Data Sorting Research Articles

KAJIAN MATERIAL SEDIMEN PANTAI BALIKPAPAN UNTUK MENGETAHUI VARIASI UKURAN BUTIR PARTIKEL PASIR (SAND) SEDIMEN

Along the Balikpapan Beach, there are abundant sand-sized sediment particles. Sand is one of the unconsolidated sediment grains based on fractions on the Wentworth Scale. Unconsolidated Sediment is defined as Loose Material and/or sedimentary rocks that is not yet solid. Balikpapan Beach, based on the Regional Geological Map of Balikpapan sheet, formed the Alluvium Formation (abbreviated as Qa) of the Holocene Epoch, Quaternary. Alluvium formation (Qa) has an arrangement of rocks in the form of pebbles, gravel, sand, and clay. Samples of Unconsolidated Sediment or loose material and sedimentary rocks that is not yet solid are determined for grain size variations using the Sieving method. 
 The Seiving method or Sieve Test is a method for determining the size of the grains which will then be viewed on a scale and followed by the amount of grain size data that will be presented in graphical form to facilitate the type of grain size that dominates. The research focused on how the grain size on Balikpapan Beach based on the Wentworth Scale and Sorting data after the Sieve Test. 
 Sieve Test results measured Mesh#10, Mesh#20, Mesh#40, Mesh#60, Mesh#120, Mesh#200 and Filter Analysis of the relationship between Filter Diameter and Retained Percentage as follows: Granule by 3.53%, Sand (Very Coarse Sand by 3.53%, Coarse Sand by 4.75%, Medium Sand by 13.26%, Fine Sand by 66.26%, Very Fine Sand by 5.15%) and Coarse Silt by 3.35%. Therefore, particles in the area of research with the Sieve Test were dominated by Fine Sand particles. Fine Sand with grain size 0.125 – 0.25 mm tested with a filter diameter of 0.125 mm got a Retained Weight of 331.29 gr, Retained Weight Precentage of 66.26%.

Counting the carnivores: Who ate meat in Republican-Era China?

AbstractIt is commonly asserted that Chinese diets before the market and production reforms of the 1980s contained little or no meat. Yet this nearly universal assumption remains untested: Unlike other forms of material consumption, the question of meat in Chinese diets has received almost no systematic attention from historians. Focusing on the early twentieth century, this article examines who in China ate meat, and how meat consumption was shaped by regional and household patterns. It combines insights from three sorts of data. First, Japanese price surveys from the 1920s show a high degree of variation in the preference for one type of meat over others, and the price availability of meat versus wages or other food products. Second, production data, including slaughterhouse tallies and industry estimates of animal by-products show the seasonality of animal slaughter and the vast scale and dispersed geography of China’s livestock production. Finally, nutrition and diet studies from the 1920 to the late 1940s examine actual household consumption, emphasizing how social forces and cyclical fortunes shaped individual choices. The composite picture from these three perspectives confirms that China’s meat consumption was hardly inconsequential. But more than simply triangulating a result, the exercise of comparing perspectives of price, production, and nutrition also highlights the collection of survey data as a series of historical moments.

Wireless Sensor Network as a Mesh: Vision and Challenges

Nowadays, network technologies are developing very rapidly. The growing volume of transmitted information (video, data, VoIP, etc.), the physical growth of networks, and inter-network traffic are forcing manufacturers to produce more powerful and “smart” devices that use new methods of transferring and sorting data. Such connected smart devices (IoT) are used in intelligently controlled traffic for self-driving vehicles in Vehicle Adhoc Networks (VANET), in electricity and water in smart cities, and in-home automation in smart homes. These types of connected Internet of Things (IoT) devices are used to leverage different types of network structures. Such IoT sensor devices can be deployed as a wireless sensor network (WSN) in a mesh topology. Both WSNs and Wireless Mesh Networks (WMNs) are easy to organize as well as to deploy. In this case, there are many reasons for combining these different types of networks. In particular, the detailed sensory capabilities of sensor networks may be improved by increasing bandwidth, reliability and power consumption in the mesh topology. However, there are currently only a handful of studies devoting to integrate these two different types of networks. In addition, there is no systematic review of existing interconnection methods. That is why in this article we explore the existing methods of these two networks and provide an analytical basis for their relationship. We introduce the definition of WSN and WMN and then look at some case studies. Afterward, we present several challenges and opportunities in the area of combined Wireless Mesh Sensor Network (WMSN) followed by a discussion on this interconnection through literature review and hope that this document will attract the attention of the community and inspire further research in this direction.

A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things. This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays’ data using one of the popular comparison-based sorting algorithms. Two frameworks, one for serial realization and another for parallel realization, are proposed. The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions. It is about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1.6\%)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3.5-4\%)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(0.3-1.8\%)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2-4\%)$ </tex-math></inline-formula> improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Merge-based and Quick-based scenarios. Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process. Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures. As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2-4)$ </tex-math></inline-formula> for small to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2-16)$ </tex-math></inline-formula> for very large data sets. The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model. Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.

Offline situational game for teaching IT systems

There are still many offline methodologies and playable games out there for teaching various curriculum elements. These games help the students in understanding difficult IT concepts. This approach is called “Computer Science Unplugged” or CSUnplugged in short. As an example, think about sorting algorithms, binary numbers, data structures, or databases. In the following article, we would like to introduce a new situational game that focuses on software systems, and network communications by simulating multiple programmable devices, that communicate with each other in real-time. We believe that the game makes the students activities and helps them to understand critical and important terms in an interesting and playful way. The game is a simulation of a smart home system in a classroom environment and does not need any digital devices.

Optimization of Human Resource Performance Management System Based on Improved R‐Means Clustering Algorithm

With the rapid development of network technology and database technology, computers have been able to store large‐scale and massive data. On the other hand, traditional data analysis and processing tools such as management information system can only process these data on the surface, but the deeper data analysis ability is not satisfactory. The contradiction between data supply ability and data analysis ability is becoming more and more prominent, so there is an urgent need for an automation technology that can deeply process data. Data mining technology came into being. Cluster analysis, as an important topic in data mining, is a data mining method that divides data into natural groups and gives the description of the characteristics of each group. It is a basic method of data mining and knowledge discovery. Cluster analysis is a data mining technology for unsupervised classification of data without prior knowledge and guidance. Through the appropriate use of advanced algorithms, it can explore the hidden valuable information, improve the quality of data analysis and interpretation, and provide a scientific judgment basis for the reprocessing or understanding of data by other data analysis and sorting tools. First, this paper briefly introduces the principle, development, and methods of cluster analysis and expounds the application of cluster analysis. Then it expounds the principle of R‐means clustering algorithm, analyzes the advantages and disadvantages of basic R‐means clustering algorithm, and expounds several existing improvement methods. An improved R‐means clustering algorithm and a clustering analysis model based on R‐means clustering algorithm are proposed, and the corresponding algorithm flow and implementation are given.

An Effective Model for Forecasting Travel Consumer Demand Using Big Data Analysis

As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.

Perception of Leprosy Patients towards the Disease in a Tertiary Care Hospital of High Prevalent District in West Bengal, India: A Qualitative Study.

Background:Leprosy is a disease having tremendous social implications due to ostracization. Despite continuous efforts made so far to eliminate leprosy, stigma/misbeliefs/adverse attitude toward leprosy still prevails among common people. Community perceptions and attitudes towards leprosy patients are critical and unique indicator of how society stereotypes leprosy.Aims:The qualitative study was conducted to explore the perceptions of leprosy patients towards leprosy.Materials and Methods:Two focused group discussions (FGDs) with 19 newly-diagnosed leprosy patients who can speak vernacular were conducted with help of a predetermined FGD-guide between April and June 2019 in the dermatology out-patient department of the tertiary-care center. Both FGD sessions were recorded, transcribed, and translated into English. These verbatims were thematically analyzed and emerging themes were identified with illustrative quotations. Free list and pile sort data obtained were analyzed by Visual Anthropac version 1.0 software for Smith's salience value Cognitive mapping with two-dimensional scaling and hierarchical cluster analysis.Results:Small size of wound, not giving importance, failure to realize, bad luck and scared of being isolated etc., were the most common reported perceptions. Overall, five themes emerged after pile sorting, namely ignorance about the disease, stigma and economic burden, positive perception after diagnosis, treatment-related suggestions, and myths and frequently asked questions.Conclusion:The study concludes that more IEC campaigns are required to abolish the stigma; and ASHA workers/front-line workers can play a pivotal role. The program (NLEP) can also utilize the role of satisfied cured-patient as peer-educator to improve the attitude of society towards this disease.

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

The concept of big data security is introduced in this article along with many features. It illustrates the need for security in healthcare systems as the volume of data increases continuously over the period of time. The necessity of big data security as well as several big data analytics phases highlighted. It covers many big data privacy-preserving strategies. Many digital storage solutions being used in today’s world are designed to work only with fixed format of the data. This paper introduces some methods for maintaining metadata obliviousness. The oblivious RAM technology mentioned in the research article address security concerns and it can be handled with the daily increase in data in several industries. Security needs are introduced at many phases of big data creation, such as information extraction, storage systems, and analytics of the information. Additionally, it presents several data recovery methods for recovering original data in the event of a data crash. This paper covers several data categorization methods for sorting data into normal and sensitive categories as well as methods for anomaly detection. It discusses the advantages and disadvantages of various security measures.

Bioinformatics Tools to Understand Notch.

As a result of the culmination of data, and the fast-paced advancement of new research, all the biological information collected can make it difficult to sort data. This is oftentimes experienced when learning about the human genome. Fortunately, with the advancement of technology, the field of bioinformatics has emerged which has allowed for the creation of a variety of biological databases. These biological databases provide a condensed reservoir of organized information that is easy to use and topic-specific. Here, we provide a list of 39 biological databases that help break down the fundamental details of a gene. This chapter uses the NOTCH1 gene as an example to demonstrate how biological databases can be used to extract gene information. Five sections were created to highlight the major areas needed to build a comprehensive foundation of NOTCH1. The first section lists databases containing basic gene and protein product information. The next section consists of protein interactions and signaling pathway databases which are essential in understanding the biological processes a gene product is involved in. Gene expression and disease databases are the next two sections which are connected since disease results from the aberrant expression of a gene product. The last database section examines model organisms which serve a key role in the study of human genetic diseases. Using these databases, we can elucidate NOTCH1's gene/protein structure, expression, and vital physiological function through the Notch signaling pathway.

Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection

Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE). The proposed method improves the accuracy of video anomaly detection by obtaining more vital information. In the data sorting phase, non-uniform chunking is proposed to divide the video frame into several chunks of different sizes to avoid obtaining unnecessary information and omitting crucial information. In order to well reflect the abnormal motion of objects in the video, a new feature, the inter frame flow feature, which is obtained by merging inter frame difference and optical flow features, is proposed to extract motion feature. Moreover, in this paper, skip connection in the auto encoder is utilized during the training phase to reduce the reconstruction error between the original frames and the reconstruction frames, so that the reconstruction error can be used to detect abnormal events during testing. Experience on three public datasets verifies the effectiveness and accuracy of our proposed method. Experimental results show that the proposed method can detect and locate abnormal events outperforms other recent methods significantly.

Implementasi Kebijakan Pengelolaan Hibah Dan Bantuan Sosial Kota Bekasi (Studi Tentang Implementasi Peraturan Menteri Dalam Negeri Nomor 32 Tahun 2011 Tentang Pedoman Pemberian Hibah dan Bantuan Sosial Yang Bersumber Dari Anggaran Pendapatan dan Belanja Daerah)

Grant Expenditures must be managed in an orderly manner, obeying laws and regulations, efficient, economic, effective, transparent and accountable by taking into account the principles of justice, appropriateness, rationality and benefits for the community. This is intended to create administrative order, accountability, transparency in the management of grant funds and accuracy in the use of aid funds by grant recipients. The formulation of the problem discussed in this study is how to manage grants and social assistance for the Bekasi City Govemment. The research method used is descriptive qualitative research, which is the process of organizing and sorting data into patterns and categories as well as basic description units so that themes can be put forward as suggested by the data. The results of the research are that the management of grants and social assistance funds in Bekasi City was not properly implemented in 2018. It was explained that this study did not use population and samples but key informants and respondents. Researcher determines the Regional Work Unit (SKPD) for Grants and Social Assistance as Key Informants which in this study are represented by the Manager of Grants and Social Assistance in each SKPD. Meanwhile, the respondents were several people who were taken from the Advisory Team for grants and social assistance. Research results, researcherconcluded that the management of grants and social assistance at the Bekasi City Regional Financial and Asset Management Agency in 2018 had not been carried out properly. Some of the obstacles to the management of grant funds and social assistance in Bekasi City are the lack of available personnel, staff who have not received training regarding grant aid management, incomplete rules and procedures for managing grant funds, and overlapping allocations of funds in grant expenditure.

Sustainable Development of Companies Using the ERP System as a Fundamental Tool of Digital Transformation

Purpose of the article: The purpose is to verify that ERP system implementations are in accordance with the company growth. There is no doubt that the ERP system as a representative of digitalisation tools aims at optimisation of resources and processes, while also contributing to sustainability.Methodology/Methods: The article is based on processing and sorting of secondary data published by the Czech Statistical Office. All relevant data were issued in different formats and structures so they needed to be parsed, cleaned and unified. It would not be possible to achieve expected results without such data transformation requiring knowledge in the field. To ensure higher validity of the study, the dependence between two researched aspects was observed in the lowest possible detail of the data and then summarised into present the final result.Scientific aim: The aim is to identify the relationship between the ERP system implementations and company growth. The study assumes that companies are motivated to digital transformation if it helps them to increase their production or do not temporarily slow it down because of the lifecycle and consequences of the implementation covering investment, deployment and usage of the ERP.Findings: Although the number of Czech companies using an ERP system is not constantly growing, the enterprise production is increasing during the reference period. This phenomenon is noticeable in almost every NACE industry in years 2015, 2017 and 2019. Even though the number of firms using the ERP is not clearly growing, as there is a fall in 2017, the general trend between 2015 and 2019 is increasing.Conclusions: As there is no negative effect of implementation of the ERP system on company growth, enterprises are willing to invest into their digital transformation and thus to support sustainable development as a whole.

Implementation of Targhib And Tarhib Methods to Teach Aqidah and Akhlaq Subject in Madrasah

This study is aimed at investigating targib and tarhib method in terms of : planning, implementation, supporting and inhibiting factors for the implementation, evaluation and success rate of application. This research applied qualitative method using descriptive analytic. The data were collected using observation, interviews, and documentation. Data were analysed by selecting, sorting data obtained, analysing and drawing conclusions. The findings show that the planning was designed in terms of learning strategies, techniques and tactics in the document of lesson plan. The implementation was in accordance with the planning written in the learning steps in the document of lesson plan. The supporting factors included innovative and competent teachers in their fields, full support from the principal, support from the teacher council and school staff, and the availability of facilities and infrastructure. The inhibiting factors covered the differences in the students’ background and the location of the classrooms which is next to public roads and residents' houses. The evaluation was done by observing students’ attitudes and administering the oral tests in learning. The success rate of implementation can be seen from the increase in the students’ awareness to behave ahlak karimah in everyday life.

Penerapan Asosiasi Algoritma Apriori Pada Data Penjualan Alat-Alat Listrik Dan Tekhnik

The incompatibility of information in reporting productsthat are sold, and data storage is very large, business people, especially in the sales business are required to find an appropriate strategy that can increase sales and marketing of products sold, one of which is by using electronic product sales data. Therefore, anapplication is needed that is able to sort and select data, so that information can be obtained that is useful for users, namely data mining. Associate patterns can be used to place products that are often purchased together into an area that is close together so as to facilitate the customer in finding the desired product and designing the appearance of products in the catalog. The method used is theApriori Algorithm method, with the help ofTanagra 1.4.50 tools and processing transaction data using Microsoft Excel 2007.

Computer Algorithms Support Physician Decisions in Traumatic Head Injury.

Computer Algorithms Support Physician Decisions in Traumatic Head Injury.

Adaptive query relaxation and top‐ k result sorting of fuzzy spatiotemporal data based on XML

With the increasing popularity of Extensible Markup Language (XML) for data representation, there is a lot of interest in searching XML data. Due to the structural heterogeneity of XML, it is daunting for users to formulate exact queries and search for accurate answers. Especially when performing queries on fuzzy spatiotemporal data, the imprecision and uncertainty of information make the amount of query results is small or even empty. In this case, query relaxation is necessary. Existing efforts are mainly made on expanding simple queries. Once there are multiple conditions in the query, the existing algorithms often have poor performance. To address this problem, we first propose an adaptive query relaxation algorithm based on XML, which can handle complex queries efficiently. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results into two sets after query relaxation. Moreover, the algorithm prestores the potential matching objects in the auxiliary structure, which provides support for efficient query. In addition, after query relaxation, we present the result sorting algorithm to address the problem of information overload. Finally, we report experimental results to show that our algorithms are superior to previous approaches.

EKSISTENSI LAFAL-LAFAL AL-MUARRAB DALAM PEMBELAJARAN AL-QUR’AN

This journal discusses the existence of al-muarrab pronunciations in learning the Qur'an from the historical, and linguistic aspects. In learning the Qur'an there is material about the vocabulary/mufrodat of the Qur'an in which there are foreign vocabularies or al-muarrab pronunciations. This vocabulary does not reduce the miracle of the Qur'an in terms of language, in fact the presence of absorption elements in it shows that the Qur'an accommodates other languages. The main factor in the existence of foreign languages in the Qur'an is because the Arabs interacted with foreign nations in pre-Islamic times through prolonged trade routes in historical records. This led to the borrowing of foreign languages which were then used by the Arabs at that time. Of course, through a long historical process naturally through a stage called arabization or language neutralization by adjusting Arabic spoken and what the Arabs wanted. This research is a qualitative research with the nature of library research so that after collecting data, researchers carry out data processing activities, which include data classification, verification, merging, and sorting data with the aim of preparing materials for data analysis activities using content analysis.

Teacher Transfers and Teachers’ Performance: Experience from Uganda Primary Schools

Achievement of excellent academic performance depends largely on the quality of the teachers deployed in the school. The main objective of this study was to examine the relationship between teacher transfer and teachers’ performance in Uganda. The study used exploratory and descriptive designs where both qualitative and quantitative approaches were applied. Quantitative data was analyzed using Statistical Package for Social Scientists (SPSS) Ver. 20 which helped to obtain descriptive statistics that were used in interpreting the data while correlation and regression analyses were used for testing and predicting the study hypotheses. Qualitative data was analyzed using content analysis where data was divided into themes, sub-themes and categories which helped to sort data according to its relevance to each piece of data that was obtained in the findings. Questionnaires and interview guide were used to collect data from a sample of 140 respondents of which 111 returned their completed usable filled questionnaires giving a response rate of 79.3%. The findings revealed that all the dimensions of teacher transfers (voluntary and involuntary transfers) were positively and significantly associated with teachers’ performance in Uganda. The major reasons for voluntary transfer were lack of accommodation, distance from families, promotion and illness while involuntary transfers were due to poor performance of teachers, misconduct, conflict with management and drunkenness. The study concludes that voluntary transfer factors are stronger than involuntary factors in accounting for a variation in teachers’ performance in Uganda primary schools. The study recommends that the district leadership should map or grade all schools in the district in terms of low, average and high performing schools as well as bring indiscipline teachers to book instead of transferring and establish adhoc transfer committees.

‘Is this something I should be worried about?’: A study of nurses' recontextualisation work when making clinical decisions based on patient reported outcome data

As clinical care practices are becoming more digitalised, information about patients is increasingly being encoded as quantified data, and the processes of sorting data are often supported by algorithmic computations. One such practice becoming more prevalent across Western countries is the clinical use of Patient Reported Outcome (PRO) data. Drawing on ethnographic fieldwork carried out in a Danish setting among nurses managing PRO-based breast cancer follow-up, we examine how clinical decisions are made on the basis of PRO-data and what this requires from the nurses. By applying the concept of recontextualisation work as an analytical perspective, we shed light on the efforts of nurses when mobilising complementary information about patients in order to recontextualise the otherwise decontextualised data, thereby giving data practical value in clinical decision-making. Recontextualisation work, we show, is shaped by organisational structure, available resources, and nurses' professional capacity. Drawing analytical attention to the work of recontextualisation allows for a nuanced understanding of the efforts required to make data workable and hence what it takes to carry out clinical decisions in today's datafied healthcare system.