The Impact of Smart City Initiatives on Cities’ Local Economic Development

The problem explored in this mixed-method action research is that challenges to sustainable economic development and basic community services increase, as populations in cities and towns increase. A city is a human settlement with well-defined demarcation points. A city’s infrastructure consists of complex systems, such as sewage treatment plants, water treatment plants, police stations, fire departments, utility services, schools, libraries, business, houses, etc. A smart city, on the other hand, is an urban vision that fosters citizens’ engagement and technological integration of the city’s infrastructure. The purpose of this mixed-method action research was to identify the characteristics of a smart city and determine to what extent smart city initiatives impact economic development. Using a combative analysis methodology, the study examined five major smart cities. The research results revealed that cities apply smart solutions by focusing on 5 major areas: Economic Development, Public Safety, Energy & Environment, Infrastructure, and Transportation. The study concluded that Smart city initiatives contribute directly and indirectly to the economic growth of cities in the United States. The study indicated that smart cities are socially engaged, financially stable, business-oriented, data-driven, environmentally friendly, and energy-efficient cities. The study also concluded that smart city initiatives can alleviate cities’ challenges, thus, enhancing economic development

Enhancement Methods for Energy Consumption Prediction in Smart House based on Machine Learning

Energy efficiency in modern homes has recently become a significant issue due to the emergence of smart home infrastructure. Numerous public structures, such as homes, hospitals, schools, and other institutions, use more energy. To come close to meeting the actual energy demand, it is crucial that we create as much energy as we can. Machine learning has various advantages for improving the effectiveness and efficiency of smart home systems and appliances, including managing and lowering energy use. Additionally, as a key component of the smart home idea, we explore the potential integration of machine learning-based on some algorithm methodologies ways to improve power energy consumption system and control. The models were used to identify patterns for smart home and variations in energy consumption. This study's conclusions were used to analyze case studies and forecast energy consumption. Detection Change (of used and generation) for all appliances, which excessive foresees energy use and stops a rise in usage. Predict Future Energy use by using meteorological data and maximizing the supply of energy to forecast future energy generation and use. Finally, using five machine learning algorithms, including the Linear Regression (LR), Gradient Boosting Regression (GBoostR), Decision Tree Regression (DTR), Stochastic Gradient Descent Regression (SGDR), and Bayesian Ridge Regression (BRR), we can measure the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Absolute Error (RMAE), and Root Mean Squared Percentage Error (RMSPE), in order to determine how well models

Development of rubberized geopolymer interlocking bricks

Waste tires contribute badly to the environment on a huge scale as they are bulky, non-biodegradable, and prone to fire and being a shelter for mosquitos and other insects. This paper reports on a novel approach towards the development of rubberized brick by utilizing crumb rubber as the sole fine aggregate in the production of geopolymer interlocking bricks. The response surface methodology (RSM) from Design Experts software has been used to determine the numbers of trial mixes and their corresponding ingredients. A total of thirteen trial mixes were carried out and tested for compressive strength and the RSM model was developed to predict the design mix based on the targeted compressive strength. The mix design was obtained to be an 18 M for NaOH and 0.8 solution to fly ash ratio. The geopolymer interlocking rubberized bricks were then produced and tested for compressive strength, dimension, modulus of rupture, water absorption, initial rate of absorption, and efflorescence. The geopolymer interlocking rubberised bricks presented a low compressive and flexural strength and a high-water absorption capacity. The bricks were rated as non-effloresced and classified as 3rd class bricks which can be used as non-load bearing material. It is recommended to utilize nano silica in order to increase the strength of the brick. Keywords: Rubberized bricks, Crumb rubber, Interlocking bricks, Geopolymer, Response surface methodolog

Properties of nano-silica modified pervious concrete

The aim of this study is to evaluate the effects of inclusion nano-silica (also known as nano-SiO2) on the properties of pervious concrete containing fly ash (FA) as a partial replacement to cement. It has been found, for cementitious paste, that incorporating NS leads to reduce the cumulative pore volume by 13.4%. While the compressive strength of NS modified pervious concrete has been improved without adversely affecting its void ratio and permeability. The workability has been adversely affected by the inclusion of NS, which can be enhanced by incorporating the fly ash and superplasticizer. The porosity of cementitious paste has increased as the FA content is increased. These results are in good agreement with SEM results. For the pervious concrete voids ratio, permeability and infiltration rate were decreased against the increase of paste to the aggregate ratio Response surface methodology (RSM) has also been used to develop a model for navigating the design space of NS modified pervious concrete. Models revealed 95% significance of confidence level with difference less than 0.2 between Pred R-Squared value of 0.9515 and Adj R-Squared. The general expression has been developed for all the responses with the different coefficients using the RSM. Keywords: Fly ash, Final setting time, Infiltration rate, Pervious concrete, Nano-silic

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research