A Fuzzy Inference Model for Predicting Irregular Human Behaviour During Stressful Missions

AbstractIn this paper a hybrid fuzzy inference and transfer function modeling is used to predict the irregular human behavior during hard and stressful tasks such as dangerous military missions. A set of affecting factors such as missioner's experience, fatigue, sunshine intensity, hungriness, thirstiness, psychological characteristics, affright, etc. may be taken to account. In this regard a dynamic system model is used to predict the convolution of the timed effects of different factors on irregular behavior of personnel during the mission. This approach of predicting irregular behavior or erroneous decision making of staff have serious usages in aerospace, military, social and similar projects where a wrong decision can have catastrophic outcome such as attempting to suicide by a pilot or killing civilians by a soldier in stressful situations. The effect of such behavior and decisions may even cause the failure of the overall project or mission. For example, killing civilians by a soldier can result to the overall failure of human terrain missions where the main objective is gaining trust between the local civilian population

Burden of tracheal, bronchus, and lung cancer in North Africa and Middle East countries, 1990 to 2019: Results from the GBD study 2019

ObjectiveTo provide estimates on the regional and national burden of tracheal, bronchus, and lung (TBL) cancer and its attributable risk factors from 1990 to 2019 in the North Africa and Middle East (NAME) region.Methods and materialsThe Global Burden of Disease (GBD) 2019 data were used. Disability-adjusted life years (DALYs), death, incidence, and prevalence rates were categorized by sex and age groups in the NAME region, in 21 countries, from 1990 to 2019. Decomposition analysis was performed to calculate the proportion of responsible factors in the emergence of new cases. Data are presented as point estimates with their 95% uncertainty intervals (UIs).ResultsIn the NAME region, TBL cancer caused 15,396 and 57,114 deaths in women and men, respectively, in 2019. The age-standardized incidence rate (ASIR) increased by 0.7% (95% UI -20.6 to 24.1) and reached 16.8 per 100,000 (14.9 to 19.0) in 2019. All the age-standardized indices had a decreasing trend in men and an increasing trend in women from 1990 to 2019. Turkey (34.9 per 100,000 [27.6 to 43.5]) and Sudan (8.0 per 100,000 [5.2 to 12.5]) had the highest and lowest age-standardized prevalence rates (ASPRs) in 2019, respectively. The highest and lowest absolute slopes of change in ASPR, from 1990 to 2019, were seen in Bahrain (-50.0% (-63.6 to -31.7)) and the United Arab Emirates (-1.2% (-34.1 to 53.8)), respectively. The number of deaths attributable to risk factors was 58,816 (51,709 to 67,323) in 2019 and increased by 136.5%. Decomposition analysis showed that population growth and age structure change positively contributed to new incident cases. More than 80% of DALYs could be decreased by controlling risk factors, particularly tobacco use.ConclusionThe incidence, prevalence, and DALY rates of TBL cancer increased, and the death rate remained unchanged from 1990 to 2019. All the indices and contribution of risk factors decreased in men but increased in women. Tobacco is still the leading risk factor. Early diagnosis and tobacco cessation policies should be improved

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

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

Enhancing displacement coefficient method for multi degree of freedom buildings (MDOF) considering nonlinear soil structure interaction

One of the main challenges in seismic assessment of existing structures is estimating the displacement demand under earthquake motions. Displacement coefficient method introduced in current code and instructors correlates displacement of an equivalent single degree of Freedom (ESDOF) system to roof or any story of corresponding MDOF. An important coefficient in this method, C1, defines the ratio of inelastic to elastic displacement of ESDOF. The effects of soil structure interaction (SSI) on parameter C1 for SDOF has been investigated by many researchers, however, this parameter on MDOF system, itself, has not been properly investigated. This is a challenging issue since many influential behaviors cannot properly be addressed in ESDOF systems such as: P–delta effects, higher mode effects, forming of plastic hinges and their sequences considering strength and stiffness deterioration, and nonlinear SSI. In this study, to investigate this approach, seven buildings representing a reasonable range of effective period as MDOF systems were selected (three moment resisting frames and four shear wall buildings) and designed with different strength reduction factors (R = 3, 4, 5 and 7). To investigate the effect of SSI on responses, the foundations were designed with (1.5, 3, 4 and 5) factor of safety vertical (FSV) to cover all probable rocking and uplifting behaviors. All designed buildings were analyzed for far-field Design Basis Earthquake (DBE), Maximum Considered Earthquake (MCE), and near field pulse-like earthquake records. The responses were investigated for the effects of SSI on global response of buildings considering both R-factor and FSV as well as MDOF displacement inelastic ratio (C1MDOF) and modified amplification factor coefficient (Cm). The results showed that the method suggested by ASCE-41-17 for prediction of inelastic displacement ratio underestimates the responses in shorter period buildings and overestimate for longer period buildings for all values of R-factors and FSV. The results showed that the coefficient of C0 introduced in ASCE-41-17 should include the effects of structural and SSI nonlinearity instead of elastic mode participation factors. Based on results, two practical equations and methods were proposed to enhancing displacement coefficient method considering SSI effects on MDOF systems

An Improvement on Association Rule Based Classification of Medical Data

Abstract Association rule based classification is one of the popular data mining techniques applied in medical domain. The major advantage is its interpretable results that medical doctors can easily adopt for diagnostic decision-making. The classification framework consists of data discretization, association rule generation, and classification. The discretization step is required to convert numerical features such as blood pressure into a categorical format, to make it suitable for association rules mining. Existing discretization methods such as Omega algorithm construct several non-adjacent intervals to represent new categorical variables. However, such algorithms are not generalizable because of failure to recognize new observations that lie between constructed intervals; this will impact the accuracy of association rules based classification. To overcome this problem, an associative classification framework based on an improved discretization algorithm is proposed. In the discretization step, a centroid of each constructed interval is identified to represent that interval. Using the identified centroids, numerical data is discretized and fed to an Apriori algorithm for rule induction. Consequently, a new observation is classified using a majority-voting scheme of the generated rules. The framework was tested on various medical datasets from the University of California Irvine repository and the Aneurisk dataset repository. The results show that the proposed framework gives a higher accuracy when compared to existing approaches

Post-earthquake seismic assessment of residential buildings following Sarpol-e Zahab (Iran) earthquake (Mw7.3) part 1: Damage types and damage states

Following a disaster, a quick and reliable evaluation of structural damage and its classification is one of the crucial steps to making decisions and disaster management. Based on a reliable evaluation of structural damage and proper classification of buildings, decisions related to structural performance (functionality), repair possibility, or in severe cases, a replacement could be made. The results of current research are obtained from a comprehensive and meticulous investigation of more than 81 damaged steel and RC buildings after the Sarpol-e Zahab (Iran) earthquake. First, a detailed explanation of various types of structural and non-structural damage to buildings is provided. Then, based on the severity, extent, and types of damage and the observed residual drift for steel structures, buildings are classified into five damage states. The damage types are the more frequently observed damage in different elements. The inherent quantitative classification of buildings into five damage states makes this approach a precise and useful tool to investigate buildings more reliably following earthquakes. Part-1 of this research (current paper) discusses various types of observed damage and the classification of buildings into damage states. In part-2 of this research (accompanying paper), a damage index is proposed based on the results of part-1 and empirical vulnerability curves for different types of damaged buildings are developed

Post-earthquake seismic assessment of residential buildings following Sarpol-e Zahab (Iran) earthquake (Mw7.3) – Part 2: Seismic vulnerability curves using quantitative damage index

One of the most crucial steps in disaster management and decision-making is a quick and reliable seismic evaluation of structures following an earthquake. Vulnerability curves, which relate the earthquake intensity measure to damage states, are key terms in performing post-earthquake assessments. In most previous research, empirical vulnerability curves were roughly developed using qualitative criteria. Current research proposes a novel damage index based on the suggested damage states resulting from field investigation of more than 81 damaged steel and RC buildings after the Sarpol-e Zahab (Iran) earthquake. The proposed damage index provides relatively precise information about damaged buildings. Then, using the spectral acceleration derived from the conditional ground motion intensity and the proposed damage index, empirical vulnerability curves are generated for RC and steel buildings and non-structural walls. The developed empirical vulnerability curves are valuable due to the lack of similar field studies on damaged buildings under severe earthquakes and are consistent with common practice of construction in Iran. Past earthquakes' data can be used in seismic risk assessment and planning for mitigation of losses in the future

Evaluation of renewable energy resources using integrated Shannon Entropy—EDAS model

To increase efficiency of energy sector, the implementation of renewable energy systems and finding most appropriate renewable energy resource is of high significance for governments. One of the fundamental decision-making problems in energy sector is to design decision making tools to find optimal energy resources. Usually, experts in the energy decision-making sector have a platform containing multiple criteria indicators that are expressed as conflicting objectives. In this paper, we proposed a Multiple Criteria Decision-Making (MCDM) model to handle conflicting objectives while evaluating five renewable resources: solar PV, Solar thermal, wind power, geothermal, and biomass with respect to economic, technical, social, and environmental aspects. Shannon Entropy method is used to determine the importance of criteria. Evaluation based on distance from average solution (EDAS) is used to prioritize the renewable energy technologies. To demonstrate the feasibility of the proposed decision-making model, a real-life case study for Saudi Arabia is investigated. Under defined socio-economic and environment criteria, results indicate that wind energy can be opted the as most suitable source fitted to the objectives and policies of the decision makers. In order to verify the results and to show the impact of parameter changes on final solutions, different sensitivity analysis tests are performed