Juliana Pazos, Danielle J. Fox, Kanwaljit Bajwa
et al.
The aim of this study is to evaluate the effect of harvest time, alcohol adjustments, and maceration time on Cabernet Sauvignon wines’ sensory profiles and polysaccharide content and composition. For achieving that, grapes were harvested at three different maturities and for each of them, the alcohol was adjusted to three different concentrations (~11%, 14%, and 16.5%). In addition, wine was macerated for either 10 days or 8 months (a maceration length not previously studied). As a result of this design, 54 wines were produced (3 harvests × 3 alcohol contents × 2 maceration treatments, in triplicate). Maceration and alcohol were the most relevant factors for explaining the sensory differences in the wine, considering aromas, taste, and mouthfeel perception. Descriptive sensory analysis of the wines revealed that astringency is better predicted using protein-precipitable tannins (0.98) and tannin galloylation (0.86) than tannin size (−0.61). The polysaccharide content did not show a correlation with mouthfeel sensory descriptors. Pectic polysaccharides were found in a higher concentration in the EM wines with low and medium alcohol. The harvest, maceration, and alcohol did not present clear effects on the rest of the polysaccharide types or overall content.
Beer is a drink that has been a staple in human history, evolving from its beginning in antiquity to the present day. Nowadays, large breweries and other companies have set up laboratories focused on finding and developing new yeast strains for the brewing sector to meet consumers’ demand for new beer styles. Monascus spp. are ascomycota that have been known for hundreds of years. They are widely popular in Asian cuisine, especially in fermented foods. Studies show that Monascus spp. produce numerous food dyes and substances that positively influence human health. In the presented work, Monascus ruber was tested as a potential microorganism for the beer industry. Experiments included fermentation trials with Monascus ruber in four regimes: in aerobic condition, anaerobic condition, anaerobic condition with pH kept above 4.5, and in anaerobic condition with pH set to 4.5. As a reference, commercial Saccharomyces cerevisiae and Saccharomyces pastorianus were used. Fermentation parameters were evaluated by measurements of ethanol and extract level. The final product was tested for its colour in order to evaluate if monascus-derived pigments were present in the beverage. Moreover, a qualitative analysis of lovastatin and citrinin was performed in order to check if those monascus metabolites were present. Finally, small-scale consumer tests were performed in order to check the organoleptic properties of the obtained beverage. Results show that Monascus ruber is able to ferment beer wort in a similar manner as Saccharomyces strains, reaching a slightly lower degree of attenuation. Nevertheless, a longer lag phase was observed in monascus trials, except for the trial with preset pH at 4.5. The most visible change in the product was a reddish colour that appeared in the sample in aerobic conditions. The qualitative analysis showed that lovastatin and citrinin were present in the tested samples. Consumer tests show that experimental beer has a different taste than Saccharomyces-fermented products. Although the presented results are preliminary, they could be a good starting point for further research on monascus-based beverages.
Hanseniaspora uvarum, formerly known as Kloeckera apiculata, is the predominant yeast species in grape musts for most wine fermentations worldwide. Despite its important impact on wine quality, its genetics has only been studied in some detail within the past decade, and methods for targeted manipulations first emerged in 2021. Since then, they have been improved and extended not only with respect to the wide applications of H. uvarum in beverage industries and as an environmental control agent, but also as tools in basic genetic research. In this review, the latest developments and future perspectives are summarized.
Discovering new yeast species can be crucial for creating new types of beers. In this study, we investigated three new yeast species, Saccharomyces bayanus, Schizosaccharomyces japonicus and Schizosaccharomyces pombe var. malidevorans, which have not been previously used in the brewing industry. Colour, total acidity, bitterness, aroma profile, total phenolic, flavonoid, mineral content and organoleptic characteristics of beers fermented by these strains were analysed to discover their applicability in the brewing industry. They did not significantly affect the nutritional value and colour of the beers, but showed increased acidity compared to the control Saccharomyces cerevisiae. GC-MS (Gas Chromatography-Mass Spectrometry) analysis revealed 33 aroma compounds, some of which were identical and some unique. S. cerevisiae and S. bayanus produced a similar number (19–20) of aroma compounds, while S. japonicus produced the fewest, including some undesirable compounds. Isobutyl alcohol, isoamyl alcohol, acetol, dimethylpyrazine, acetic acid, 4-cyclopentene-1,3-dione, butyrolactone, 2-furanmethanol, phenylethyl alcohol, maltol and pyranone that provide desired aromas in beers could be found in every sample. The new yeasts significantly increased polyphenols and decreased flavonoid content. Based on the results above and the taste scores, the strains S. bayanus and S. pombe var. malidevorans may be suitable for brewing, while S. japonicus is less or only suitable for combined fermentation.
Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.
Toni Seibold, Fabian Neumann, Falko Ueckerdt
et al.
Greenhouse gas emissions from the steel, fertiliser and plastic industries can be mitigated by producing their precursors with green hydrogen. In Germany, green production may be economically unviable due to high energy costs. This study quantifies the 'renewables pull' of cheaper production abroad and high-lights trade-offs between cost savings and import dependence. Using a detailed European energy system model coupled to global supply curves for hydrogen and industry precursors (hot briquetted iron, ammonia and methanol), we assess five scenarios with increasing degrees of freedom with respect to imports. We find that precursor import is preferred over hydrogen import because there are significant savings in hydrogen infrastructure. Cost savings in the German industry sector from shifting precursor production to European partners compared to domestic production are at 4.1 bnEUR/a or 11.2 %. This strategy captures 47.7 % of the cost savings achievable by precursor import from non-European countries, which lowers industry costs by 8.6 bnEUR/a (23.3 %). Moving energy-intensive precursor production abroad allows Germany to save costs while still retaining a substantial share of subsequent value-creating industry. However, cost savings must be weighed against the risks of import dependence, which can be mitigated by sourcing exclusively from regional partners.
With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.
Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.
Gauthier Roussilhe, Thibault Pirson, David Bol
et al.
Growing attention is given to the environmental impacts of the digital sector, exacerbated by the increase of digital products and services in our globalized societies. The materiality of the digital sector is often presented through the environmental impacts of mining activities to point out that digitization does not mean dematerialization. Despite its importance, such a narrative is often restricted to a few minerals (e.g., cobalt, lithium) that have become the symbols of extractive industries. In this paper, we further explore the materiality of the digital sector with an approach based on the diversity of elements and their purity requirements in the semiconductor industry. Semiconductors are responsible for manufacturing the key building blocks of the digital sector, i.e., microchips. Given that the need for ultra-high purity materials is very specific to the semiconductor industry, a few companies around the world have been studied, revealing new critical actors in complex supply chains. This highlights strong dependencies towards other industrial sectors with mass production and the need for a deeper investigation of interactions with the chemical industry, complementary to the mining industry.
Okara, a protein-rich byproduct of soymilk production, is highly perishable because of its high moisture content. This study evaluated the preservation and nutritional value of okara fermented by lactic acid bacteria for use in dairy cattle diets. Fermentation effectively reduced pH within 2 weeks and maintained quality for up to 6 weeks. However, aerobic exposure increased the concentration of ammonia, indicating a decline in stability. In vitro assessments revealed no significant differences in in vitro true dry matter digestibility, in vitro neutral detergent fiber digestibility, or gas production between fermented and fresh okara, although fermented okara had a higher concentration of ammonia nitrogen. In situ analysis revealed slightly lower dry matter effective degradability (ED) in fermented okara, but similar rumen-degradable and undegradable protein fractions. When fermented okara was used to replace soybean meal in total mixed rations, 25–50% inclusion-maintained digestibility and fermentation characteristics, with 25% replacement yielding the highest ED at a low ruminal passage rate (0.02 h<sup>−1</sup>). Taken together, these results suggest that fermented okara can be strategically incorporated into dairy rations as a sustainable protein alternative, supporting both rumen function and bypass protein supply.
Iwona Gientka, Alicja Synowiec, Katarzyna Pobiega
et al.
To diversify fermented plant-based products, vegetables can be fermented with fruits. This study aimed to evaluate the effect of spontaneous fermentation on the amino acid nutritional profile of sliced carrot (<i>Daucus carota</i>) and celeriac (<i>Apium graveolens</i>), with and without the addition of apple (<i>Malus domestica</i>). Lactic acid bacteria rapidly lowered the pH, ensuring the product’s microbiological stability and safety. The addition of apples to the fermentation of root vegetables had a beneficial effect on maintaining a high number of active lactic acid bacteria. The fermentation process significantly influenced the amino acid composition, reducing the content of acidic amino acids (aspartic acid and glutamic acid) while increasing the levels of several essential amino acids. Leucine was found to be the limiting amino acid in all fermented variants, although its content increased during fermentation. The Essential Amino Acid Index (EAAI) of the fermented products was generally higher in variants with apple (carrot with apple = 1.13, and celeriac with apple = 1.03). The results indicate that the fermentation of root vegetables with apple enhances protein quality and contributes to the development of nutritionally valuable, microbiologically safe fermented foods. This study supports sustainable dietary practices by utilizing locally available root vegetables to create innovative plant-based fermented products.
Blanca Rosa Aguilar-Uscanga, Esperanza González-Quezada, Josue Raymundo Solis-Pacheco
In Mexico, there is a wide variety of fermented drinks that represent the social and cultural traditions of the country, such as pozol, tejiuno, tepache, and pulque, which are associated with the climate, natural microbiota, and the local production of raw materials for the obtaining fermented drinks. Fermentation processes for the production of ancestral drinks are an ancient technology used by ethnic groups, since pre- and post-Hispanic times, to satisfy their nutritional and medicinal needs. These fermented drinks represent the characteristic flavor, texture, and aroma that each cultural society has imposed on its traditional products in Mexico. Since ancient times, these fermented drinks have been attributed a health impact, due to their beneficial effects in the prevention of certain gastrointestinal diseases, and for this reason, today, the production of pozol, tejuino, tepache, and pulque, on an industrial scale, represents an important challenge for the industry.
The endo-1,4-β-xylanases (EC 3.2.1.8) are the largest group of hydrolytic enzymes that degrade xylan, the major component of hemicelluloses, by catalyzing the hydrolysis of glycosidic bonds β-1,4 in this polymer, releasing xylooligosaccharides of different sizes. Xylanases have considerable potential in producing bread, animal feed, food, beverages, xylitol, and bioethanol. The fungus Aspergillus tamarii Kita produced xylanases in Adams’ media supplemented with barley bagasse (brewer’s spent grains), a by-product from brewery industries. The culture extract exhibited two xylanase activities in the zymogram, identified by mass spectrometry as glycosyl hydrolase (GH) families 10 and 11 (GH 10 and GH 11). The central composite design (CCD) showed excellent predictive capacity for xylanase production (23.083 U mL−1). Additionally, other enzyme activities took place during the submerged fermentation. Moreover, enzymatic saccharification based on a mixture design (MD) of three different lignocellulosic residues was helpful in the production of fermentable sugars by the A. tamarii Kita crude extract.
Naiara Jacinta Clerici, D. Daroit, Aline Aniele Vencato
et al.
Microbial fermentation represents an interesting strategy for the management and valorization of agro-industrial byproducts. In this study, the proteolytic strain Bacillus sp. CL18 was used to produce bioactive hydrolysates during submerged cultivation with various protein-containing substrates, including byproducts from the poultry (feathers), cheese (whey), fish (scales), and vegetable oil (soybean meal) industries. The bioactive feather hydrolysates (BFHs) showing high antioxidant activity were incorporated in poly(vinyl alcohol) (PVA) and poly-ε-caprolactone (PCL) nanofibers by the electrospinning technique. The PVA nanofibers containing 5% BFH reached antioxidant activities of 38.7% and 76.3% for DPPH and ABTS assays, respectively. Otherwise, the PCL nanofibers showed 49.6% and 55.0% scavenging activity for DPPH and ABTS radicals, respectively. Scanning electron microscopy analysis revealed that PVA and PCL nanofibers containing BFH had an average diameter of 282 and 960 nm, respectively. Moreover, the results from thermal analysis and infrared spectroscopy showed that the incorporation of BFH caused no significant modification in the properties of the polymeric matrix. The bioconversion of feathers represents an interesting strategy for the management and valorization of this byproduct. Furthermore, the effective incorporation of BFH in polymeric nanofibers and validation of the biological activity suggest the application of these materials as antioxidant coatings and packaging.
Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems. In an era in which smart devices typically adopted in the industry domain are more and more sophisticated and autonomous, the Internet of Things and its evolution, known as the Internet of Everything (IoE, for short), involving also people, robots, processes and data in the network, represent the main driver to allow industries to put the experiences and needs of human beings at the center of their ecosystems. However, due to the extreme heterogeneity of the involved entities, their intrinsic need and capability to cooperate, and the aim to adapt to a dynamic user-centric context, special attention is required for the integration and processing of the data produced by such an IoE. This is the objective of the present paper, in which we propose a novel semantic model that formalizes the fundamental actors, elements and information of an IoE, along with their relationships. In our design, we focus on state-of-the-art design principles, in particular reuse, and abstraction, to build ``SemIoE'', a lightweight ontology inheriting and extending concepts from well-known and consolidated reference ontologies. The defined semantic layer represents a core data model that can be extended to embrace any modern industrial scenario. It represents the base of an IoE Knowledge Graph, on top of which, as an additional contribution, we analyze and define some essential services for an IoE-based industry.
Employee turnover remains a pressing issue within high-tech sectors such as IT firms and research centers, where organizational success heavily relies on the skills of their workforce. Intense competition and a scarcity of skilled professionals in the industry contribute to a perpetual demand for highly qualified employees, posing challenges for organizations to retain talent. While numerous studies have explored various factors affecting employee turnover in these industries, their focus often remains on overarching trends rather than specific organizational contexts. In particular, within the software industry, where projectspecific risks can significantly impact project success and timely delivery, understanding their influence on job satisfaction and turnover intentions is crucial. This study aims to investigate the influence of project risks in the IT industry on job satisfaction and employee turnover intentions. Furthermore, it examines the role of both external and internal social links in shaping perceptions of job satisfaction.
Benjamin Alt, Florian Stöckl, Silvan Müller
et al.
Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.
The acidity of wine is a crucial factor affecting its quality and stability, serving as an important index for wine evaluation. The paper reviewed the application of acid-lowering yeast pathways and screening methods in wine, kiwi wine, and milone. As a biological acid-lowering method, acid-lowering yeast primarily relies on the lactose-lactate fermentation pathway and the malate-lactic acid fermentation route to reduce acidity. The method of acid reduction varies among the three types of beverages. Additionally, the paper explored the screening pathways for acid-lowering yeast. Beyond traditional screening methods, molecular biology techniques, gene editing techniques and adaptive evolution have further improved the screening efficiency and accuracy. By analyzing the fermentation conditions of different alcoholic beverages, different screening methods can be employed to improve the fermentation effect of acid reduction. Acid-lowering yeast has broad application prospects in the alcohol industry. Combined with gene technology to further study and innovate the yeast acid-lowering way, apply it to more alcohol, and promote the development and progress of the industry.