Search Results (454)

The growing complexity of production systems in the technology sector demands advanced tools to ensure efficiency, flexibility, and cost-effectiveness. This study presents the development of a simulation model for a selective soldering line at a technology manufacturing company in Poland, created during an engineering internship. Using FlexSim 24.2 software, the real production process was replicated, including input/output queues, manual insertion (MI) stations, soldering machines, and quality control points. Special emphasis was placed on implementing dynamic process logic via ProcessFlow, enabling detailed modeling of token flow and system behavior. Through experimentation, various configurations were tested to optimize process time and the number of soldering pallets in circulation. The results revealed that reducing pallets from 12 to 8 maintains process continuity while offering cost savings without impacting performance. An intuitive operator panel was also developed, allowing users to adjust parameters and monitor outcomes in real time. The project demonstrates that simulation not only supports operational decision-making and resource planning but also enhances interdisciplinary communication by visually conveying complex workflows. Ultimately, the study confirms that simulation modeling is a powerful and adaptable approach to production optimization, contributing to long-term strategic improvements and innovation in technologically advanced manufacturing environments. Full article

Avian influenza A viruses (IAVs) pose a persistent threat to the poultry industry, causing substantial economic losses. Although traditional vaccines have helped reduce the disease burden, they typically rely on multivalent antigens, emphasize humoral immunity, and require intensive production. This study aimed to establish a genetically matched host–cell system to evaluate antigen-specific immune responses and identify conserved CD8+ T cell epitopes in avian influenza viruses. To this end, we developed an MHC class I genotype (B21)-matched host (Lohmann VALO SPF chicken) and cell vector (DF-1 cell line) model. DF-1 cells were engineered to express the hemagglutinin (HA) gene of clade 2.3.4.4b H5N1 either transiently or stably, and to stably express the matrix 1 (M1) and nucleoprotein (NP) genes of A/chicken/South Korea/SL20/2020 (H9N2, Y280-lineage). Following prime-boost immunization with HA-expressing DF-1 cells, only live cells induced strong hemagglutination inhibition (HI) and virus-neutralizing (VN) antibody titers in haplotype-matched chickens. Importantly, immunization with DF-1 cells transiently expressing NP induced stronger IFN-γ production than those expressing M1, demonstrating the platform’s potential for differentiating antigen-specific cellular responses. CD8+ T cell epitope mapping by mass spectrometry identified one distinct MHC class I-bound peptide from each of the HA-, M1-, and NP-expressing DF-1 cell lines. Notably, the identified HA epitope was conserved in 97.6% of H5-subtype IAVs, and the NP epitope in 98.5% of pan-subtype IAVs. These findings highlight the platform’s utility for antigen dissection and rational vaccine design. While limited by MHC compatibility, this approach enables identification of naturally presented epitopes and provides insight into conserved, functionally constrained viral targets. Full article

Biocatalysis is one of the oldest fields that has been used in industrial applications, with one of the earliest purposeful examples being the mass production of acetic acid from an immobilized Acinetobacter strain in the year 1815. Efficiency, specificity, reduced reaction times, lower overall costs, and environmental friendliness are some advantages biocatalysis has over conventional chemical synthesis, which has made biocatalysis increasingly used in industry. We highlight three necessary fields that are fundamental to advancing industrial biocatalysis, including biocatalyst engineering, solvent engineering, and mechanistic engineering. However, the fundamental mechanism of enzyme function is often overlooked or given less attention, which can limit the engineering process. In this review, we describe how mechanistic enzymology benefits industrial biocatalysis by elucidating key fundamental principles, including the k_cat and k_cat/K_m parameters. Mechanistic enzymology presents a unique field that provides in-depth insights into the molecular mechanisms of enzyme activity and includes areas such as reaction kinetics, catalytic mechanisms, structural analysis, substrate specificity, and protein dynamics. In line with the objective of protein engineering to optimize enzyme activity, we summarize a range of strategies reported in the literature aimed at improving the product release rate, the chemical step of catalysis, and the overall catalytic efficiency of enzymes. Further into this review, we delineate kinetic solvent viscosity effects (KSVEs) as a very efficient, cost-effective, and easy-to-perform method to probe different aspects of enzyme reaction mechanisms, including diffusion-dependent kinetic steps and rate-limiting steps. KSVEs are cost-effective because simple kinetic enzyme assays, such as the Michaelis–Menten kinetic approach, can be combined with them without the need for specialized and costly equipment. Other techniques in protein engineering and genetic engineering are also covered in this review. Additionally, we provide information on solvent systems in enzymatic reactions, details on immobilized biocatalysts, and common misconceptions that misguide enzyme design and optimization processes. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

The development of ceria (CeO_2−x)-based nanoantioxidants requires fine-tuning of structural and surface properties for enhancing antioxidant behavior in biological environments. In this contest, here ultrasmall water-dispersible CeO_2−x nanoparticles (NPs), characterized by a high Ce³⁺/Ce⁴⁺ ratio, were synthesized in a non-polar solvent and phase-transfer to an aqueous environment through ligand-exchange reactions using citric acid (CeO_2−x@Cit) and post-treatment with dopamine hydrochloride (CeO_2−x@Dopa). The concept behind this work is to enhance via surface engineering the intrinsic antioxidant properties of CeO_2−x NPs. For this purpose, thanks to electron transfer reactions between dopamine and CeO_2−x, the CeO_2−x@Dopa was obtained, characterized by increased surface Ce³⁺ sites and surface functionalized with polydopamine bearing o-quinone structures as demonstrated by complementary spectroscopic (UV–vis, FT-IR, and XPS) characterizations. To test the antioxidant properties of CeO_2−x NPs, the scavenging activity before and after dopamine treatment against artificial radical 1,1-diphenyl-2-picrylhydrazyl (DPPH^·) and the ability to reduce the reactive oxygen species in Diencephalic Immortalized Type Neural Cell line 1 were evaluated. CeO_2−x@Dopa demonstrated less efficiency in DPPH^· scavenging (%radical scavenging activity 13% versus 42% for CeO_2−x@Cit before dopamine treatment at 33 μM DPPH^· and 0.13 mg/mL loading of NPs), while it markedly reduced intracellular ROS levels (ROS production 35% compared to 66% of CeO_2−x@Cit before dopamine treatment with respect to control—p < 0.001 and p < 0.01, respectively). While steric hindrance from the dopamine-derived polymer layer limited direct electron transfer from CeO_2−x NP surface to DPPH^·, within cells the presence of o-quinone groups contributed with CeO_2−x NPs to break the autoxidation chain of organic substrates, enhancing the antioxidant activity. The functionalization of NPs with o-quinone structures represents a valuable approach to increase the inherent antioxidant properties of CeO_2−x NPs, enhancing their effectiveness in biological systems by promoting additional redox pathways. 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

This paper presents a declarative framework for resilient machining line planning, integrating line balancing and disruption handling within a unified, interactive decision-support environment. Building upon earlier constraint-based models, the proposed approach incorporates sustainability-oriented improvements through Pareto-based multi-criteria optimization. The model supports trade-off analysis across cost, energy consumption, tool wear, and schedule continuity, enabling predictive planning and adaptive dispatching under operational uncertainty. By combining proactive balancing with reactive disruption handling in a single declarative formulation, the framework addresses a key gap in the current production engineering methodologies. A case study employing real data and real-world-inspired disruption scenarios demonstrates the effectiveness of the approach. Compared to traditional sequential strategies, the framework yields superior performance in terms of solution diversity, responsiveness, and sustainability alignment, confirming its value for next-generation, resilient manufacturing systems. 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

Trichomes—microscopic appendages on the plant epidermis—play vital roles as both protective barriers and specialized biosynthetic factories. Acting as the first line of defense against environmental stressors, they also produce a wide range of pharmaceutically valuable secondary metabolites. This mini-review highlights recent advances in understanding the development, structure, and function of trichomes, with a focus on glandular secretory trichomes (GSTs) in key species such as Artemisia annua and Solanum lycopersicum. We explore how insights from these systems are driving innovation in plant synthetic biology, including modular genetic engineering and metabolic channeling strategies. These breakthroughs are paving the way for scalable, plant-based platforms to produce high-value compounds. By integrating molecular mechanisms with emerging technologies, this review outlines a forward-looking framework for leveraging trichomes in sustainable agriculture, natural product discovery, and next-generation biomanufacturing. Full article

There has been a great need for replacing combustion-powered vehicles with electric vehicles (EV), and fully electric cars are meant to replace combustion engine cars. This has led to considerable research into improving the performance of EVs, especially via electric motor voltage control. A wide range of optimization algorithms have been used as traditional approaches, but the dynamic parameters of electric motors, impacted by temperature and different driving cycles, continue to be a problem. This study introduces an improved version of the Parrot Optimizer (IPO) aimed at enhancing voltage regulation in EVs. The algorithm can intelligently adjust certain motor parameters for adaptive management to maintain performance based on different situations. To ensure a stable and sustainable power supply for the powertrain of the EV, a photovoltaic (PV) system is used with energy storage batteries. Such an arrangement seeks to deliver permanent electric energy, a solution to traditional grid electricity reliance. This demonstrates the effectiveness of IPO, with the resultant motor performance remaining optimal despite parameter changes. It is also illustrated that energy production, by integrating PV systems, prevents excessive voltage line drops and thus voltage imbalances. The proposed intelligent controller is verified based on multiple simulations, demonstrating and ensuring significant improvements in EV efficiency and reliability. Full article

Background: Modern viral vector production needs to consider process intensification for higher yields from smaller production volumes. However, innate antiviral immunity triggered in the producer cell may limit virus replication. While commonly used cell lines (e.g., Vero or E1A-immortalised cells) are already compromised in antiviral pathways, the redundancy of innate signaling complicates host cell optimization by genetic engineering. Small molecules that are hypothesized to target antiviral pathways (Viral Sensitizers, VSEs) added to the culture media offer a versatile alternative to genetic modifications to increase permissiveness and, thus, viral yields across multiple cell lines. Methods: To explore how the yield for a chimeric Zika vaccine candidate (YF-ZIK) could be further be increased in an intensified bioprocess, we used spin tubes or an Ambr15 high-throughput microbioreactor system as scale-down models to optimize the dosing for eight VSEs in three host cell lines (AGE1.CR.pIX, BHK-21, and HEK293-F) based on their tolerability. Results: Addition of VSEs to an already optimized infection process significantly increased infectious titers by up to sevenfold for all three cell lines tested. The development of multi-component VSE formulations using a design of experiments approach allowed further synergistic titer increases in AGE1.CR.pIX cells. Scale-up to 1 L stirred-tank bioreactors and 3D-printed mimics of 200 or 2000 L reactors resulted in up to threefold and eightfold increases, respectively. Conclusions: Addition of single VSEs or combinations thereof allowed a further increase in YF-ZIK titers beyond the yield of an already optimized, highly intensified process. The described approach validates the use of VSEs and can be instructive for optimizing other virus production processes. Full article

This paper introduces arguments in favor of the intensive use of standards in both teaching the Machine Elements discipline and solving the first projects of mechanical design (gearboxes, jacks, pumps, tanks, etc.). The paper presents a SWOTT approach to the use of new in-force standards in teaching the design of machine elements. The use of information from standards in courses and design handbooks is regulated by various standardization associations at different levels internationally, such as the ISO (International Organization of Standardization), IEC (International Electrotechnical Commission), and ITU (International Telecommunication), and regional associations such as the CEN (European Commission for Standardization), CENELEC (European Committee for Electrotechnical Standardization) and ETSI (European Telecommunications Standards Institute), and national associations (for instance, the ASRO—Association of Standardization of Romania). In general, the conditions for using partial information from standards vary, but the authors present common lines and recommendations for introducing information from standards in books and design handbooks for engineering students. The use of information from standards for terms, materials, calculation models, test methods etc. is beneficial for students. This will provide them a good professional education towards adapting to a specific job in the field of mechanical engineering, where conformity to norms and standards is required by the dynamics of production, product quality and, not least, the safety of machines and operators. Full article

Background/Objectives: Although the nanocomposite of poly(L-lactic acid) with graphene oxide (PLLA-GO) shows promise for tissue engineering, its specific bioactive interactions with diverse cell lineages during early tissue regeneration remain unclear. This study comprehensively investigated the in vitro multifaceted biocompatibility of PLLA-GO using human fibroblasts (FN1 cells), murine mesenchymal stem cells (mBMSCs), and human umbilical vein endothelial cells (HUVECs). Methods: Morphological analyses were performed using optical and scanning electron microscopy, while proliferation dynamics were assessed via CFSE staining. Cell cycle progression was evaluated using flow cytometry, mitochondrial activity was examined through TMRE staining, and inflammatory cytokine profiling was performed via Cytometric Bead Array (CBA). Results: PLLA-GO exhibited primary biocompatibility across all evaluated cell lines, characterized by efficient adhesion and proliferation. However, significant cell-type-dependent modulations were observed. The FN1 cells exhibited proliferative adaptation but induced accelerated scaffold degradation, as evidenced by a substantial increase in cellular debris (5.93% control vs. 34.38% PLLA-GO; p = 0.03). mBMSCs showed a transient initial proliferative response and a significant 21.66% increase in TNF-α production (179.67 pg/mL vs. 147.68 pg/mL in control; p = 0.03). HUVECs demonstrated heightened mitochondrial sensitivity, exhibiting a 32.19% reduction in mitochondrial electrical potential (97.07% control vs. 65.82% PLLA-GO; p ≤ 0.05), alongside reductions in pro-inflammatory cytokines TNF-α (8.73%) and IL-6 (12.47%). Conclusions: The PLLA-GO processing method is crucial for its properties and subsequent cellular interactions. Therefore, rigorous and specific preclinical evaluations—considering both cellular contexts and fabrication—are indispensable to ensure the safety and therapeutic potential of PLLA-GO in tissue engineering and regenerative medicine. Full article

Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets—such as datasets, feature pipelines, and hyperparameters—and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entire lifecycle of ML development, including datasets, pipelines, models, and configuration assets. The proposed framework introduces the key requirements for ML PLE and the lifecycle process tailored to machine-learning-intensive systems. We illustrate the approach using an industrial case study in the context of space systems, where ML PLE is applied for data analytics of satellite missions. Full article

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications. Full article