This paper examines the potentials of augmented reality (AR) technology in supply chain management (SCM) and logistics. Specifically, we provide an overview of the technology’s various value propositions and its ability to support companies’ business processes. Although the emergence of Industry 4.0 has renewed the interest in AR, and how it can address several issues challenging existing business models, rigorous studies investigating the potentials of AR for SCM and logistics activities are scant. To bridge this knowledge gap, we conducted a systematic literature review to compile existing literature, identify current research gaps, and systematize AR research in SCM and logistics activities. In total, forty-three (43) papers were thoroughly analyzed. The findings of this study reveal that AR can add value in five main areas, namely warehousing, manufacturing, sales and outdoor logistics, planning and design and human resource management. Moreover, we discuss organizations’ challenges when deploying AR in SCM and logistics and propose exploratory research opportunities for further investigation. In this paper, we highlight numerous practical implications of AR in SCM and recommend that organizations consider AR as a potential solution for enhancing business processes, improving operational efficiencies, and increasing overall competitiveness. This study represents one of the first attempts to synthesize AR’s literature from an SCM and logistics perspective.

Nowadays, an increasing number of organisations have to make decisions about the change and optimization of their enterprise infocommunication infrastructure. The usual approach of performance (using QoS, SLA) is not user (enterprise) centred and complex enough to help ICT (Information and Communication Technology) experts to support management decisions. © 2012 Society for Modeling & Sim.

One of education's most challenging tasks is accurately assessing student performance. Unfortunately, even nowadays, assessment is mainly done manually, with teachers manually correcting submitted work, essays, and dissertations. There is plagiarism checking services and algorithms, but these search for matches in theses and publications on the Internet, so they need to be more suitable for facilitating teachers' daily work. In this paper, we present a self-developed plagiarism screening application that can find duplicates in students' submitted work. The tool performs simple text comparison and syntactic and semantic checking. We also created an easy-to-use online interface where users can easily create and run projects.

We suggest a distance measure for OWA operators. First we associate an OWA operator with a unique regular increasing monotone quantifier and then define the distance between two OWA operators as the Wasserstein-1 distance between their associated quantifiers.

For the purpose of efficient algae cultivation, the Photo Bio-Reactor (PBR) must be designed according to the needs of the algae to be cultivated. We performed our experiment with a loop reactor with a total volume of 14 liters. Among other things, the mixing of the gas and liquid phases, the value and change of the light intensity reaching the algae, and the degree of algae deposition on the walls of the equipment depend on the flow caused by the bubble column in the equipment. Using the ANSYS FLUENT simulation environment, we optimized the efficiency of the gas intake that determines the flow.

In addition to the specific characteristics of the teaching-learning process, we have faced and continue to face a number of challenges and constraints in the digital era. A common set of challenges has been and will continue to be, at the end of 2021, the issue of digital competences, which is one of the main focal points (in addition to the provision of a modern high-capacity server backbone) of the new European Digital Agenda for Education (2021-27). The focus of our paper is therefore on the study of digital competences among teachers. In Hungary, the adapted frameworks based on the central EU Recommendation were published in 2019 and 2020, the first of which was the digital competence framework for teachers based on DigCompEdu. The digital skills and ICT attitudes of teachers play a key role in digital education. In this article, we present the results of a snow-ball sampling based pilot questionnaire administered to practising teachers in addition to a description and overview of the theoretical framework. Our research questionnaire consisted of 34 items, with N=112 respondents providing a scored response to our survey. The questionnaire survey consisted of a total of six sections, and we intend to use the responses and results obtained in our further research, seeking answers to the main research questions.

Background For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. Methods The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010–23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Findings Total numbers of global DALYs grew 6·1% (95% UI 4·0–8·1), from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0–14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31–1·61) global DALYs in 2010, increasing to 1·80 billion (1·63–2·03) in 2023, alongside a concurrent 4·1% (1·9–6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176–209] DALYs), stroke (157 million [141–172]), and diabetes (90·2 million [75·2–107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0–107·5]), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and a 25·8% (22·6–28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7–61·0) for diarrhoeal diseases, 42·9% (38·0–48·0) for HIV/AIDS, and 42·2% (23·6–56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6–22·0) and 24·8% (7·4–36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7–19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18–1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation—with high SBP accounting for 8·4% (6·9–10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories—behavioural, metabolic, and environmental and occupational—risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8–37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0–11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023—eg, declining by 54·4% (38·7–65·3) for unsafe sanitation, 50·5% (33·3–63·1) for unsafe water source, and 45·2% (25·6–72·0) for no access to handwashing facility, and by 44·9% (37·3–53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [–2·7 to 15·6]; non-significant). Interpretation Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors—eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG—including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic—the complex interaction of multiple health risks, social determinants, and systemic challenges—will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Funding Gates Foundation and Bloomberg Philanthropies.

This paper focuses on the use of model and simulation one of renewable energy. In this paper the results of simulation models by Matlab-Simulink for an urban-metro railcar and some newer methods for reducing the need value of capacitance for energy storage in cooperation by a Li-ion battery are presented. In this research was been investigated the Li-ion battery and the supercapacitor as hybrid energy storing device for the same task and its effectivness under operation of a suitable energy control system. The available decreasing ratio of the needed energy storage at case SCAP is 25 % to 40 % with this improved energy control method, which are significant values as decreasing in volume, mass and price. Mass reduction of our hybrid storage system is significant, about 60%.

In recent years, investors, corporations, and enterprises have shown great interest in the Bitcoin network; thus, promoting its products and services is crucial. This study utilizes an empirical analysis for financial time series and machine learning to perform prediction of bitcoin price and Garman-Klass (GK) volatility using Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Facebook prophet models. The performance findings show that the LTSM boost has a noticeable improvement compared to SARIMA and Facebook Prophet in terms of MSE (Mean Squared Error) and MAE (Mean Average Error). Unlike Long Short-Term Memory (LSTM), a component of Deep Learning (DL), the finding explains why the bitcoin and its volatility forecasting difficulty has been partially met by traditional time series forecasting (SARIMA) and auto-machine-learning technique (Fb-Prophet). Furthermore, the finding confirmed that Bitcoin values are extremely seasonally volatile and random and are frequently influenced by external variables (or news) such as cryptocurrency laws, investments, or social media rumors. Additionally, results show a robust optimistic trend, and the days when most people commute are Monday and Saturday and an annual seasonality. The trend of the price and volatility of bitcoin using SARIMA and FB-Prophet is more predictable. The Fb-Prophet cannot easily fit within the Russian-Ukrainian conflict period, and in some COVID-19 periods, its performance will suffer during the turbulent era. Moreover, Garman-Klass (GK) forecasting seems more effective than the squared returns price measure, which has implications for investors and fund managers. The research presents innovative insights pertaining to forthcoming cryptocurrency regulations, stock market dynamics, and global resource allocation.