Database Technology Research Articles

Penerapan Customer Relationship Management sebagai strategi pemasaran berbasis data di PT.Gragaz Berkah Sejahtera

Implementing Customer Relationship Management (CRM) as a data-based marketing strategy has become the main focus for many companies in optimizing relationships with customers. This research explores this through a case study at PT. Gragaz Berkah Sejahtera, a pharmaceutical retail company or pharmacy business that adopts this approach. This research aims to identify how PT. Gragaz Berkah Sejahtera integrates technology and Customer Relationship Management (CRM) theory to improve interactions with customers and how to optimize data-based marketing and sales processes. The research method used is a qualitative descriptive analysis method with the object studied being PT. Gragaz Berkah Sejahtera which involves collecting data, implementing CRM technology, and evaluating its impact on customer loyalty, retention, and increasing sales. While the results of this research highlight the company's success in implementing data-based marketing strategies in an effort to improve marketing and sales, as well as increase customer satisfaction, however these results have not achieved maximum success because there are obstacles in implementing the concept of customer relationship management such as system failures. data-based information technology applications. So that efforts to implement CRM programs are disrupted and have an impact on sales which have not achieved significant growth through this data-based marketing strategy. This research can provide important information and insights for other companies wishing to adopt the same approach in an effort to improve relationships with customers and achieve optimal and sustainable marketing and sales.

IDify - Distributed Database System for Digital Identification

”IDify” Distributed database system for digital identification is a big data handling system to centralize the important services provided by the government to make the facilities efficient and reliable. It has integrated services such as digital authentication, E-Banking, Personal Information Management, and Government Surveys to provide users with a centralized and digitized platform to make their lives easier. In this system there is a distributed database platform to carry out all the data storing and data processing tasks to facilitate a large user count and to avoid the drawbacks of monolithic systems. The database system has been designed by integrating several modern database technologies to make the system efficient and coherent as the facilities provided by the system are extremely mission-critical. To make the system low latent on data ingestion and data reading tasks a special library is also implemented in collaboration with modern query processing engines. It is proposed to utilize the thread system of a processor to implement parallelism to enhance the performance of data read/write tasks. Apart from that a fault-tolerant framework has been implemented to free the system from any erroneous scenario taking place.

Ethnicity and In Vitro Fertilization Outcomes in an Equal Access to Care Setting [ID 2683634

INTRODUCTION: Many studies report racial disparities in assisted reproductive technology (ART) outcomes, specifically, lower pregnancy rates and higher miscarriage rates in Black women. In an equal access to care setting, we wanted to delineate causes of this ongoing racial disparity. METHODS: This is a retrospective review of ART cycles at Brooke Army Medical Center (BAMC) from 2013 to 2021 via the Society for Assisted Reproductive Technology database. This study received a nonresearch designation from the BAMC IRB. RESULTS: A total of 1,211 in vitro fertilization (IVF) cycles were reviewed, comparing outcomes by race from first-time embryo transfers, fresh or frozen after “freeze-all” IVF cycles. Black women (19% of all cycles) experienced a lower live birth rate (27.3% versus 34.6%; risk ratio [RR] 0.75; 95% CI 0.6–0.94), but did not experience a statistically significant increase in pregnancy loss (36.8% versus 30.6%; RR 1.26; 95% CI, 0.94–1.68] compared to all other patients. Black women were found to have a higher prevalence of uterine factor infertility (RR 2.87; 95% CI, 2.24–3.66) and tubal factor infertility (RR 1.79; 95% CI, 1.51–2.12). When controlling for uterine factor infertility, Black racial identity was no longer a risk factor for decreased live birth rate (adjusted RR 0.85; 95% CI, 0.69–1.07). CONCLUSION: Recent literature demonstrates poorer ART outcomes for Black women. Our study suggests that this may be attributable to uterine fibroids, and an increased research focus on uterine fibroid treatment in patients desiring fertility may improve ART outcomes in Black women.

InGaZnO-based synaptic transistor with embedded ZnO charge-trapping layer for reservoir computing

In this study, we design an IGZO/ZnO/IGZO-based synaptic transistor to implement robust reservoir computing. Short-term memory characteristics are achieved using the charge trapping and detrapping effects of the ZnO layer. We verify excellent cell-to-cell and cycle-to-cycle uniformity regarding the memory characteristics of the device. Moreover, various synaptic behaviors, including short-term potentiation, short-term depression (STD), excitatory postsynaptic currents (EPSC), and paired-pulse facilitation (PPF) are emulated to check the suitability of neuromorphic properties. Finally, reservoir computing trained on the modified National Institute of Standards and Technology database dataset is presented for temporal data learning. As a physical reservoir, the device can achieve 16 different using 4 bits depending on the applied pulse stream. The results include a confusion matrix covering all recognition scenarios, with an average recognition accuracy of 93.87%, closely approaching the theoretical recognition accuracy of 94.1%. This study sheds light on a computational framework for physical reservoir computing by reducing the training cost.

Research on the Teaching Resource Construction System of Computer VR Online Interactive Experiment Platform for Mechanical Engineering Graphics

The introduction of three-dimensional design experiment teaching in engineering drawing teaching. The experimental platform of "network engineering drawing" is established, and the corresponding multimedia teaching software is developed using WebVR, ASP, database and other technologies. The 3D modeling method of the reducer is established by using UGNX and optimized accordingly. In order to solve the problem that UGNX mode introduces packet loss in the simulation system, the model and data are reconstructed separately. Using VisualStudio2019 as the development platform, the 3D modeling of the reducer is realized. The establishment of the engineering graphics design virtual simulation system helps enterprises to simulate operations before actual production and processing. It greatly improves production efficiency and reduces production costs.

Artificial Synapse Based on a δ-FAPbI3/Atomic-Layer-Deposited SnO2 Bilayer Memristor.

Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.

The Blockchain: Opportunities for Research in Information Technology

Blockchain technology has come to be seen as a radically different kind of information technology due to its role as a disruptive agent that challenges incumbent actors and their assumptions. With this research paper, I cover the basics and benefits of blockchain technology as well as its possible industries residing, organization and even changing. The blockchain is a decentralized database technology, which had brought the rise and the existence of cryptocurrencies. Similar to Bitcoin, the magnitude of its operation is wielded not only for digital currency. This article will be carrying on the matter of blockchain systems type diversity with public, private, and consortium chains detailed, and the role of the network nodes in achieving consensus being specified. Keywords: - Cryptocurrencies (e.g., Bitcoin, Ethereum), Smart contracts, Digital Signatures, Supply chain applications, Blockchain applications, Blockchain in finance and healthcare

Lessons Learned: Musings on Teaching Technology in Library and Information Science

Information professionals integrate a variety of technologies and tools into their work practices and are often introduced to these while they are students in library and information science programs. This commentary examines recent trends, barriers, and potential approaches in teaching technology for LIS graduate students. Here we draw on our expertise teaching several technology topics, including an introductory technology survey course for graduate students, programming and database management, and intersectionality and technology. Rooted in a reflection on our perspectives as technology educators, our analysis focuses on this central question: How can library and information science instructors foster the confidence, curiosity, and comfort that students need to continually engage with an ever-changing technological landscape throughout the course of their careers?

Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles

This paper presents a methodology by which kinematic variables of road vehicles can be extracted from unmanned aerial vehicle (UAV) footage. The oriented bounding boxes of the vehicles are identified based on the aerial view of the intersection, and the kinematic variables, such as position, longitudinal velocity, lateral velocity, yaw angle and yaw rate, are determined. The bounding boxes are converted to the perspective of a roadside camera using homography, to generate labeled data sets for training the machine learning-based perception systems of smart intersections. Compared to ordinary GPS data-based technology, the proposed method provides smoother data and more information about the dynamics of the vehicles. In the meantime, it does not require any additional instrumentation on the vehicles. The extracted kinematic variables can be used for motion prediction of road traffic participants and for control of connected automated vehicles (CAVs) in intelligent transportation systems.

Theoretical prediction of molar entropy of modified shifted Morse potential for gaseous molecules

The modified shifted Morse potential is a molecular potential that describes the molecular vibration and atomic interaction. This potential has not received much report as one of the useful potential function. In this study, the molar entropy of a modified shifted Morse is obtained via the vibrational partition function, rotational partition function and translational partition function. Numerical results are obtained for iodine molecule (I2), silicon carbide (SiC), carbon phosphide (CP) and fluorine molecule (F2) for temperatures of 200 K to 6000 K. The predicted results are compared with the experimental results obtained from the National Institute of Standards and Technology (NIST) data base. The predicted values aligned with the experimental results for the four molecules. The average absolute percentage deviation obtained for each molecule is less than one, which shows a good representation of the modified shifted Morse potential for thermodynamic functions.

Seizure stage detection of epileptic seizure using convolutional neural networks

According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

Implementasi Big Data terhadap Pengecekan Medis dan Konsultasi Kesehatan di Indonesia

The occurrence of the Covid-19 pandemic tests the resilience of the health service system in countries around the world, especially Indonesia, in responding and acting quickly and appropriately. This incident is a separate evaluation of the health service system in this country. As part of the global action plan to achieve Sustainable Development Goals (SDGs) point number 3, namely ensuring a healthy life and supporting welfare for all and efforts to improve public health as measured by the Public Health Development Index (IPKM), therefore, an increase in quality of health services is needed. The quality of health services includes issues of performance and affordability among society. With the development of innovations in database technology aspect, namely big data and artificial intelligence, it is hoped that this can be a breakthrough in the world of Indonesian health so that the goals of Indonesia in improving public health can be achieved.

A Study on Innovative Design Approaches for Implementing an Intelligent ICT-Based Smart Highway

This research, conducted over a three-year period starting from 2020, was a collaborative project between practitioners in the field of VE design and the Korea Expressway Corporation, aimed at innovating the design VE project to construct an intelligent ICT-based smart highway and enhancing highway driving performance and customer satisfaction.
 The research included R&D support for payment and management systems integrated with IOT technology, the application of the developed prototype at highway sites, and analysis of user performance evaluation. Based on the Innovation Work Roadmap of the Korea Expressway Corporation, the research process involved collaboration between a public participation group and an expert group, considering the technical feasibility of data-based ICT technology through the National Idea VE Contest.
 The final VE design results were implemented at the Seongnam tollgate site with budget support from relevant departments, followed by a satisfaction survey. It is expected that the continuous contributions to achieving an ESG society, such as the increase in the number of customers using the tolling zone equipped with smart upgrades, along with their satisfaction rate, and the reduction in carbon emissions and total settlement time of highway users, will be sustained.

Effect of dense plasma environment and external magnetic field on atomic structure and radiative properties of Ar XVII

Atomic structures and radiative properties of He like Ar XVII in the presence of dense plasma environments and external magnetic field have been presented within the framework of the relativistic theory. For this purpose, the relativistic configuration interaction method by incorporating the generalized b-potential and external magnetic field has been implemented. Plasma screening effect on 1s2 1S0 → 1s2p 3P1 transitions of Ar XVII ion in the presence of an external magnetic field has been investigated. We also studied the Zeeman splitted energies and predicted the excitation energies of Ar XVII ion for the magnetic sublevels 1s2p 3P0 (MJ = 0), 3P1 (MJ = 0, ±1), 3P2 (MJ = 0, ±1, ±2), 1P1 (MJ =0, ±1). Our calculated results are in excellent agreement with National Institute of Science and Technology (NIST) database and other theoretical available data.

Neural Network-based Pattern Recognition in the Framework of Edge Computing

Neural network (NN) model has been widely used in pattern recognition (PR), speech recognition, image processing and other fields, but its application in edge computing (EC) environment faces performance and energy consumption problems. This article first introduced the basic structure and training process of NN, including backpropagation algorithms. Then, this article presented a NN modeling approach based on EC, including NN model compression, distributed NN model and knowledge distillation approach. Finally, this article implemented a PR model for the MNIST (Mixed National Institute of Standards and Technology database) dataset and analyzed the experimental results. The experimental outcomes indicated that the presented approach can significantly enhance the performance of the NN model in the EC environment, while ensuring a high recognition accuracy. The NN modeling approach based on EC can reduce the amount of computation and storage of the NN, thus improving the operating efficiency of the NN in the EC environment by 6%-12%. The NN modeling approach based on EC can optimize the performance and efficiency of the NN model in the EC environment, and provide new ideas and approaches for the application of NN in the EC environment.

Adjustable artificial neuron based on vortex magnetic tunnel junction

In this Letter, we demonstrate an adjustable artificial neuron based on vortex magnetic tunnel junction (MTJ). By applying a bias current to vortex MTJ, the device exhibits splendid characteristics of stochastic switching and nonlinear rectification. The stochastic switching probability induced by spin transfer torque as a function of bias current can simulate sigmoid activation functions. The nonlinear spin-torque microwave rectification through injection locking is similar to a ReLU-like activation function. These two behaviors further are used to perform the recognition of handwritten digits in the Mixed National Institute of Standards and Technology database, with a produced accuracy of up to 93.56% and 93.25%, respectively. Our work provides a potential way for the construction of artificial neuron based on vortex MTJ.

Construction of environmental vibration prediction model for subway transportation based on machine learning algorithm and database technology

Vibrations generated in the metro transport environment are mainly caused by, vibrations generated by the interaction between the metro and the track during operation. and the change of vibration factors will affect the normal operation of the subway. However, it is difficult to have a model that can achieve the characteristics of high accuracy, fast computing speed and wide range of use in the traditional metro rail transportation environment prediction. Therefore, this research uses database theory and machine learning algorithms to predict the vibration of subway transportation environment. The experimental results show that the average difference between the whole prediction value and the real value is 1.4 dB, of which the maximum difference error value is 0.29%, the maximum error difference is 8.2%, and the approximate value is 6.2 dB, and the four averages predicted in 40 m are relatively small as 1.6 dB, and the average error value of prediction ability between 40 and 100 m is 1.72 dB, and the experimental prediction value and real value are in good agreement. The agreement between the experimental prediction and the real value is very good. Therefore, the model is able to predict the vibration model of the subway transportation environment with a high degree of agreement and accuracy.

Prospects for applying distributed database technology in medical property resources management within military health care

When considering the problems of digital transformation of various spheres of life support for the country and maintaining the defense capability of the state, it seems advisable to focus on the most effective digital technologies. Among them, in the context of an increasing risk of cyber threats, blockchain technology, which has a promisingly increasing potential for information security and demonstrates dynamic development in many areas of socio-economic life, primarily attracts at- tention. The article discusses the historical aspects of the development of blockchain tech- nology, presents the results of a study of its main characteristics, international and domestic experience of application, and also presents prospects for implementa- tion in the management of medical property resources. It has been determined that in the context of using distributed database technology in the field of military healthcare, the preferred solution is to use a closed type of blockchain, characterized by a high degree of digital security and scalability. Based on the results obtained during the study, the main advantages and limita- tions of implementing distributed database technology within military healthcare were highlighted. It has been determined that in order to increase the efficiency of the medical ser- vice, it is promising to build a single digital loop for managing medical property resources on the basis of blockchain technology in accordance with the territorial medical support system. It is concluded that digital transformation through the integration of blockchain technology into the practical activities of a medical service can be an effective tool for optimizing the processes of managing medical property resources. However, further comprehensive study of this issue is required, as well as justification and development of possible modifications of existing digital solutions, taking into ac- count the needs of military healthcare and the need to rationalize the provision of medical equipment to assigned contingents.

A Reflective Study of Computerized Database Application Technology and Maintenance

In the era of digitalization, computer databases have become an indispensable technological support across various industries. They not only house a vast amount of data but also enable efficient management and swift retrieval of this information. Concurrently, advancements in database application technology and maintenance have become crucial in ensuring the stable operation of information systems. However, contemporary computer database technology still faces numerous challenges such as data compatibility, automation levels, and flexibility in data transmission. Therefore, this study focuses on the key application techniques and maintenance mechanisms of computer databases, exploring measures to enhance data compatibility, improve production automation, strengthen the flexibility of data transmission, and bolster technical support. These efforts aim to drive the development of computer databases towards efficiency, stability, and security, thus laying a solid data foundation for the sustainable development of the entire information society.

Anomaly detection using deep learning based model with feature attention

<span lang="EN-US">Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.</span>