Data analytics on key indicators for the city's urban services and dashboards for leadership and decision-making

Cities are continuously evolving human settlements. Our cities are under strain in an increasingly urbanized world, and planners, decision-makers, and communities must be ready to adapt. Data is an important resource for municipal administration. Some technologies aid in the collection, processing, and visualization of urban data, assisting in the interpretation and comprehension of how urban systems operate. The relationship between data analytics and smart cities has come to light in recent years as interest in both has grown. A sophisticated network of interconnected systems, including planners and inhabitants, is what is known as a smart city. Data analysis has the potential to support data-driven decision-making in the context of smart cities. Both urban managers and residents are becoming more interested in city dashboards. Dashboards may collect, display, analyze, and provide information on regional performance to help smart cities development having sustainability. In order to assist decision-making processes and enhance the performance of cities, we examine how dashboards might be used to acquire accurate and representative information regarding urban challenges. This chapter culminates Data Analytics on key indicators for the city's urban services and dashboards for leadership and decision-making. A single web page with consolidated information, real-time data streams pertinent to planners and decision-makers as well as residents' everyday lives, and site analytics as a method to assess user interactions and preferences are among the proposals for urban dashboards. Keywords: -Dashboard, data analytics, smart city, sustainability

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research

This research investigates road traffic accident severity in the UK, using a combination of machine learning, econometric, and statistical methods on historical data. We employed various techniques, including correlation analysis, regression models, GMM for error term issues, and time-series forecasting with VAR and ARIMA models. Our approach outperforms naive forecasting with an MASE of 0.800 and ME of -73.80. We also built a random forest classifier with 73% precision, 78% recall, and a 73% F1-score. Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for Explainable AI, highlighting influential factors like Driver_Home_Area_Type and Road_Type. Our study enhances understanding of accident severity and offers insights for evidence-based road safety policies.Comment: 3

EFL Learners’ Participation in Primary Schools of Coastal Areas in Bangladesh

Despite numerous initiatives by both governmental and non-governmental organizations, primary level students’ skills in English language are still below the expected level in Bangladesh (Hamid & Honan, 2012; Sultana, 2010). Our study examined reasons behind the limited participation of EFL (English as a Foreign Language) learners in primary level classrooms in the coastal areas of Bangladesh. To conduct the research, we followed an explanatory sequential mixed methods design (Creswell, 2014; Creswell & Creswell, 2018; Ivankova & Stick, 2007). We collected data from 37 male and 23 female students in grades four and five through questionnaire surveys and three focus group discussions (FGDs). We also collected data from five teachers through interviews and three class observations. We found that teachers had less motivation to create an interactive learning environment for the students due to heavy teaching loads and administrative assignments. Many of the students had low academic expectations and motivation, lived in poor socio-economic conditions that required them to work, and were impacted by frequent natural disasters that interrupted their regular classes. The results of our research provide insights for educationists and policymakers related to primary education in disaster-prone coastal areas as well as other rural parts of the country

Kepentingan modal sosial dalam pertumbuhan ekonomi

Social capital is just like one of the many forms of capital, such as physical capital, produced economic capital and human capital which has individual role that contribute to the country's growth and prosperity. Physical capital is the combination of production input and labour input in order to produce output. Human capital investment is considered as one of the catalysts to the county's growth. Nowadays, social capital is an indirect new source that contribute to the county's growth. Nevertheless, the study on contribution of social capital is still new and has various definitions. The measurement of social capital needs insight on social functioning, and the network and link between individuals in a community. All these insights can be utilised to contribute positive outcomes for the individual, ethnic group and community a like. This understanding can provide a picture of how individuals in a community cooperate in achieving mutual goals for building a prosperous county. This article looks at the role of social capital in assisting a county's economic growth. The Human Development Index (HDI) which is utilised by the World Bank will be used depict the relationship between economic growth and social capital. The regression analysis outcome on the relationship between HDI and Gross Domestic Product (GDP) shows a positive and significant relationship

Comparative Study Of Fastness Properties And Color Absorbance Criteria Of Conventional And Avitera Reactive Dyeing On Cotton Knit Fabric

160 GSM Single Jersey cotton knitted fabric was dyed with conventional Remazol reactive dye and latest Avitera reactive dye (Huntsman). Detailed comparison of the process parameters and fastness properties of these dyed fabrics were studied. Investigation exposed that Avitera delivered better dyeing performance including fastness to washing, perspiration, rubbing than conventionally dyed fabrics. Concerning process parameters Avitera dye required less soda, salt and no addition of other auxiliaries. Also this new Avitera reactive dye is more eco-friendly, cost effective and energy saving than conventional Remazol reactive dye. CMC DE and Da* color deviation were significantly higher between the dyed samples. Again K/S value of Avitera dyed sample was superior to that of Remazol dyed samples as because of enhanced dye uptake. Sequentially reflectance and relative indicators of the latest reactive dyed samples were also experimented

Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases. CCS CONCEPTS Covid,Deep Learning, Image Processing KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet, GoogleNet, Image Processing, Disease Detection

In Vitro Screening for Antioxidant and Anticholinesterase Effects of Uvaria littoralis Blume.: A Nootropic Phytotherapeutic Remedy

Background: Oxidative stress is strongly linked in the development of numerous chronic and degenerative disorders. Medicinal plants with antioxidant and anticholinesterase activities exert a key role for the management of oxidative stress related disorders mainly Alzheimer's disease (AD). Therefore the purpose of this study was to assess antioxidant potentiality and anticholinesterase inhibitory activity of crude methanolic extract (CME), petroleum ether fraction (PEF), chloroform fraction (CLF), ethyl acetate fraction (EAF) and aqueous fraction (AQF) of Uvaria littoralis (U. littoralis) leaves. Methods: The antioxidant compounds namely total flavonoids contents (TFCs) and total proanthocyanidins contents (TPACCs) were determined for quantities constituent’s characterization. Antioxidant capacity of U. littoralis leaves were estimated by the iron reducing power (IRPA), 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and nitric oxide (NO) radical scavenging capacity. Anticholinesterase effects were estimated for acetylcholinesterase (AChE) and butyrylcholinestrase (BChE) activity. Results: The EAF of U. littoralis leaves showed the highest TFCs as compared to CLF, CME, PEF and AQF. TPACCs were also found highest in EAF. The highest absorbance for IRPA was found in EAF (2.220 nm) with respect to CME and other fractions at the highest concentration. The EAF showed best DPPH and NO radical scavenging activity with IC50 values of 31.63 and 55.47 μg/mL, respectively with regard to CME and remaining fractions. The PEF represents highest AChE inhibitory activity with IC50 values of 35.19 μg/mL and CLF showed highest BChE inhibitory activity with IC50 values of 32.49 μg/mL. Conclusions: The findings of the current study demonstrate the presence of antioxidant phytochemicals, likewise, turns out antioxidant and anticholinesterase potentiality of U. littoralis leaves which could be a prestigious candidate for the treatment of neurodegenerative diseases especially AD

Innovative machine learning strategies for early detection and prevention of pregnancy loss: the Vitamin D connection and gestational health

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study’s findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030