Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. Methods We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. Findings Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. Interpretation As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. Funding Bill & Melinda Gates Foundation

Applications of the ANFIS and LR models in the prediction of shear connection in composite beams / Ali Toghroli

Channel connectors are fairly new alternatives to the shear connectors. Due to their complex behavior and lack of valid approaches, the prediction of shear capacity of these shear connectors is very difficult. The conventional push-out tests and simple modelling of these connectors provide limited guidance in their structural behavior and the results are valid only for the selected testing protocol. Recent advancements in the area of Artificial Intelligence (AI) have made it feasible to utilize the application of these technologies in the construction industry and structural analysis. Particularly, the use of Artificial Neural Networks (ANNs) has been widely accepted as a reliable tool to solve complex problems in an accurate way. The collective behavior of an ANNs is like a human brain that demonstrates the ability to learn, recall and generalize from training patterns or data. A sub-type of ANNs is Adaptive neuro fuzzy inference system (ANFIS). It integrates both neural networks and fuzzy logic principles with a potential to capture the benefits of both in a single framework. This study aims at predicting the shear strength of channel shear connectors in composite beam comprised of steel and concrete sections using ANFIS as a non-linear modelling tool and the classical Linear Regression (LR) as a linear modelling tool. A set of 1200 experimental data is collected till date and used as an input data of the push-out tests and the output data being the corresponding shear strength which were recorded at all loading stages. The results derived from the use of ANFIS and the LR was then compared. The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as compared to LR. Afterwards, ANFIS network was used to determine which parameters are the most influential on shear strength of channel shear connectors. Two output parameters were analysed, namely load per connector and slip of the shear connectors. To assess the shear strength of channel shear connectors, it is desirable to select and analyse factors or parameters that are truly relevant or the most influential to the shear strength estimation and prediction. This procedure is typically called variable selection that corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. Variable searching using the ANFIS network was performed to determine how the selected parameters affect the shear strength

Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

Evaluation of the parameters affecting the shear strength and ductility of steel–concrete composite beam is the goal of this study. This study focuses on predicting the future output of beam’s strength and ductility based on relative inputs using a soft computing scheme, extreme learning machine (ELM). Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. Referring to the experimental results, as opposed to the GP and ANN methods, the ELM approach enhanced generalization ability and predictive accuracy. Moreover, achieved results indicated that the developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in shear strength and ductility of steel concrete composite. Furthermore, the experimental results indicate that on the whole, the newflanged algorithm creates good generalization presentation. In comparison to the other widely used conventional learning algorithms, the ELM has a much faster learning ability

A review on pavement porous concrete using recycled waste materials

Pavements porous concrete is a noble structure design in the urban management development generally enabling water to be permeated within its structure. It has also capable in the same time tocater dynamic loading. During the technology development, the quality and quantity of waste materials have led to a waste disposal crisis. Using recycled materials (secondary) instead of virgin ones (primary) have reduced landfill pressure and extraction demanding. This study has reviewedthe waste materials (Recycled crushed glass (RCG), Steel slag, Steel fiber, Tires, Plastics, Recycled asphalt) used in the pavement porous concretes and report their respective mechanical, durability and permeability functions. Waste material usagein the partial cement replacement will cause the concrete production cost to be reduced; also, the concretes' mechanical features have slightly affected to eliminate the disposal waste materials defects and to use cement in Portland cement (PC) production. While the cement has been replaced by different industrial wastes, the compressive strength, flexural strength, split tensile strength and different PC permeability mixes have depended on the waste materials' type applied in PC production

Behavior of steel storage pallet racking connection - a review

Steel pallet racking industry has globally used from the industrial revolution and has deeply evolved from hot-rolled profile into cold-formed profile to raise the optimization in engineering field. Nowadays, some studies regarding cold-formed steel profile have been performed, but fewer studies in terms of cold-formed pallet racking specifically in connection due to the semi-rigid behavior by lug-hooked into the upright have been conducted. The objective of this study is to review the related literature on steel storage racking connection behavior

Application of polymer, silica-fume and crushed rubber in the production of Pervious concrete

Achieving a pervious concrete (PC) with appropriate physical and mechanical properties used in pavement have been strongly investigated through the use of different materials specifically from the global waste materials of the populated areas. Discarded tires and the rubber tire particles have been currently manufactured as the recycled waste materials. In the current study, the combination of polymer, silica fume and rubber aggregates from rubber tire particles have been used to obtain an optimized PC resulting that the PC with silica fume, polymer and rubber aggregate replacement to mineral aggregate has greater compressive and flexural strength. The related flexural and compressive strength of the produced PC has been increased 31% and 18% compared to the mineral PC concrete, also, the impact resistance has been progressed 8% compared to the mineral aggregate PC and the permeability with Open Graded Fraction Course standard (OGFC). While the manufactured PC has significantly reduced the elasticity modulus of usual pervious concrete, the impact resistance has been remarkably improved

Application of waste tire rubber aggregate in porous concrete

This study aimed to categorize pervious rubberized concrete into fresh and hardened concrete analyzing its durability, permeability and strength. During the globalization of modern life, growing population and industry rate have signified a sustainable approach to all aspects of modern life. In recent years, pervious concrete (porous concrete) has significantly substituted for pavements due to its mechanical and environmental properties. On the other hand, scrap rubber tire has been also contributed with several disposal challenges. Considering the huge amount of annually tire wastes tossing out, the conditions become worse. Pervious concrete (PC) gap has graded surface assisted with storm water management, recharging groundwater sources and alleviate water run-offs. The results have shown that the use of waste tires as aggregate built into pervious concrete has tremendously reduced the scrap tire wastes enhancing environmental compliance

Computational investigation of the comparative analysis of cylindrical barns subjected to earthquake

The structural behaviors of cylindrical barns as a specific engineering structure have been considered as a complicated computing process. The structure design against the earthquake load, to protect by using the code, is an urgency avoiding unexpected damages. The situation has been subjected to the applied design method if there would be no failure across the construction procedures. The purpose of the current study is to clarify the behaviors of cylindrical reinforced concrete barns through the analytic methods across the mass and Lagrangian approaches through the whole outcomes comparison indicating that the isoparametric element obtained from the Lagrangian approach has been successfully applied in the barns earthquake analysis when the slosh effects have been discarded. The form of stress distributions is equal with sz closed distributions to one another

Application of ANFIS technique on performance of C and L shaped angle shear connectors

The behavior of concrete slabs in composite beam with C and L shaped angle shear connectors has been studied in this paper. These two types of angle shear connectors' instalment have been commonly utilized. In this study, the finite element (FE) analysis and soft computing method have been used both to present the shear connectors' push out tests and providing data results used later in soft computing method. The current study has been performed to present the aforementioned shear connectors' behavior based on the variable factors aiming the study of diverse factors' effects on C and L shaped angle in shear connectors. ANFIS (Adaptive Neuro Fuzzy Inference System), has been manipulated in providing the effective parameters in shear strength forecasting by providing input-data comprising: height, length, thickness of shear connectors together with concrete strength and the respective slip of shear connectors. ANFIS has been also used to identify the predominant parameters influencing the shear strength forecast in C and L formed angle shear connectors