Background: Chronic respiratory diseases are an important global issue, particularly in Asia, where burden patterns vary widely across countries. With more than half the world's population living in Asia, understanding the national and regional burden of chronic respiratory diseases is essential; however, research on this area remains inadequate. We aimed to investigate the burden of chronic respiratory diseases in Asia at national and regional levels, and to identify key risk factors. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study 2023 provides estimates for assessing the burden of chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD), and pulmonary sarcoidosis. We focused on 34 countries in Asia, encompassing the high-income Asia Pacific region and central, east, south, and southeast Asia. Estimates for age-standardised prevalence and disability-adjusted life-year (DALY) rates per 100 000 population, including 95% uncertainty intervals (UIs), were extracted by location, sex, year, and Socio-demographic Index (SDI). The average annual percentage change was calculated and presented as a percentage with 95% CIs. Estimates of modifiable attributable risk factors for DALYs and mortality were also included. Findings: In Asia, the age-standardised prevalence and DALY rates for chronic respiratory diseases generally declined from 1990 to 2023; however, the trend varied substantially by disease and country. In 2023, the age-standardised prevalence rate of COPD was highest in south Asia (3044·18 [95% UI 2748·67–3303·04] per 100 000 population), while the age-standardised asthma prevalence rate was highest in the high-income Asia Pacific region (4870·24 [4046·70–5962·78] per 100 000 population) and southeast Asia (4778·18 [3970·25–5735·61] per 100 000 population). Despite southeast Asia and the high-income Asia Pacific region having a similar age-standardised asthma prevalence rate, southeast Asia had a higher age-standardised DALY rate (508·67 [95% UI 394·89–669·92] per 100 000 population) compared with the high-income Asia Pacific region (204·40 [129·23–290·41] per 100 000 population). A decrease in the age-standardised DALY rate for chronic respiratory diseases was observed with increasing SDI, contrasting with its prevalence patterns. Age-standardised DALY rates of COPD decreased in all Asian countries except for Georgia (average annual percentage change 1·37 [95% CI 1·26–1·48]) and Kazakhstan (0·73 [0·55–0·93]), and age-standardised DALY rates of asthma decreased in all countries. Smoking and ambient particulate matter pollution were identified as leading attributable risk factors for chronic respiratory diseases across Asia. Household air pollution from solid fuels was a regionally pronounced risk factor for chronic respiratory diseases, particularly in south Asia (age-standardised DALY rate 657·58 [95% UI 485·04–880·45] per 100 000 population). Although smoking was a major risk factor in males, ambient particulate matter pollution and secondhand smoke emerged as important attributable risk factors for chronic respiratory diseases in females. Interpretation: Countries with lower SDI had markedly higher DALY rates, highlighting the need to address socioeconomic and health-care inequities. Household air pollution from solid fuels continues to impose a substantial but preventable burden in south Asia, calling for clean energy adoption and improved ventilation. Funding: Gates Foundation.

Optimization methods known from the literature include gradient techniques and evolutionary algorithms. The main idea of gradient methods is to calculate the gradient of the objective function at the actual point and then to step towards better values according to this value. Evolutionary algorithms imitate a simplified abstract model of evolution observed in nature. Memetic algorithms traditionally combine evolutionary and gradient techniques to exploit the advantages of both methods. Our current research aims to discover the properties, especially the efficiency (i.e. the speed of convergence) of particular evolutionary and memetic algorithms. For this purpose the techniques are compared by applying them on several numerical optimization benchmark functions and on fuzzy rule base identification.

Featured Application: The selected attributes and their ranking and weights can be used in decision support algorithms in computer aided diagnosis. The liver is one of the most common locations for incidental findings during abdominal CT scans. There are multiple types of disease that can arise within the liver and many of them are nodular. The ultimate goal of our research is to develop an expert knowledge-based system using fuzzy signatures, to support decisions during diagnosis of the most frequent of these nodular lesions. Since the literature contains limited information about the graphical properties of CT images that must be taken into consideration and their relationship to one another, in this paper we focused on selecting and ranking the input parameters using expert knowledge and determining their importance. Six visual attributes of lesions (size, shape, density, homogeneity contour, and other features) were selected based on textbooks of radiology and expert opinion. The importance of these attributes was ranked by radiologist experts using questionnaires and a pairwise comparison technique. The most important feature was found to be the density of the lesion on the various CT phases, and the least important was the size, the order of the other attributes was other features, contour, homogeneity, and shape, with a Kendall concordance coefficient of 0.612. Weights for the attributes, to be used in the future fuzzy signatures, were also determined. As a last step, several statistical parameter-based quantities were generated to represent the above abstract attributes and evaluated by comparing them to expert opinions.

Nowadays, people find themselves under a high degree of information pressure, both in terms of the quantity and quality of information. This paper investigates how workflows can be extended in today's digital information deluge. In the scope of this study, we observe the behaviors and habits of generation Z and older (X, Y) generation students under information pressure. The study is based on empirical research, direct observations, and a questionnaire survey, focusing on information processing and time management. We conclude that preferred digital content and time management strategies differ across generations based on the test results. Furthermore, the speed of information processing negatively correlates with student age; and the frequency and quality of collaboration shows differences across user generations.

The live demonstration will show two examples of 3D Webspaces. It will be demonstrated that 3D Websapces are considerable more effective in communication comparing to 2D Websites.

Background Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Methods Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. Findings In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (–11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by –29·9% (–33·6 to –25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. Interpretation The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Funding Gates Foundation, St Jude Children's Research Hospital.

Route optimization is an extensively studied NP-hard graph search problem. Many researchers applied numerous techniques to find the optimum or semi optimum solution (the one with least cost). There are many practical extensions and modifications of this problem applied using deterministic methods. However, traveling between nodes (locations) might encounter additional fuzzy cost (time) on the overall trip, whether they are traveled during the rush hour periods or if they crossed traffic regions (the city centers). Since, those factors are non-deterministic; it would be closer to reality to represent them using fuzzy numbers. In this paper, we propose a novel route optimization under road uncertainties using Interval-Valued Fuzzy Soft Sets. We use scoring technique to help determining the optimum route amongst all alternatives. Our novel approach can be looked at as a practical and closer to reality estimation for non-deterministic factors of the original abstract route optimization problem.

The 1st Joint (Host Laboratory) Experiment on 'joint research using small tokamaks' was carried out using the IPP Prague experimental facility 'CASTOR tokamak'. The main experimental programme was aimed at characterizing the edge plasma in a tokamak by using different advanced diagnostic techniques. It is widely recognized that characterization of phenomena occurring at the plasma edge is essential for understanding the plasma confinement in a tokamak. The edge plasma in small and large scale experiments has many similar features, and the results obtained through detailed measurements in a small flexible device such as CASTOR are in many aspects still relevant to those in large tokamaks. Therefore, it is expected that the results of this joint experiment will have general validity. The radial and poloidal structure of electrostatic turbulence was characterized. The effects of edge biasing were analysed. Radiation fluctuations and profile measurements were performed using fast bolometry. Plasma position measurements were performed using novel Hall sensors. © 2007 IAEA, Vienna.

Until now, no general numerical method has been published for characterizing the viscous characteristic length, although there is a high demand to assist whole vehicle development with accurate geometric parameters. A new formula was proposed to determine the viscous characteristic length (Λ) directly based on the viscous dissipation work, which was combined with the general definition of the characteristic length. This new expression facilitated significantly faster convergence than the known formula (Johnson) for inviscid fluids by representing viscous fluids through the material sample. The complete CFD simulation procedure was introduced based on the solution of Navier-Stokes equations. The novel expression is capable of any homogeneous material samples assuming that high-resolution reconstruction can be carried out, compared to Johnson's formula, which is limited to idealized cases. Good agreement between simulation and measurement results was found by applying fluid velocity instead of frequency. It was found that viscous losses dominate not only inside the pore connections but also in other regions, and the Reynolds number must be limited to the laminar regime. On that basis, the implemented model has more advantages against measurements and Johnson's formula, like the repeatability as well as ensuring analyses under steady-state and real conditions.