Search Results (12,256)

Recently, diffractive optics systems have garnered increasing attention due to their myriad benefits in various applications, such as creating vortex beams, Bessel beams, or optical traps, while refractive optics systems still exhibit some disadvantages related to materials, substrates, and intensity shapes. The manufacturing of diffractive optical elements has become easier due to the development of lithography techniques such as direct laser writing, photo lithography, and electron beam lithography. In this paper, we improve the results from previous research and propose a new methodology to design and fabricate advanced binary diffractive optical elements that achieve a square focal spot independently, reducing reliance on additional components. By integrating a binary square zone plate with an axicon zone plate of the same scale, we employ machine learning for laser path optimization and direct laser lithography for manufacturing. This streamlined approach enhances simplicity, accuracy, efficiency, and cost effectiveness. Our upgraded binary diffractive optical elements are ready for real-world applications, marking a significant improvement in optical capabilities. Full article

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace due to their excellent strength-to-weight ratio and tailorable properties. However, these properties critically depend on the CFRP curing cycle. The commonly adopted manufacturer-recommended curing cycle (MRCC), designed to accommodate the most conservative conditions, involves prolonged curing times and high energy consumption. To overcome these limitations, this study proposes an efficient and adaptable method to determine the optimal curing cycle. The effects of varying heating rates on resin dynamic and isothermal–exothermic behavior were characterized via reaction kinetics analysis using differential scanning calorimetry (DSC) and rheological measurements. The activation energy of the reaction system was substituted into the modified Sun–Gang model, and the parameters were estimated using a particle swarm optimization algorithm. Based on the curing kinetic behavior of the resin, CFRP compression molding process orthogonal experiments were conducted. A weighted scoring system incorporating strength, energy consumption, and cycle time enabled multidimensional evaluation of optimized solutions. Applying this curing cycle optimization method to a commercial epoxy resin increased efficiency by 247.22% and reduced energy consumption by 35.7% while meeting general product performance requirements. These results confirm the method’s reliability and its significance for improving production efficiency. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

Colchicine is a potent alkaloid with well-established anti-inflammatory properties. It shows significant promise in treating classic immune-mediated inflammatory diseases, as well as associated cardiovascular diseases, including atherosclerosis. However, its clinical use is limited by a narrow therapeutic window, dose-limiting systemic toxicity, variable bioavailability, and clinically significant drug–drug interactions, partly mediated by modulation of P-glycoprotein and cytochrome P450 3A4 metabolism. This review explores advanced delivery strategies designed to overcome these limitations. We critically evaluate lipid-based systems, such as solid lipid nanoparticles, liposomes, transferosomes, ethosomes, and cubosomes; polymer-based nanoparticles; microneedles; and implants, including drug-eluting stents. These systems ensure targeted delivery, improve pharmacokinetics, and reduce toxicity. Additionally, we discuss chemical derivatization approaches, such as prodrugs, codrugs, and strategic ring modifications (A-, B-, and C-rings), aimed at optimizing both the efficacy and safety profile of colchicine. Combinatorial nanoformulations that enable the co-delivery of colchicine with synergistic agents, such as glucocorticoids and statins, as well as theranostic platforms that integrate therapeutic and diagnostic functions, are also considered. These innovative delivery systems and derivatives have the potential to transform colchicine therapy by broadening its clinical applications while minimizing adverse effects. Future challenges include scalable manufacturing, long-term safety validation, and the translation of research into clinical practice. Full article

High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article

Highly pathogenic avian influenza (HPAI) remains a persistent threat to global poultry production and public health. Current vaccine platforms show limited cross-clade efficacy and often fail to induce mucosal immunity. Recent advances in microbiome research reveal critical roles for gut commensals in modulating vaccine-induced immunity, including enhancement of mucosal IgA production, CD8⁺ T-cell activation, and modulation of systemic immune responses. Engineered commensal bacteria such as Lactococcus lactis, Bacteroides ovatus, Bacillus subtilis, and Staphylococcus epidermidis have emerged as promising live vectors for antigen delivery. Postbiotic and synbiotic strategies further enhance protective efficacy through targeted modulation of the gut microbiota. Additionally, artificial intelligence (AI)-driven tools enable predictive modeling of host–microbiome interactions, antigen design optimization, and early detection of viral antigenic drift. These integrative technologies offer a new framework for mucosal, broadly protective, and field-deployable vaccines for HPAI control. However, species-specific microbiome variation, ecological safety concerns, and scalable manufacturing remain critical challenges. This review synthesizes emerging evidence on microbiome–immune crosstalk, commensal vector platforms, and AI-enhanced vaccine development, emphasizing the urgent need for One Health integration to mitigate zoonotic adaptation and pandemic emergence. Full article

Additive manufacturing (AM) techniques, especially fused deposition modeling (FDM), offer significant advantages in terms of cost, material efficiency, and design flexibility. In this study, the mechanical performance of biodegradable PLA/PHA composite samples produced via FDM was optimized by evaluating the influence of key printing parameters—layer height, printing orientation, and printing speed—on both the tensile and compressive strength. A full factorial design (3 × 3 × 3) was employed, and all of the samples were triplicated to ensure the consistency of the results. Grey relational analysis (GRA) was used as a multi-objective optimization method to determine the optimal parameter combinations. An analysis of variance (ANOVA) was also conducted to assess the statistical significance of each parameter. The ANOVA results revealed that printing orientation is the most significant parameter for both tensile and compression strength. The optimal parameter combination for maximizing mechanical properties was a layer height of 0.1 mm, an X printing orientation, and a printing speed of 50 mm/s. This study demonstrates the effectiveness of GRA in optimizing the mechanical properties of biodegradable composites and provides practical guidelines to produce environmentally sustainable polymer parts. Full article

Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. Future research directions include developing adaptive threshold learning mechanisms for varying industrial conditions and integration with surface defect detection capabilities to enhance comprehensive quality control in intelligent manufacturing systems. Full article

Directed Energy Deposition is an additive manufacturing subgroup that uses a laser beam to melt the wire or powder to create a melt pool. In the current study, a scientometric analysis has been carried out to analyze the contribution of countries, publication type analysis, distribution of publications over the years, keywords analysis, author analysis, cited journal, categories, institutes of publication, and report the practical implications. Firstly, the database was extracted from the Web of Science and then post-processed with CiteSpace 6.2.R4 and VOSviewer 1.6.20 software. Afterward, the associated results had been extracted and reported. It was found that China is the leader according to publication, followed by the USA and Germany, which mostly published their achievements in article and proceeding paper formats, which are increasing annually. According to the keywords, additive manufacturing, Laser Metal Deposition, and fabrication are the most commonly used. Based on the CiteSapce and VOSviewer results, Lin, Xin and Huang, Weidong are the authors with the highest publication rates. In addition, Additive Manufacturing, Materials & Design, and Materials Science and Engineering: A are the most cited journals, and regarding the categories, materials science, multidisciplinary, applied physics, and manufacturing engineering are the most commonly used DED processes. Northwestern Polytechnical University, Fraunhofer Gesellschaft, and the United States Department of Energy (DOE) have performed the most research in the field of DED. Full article

Although cemented carbides have been manufactured by the powder metallurgy (P/M) technology for over a century now, systematic developmental efforts are still underway. In the present study, tool life improvements in metal grooving applications are the key objective. Four PVD-coated cemented carbides compositions, dedicated to groove steel, stainless steel, cast iron, and aluminium alloys, have been newly designed, along with their manufacturing conditions. Physical, mechanical and chemical characteristics—such as sintered density, modulus of elasticity, hardness, fracture toughness, WC grain size, and the chemical composition of the substrate material, as well as the chemical composition, microhardness, structure, and thickness of the coatings—have been studied. A series of grooving tests have also been conducted to assess whether modifications to the thus far marketed tool materials, tool geometries, and coatings can improve cutting performance. In order to compare the laboratory and application properties of the investigated materials with currently produced by reputable companies, commercial inserts have also been tested. The experimental results obtained indicate that the newly developed grooving inserts exhibit excellent microstructural characteristics, high hardness, fracture toughness, and wear resistance and that they show slightly longer tool life compared to the commercial ones. Full article

The article presents a comprehensive review of key issues and challenges related to enhancing the durability of hot forging tools. It discusses modern strategies aimed at increasing tool life, including modifications to tool materials, heat treatment, surface engineering, tool and die design, die geometry, tribological conditions, and lubrication. The review is based on extensive literature data, including recent publications and the authors’ own research, which has been implemented under industrial conditions at the modern forging facility in Forge Plant “Glinik” (Poland). The study introduces original design and technological solutions, such as an innovative concept for manufacturing forging dies from alloy structural steels with welded impressions, replacing traditional hot-work tool steel dies. It also proposes a zonal hardfacing approach, which involves applying welds with different chemical compositions to specific surface zones of the die impressions, selected according to the dominant wear mechanisms in each zone. General guidelines for selecting hardfacing material compositions are also provided. Additionally, the article presents technological processes for die production and regeneration. The importance and application of computer simulations of forging processes are emphasized, particularly in predicting wear mechanisms and intensity, as well as in optimizing tool and forging geometry. Full article

This study investigates the sustainable production of NdFeB permanent magnets using powder extrusion molding (PEM) with in situ magnetic alignment, utilizing recycled powder from an end-of-life (Eol) wind turbine magnet obtained via hydrogen processing of magnetic scrap (HPMS). Finite Element Method (FEM) simulations were conducted to design and optimize alignment tool geometries and magnetic field parameters. A key challenge in the PEM process is achieving effective particle alignment while the continuous strand moves through the magnetic field during extrusion. To address this, extrusion experiments were performed using three different alignment tool geometries and varying magnetic field strengths to determine the optimal configuration for particle alignment. The experimental results demonstrate a high degree of alignment (B_r/J_s = 0.95), exceeding the values obtained with PEM without an external magnetic field (0.78). The study confirms that optimizing the alignment tool geometry and applying sufficiently strong magnetic fields during extrusion enable the production of anisotropic NdFeB permanent magnets without post-machining, providing a scalable route for permanent magnet recycling and manufacturing. Moreover, PEM with in situ magnetic particle alignment allows for the continuous fabrication of near-net-shape strands with customizable cross-sections, making it a scalable approach for permanent magnet recycling and industrial manufacturing. Full article

Collagen implants in neurosurgery are widely used due to their biocompatibility, biodegradability, and ability to support tissue regeneration, but their mechanical properties, such as low tensile strength and susceptibility to enzymatic degradation, remain challenging. Current technologies are improving these implants through cross-linking, synthetic reinforcements, and advanced manufacturing techniques such as 3D bioprinting to improve durability and predictability. Industry 4.0 is contributing to this by automating production, using data analytics and machine learning to optimize implant properties and ensure quality control. In Industry 5.0, the focus is shifting to personalization, enabling the creation of patient-specific implants through human–machine collaboration and advanced biofabrication. eHealth integrates digital monitoring systems, enabling real-time tracking of implant healing and performance to inform personalized care. Despite progress, challenges such as cost, material property variability, and scalability for mass production remain. The future lies in smart biomaterials, AI-driven design, and precision biofabrication, which could mean the possibility of creating more effective, accessible, and patient-specific collagen implants. The aim of this article is to examine the current state and determine the prospects for the development of mechanical properties of collagen implant used in neurosurgery towards Industry 4.0/5.0, including ML model. Full article

The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. Full article

The rational design of high-performance Cu-SSZ-13 catalysts with enhanced low-temperature activity represents a critical challenge for meeting stringent Euro VII emission standards in diesel aftertreatment systems. Elevating Cu loading can theoretically improve catalytic performance; however, one-time ion exchange using common CuSO₄ solution makes it hard to accomplish high Cu-ion contents. Herein, we demonstrate that the conventional ion-exchange method, adopting Cu(CH₃COO)₂ as precursor in NH₄-SSZ-13 zeolite with a low Si/Al ratio (≈6–7), can achieve higher Cu content while maintaining superior dispersion of active sites. Comprehensive characterizations reveal a dual incorporation mechanism: canonical Cu²⁺ ion exchange and unique adsorption of the [Cu(CH₃COO)]⁺ complex. In the latter case, the surface-adsorbed [Cu(CH₃COO)]⁺ ions form high-dispersion CuO_x species, while the framework-confined ones convert to active Z[Cu²⁺(OH)]⁺ ions. The Cu(CH₃COO)₂-exchanged Cu-SSZ-13 catalyst exhibits superior low-temperature SCR activity and hydrothermal stability to its CuSO₄-exchanged counterpart, making it particularly suitable for close-coupled SCR applications. Our findings provide fundamental insights into Cu speciation control in zeolites and present a scalable, industrially viable approach for manufacturing next-generation SCR catalysts capable of meeting future emission regulations. Full article