Search results for: strong ground motion data

Authors: Rauha Salam

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In Pakistan, over the past decade, the notion of what counts as a true ‘masculine and feminine’ behaviour has become more complicated with the inspection of social media. Given its strong religious and socio-cultural norms, patriarchal values are entrenched in the local and cultural traditions of the Pakistani society and regulate the social value of gender. However, the increasing use of internet among Pakistani men and women, especially in the form of social media uses by the youth, is increasingly becoming disruptive and challenging to the strict modes of behavioural monitoring and control both at familial and state level. Facebook, being the prime social media communication platform in Pakistan, provide its users a relatively ‘safe’ place to embrace how they want to be perceived by their audience. Moreover, the availability of an array of semiotic resources (e.g. the videos, audios, visuals and gifs) on Facebook makes it possible for the users to create a virtual identity that allows them to describe themselves in detail. By making use of Multimodal Discourse Analysis, I aimed to investigate how men and women in Pakistan construct their gendered identities multimodally (visually and linguistically) through their Facebook posts and how these semiotic modes are interconnected to communicate specific meanings. In case of the female data, the analysis showed an ambivalence as females were found to be conforming to the existing socio-cultural norms of the society and they were also employing social media platforms to deviate from traditional gendered patterns and to voice their opinions simultaneously. Similarly, the male data highlighted the reproduction of the prevalent cultural models of masculinity. However, there were instances in the data that showed a digression from the standard norms and there is a (re)negotiation of the traditional patriarchal representations.

Keywords: Facebook, Gendered Identities, Multimodal Discourse Analysis, Pakistan

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Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

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Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: D. Kuvshinov, A. Siswanto, W. Zimmerman

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A phorbol-12-myristate-13-acetate (TPA) is a synthetic analogue of phorbol ester (PE), a natural toxic compound of Euphorbiaceae plant. The oil extracted from plants of this family is useful source for primarily biofuel. However this oil can also be used as a food stock due to its significant nutrition content. The limitations for utilizing the oil as a food stock are mainly due to a toxicity of PE. Nowadays a majority of PE detoxification processes are expensive as include multi steps alcohol extraction sequence. Ozone is considered as a strong oxidative agent. It reaction with PE it attacks the carbon double bond of PE. This modification of PE molecular structure results into nontoxic ester with high lipid content. This report presents data on development of simple and cheap PE detoxification process with water application as a buffer and ozone as reactive component. The core of this new technique is a simultaneous application of new microscale plasma unit for ozone production and patented gas oscillation technology. In combination with a reactor design the technology permits ozone injection to the water-TPA mixture in form of microbubbles. The efficacy of a heterogeneous process depends on diffusion coefficient which can be controlled by contact time and interface area. The low velocity of rising microbubbles and high surface to volume ratio allow fast mass transfer to be achieved during the process. Direct injection of ozone is the most efficient process for a highly reactive and short lived chemical. Data on the plasma unit behavior are presented and influence of the gas oscillation technology to the microbubbles production mechanism has been discussed. Data on overall process efficacy for TPA degradation is shown.

Keywords: microbubble, ozonolysis, synthetic phorbol ester, chemical engineering

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: Miao Li, Mengyuan Xu

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As a low-carbon travel mode, bicycling has drawn increasing political interest in the contemporary Chinese urban context, and the public sharing bikes have become the most popular ways of bike usage in China now. This research aims to explore the spatial-temporal relationship between sharing bike usage and different characteristics of the urban street built environment. In the research, street segments were used as the analytic unit of the street built environment defined by street intersections. The sharing bike usage data in the research include a total of 2.64 million samples that are the entire sharing bike distribution data recorded in two days in 2018 within a neighborhood of 185.4 hectares in the city of Wuhan, China. And these data are assigned to the 97 urban street segments in this area based on their geographic location. The built environment variables used in this research are categorized into three sections: 1) street design characteristics, such as street width, street greenery, types of bicycle lanes; 2) condition of other public transportation, such as the availability of metro station; 3) Street function characteristics that are described by the categories and density of the point of interest (POI) along the segments. Spatial Lag Models (SLM) were used in order to reveal the relationships of specific urban streets built environment characteristics and the likelihood of sharing bicycling usage in whole and different periods a day. The results show: 1) there is spatial autocorrelation among sharing bicycling usage of urban streets in case area in general, non-working day, working day and each period of a day, which presents a clustering pattern in the street space; 2) a statistically strong association between bike sharing usage and several different built environment characteristics such as POI density, types of bicycle lanes and street width; 3) the pattern that bike sharing usage is influenced by built environment characteristics depends on the period within a day. These findings could be useful for policymakers and urban designers to better understand the factors affecting bike sharing system and thus propose guidance and strategy for urban street planning and design in order to promote the use of sharing bikes.

Keywords: big data, sharing bike usage, spatial statistics, urban street built environment

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Umair Nadeem, Sidra Umair

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Political behavior of voters and democratic values have been observed an emerging phenomenon in recent years in Pakistan. Privatized TV news channels are taking one sided position on the political issues, corresponding with respective political parties. Since last decade, TV News Channels have undermined this monopoly. Elections 2013 were unique in Pakistan with reference to political behavior and democratic values. Partisan narratives and counter narratives have been witnessed on different TV channels, in last few years. These mediated events seem very important to study the political behavior and democratic values as the country is approaching towards elections 2018. This endeavor is an attempt to capture the framing of the parties, issues in the partisan media culture and framing effects on political behavior of voters. Data for this research come from two data set. Content analysis of selected representative talks shows broadcast on mainstream news channels provide an assessment of the framing while quantitative survey of the discussion program’s viewers from Lahore city provide an evidence of framing effects on political behavior on voters and on democratic values. Regression results help us to argue that the highly partisan shows are strong predictors of polarized views among the audience. Study also grasp the attention of scholars towards the implications of this phenomenon.

Keywords: democratic values, partisan media, polarized views, political behavior

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Authors: M. Berrehail, F. Benamira

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We study the quantum motion of a particle in the presence of a time- dependent linear potential using an operator invariant that is quadratic in p and linear in q within the framework of the Lewis-Riesenfeld invariant, The special invariant operator proposed in this work is demonstrated to be an Hermitian operator which has an Airy wave packet as its Eigenfunction

Keywords: airy wave packet, ivariant, time-dependent linear potential, unitary transformation

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Akinwale S. O., Koya O. A.

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The need to provide ecologically-friendly and effective lawn mowing solution is crucial for the well-being of humans. This study involved the modification of a human-powered lawn mower designed to cut tall grasses in residential areas. This study designed and fabricated a reel-type mower blade system and a pedal-powered test rig for the blade system. It also evaluated the performance of the machine. The machine was tested on some overgrown grass plots at College of Education Staff School Ilesa. Parameters such as theoretical field capacity, field efficiency and effective field capacity were determined from the data gathered. The quality of cut achieved by the unit was also documented. Test results showed that the fabricated cutting system produced a theoretical field capacity of 0.11 ha/h and an effective field capacity of 0.08ha/h. Moreover, the unit’s cutting system showed a substantial improvement over existing reel mower designs in its ability to cut on both the forward and reverse phases of its motion. This study established that the blade system described herein has the capacity to cut tall grasses. Hence, this device can therefore eliminate the need for powered mowers entirely on small residential lawns.

Keywords: effective field capacity, field efficiency, theoretical field capacity, quality of cut

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Hafiz Haseeb Nisar, Maryam Bilal

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This qualitative study was conducted to investigate the underlying dynamics, key success factors and characteristics of Pakistani business leaders. The sample included 5 top businessmen from different business sectors. They were approached through the magazine “Manager Today” office in Lahore. Semi-structured interview technique was used to understand their experiences, attitudes, styles of management and key factors in their business success. Grounded theory was employed to analyze data. The following themes emerged as characteristics of business leaders: optimism, assertiveness, professionalism, effective management style, business knowledge/ excellence, supportive family and strong faith in God. It was noted that all these were interlinked, which leads the businessmen to become a successful business tycoon of this country. The importance of this study rests in its eastern contribution to the theoretical framework of the psychology of business leaders and particularly these findings will help to realize the basic key factors of success behind a business leader in Pakistan.

Keywords: business psychology, characteristics, key personality factors, leadership

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Authors: Muluwerk Ayele Derebe

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Background: Antenatal care (ANC) is an essential element in the continuum of reproductive health care for preventing preventable pregnancy-related morbidity and mortality. Objective: The aim of this study is to assess the spatial pattern and predictors of ANC visits in Ethiopia. Method: This study was done using Ethiopian Demographic and Health Survey data of 2016 among 7,174 pregnant women aged 15-49 years which was a nationwide community-based cross-sectional survey. Spatial analysis was done using Getis-Ord Gi* statistics to identify hot and cold spot areas of ANC visits. Multilevel glmmTMB packages adjusted for spatial effects were used in R software. Spatial multilevel count regression was conducted to identify predictors of antenatal care visits for pregnant women, and proportional change in variance was done to uncover the effect of individual and community-level factors of ANC visits. Results: The distribution of ANC visits was spatially clustered Moran’s I = 0.271, p<.0.001, ICC = 0.497, p<0.001). The highest spatial outlier areas of ANC visit was found in Amhara (South Wollo, Weast Gojjam, North Shewa), Oromo (west Arsi and East Harariga), Tigray (Central Tigray) and Benishangul-Gumuz (Asosa and Metekel) regions. The data was found with excess zeros (34.6%) and over-dispersed. The expected ANC visit of pregnant women with pregnancy complications was higher at 0.7868 [ARR= 2.1964, 95% CI: 1.8605, 2.5928, p-value <0.0001] compared to pregnant women who had no pregnancy complications. The expected ANC visit of a pregnant woman who lived in a rural area was 1.2254 times higher [ARR=3.4057, 95% CI: 2.1462, 5.4041, p-value <0.0001] as compared to a pregnant woman who lived in an urban. The study found dissimilar clusters with a low number of zero counts for a mean number of ANC visits surrounded by clusters with a higher number of counts of an average number of ANC visits when other variables held constant. Conclusion: This study found that the number of ANC visits in Ethiopia had a spatial pattern associated with socioeconomic, demographic, and geographic risk factors. Spatial clustering of ANC visits exists in all regions of Ethiopia. The predictor age of the mother, religion, mother’s education, husband’s education, mother's occupation, husband's occupation, signs of pregnancy complication, wealth index and marital status had a strong association with the number of ANC visits by each individual. At the community level, place of residence, region, age of the mother, sex of the household head, signs of pregnancy complications and distance to health facility factors had a strong association with the number of ANC visits.

Keywords: Ethiopia, ANC, spatial, multilevel, zero inflated Poisson

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Stephen Pembi, Samuel K. Msheliza, Helen A. Andow

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Organizational culture is very important in the organization because it enhances organizational performance and serves as a sense of making and control mechanism that guides and shapes the attitude and behaviour of employees. However, organizational culture issues are frequently disregarded in lieu of activities that may or may not have a good impact on performance. This study examines the relationship between organizational culture and organizational performance of Adama Beverages Ltd, Adamawa State. The study employed an explanatory survey research design with a questionnaire as a source of data collection. One hundred and thirty-five copies of the questionnaire were administered using the convenience method of sampling, out of which one hundred and twenty were retrieved and well answered. The data collected were subjected to the Pearson product-moment correlation technique to test the hypotheses of the study using SPSS. The overall results signify that organizational culture has a significant positive relationship with organizational performance. The multiple regression results show that mission, adaptability, and involvement have a significant positive influence on organizational performance, while consistency has a significant negative influence on organizational performance. Therefore, this study concluded that organizational culture is a strong determinant of organizational performance in Adama Beverages Ltd, Adamawa State. The study recommends that the level of employee input into decision-making, flexibility in responding to changes in the business environment, consistency with values and traditions, and organizational performance should all be maintained by Adama Beverages Ltd.

Keywords: adaptability, consistency, involvement, mission, organizational performance

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Authors: Asinguza P. Allan

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The Advocacy for Better Health aims to equip citizens with enabling environment and systems to effectively advocate for strong action plans to improve health services. This is because the 2020 Government target for Uganda to transform into a middle income country will be achieved if investment is made in keeping the population healthy and productive. Citizen participation as an important foundation for change has been emphasized to gather data through participatory rural appraisal and inform evidence-based advocacy for recruitment and motivation of human resources. Citizens conduct problem ranking during advocacy forums on staffing levels and health worker absenteeism. Citizens prioritised inadequate number of midwives and absenteeism. On triangulation, health worker to population ratio in Uganda remains at 0.25/1,000 which is far below the World Health Organization (WHO) threshold of 2.3/1,000. Working with IntraHealth, the project advocated for recruitment of critical skilled staff (doctors and midwives) and scale up health workers motivation strategy to reduce Uganda’s Neonatal Mortality Rate of 22/1,000 and Maternal Mortality Ratio of 320/100,000. Government has committed to increase staffing to 80% by 2018 (10 districts have passed ordinances and revived use of duty rosters to address health worker absenteeism. On the other hand, the better health advocacy debate has been elevated with need to increase health sector budget allocations from 8% to 10%. The project has learnt that building a body of evidence from citizens enhances the advocacy agenda. Communities will further monitor government commitments to reduce Neonatal Mortality Rate and Maternal Mortality Ratio. The project has learnt that interface meeting between duty bearers and the community allows for immediate feedback and the process is a strong instrument for empowerment. It facilitates monitoring and performance evaluation of services, projects and government administrative units (like district assemblies) by the community members themselves. This, in turn, makes the human resources in health to be accountable, transparent and responsive to communities where they work. This, in turn, promotes human resource performance.

Keywords: advocacy, empowerment, evidence, human resources

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Abdulfatah Faraj Aboufayed

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Water erosion is the most important problems of the soil in the Jebel Nefusa area located in north west of Libya, therefore erosion station had been established in the Faculty of Veterinary and rainfed agriculture research Station, University of the Jepel Algherbee in Zentan. The length of the station is 72.6 feet, 6 feet width, and the percentage of it's slope is 3%. The station was established to measure the mount of soil eroded and amount of surface water produced during the seasons 95/96 and 96/97 from each rain storms. The Monitoring shows that there was a difference between the two seasons in the number of rainstorms which made differences in the amount of surface runoff water and the amount of soil eroded between the two seasons. Although the slope is low (3%), the soil texture is sandy and the land ploughed twice during each season surface runoff and soil eroded occurred. The average amount of eroded soil was 3792 grams (gr) per season and the average amount of surface runoff water was 410 litter (L) per season. The amount of surface runoff water would be much greater from Jebel Nefusa upland with steep slopes and collecting of them will save a valuable amount of water which lost as a runoff while this area is in desperate of this water. The regression analysis of variance show strong correlation between rainfall depth and the other two depended variable (the amount of surface runoff water and the amount of eroded soil). It shows also strong correlation between amount of surface runoff water and amount of eroded soil.

Keywords: rain, surface runoff water, soil, water erosion, soil erosion

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Franklin Nantui Mabe, Gideon Danso-Abbeam, Dennis Sedem Ehiakpor

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In order for the government of Ghana through the Ministry of Food and Agriculture to motivate farmers to adopt improved agricultural technologies, expand their farms and encourage youth to enter into agricultural production so as to increase crop productivity, “Planting for Food and Jobs” (PFJ) programme was launched in April 2017. The PFJ programme covers five pillars, namely, provision of subsidized and improved seeds; subsidized fertilizer; agricultural extension services; establishment of markets; and e-agriculture. This study assesses the awareness of farmers about the packages of these pillars using the Likert scale, paired t-test and Spearman’s rank correlation coefficient. The study adopted a mixed research design. A semi-structured questionnaire and checklist were used to collect data. The data collection was done using interviews and focus group discussions. The PFJ pillar farmers are much aware is a subsidy on fertilizer followed by a subsidy on improved seeds. Electronic agriculture is a pillar with the lowest level of awareness. There is a strong positive correlation between awareness of fertilizer and seed packages suggestion their complementarities. Lack of information/awareness of the packages of the programme can affect farmers’ participation in all the pillars. Farmers, in particular, should be educated for them to know what they are entitled to in each of the pillars. The programme implementation plan should also be made available to farmers as a guide.

Keywords: awareness, planting for food and jobs, programme, farmers, likert scale

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Authors: Md. Abdul Kader

Abstract:

Several reports released by Global Financial Integrity (GFI) in recent times have identified Bangladesh as being among the worst affected countries to the scourge of money laundering (ML) and terrorist financing (TF). The money laundering (ML) and terrorist financing (TF) risks associated with conventional finance are generally well identified and understood by the relevant national authorities. There is, however, no common understanding of ML/TF risks associated with Islamic Banking. This paper attempts to examine the issues of money laundering (ML) and terrorist financing (TF) in Islamic Banks of Bangladesh. This study also investigates the risk factors associated with Islamic Banking system of Bangladesh that are favorable for ML and TF and which prevent the government to control such issues in the Islamic Banks of Bangladesh. Qualitative research methods were employed by studying various reports from journals, newspapers, bank reports and periodicals. In addition, five ex-bankers who were in the policy making bodies of three Islamic Banks were also interviewed. Findings suggest that government policies regarding Islamic Banking system in Bangladesh are not well defined and clear. Shariah law, that is the guiding principle of Islamic Banking, is not well recognized by the government policy makers, and thus they left the responsibility to the governing bodies of the banks. Other challenges that were found in the study are: the complexity of some Islamic banking products, the different forms of relationship between the banks and their clients, the inadequate ability and skill in the supervision of Islamic finance, particularly in jurisdictions, to evaluate their activities. All these risk factors paved the ground for ML and TF in the Islamic Banks of Bangladesh. However, due to unconventional nature of Banking and lack of investigative reporting on Islamic Banking, this study could not cover the whole picture of the ML/TF of Islamic Banks of Bangladesh. However, both qualitative documents and interviewees confirmed that Islamic Banking in Bangladesh could be branded as risky when it comes to money laundering and terror financing. This study recommends that the central bank authorities who supervise Islamic finance and the government policy makers should obtain a greater understanding of the specific ML/TF risks that may arise in Islamic Banks and develop a proper response. The study findings are expected to considerably impact Islamic banking management and policymakers to develop strong and appropriate policy to enhance transparency, accountability, and efficiency in banking sector. The regulatory bodies can consider the findings to disseminate anti money laundering and terror financing related rules and regulations.

Keywords: money laundering, terror financing, islamic banking, bangladesh

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

Abstract:

Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Khalid Soussi

Abstract:

The study at hand has attempted to describe the acquisition of three EFL lexical knowledge aspects - meaning, synonymy and collocation – across three academic levels: Baccalaureate, second year and fourth year university levels in Morocco. The research also compares the development of the three lexical knowledge aspects between knowledge (reception) and use (production) and attempts to trace their order of acquisition. This has led to the use of three main data collection tasks: translation, acceptability judgment and multiple choices. The study has revealed the following findings. First, L1 and EFL mental lexicons are connected at the lexical knowledge depth. Second, such connection is active whether in language reception or use. Third, the connectivity between L1 and EFL mental lexicons tends to relatively decrease as the academic level of the learners increases. Finally, the research has revealed a significant 'order' of acquisition between the three lexical aspects, though not a very strong one.

Keywords: vocabulary acquisition, EFL lexical knowledge, mental lexicon, vocabulary knowledge depth

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Authors: David Krejci, Tony Schönherr, Quirin Koch, Valentin Hugonnaud, Lou Grimaud, Alexander Reissner, Bernhard Seifert

Abstract:

In 2018 the NANO thruster became the first Field Emission Electric Propulsion (FEEP) system ever to be verified in space in an In-Orbit Demonstration mission conducted together with Fotec. Since then, 160 additional ENPULSION NANO propulsion systems have been deployed in orbit on 73 different spacecraft across multiple customers and missions. These missions included a variety of different satellite bus sizes ranging from 3U Cubesats to >100kg buses, and different orbits in Low Earth Orbit and Geostationary Earth orbit, providing an abundance of on orbit data for statistical analysis. This large-scale industrialization and flight heritage allows for a holistic way of gathering data from testing, integration and operational phases, deriving lessons learnt over a variety of different mission types, operator approaches, use cases and environments. Based on these lessons learnt a new generation of propulsion systems is developed, addressing key findings from the large NANO heritage and adding new capabilities, including increased resilience, thrust vector steering and increased power and thrust level. Some of these successor products have already been validated in orbit, including the MICRO R3 and the NANO AR3. While the MICRO R3 features increased power and thrust level, the NANO AR3 is a successor of the heritage NANO thruster with added thrust vectoring capability. 5 NANO AR3 have been launched to date on two different spacecraft. This work presents flight telemetry data of ENPULSION NANO systems and onorbit statistical data of the ENPULSION NANO as well as lessons learnt during onorbit operations, customer assembly, integration and testing support and ground test campaigns conducted at different facilities. We discuss how transfer of lessons learnt and operational improvement across independent missions across customers has been accomplished. Building on these learnings and exhaustive heritage, we present the design of the new generation of propulsion systems that increase the power and thrust level of FEEP systems to address larger spacecraft buses.

Keywords: FEEP, field emission electric propulsion, electric propulsion, flight heritage

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Authors: Abdalrahman D. Alsulaili, Sheikha Y. Aljeraiwi, Athba N. Almanaie, Raghad Y. Alhajeri, Mariam Z. Almijren

Abstract:

The water accompanying oil in the oil production process is increasing and due to its increasing rates a problem with handling it occurred. Current solutions like discharging into the environment, dumping water in evaporation pits, usage in the industry and reinjection in oil reservoirs to enhance oil production are used worldwide. The water injection method has been introduced to the oil industry with a process that either immediately injects water to the reservoir or goes to the filtration process before injection all depending on the porosity of the soil. Reinjection of unfiltered effluent water with high Total Suspended Solid (TSS) and Oil in Water (O/W) into soils with low porosity cause a blockage of pores, whereas soils with high porosity do not need high water quality. Our study mainly talks about the filtration and adsorption of the water using organic media as the adsorbent. An adsorbent is a substance that has the ability to physically hold another substance in its surface. Studies were done on nutshell and date pits with different surface areas and flow rates by using a 10inch filter connected with three tanks to perform as one system for the filtration process. Our approach in the filtration process using different types of medias went as follow: starting first with crushed nutshell, second with ground nutshell, and third using carbonized date pits with medium flow rate then high flow rate to compare different results. The result came out nearly as expected from our study where both O/W and TSS were reduced from our oily water sample by using this organic material. The effect of specific area was noticed when using nutshell as the filter media, where the crushed nutshell gave us better results than ground nutshell. The effect of flow rate was noticed when using carbonized date pits as the filter media whereas the treated water became more acceptable when the flow rate was on the medium level.

Keywords: date pits, nutshell, oil water, TSS

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Authors: X. Wang

Abstract:

Timely and accurate information on urban building damage caused by earthquake is important basis for disaster assessment and emergency relief. Very high resolution (VHR) remotely sensed imagery containing abundant fine-scale information offers a large quantity of data for detecting and assessing urban building damage in the aftermath of earthquake disasters. However, the accuracy obtained using spectral features alone is comparatively low, since building damage, intact buildings and pavements are spectrally similar. Therefore, it is of great significance to detect urban building damage effectively using multi-source data. Considering that in general height or geometric structure of buildings change dramatically in the devastated areas, a novel multi-stage urban building damage detection method, using bi-temporal spectral, height and corner information, was proposed in this study. The pre-event height information was generated using stereo VHR images acquired from two different satellites, while the post-event height information was produced from airborne LiDAR data. The corner information was extracted from pre- and post-event panchromatic images. The proposed method can be summarized as follows. To reduce the classification errors caused by spectral similarity and errors in extracting height information, ground surface, shadows, and vegetation were first extracted using the post-event VHR image and height data and were masked out. Two different types of building damage were then extracted from the remaining areas: the height difference between pre- and post-event was used for detecting building damage showing significant height change; the difference in the density of corners between pre- and post-event was used for extracting building damage showing drastic change in geometric structure. The initial building damage result was generated by combining above two building damage results. Finally, a post-processing procedure was adopted to refine the obtained initial result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010, using pre-event GeoEye-1 image, pre-event WorldView-2 image, post-event QuickBird image and post-event LiDAR data. The results showed that the method proposed in this study significantly outperformed the two comparative methods in terms of urban building damage extraction accuracy. The proposed method provides a fast and reliable method to detect urban building collapse, which is also applicable to relevant applications.

Keywords: building damage, corner, earthquake, height, very high resolution (VHR)

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