Due to the explosive increment of Data in recent application, available Data storage cannot respond to this volume of Data, for this reason molecular memory have been suggested in recent research. DNA is molecular Data storage that can store a large amount of Data in a limited space with high endurance. Storing Data in low volumes can be provided using appropriate mapping. In this paper, a new method for mapping digital Data to DNA have been proposed with the aim of simple coding, omitting the decoding faults, increasing the speed of coding and storing digital Data and sign-digit with sufficient compression. Studies show that the proposed method can guarantee long-term retrieval of information from DNA compared to previous methods. It also uses less compression to store digital Data as compared to the previous methods.

The neoclassical growth model is tested by use of panel Data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel Data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

We present a novel approach to Data storage based on holographic principles that encodes information in geometric structures rather than discrete units. Building on recent advances in geometric error correction and holographic duality, we develop a mathematical framework for storing and retrieving information using topological invariants that provide natural error protection. We prove that this approach achieves information preservation without active error correction, leading to inherently robust memory systems. Our framework provides explicit constructions for encoding classical and quantum Data in geometric structures while maintaining error protection through topological invariance. We demonstrate theoretical bounds showing superior error resistance compared to traditional storage methods, along with practical implementation strategies using current technology.

Background: The characteristics of medical Data in Precision Medicine (PM), the challenges related to their storage and retrieval, and the effective facilities to address these challenges are importantly considered in implementing PM. For this purpose, a secured and scalable infrastructure for various Data integration and storage is needed.Objective: This study aimed to determine the characteristics of PM Data and recognize the challenges and solutions related to appropriate infrastructure for Data storage and its related issues.Material and Methods: In this systematic study, coherent research was conducted on Web of Science, Scopus, PubMed, Embase, and Google Scholar from 2015 to 2023. A total of 16 articles were selected and evaluated based on the inclusion and exclusion criteria and the central search theme of the study.Results: A total of 1,961 studies were identified from designated Databases, 16 articles met the eligibility criteria and were classified into five main sections: PM Data and its major characteristics based on the volume, variety and velocity (3Vs) of medical big Data, Data quality issues, appropriate infrastructure for PM Data storage, cloud computing and PM infrastructure, and security and privacy. The variety of PM Data is categorized into four major categories. Conclusion: A suitable infrastructure for precision medicine should be capable of integrating and storing heterogeneous Data from diverse departments and sources. By leveraging big Data management experiences from other industries and aligning their characteristics with those in precision medicine, it is possible to facilitate the implementation of precision medicine while avoiding duplication.

This study aims to design a model of cyber defense in Data-driven organizations in the field of Data storage. In this exploratory and applied research, the approach was mixed and the method was both Delphi and survey. First, by studying the existing documents and resources related to Data-driven organizations, the field of Data storage, because of its importance in compared to other organizations and five dimensions, (identification, protection, detection, responsibility and retrieval) of cyber defense were identified and counted based on the NIST framework. A Likert scale questionnaire by using 15 experts and implementation of two Delphi stages, was designed, refined and finalized based on the Data retention characteristics of Data-driven organizations. Then, using the analytical survey method, in order to answer the main research question, the questionnaire was given to a sample size of 288 people who were randomly selected from the study population. Finally, the results were analyzed using structural equation modeling and confirmatory factor analysis, and as a result, a cyber defense model was presented. Five dimensions along with 21 components and 70 cyber defense indicators in the field of Data storage in Data-driven organizations were identified and counted. In addition, by confirming the confirmatory factor analysis on the computed model, the effect of each dimension of cyber defense on the Data-driven organization is verified,and the experimental Data support the theoretical Data. At the end, the relevant cyber defense model in Data-driven organizations is presented.

Introduction: This paper presents a portable device of Air Quality Monitoring System (AQMS) based on a sensor platform. The purpose of the study is to power awareness, warning indication, and minimal Data storage capability. Materials and methods: AQMS is developed by embedded design methodology. The software part is based on the C programming language. AQMS device is made up of “, transmitter”,and “, receiver”,sections through the Zigbee wireless network. The objective is to collect concentrations of CO, NO2, CO2, humidity, and temperature to check air pollution for health awareness. A power-saving strategy is adopted in the "transmitter" of AQMS through a Demultiplexer circuit. To minimize the space complexity of storage and availability of long-term Data, Data encoding techniques are implemented. Results: By implementing switching activity on the sensors in AQMS, the active mode of sensing operations are controlled and a power saving of 18. 41% is achieved. Extending the duration of transmission operation increases Data storage in the “, receiver”,unit. Hence, two encoding techniques are developed where real-time Data are encoded in binary form: 2-bit encoding and 3-bit encoding. According to the results, 2-bit encoding saves 50% of storage space and 3-bit encoding saves 25%, compared to not utilizing any encoding strategy, sacrificing Data accuracy by less than 5%. Conclusion: AQMS design is created with the implementations of low power consumption and low storage. Additionally, an alarming condition is set in AQMS for indicating the level of pollutants in the air to determine the risk level of exposure which is dangerous for human health.

Hash or B-Tree based composite indexes, are the two most commonly used techniques for searching and re-trieving Data from memory. Although these techniques have a serious memory limitation, that restricts free-dom to search by any combination of single key/Data attribute, that comprises the composite search key, the techniques are still accepted considering the trade o s with better performance on insert and update opera-tions. But when the Data is semi-static, which does not change often, there is a need and scope for a better tech-nique that provides the exibility and freedom to e-ciently search by any possible key, without creating any composite index. This paper explains such algorithmic technique along with its Data structures.

Todays gamma knife radiosurgery is used widely for treatment of very small brain tumors. In order to investigate accuracy of dosimetry and treatment planning calculations, using Monte Carlo simulation with dedicated code named as beamnrc including non-CT Data and CT Data options is necessary. The aim of this study is choosing the best options in order to have an accurate tools based on their advantages and disadvantages. In this study, gamma knife unit 4C along with standard water equivalent phantom and EBT3 films were used to obtain dose distributions. Monte Carlo simulation was done with non-CT Data and CT Data options of the code and their resulting dose were compared. Comparison the calculated and measured dose distributions at X, Y and Z axis showed gamma value below 1 which verified Monte Carlo simulations. Also comparing the dose distributions from both non-CT Data and CT Data with each other implies that there is no significant difference between two methods. Based on the obtained results using non-CT Data and CT Data results in the same dose distribution. So for simplicity, using non CT Data for regular phantom shapes is preferred.