Search Results (2,938)

Kynurenic acid (KYNA), a tryptophan metabolite, possesses immunomodulatory properties, although the molecular mechanism of this action has not yet been resolved. In the present study, the effects of KYNA on the secretion of selected cytokines and chemokines by macrophages derived from the human THP-1 cell line are investigated. Furthermore, the involvement of the aryl hydrocarbon receptor (AhR) and the G protein-coupled receptor 35 (GPR35) in mediating the effects of KYNA was examined. In lipopolysaccharide (LPS)-stimulated THP-1-derived macrophages, KYNA significantly reduced IL-6 and CCL-2, but increased IL-10 and M-CSF levels. AhR antagonist CH-223191 reduced the KYNA influence on IL-6, CCL-2, and M-CSF production, while the GPR35 antagonist, ML-145, blocked KYNA-induced IL-10 production. Furthermore, it was shown that THP-1 derived macrophages were capable of synthesizing and releasing KYNA and that its production was increased in the presence of LPS. These findings suggest that THP-1-derived macrophages are a source of KYNA and that KYNA modulates inflammatory responses predominantly through AhR and GPR35 receptors. Our study provides further evidence for the involvement of macrophages in immunomodulatory processes that are dependent on AhR and GPR35 receptors, as well as the potential role of KYNA in these phenomena. Full article

Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing. Full article

Shift work can have an adverse impact on sleep and wellbeing, as well as negative consequences for workplace safety and productivity. SleepShifters is a co-developed sleep management programme that aims to equip shift workers and employers with the skills needed to manage sleep from the onset of employment, thus preventing sleep problems and their associated consequences from arising. This paper describes the co-development process and resulting programme protocol of SleepShifters, designed in line with the Medical Research Council’s framework for the development and evaluation of complex interventions. Programme components were co-produced in partnership with stakeholders from four organisations across the United Kingdom, following an iterative, four-stage process based on focus groups and interviews. As well as a handbook containing guidance on shift scheduling, workplace lighting, and controlled rest periods, SleepShifters consists of five key components: (1) an annual sleep awareness event; (2) a digital sleep training induction module for new starters; (3) a monthly-themed sleep awareness campaign; (4) a website, hosting a digital Cognitive Behavioural Therapy for insomnia platform and supportive video case studies from shift-working peers; (5) a sleep scheduling app for employees. Future work will implement and assess the effectiveness of delivering SleepShifters in organisational settings. Full article

This study evaluates the agronomic potential of two types of vermicompost—one produced solely from wine industry residues (WIR) and one incorporating sewage sludge (WIR + SS)—under rainfed and deficit irrigation conditions in Mediterranean vineyards. The vermicompost was obtained through a two-phase process involving initial thermophilic pre-composting, followed by vermicomposting using Eisenia fetida for 90 days. The conditions were optimized to ensure aerobic decomposition and maintain proper moisture levels (70–85%) and temperature control. This resulted in end products that met the legal standards required for agricultural use. However, population dynamics revealed significantly higher worm reproduction and biomass in the WIR treatment, suggesting superior substrate quality. When applied to grapevines, WIR vermicompost increased soil organic matter, nitrogen availability, and overall fertility. Under rainfed conditions, it improved vegetative growth, yield, and must quality, with increases in yeast assimilable nitrogen (YAN), sugar content, and amino acid levels comparable to those achieved using chemical fertilizers, as opposed to the no-fertilizer trial. Foliar analyses at veraison revealed stronger nutrient uptake, particularly of nitrogen and potassium, which was correlated with improved oenological parameters compared to the no-fertilizer trial. In contrast, WIR + SS compost was less favorable due to lower worm activity and elevated trace elements, despite remaining within legal limits. These results support the use of vermicompost derived solely from wine residues as a sustainable alternative to chemical fertilizers, in line with the goals of the circular economy in viticulture. Full article

Chinese hamster ovary (CHO) cells are the most common protein production platform for glycosylated biopharmaceuticals due to their relatively efficient secretion systems, post-translational modification (PTM) machinery, and quality control mechanisms. However, high productivity and titer demands can overburden these processes. In particular, the endoplasmic reticulum (ER) can become overwhelmed with misfolded proteins, triggering the unfolded protein response (UPR) as evidence of ER stress. The UPR increases the expression of multiple genes/proteins, which are beneficial to protein folding and secretion. However, if the stressed ER cannot return to a state of homeostasis, a prolonged UPR results in apoptosis. Because ER stress poses a substantial bottleneck for secreting protein therapeutics, CHO cells are both selected for and engineered to improve high-quality protein production through optimized UPR and ER stress management. This is vital for optimizing industrial CHO cell fermentation. This review begins with an overview of common ER-stress related markers. Next, the optimal UPR profile of high-producing CHO cells is discussed followed by the context-dependency of a UPR profile for any given recombinant CHO cell line. Recent efforts to control and engineer ER stress-related responses in CHO cell lines through the use of various bioprocess operations and activation/inhibition strategies are elucidated. Finally, this review concludes with a discussion on future directions for engineering the CHO cell UPR. Full article

Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article

The stretch film production is highly energy intensive. The components of the technological line are powered by electrical energy, and the heat is used to change the physical state of the raw material (granules). The raw material is poured into FCR (the first calender roller). To solidify the liquid raw material, the calendar must be cooled. The low-temperature heat, treated as waste heat, has dissipated in the atmosphere. Technological innovations were proposed: (a) the raw material comprises raw material (primary) and up to 80% recyclate (waste originating mainly from agriculture), (b) the use of low-temperature waste heat (the cooling of FCR in the process of foil stretch production). A heat recovery line based on two compressor heat pumps (HP, hydraulically coupled) was designed. The waste heat (by low-temperature HP) was transformed into high-temperature heat (by high-temperature HP) and used to prepare the raw material. The proposed technological line enables the management of difficult-to-manage post-production waste (i.e., agriculture and other economic sectors). It reduces energy consumption and raw materials from non-renewable sources (CO₂ and other greenhouse gas emissions are reducing). It implements a closed-loop economy based on renewable energy sources (according to the European Green Deal). Full article

This study investigates the effectiveness of the Xsens motion capture system in performing ergonomic analysis compared to traditional manual assessments by experts in the specific environment of assembly lines. A comprehensive literature review emphasizes the need to investigate the reliability of new, promising high-tech systems. The main objective was therefore to compare the Ergonomic Assessment Worksheet (EAWS) assessment approach performed with Xsens motion capture technology and Process Simulate V16 software with the manual method using EAWS digital prepared by experts in the controlled workflow. The greatest value of the research conducted lies in the novel integration of the state-of-the-art Xsens motion capture technology with the Process Simulate V16 software environment and the use of the licensed EAWS ergonomic method and Methods-Time Measurement Universal Analyzing System (MTM-UAS). The results are presented in the form of a case study. The results show a large similarity between the whole-body results and a large difference in the upper limb results, confirming the initial benefits of the Xsens equipment but also pointing to the need to verify its reliability on larger samples. The study highlights the potential of integrating Xsens motion capture data into ergonomic assessments and tuning of the assembly line to increase productivity and worker safety. Full article

The article examines the application of collaborative robots (cobots) as a modern engineering tool for the transformation of the food industry following the principles of Industry 5.0. A conceptual engineering model has been developed that integrates collaborative robots with IoT systems, digital twins, and predictive analytics to increase the flexibility, safety, and sustainability of production processes. The proposed model is validated through a practical case study focused on a yogurt packaging line in the dairy sector, where cobot systems demonstrate a significant improvement in operational efficiency and process safety. A step-by-step strategic roadmap is presented to guide industrial enterprises through the various stages of implementation, from the initial assessment to the full-scale integration of solutions. Additionally, a comparative analysis has been performed between traditional automated systems and the integrated approach with collaborative robots, which highlights the technological, economic, and human-oriented advantages of the latter. The results of the study confirm that collaborative robotics offers an effective and applicable path for transforming the food and beverage industry towards a sustainable, adaptive, and human-centered manufacturing ecosystem characteristic of Industry 5.0. Full article

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article

Due to increasing environmental concerns and the constant development of the bottling industry, research into the environmental impact of beverage packaging processes is crucial. The aim of this article is to determine the environmental impact, in selected aspects, of automated beverage bottling and packaging processes using life cycle analysis (LCA). The analysis covers key process stages, such as filling, packaging and internal transport, in the context of raw material consumption, but also energy and waste generation. This work focuses primarily on the impact of changing the raw material used for bottle and shrink film production on the environmental impact of the studied technical facility within the adopted system boundaries and on analyzing scenarios for the management of these post-consumer materials. This research has shown that the stage associated with the greatest negative environmental impact is the shrinking of the film around the bottles. Furthermore, it has been demonstrated that recycling plastic film and bottle waste is a more environmentally friendly solution than landfill disposal. The analysis shows that using recycled materials in the tested production line allows for the reduction of harmful emissions and a reduction in the overall environmental footprint of the tested system. Full article

This paper focuses on the complex dynamics that concern assembly line balance in the context of mass customization within manufacturing. In fact, the increase in demand for customized products has heightened the complexities associated with achieving optimal efficiency, productivity, product quality, and customer satisfaction. The research proposes a multi-criteria analysis of statistical methods to determine the fluctuation of parameters affecting the state of balance of an assembly line. A 3D matrix model is suggested to analyze the parameters managing the assembly line. This representation is executed using the MATLAB R2024b tool, and a methodology for finding the variability of parameters affecting balance through statistical approaches is proposed. We observed that changes in parameters such as task times, worker efficiency, or material flow led to significant changes in the line’s overall balance. As a result, static balancing becomes inadequate to deal with the complexities introduced by these highly variable parameters. The novelty of this paper consists of the innovative integration of multi-criteria statistical analysis and 3D matrix modeling to detect parameter variability and optimize assembly line balancing. Conventional static approaches are often unable to capture the process-dynamic aspect of modern manufacturing. This work presents a systematic methodology capable of identifying, quantifying, and moderating the variability of key operating parameters. This methodology, carried out using MATLAB-based simulations, is based on principal component analysis (PCA) and correlation analysis to detect critical factors influencing balancing efficiency. By structuring assembly line parameters in a 3D matrix representation, this research gives a holistic, data-based method for improving decision-making in balancing procedures. The research goes beyond theoretical modeling by applying the approach to a real automotive assembly line, validating its effectiveness and demonstrating its practical applicability in industrial conditions. Full article

Currently, the Longmaxi shale in the Sichuan Basin is the most successful stratum of shale gas production in China. However, because Longmaxi shale mostly has high over-maturity, a low-maturity sample cannot be obtained for gas generation thermal simulations, and as a result, a gas generation model has not yet been established for it. Therefore, models of other shales are usually used to calculate the amount of gas generated from Longmaxi shale, but they may produce inaccurate results. In this study, a Longmaxi shale sample with an equivalent vitrinite reflectance calculated from Raman spectroscopy (EqVR_o) of 1.26% was obtained from Well Yucan 1 in the Chengkou area, northeast Sichuan Province. This Longmaxi shale may have the lowest maturity in nature. Pyrolysis simulations based on gold tubes were performed on this sample, and the gas generation line was obtained. The amount of gas generated during the low-maturity stage was compensated by referring to gas generation data obtained from Lower Silurian black shale in western Lithuania. Thus, a gas generation model of the Longmaxi shale was built. The model showed that the gas generation process of Longmaxi shale could be divided into three stages: (1) First, there is the quick generation stage (EqVR_o 0.5–3.0%), where hydrocarbon gases were generated quickly and constantly, and the generation rate was steady. A maximum of 458 mL/g TOC was reached at a maturity of 3.0% EqVR_o. (2) Second, there is the stable stage (EqVR_o 3.0–3.25%), where the amount of generated gas reached a plateau of 453–458 mL/g TOC. (3) Third, there is the rapid descent stage (EqVR_o > 3.25%), where the amount of generated gas started to decrease, and it was 393 mL/g TOC at an EqVR_o of 3.34%. This model allows us to more accurately calculate the amount of gas generated from the Longmaxi shale in the Sichuan Basin. Full article

Dietary choices and patterns have enormous consequences along the lines of individual, community, and planetary health. Excess meat consumption has been linked to chronic disease risk, and at large scales, the underlying industries maintain a massive environmental footprint. For these reasons, public and planetary health experts are unified in emphasizing a whole or minimally processed plant-based diet. In response, the purveyors of ultra-processed foods have added “meat alternatives” to their ultra-processed commercial portfolios; multinational corporations have been joined by “start-ups” with new ultra-processed meat analogues. Here, in our Viewpoint, we revisit the 1990s food industry rhetoric and product innovation, a time in which multinational corporations pushed a great “low-fat transition.” We focus on the McLean Deluxe burger, a carrageenan-rich product introduced by the McDonald’s Corporation in 1991. Propelled by a marketing and media-driven fear of dietary fats, the lower-fat burger was presented with great fanfare. We reflect this history off the current “great protein transition,” a period once again rich in rhetoric, with similar displays of industry detachment from concerns about the health consequences of innovation. We scrutinize the safety of carrageenan and argue that the McLean burger should serve as a cautionary tale for planetary health and 21st century food innovation. Full article

The transition toward a circular water economy addresses accelerating water scarcity and pollution. A PRISMA-2020 systematic review of 50 peer-reviewed articles (January 2018–April 2024) mapped current technologies and management strategies, seeking patterns, barriers, and critical bottlenecks. Bibliometric analysis revealed the following three dominant patterns: (i) rapid diffusion of membrane bioreactors, constructed wetlands, and advanced oxidation processes; (ii) research geographically concentrated in Asia and the European Union; (iii) industry’s marked preference for by-product valorization. Key barriers—high energy costs, fragmented regulatory frameworks, and low social acceptance—converge as critical constraints during scale-up. The following three practical action lines emerge: (1) adopt progressive tariffs and targeted tax credits that internalize environmental externalities; (2) harmonize water-reuse regulations with comparable circularity metrics; (3) create multi-actor platforms that co-design projects, boosting local legitimacy. These findings provide policymakers and water-sector practitioners with a clear roadmap for accelerating Sustainable Development Goals 6, 9, and 12 through circular, inclusive, low-carbon water systems. Full article