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.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

مقدمه: استارت آپ ها در سال های اخیر توانسته اند خدمات و ارزش های جدیدی را با بهره وری بالاتر و با قیمت پایین تر به مشتریان عرضه کنند. ازاین رو، پژوهش حاضر با هدف شناسایی نقش و جایگاه استارت آپ ها در بهبود مدیریت نظم و امنیت ترافیکی کشور انجام شده است. روش: مطالعه حاضر، پژوهشی توسعه ای-کاربردی، مبتنی بر رویکرد آمیخته (کیفی-کمی) است. شرکت کنندگان بخش کیفی شامل 16 نفر از متخصصان و خبرگان حوزه فن آوری و ترافیک هستند که به صورت هدفمند انتخاب و با استفاده از فن مصاحبه نیمه ساختاریافته داده های مدنظر از آن ها دریافت شد. جامعه آماری بخش کمی نیز شامل 32 نفر از دانشجویان و دانش آموختگان دکتری رشته مدیریت ایمنی ترافیک، رییس و اساتید دانشکده پلیس راهور، مدیران ارشد پلیس راهور ناجا بوده که به صورت تمام شمار انتخاب شدند. همچنین، برای تحلیل داده های بخش کمی از نرم افزار Smart-PLS استفاده شد. یافته: یافته های بخش کیفی پژوهش نشان داد که استارت آپ ها با 4 نقش در بهبود مدیریت ترافیک تاثیرگذار هستند که عبارت اند از: «نظارت بر ترافیک»، «ایمنی ترافیک»، «مدیریت عرضه و تقاضا» و «بهینه سازی سفر» که نتایج بخش کمی نیز یافته های بخش کیفی را تایید کرد. نتیجه گیری: با توجه به یافته های به دست آمده، پیشنهاد می شود پلیس راهور نیروی انتظامی به منظور توزیع بهینه ترافیک با حمایت از ظرفیت نهادهای دانش بنیان و نخبگان دانشگاهی در راستای راه اندازی استارت آپ های مسیریاب قوی و قابل اعتماد داخلی سازگار با محیط داخل کشور تلاش کند.