Search results for: planning patient care information system.

Authors: Imad M. Assali

Abstract:

Recently, the world main problem is a global warming and climate change affecting both outdoor and indoor environments, especially the air quality (AQ) as a result of vast migration of people from rural areas to urban areas. Therefore, cities became more crowded and denser from an irregular population increase, along with increasing urbanization caused many problems for the environment such as increasing the land prices, changes in life style, and the new buildings are not adapted to the climate producing uncomfortable and unhealthy indoor building conditions. As interior environments are the places that create the most intimate relationship with the user. Consequently, the indoor environment quality (IEQ) for buildings became uncomfortable and unhealthy for its occupants. The symptoms commonly associated with poor indoor environment such as itchy, headache, fatigue, and respiratory complaints such as cough and congestion, etc. The symptoms tend to improve over time or even disappear when people are away from the building. Therefore, designing a healthy indoor environment to fulfill human needs is the main concern for architects and interior designer. However, this research explores how occupant expectations and environmental attitudes may influence occupant health and satisfaction within the context of the indoor environment. In doing so, it reviews and contributes to the methods and tools used to evaluate only the indoor environment quality (IEQ) components of building performance. Its main aim is to review the literature on indoor human comfort. This is followed by a review of previous papers published related to human comfort. Finally, this paper will provide possible approaches in design level of healthy buildings.

Keywords: Sustainable building, indoor environment quality (IEQ), occupant's health, active system, sick building syndrome (SBS).

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Authors: Anamika Jain, A. S. Thoke, R. N. Patel

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This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.

Keywords: Double circuit transmission line, Fault detection and classification, High impedance fault and Artificial Neural Network.

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Authors: A. Banovcinova, M. Zakova

Abstract:

Labor market activity and paid employment should be a key factor in protecting individuals and families from falling into poverty and providing them with sufficient resources to meet the needs of their members. However, due to various processes in the labor market as well as the influence of individual factors and often insufficient social capital, there is a relatively large group of households that cannot eliminate paid employment and find themselves in a state of so-called working poverty. The aim of the research was to find out what strategies families use in managing poverty and meeting their needs and which of these strategies prevail in the Slovak population. A quantitative research strategy was chosen. The method of data collection was a structured interview focused on finding out the use of individual management strategies and also selected demographic indicators. The research sample consisted of members of families in which at least one member has a paid job. The condition for inclusion in the research was that the family's income did not exceed 60% of the national median equalized disposable income. The analysis of the results showed 5 basic areas to which management strategies are related - work, financial security, needs, social contacts and perception of the current situation. The prevailing strategies were strategies aimed at increasing and streamlining labor market activity and the planned and effective management of the family budget. Strategies that were rejected were mainly related to debt creation. The results make it possible to identify the preferred ways of managing poverty in individual areas of life, as well as the factors that influence this behavior. This information is important for working with families living in a state of working poverty and can help professionals develop positive ways of coping for families.

Keywords: Copying strategies, family, in-work poverty, quantitative research.

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Authors: Takashi Kaburagi, Masashi Takenaka, Yosuke Kurihara, Takashi Matsumoto

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Major depressive disorder (MDD) is one of the most common mental illnesses today. It is believed to be caused by a combination of several factors, including stress. Stress can be quantitatively evaluated using the State-Trait Anxiety Inventory (STAI), one of the best indices to evaluate anxiety. Although STAI scores are widely used in applications ranging from clinical diagnosis to basic research, the scores are calculated based on a self-reported questionnaire. An objective evaluation is required because the subject may intentionally change his/her answers if multiple tests are carried out. In this article, we present a modified index called the “multi-channel Laterality Index at Rest (mc-LIR)” by recording the brain activity from a wider area of the frontal lobe using multi-channel functional near-infrared spectroscopy (fNIRS). The presented index aims to measure multiple positions near the F_pz defined by the international 10-20 system positioning. Using 24 subjects, the dependencies on the number of measuring points used to calculate the mc-LIR and its correlation coefficients with the STAI scores are reported. Furthermore, a simple linear regression was performed to estimate the STAI scores from mc-LIR. The cross-validation error is also reported. The experimental results show that using multiple positions near the Fpz will improve the correlation coefficients and estimation than those using only two positions.

Keywords: Stress, functional near-infrared spectroscopy, frontal lobe, state-trait anxiety inventory score.

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Authors: Abdulrahman Abdulrahman

Abstract:

A side weir is a hydraulic structure set into the side of a channel. This structure is used for water level control in channels, to divert flow from a main channel into a side channel when the water level in the main channel exceeds a specific limit and as storm overflows from urban sewerage system. Computation of water surface over the side weirs is essential to determine the flow rate of the side weir. Analytical solutions for water surface profile along rectangular side weir are available only for the special cases of rectangular and trapezoidal channels considering constant specific energy. In this paper, a rectangular side weir located in a combined (trapezoidal with exponential) channel was considered. Expanding binominal series of integer and fraction powers and the using of reduction formula of cosine function integrals, a general analytical formula was obtained for water surface profile along a side weir in a combined (trapezoidal with exponential) channel. Since triangular, rectangular, trapezoidal and parabolic cross-sections are special cases of the combined cross section, the derived formula, is applicable to triangular, rectangular, trapezoidal cross-sections as analytical solution and semi-analytical solution to parabolic cross-section with maximum relative error smaller than 0.76%. The proposed solution should be a useful engineering tool for the evaluation and design of side weirs in open channel.

Keywords: Analytical solution, combined channel, exponential channel, side weirs, trapezoidal channel, water surface profile.

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: Aero-thermo-elasticity, elastic deformation, structural temperature, multi-field coupling.

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Authors: A. A. Njoh, S. T. Ndoula, A. Adidja, G. N. Menan, A. Mengue, E. Mboke, H. B. Bachir, S. C. Nchinjoh, L. Adisso, Y. Saidu, L. Cleenewerck de Kiev

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Vaccines serve a great deal in protecting the population globally. Vaccine products are subject to rigorous quality control and approval before use to ensure safety. Even if all actors take the required precautions, some people could still have adverse events following immunization (AEFI) caused by the vaccine composition or an error in its administration. AEFI underreporting is pronounced in low-income settings like Cameroon. The Country introduced electronic platforms to strengthen surveillance. With the introduction of many novel vaccines, like COVID-19 and the novel Oral Polio Vaccine (nOPV) 2, there was a need to monitor AEFI in Cameroon. A cross-sectional study was conducted from July to December 2022. Data on AEFI per region of Cameroon were reviewed for the previous five years. Data were analyzed with MS Excel, and the results were presented in proportions. AEFI reporting was uncommon in Cameroon. With the introduction of novel vaccines in 2021, the health authorities engaged in new tools and training to capture cases. AEFI detected almost doubled using the open data kit (ODK) compared to previous platforms, especially following the introduction of the nOPV2 and COVID-19 vaccines. The AEFI rate was 1.9 and 160 per administered 100,000 doses of nOPV2 and COVID-19 vaccines, respectively. This mobile tool captured individual information for people with AEFI from all regions. The platform helped to identify common AEFI following the use of these new vaccines. The ODK mobile technology was vital in improving AEFI reporting and providing data to monitor the use of new vaccines in Cameroon.

Keywords: Adverse events following immunization, AEFI, Cameroon, COVID-19 vaccines, novel oral polio vaccine 2, open data kit.

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Authors: Ying-Chang Yu, Yuan-Lung Lo

Abstract:

Building ventilation performance is an important indicator of indoor comfort. However, in addition to the geometry of the building or the proportion of the opening, the ventilation performance is also very much related to the actual wind pressure of the building. There are more and more contemporary building designs built with multi-layer exterior envelope. Due to ventilation and view observatory requirement, the porous outer layer of the building is commonly adopted and has a significant wind damping effect, causing the phenomenon of actual wind pressure loss. However, the relationship between the wind damping effect and the actual wind pressure is not linear. This effect can make the indoor ventilation of the building rationalized to reasonable range under the condition of high wind pressure, and also maintain a good amount of ventilation performance under the condition of low wind pressure. In this study, wind tunnel experiments were carried out to simulate the different wind pressures flow through the porous outer layer, and observe the actual wind pressure strength engage with the window layer to find the decreasing relationship between the damping effect of the porous shell and the wind pressure. Experiment specimen scale was designed to be 1:50 for testing real-world building conditions; the study found that the porous enclosure has protective shielding without affecting low-pressure ventilation. Current study observed the porous skin may damp more wind energy to ease the wind pressure under high-speed wind. Differential wind speed may drop the pressure into similar pressure level by using porous skin. The actual mechanism and value of this phenomenon will need further study in the future.

Keywords: Renault number, porous media, wind damping, wind tunnel test, building ventilation.

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Authors: Arash Taheri, Meysam Mohammadi-Amin, Seyed Hossein Moosavy

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Medical applications are among the most impactful areas of microrobotics. The ultimate goal of medical microrobots is to reach currently inaccessible areas of the human body and carry out a host of complex operations such as minimally invasive surgery (MIS), highly localized drug delivery, and screening for diseases at their very early stages. Miniature, safe and efficient propulsion systems hold the key to maturing this technology but they pose significant challenges. A new type of propulsion developed recently, uses multi-flagella architecture inspired by the motility mechanism of prokaryotic microorganisms. There is a lack of efficient methods for designing this type of propulsion system. The goal of this paper is to overcome the lack and this way, a numerical strategy is proposed to design multi-flagella propulsion systems. The strategy is based on the implementation of the regularized stokeslet and rotlet theory, RFT theory and new approach of “local corrected velocity". The effects of shape parameters and angular velocities of each flagellum on overall flow field and on the robot net forces and moments are considered. Then a multi-layer perceptron artificial neural network is designed and employed to adjust the angular velocities of the motors for propulsion control. The proposed method applied successfully on a sample configuration and useful demonstrative results is obtained.

Keywords: Artificial Neural Network, Biomimetic Microrobots, Flagellar Propulsion, Swimming Robots.

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Authors: Amos Barkai, Guy Meredith, Fatima Felaar, Zahrah Dantie, Dave de Buys

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Fisheries management all around the world is hampered by the lack, or poor quality, of critical data on fish resources and fishing operations. The main reasons for the chronic inability to collect good quality data during fishing operations is the culture of secrecy common among fishers and the lack of modern data gathering technology onboard most fishing vessels. In response, OLRAC-SPS, a South African company, developed fisheries datalogging software (eLog in short) and named it Olrac. The Olrac eLog solution is capable of collecting, analysing, plotting, mapping, reporting, tracing and transmitting all data related to fishing operations. Olrac can be used by skippers, fleet/company managers, offshore mariculture farmers, scientists, observers, compliance inspectors and fisheries management authorities. The authors believe that using eLog onboard fishing vessels has the potential to revolutionise the entire process of data collection and reporting during fishing operations and, if properly deployed and utilised, could transform the entire commercial fleet to a provider of good quality data and forever change the way fish resources are managed. In addition it will make it possible to trace catches back to the actual individual fishing operation, to improve fishing efficiency and to dramatically improve control of fishing operations and enforcement of fishing regulations.

Keywords: data management, electronic logbook (eLog), electronic reporting system (ERS), fisheries management

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Authors: Tien-Shun Chan, Yung-Chung Chang, Cheng-Yu Chu, Wen-Hui Chen, Yuan-Lin Chen, Shun-Chong Wang, Chang-Chun Wang

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98% of the energy needed in Taiwan has been imported. The prices of petroleum and electricity have been increasing. In addition, facility capacity, amount of electricity generation, amount of electricity consumption and number of Taiwan Power Company customers have continued to increase. For these reasons energy conservation has become an important topic. In the past linear regression was used to establish the power consumption models for chillers. In this study, grey prediction is used to evaluate the power consumption of a chiller so as to lower the total power consumption at peak-load (so that the relevant power providers do not need to keep on increasing their power generation capacity and facility capacity). In grey prediction, only several numerical values (at least four numerical values) are needed to establish the power consumption models for chillers. If PLR, the temperatures of supply chilled-water and return chilled-water, and the temperatures of supply cooling-water and return cooling-water are taken into consideration, quite accurate results (with the accuracy close to 99% for short-term predictions) may be obtained. Through such methods, we can predict whether the power consumption at peak-load will exceed the contract power capacity signed by the corresponding entity and Taiwan Power Company. If the power consumption at peak-load exceeds the power demand, the temperature of the supply chilled-water may be adjusted so as to reduce the PLR and hence lower the power consumption.

Keywords: Gery system theory, grey prediction, chller.

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Authors: R. B. Ibrahim, M. S. Argungu, I. M. Mungadi

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It is obvious in this present time, internet has become an indispensable part of human life since its inception. The Internet has provided diverse opportunities to make life so easy for human beings, through the adoption of various channels. Among these channels are email, internet banking, video conferencing, and the like. Email is one of the easiest means of communication hugely accepted among individuals and organizations globally. But over decades the security integrity of this platform has been challenged with malicious activities like Phishing. Email phishing is designed by phishers to fool the recipient into handing over sensitive personal information such as passwords, credit card numbers, account credentials, social security numbers, etc. This activity has caused a lot of financial damage to email users globally which has resulted in bankruptcy, sudden death of victims, and other health-related sicknesses. Although many methods have been proposed to detect email phishing, in this research, the results of multiple machine-learning methods for predicting email phishing have been compared with the use of filter-wrapper feature selection. It is worth noting that all three models performed substantially but one outperformed the other. The dataset used for these models is obtained from Kaggle online data repository, while three classifiers: decision tree, Naïve Bayes, and Logistic regression are ensemble (Bagging) respectively. Results from the study show that the Decision Tree (CART) bagging ensemble recorded the highest accuracy of 98.13% using PEF (Phishing Essential Features). This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, hybrid, filter-wrapper, phishing.

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Authors: John Michael Russo

Abstract:

Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: Apiculture, precision apiculture, RNA varroa treatment, honeybee feed applications.

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Authors: Jung-Chuan Chou, Cheng-Wei Chen

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The hard clam (meretrix lusoria) cultivated industry has been developed vigorously for recent years in Taiwan, and seawater quality determines the cultivated environment. The pH concentration variation affects survival rate of meretrix lusoria immediately. In order to monitor seawater quality, solid-state sensing electrode of ruthenium-doped titanium dioxide (TiO2:Ru) is developed to measure hydrogen ion concentration in different cultivated solutions. Because the TiO2:Ru sensing electrode has high chemical stability and superior sensing characteristics, thus it is applied as a pH sensor. Response voltages of TiO2:Ru sensing electrode are readout by instrument amplifier in different sample solutions. Mean sensitivity and linearity of TiO2:Ru sensing electrode are 55.20 mV/pH and 0.999 from pH1 to pH13, respectively. We expect that the TiO2:Ru sensing electrode can be applied to real environment measurement, therefore we collect two sample solutions by different meretrix lusoria cultivated ponds in the Yunlin, Taiwan. The two sample solutions are both measured for 200 seconds after calibration of standard pH buffer solutions (pH7, pH8 and pH 9). Mean response voltages of sample 1 and sample 2 are -178.758 mV (Standard deviation=0.427 mV) and -180.206 mV (Standard deviation =0.399 mV), respectively. Response voltages of the two sample solutions are between pH 8 and pH 9 which conform to weak alkali range and suitable meretrix lusoria growth. For long-term monitoring, drift of cultivated solutions (sample 1 and sample 2) are 1.16 mV/hour and 1.03 mV/hour, respectively.

Keywords: Co-sputtering system, Hard clam (meretrix lusoria), Ruthenium-doped titanium dioxide, Solid-state sensing electrode.

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Authors: S. Vignesh, N. Vishnu, S. Vigneshwaran, M. Vishnu Anand, Dinesh Kumar Babu, V. R. Sanal Kumar

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Numerical studies have been carried out using a validated two-dimensional standard k-omega turbulence model for the design optimization of a thrust vector control system using shock induced self-impinging supersonic secondary double jet. Parametric analytical studies have been carried out at different secondary injection locations to identifying the highest unsymmetrical distribution of the main gas flow due to shock waves, which produces a desirable side force more lucratively for vectoring. The results from the parametric studies of the case on hand reveal that the shock induced self-impinging supersonic secondary double jet is more efficient in certain locations at the divergent region of a CD nozzle than a case with supersonic single jet with same mass flow rate. We observed that the best axial location of the self-impinging supersonic secondary double jet nozzle with a given jet interaction angle, built-in to a CD nozzle having area ratio 1.797, is 0.991 times the primary nozzle throat diameter from the throat location. We also observed that the flexible steering is possible after invoking ON/OFF facility to the secondary nozzles for meeting the onboard mission requirements. Through our case studies we concluded that the supersonic self-impinging secondary double jet at predesigned jet interaction angle and location can provide more flexible steering options facilitating with 8.81% higher thrust vectoring efficiency than the conventional supersonic single secondary jet without compromising the payload capability of any supersonic aerospace vehicle.

Keywords: Fluidic thrust vectoring, rocket steering, self-impinging secondary supersonic jet, TVC in aerospace vehicles.

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Authors: Muhammad Rehan Khalid, Muhammad Haroon Siddiqui, Danish Ilyas

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This paper explores the implementation of adaptive coding and modulation schemes for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) feedback systems. Adaptive coding and modulation enables robust and spectrally-efficient transmission over time-varying channels. The basic premise is to estimate the channel at the receiver and feed this estimate back to the transmitter, so that the transmission scheme can be adapted relative to the channel characteristics. Two types of codebook based channel feedback techniques are used in this work. The longterm and short-term CSI at the transmitter is used for efficient channel utilization. OFDM is a powerful technique employed in communication systems suffering from frequency selectivity. Combined with multiple antennas at the transmitter and receiver, OFDM proves to be robust against delay spread. Moreover, it leads to significant data rates with improved bit error performance over links having only a single antenna at both the transmitter and receiver. The coded modulation increases the effective transmit power relative to uncoded variablerate variable-power MQAM performance for MIMO-OFDM feedback system. Hence proposed arrangement becomes an attractive approach to achieve enhanced spectral efficiency and improved error rate performance for next generation high speed wireless communication systems.

Keywords: Adaptive Coded Modulation, MQAM, MIMO, OFDM, Codebooks, Feedback.

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Authors: Raphael de Oliveira Garcia, Samuel Rocha de Oliveira

Abstract:

We have developed a new computer program in Fortran 90, in order to obtain numerical solutions of a system of Relativistic Magnetohydrodynamics partial differential equations with predetermined gravitation (GRMHD), capable of simulating the formation of relativistic jets from the accretion disk of matter up to his ejection. Initially we carried out a study on numerical methods of unidimensional Finite Volume, namely Lax-Friedrichs, Lax-Wendroff, Nessyahu-Tadmor method and Godunov methods dependent on Riemann problems, applied to equations Euler in order to verify their main features and make comparisons among those methods. It was then implemented the method of Finite Volume Centered of Nessyahu-Tadmor, a numerical schemes that has a formulation free and without dimensional separation of Riemann problem solvers, even in two or more spatial dimensions, at this point, already applied in equations GRMHD. Finally, the Nessyahu-Tadmor method was possible to obtain stable numerical solutions - without spurious oscillations or excessive dissipation - from the magnetized accretion disk process in rotation with respect to a central black hole (BH) Schwarzschild and immersed in a magnetosphere, for the ejection of matter in the form of jet over a distance of fourteen times the radius of the BH, a record in terms of astrophysical simulation of this kind. Also in our simulations, we managed to get substructures jets. A great advantage obtained was that, with the our code, we got simulate GRMHD equations in a simple personal computer.

Keywords: Finite Volume Methods, Central Schemes, Fortran 90, Relativistic Astrophysics, Jet.

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Authors: Y. Harold Robinson, M. Rajaram

Abstract:

Wireless mesh networking is rapidly gaining in popularity with a variety of users: from municipalities to enterprises, from telecom service providers to public safety and military organizations. This increasing popularity is based on two basic facts: ease of deployment and increase in network capacity expressed in bandwidth per footage; WMNs do not rely on any fixed infrastructure. Many efforts have been used to maximizing throughput of the network in a multi-channel multi-radio wireless mesh network. Current approaches are purely based on either static or dynamic channel allocation approaches. In this paper, we use a hybrid multichannel multi radio wireless mesh networking architecture, where static and dynamic interfaces are built in the nodes. Dynamic Adaptive Channel Allocation protocol (DACA), it considers optimization for both throughput and delay in the channel allocation. The assignment of the channel has been allocated to be codependent with the routing problem in the wireless mesh network and that should be based on passage flow on every link. Temporal and spatial relationship rises to re compute the channel assignment every time when the pattern changes in mesh network, channel assignment algorithms assign channels in network. In this paper a computing path which captures the available path bandwidth is the proposed information and the proficient routing protocol based on the new path which provides both static and dynamic links. The consistency property guarantees that each node makes an appropriate packet forwarding decision and balancing the control usage of the network, so that a data packet will traverse through the right path.

Keywords: Wireless mesh network, spatial time division multiple access, hybrid topology, timeslot allocation.

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

Abstract:

With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: Machine learning, Imbalanced data, Data mining, Big data.

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000cm^-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R², the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R² = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R² = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R²) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, Pasta, moisture determination.

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: Autonomous vehicles, deformable part model, dpm, pedestrian recognition.

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Authors: Nihat Hakan Akyol, Irfan Yolcubal

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In-situ chemical oxidation (ISCO) has been widely used for source zone remediation of Dense Nonaqueous Phase Liquids (DNAPLs) in subsurface environments. DNAPL source zones for karst aquifers are generally located in epikarst where the DNAPL mass is trapped either in karst soil or at the regolith contact with carbonate bedrock. This study aims to investigate the performance of oxidation of residual trichloroethylene found in such environments by potassium permanganate. Batch and flow cell experiments were conducted to determine the kinetics and the mass removal rate of TCE. pH change, Cl production, TCE and MnO4 destruction were monitored routinely during experiments. Nonreactive tracer tests were also conducted prior and after the oxidation process to determine the influence of oxidation on flow conditions. The results show that oxidant consumption rate of the calcareous epikarst soil was significant and the oxidant demand was determined to be 20 g KMnO4/kg soil. Oxidation rate of residual TCE (1.26x10-3 s-1) was faster than the oxidant consumption rate of the soil (2.54 - 2.92x10-4 s-1) at only high oxidant concentrations (> 40 mM KMnO4). Half life of TCE oxidation ranged from 7.9 to 10.7 min. Although highly significant fraction of residual TCE mass in the system was destroyed by permanganate oxidation, TCE concentration in the effluent remained above its MCL. Flow interruption tests indicate that efficiency of ISCO was limited by the rate of TCE dissolution and the rate-limited desorption of TCE. The residence time and the initial concentration of the oxidant in the source zone also controlled the efficiency of ISCO in epikarst.

Keywords: Epikarst, in-situ chemical oxidation, permanganate.

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: Political tendency, prediction, sentiment analysis, Twitter.

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Authors: J. Martin, A. Dannenberg, G. Detrell, R. Ewald, S. Fasoulas

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Microalgae-based photobioreactors (PBR) for Life Support Systems (LSS) are currently being investigated for future space missions such as a crewed base on planets or moons. Biological components may help reducing resupply masses by closing material mass flows with the help of regenerative components. Via photosynthesis, the microalgae use CO₂, water, light and nutrients to provide oxygen and biomass for the astronauts. These capabilities could have synergies with Earth applications that tackle current problems and the developed technologies can be transferred. For example, a current worldwide discussed issue is the increased nitrate and phosphate pollution of ground water from agricultural waste waters. To investigate the potential use of a biological system based on the ability of the microalgae to extract and use nitrate and phosphate for the treatment of polluted ground water from agricultural applications, a scalable test stand is being developed. This test stand investigates the maximization of intake rates of nitrate and quantifies the produced biomass and oxygen. To minimize the required energy, for the uptake of nitrate from artificial waste water (AWW) the Flashing Light Effect (FLE) and the adaption of the illumination spectrum were realized. This paper describes the composition of the AWW, the development of the illumination unit and the possibility of non-invasive process optimization and control via the adaption of the illumination spectrum and illumination cycles. The findings were a doubling of the energy related growth rate by adapting the illumination setting.

Keywords: Microalgae, illumination, nitrate uptake, flashing light effect.

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Authors: Pedro Pablo González Pérez, Hiram I. Beltrán, Arturo Rojo-Domínguez, Máximo EduardoSánchez Gutiérrez

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Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.

Keywords: multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.

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Authors: M. F. Nawaz, S. Gul, T. H. Farooq, M. T. Siddiqui, M. Asif, I. Ahmad, N. K. Niazi

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In Pakistan, major demands of fuel wood and timber wood are fulfilled by agroforestry. However, the information regarding economic significance of agroforestry and its productivity in Pakistan is still insufficient and unreliable. Survey of field conditions to examine the agroforestry status at local level helps us to know the future trends and to formulate the policies for sustainable wood supply. The objectives of this research were to examine the actual status and potential of agroforestry and to point out the barriers that are faced by farmers in the adoption of agroforestry. Research was carried out in Chiniot district, Pakistan because it is the famous city for furniture industry that is largely dependent on farm trees. A detailed survey of district Chiniot was carried out from 150 randomly selected farmer respondents using multi-objective oriented and pre-tested questionnaire. It was found that linear tree planting method was more adopted (45%) as compared to linear + interplanting (42%) and/or compact planting (12.6%). Chi-square values at P-value <0.5 showed that age (11.35) and education (17.09) were two more important factors in the quick adoption of agroforestry as compared to land holdings (P-value of 0.7). The major reason of agroforestry adoption was to obtain income, fodder and fuelwood. The most dominant species in farmlands was shisham (Dalbergia sissoo) but since last five years, mostly farmers were growing Sufeida (Eucalyptus camaldulensis), kikar (Acacia nilotica) and popular (Populus deltoides) on their fields due to “Shisham die-back” problem. It was found that agro-forestry can be increased by providing good quality planting material to farmers and improving wood markets.

Keywords: Agroforestry, trees, services, agriculture, farmers.

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: Constraint parameters, derivative matrix, magnetocardiography, regular term, total variation.

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Authors: Abdulkareem Hussein Bibire

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Job satisfaction and motivation have been given an important attention in psychology because they are seen as main instruments in maintaining organizational growth and development; they are also used to accomplish organizational aims and objectives. However, it has been observed that some institutions failed in motivating and stimulating their workers; in contrast, workers may be motivated but not satisfied with the job and failed to perform efficiently and effectively. It is hoped that the study of this nature would be of significance value to all stakeholders in education specifically, lecturers in higher institutions in Nigeria. Also, it is hoped that the findings of this study will enhance lecturers’ effectiveness and performance in discharging their duties. In the light of the above statements, this study investigated whether job satisfaction and motivation predict lecturers’ effectiveness in Nigeria Police Academy, Wudil, Kano State. Correlational research method was adopted for the study, while purposive sampling technique was used to choose the institution and the sampled lectures (70). Simple random sampling technique was used to select one hundred cadets across the academy. Two instruments were used to elicit information from both lecturers and cadets. These were job satisfaction and motivation; and lecturers’ effectiveness Questionnaires. The instruments were subjected to pilot testing and found to have reliability coefficient of 0.69 and 0.71 respectively. The results of the study revealed that there was a significance relationship among job satisfaction, motivation and lecturers effectiveness in Nigeria Police Academy. There was a significance relationship between job satisfaction and lecturers’ effectiveness in Nigeria Police Academy the cal r is 0.21 while the crt r is 0.19. at p<0.05 and; there was a significance relationship between job motivation and lecturers effectiveness in Nigeria Police Academy the cal r is 0.20 while the crt r is 0.19 at p<0.05This study therefore concluded that there was a significance relationship among job satisfaction, motivation and lecturers effectiveness in Nigeria Police Academy. Based on the data collected, collated and analyzed Recommendations were made for both the lecturers and the Academy management. It is also suggested that lecturers should be industrious in their primary assignment in other to make values to cadets lives and career. And management should also try to enhance lecturers performance by more motivational needs for the lecturers.

Keywords: Satisfaction, motivation, lecturer effectiveness, academy.

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Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim

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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.

Keywords: Face Alignment, AAM, facial feature detection, model matching.

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Authors: Ajay Mandal, Achinta Bera

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Nanoemulsions are a class of emulsions with a droplet size in the range of 50–500 nm and have attracted a great deal of attention in recent years because it is unique characteristics. The physicochemical properties of nanoemulsion suggests that it can be successfully used to recover the residual oil which is trapped in the fine pore of reservoir rock by capillary forces after primary and secondary recovery. Oil-in-water nanoemulsion which can be formed by high-energy emulsification techniques using specific surfactants can reduce oil-water interfacial tension (IFT) by 3-4 orders of magnitude. The present work is aimed on characterization of oil-inwater nanoemulsion in terms of its phase behavior, morphological studies; interfacial energy; ability to reduce the interfacial tension and understanding the mechanisms of mobilization and displacement of entrapped oil blobs by lowering interfacial tension both at the macroscopic and microscopic level. In order to investigate the efficiency of oil-water nanoemulsion in enhanced oil recovery (EOR), experiments were performed to characterize the emulsion in terms of their physicochemical properties and size distribution of the dispersed oil droplet in water phase. Synthetic mineral oil and a series of surfactants were used to prepare oil-in-water emulsions. Characterization of emulsion shows that it follows pseudo-plastic behaviour and drop size of dispersed oil phase follows lognormal distribution. Flooding experiments were also carried out in a sandpack system to evaluate the effectiveness of the nanoemulsion as displacing fluid for enhanced oil recovery. Substantial additional recoveries (more than 25% of original oil in place) over conventional water flooding were obtained in the present investigation.

Keywords: Nanoemulsion, Characterization, Enhanced Oil Recovery, Particle Size Distribution

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