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The continuously increasing complexity of modern and sustainable power and energy systems leads to a wide range of solutions developed by industry and academia. To manage such complex system-of-systems, proper engineering and validation approaches, methods, concepts, and corresponding tools are necessary. The Smart Grid Architecture Model (SGAM), an approach that has been developed during the last couple of years, provides a very good and structured basis for the design, development, and validation of new solutions and technologies. This review therefore provides a comprehensive overview of the state-of-the-art and related work for the theory, distribution, and use of the aforementioned architectural concept. The article itself provides an overview of the overall method and introduces the theoretical fundamentals behind this approach. Its usage is demonstrated in several European and national research and development projects. Finally, an outlook about future trends, potential adaptations, and extensions is provided as well.

The roll-out of decentralized energy resources introduces a set of new challenges for system operators. In order to address these requirements it is necessary to utilize modern control strategies, and consequently the use of automation and Information and Communication Technologies. Nevertheless, the use of multiple control schemes in a complex grid may result in a conflicting behavior of two or more control devices which ultimately lead to an undesired behavior of the controlled power plants. Nowadays, there are several efforts toward the early identification and mitigation of controller conflicts within complex energy systems. However, a comprehensive methodology to address the dynamics of the system from a broader perspective is not yet fully established. For this reason, this research project presents a method for identifying and testing potential controller conflicts considering the dynamic behavior of the components within the system under study by using multiple models and analysis tools as a part of a toolchain integration approach.

Background In South America, fascioliasis stands out due to the human endemic areas in many countries. In Argentina, human endemic areas have recently been detected. Lymnaeid vectors were studied in two human endemic localities of Catamarca province: Locality A beside Taton and Rio Grande villages; Locality B close to Recreo town. Methods Lymnaeids were characterised by the complete sequences of rDNA ITS-2 and ITS-1 and fragments of the mtDNA 16S and cox 1. Shell morphometry was studied with the aid of a computer image analysis system. Climate analyses were made by nearest neighbour interpolation from FAO data. Koeppen & Budyko climate classifications were used. De Martonne aridity index and Gorczynski continentality index were obtained. Lymnaeid distribution was assessed in environmental studies. Results DNA sequences demonstrated the presence of Lymnaea neotropica and L. viator in Locality A and of L. neotropica in Locality B. Two and four new haplotypes were found in L. neotropica and L. viator , respectively. For interspecific differentiation, ITS-1 and 16S showed the highest and lowest resolution, respectively. For intraspecific analyses, cox 1 was the best marker and ITS-1 the worst. Shell intraspecific variability overlapped in both species, except maximum length which was greater in L. viator . The desertic-arid conditions surrounding Locality A, the semiaridity-aridity surrounding Locality B, and the very low yearly precipitation in both localities, are very different from the typical fascioliasis transmission foci. Lymnaeids are confined to lateral river side floodings and small man-made irrigation systems. Water availability only depends on the rivers flowing from neighbouring mountains. All disease transmission factors are concentrated in small areas where humans and animals go for water supply, vegetable cultures and livestock farming. Conclusions The unusually high number of DNA haplotypes and the extreme climate unsuitable for F. hepatica and lymnaeid development, demonstrate that the transmission foci are isolated. Seasonal transmission may depend on the timely overlap of appropriate temperature and river water availability. Lymnaeids and F. hepatica have probably reached these localities by livestock introduction. DNA differences regarding other populations of L. neotropica and L. viator in Argentina suggest an introduction independent from the spreading movements which allowed these two lymnaeids to expand throughout the country.

Abstract Information on species distribution is recognized as a crucial input for biodiversity conservation and management. To that end, considerable resources have been dedicated towards increasing the quantity and availability of species occurrence data, boosting their use in species distribution modeling and online platforms for their dissemination. Currently, those platforms face the challenge of bringing biology into modeling by making informed decisions that result in meaningful models, based on limited occurrence and ecological data. Here we describe BioModelos

Corn processing generates substantial volumes of agricultural by-products, collectively referred to as corn stover, comprising husks, cobs, stalks, leaves, and silks. Although rich in bioactive compounds, these by-products are still predominantly destined for low-value uses such as landfilling and open-field burning. They contain valuable biomolecules such as lignocellulosic fibers, starch, pectin, proteins, and polyphenols, all of which hold significant potential for applications in agricultural and food industries. These compounds offer opportunities as sustainable alternatives to conventional ingredients and as novel functional additives. However, utilization of corn stover remains focused on biofuel production, limiting the development of applications in broader, high-value fields such as functional food ingredients. This review aims to highlight the opportunities that corn stover presents for developing solutions for food production, which is becoming increasingly important as the global population continues to grow and food demand rises, particularly in regions where access to sufficient and nutritious food remains limited. It also considers the challenges to be solved in order to incorporate corn stover in circular economies, like the impact of pesticide presence on derived products and gaps of emerging strategies for scaling up production in alignment with circular economy goals and the high-value utilization of corn stover.

Species distribution modeling (SDM) is a booming area of research that has had an exponential increase in use and development in recent years. We performed a search of scientific literature and found 5,533 documents published from 1993 to 2018 using SDM, representing a global network of 4,329 collaborating institutions from 155 countries, with Brazil and Mexico being in the top 10 of the most prolific countries globally. National Autonomous University of Mexico, Chinese Academy of Sciences, University of Kansas, and U.S. Geological Survey are the most prolific institutions worldwide. Latin American institutions (n = 556) participated in 1,000 (18% of global productivity) documents published in collaboration with 591 institutions outside Latin American countries, from which the National Autonomous University of Mexico, Federal University of Goiás, Institute of Ecology A.C., National Scientific and Technical Research Council in Argentina, University of São Paulo, and University of Brasilia were the most productive. From this body of literature, the most frequently modeled taxonomic groups were Chordata and Insecta, and the most common realms of application were conservation planning and management, climate change, species conservation, epidemiology, evolutionary biology, and biological invasions. From the 36 modeling methods identified to generate SDMs, MaxEnt is used in 73.5% of the papers, followed by Genetic Algorithm for Rule-Set Prediction (GARP) with 18.7%, and just 7.4% of the papers compared between 3 and 10 modeling methods. In Latin American countries, productivity in SDM research could be improved as the network of collaborations diversifies and connects with other productive countries (such as United Kingdom, China, Spain, Germany, Australia, and France). The scientific collaboration between Latin American countries should be increased, as the most prolific countries (Brazil, Mexico, Argentina, and Colombia) share less than 10% of its productivity. Some of the main challenges for SDM development in Latin America include bridging the gaps from (a) software use to research productivity and (b) translation to decision-making. To address these challenges, we propose to strengthen communities of practice where modelers, species experts, and decision-makers come together to discuss and develop SDM to shift and enhance current paradigms on how science and decision-making are linked.

The present study explores novel alternatives for the exploitation of sugarcane bagasse ash by obtaining and modifying SiO2 nanoparticles through a green synthesis method. The hydrophilic nature of the nanoparticles was modified using oleic acid. The nanoparticles were characterized using FTIR, FESEM, and DLS, and their performance in the stabilization of Pickering emulsions was also studied. FESEM micrographs of the nanoparticles revealed an irregular and agglomerated structure. EDS confirmed that their main components are oxygen and silicon, and ATR-FTIR spectra demonstrated that oleic acid effectively modified the nanoparticles. Subsequently, O/W Pickering emulsions were fabricated by combining rotor–stator homogenization and probe ultra-sonication, using dodecane and liquid paraffin as model oil phases and SiO2 NPs as stabilizers. Static light scattering measurements showed that the emulsions exhibited polydispersity, while photographic monitoring confirmed that their physical stability was affected by the concentrations of oleic acid and nanoparticles: concentrations of up to 20.0 wt% and 1.0 wt%, respectively, produced emulsions that remained stable for 7 to 15 days. This study identifies the behavior and challenges associated with novel pathways for the valorization of sugarcane bagasse ash. The stabilization of Pickering emulsions using the obtained SiO2 NPs highlights their potential in pharmaceutical, cosmetic, and food applications.

CO2 enrichment in protected agriculture has been extensively studied as a strategy to enhance crop productivity, resource use efficiency, and climate resilience. This systematic review examines the scientific literature on CO2 enrichment in greenhouses, vertical farms, and controlled environment agriculture (CEA) systems, with a focus on its impact on crop physiology, photosynthesis, agricultural yield, modeling and simulation techniques, injection technologies, and sustainability challenges. A comprehensive bibliometric and systematic search was conducted in the Scopus database using key terms related to CO2 enrichment and sustainable protected agriculture, following the PRISMA methodology. From an initial set of 212 documents, 171 were selected after removing duplicates, inaccessible articles, and studies not directly relevant to this context. The findings indicate that CO2 enrichment can significantly improve photosynthetic efficiency, water use efficiency, and crop productivity, although its impact varies depending on species, environmental conditions, and application strategies. Computational models, such as CFD and machine learning, have optimized CO2 distribution in controlled environments, contributing to more precise and resource-efficient agricultural practices. However, environmental and economic concerns, particularly energy consumption, carbon footprint, and the sustainability of CO2 sources, remain critical challenges. To ensure the sustainable adoption of CO2 enrichment, it is essential to integrate renewable energy sources, carbon capture and reuse technologies, and advanced CO2 injection systems. This review provides a holistic assessment of current knowledge, identifying opportunities and barriers for the development of climate-smart protected agriculture systems that align with global sustainability goals and contribute to food security and environmental stewardship.

The objective was to analyze the association between alcohol consumption and abdominal adiposity in adults. Cross-sectional study conducted at baseline data from ELSA-Brasil (2008- 2010). The sample consisted of 15,065 civil servants from six education and research institutions (35 to 74 years old, both sexes). To identify central adiposity by measuring waist circumference (WC) and waist-to-hip ratio (WHR), the cutoff points recommended by the World Health Organization were used. Poisson regression models adjusted for potentially confounding variables were tested. About 40% of the sample had elevated WC and WHR. The probability of having elevated WC was 5% and 3% higher in the most exposed group of beer consumption in men and women when compared to the reference group [PR= 1.05 (95% CI 1.02-1.08) and P R= 1.03 (95% CI 1.00-1.07)]. A higher probability of having a high WHR was also found among the highest beer consumers [PR = 1.03 (95% CI 1.00-1.07) in men and PR = 1.10 (95% CI 1.04-1.15) in women]. A greater number of doses/week of alcoholic drink increased the probability of occurrence of high WC and WHR, with the beer contribution being more important.

Estimates of species’ ranges can inform many aspects of biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species’ geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species’ current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species’ habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species’ current ranges, including quantifications of biodiversity and its change over time.