A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars

NetShaper: A Differentially Private Network Side-Channel Mitigation System

The widespread adoption of encryption in network protocols has significantly improved the overall security of many Internet applications. However, these protocols cannot prevent network side-channel leaks -- leaks of sensitive information through the sizes and timing of network packets. We present NetShaper, a system that mitigates such leaks based on the principle of traffic shaping. NetShaper's traffic shaping provides differential privacy guarantees while adapting to the prevailing workload and congestion condition, and allows configuring a tradeoff between privacy guarantees, bandwidth and latency overheads. Furthermore, NetShaper provides a modular and portable tunnel endpoint design that can support diverse applications. We present a middlebox-based implementation of NetShaper and demonstrate its applicability in a video streaming and a web service application

A differentially private network traffic shaping framework

Many Internet applications depend exclusively on end-to-end encryption of network traffic as the primary means to guarantee the users' privacy. However, encryption alone cannot prevent network side-channel attacks--leaks of sensitive information through the sizes and timing of network packets. We present NetShaper, a traffic shaping framework to mitigate network side-channel attacks. NetShaper’s traffic shaping provides differential privacy guarantees, allowing users to adjust the trade-off between privacy guarantees and bandwidth overhead according to their specific requirements. We design NetShaper as a modular tunnel endpoint that can be deployed anywhere along the path of traffic. We implement a simulator to assess the privacy and bandwidth trade-offs of our framework and demonstrate its applicability in a video streaming and a web service and its effectiveness in thwarting state-of-the-art network side-channel attacks.Science, Faculty ofComputer Science, Department ofGraduat

تأثیر تمرینات ادراکی-حرکتی و دهلیزی بر تعادل ایستا و پویای کودکان مبتلا به اختلال هماهنگی رشدی

Background and aim: One of functional characteristic of children with developmental coordination disorder (DCD) is poor postural control. These children are less capable of controlling their balance during dynamic circumstances due to slower response to balance disturbances compared with their normal peers. The purpose of this study was to investigate effect of perceptual-motor and vestibular training on static and dynamic balance in children with DCD. Methods: This study was experimental with pretest-posttest, and control group, in which 45 children with DCD, divided randomly into two experiment groups and a control.  Heel to Toe Walking Test and Modified Stroke Stand Test were used to measure dynamic and static balance respectively. Vestibular and perceptual-motor training groups trained for 12 weeks, 3 sessions of 45 minutes per week. Pre-test and post-test data was analyzed using Multivariate Analysis of Covariance method. Results: Results revealed that both intervention programs improved significantly static and dynamic balance in children with DCD compared to the control group, but there was no between groups effectiveness. Conclusion: 12 week perceptual-motor and also vestibular training can improve dynamic and static balance in children with DCD.مقدمه: یکی از مشخصه‌های بارز کودکان مبتلا به اختلال هماهنگی رشدی، کنترل قامت ضعیف است. این کودکان کمتر قادر به کنترل تعادل خود در موقعیت‌های متغیر هستند به این دلیل که آن‌ها به اختلالات تعادلی در مقایسه با همسالان خود آهسته‌تر پاسخ می‌دهند. ازاین‌رو هدف از انجام این تحقیق بررسی تأثیر تمرینات ادراکی-حرکتی و دهلیزی بر عملکرد تعادلی کودکان مبتلابه اختلال هماهنگی رشدی بود. روش کار: این مطالعه از نوع تجربی با طرح پیش‌آزمون- پس‌آزمون با گروه کنترل بود که در آن 45  کودک مبتلابه اختلال هماهنگی رشدی به‌صورت تصادفی انتخاب و در دو گروه آزمایش و یک گروه کنترل قرار گرفتند.  به‌منظور ارزیابی تعادل پویا و ایستا به ترتیب از آزمونهای راه رفتن پاشنه - پنجه و آزمون اصلاح‌شده لک‌لک استفاده گردید. یک گروه آزمایش پروتکل‌های تمرینات دهلیزی و و گروه دیگر تمرینات ادراکی-حرکتی را به مدت 12 جلسه، هر هفته سه جلسه و هر جلسه 45 دقیقه انجام دادند. داده‌های به‌دست‌آمده از سنجش‌های پیش‌آزمون و پس‌آزمون با استفاده از تحلیل کوواریانس چند متغیره مورد تجزیه‌وتحلیل قرار گرفتند. یافته‌ها: براساس نتایج بدست آمده، هر دو برنامه تمرینی یعنی تمرینات دهلیزی و ادراکی-حرکتی بر بهبود تعادل ایستا و پویا در کودکان مبتلابه اختلال هماهنگی رشدی نسبت به گروه کنترل تأثیر داشت اما تفاوتی بین میزان تأثیرگذاری آن‌ها یافت نشد به عبارت دیگر، بین دو نوع تمرین در بهبود عملکرد تعادل ایستا و پویا کودکان مبتلابه اختلال هماهنگی رشدی تفاوت معناداری وجود نداشت. نتیجه‌گیری: مداخلات 12 هفته‌ای تمرینات دهلیزی و همچنین تمرینات ادراکی-حرکتی می‌تواند در بهبود تعادل پویا و ایستا کودکان مبتلابه اختلال هماهنگی رشدی مؤثر باشد

A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars

Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars

Rehabilitation of reinforced concrete beam: Sustainable restoration mortar with waste materials

The current application of epoxy resin in the installation of fiber sheets for concrete beam restoration presents practical challenges, limited fire resistance, and lacks environmental sustainability. Additionally, epoxy resin cannot be used on wet surfaces as it compromises adhesion and reduces durability. In this study, we propose an effective mortar formulation that incorporates waste materials such as marble powder (MP), red mud (RM), and electric arc furnace dust (EAFD) for fiber sheet installation. The performance of the developed mortar was comprehensively assessed through various experiments, evaluating compressive and tensile strengths, water absorption (WA), sulfuric acid resistance (SAR), and microstructural characteristics of the restoration mortars. Furthermore, three reinforced concrete (RC) beams were constructed and subjected to a four-point bending test. One beam was strengthened with carbon fiber-reinforced polymer (CFRP), while the other two utilized fiber-reinforced cementitious material (FRCM) with either CFRP mesh or glass fiber-reinforced polymer (GFRP) bar. The findings reveal that RC beams strengthened with CFRP mesh-restoration mortar and GFRP rebar-restoration mortar exhibit load-carrying capacities 13% and 36% higher, respectively, compared to that reinforced with CFRP sheets. This study lays the foundation for future research by demonstrating, for the first time, the superior performance of mortar-based restoration over epoxy resin, thereby opening up new possibilities for the development of concrete element restoration

Examining the psychosocial impacts of the COVID-19 pandemic: an international cross-sectional study protocol

Introduction The COVID-19 pandemic exposed people to significant and prolonged stress. The psychosocial impacts of the pandemic have been well recognised and reported in high-income countries (HICs) but it is important to understand the unique challenges posed by COVID-19 in low- and middle-income countries (LMICs) where limited international comparisons have been undertaken. This protocol was therefore devised to study the psychosocial impacts of the COVID-19 pandemic in seven LMICs using scales that had been designed for or translated for this purpose.Methods and analysis This cross-sectional study uses an online survey to administer a novel COVID Psychosocial Impacts Scale (CPIS) alongside established measures of psychological distress, post-traumatic stress, well-being and post-traumatic growth in the appropriate language. Participants will include adults aged 18 years and above, recruited from Indonesia, Iraq, Iran, Malaysia, Pakistan, Somalia and Turkey, with a pragmatic target sample size of 500 in each country.Data will be analysed descriptively on sociodemographic and study variables. In addition, CPIS will be analysed psychometrically (for reliability and validity) to assess the suitability of use in a given context. Finally, within-subjects and between-subjects analyses will be carried out using multi-level mixed-effect models to examine associations between key sociodemographic and study variables.Ethics and dissemination Ethical approval was granted by the Human Ethics Committee, University of Otago, New Zealand (Ref. No. 21/102). In addition, international collaborators obtained local authorisation or ethical approval in their respective host universities before data collection commenced.Participants will give informed consent before taking part. Data will be collected and stored securely on the University of Otago, New Zealand Qualtrics platform using an auto-generated non-identifiable letter-number string. Data will be available on reasonable request. Findings will be disseminated by publications in scientific journals and/or conference presentations.Trial registration number NCT05052333