Metal–organic frameworks (MOFs) have emerged as a transformative class of materials, offering unprecedented versatility in applications ranging from energy storage to environmental remediation and photocatalysis.

The global increase in early-onset cancers among adolescents and young adults has happened at the same time as the rise in the consumption of ultra-processed foods (UPFs). Far beyond their poor nutritional quality, UPFs are increasingly seen as Trojan horses, complex biological agents that interfere with many functions of the human organism. In this review, we utilise the Trojan horse model to explain the quiet and building health risks from UPFs as foods that seem harmless, convenient, and affordable while secretly delivering endocrine-disrupting chemicals (EDCs), causing chronic low-grade inflammation, altering the microbiome, and producing epigenetic alterations. We bring together new proof showing that UPFs mess up hormonal signals, harm the body’s ability to fight off harmful germs, lead to an imbalance of microbes, and cause detrimental changes linked to cancer. Important components, such as bisphenols and phthalates, can migrate from containers into food, while additional ingredients and effects from cooking disrupt the normal balance of cells. These exposures are especially harmful during vulnerable developmental periods and may lay the groundwork for disease many years later. The Trojan horse model illustrates the hidden nature of UPF-related damage, not through a sudden toxin but via chronic dysregulation of metabolic, hormonal, and genetic control. This model changes focus from usual diet worries to a bigger-picture view of UPFs as causes of life-disrupting damage. Ultimately, this review aims to identify gaps in current knowledge and epidemiological approaches and highlight the need for multi-omics, long-term studies and personalised nutrition plans to assess and reduce the cancer risk associated with UPFs. Recognising UPFs as a silent disruptor is crucial in shaping public health policies and cancer prevention programs targeting younger people.

Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to balance economic efficiency and environmental sustainability by minimizing both transportation costs and delivery time. A bi-criteria mathematical model is developed and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which is well-suited for handling complex, large-scale optimization problems under multiple constraints. The aim of the study is to develop and implement this technology that balances economic efficiency, environmental sustainability and manageability of operational processes. The research includes the development of a two-criteria model that takes into account both temporal and economic parameters of the routes. The optimization method employs the NSGA-III, a well-known metaheuristic that generates a diverse set of near-optimal Pareto-efficient solutions. This enables the selection of trade-off alternatives depending on the decision-maker’s preferences and specific operational constraints. Simulation results show that the implementation of the proposed technology can reduce the costs of intermodal operators by an average of 8% and the duration of transportation by up to 50% compared to traditional planning methods. In addition, the automation of the process contributes to a more rational use of resources, reducing carbon emissions and increasing the sustainability of transportation networks. This approach is in line with the principles of sustainable economic development, as it improves the efficiency of logistics operations, reduces pressure on infrastructure and minimizes the environmental impact of the transport sector. Route optimization and digitalization of transport processes can increase resource efficiency, improve freight flow management and contribute to the long-term stability of transport systems. The developed technology of automated planning of intermodal transportation is oriented to application in large-scale production systems, providing effective management of cargo flows within complex logistics chains. The proposed method supports the principles of sustainable development by providing a formal decision-making framework that balances transportation cost, delivery time and environmental objectives. Instead of optimizing for a single goal, the model enables the identification of efficient trade-offs between economic performance and ecological impact. Moreover, by generating multiple routing scenarios under varying operational constraints, the approach enhances the adaptability and robustness of freight transportation systems in dynamic and uncertain environments.

An important target of the UN Sustainable Development Goals (SDGs) is the efficient use of the planet’s resources. In this study, the authors show a strong exponential relationship between the economic complexity index and the efficiency of resource use in a country. The economic complexity index is a characterization of the productive capacity of large economies. This index measures the level of knowledge accumulated by a society that enables production. Assessing the level of a country’s index also makes it possible to predict future trends in the region’s economy. The model of economic sophistication index proposed by the authors includes the service economy, retail trade and manufacturing. Thus, in the paper, the authors identify how the economic complexity index affects the product level by defining the product space for each country and identifying the main products that contribute to a high product complexity index and prospective scalability, indicating the potential to produce better products in the future. Policies focused on increasing economic complexity and investing in staple products appear to be a priority for achieving sustainable development.

The sustainability of large economies is one of the most important challenges in today’s world. As the world strives to create a greener and more efficient future, it becomes necessary to accurately analyze and forecast freight volumes. By developing a reliable freight transportation forecasting model, the authors will be able to gain valuable insights into the trends and patterns that determine the development of economic systems. This will enable informed decisions on resource allocation, infrastructure development, and environmental impact mitigation. Such a model takes into account various factors such as market demand, logistical capabilities, fuel consumption, and emissions. Understanding these dynamics allows us to optimize supply chains, reduce waste, minimize our carbon footprint, and, ultimately, create more sustainable economic systems. The ability to accurately forecast freight volumes not only benefits businesses by enabling better planning and cost optimization but also contributes to the overall sustainable development goals of society. It can identify opportunities to shift to more sustainable modes of transportation, such as rail or water, and reduce dependence on carbon-intensive modes, such as road or air. In conclusion, the development and implementation of a robust freight forecasting model is critical to the sustainability of large-scale economic systems. Thus, by utilizing data and making informed decisions based on these forecasts, it is possible to work toward a more sustainable future for future generations.