Using Waste Vermiculite and Dolomite as Eco-Friendly Additives for Improving the Performance of Porous Concrete

The present study investigated the applicability of waste vermiculite and dolomite as fine aggregate, known as appropriate mineral adsorbents to enhance the quality of urban runoff, for improving the mechanical properties of porous concrete. 180 samples were mixed by adding 5-30% vermiculite and dolomite, as fine aggregate, and combining them with ordinary sand; lime sand (combining of 5-15% of each). Results showed that although adding dolomite culminated in a minor reduction of permeability– average of about 30%-, the average of compressive strength was augmented by 120%. Results of compressive strength of dolomite samples were repeated in mixtures containing vermiculite (an increase of 57%). While exploiting vermiculite in high percentages (20, 25, and 30) resulted in an extensive decrease in the permeability (94%), it was improved to an acceptable level (about 40%) after using vermiculite in combination with ordinary sand (lime sand). All dolomite and improved vermiculite mixtures, after combining vermiculite with ordinary sand, had appropriate performance in draining storm-urban runoff; such that in the weakest case, stimulated storm runoffs with heights of 10, 20, 30 and 40 cm were completely drained only after 17, 36, 59 and 87 seconds, respectively. Also, using vermiculite resulted in reducing the concrete weight (about 100 kg). Generally, although a little reduction in the permeability was seen, but using waste vermiculite and dolomite improved the mechanical properties of porous concrete significantly

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Traditionally, climate conditions has been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. Firstly, the most appropriate inputs were selected for models using sensitivity analysis. After that, long-term air temperatures (1 month, 1, 2 and 3 years ahead) were modeled.  Results showed that: 1) the given algorithms had acceptable results in improving the models’ performance in modeling minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R2), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals

Comparison of the Effect of Root Canal Preparation by Using Wave One and ProTaper on Postoperative Pain: A Randomized Clinical Trial

Introduction: WaveOne is a single-file reciprocating instrumentation system with the benefits of M-Wire alloy that has increased flexibility and improved resistance to cyclic fatigue over the conventional alloy. Root canal preparation techniques may cause postoperative pain. The goal of the present study was to compare the intensity and duration of postoperative pain when using WaveOne or ProTaper Universal systems for instrumentation of root canals. Methods: Forty-two patients who fulfilled specific inclusion criteria were assigned to 2 groups according to the root canal instrumentation technique used, WaveOne or ProTaper Universal. Root canal treatment was carried out in 2 appointments, and the severity of postoperative pain was assessed by numerical rating scale (NRS) score after each session until complete pain relief was achieved. Analgesic consumption, duration of pain, and root canal preparation time were also recorded. Results: The mean NRS score and duration of pain after both appointments were significantly higher in the WaveOne group (P < .05); however, the mean analgesic consumption was only significantly higher in the WaveOne group after the first appointment (P < .05). In all groups the highest mean NRS score was seen 6 hours after each therapeutic appointment. Canal preparation time was significantly shorter in the WaveOne group (P < .001). Conclusions: Postoperative pain was significantly lower in patients undergoing canal instrumentation with ProTaper Universal rotary instruments compared with the WaveOne reciprocating single-file technique

Designing a Supply Chain Network under the Risk of Disruptions

This paper studies a supply chain design problem with the risk of disruptions at facilities. At any point of time, the facilities are subject to various types of disruptions caused by natural disasters, man-made defections, and equipment breakdowns. We formulate the problem as a mixed-integer nonlinear program which maximizes the total profit for the whole system. The model simultaneously determines the number and location of facilities, the subset of customers to serve, the assignment of customers to facilities, and the cycle-order quantities at facilities. In order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, two solution methods based on Lagrangian relaxation and genetic algorithm are developed. The effectiveness of the proposed solution approaches is shown using numerical experiments. The computational results, in addition, demonstrate that the benefits of considering disruptions in the supply chain design model can be significant

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

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

Designing a Supply Chain Network under the Risk of Disruptions

This paper studies a supply chain design problem with the risk of disruptions at facilities. At any point of time, the facilities are subject to various types of disruptions caused by natural disasters, man-made defections, and equipment breakdowns. We formulate the problem as a mixed-integer nonlinear program which maximizes the total profit for the whole system. The model simultaneously determines the number and location of facilities, the subset of customers to serve, the assignment of customers to facilities, and the cycle-order quantities at facilities. In order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, two solution methods based on Lagrangian relaxation and genetic algorithm are developed. The effectiveness of the proposed solution approaches is shown using numerical experiments. The computational results, in addition, demonstrate that the benefits of considering disruptions in the supply chain design model can be significant

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters. Keywords: Water quality parameters, ANFIS, Evolutionary algorithm, Particle swarm optimization, Ant colony optimization for continuous domain