A novel approach to measure rotational dynamics of valvetrain components in production engines using miniature GMR chip

Biocarrier-driven enhancement of caproate production via microbial chain elongation: Linking metabolic redirection and microbiome assembly.

Bacterial polysaccharides in the food industry: Synthesis-structure-properties relationships, AI-driven innovations, regulatory challenges, and bioeconomy prospects.

Purpose: This study addresses the development of a variant process planning tool, following the Knowledge-Based Variant Process Planning methodology, applied in a case study and presents the gains achieved. Methodology/Approach: Case study supported by six steps: (1) Feature Analysis, (2) Knowledge Retrieval, (3) Inference, (4) Plan Adaptation, (5) Knowledge Update, and (6) Plan Validation/Optimisation.. Findings: The implementation of the Knowledge-Based Variant Process Planning tool led to significant improvements: planner time reduced by 70%, analyst workload by 90%, and process plan errors to 0%. Results show this approach significantly improves process planning in customised production. Research Limitation/Implication: The limitations are associated with the specificity of the case study problem – the electric engine production systems. Originality/Value of paper: This study helps fill the gap in case studies on the Variant Process Planning approach, specifically for electric engine production systems, paving the way for similar companies to adopt Knowledge-Based Variant Process Planning.

ABSTRACT This study presents key perspectives and challenges in relation to artificial intelligence in green production and industrial engineering. Numerous studies have examined the benefits of artificial intelligence for achieving operational efficiency and improved environmental performance; however, few studies have identified future perspectives and challenges. In this study, content, bibliographic and cluster analyses are conducted to extract and cluster attributes related to artificial intelligence in green production and challenges in industrial engineering from the Scopus database, and meta-analysis is used to validate the topics and refine the attributes and clusters. The entropy weighted method and fuzzy Delphi method are utilized to identify key challenges. Fuzzy decision-making trials and laboratory evaluations are performed to analyze perspectives that should be prioritized on the basis of cause-and-effect interrelationships. The results reveal that data management and analysis as well as waste and risk management are key future perspectives that must be addressed.

The key technologies and main challenges for the engineering production of animal cell meat cultured in bioreactors

Microchannel reactors are among the most important tools used for high-performance continuous-flow synthesis. However, most microchannel reactors manufactured with conventional micromachining techniques are limited to two-dimensional (2D) planar geometries, which pose significant challenges for the custom production of three-dimensional (3D) architectures that offer superior microchemical performance. Using unique nonlinear optical effects of ultrafast lasers, hollow microchannel structures with 3D configurations can be flexibly created within transparent glass materials through either direct removal or subsequent chemical etching methods. This review provides an overview of typical fabrication techniques for 3D glass microchannel reactors based on ultrafast laser microfabrication, as well as their state-of-the-art advancements, including large-scale and high-precision manufacture of all-glass microchannels and the facile integration of online monitoring modules. Moreover, the applications of these fabricated microchannel reactors for various continuous-flow microchemical reactions are introduced. Ultrafast laser-enabled 3D glass microchannel reactors hold great potential for developing innovative and industrial-scale continuous-flow manufacturing processes in chemical engineering and pharmaceutical production.

The model predictive control (MPC) method to optimize the balance of target tracking performance of the FC net power, fuel efficiency, and degradation rate was investigated. To evaluate control performance, an integrated FC system simulator ‘FC-DynaMo’, which consists of physical and semi-empirical models of FC system components, such as a FC stack including degradation models, air and H2 supply, thermal management, and electric power systems, and the controllers for them[1]-[5]. The original controllers in FC-DynaMo consist of rule-based controllers with feed-forward and feed-back controllers and are designed to maximize target tracking performance and fuel efficiency[6]. They were customized by replacing the original controllers with MPC which considers the optimization of the balance of target tracking performance, fuel economy, and degradation rates of Pt coarsening and carbon support corrosion. The following 3 cases were investigated: target tracking performance and Pt degradation rate are considered (case 1); target tracking performance and carbon support degradation rate are considered (case 2); target tracking performance and fuel economy are considered (case 3). In case 1, MPC maintained a comparable setpoint tracking performance to conventional control system even though the platinum catalyst degradation rate was reduced by 61%, as shown in Fig. 1. In case 2, MPC maintained a comparable setpoint tracking performance to conventional control system even though the carbon catalyst corrosion rate was reduced by 26%. In case 3, although the setpoint tracking error increased by a factor of 1.6, the hydrogen consumption was reduced by 6.4%. As the computational speed of the developed MPC is not acceptable to implement in a product engine control unit (ECU) due to iterative calculation in the numerical solver to obtain a converged solution, the authors will develop fast computational methods with similar control performance.

Genomic characterisation and gene editing of Marinibacterium sp. CCB-SX1 as a new marine chassis for polyhydroxyalkanoate production.

This article presents the initial application of the Project-Based Learning (PBL) model in an undergraduate Production Engineering course at Fluminense Federal University, at the Petrópolis School of Engineering. Initially, the main models found in the literature for studying goods and services transformation processes are identified (Slack, 2013). Subsequently, through a case study, the guiding concepts and principles of the course are presented, along with the skills and abilities developed in students, the structure of its pedagogical project, and the integration of PBL with the course's curricular structure. This integration occurs through a group of courses that use the PBL methodology and another that adopts the conventional methodology, with the support of thematic laboratories that provide support. The main results highlighted are: the desired profile of attributes that constitute the overall course conception, the proposed curricular organization, and the initial implementation model of the Project-Based Learning approach.

The study raises the problem of changing landscape of the banking sector in view of upgraded ways of interaction between financial intermediaries and their clients and financial product engineering in the digital era. The relevance is determined by the weakening positions of traditional banks and the need to develop a competitive strategy. The research is aimed to identify a list of main directions of the banking sector adaptation to digital realities. With the help of theoretical systematization method the authors reveal that through digitalization of a banking client customer journey that eliminates physical barriers of money transfer between market participants and through the modification of the banking product profile (including product bundling approach) digital technologies make price, product and sales of distribution competition tougher. Rivalry is also fostered by new market entrants whose organizational and legal status may range from a traditional bank’s digital satellite (with a shared banking license but on a separate brand) to a IT company’s fintech project in partnership with a bank. As a consequence, traditional banks face diminishing operations margins and returns on capital. In order to maintain financial soundness banks should follow the best practices of new entrants’ business model organization, including omnichannel approach, maximum monetization of the bank’s infrastructure, banking services technologization, cross-industry partnerships for “package” sale of products.

Abstract The efficient and stable capture of cells within microfluidic platforms is essential for cellular biology analyses, offering insights into the heterogeneity of cell properties and cellular processes, for example, among cancer cells. However, conventional microfluidic cell confinement modalities, such as water‐in‐oil emulsions and microstructure trapping, face inherent limitations in biological applicability and precise control. Here an approach is introduced to confine cells in dead‐end microstructures leveraging a dextran concentration gradient. This method allows for the fine‐tuned capture of cells, reaching the precision of single‐cell culture, as demonstrated for yeast and leukemia cells. By incorporating polyethylene glycol (PEG) solutions, phase separation is induced within the microfluidic environment, encapsulating single cells within dextran droplets. The technique is distinguished by its stability, control, and adaptability, paving a new way for innovations not only in cellular biology, but broadly in chemical and biological applications, including the synthesis of bio‐oriented particles, microcarrier production, and advancements in tissue engineering.

A new genome-scale metabolic model of oleaginous microalgae with refined lipid metabolism clarifies Microchloropsis gaditana mutant phenotypes.

Covering: 2020 to 2025Natural products are a major source of bioactive compounds, yet elucidating their biosynthetic pathways remains a major challenge due to complex genotype-phenotype relationships. Recent advances in computational approaches, particularly artificial intelligence (AI) and mechanistic modeling, are transforming this field. This highlight examines key databases that underpin computational studies, AI-driven methods for predicting biosynthetic pathways and enzyme-substrate interactions, and mechanistic simulations that provide energetic and structural insights. We also discuss current challenges and future opportunities for integrating these strategies to accelerate discovery, engineering, and application of natural products in drug discovery, biotechnology, and synthetic biology.

1-Deoxynojirimycin (DNJ) is an iminosugar with notable pharmacological properties, and mulberry (Morus spp.) represents the richest natural source of this compound, particularly in young leaves and roots. Despite its tissue-specific distribution, the transcriptional regulatory mechanisms underlying its biosynthesis remain poorly understood. In this study, we integrated transcriptomic profiling, molecular assays, and phytohormone analyses to uncover a multilayered transcriptional network controlling DNJ biosynthesis in mulberry. Weighted gene co-expression network analysis (WGCNA) identified a DNJ-associated module (MEblue) that includes four potential transcription factors (TFs): zinc-finger homeodomain-6 (ZFHD6), HOMEODOMAIN GLABROUS11 (HDG11), TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR-14 (TCP14), and ARABIDOPSIS THALIANA HOMEBOX-40 (ATHB40). ZFHD6, TCP14, and ATHB40 directly interacted with and activated the MnGutB1 promoter, a key biosynthetic gene. Although HDG11 did not target MnGutB1 directly, it transcriptionally activated ZFHD6, TCP14, and ATHB40, and formed protein complexes with TCP14 or ATHB40, thereby indirectly promoting DNJ biosynthesis. Furthermore, exogenous application of trans-zeatin riboside (tZR) or abscisic acid (ABA) induced HDG11 and MnGutB1 expression, concomitant with elevated DNJ levels. Our findings identify an HDG11-centered regulatory hierarchy that integrates phytohormonal and developmental signals to regulate DNJ biosynthesis, offering insights for metabolic engineering of DNJ production in plants.

Escherichia coli and Pseudomonas putidaKT2440 as cell factories for free fatty acid production: A comparative review.

Microfluidic technology for cleaner production in microchemical engineering: design, fabrication and application panorama

This study considers components manufactured by additive electron beam manufacturing (EBM) from Ti-6Al-4V (VT6) titanium alloy powder. This material is one of the most widely used in aircraft engine production due to its combination of high weldability, strength, as well as resistance to fatigue loading. The task addressed is to achieve consistently high density, structural uniformity, and stable operational properties in gas turbine engines (GTEs) components manufactured from VT6 alloy by the EBM method. To fabricate specimens, a digital model was constructed in Materialise Magics, while layer-by-layer analysis and optimization of process parameters were carried out in Simufact Additive. Using the EBM process, experimental specimens were produced, including four GTE blades, a turbine wheel, and control samples. The chemical composition confirmed full compliance of the parts with the VT6 (Ti-6Al-4V) alloy standard. The microstructure is characterized by a lamellar α′-phase with a minor fraction of β-phase; the α-phase exhibits an acicular morphology with crystal thickness ranging from 0.5 to 1.5 μm. A uniform distribution of alloying elements, as well as the absence of segregation and porosity, was observed. The average microhardness value HV100 was determined to be 3.71 GPa. The results confirmed that the manufactured parts met the requirements for GTE components, demonstrating high density, strength, and operational reliability. The integration of simulation with subsequent EBM fabrication, optimization of process parameters, and the use of produced VT6 powder enabled the production of parts with zero porosity and stable microstructure. The components also showed controlled texture and high geometric accuracy. This confirms the effectiveness of the proposed approach and highlights its potential for scaling into serial production of critical components with predictable performance characteristics

Collagen is a widely distributed extracellular matrix protein crucial for various biological processes and is extensively used as a biomaterial in medical applications. While animal-derived collagen is commonly used in tissue engineering, it often suffers from batch-to-batch variability and potential immunogenicity. As a promising alternative, prokaryotic sources, specifically bacterial collagen-like proteins (CLPs), offer easier scalability and a lower risk of immune reactions. This Perspective discusses the structural and functional properties of collagen by type and source, emphasizing its biomedical relevance. It also explores the limitations of animal-derived collagen and examines the advantages of bacterial collagen as a sustainable and biocompatible option. Additionally, recent advancements in bacterial collagen production, purification, scalability, and applications in tissue engineering are outlined. In conclusion, bacterial collagen addresses many shortcomings of animal-derived collagen and holds significant promise for innovative tissue engineering biomaterials. However, it still requires further comprehensive validation using in vivo models to ensure robust clinical translation and ensure its readiness for commercial use.

This study systematically develops a comprehensive framework for human-centered prosthetic design, directly responding to the evolving needs of domestic prosthesis users. Integrating theoretical modeling with empirical investigation, the research identifies core design determinants aligned with user expectations. Employing a mixed-method approach, including large-scale surveys and deep interviews, the study explores Chinese prosthetic users’ functional, emotional, and social integration needs. Findings reveal a notable gap between current products and user aspirations, especially in biomechanical adaptability, aesthetic customization, intelligent interaction, and seamless daily integration. A novel ‘cultural context–social integration’ design paradigm is proposed, transcending purely technical models. These interdisciplinary insights support progress in rehabilitation engineering, assistive technology, and inclusive product policy. The system establishes a comprehensive framework for human-centered prosthetic design, directly addressing the evolving needs of domestic prosthetic users. Through integrating theoretical modeling with empirical research, it identifies core design elements that align with user expectations.