Background: Cancer is a leading cause of death globally. Accurate cancer burden information is crucial for policy planning, but many countries do not have up-to-date cancer surveillance data. To inform global cancer-control efforts, we used the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework to generate and analyse estimates of cancer burden for 47 cancer types or groupings by age, sex, and 204 countries and territories from 1990 to 2023, cancer burden attributable to selected risk factors from 1990 to 2023, and forecasted cancer burden up to 2050. Methods: Cancer estimation in GBD 2023 used data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Cancer mortality was estimated using ensemble models, with incidence informed by mortality estimates and mortality-to-incidence ratios (MIRs). Prevalence estimates were generated from modelled survival estimates, then multiplied by disability weights to estimate years lived with disability (YLDs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the GBD standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. We used the GBD 2023 comparative risk assessment framework to estimate cancer burden attributable to 44 behavioural, environmental and occupational, and metabolic risk factors. To forecast cancer burden from 2024 to 2050, we used the GBD 2023 forecasting framework, which included forecasts of relevant risk factor exposures and used Socio-demographic Index as a covariate for forecasting the proportion of each cancer not affected by these risk factors. Progress towards the UN Sustainable Development Goal (SDG) target 3.4 aim to reduce non-communicable disease mortality by a third between 2015 and 2030 was estimated for cancer. Findings: In 2023, excluding non-melanoma skin cancers, there were 18·5 million (95% uncertainty interval 16·4 to 20·7) incident cases of cancer and 10·4 million (9·65 to 10·9) deaths, contributing to 271 million (255 to 285) DALYs globally. Of these, 57·9% (56·1 to 59·8) of incident cases and 65·8% (64·3 to 67·6) of cancer deaths occurred in low-income to upper-middle-income countries based on World Bank income group classifications. Cancer was the second leading cause of deaths globally in 2023 after cardiovascular diseases. There were 4·33 million (3·85 to 4·78) risk-attributable cancer deaths globally in 2023, comprising 41·7% (37·8 to 45·4) of all cancer deaths. Risk-attributable cancer deaths increased by 72·3% (57·1 to 86·8) from 1990 to 2023, whereas overall global cancer deaths increased by 74·3% (62·2 to 86·2) over the same period. The reference forecasts (the most likely future) estimate that in 2050 there will be 30·5 million (22·9 to 38·9) cases and 18·6 million (15·6 to 21·5) deaths from cancer globally, 60·7% (41·9 to 80·6) and 74·5% (50·1 to 104·2) increases from 2024, respectively. These forecasted increases in deaths are greater in low-income and middle-income countries (90·6% [61·0 to 127·0]) compared with high-income countries (42·8% [28·3 to 58·6]). Most of these increases are likely due to demographic changes, as age-standardised death rates are forecast to change by –5·6% (–12·8 to 4·6) between 2024 and 2050 globally. Between 2015 and 2030, the probability of dying due to cancer between the ages of 30 years and 70 years was forecasted to have a relative decrease of 6·5% (3·2 to 10·3). Interpretation: Cancer is a major contributor to global disease burden, with increasing numbers of cases and deaths forecasted up to 2050 and a disproportionate growth in burden in countries with scarce resources. The decline in age-standardised mortality rates from cancer is encouraging but insufficient to meet the SDG target set for 2030. Effectively and sustainably addressing cancer burden globally will require comprehensive national and international efforts that consider health systems and context in the development and implementation of cancer-control strategies across the continuum of prevention, diagnosis, and treatment. Funding: Gates Foundation, St Jude Children's Research Hospital, and St Baldrick's Foundation.

This study investigates the effect of annealing heat treatment on polymer composites composed entirely of biodegradable components. Poly(lactic acid) (PLA) was used as the matrix material paired with three different types of commercial lignocellulose fibers of varying sizes. Composites containing 10 wt.% fibers were processed through extrusion followed by injection molding. The amorphous (unannealed) and semi-crystalline (annealed) samples were characterized for their morphological, thermal, mechanical, and water absorption properties. Scanning electron microscopic analysis revealed a homogenous distribution of cellulose fibers within the PLA matrix, even though the composite with the smallest fiber size exhibited slight agglomeration. Differential scanning calorimetric measurements indicated that the annealing heat treatment successfully induced crystallization, with the filler particles capable of increasing the extent of crystallinity formed during the annealing heat treatment from 28% to 36%. Based on the tensile tests, as a result of annealing heat treatment, the composites' strength increased from 48–50 to 53–56 MPa, while their Young's modulus increased from 3.1 GPa to 3.3-3.5 GPa. The Charpy impact tests also revealed an enhanced toughness for the samples exposed to the annealing heat treatment. In terms of water absorption, annealing enhanced the hydrophobic nature of PLA. In addition, the semi-crystalline structure formed during the heat treatment also inhibited the highly hydrophilic cellulose fibers from absorbing as much moisture as they did when incorporated inside amorphous PLA; cellulose fibers embedded in the semi-crystalline PLA matrix consequently exhibited less moisture absorption than the ones in amorphous PLA. Highlights: Through annealing, crystallinity was developed in PLA/lignocellulose composites. Lignocellulose fibers facilitated crystallization by acting as a nucleating agent. Crystallized biocomposites exhibited superior mechanical properties Crystalline segments hindered the water absorption of embedded lignocellulose.

The rise of questionable journals poses a significant threat to academic integrity, resulting in substantial waste of institutional and university resources. This commentary analysis focuses on six hijacked medical journals, a specific type of questionable publication. We utilized Semrush, an online Search Engine Optimization auditing platform, to analyse our data, which revealed that hijacked journals disseminate their content through search engines. Specifically, searches for certain medical keywords return hijacked medical journals’ content among the top 20 results. Evidence from both previous research and the current study suggests that hijacked journals leverage various channels for content dissemination, including artificial intelligence chatbots, citation databases, spam emails, and search engines. Raising awareness about this issue is crucial to mitigating the immediate harm caused by these journals. Furthermore, long-term solutions will necessitate advancements in technological development to combat this evolving problem effectively.

The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.

The publication explores the development of the Augmented Medication Risk Score (AUGMERIS) calculator, a web application supported by artificial intelligence, designed to automate the evaluation of medication therapies with the Danish Medication Risk Score (MERIS) method. It is a tool that assesses drug combinations and kidney function in estimated glomerular filtration rate (eGFR), which helps clinical pharmacists identify high-risk patients. To overcome the problem of processing unstructured electronic health records (EHRs), a hybrid text processing model was created by combining rigorous algorithms and Generative Pre-trained Transformer (GPT) technology, which was integrated into a web application along with an automated risk calculation programme. Our objective was to develop and test a globally accessible calculator application with the validation of performance on poor-quality data. Despite the validation limitations, the text processing function serves the application satisfactorily. The AUGMERIS web app is built with Python 3 and shared globally by Streamlit. Volunteer testers from eight different countries performed a total of 383 trial calculations. The application has the potential to improve global pharmacotherapy by identifying patients requiring medication reviews. Its wider adoption might enhance patient safety and optimize treatments in a variety of healthcare systems.

Round-leaved sundew (Drosera rotundifolia L.) is a protected glacial relict plant inhabiting Sphagnum bogs, which are endangered habitats in Hungary. In 2020 and 2021 greyish mycelium growth was observed on the hibernacula of D. rotundifolia in Czech Republic, Germany and Hungary. Samples have been collected in possession of the required permits. The fungus was isolated and identified with classical and molecular methods. Koch's postulates were fulfilled. The novel pathogen was identified as the highly polyphagous Botrytis cinerea in each sample. Simultaneously, field assessments of wild Hungarian populations were carried out. Throughout the survey of three different Hungarian collection sites, altogether 207 hibernacula were carefully examined for gray mold symptoms. Interestingly, only plants grown on milled peat substrate were affected by the pathogen. The antifungal and antimicrobial properties of Sphagnum mosses have been reported by other researchers, which could aid in the protection of D. rotundifolia hibernacula. These results indicate that live Sphagnum moss is a better substrate for this species than milled peat, both for commercial production and for in situ conservation. This information can be vital to the survival and conservation of this species. Sphagnum bogs may protect and allow the expansion and re-establishment of D. rotundifolia.

This study examines the current state of sustainable finance and proposes a strategic roadmap for its advancement in policy and practice, emphasizing the integration of sustainability principles into financial systems to address global environmental and social challenges. Using an integrative literature review of 684 scholarly articles—combining bibliometric analysis with manual review—the research identifies six critical themes and highlights major barriers such as regulatory ambiguity, lack of standardized metrics, and limited data availability. It offers targeted recommendations for policymakers, financial institutions, and stakeholders to overcome these challenges. The study provides a novel methodological contribution by merging bibliometric and qualitative insights, and outlines practical strategies to enhance regulatory frameworks, encourage innovation in sustainable finance, and promote emerging technologies like blockchain and artificial intelligence. Ultimately, it supports the integration of environmental, social, and governance (ESG) considerations into financial practices, fostering a more responsible and inclusive financial ecosystem.

This study aims to investigate the moderating effect of green competencies building (GCB) and green passion (GP) on the relationship between green human resource management (GHRM) and sustainable performance (SP). Moreover, it aims to find out the joint moderating effect of GCB and GP on the relationship between GHRM and SP. An online survey was used to gather 410 samples from various manufacturing organizations in Bangladesh, and the data was analyzed using structural equation modeling (SEM). The study found that GCB and GP separately and jointly moderate the relationship between GHRM and SP. This study uniquely explores how green competencies and green passion, both individually and jointly, moderate the relationship between GHRM and sustainable performance.

Chatbots have become a pivotal technology in business and management. Despite their growing adoption, existing research on chatbots is diverse and fragmented, lacking a cohesive overview of the field’s development and emerging trends. This study aims to answer the core research question: “What are the major research patterns, influential contributors, and emerging themes in chatbot-related literature within business and management between 2018 and 2024?” To address this, a structured four-step bibliometric methodology was applied to systematically collect, screen, and analyze relevant literature. A total of 331 peer-reviewed journal articles published between 2018 and 2024 were retrieved from the Web of Science database. Bibliometric and metadata analyses were conducted using CiteSpace software, including keyword co-occurrence, co-citation, and collaboration network visualizations. The findings show a steady rise in chatbot publications, with the highest output in 2023 (88 articles) and 2024 (85 articles). Prolific authors include Mou Jian, Lova Rajaobelina, and Xueming Luo, while top institutions are the University System of Ohio and the Indian Institute of Management. Core themes include AI, customer service, trust, and consumer experience, with emerging topics such as large language models and service quality. Influential cited works focus on anthropomorphism, technology acceptance, and generative AI. This study provides quantitative insights into the evolution of chatbot research, highlights key contributors and trends, and offers practical implications for improving chatbot design and adoption in business contexts.