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The integration of modern service and advanced manufacturing industries represents deep industrial reform and profound change in quality concepts, mechanisms, and practices. Integrating these two industries is important for achieving high-quality development and is an area of interest for policymakers and academia. This study analyzes the data of 11 regions in 2021 in the Guangdong-Hong Kong-Macao Greater Bay Area using the fuzzy set qualitative comparative analysis method from the configuration perspective. This study explores the effects of multiple factors, such as economic efficiency, technological innovation, and the policy environment, on improving the coupling coordination level of the modern service and advanced manufacturing industries, and analyzes the path selection of industrial integration development. Overall, economic efficiency, technological innovation, and policy environment factors are not necessary conditions for the integration development of the modern service and advanced manufacturing industries. The multiple impacts of economic efficiency, technological innovation, and policy environment form a multi-restructuring path that drives the integration of the two industries. Owing to the economic development level and resource endowment of different regions, the path dictating the degree of integration between the two industries varies by region. From a holistic perspective, all regions should strive to coordinate the multiple conditions of economic efficiency, technological innovation, and policy environment to break through objective constraints such as the government's conditions and external environment based on their own organizational conditions and resource endowments. Additionally, all regions should strengthen exchanges, cooperation, and integration mechanisms and implement complementary development and distinctive development in core cities in the Greater Bay Area. The results clarify the factors affecting industrial convergence in the Greater Bay Area, enriching existing research methods and laying a foundation for a decision-making theory of industrial integration development.

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In response to the escalating demand for flexible devices in applications such as wearables, sensors, and touch panels, there is a need for innovative fabrication approaches for devices made from nanomaterial-based inks. Subsequent to ink deposition, a pivotal stage in device manufacturing typically involves high-temperature sintering, posing challenges for heat-sensitive substrates. Nonthermal plasma jet sintering utilizing an atmospheric pressure dielectric barrier discharge (DBD) plasma jet enables sintering at room temperature and standard pressure, facilitating the sintering of printed nanoparticle films without compromising substrate or film surface integrity. However, determining optimal plasma jet sintering conditions can be challenging due to multiple processing variables with intricate interrelationships. This work employed Bayesian optimization (BO) and machine learning (ML) to identify optimal values for seven primary plasma jet sintering variables. Optimization yielded a 99.2% increase in the measured electrical conductivity for plasma jet-sintered indium tin oxide (ITO) films after five rounds of experiments. Moreover, the optimal sintering conditions achieved an electrical conductivity that was 81.4% of conventional furnace sintering at 300 °C, but was three times faster and with a peak substrate temperature below 47 °C. This result demonstrates the prospect of applying BO to optimize processing techniques for emerging low-temperature requirements.

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The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into "Beyond CMOS" and "More than Moore" paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. The outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.

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3D-printing technology has revolutionized spinal implant manufacturing, particularly in developing personalized and custom-fit titanium interbody fusion cages. These cages are pivotal in supporting inter-vertebral stability, promoting bone growth, and restoring spinal alignment. This article reviews the latest advancements in 3D-printed titanium interbody fusion cages, emphasizing their relevance in modern personalized surgical spine care protocols applied to common clinical scenarios. Furthermore, the authors review the various printing and post-printing processing technologies and discuss how engineering and design are deployed to tailor each type of implant to its patient-specific clinical application, highlighting how anatomical and biomechanical considerations impact their development and manufacturing processes to achieve optimum osteoinductive and osteoconductive properties. The article further examines the benefits of 3D printing, such as customizable geometry and porosity, that enhance osteointegration and mechanical compatibility, offering a leap forward in patient-specific solutions. The comparative analysis provided by the authors underscores the unique challenges and solutions in designing cervical, and lumbar spine implants, including load-bearing requirements and bioactivity with surrounding bony tissue to promote cell attachment. Additionally, the authors discuss the clinical outcomes associated with these implants, including the implications of improvements in surgical precision on patient outcomes. Lastly, they address strategies to overcome implementation challenges in healthcare facilities, which often resist new technology acquisitions due to perceived cost overruns and preconceived notions that hinder potential savings by providing customized surgical implants with the potential for lower complication and revision rates. This comprehensive review aims to provide insights into how modern 3D-printed titanium interbody fusion cages are made, explain quality standards, and how they may impact personalized surgical spine care.

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Currently, the lengthy time needed to bring new drugs to market or to implement postapproval changes causes multiple problems, such as delaying patients access to new lifesaving or life-enhancing medications and slowing the response to emergencies that require new treatments. However, new technologies are available that can help solve these problems. The January 2023 NIPTE pathfinding workshop on accelerating drug product development and approval included a session in which participants considered the current state of product formulation and process development, barriers to acceleration of the development timeline, and opportunities for overcoming these barriers using new technologies. The authors participated in this workshop, and in this article have shared their perspective of some of the ways forward, including advanced manufacturing techniques and adaptive development. In addition, there is a need for paradigm shifts in regulatory processes, increased pre-competitive collaboration, and a shared strategy among regulators, industry, and academia.

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The growing expansion of the manufacturing sector, particularly in Mexico, has revealed a spectrum of nearshoring opportunities yet is paralleled by a discernible void in educational tools for various stakeholders, such as engineers, students, and decision-makers. This paper introduces a state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems. Through a case study focused on the design, production, and evaluation of a robotic platform, the framework endeavors to offer an exhaustive educational experience via an interactive VR environment, encapsulating (1) Robotic platform system design and modeling, enabling users to immerse themselves in the design and simulation of robotic platforms under varied conditions; (2) Virtual manufacturing company, presenting a detailed virtual manufacturing setup to enhance users' comprehension of manufacturing processes and systems, and problem-solving in realistic settings; and (3) Product evaluation, wherein users employ VR to meticulously assess the robotic platform, ensuring optimal functionality and customer satisfaction. This innovative framework melds theoretical acumen with practical application in advanced manufacturing, preparing entities to navigate Mexico's manufacturing sector's vibrant and competitive nearshoring landscape. It creates an immersive environment for understanding modern manufacturing challenges, fostering Mexico's manufacturing sector growth, and maximizing nearshoring opportunities for stakeholders.

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Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

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Microfluidics, with its remarkable capacity to manipulate fluids and droplets at the microscale, has emerged as a powerful platform in numerous fields. In contrast to conventional closed microchannel microfluidic systems, free-boundary microfluidic manufacturing (FBMM) processes continuous precursor fluids into jets or droplets in a relatively spacious environment. FBMM is highly regarded for its superior flexibility, stability, economy, usability, and versatility in the manufacturing of advanced materials and architectures. In this review, a comprehensive overview of recent advancements in FBMM is provided, encompassing technical principles, advanced material manufacturing, and their applications. FBMM is categorized based on the foundational mechanisms, primarily comprising hydrodynamics, interface effects, acoustics, and electrohydrodynamic. The processes and mechanisms of fluid manipulation are thoroughly discussed. Additionally, the manufacturing of advanced materials in various dimensions ranging from zero-dimensional to three-dimensional, as well as their diverse applications in material science, biomedical engineering, and engineering are presented. Finally, current progress is summarized and future challenges are prospected. Overall, this review highlights the significant potential of FBMM as a powerful tool for advanced materials manufacturing and its wide-ranging applications.

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Chronic kidney disease (CKD) is characterised by gradual loss of renal function and cardiovascular disease (CVD) as its principal consequence. CVD is a substantial source of morbidity and death in the CKD population and a growing global concern. Because there are no reliable early biomarkers to follow the progression of CKD and predict the risk of complications, research into such molecules continues. Many studies have demonstrated that miRNAs are potentially important variables in CKD, are very stable in blood, and may be employed as diagnostic and prognostic markers for various disorders. Vascular calcification (VC) is a cell-mediated process that necessitates genetic defects in the combined cardiovascular issues of CKD and may be modulated in part by miRNAs. Numerous miRNAs have been linked to the progression of vascular calcification. Many miRNAs have been discovered as being important in ventricular hypertrophy, including miRNA-30, miRNA-212, and miRNA-133. Endothelium miR-126, miR-92a-3p, and others are important regulators of angiogenesis, endothelium repair, and homeostasis. Several interesting non-invasive miRNA biomarkers in CKD/CVD have been found, with the potential to enhance diagnostic accuracy, predict prognosis, track disease progression, and serve as novel therapy targets. However, large-scale clinical studies are still needed to determine the therapeutic utility of miRNA.

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Multimaterial aerosol jet printing offers a unique capability to freely mix inks with different chemical compositions in the aerosol phase, enabling one-step digital fabrication with tailored compositions or functionally graded structures, including in the x-y plane. Here, in situ mixing of two carbon nanomaterial inks with distinct electrical properties is demonstrated. By tailoring the mixing ratio of the constituent inks, electrical conductivity is modulated by 130×, and sheet resistance values for a single pass span approximately 2 orders of magnitude. The ability to manufacture components with tailored electrical properties offers significant value for hybrid and flexible electronic device applications, such as microelectronics packaging. Moreover, grading properties within a part provides a new dimension of design freedom for complex assemblies.

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Aligned and suspended carbon nanotubes can outperform randomly oriented networks in electronic biosensing and thin-film electronics. However, carbon nanotubes tend to bundle and form random networks. Here, we show that carbon nanotubes spontaneously align in an ammonium deoxycholate surfactant gel even under low shear forces, allowing direct writing and printing of nanotubes into electrically conducting wires and aligned thin layers across trenches. To demonstrate its application potential, we directly printed arrays of disposable electrical biosensors, which show femtomolar sensitivity in the detection of DNA and SARS-CoV-2 RNA.

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Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

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Automation of visual quality inspection tasks in manufacturing with machine vision is beginning to be the de facto standard for quality inspection as manufacturers realize that machines produce more reliable, consistent and repeatable analyses much quicker than a human operator ever could. These methods generally rely on the installation of cameras to inspect and capture images of parts; however, there is yet to be a method proposed for the deployment of cameras which can rigorously quantify and certify the performance of the system when inspecting a given part. Furthermore, current methods in the field yield unrealizable exact solutions, making them impractical or impossible to actually install in a factory setting. This work proposes a set-based method of synthesizing continuous pose intervals for the deployment of cameras that certifiably satisfy constraint-based performance criteria within the continuous interval.

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The aim of this study was to probe an unexpected relationship between the ice nucleation temperature (TIN), process efficiency and product attributes in a controlled ice nucleation (CIN) lyophilization process. An amorphous product was lyophilized with (CIN-5 °C, CIN-7 °C or CIN-10 °C) or without (NOCIN) control of ice nucleation. Process parameters and product attributes were monitored and compared using a series of advanced in-line and off-line process analytical technology (PAT) tools. Unexpectedly, an indirect relationship was observed between TIN and primary drying efficiency for the CIN processes. Further, the CIN-5 °C process was associated with higher product resistance to mass flow than corresponding CIN-7 °C and CIN-10 °C processes. Surprisingly, the air voids in some NOCIN products were larger than CIN-5 °C products but comparable to CIN-7 °C. Heat flux analysis revealed an indirect relationship between TIN and the minimum hold time required to complete solidification. The heat flux analysis also revealed all products underwent complete solidification prior to primary drying. The order of homogeneity in water activity of the products was CIN-5 °C ≥NOCIN>CIN-7 °C. The higher homogeneity in water activity of CIN-5 °C than corresponding CIN-7 °C processes indicated that the lower process efficiency of CIN-5 °C could not be attributed to unsuccessful induction of ice nucleation during CIN-5 °C. High resolution micro-CT imaging and Artificial Intelligence Image analysis revealed cake wall deformation in CIN-7 °C and NOCIN products but not in CIN-5 °C. In addition, NOCIN products had bimodal distribution in air voids with median size range of 4-5 µm and 151.9-309 µm, respectively, hence the lower process efficiency of NOCIN despite the higher D90. Thus, the observed relationship between TIN and process efficiency may be attributed to microstructural changes post freezing. This hypothesis was corroborated by visible macroscopic cake collapse in NOCIN products but not in CIN products after lyophilization at a higher shelf temperature. In conclusion, the advantages of controlling the ice nucleation temperature of a lyophilization process may only be attained through a robust process design that takes into consideration the primary and secondary drying process parameters. Further, combined use of advanced in-line and off-line PAT tools for process and product characterization may hasten the at scale adoption of advance techniques such as CIN.

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Remote epitaxy has opened novel opportunities for advanced manufacturing and heterogeneous integration of two-dimensional (2D) materials and conventional (3D) materials. The lattice transparency as the fundamental principle of remote epitaxy has been studied and challenged by recent observations defying the concept. Understanding remote epitaxy requires an integrated approach of theoretical modeling and experimental validation at multi-scales because the phenomenon includes remote interactions of atoms across an atomically thin material and a few van der Waals gaps. The roles of atomically thin 2D material for the nucleation and growth of a 3D material have not been integrated into a framework of remote epitaxy research. Here, we summarize studies of remote epitaxy mechanisms with a comparison to other epitaxy techniques. In the end, we suggest the crucial topics of remote epitaxy research for basic science and applications.

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The use of selected multi-criteria decision methods for the optimization of cutting processes by abrasive water jet methods is increasingly being used in industrial processes. This is due to the complexity of the processes and the need to reduce operating costs. Process optimization methods are available to support organizational processes including the design phase, quality assurance, production automation, and many more. This article presents the current state of research on the water-abrasive cutting process and the use of multi-criteria methods in optimizing this process. This article presents a detailed methodological study of the VIKOR approach to optimization, indicating the applicability conditions, assumptions, and limitations on the example of high-pressure abrasive water jet cutting of elements made of titanium alloy utilizing HPX garnet abrasive. As a result of the research conducted, the best input parameters of the cutting process for abrasive flow rate, pressure, and the traverse speed of the cutting process were determined. The achieved result is consistent with the assumption that the most favorable output parameters are the highest cutting depth and the lowest level of roughness.

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Integrating the modern service industry with the advanced manufacturing industry is an important way to cultivate a modern industrial system and achieve high-quality development of economy. This study aims to enhance the supporting and leading role of the Greater Bay Area of Guangdong, Hong Kong, and Macao in China's national economic development and opening up by promoting the in-depth integration of these two industries. A new monitoring system is developed to simulate the level of integration of the modern service and advanced manufacturing industries in the Guangdong-Hong Kong-Macao Greater Bay Area. The dynamic comprehensive evaluation model of stock increment was used to simulate the coupling coordination degree of these two industries. Empirical results reveal that the development of these industries has uneven stock and incremental resource advantages, and their development has not been balanced. The coupling coordination degree of these industries in some areas has become maladjusted, while in other areas, it has fluctuated or developed from coordination to disorderly leap-forward. These findings demonstrate that the study methods and results are significant for analyzing industrial integration processes and promoting in-depth integration development in the Guangdong-Hong Kong-Macao Greater Bay Area. In conclusion, building a monitoring system using the dynamic comprehensive evaluation model of stock increment is an effective way to evaluate the level of integration of these two industries and promote their in-depth integration in the Greater Bay Area.

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Innovations in small-angle X-ray and neutron scattering (SAXS and SANS) at major X-ray and neutron facilities offer new characterization tools for researching materials phenomena relevant to advanced applications. For SAXS, the new generation of diffraction-limited storage rings, incorporating multi-bend achromat concepts, dramatically decrease electron beam emittance and significantly increase X-ray brilliance over previous third-generation sources. This results in intense X-ray incident beams that are more compact in the horizontal plane, allowing significantly improved spatial resolution, better time resolution, and a new era for coherent-beam SAXS methods such as X-ray photon correlation spectroscopy. Elsewhere, X-ray free-electron laser sources provide extremely bright, fully coherent, X-ray pulses of <100 fs and can support SAXS studies of material processes where entire SAXS data sets are collected in a single pulse train. Meanwhile, SANS at both steady-state reactor and pulsed spallation neutron sources has significantly evolved. Developments in neutron optics and multiple detector carriages now enable data collection in a few minutes for materials characterization over nanometre-to-micrometre scale ranges, opening up real-time studies of multi-scale materials phenomena. SANS at pulsed neutron sources is becoming more integrated with neutron diffraction methods for simultaneous structure characterization of complex materials. In this paper, selected developments are highlighted and some recent state-of-the-art studies discussed, relevant to hard matter applications in advanced manufacturing, energy and climate change.

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Characterization of live biotherapeutic product (LBP) batches typically includes a measurement of viability, such as colony forming units (CFU). However, strain-specific CFU enumeration assays can be complicated by the presence of multiple organisms in a single product with similar growth requirements. To overcome specific challenges associated with obtaining strain-specific CFU values from multi-strain mixtures, we developed a method combining mass spectrometry-based colony identification with a traditional CFU assay. This method was assessed using defined consortia made from up to eight bacterial strains. Among four replicate batches of an eight-strain mixture, observed values differed from expected values by less than 0.4 log10 CFU among all strains measured (range of differences, -0.318 to + 0.267). The average difference between observed and expected values was + 0.0308 log10 CFU, with 95% limits of agreement from -0.347 to 0.408 (Bland-Altman analysis). To estimate precision, a single batch of eight-strain mixture was assayed in triplicate by three different users, for a total of nine measurements. Pooled standard deviation values ranged from 0.067 to 0.195 log10 CFU for the eight strains measured, and user averages did not differ significantly. Leveraging emerging mass-spectrometry-based colony identification tools, a novel method for simultaneous enumeration and identification of viable bacteria from mixed-strain consortia was developed and tested. This study demonstrates the potential for this approach to generate accurate and consistent measurements of up to eight bacterial strains simultaneously and may provide a flexible platform for future refinements and modifications. KEY POINTS: • Enumeration of live biotherapeutics is essential for product quality and safety. • Conventional CFU counting may not differentiate between strains in microbial products. • This approach was developed for direct enumeration of mixed bacterial strains simultaneously.

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The challenge of introducing new technologies into established industries is not a problem unique to the biopharmaceutical industry. However, it may be critical to the long-term competitiveness of individual manufacturers and, more importantly, the ability to deliver therapies to patients. This is especially true for new treatment modalities including cell and gene therapies. We review several barriers to technology adoption which have been identified in various public forums including business, regulatory, technology, and people-driven concerns. We also summarize suitable enablers addressing one or more of these barriers along with suggestions for developing synergies or connections between innovation in product discovery and manufacturing or across the supplier, discovery, manufacturing, and regulatory arms of the holistic innovation engine.

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