec-A) providing antibiotic resistance to the bacteria were screened by Polymerase Chain Reaction and results were validated with traditional antibiotic assay. Total 3 bacteriophages were isolated from nearby environment of the cattle farm. The efficacy of phages was checked against multi-drug resistant bacteria, identified by PCR. In-vivo study was carried out for phage therapy in mammary glands of female rats “Wister albino”. Mammary glands were infused with MDR isolates for 3 consecutive days. Recovery was observed in infected rats after intramammary infusion of sterile phage suspension. From day 4th onwards, level of C-reactive protein was significant increases up to day 12th . However, significant reduction was observed between days 12th to 18th post treatment. Bacteriophages have significant potential as antibacterial agents and their ability to replicate exponentially within their hosts and their specificity, make them ideal candidates for more sustainable mastitis control.

Keywords: bacteriophages, c-reactive protein, mastitis, metagenomic analysis

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Authors: Atousa Ataieyan, Salvador A. Gomez-Lopera, Gennaro Sepede

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Today, due to the growing urban population and consequently, the increasing water demand in cities, the amount of contaminants entering the water resources is increasing. This can impose harmful effects on the quality of the downstream water. Therefore, predicting the concentration of discharged pollutants at different times and distances of the interested area is of high importance in order to carry out preventative and controlling measures, as well as to avoid consuming the contaminated water. In this paper, the concentration distribution of an injected conservative pollutant in a square reservoir containing four symmetric blocks and three sources using Finite Volume Method (FVM) is simulated. For this purpose, after estimating the flow velocity, classical Advection-Diffusion Equation (ADE) has been discretized over the studying domain by Backward Time- Backward Space (BTBS) scheme. Then, the discretized equations for each node have been derived according to the initial condition, boundary conditions and point contaminant sources. Finally, taking into account the appropriate time step and space step, a computational code was set up in MATLAB. Contaminant concentration was then obtained at different times and distances. Simulation results show how using BTBS differentiating scheme and FVM as a numerical method for solving the partial differential equation of transport is an appropriate approach in the case of two-dimensional contaminant transfer in an advective-diffusive flow.

Keywords: BTBS differentiating scheme, contaminant concentration, finite volume, mass transfer, water pollution

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Authors: Kevin Soubly, Kaysara Khatun

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This study applies the existing Middle-Out Perspective (MOP) as a complementing analytical alternative to the customary dichotomous options of top-down vs. bottom-up strategies of international development and commons governance. It expands the framework by applying it to a new context of land management and environmental change, enabling fresh understandings of decision making around land use. Using a case study approach in Central Kalimantan, Indonesia among a village of indigenous Dayak, this study explores influences from both internal and external middle actors, utilizing qualitative empirical evidence and incorporating responses across 25 village households and 11 key stakeholders. Applying the factors of 'agency' and 'capacity' specific to the MOP, this study demonstrates middle actors’ unique capabilities and criticality to change due to their influence across various levels of decision-making. Study results indicate that middle actors play a large role, both passively and actively, both directly and indirectly, across various levels of decision-making, perception-shaping, and commons governance. In addition, the prominence of novel 'passive' middle actors, such as the internet, can provide communities themselves with a level of agency beyond that provided by other middle actors such as NGOs and palm oil industry entities – which often operate at the behest of the 'top' or out of self-interest. Further, the study posits that existing development and decision-making frameworks may misidentify the 'bottom' as the 'middle,' raising questions about traditional development and livelihood discourse, strategies, and support, from agricultural production to forest management. In conclusion, this study provides recommendations including that current policy preconceptions be reevaluated to engage middle actors in locally-adapted, integrative manners in order to improve governance and rural development efforts more broadly.

Keywords: environmental management, governance, Indonesia, land use, middle actors, middle-out perspective

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Authors: Abderrahim Elmoataz

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More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

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Authors: Amos Sunday Onikanni, Bashir Lawal, Babatunji Emmanuel Oyinloye, Gomaa Mostafa-Hedeab, Mohammed Alorabi, Simona Cavalu, Augustine O. Olusola, Chih-Hao Wang, Gaber El-Saber Batiha

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The increasing global burden of diabetes mellitus has called for the search for a therapeutic alternative that offers better activities and safety than conventional chemotherapy. Herein, we evaluated the neuroprotective and antioxidant properties of different fractions (ethyl acetate, N-butanol and residual aqueous) of Clompanus pubescens leaves in streptozotocin (STZ)-induced diabetic rats. Our results revealed a significant elevation in the levels of blood glucose, pro-inflammatory cytokines, lipid peroxidation, neuronal activities of acetylcholinesterase, butyrylcholinesterase, nitric oxide, epinephrine, norepinephrine, and Na+/K+-ATPase in diabetic non treated rats. In addition, decreased levels of enzymatic and non-enzymatic antioxidants were observed. Treatment with different fractions of C. pubescens leaves resulted in a significant reversal of the biochemical alteration and improved the neurocognitive deficit in STZ-induced diabetic rats. However, the ethyl-acetate fraction demonstrated higher activities than the other fractions and was characterized for its phytoconstituents, revealing the presence of Gallic acid (713.00 ppm), catechin (0.91 ppm), ferulic acid (0.98 ppm), rutin (59.82 ppm), quercetin (3.22 ppm) and kaempferol (4.07 ppm). Our molecular docking analysis revealed that these compounds exhibited different binding affinities and potentials for targeting BChE/AChE/ IL-1 β/Na+-K+-ATPase. However, only Kampferol and ferulic exhibited good drug-like, ADMET, and permeability properties suitable for use as a neuronal drug target agent. Hence, the ethyl-acetate fraction of C. pubescent leaves could be considered a source of promising bioactive metabolite for the treatment and management of cognitive impairments related to type II diabetes mellitus.

Keywords: diabetes mellitus, neuroprotective, antioxidant, pro-inflammatory cytokines

Procedia PDF Downloads 116

Authors: Sun-Jin Han, Deuck Hang Lee, Hyo-Eun Joo, Hyun Kang, Kang Su Kim

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In pretensioned concrete members, the transfer length region is existed, in which the stress in prestressing strand is developed due to the bond mechanism with surrounding concrete. The stress of strands in the transfer length zone is smaller than that in the strain plateau zone, so-called effective prestress, therefore the web-shear strength in transfer length region is smaller than that in the strain plateau zone. Although the transfer length is main key factor in the shear design, a few analytical researches have been conducted to investigate the transfer length. Therefore, in this study, a theoretical approach was used to estimate the transfer length. The bond stress developed between the strands and the surrounding concrete was quantitatively calculated by using the Thick-Walled Cylinder Model (TWCM), based on this, the transfer length of strands was calculated. To verify the proposed model, a total of 209 test results were collected from the previous studies. Consequently, the analysis results showed that the main influencing factors on the transfer length are the compressive strength of concrete, the cover thickness of concrete, the diameter of prestressing strand, and the magnitude of initial prestress. In addition, the proposed model predicted the transfer length of collected test specimens with high accuracy. Acknowledgement: This research was supported by a grant(17TBIP-C125047-01) from Technology Business Innovation Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Keywords: bond, Hoyer effect, prestressed concrete, prestressing strand, transfer length

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Authors: Seyed M. Khatami, H. Naderpour, R. Vahdani, R. C. Barros

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Many previous studies have been carried out to calculate the impact force and the dissipated energy between two neighboring buildings during seismic excitation, when they collide with each other. Numerical studies are an important part of impact, which several researchers have tried to simulate the impact by using different formulas. Estimation of the impact force and the dissipated energy depends significantly on some parameters of impact. Mass of bodies, stiffness of spring, coefficient of restitution, damping ratio of dashpot and impact velocity are some known and unknown parameters to simulate the impact and measure dissipated energy during collision. Collision is usually shown by force-displacement hysteresis curve. The enclosed area of the hysteresis loop explains the dissipated energy during impact. In this paper, the effect of using different types of impact models is investigated in order to calculate the impact force. To increase the accuracy of impact model and to optimize the results of simulations, a new damping equation is assumed and is validated to get the best results of impact force and dissipated energy, which can show the accuracy of suggested equation of motion in comparison with other formulas. This relation is called "n-m". Based on mathematical relation, an initial value is selected for the mentioned coefficients and kinetic energy loss is calculated. After each simulation, kinetic energy loss and energy dissipation are compared with each other. If they are equal, selected parameters are true and, if not, the constant of parameters are modified and a new analysis is performed. Finally, two unknown parameters are suggested to estimate the impact force and calculate the dissipated energy.

Keywords: impact force, dissipated energy, kinetic energy loss, damping relation

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Authors: Omran M. Kenshel, Alan J. O'Connor

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Estimating the service life of Reinforced Concrete (RC) bridge structures located in corrosive marine environments of a great importance to their owners/engineers. Traditionally, bridge owners/engineers relied more on subjective engineering judgment, e.g. visual inspection, in their estimation approach. However, because financial resources are often limited, rational calculation methods of estimation are needed to aid in making reliable and more accurate predictions for the service life of RC structures. This is in order to direct funds to bridges found to be the most critical. Criticality of the structure can be considered either form the Structural Capacity (i.e. Ultimate Limit State) or from Serviceability viewpoint whichever is adopted. This paper considers the service life of the structure only from the Structural Capacity viewpoint. Considering the great variability associated with the parameters involved in the estimation process, the probabilistic approach is most suited. The probabilistic modelling adopted here used Monte Carlo simulation technique to estimate the Reliability (i.e. Probability of Failure) of the structure under consideration. In this paper the authors used their own experimental data for the Correlation Length (CL) for the most important deterioration parameters. The CL is a parameter of the Correlation Function (CF) by which the spatial fluctuation of a certain deterioration parameter is described. The CL data used here were produced by analyzing 45 chloride profiles obtained from a 30 years old RC bridge located in a marine environment. The service life of the structure were predicted in terms of the load carrying capacity of an RC bridge beam girder. The analysis showed that the influence of SV is only evident if the reliability of the structure is governed by the Flexure failure rather than by the Shear failure.

Keywords: Chloride-induced corrosion, Monte-Carlo simulation, reinforced concrete, spatial variability

Procedia PDF Downloads 472