Delicar: A smart deep learning based self driving product delivery car in perspective of Bangladesh

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.publishedVersio

Domestic violence and decision-making power of married women in Myanmar: analysis of a nationally representative sample

BACKGROUND: Women in Myanmar are not considered decision makers in the community and the physical and psychological effect of violence makes them more vulnerable. There is a strong negative reaction, usually violent, to any economic activity generated by women among poorer and middle-class families in Myanmar because a woman's income is not considered necessary for basic survival. OBJECTIVE: Explore the relationship between domestic violence on the decision-making power of married women in Myanmar. DESIGN: Cross-sectional. SETTING: National, both urban and rural areas of Myanmar. PATIENTS AND METHODS: Data from the Myanmar Demographic and Health Survey 2015-16 were used in this analysis. In that survey, married women aged between 15 to 49 years were selected for interview using a multistage cluster sampling technique. The dependent variables were domestic violence and the decision-making power of women. Independent variables were age of the respondents, educational level, place of residence, employment status, number of children younger than 5 years of age and wealth index. MAIN OUTCOME MEASURES: Domestic violence and decision-making power of women. SAMPLE SIZE: 7870 currently married women. RESULTS: About 50% respondents were 35 to 49 years of age and the mean (SD) age was 35 (8.4) years. Women's place of residence and employment status had a significant impact on decision-making power whereas age group and decision-making power of women had a relationship with domestic violence. CONCLUSION: Giving women decision making power will be indispensable for the achievement of sustainable development goals. Government and other stakeholders should emphasize this to eliminate violence against women. LIMITATIONS: Use of secondary data analysis of cross-sectional study design and cross-sectional studies are not suitable design to assess this causality. Secondly the self-reported data on violence may be subject to recall bias. CONFLICT OF INTEREST: None

Evaluation of macrophage injury and activation by amphotericin B-loaded polymeric nanoparticles

<p>The objectives of present work were to prepare water soluble nanoformulation of amphotericin B by radical polymerization using <i>N</i>-isopropylacrylamide–acrylic acid and vinyl pyrrolidone for targeted delivery and minimized toxicity in mild experimental conditions with relatively narrow size distribution. The prepared formulations were extensively characterized in terms of size, morphology, thermal stability, drug loading and toxicity. The results demonstrates that the prepared nanoformulation have the potential to deliver amphotericin B with increased bioavailability and cellular uptake.</p

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

The rapid expansion of a country&rsquo;s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system&rsquo;s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system&rsquo;s infrastructure is far too low-cost and easy to install

Molecular docking of genistein on estrogen receptors, promoter region of BCLX, caspase-3, Ki-67, cyclin D1, and telomere activity

الملخص: أهداف البحث: تهدف هذه الدراسة للتحقيق في تشكيل مستقبلات هرمون الاستروجين بواسطة تركيب مضاد –الاستروجين ودور الجينيستين ضد تنظيم عملية النسخ من الجينات المشاركة في الانتشار، وموت الخلايا المبرمج ونشاط التيلومير. طرق البحث: تم إجراء البحث باستخدام أسلوب سيليكو بحيث يكون الإرساء هو أهم طريقة تم تنفيذها بواسطة برمجيات الهيكس ٨.٠ وقاعدة البيانات هادوك. ثم تم عمل تحليل التفاعل لملاحظة التفاعلات بين الجينيستين وعدد من البروتينات والجينات ذات الصلة باستخدام برامج الاكتشاف. النتائج: لم يظهر التفاعل بين مستقبلات هرمون الاستروجين – الفا مع الجينيستين تشكيل أي رابطة. وهكذا التفاعل، الذي ممكن حدوثه، لن يكون فاعلا لأنه ليس مستقرا. وعلى العكس، عندما يكون التفاعل مع مستقبلات هرمون الاستروجين- بيتا، اثنان من الروابط الهيدروجينية وأربع من الروابط الطاردة للماء، هيدروكلوريد تفاعل مع مستقبلات هرمون الاستروجين – الفا بواسطة اثنان من الروابط الهيدروجينية وثلاثة من الروابط الطاردة للماء. سيكون من السهل للمركب الحث على تنشيط النسخ للجينات المدروسة. الاستنتاجات: إعطاء الجينيستين ممكن أن يزيد النشاط الجينومي لمركبات مستقبلات هرمون الاستروجين التي ترتبط بموت الخلايا المبرمج، والانتشار ونشاط التيلومير. Abstract: Objectives: This study aims to investigate the modulation of estrogen receptors by estrogen and the role of genistein in the transcriptional process that regulates genes involved in the proliferation, apoptosis, and telomere activity. Methods: The research was conducted in silico, wherein docking, the most important method, was carried out using Hex 8.0 software and HADDOCK web server. Interaction analysis was subsequently done to observe the interactions between genistein and several related proteins and BCLX, Casp3, Ki-67, CyclinD1, hTERT, and POT1 genes using Discovery Studio, LigPlus, and NUCPLOT. Results: The interaction between ERα with genistein was not found to form a single bond. Thus, the interaction that may occur will not be effective because it is not stable. Conversely, when interacting with ERβ, two hydrogen bonds and four hydrophobic bonds, MPP dihydrochloride interacted with ERα via two hydrogen bonds and three hydrophobic bonds. The ERβ/eNOS complex will be comparatively easier to induced by the transcriptional activation of BCLX, Casp3, Ki-67, CyclinD1, hTERT and POT1 genes. Conclusions: Administration of genistein can increase the genomic activities of the estrogen-eNOS receptor complexes related to apoptosis, proliferation, and telomere activity. الكلمات المفتاحية: موت الخلايا المبرمج, مستقبل هرمون الاستروجين, جينيستين, Keywords: Apoptosis, Genistein, hTERT, Estrogen receptor, POT

TEZEM: A new energy-efficient routing protocol for next-generation wireless sensor networks

The design and implementation of energy-efficient routing protocols for next-generation wireless sensor networks is always a challenge due to limited power resource capabilities. Hierarchical (clustering) routing protocols appeared to be a remarkable solution for extending the lifetime of wireless sensor networks, particularly in application-aware (threshold-sensitive) and heterogeneity-aware cluster-based routing protocols. In this article, we propose a protocol, namely, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol. It is a heterogeneity-aware and threshold-based protocol that provides a better solution to existing problems in next-generation wireless sensor networks. During execution, the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol splits the entire network area into several zones to manage network traffic efficiently. In the first step, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is designed for a homogeneous network where the initial energy of all the nodes is the same. Thereafter, we bring in heterogeneity in the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol execution environment to optimize its energy consumption. By investigating the performance of the various numbers of divisions, it is proved that the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol with 9 zonal divisions has higher stability and throughput. The performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is compared with those of Stable Election Protocol, Low-Energy Adaptive Clustering Hierarchy, Modified Low-Energy Adaptive Clustering Hierarchy, and Gateway-Based Energy-Efficient Routing Protocol through computer simulations. Simulation results verify the improved performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol in terms of network stability, lifetime, and throughput

Is post-COVID osteonecrosis of jaw (PCONJ) Masquerading as osteomyelitis ? A largest unicentric report of 13 cases

Objective: The purpose of this study was to ascertain the correlation between COVID-19 infection and jaw osteonecrosis, along with the identification of risk factors that could be associated with the development of the condition. Another aim of our study is to establish whether maxillofacial osteonecrosis is an early or late complication seen in COVID-19 patients. Material and method: This was a retrospective study conducted over a period of two years. Case records of patients with a history of severe COVID and steroid treatment who later developed jaw osteonecrosis were evaluated. Result: 13 patients with an age range from 8 years to 70 years were identified. Osteonecrosis was seen as late as 21 months after COVID-19. The majority of the cases involved maxilla, one case was of bi-jaw involvement, and one case presented with isolated mandibular involvement. 6 patients were diabetic and 11 patients gave a history of provocative dental treatment like extraction. Conclusion: A triad of post-COVID coagulopathy, steroid administration, and a provocative dental treatment may contribute to jaw osteonecrosis which may be seen in patients without pre-existing systemic illness and may present as late as 21 months after COVID-19

Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data