Search Results (2,726)

Background/Objectives: Pharmaceutical formulation involves designing a drug product by combining the properties of an active pharmaceutical ingredient (API) with suitable excipients and processing strategies to produce a safe, effective, and manufacturable dosage form. However, data in formulation science are often limited, expensive to generate, and frequently restricted by proprietary and confidentiality constraints. Interactive digital tools can support formulators during early drug product development by improving the structure, transparency, and efficiency of formulation decision-making. While the current system focuses on structured decision support, future extensions may incorporate machine-learning methods for recommendation and knowledge extraction. Methods: In this work, we developed the Formulation tool, an interactive decision-support and visualization system for formulation development based on a hierarchical formulation-strategy framework commonly used in pharmaceutical practice. The tool is designed to prioritize suitable formulation principles and associated processing routes, with oral solid formulation as the initial application domain. The evaluated scenarios also include pathway regions relevant to oral liquid formulations. Its modular architecture also makes it adaptable to other formulation scenarios. To assess practical applicability, the tool was evaluated in a structured expert study involving five expert users across six predefined formulation scenarios (n = 30 runs) , covering three drugs under adult and pediatric conditions. Results: The tool showed agreement with the expected dosage-form families and overall formulation properties, with adult scenarios converging to oral solid regions and pediatric scenarios converging to oral liquid regions. At the downstream formulation-profile level, users converged either to the dominant expected pathway or to alternative feasible pathways within the same formulation region. Variability in full pathway completion was observed and was primarily associated with differences in user interaction behavior and exploratory usage patterns. The median task completion time was 113.5 s. Conclusions: In addition to organizing formulation knowledge, the Formulation tool records user interactions in a structured manner, which may support future learning from usage patterns. Because detailed downstream formulation constraints are often institution-specific and are typically not available in the public domain, the present evaluation focused on agreement at the dosage-form-family level and on overall formulation properties rather than on highly specialized constraint logic. The system is based on a constraint satisfaction problem (CSP) framework, which is well suited for modeling complex decision processes under explicit constraints. CSP has also been widely applied in intelligent scheduling systems, supporting its suitability for structured, constraint-rich decision-making tasks such as pharmaceutical formulation strategy development. Full article

Synthetic biology is reshaping in vitro diagnostics (IVD) by enabling programmable and modular biosensing elements that can be integrated into point-of-care testing (POCT) platforms. Compared with conventional assays that depend on fixed chemistries and centralized instrumentation, synthetic biology-based systems offer adaptable molecular recognition, tunable signal processing, and flexible readout formats for decentralized diagnostics. In this review, we present synthetic biology-enabled IVD as programmable biosensing platforms organized into four functional layers: molecular recognition, signal transduction and amplification, output generation, and system integration. We discuss four major enabling modules, including cell-free protein synthesis (CFPS) systems, aptamer and riboswitch sensors, CRISPR-Cas diagnostic platforms, and microfluidic integration technologies. We summarize representative clinical applications from 2021 to 2025 in infectious disease detection, cancer biomarker analysis, and drug metabolism/toxicity screening. In addition, we examine practical considerations beyond analytical sensitivity, including matrix tolerance, workflow complexity, manufacturability, quantitative capability, and regulatory readiness. Finally, we highlight future directions for programmable diagnostics, including AI-assisted biosensor design, multimodal readouts, interoperable platform architectures, and real-world clinical validation. Full article

Hydrogen-propelled aircraft can enable net-zero CO₂ emissions in aviation, which is the goal of the International Civil Aviation Organization (ICAO) for 2050. One drawback of onboard hydrogen storage in aircraft is the necessity for relatively large, pressurised storage volumes. To maximise H₂ volumetric efficiency, the COmposite COnformal LIquid H₂ Tank (COCOLIH₂T) project attempts to design, build and test a vacuum-insulated liner-less cryogenic conformal thermoplastic composite tank with conditioning subsystems for safe operation. The tank must be compatible with the design envelope in the empennage, specifically towards the aft of the pressure bulkhead of an ATR 72-like aircraft. The tank design, analysis, optimisation and demonstrator manufacturing are presented in this paper. Full article

In scientific research, the optimization of organic light-emitting diodes (OLEDs) is generally achieved through a lengthy and expensive experimental process as new ideas and configurations are tested on real devices. Electro-optical simulation allows for the rapid evaluation of key performance parameters of device structures, thus reducing manufacturing time and costs. This paper presents an original contribution to the electro-optical modeling and optimization of multilayer OLED devices using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This optimization explicitly incorporates defect states within the ITO/NPB/Alq₃:C545T/Alq₃/LiF-Al structure. The simulated model is calibrated using experimental data by fitting the trap state distribution. The Pareto front resulting from the multi-objective optimization identifies a set of non-dominated configurations, including an optimal intermediate structure defined by an electron transport layer (ETL) thickness of approximately 42 nm and a hole transport layer (HTL) thickness of approximately 53 nm. This configuration leads to a limited reduction of 1.75–2% in current efficiency (${\eta }_c$) while offering a remarkable improvement of 23–30% in power efficiency (${\eta }_p$) compared to the extreme configurations of the optimal Pareto set. Thus, this solution represents an optimal Pareto trade-off between high current efficiency and improved power efficiency. This paper shows that combining defect modeling and thickness optimization provides a reliable framework for the electro-optical optimization of OLED devices. Future work will extend this approach to spectral and colorimetric analysis. Full article

Tropical lignocellulosic residues are increasingly relevant feedstocks for lignin-containing nanocellulose composites, but their performance cannot be predicted from botanical origin or bulk lignin percentage alone. This review defines the interface as the geometrical boundary between phases and the interphase as the finite, compositionally graded region in which lignin distribution, nanocellulose morphology, adsorbed water, and the surrounding matrix jointly govern stress transfer and mass transport. Using an evidence-weighted framework, the literature is organized into the following categories: residual-lignin nanofibrils, redeposited-lignin systems, lignin nanoparticle assemblies, compatibilized thermoplastic hybrids, and all-lignocellulosic sheets. Representative quantitative observations show that controlled residual lignin can the increase water contact angle from approximately 35 degrees to 78 degrees and reduce oxygen permeability by up to 200-fold in nanopapers, while selected PLA/LCNF systems show tensile-strength and modulus increases of 37% and 61%, respectively; however, high or poorly distributed lignin can suppress fibrillation, lower viscosity, weaken gel networks, and reduce reproducibility. The most defensible near-term product windows are packaging layers, grease/oil barrier papers, coatings, paper-like multilayers, and selected porous media. Thermoplastic matrices remain process-sensitive, and biomedical, additive-manufacturing, nano-reactor, and energy-material claims require stronger validation of the extractables, rheology, humidity history, TEA/LCA metrics, and end-of-life behavior. This review, therefore, provides a critical, application-backward roadmap for tropical biorefineries in which interfacial function, wet handling, drying energy, and process integration are assessed together rather than treated as independent variables. The abbreviations used in the abstract are defined as follows: CNFs, cellulose nanofibrils; CNC, cellulose nanocrystals; LCNF, lignin-containing cellulose nanofibrils; LCNCs, lignin-containing cellulose nanocrystals; PLA, poly(lactic acid); PHB, polyhydroxybutyrate; PHAs, polyhydroxyalkanoates; PVA, poly(vinyl alcohol); DESs, deep eutectic solvents; TEA, techno-economic analysis; LCA, life-cycle assessment; ML, machine learning. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

High-tech manufacturing is a technology- and knowledge-intensive strategic industry. Its total factor productivity (TFP) directly impacts national competitiveness and economic quality. In China, despite rapid growth, TFP performance varies across sub-sectors and firms. In this study, TFP was adopted as the central outcome variable to capture the comprehensive production and technological efficiency of high-tech manufacturing firms. The Technology–Organization–Environment (TOE) framework was integrated with Dynamic Qualitative Comparative Analysis (Dynamic QCA) to examine the causal complexity, dynamic evolution, and industrial heterogeneity of TFP, using a sample of Chinese A-share-listed companies from 2015 to 2024. The results showed that high TFP depends on configurations rather than on a single factor. Three configurational paths were identified, including “technology–innovation–scale synergy,” “technology–scale dual core,” and “technology-led productivity optimization.” All paths require a strong technological foundation. Conversely, a lack of technology leads to low total factor productivity across all sectors. Moreover, the effectiveness of these pathways evolves over time. The dual-core pathway serves as a stable baseline model. The synergy pathway is reinforced in fast-iteration sectors. Due to weak innovation support, the productivity optimization pathway declined after 2019. Third, different sectors show distinct patterns. Fast-iteration sectors use synergy to handle rapid technical changes. Slow-iteration sectors use the dual-core model to share R&D risks. Productivity-optimized sectors stagnate because they focus on automation instead of innovation. This work reveals deep patterns in TFP growth and provides theoretical support and practical insight for strategic choices of firms, industry resource allocation, and industrial policy optimization. Full article

The increasing demand for sustainable industrial practices has intensified the search for manufacturing processes that minimize environmental impacts without compromising technical performance or economic viability. In this context, powder metallurgy has emerged as a promising alternative in mechanical manufacturing due to its potential for raw material reuse, waste reduction, lower energy consumption, and near-net-shape production. However, despite the growing body of research on this topic, there is still a lack of a comprehensive and integrated framework that systematically organizes and correlates the factors influencing sustainability across environmental, economic, and social dimensions, which limits a holistic understanding of the process. Therefore, this study aims to analyze and classify the main factors affecting sustainability in powder metallurgy. A Systematic Literature Review was conducted following the PRISMA method, using the Scopus, Web of Science and Wiley databases. The initial search identified 1753 articles, of which 56 were selected after applying inclusion and exclusion criteria. The analysis considers the three pillars of sustainability and examines how variables related to raw materials, energy consumption, processing technologies, waste reuse, product performance, and operational conditions influence process sustainability. The results enable the identification of the most recurrent factors in the literature and support the development of a structured theoretical framework, contributing to a more integrated understanding of sustainability in powder metallurgy. Full article

Industrial control of volatile organic compound (VOC) emissions from medium-density fibreboard (MDF) production remains constrained by a shortage of compound-resolved evidence from full-scale plants, where wood furnish, amino resin chemistry, heat transfer, gas flow, and wet gas cleaning act simultaneously. Here, we analysed more than 20,000 synchronized operating records from a full-scale single-stage flash-tube MDF dryer at an industrial SWISS KRONO production line and linked total VOC (TVOC) measurements from flame ionization detection with Fourier-transform infrared speciation on the cleaned stack. Five compounds—α-pinene, 3-carene, limonene, methanol, and formaldehyde—accounted for more than 80% of the resolved VOC signal. Process–state contrasts showed that higher digester residence time, discharge screw speed, adhesive amount, urea amount, dryer inlet temperature, and scrubber–water temperature increased one or more representative compounds, whereas higher hardwood share, additional flue-gas supply, and higher scrubber–water pH decreased them. Limonene, methanol, and formaldehyde were substantially more process-sensitive than α-pinene. An exploratory decorrelation step further showed that a drying/throughput domain explained about half of the variability of the screened process space. The study therefore identifies the small set of compounds and operating domains that most strongly govern the cleaned dryer-stack signature and provides a mechanistically grounded prioritization framework for follow-up causal experiments, source apportionment, and emission-mitigation design in industrial MDF manufacture. Unlike product or chamber emission studies, this work links the compound-resolved FTIR/FID chemistry of the final cleaned industrial stack with synchronized production variables; it therefore addresses a scale-integration gap by transforming routine compliance-type exhaust monitoring into a process-diagnostic framework for ranking emission sources, abatement-sensitive variables, and mitigation experiments. Full article

Molybdenum (Mo) is a strategic raw material for high-end equipment manufacturing, aerospace technologies, and advanced alloys, and approximately 50% of global molybdenum resources are hosted in porphyry Cu–Mo deposits. To address the long-standing challenge of selectively separating chalcopyrite and molybdenite by flotation, this study screened five sulfur-containing organic depressants and investigated their effects on the flotation responses of the two minerals, motivated by the strong affinity of sulfur donor atoms for surface Cu sites on chalcopyrite. The results indicate that thiomalic acid, 4-hydroxythiobenzamide, and 6-methyl-2-thiouracil markedly depress chalcopyrite flotation, whereas 2-(methylthio)acetic acid and N-phenylthiourea exert only minor effects. In contrast, none of the five reagents significantly affects the floatability of molybdenite. Among these depressants, thiomalic acid exhibited the best selectivity. In practical Cu–Mo bulk concentrate flotation, it showed a clear dosage advantage at low addition levels and improved Cu–Mo separation performance; at a Mo recovery of 76.09% and a Mo grade of 5.45%, Cu recovery was reduced to 9.54%. The adsorption mechanism of thiomalic acid on chalcopyrite was further investigated using FT-IR spectroscopy, X-ray photoelectron spectroscopy, and self-consistent charge density-functional tight-binding (SCC-DFTB) calculations. The results suggest that thiomalic acid interacts strongly with surface Cu sites on chalcopyrite via its S- and O-containing functional groups, likely increasing surface hydrophilicity and inhibiting collector adsorption (and subsequent bubble attachment), thereby contributing to selective chalcopyrite depression. Full article

Cancer continues to present a profound challenge due to high mortality and the inherent limitations of conventional treatments, including suboptimal targeting, systemic toxicity, and difficulty in overcoming physiological barriers. Micro/nanorobots (MNRs) offer a promising enhanced precision and efficacy in cancer therapy. This review systematically analyzes recent advancements in MNR applications, establishing a consistent framework that interlinks their diverse material compositions, propulsion strategies, and therapeutic functions. We critically compare various materials (inorganic, organic/polymeric, and biological/hybrid materials), elucidating their respective trade-offs in biocompatibility, biodegradability, and stimulus responsiveness. This paper further examines both internal (chemical and biological) and external (magnetic, light, and ultrasound) propulsion mechanisms, highlighting their strengths in overcoming biological barriers and enabling complex in vivo navigation, while also discussing their inherent limitations in control, fuel dependency, and tissue penetration. We then synthesize the therapeutic capabilities of MNRs across targeted drug delivery, phototherapy, radiotherapy, and immunotherapy, emphasizing common advantages like enhanced tumor specificity and reduced systemic side effects. A forward-looking perspective was also provided on the remaining challenges, particularly focusing on in vivo controllability, long-term biosafety, manufacturing scalability, and the significant hurdles in clinical translation. By offering a more critical and integrated analysis, this review underscores the immense potential of MNRs to revolutionize personalized precision cancer treatment, while candidly addressing the complex obstacles that must be surmounted for their successful clinical adoption. Full article

Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures. Full article

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm. Full article

Intelligent detection in modern manufacturing, healthcare, process industries, and structural monitoring is fundamentally enabled by heterogeneous sensor systems. Rather than being viewed as a purely image-centered recognition task, intelligent detection is more appropriately formulated as a sensor-driven state inference problem in which sensing physics, signal quality, temporal synchronization, modality availability, and deployment conditions jointly determine what can be reliably detected, localized, interpreted, and acted upon. Against this background, this review provides a structured synthesis of the field through three coupled dimensions, namely methods, systems, and governance, and organizes the literature around four recurring engineering components: signal unification, representation unification, alignment mechanisms, and robustness mechanisms. Using a structured review protocol with explicit source selection, screening, and study coding, the paper traces the methodological evolution from traditional feature-engineering and model-based pipelines to deep learning for visual, temporal, multimodal, generative, and mechanism-constrained sensing, and further to foundation-model-based and multimodal sensor intelligence. Cross-domain evidence is synthesized from industrial defect detection, fault diagnosis, remaining useful life prediction, non-destructive testing, structural health monitoring, medical lesion analysis, and process monitoring. The review argues that recent progress has substantially strengthened learned representations, multimodal interaction, and semantic extensibility, but has not removed persistent constraints arising from domain shift, missing modalities, calibration instability, privacy-preserving collaboration, and edge-side resource limits. Accordingly, the central challenge is no longer how to optimize isolated detection models, but how to build sensor-enabled intelligent systems that remain physically grounded, trustworthy, transferable, and maintainable under real operational conditions. On this basis, the paper concludes by identifying future directions in mechanism-aware modeling, trustworthy evaluation, missing-modality-robust multimodal systems, privacy-preserving cross-site collaboration, and edge-native lifecycle-aware deployment. Full article

Noise is an environmental factor that negatively affects the health of living organisms and must therefore be mitigated. One effective approach to noise reduction is the use of passive materials for sound absorption. Moreover, with the increasing use of 3D printing technology, it is now possible to produce complex material structures for noise reduction that cannot be manufactured using conventional manufacturing techniques. This study investigates the sound absorption performance of novel 3D-printed concentric tubular structures made of acrylonitrile styrene acrylate (ASA) with intermediate lattice inserts. The sound absorption properties of these structures were experimentally evaluated in the frequency range of 200–1600 Hz using a two-microphone acoustic impedance tube. Various factors influencing sound absorption properties were investigated, including the number of concentric tubes, sample height, strut diameter, and back air cavity thickness. The experimental results show that the sound absorption performance depends significantly on the design parameters of the proposed system. The average sound absorption coefficient (α_avg) increased with the number of concentric tubes and reached a maximum value of 0.264 for the configuration with five tubes. The highest sound absorption peak (α_max = 0.623) was achieved for the structure with two concentric tubes, a strut diameter of 3 mm, a height of 30 mm, and a back air cavity of 10 mm at a frequency of approximately 1548 Hz. Furthermore, increasing the strut diameter and sample height generally improved sound absorption performance, while the presence of a back air cavity significantly shifted the absorption peak toward lower frequencies, thereby enhancing low-frequency sound absorption. Full article