Search results for: minimum data set

Authors: Shittu Ahmed Tajudeen

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This study examines student-teachers’ attitude and intention towards cell-phone use for learning. The study involves one hundred and ninety (190) trainee teachers in one of the Institutes of Education in Nigeria. The data of the study was collected through a questionnaire on a rating of seven point likert-type Scale. The data collected was used to test the hypothesized model of the study using Structural Equation Modeling approach. The finding of the study revealed that Perceived Usefulness (PU), Perceived Ease of Use (PEU), Subjective Norm (SN) and Attitude significantly influence students’ intention towards adoption of cell-phone for learning. The study showed that perceived ease of use stands to be the strongest predictor of cell-phone use. The model of the study exhibits a good-fit with the data and provides an explanation on student- teachers’ attitude and intention towards cell-phone for learning.

Keywords: cell-phone, adoption, structural equation modeling, technology acceptance model

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Authors: Lucia Carrion Gordon, Jaime Santiago Sanchez Reinoso

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According to the relation of Digital Preservation and the Health field as a case of study, the architectural model help us to explain that definitions. .The principal goal of Data Preservation is to keep information for a long term. Regarding of Mediacal information, in order to perform a heart transplant, physicians need to preserve this organ in an adequate way. This approach between the two perspectives, the medical and the technological allow checking the similarities about the concepts of preservation. Digital preservation and medical advances are related in the same level as knowledge improvement.

Keywords: medical management, digital, data, heritage, preservation

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: M. A. Meslem

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For precise geoid determination, we use a reference field to subtract long and medium wavelength of the gravity field from observations data when we use the remove-compute-restore technique. Therefore, a comparison study between considered models should be made in order to select the optimal reference gravity field to be used. In this context, two recent global geopotential models have been selected to perform this comparison study over Northern Algeria. The Earth Gravitational Model (EGM2008) and the Global Gravity Model (GECO) conceived with a combination of the first model with anomalous potential derived from a GOCE satellite-only global model. Free air gravity anomalies in the area under study have been used to compute residual data using both gravity field models and a Digital Terrain Model (DTM) to subtract the residual terrain effect from the gravity observations. Residual data were used to generate local empirical covariance functions and their fitting to the closed form in order to compare their statistical behaviors according to both cases. Finally, height anomalies were computed from both geopotential models and compared to a set of GPS levelled points on benchmarks using least squares adjustment. The result described in details in this paper regarding these two models has pointed out a slight advantage of GECO global model globally through error degree variances comparison and ground-truth evaluation.

Keywords: quasigeoid, gravity aomalies, covariance, GGM

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Heesuk Seo, Yongtae Kim

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Online learning started in the 1990s, the spread of the Internet has been through the era of e-learning paradigm of online education in the era of smart learning change. Reflecting the different nature of the mobile to anywhere anytime, anywhere was also allows the form of learning, it was also available through the learning content and interaction. We are developing a cloud system, 'TLINKS CLOUD' that allows you to configure the environment of the smart learning without the need for additional infrastructure. Using the big-data analysis for e-learning contents, we provide an integrated solution for e-learning tailored to individual study.

Keywords: authority tool, big data analysis, e-learning, HTML5

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Authors: Andrew Hwang

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The average American worker sits for thirteen hours a day, often with poor posture and infrequent breaks, which can lead to health issues and back problems. The Smart Cushion was created to alert individuals of their poor postures, and may potentially alleviate back problems and correct poor posture. The Smart Cushion is a portable, rectangular, foam cushion, with five strategically placed pressure sensors, that utilizes an Arduino Uno circuit board and specifically designed software, allowing it to collect data from the five pressure sensors and store the data on an SD card. The data is then compiled into graphs and compared to controlled postures. Before volunteers sat on the cushion, their levels of back pain were recorded on a scale from 1-10. Data was recorded for an hour during sitting, and then a new, corrected posture was suggested. After using the suggested posture for an hour, the volunteers described their level of discomfort on a scale from 1-10. Different patterns of sitting postures were generated that were able to serve as early warnings of potential back problems. By using the Smart Cushion, the areas where different volunteers were applying the most pressure while sitting could be identified, and the sitting postures could be corrected. Further studies regarding the relationships between posture and specific regions of the body are necessary to better understand the origins of back pain; however, the Smart Cushion is sufficient for correcting sitting posture and preventing the development of additional back pain.

Keywords: Arduino Sketch Algorithm, biomedical technology, pressure sensors, Smart Cushion

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Authors: Mohammed Alenezy

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The function of cardiac motion can be used as an indicator of the heart abnormality by evaluating longitudinal, circumferential, and Radial Strain of the left ventricle. In this paper, the Radial Strain and SR is studied using tagged MRI (tMRI) data during the cardiac cycle on the mid-ventricle level of the left ventricle. Materials and methods: The short-axis view of the left ventricle of five healthy human (three males and two females) and four healthy male rats were imaged using tagged magnetic resonance imaging (tMRI) technique covering the whole cardiac cycle on the mid-ventricle level. Images were processed using Image J software to calculate the left ventricle wall Radial Strain and radial SR. The left ventricle Radial Strain and radial SR were calculated at the mid-ventricular level during the cardiac cycle. The peak Radial Strain for the human and rat heart was 40.7±1.44, and 46.8±0.68 respectively, and it occurs at 40% of the cardiac cycle for both human and rat heart. The peak diastolic and systolic radial SR for human heart was -1.78 s-1 ± 0.02 s-1 and 1.10±0.08 s-1 respectively, while for rat heart it was -5.16± 0.23s-1 and 4.25±0.02 s-1 respectively. Conclusion: This results show the ability of the tMRI data to characterize the cardiac motion during the cardiac cycle including diastolic and systolic phases which can be used as an indicator of the cardiac dysfunction by estimating the left ventricle Radial Strain and radial SR at different locations of the cardiac tissue. This study approves the validity of the tagged MRI data to describe accurately the cardiac radial motion.

Keywords: left ventricle, radial strain, tagged MRI, cardiac cycle

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Authors: Juan Patricio Ibáñez, Exequiel López

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A method for synthesizing copper nanoparticles through an electrochemical approach is proposed, employing surfactants to stabilize the size of the newly formed nanoparticles. The electrolyte was made up of a matrix of H₂SO₄ (190 g/L) having Cu²⁺ (from 3.2 to 9.5 g/L), sodium dodecyl sulfate -SDS- (from 0.5 to 1.0 g/L) and Tween 80 (from 0 to 7.5 mL/L). Tween 80 was used in a molar relation of 1 to 1 with SDS. A glass cell was used, which was in a thermostatic water bath to keep the system temperature, and the electrodes were cathodic copper as an anode and stainless steel 316-L as a cathode. This process was influenced by the control exerted through the initial copper concentration in the electrolyte and the applied current density. Copper nanoparticles of electrolytic purity, exhibiting a spherical morphology of varying sizes with low dispersion, were successfully produced, contingent upon the chemical composition of the electrolyte and current density. The minimum size achieved was 3.0 nm ± 0.9 nm, with an average standard deviation of 2.2 nm throughout the entire process. The deposited copper mass ranged from 0.394 g to 1.848 g per hour (over an area of 25 cm²), accompanied by an average Faradaic efficiency of 30.8% and an average specific energy consumption of 4.4 kWh/kg. The chemical analysis of the product employed X-ray powder diffraction (XRD), while physical characteristics such as size and morphology were assessed using atomic force microscopy (AFM). It was identified that the initial concentration of copper and the current density are the variables defining the size and dispersion of the nanoparticles, as they serve as reactants in the cathodic half-reaction. The presence of surfactants stabilizes the nanoparticle size as their molecules adsorb onto the nanoparticle surface, forming a thick barrier that prevents mass transfer with the exterior and halts further growth.

Keywords: copper nanopowder, electrochemical synthesis, current density, surfactant stabilizer

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Authors: Felix Platzer, Eric Fimbinger

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In Discrete Element Method (DEM) simulations, the breakage behavior of particles can be simulated based on different principles. In the case of large, complex-shaped particles that show various breakage patterns depending on the scenario leading to the failure and often only break locally instead of fracturing completely, some of these principles do not lead to realistic results. The reason for this is that in said cases, the methods in question, such as the Particle Replacement Method (PRM) or Voronoi Fracture, replace the initial particle (that is intended to break) into several sub-particles when certain breakage criteria are reached, such as exceeding the fracture energy. That is why those methods are commonly used for the simulation of materials that fracture completely instead of breaking locally. That being the case, when simulating local failure, it is advisable to pre-build the initial particle from sub-particles that are bonded together. The dimensions of these sub-particles consequently define the minimum size of the fracture results. This structure of bonded sub-particles enables the initial particle to break at the location of the highest local loads – due to the failure of the bonds in those areas – with several sub-particle clusters being the result of the fracture, which can again also break locally. In this project, different methods for the generation and calibration of complex-shaped particle conglomerates using bonded particle modeling (BPM) to enable the ability to depict more realistic fracture behavior were evaluated based on the example of filter cake. The method that proved suitable for this purpose and which furthermore allows efficient and realistic simulation of breakage behavior of complex-shaped particles applicable to industrial-sized simulations is presented in this paper.

Keywords: bonded particle model, DEM, filter cake, particle breakage

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Authors: Enoch F. Sam, Kris Brijs, Stijn Daniels, Tom Brijs, Geert Wets

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Notwithstanding the growing body of literature that recognises the importance of personal safety to public transport (PT) users, it remains unclear what PT users consider regarding their safety. In this study, we explore the criteria PT users in Ghana use to assess bus safety. This knowledge will afford a better understanding of PT users’ risk perceptions and assessments which may contribute to theoretical models of PT risk perceptions. We utilised phenomenological research methodology, with data drawn from 61 purposively sampled participants. Data collection (through focus group discussions and in-depth interviews) and analyses were done concurrently to the point of saturation. Our inductive data coding and analyses through the constant comparison and content analytic techniques resulted in 4 code categories (conceptual dimensions), 27 codes (safety items/criteria), and 100 quotations (data segments). Of the number of safety criteria participants use to assess bus safety, vehicle condition, driver’s marital status, and transport operator’s safety records were the most considered. With each criterion, participants rightly demonstrated its respective relevance to bus safety. These findings imply that investment in and maintenance of safer vehicles, and responsible and safety-conscious drivers, and prioritization of passengers’ safety are key-targets for public bus/minibus operators in Ghana.

Keywords: safety evaluations, public bus/minibus, passengers, phenomenology, Ghana

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Juliana Janet R. Martin-Puzon, Demetrio L. Valle, Windell L. Rivera

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This study aimed to determine the presence of bioactive phytochemical constituents and evaluate the in vitro antibacterial activities of Glinus oppositifolius or carpet weed, a plant valued for its use in traditional medicine and as a vegetable. The leaves, stems, and roots were extracted using chloroform, ethanol, and methanol. Phytochemical screening revealed that the entire G. oppositifolius plant, i.e. roots, stems, and leaves, is a rich source of alkaloids, flavonoids, glycosides, saponins, sterols, tannins, and triterpenes. The antibacterial activity of the leaf and stem extracts were evaluated through disc diffusion, minimum inhibitory concentration, and bactericidal concentration assays against methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), extended spectrum β-lactamase-producing (ESβL+), carbapenem-resistant Enterobacteriaceae (CRE), and metallo-β-lactamase-producing (MβL+) Pseudomonas aeruginosa and Acinetobacter baumannii. The leaf extracts revealed antibacterial activities, inhibiting the growth of non-resistant and multidrug-resistant (MDR) strains of the Gram-negative bacteria E. coli, P. aeruginosa, and A. baumanii. In conclusion, the various biological activities of G. oppositifolius, including its antibacterial activity, are due to the presence of diverse bioactive secondary metabolites. The presence of phytochemical compounds in G. oppositifolius is scientific evidence on its use for treatment of many ailments. Thus, the results demonstrate the great potential of the plant as a new, alternative source of antimicrobials and other components with therapeutic value.

Keywords: antibacterial, Glinus oppositifolius, multidrug-resistant, secondary metabolites

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Authors: Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan

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We perform an investigation of the unclosed terms in the evolution equation of the velocity gradient tensor (VGT) in compressible decaying turbulent flow. Velocity gradients in a compressible turbulent flow field influence several important nonlinear turbulent processes like cascading and intermittency. In an attempt to understand the dynamics of the velocity gradients various researchers have tried to model the unclosed terms in the evolution equation of the VGT. The existing models proposed for these unclosed terms have limited applicability. This is mainly attributable to the complex structure of the higher order gradient terms appearing in the evolution equation of VGT. We investigate these higher order gradients using the data from direct numerical simulation (DNS) of compressible decaying isotropic turbulent flow. The gas kinetic method aided with weighted essentially non-oscillatory scheme (WENO) based flow- reconstruction is employed to generate DNS data. By applying neural-network to the DNS data, we map the structure of the unclosed higher order gradient terms in the evolution of the equation of the VGT with VGT itself. We validate our findings by performing alignment based study of the unclosed higher order gradient terms obtained using the neural network with the strain rate eigenvectors.

Keywords: compressible turbulence, neural network, velocity gradient tensor, direct numerical simulation

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Authors: Hafizah Che Hasan, Fateen Nazwa Yusof, Maslina Daud

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Internet of Things (IoT) is a powerful industry system, which end-devices are interconnected and automated, allowing the devices to analyze data and execute actions based on the analysis. The IoT technology leverages the technology of Radio-Frequency Identification (RFID) and Wireless Sensor Network (WSN), including mobile and sensor. These technologies contribute to the evolution of IoT. However, due to more devices are connected each other in the Internet, and data from various sources exchanged between things, confidentiality of the data becomes a major concern. This paper focuses on one of the major challenges in IoT; authentication, in order to preserve data integrity and confidentiality are in place. A few solutions are reviewed based on papers from the last few years. One of the proposed solutions is securing the communication between IoT devices and cloud servers with Elliptic Curve Cryptograhpy (ECC) based mutual authentication protocol. This solution focuses on Hyper Text Transfer Protocol (HTTP) cookies as security parameter.  Next proposed solution is using keyed-hash scheme protocol to enable IoT devices to authenticate each other without the presence of a central control server. Another proposed solution uses Physical Unclonable Function (PUF) based mutual authentication protocol. It emphasizes on tamper resistant and resource-efficient technology, which equals a 3-way handshake security protocol.

Keywords: Internet of Things (IoT), authentication, PUF ECC, keyed-hash scheme protocol

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Fakharuddin Ibrahim, Jamaludin Md. Ali, Ahmad Ramli

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In this paper, we will discuss about the data interpolation by using the rational cubic Ball curve. To generate a curve with a better and satisfactory smoothness, the curve segments must be connected with a certain amount of continuity. The continuity that we will consider is of type G¹ continuity. The conditions considered are known as the G¹ Hermite condition. A simple application of the proposed method is to generate an Arabic font satisfying the required continuity.

Keywords: data interpolation, rational ball curve, hermite condition, continuity

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Authors: Babak Nematshahrbabaki, Fallahi Arezoo

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Objective: The aim of this study was to describe teenagers’ decisions to undergo orthodontic treatment through a qualitative study. Materials and methods: Twenty-three patients (12 girls), aged 12–18 years, at a dental clinic in Sanandaj the western part of Iran participated. Face-to-face and semi-structured interviews and two focus group discussions were held to gather data. Data analyzed by the grounded theory method. Results: ‘Decision-making’ was the core category. During the data analysis four main themes were developed: ‘being like everyone else’, ‘being diagnosed’, ‘maintaining the mouth’ and ‘cultural-social and environmental factors’. Conclusions: cultural- social and environmental factors have crucial role in decision-making to undergo orthodontic treatment. The teenagers were not fully conscious of these external influences. They thought their decision to undergo orthodontic treatment is independent while it is related to cultural- social and environmental factors.

Keywords: decision-making, qualitative study, teenager, orthodontic treatment

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Sonny Dewi Judiasih

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Every person retains the right to marry and starts a family through a legitimized marriage. Indonesian Marriage Act has regulated the minimum age for boys to marry is 19 while the girls is 16, with an exception that the parents could ask for an exemption to the court or to the authorized official. Despite the age limit is set by the Marriage Act, however, with the influences from adat law and islamic law which allows younger persons to marry, the child marriage phenomenon is inevitable to happen in Indonesia. Child marriages in Indonesia have shown such alarming fact where 4.8 percentage of total marriage number come from persons with the age of 10 to 14 years old. The percentage was the result from a research conducted by the National Population and Family Planning Board (BKKBN). The result shows 41.9 percent of child marriages was contributed by girls who marry in the age of 15 to 19, which mostly comes from villagers. Other fact shows 50 percent of child marriages end in divorce with grounds varied from the mental health of the children, economic situation, and so on. With more children committed early marriages, more babies will be borned from indebted families. Subsequently, the government’s program to alleviate poverty will be short of expectations. Other risk for child marriages includes death of the mother and the child after giving birth. The people have challenged the legality of child marriages through judicial review filed to the Constitutional Court. The Court decided to reinforce the age limitation previously set by the Marriage Act by issuing judicial decision no: 30-74/PUU-Xii/2014. The Court stated that changes to the age limit must be in conform with cultural and traditional situation. Further, it stated child marriages are allowed to be arranged as an “emergency exit” if the parents filed such request to an exemption on the grounds of coercive situation and after the court or the authorized officials issued its approval.

Keywords: child, marriage, court, Indonesia

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Sudhaveni Naresh

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International migration is an important subject and gaining more significance andinterest among scholars in recent years. It is defined as crossing of the boundaries of political or administrative units for a certain minimum period for reasons such as education, employment, etc.International migration is not new for India because it has a long history with the Gulf region since ancient period. India is also one of the largest migrant-sending countries after China in the world. Migration towards the Gulf region became more prominent during early 1970s due to oil boom which led to rapid increase in the demand for foreign labour. Of 25 million Indian emigrants are living across the world, about six million Indian emigrants working in the Gulf. Most of these migrants were either unskilled or semi-skilled. Both the pull and push factors behind labour emigrate to Gulf countries. India is world’s leading receiver of remittances and the flow of remittances to India has been increasing steadily since the 1970s. In 2011-12, it was about 4 percent of GDP.Emigrants play a significant role in the economic development and growth of the country via the remittances and knowledge and skill transfer. Scholars see remittances as vital tool in the development for origin country. This paper examines the recent trend and pattern of migration from India to Gulf countries and explores impact of remittances on emigrants’ families at home country. It also highlights opportunities, challenges and the need for strengthening multilateral cooperation to transform migration into an efficient, orderly and humane process.The study propose to undertake a primary survey for this purpose. Both quantitative and qualitative research methods will be used to study the above issues.

Keywords: development, international migration, remittances, unskilled labour

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Authors: Nasir Ashraf, Sajid Rahid Ahmad, Adeel Ahmad

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Pakistan is a major tea consumer country and ranked as the third largest importer of tea worldwide. Out of all beverage consumed in Pakistan, tea is the one with most demand for which tea import is inevitable. Being an agrarian country, Pakistan should cultivate its own tea and save the millions of dollars cost from tea import. So the need is to identify the most suitable areas with favorable weather condition and suitable soils where tea can be planted. This research is conducted over District Mansehra and District Abbottabad in Khyber Pakhtoonkhwah Province of Pakistan where the most favorable conditions for tea cultivation already exist and National Tea Research Institute has done successful experiments to cultivate high quality tea. High tech approach is adopted to meet the objectives of this research by using the remotely sensed data i.e. Aster DEM, Landsat8 Imagery. The Remote Sensing data was processed in Erdas Imagine, Envi and further analyzed in ESRI ArcGIS spatial analyst for final results and representation of result data in map layouts. Integration of remote sensing data with GIS provided the perfect suitability analysis. The results showed that out of all study area, 13.4% area is highly suitable while 33.44% area is suitable for tea plantation. The result of this research is an impressive GIS based outcome and structured format of data for the agriculture planners and Tea growers. Identification of suitable tea growing areas by using remotely sensed data and GIS techniques is a pressing need for the country. Analysis of this research lets the planners to address variety of action plans in an economical and scientific manner which can lead tea production in Pakistan to meet demand. This geomatics based model and approach may be used to identify more areas for tea cultivation to meet our demand which we can reduce by planting our own tea, and our country can be independent in tea production.

Keywords: agrarian country, GIS, geoinformatics, suitability analysis, remote sensing

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

Abstract:

In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Soo Hong Da, Ng Zheng Yao, Burra Venkata Durga Kumar

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The Healthcare sector is a business that consists of providing medical services, manufacturing medical equipment and drugs as well as providing medical insurance to the public. Most of the time, the data stored in the healthcare database is to be related to patient’s information which is required to be accurate when it is accessed by authorized stakeholders. In distributed systems, one important issue is concurrency in the system as it ensures the shared resources to be synchronized and remains consistent through multiple read and write operations by multiple clients. The problems of concurrency in the healthcare sector are who gets the access and how the shared data is synchronized and remains consistent when there are two or more stakeholders attempting to the shared data simultaneously. In this paper, a framework that is beneficial to distributed healthcare sector concurrent environment is proposed. In the proposed framework, four different level nodes of the database, which are national center, regional center, referral center, and local center are explained. Moreover, the frame synchronization is not symmetrical. There are two synchronization techniques, which are complete and partial synchronization operation are explained. Furthermore, when there are multiple clients accessed at the same time, synchronization types are also discussed with cases at different levels and priorities to ensure data is synchronized throughout the processes.

Keywords: resources, healthcare, concurrency, synchronization, stakeholders, database

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Authors: M. G. R. S. Perera, B. S. Weerakoon, L. P. G. Sherminie, M. L. Jayatilake, R. D. Jayasinghe, W. Huang

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The aim of this study was to measure and evaluate the Longitudinal Relaxation Times (T1) in bone marrow of an Acute Myeloid Leukaemia (AML) patient in order to explore the potential for a prognostic biomarker using Magnetic Resonance Imaging (MRI) which will be a non-invasive prognostic approach to AML. MR image data were collected in the DICOM format and MATLAB Simulink software was used in the image processing and data analysis. For quantitative MRI data analysis, Region of Interests (ROI) on multiple image slices were drawn encompassing vertebral bodies of L3, L4, and L5. T1 was evaluated using the T1 maps obtained. The estimated bone marrow mean value of T1 was 790.1 (ms) at 3T. However, the reported T1 value of healthy subjects is significantly (946.0 ms) higher than the present finding. This suggests that the T1 for bone marrow can be considered as a potential prognostic biomarker for AML patients.

Keywords: acute myeloid leukaemia, longitudinal relaxation time, magnetic resonance imaging, prognostic biomarker.

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

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Authors: Abdulkadir I.

Abstract:

Scientists have come to terms with the fact that climate change has been and is expected to cause a significant increase in the severity and frequency of drought events. The northeast region of Nigeria is one of the most, if not the most, affected regions by drought in the country. Therefore, it is on this note that the present study applied ArcGIS and XLSTAT Software and explored drought and its trend in the northeast region of the country using the vegetation health index (VHI), Mann-Kendal, and Sen’s slope between 2001 and 2020. The study also explored the areas that remained under drought and no-drought conditions at intervals of five years for the period under review. The result of Mann-Kendal (-0.07) and Sen’s slope (-0.19) revealed that there was a decreasing trend in VHI over the period under review. The result further showed that the period between 2010 and 2015 had a minimum area of no-drought conditions of about 24%, with Gombe State accounting for the lowest percentage among the six States, about 0.9% of the total area of no-drought conditions. The result further showed the areas that were under drought conditions between 2010 and 2015 represented about 9.1%, with Borno State accounting for the highest percentage among the six States, about 2.5% of the total area under drought conditions. The masked-out areas stood at 66.8%, with Borno State accounting for the highest percentage among the six States, about 20.2% of the total area under drought conditions. Therefore, collective efforts are needed to put in place sustainable land management in the affected areas so as to mitigate the sprawl of desertification in the region.

Keywords: climate change, drought, Mann Kendal, sustainable land management, vegetation health index

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Authors: Sarah K. Basudan, Ragad I. Al Jazzar, Zeinah Sulaihim, Hanan M. Al-Kadri

Abstract:

Background: Cesarean section has been increasing in recent years, with a wide range of etiologies contributing to this rise. This study aimed to assess the indications, outcomes, and complications in Riyadh, Saudi Arabia. Methods: A Retrospective Cohort study was conducted at King Abdulaziz medical city. The study includes two cohorts: G1 (2009) and G2 (2020) groups who met the inclusion criteria. The data was transferred to the SPSS (statistical package for social sciences) version 24 for analysis. The initial descriptive statistics were run for all variables, including numerical and categorical data. The numerical data were reported as median, and standard deviation and categorical data were reported as frequencies and percentages. Results: The data were collected from 399 women who were divided into two groups, G1(199) and G2(200). The mean age of all participants is 32+-6​; G1 and G2 had significant differences in age means with 30+-6 and 34+-5, respectively, with a p-value of <0.001, which indicates delayed fertility by four years. Moreover, a breech presentation was less likely to occur in G2 (OR 0.64, CI: 0.21-0.62. P<0.001). Nonetheless, maternal causes such as repeated C-sections and maternal medical conditions were more likely to happen in G2 (OR 1.5, CI: 1.04-2.38, p=0.03) and (OR 5.4, CI: 1.12-23.9, P=0.01), respectively. Furthermore, postpartum hemorrhage showed an increase of 12% in G2 (OR 5.4, CI: 2.2-13.4, p<0.001). G2 was more likely to be admitted to the neonatal intensive care unit (NICU) (OR 16, CI: 7.4-38.7) and to special care baby (SCB) (OR 7.2, CI: 3.9-13.1), both with a p-value<0.001 compared to regular nursery admission. Conclusion: There are multiple factors that are contributing to the increase in c section rate in a Saudi tertiary hospitals. The factors were suggested to be previous c-sections, abnormal fetal heart rate, malpresentation, and maternal or fetal medical conditions.

Keywords: cesarean sections, maternal indications, maternal complications, neonatal condition

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