The World Academy of Sciences

Planetary landings can only be autonomous by having control systems that are able to balance accuracy and efficiency under nonlinear conditions. This research project examines the use of reinforcement learning (RL) which in this case is Proximal Policy Optimization (PPO) algorithm to simulate and optimize the dynamics of lunar descent in a one dimensional environment with Newtonian physics. The agent was trained to successfully complete soft landings at different fuel and thrust-cost settings, where a reward function punished the use of fuel and high terminal velocity. On 40 000 training episodes, the agent always shared similar stable policies that reduced fuel usage and yet reached safe touchdown speeds (less than 2 m/s-1). Quantitative analysis showed the existence of a strong negative correlation between available fuel and final velocity and unique dual-phase kinetics of fuel-use patterns were found which were similar to real-world powered-descent sequences. These findings confirm that reinforcement learning is a physically consistent, adaptive control technique to optimize descent, and has the potential to be useful in autonomous guidance systems in future trips to the moon and other planets.

Structured quantitative data, such as survey responses in human resource management research, are often analysed using machine learning methods, including logistic regression. Although these methods provide accurate statistical predictions, their results are frequently abstract and difficult for non-specialists to comprehend. This limits their usefulness in practice, particularly in contexts where eXplainable Artificial Intelligence (XAI) is essential. This study proposes a domain-independent approach for the autonomous classification and interpretation of quantitative data using visual processing. This method transforms individual responses based on rating scales into visual representations, which are subsequently processed by Convolutional Neural Networks (CNNs). In combination with Class Activation Maps (CAMs), image-based CNN models enable not only accurate and reproducible classification but also visual interpretability of the underlying decision-making process. Our evaluation found that CNN models with bar chart coding achieved an accuracy of between 93.05% and 93.16%, comparable to the 93.19% achieved by logistic regression. Compared with conventional numerical approaches, exemplified by logistic regression in this study, the approach achieves comparable classification accuracy while providing additional comprehensibility and transparency through graphical representations. Robustness is demonstrated by consistent results across different visualisations generated from the same underlying data. By converting abstract numerical information into visual explanations, this approach addresses a core challenge: bridging the gap between model performance and human understanding. Its transparency, domain-agnostic design, and straightforward interpretability make it particularly suitable for XAI-driven applications across diverse disciplines that use quantitative response data.

O mobiliário urbano tem papel multifacetado nas cidades contemporâneas, englobando aspectos como praticidade, estética, ergonomia, identidade local, interação social, abrigo, iluminação, segurança e conforto. Este artigo disserta sobre o planejamento e concepção de um abrigo para ponto de ônibus de uma cidade universitária, concebido por meio do design colaborativo, com metodologia de pesquisa-ação aliada ao design thinking. O abrigo é o resultado de um projeto de pesquisa interdisciplinar, que integra as áreas do design, da engenharia civil e da arquitetura para propor uma solução acessível que atenda às demandas da comunidade local. Apresenta-se o processo criativo envolvido na concepção do abrigo, os materiais e tecnologias utilizados, além de uma reflexão sobre a importância do design colaborativo neste tipo de iniciativa. O projeto foi destaque em eventos acadêmicos, participou de feira internacional para projeto inovadores e, por fim, foi tema de uma reportagem internacional que destacou iniciativas de impacto ambiental positivo.

Obesity poses significant health and economic burdens globally, with interventions requiring robust cost-effectiveness evaluations. Markov models are widely utilized in economic evaluation of obesity interventions, their structure, assumptions, and related uncertainties have not yet been thoroughly evaluated. This study aimed to systematically review the Markov models used for the economic evaluation of anti-obesity interventions, describe their structural characteristics, identify key uncertainties, and provide insights for future research. The review was conducted across three databases (PubMed, Embase, the Cochrane Library) and health technology assessment agency websites to identify published Markov model-based full economic evaluations in adults with obesity from their inception to 2 June 2024. Model structure, model uncertainty, and validation were extracted from the included studies. Philips checklist for the methodology quality of modeling studies was performed. The review included 21 primary publications with 21 unique Markov models. Two modeling approaches regarding the progression of obesity and its impact were identified: direct BMI to cost and utility; and BMI-linked complications, with diabetes and cardiovascular diseases most frequently modeled. Validation practices were inconsistently reported (43% of models), and structural uncertainty (eg, BMI trajectory assumptions) was rarely addressed. Quality assessment revealed moderate rigor (a mean compliance rate of 78% across all criteria), with gaps in transparency and generalizability, particularly for non-Western populations. Probabilistic sensitivity analysis was universal, yet scenario analyses highlighted outcome sensitivity to complication inclusion and time horizons. While Markov models are commonly utilized in obesity intervention evaluations, methodological heterogeneity and insufficient validation limit comparability and reliability. Future models should prioritize standardized validation (eg, ISPOR guidelines), broader complication spectrum, and diverse population data. Enhancing transparency in structural assumptions and uncertainty analysis is critical for robust policy recommendations.

This study focused on the Middle Ages, spanning from 400 to 1500 CE. The period's religious and social discourses often associated blindness with a lack of spiritual enlightenment, a concept that was depicted in various forms of art and literature. The research delves deeply into the multifaceted concept of blindness as portrayed in medieval biblical texts, art, and literature. Through a combination of textual analysis and art historical methods, the study investigates how blindness was depicted in medieval religious manuscripts, stained glass windows, and ivory carvings. This approach allows for a comprehensive understanding of the symbolic meanings attributed to blindness and their impact on medieval thought and spirituality in different social classes. The analysis highlights how the metaphor of blindness was utilized to underscore themes of faith, redemption, and spiritual transformation. For instance, the healing miracles of Jesus Christ, particularly the restoration of sight to the blind, were not only seen as acts of compassion but also as symbols of spiritual enlightenment and divine intervention. These narratives challenged contemporary societal and religious norms, emphasizing the transformative power of encountering Christ. This study helps us understand how the metaphorical portrayal of blindness in medieval times influenced both the perception of visual impairments and the theological discourse surrounding faith and redemption. It demonstrates that these artistic and literary representations served as educational tools and spiritual reflections, reinforcing the Christian doctrine of spiritual awakening and salvation through faith in Christ.

This study aims to identify medical care transformations during the COVID-19 pandemic and to assess the economic efficiency of these care transformations. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviewing and Meta-Analysis (PRISMA) guidelines. The databases used in the search protocol included PubMed, RSCI, and Google Scholar. Results: Ten eligible studies in English and one publication in Russian were identified. In general, the following changes in organization of health care processes since 2020 are observed: hospital at home, telemedicine (physician-to-patient), and the adoption of new information communication technologies within physician-to-physician and physician-to-nurse communication. Earlier trends, such as (a) wider use of electronic devices, (b) adoption of Lean techniques, (c) the incorporation of patient and other customer experience feedback, and (d) the implementation of clinical decision support systems and automation of workflow, tend to be preserved. The most common changes in hospital care organization and the respective impacts of workflow changes (ie, workflow interventions, redesign, and transformations) on the efficiency of hospital care were summarized and avenues for future research and policy implications were discussed. The pandemic demonstrated a need for building more resilient and adaptive healthcare systems, enhancing crisis preparedness along with rapid and effective responses.

High prices of anticancer drugs have raised concerns due to their financial impact on patients and healthcare systems. This study aimed to assess the initial and latest list prices and clinical value of reimbursed anticancer drugs in China, Japan, and South Korea. We identified anticancer drugs newly approved by the National Medical Products Administration of China from January 2012 to June 2022, and by the Pharmaceuticals and Medical Devices Agency of Japan and the Ministry of Food and Drug Safety of South Korea up until June 2022. We compared initial and latest treatment prices between countries and assessed clinical value using patients' survival, quality of life (QoL), and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). We calculated Spearman rank correlation coefficients of treatment prices with clinical value for individual countries and employed regression analyses to investigate whether the relationship between prices and clinical value was modified by the country setting. Our cohort included 91 anticancer drug indications, with 60 listed for reimbursement in China, 91 in Japan, and 87 in South Korea. Median treatment prices were highest in Japan, followed by South Korea, and lowest in China, both for initial prices (US$64082 vs. US$45529 vs. US$19144, p<0.0001) and latest prices (US$50859 vs. US$31611 vs. US$18666, p<0.0001). Over time, China (β=-0.047, p<0.0001) and South Korea (β=-0.049, p<0.0001) witnessed more significant price reductions compared to Japan (β=-0.013, p=0.011). The correlations between both initial and latest treatment prices and clinical value (QoL and ESMO-MCBS) were more significant and stronger in China and South Korea than in Japan, although Japan exhibited slightly stronger correlations in terms of survival compared to China and South Korea. The relationship between clinical value and treatment prices may not be modified by the country setting. In comparison, South Korea's list prices and their correlations with clinical value appear reasonable. Policymakers in Japan could enhance efficiency by controlling prices and aligning them with clinical value, while China would need to take substantial steps to expand anticancer drug coverage. National Natural Science Foundation of China (72374149 and 72074163), and China Center for South Asian Studies, Sichuan University.

ObjectivesThe population of older adults continues to grow in Iran, with pharmaceutical costs as a leading driver of household health-related costs. The present study was conducted to estimate the out-of-pocket pharmaceutical expenditure and its socioeconomic predictors among households with the elderly in Iran.MethodThis study is a secondary analysis using 2019 national household expenditure and income survey data in Iran. The sample size was 9381 households with at least one member older than 65. The double-hurdle model in STATA 16 was used to examine the association between independent variables and households’ out-of-pocket pharmaceutical expenditures.ResultsThe mean out-of-pocket pharmaceutical expenditures for each household with elderly member was $8065 per year. There was a positive association between the (female) gender of the household head, urban residence, employment status, insurance expenditure and a higher level of education of the head of the household with the out-of-pocket pharmaceutical expenditures (P < 0.05). The income of elderly households did not affect these expenditures (P > 0.05).ConclusionsThis study showed that the socioeconomic characteristics of elderly families not only influenced their decision to enter the medicine market, but also the rate of medicine purchase. It is helpful to manage and control the pharmaceutical costs among the elderly.