Purpose: Food security is a critical global challenge that is influenced by research and innovation in the field. Therefore, the objective of this study is to analyze the scientific output of developing countries in food security and examine its relationship with patents and Gross Domestic Product (GDP).Methodology: This applied research utilized the Scientometric approach. A total of 8,416 papers published between 1992-2023 in the field of food security by developing countries were included in the study using citation databases from Clarivate Analytics. Additionally, patent registrations from the WIPO database and GDP data from the World Bank were analyzed. Information was collected through note-taking, and the data was analyzed using Pearson's correlation coefficient.Findings: The findings reveal an upward trend in the publication and citation of scientific outputs related to food security in developing countries. China has higher numbers of papers, patents, GDP, and food production index compared to Iran, Japan, and South Korea. There is also a positive correlation observed between population and the number of papers, gross production and the number of papers, food production and the number of published papers, as well as the number of patents and papers citing scientific outputs of countries.Conclusion: These results highlight the significant relationship between increasing scientific output, GDP, the number of patents, and food security. Greater emphasis on food security contributes to enhanced scientific output, GDP, and innovation. Similarly, increasing scientific output, GDP, and innovation positively impact food security in countries.Value: This study emphasizes the importance of scientific outputs in driving technological advancements, innovations, and ultimately, ensuring food security in developing countries.

Currently, the significant growth of the number of users of online services and distributed systems generate a large amount of database information at the level of data warehouses and big data. data warehouses are large collections of business data that help organizations and businesses make more accurate and intelligent decisions. Big data is also a very large collection of data collected from multiple sources. These data can be the results of evaluating the performance of an organization or the interactions of its audience in social networks. Due to the increasing importance of big data functions and data warehouses, the issue of maintaining data security is the biggest threat these systems face. Basically, dealing with the category of information security and security of internet networks, data warehouses and services based on big data, requires special attention of the organization to the position of information security and all-round security of these systems, and this category should be considered at the macro level and from the perspective of benefits and benefits. it looked The existence of security weaknesses in these systems, the lack of proper training and justification of users regardless of their point of view regarding the position and importance of security, the absence of necessary instructions to prevent security defects, the absence of specific and codified policies in order to properly and timely deal with security flaws will lead to issues that harm all users and the operational capabilities of these systems and actually expose the information infrastructure of organizations to serious damage and threats. In this article, we are going to research the security aspects of data warehouses and big data and examine effective and logical solutions in this area.

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.

With the emergence of new phenomena in the telecommunications and information technology fields, such as cloud computing and smart networks, we are witnessing new challenges in these areas. One of the most significant challenges is the privacy of outsourced data. Due to the limited processing power of new intelligent devices such as tablets and mobile phones, outsourcing computations to these platforms has gained more attention from users. In addition to data privacy, the security of algorithms used in online software is also of great importance. Therefore, software providers may be concerned about the disclosure of their algorithms after outsourcing them to cloud environments. Existing homomorphic encryption systems can provide privacy for data that needs to be processed online. However, the concurrent privacy of algorithms in these systems has not been addressed. To address this, we introduce a simultaneous homomorphic encryption of data and function called SHDF. This system can homomorphically encrypt all algorithms used in the software and the data to be processed on them, enabling necessary computations to be performed on an insecure server. Furthermore, we show that the proposed system is provably secure. Our implementation results indicate that it is usable in cloud environments with the desired efficiency.

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.

The issue of online privacy and security is a challenging subject, as it concerns the privacy of data that are increasingly more accessible via the internet. In other words, people who intend to access the private information of other users can do so more efficiently over the internet. This study is an attempt to address the privacy issue of distributed big data in the context of cloud computing. One of the cases where data privacy is of great importance is the authentication and protection of ownership data. In this paper, this privacy issue is analyzed by Petri net modeling. What today’ s organizations need for their clouds are integrated comprehensive solutions that can deliver security intelligence. Advanced security intelligence solutions can close security gaps by using labor-saving automation to analyze millions of events occurring within the cloud, and discover system vulnerabilities through the normalization and correlation of these events. Using the proposed method, a model of security, including control of user access to databases of big data with RMS, the multiplicity and the virtual machine to prevent internal threats, deleting data, insecure or incomplete data protection and control of a third-party can be provided to improve the operation according to the rules of Petri net modeling and simulation.