Integrating social and environmental impacts of green transportation infrastructure : a framework for effective decision-making

Green Infrastructure (GI) gains recognition as a viable alternative to traditional infrastructure due to its economic, environmental, and social benefits. However, quantifying and monetizing GI\u27s social and environmental impacts pose challenges, leading to their neglect in comparative evaluations. To heighten GI\u27s appeal, this study introduces a novel framework that incorporates social and environmental impacts and public opinion using the Analytical Hierarchy Process and Monte Carlo simulation. The framework offers a comprehensive approach to evaluate GI\u27s impact. Findings from a Philadelphia project demonstrate that projects with more GI elements are cost-effective when considering public opinion and long-term benefits. The research emphasizes the importance of incorporating GI\u27s threefold benefits into evaluation frameworks, aiding decision-makers in making informed choices by accounting for social, environmental, and economic impact

Diabetes Mellitus Management: An Extensive Review of 37 Medicinal Plants

Plants have been used as sources of medicine since ancient times. Natural products have been used extensively in Chinese, ayurvedic and folk medicine. In addition, a significant portion of the world’s population still utilizes herbal medicine. Diabetes is a common ailment affecting almost 463 million people in the world. However, current medications exert harmful after-effects on patients, while herbal medicines have fewer adverse effects. Plants possess secondary metabolites, such as alkaloids, flavonoids, tannins, steroids, etc., which exert numerous beneficial effects on health. Extensive research has been conducted over the years investigating and proving the hypoglycemic potential of various plants. The present paper reviews 37 such plants that are rich in phytoconstituents that possess a variety of pharmacological activities and have been experimentally proven to possess potentially hypoglycemic properties in animal models: Ficus racemosa, Agremone mexicana, Bombax ceiba, Cajanus cajan, Coccinia cordifolia, Momordica charantia, Syzygium cumini, Neolamarckia cadamba, Mangifera indica, Cocos nucifera, Tamarindus indica, Punica granatum, Azadirachta indica, Costus speciosus, Moringa oleifera, Andrographis paniculata, Ficus benghalensis, Anacardium occidentale, Annona squamosa, Boerhaavia diffusa, Catharanthus roseus, Cocculus hirsutus, Ficus hispida, Terminalia chebula, Terminalia catappa, Amaranthus tricolor, Blumea lacera, Piper betle leaves, Achyranthes aspera, Kalanchoe pinnata, Nelumbo nucifera, Mikania cordata, Wedelia chinensis, Murraya koenigii, Aloe barbadensis, Bryophyllum pinnatum and Asparagus racemosus. These 37 plant extracts exhibit antidiabetic activities through different mechanisms, including α-amylase and α-glucosidase inhibition, increases in glucose uptake and the stimulation of insulin secretion

Facial Recognition-Based Entry System for Student Residence Halls: Enhancing Security and Accessibility

Protecting an organization from numerous threats both inside and outside is the primary function of the security system. Automated embedded systems have come a long way in the contemporary era and have shown to be highly beneficial in applications related to security and surveillance. Face recognition is one of the study fields in computer vision, which is commonly used in security systems for video surveillance. Even though facial recognition technology has advanced significantly and is employed in several significant applications, numerous challenges need to be solved. These challenges include changes in posture, occlusions, expression, aging, lighting, and other elements. Deep learning can be useful in these situations. By using several processing layers to develop data representations with multiple feature extraction layers, deep learning may achieve higher accuracy. With the purpose of providing better security for student residence halls, in this work, we present a real-time deep learning-based facial recognition system that can be used to identify an individual's identity and give a warning when the individual's face is not recognized in front of the system. Here, faces from the face database are matched in order to identify students based on a video of their arrival into the residence halls. This process begins with face detection and ends with face recognition. We used a Convolutional Neural Network (CNN) based model Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection and recognizing faces using the Google FaceNet model. The model was trained on around 3000 photos, taken by 30 distinct people

Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm

Depression stands as a prominent and prevalent mental health issue, representing a significant global public health concern. Its emergence can be attributed to diverse factors. Suicide stands as a prominent global cause of death, eliciting concern on a widespread scale. This study was to analyze the perceived factors for depression and suicidal ideation among Bangladeshi university students in Bangladesh. There are so many factors such as Loneliness, Hopelessness, Helplessness, Relationship Issues, Grade problems, Academic Pressure, Parental problems, Money problems, Social Comparison, Social Media Influence, Family Expectations, Lack of Sleep, Uncertain Future, Health Issues, Bullying, Substance Abuse and Unemployment etc. These factors vary among male and female students. Apriori association algorithm were used to calculate support, confidence and lift of factors sets. Frequent factors sets and relationship were found from the work using Apriori association algorithm. The work is an online survey-based study about psychological and stress status of participants and statistical analysis is used for concluding the results. The research participants are Bangladeshi university students, Data collection carried out by online questionnaire. The findings from data analysis indicated that academic pressure (72.41%), uncertain future (56.32%), hopelessness (48.28%), family expectation (47.13%), financial crisis (42.53%), loneliness (41.38%) and unemployment (37.93%) are the key factors. The prevention of suicides is achievable. Hence, identifying depression and forecasting the potential for suicide risk serves as a means to prevent instances of self-harm within the university student population

Prevention of Alzheimer's disease through diet: An exploratory review

Introduction: This exploratory review article describes about the genetic factors behind Alzheimer's disease (AD), their association with foods, and their relationships with cognitive impairment. It explores the dietary patterns and economic challenges in AD prevention. Methods: Scopus, PubMed and Google Scholar were searched for articles that examined the relationships between Diets, Alzheimer's Disease (AD), and Socioeconomic conditions in preventative Alzheimer's disease studies. Graphs and Network analysis data were taken from Scopus under the MeSH search method, including words, Alzheimer's, APoE4, Tau protein, APP, Amyloid precursor protein, Beta-Amyloid, Aβ, Mediterranean Diet, MD, DASH diet, MIND diet, SES, Socioeconomic, Developed country, Underdeveloped country, Preventions. The network analysis was done through VOS viewer. Results: Mediterranean diet (MD) accurately lowers AD (Alzheimer's Disease) risk to 53% and 35% for people who follow it moderately. MIND scores had a statistically significant reduction in AD rate compared to those in the lowest tertial (53% and 35% reduction, respectively). Subjects with the highest adherence to the MD and DASH had a 54% and 39% lower risk of developing AD, respectively, compared to those in the lowest tertial. Omega-6, PUFA, found in nuts and fish, can play most roles in the clearance of Aβ. Vitamin D inhibits induced fibrillar Aβ apoptosis. However, the high cost of these diet components rise doubt about the effectiveness of AD prevention through healthy diets. Conclusion: The finding of this study revealed an association between diet and the effects of the chemical components of foods on AD biomarkers. More research is required to see if nutrition is a risk or a protective factor for Alzheimer's disease to encourage research to be translated into therapeutic practice and to clarify nutritional advice

Effect of charge on the antimicrobial activity of alpha-helical amphibian antimicrobial peptide

Antibiotic resistance is a severe threat to the world's public health, which has increased the need to discover novel antibacterial molecules. In this context, an emerging class of naturally occurring short peptide molecules called antimicrobial peptides (AMPs) has been considered potent antibacterial agents. Amphibians are one of the significant sources of AMPs, which have been extensively studied for the last few decades. Most amphibian AMPs are cationic, and several of these cationic AMPs adopt a well-defined alpha-helical structure in the presence of bacterial membranes. These cationic alpha-helical amphibian AMPs (CαAMPs) can selectively and preferentially bind with the negatively charged surfaces of Gram-positive and Gram-negative bacteria through electrostatic interaction, considered the main reason for their antibacterial activities. Here, we categorized these CαAMPs according to their charge, and to calculate the charge density; we divided the charge of each peptide by its corresponding length. To investigate the effect of charge among these categories, charge or charge density under each charge category was plotted against their corresponding minimum inhibitory concentration (MIC). Moreover, the effect of charge modification of some CαAMPs under specific charge categories in the context of MIC and hemolysis was also discussed. The information in this review will help us understand the antibacterial activity of accessible CαAMPs depending on each charge category across species. Additionally, this study suggests that designing novel functional antibacterial agents requires charge modification optimally

Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches

Reading newspapers is beneficial for people of all ages and the global community. The enjoyment of gathering diverse data from various sources adds to the overall experience. To enhance specificity in Bengali news headlines, recognizing the news genre becomes crucial. Recognizing the genre of the news, it is a very challenging task in Bengali Text Classification with the help of AI. A very few research works is done on Bengali News headline classification and we have done a model to provide a solution to the addressed issue. Due to the continuous change of the structure of the news headlines, we have employed a neural network adoption connection to our methodology experiment on a mixture of primary and secondary dataset. Achieving significant results, we implemented a Bengali dataset in Multi Classification using Long-Short Term Memory (LSTM), Bi- Long-Short Term Memory (Bi-LSTM), and Bi-Gated Recurrent Unit (Bi-GRU). The dataset is established by aggregating news headlines from various Bengali news portals and websites, showcasing robust categorization performance in the end product. Six categories were employed for the classification of Bengali newspaper headlines. The Bi-LSTM Model emerged with the highest training accuracy at 97.96% and the lowest validation accuracy at 77.91%. Furthermore, it demonstrated enhanced sensitivity and specificity