Search Results (430)

Circular economy (CE) decouples value creation from virgin resource use and waste in the medical device sector, which faces stringent patient-safety, quality, and regulatory obligations. Green Additive Manufacturing (AM) offers a precise, digitally driven route to implement CE through dematerialization, on-demand localized production, topology optimization, and material circularity. In this study, a comprehensive CE framework is tailored to medical device manufacturing that integrates eco-design, material circularity, remanufacturing, and regulatory compliance across the product life cycle. Methods include an International Organization for Standardization (ISO) 14040/44-aligned life cycle assessment, process energy metering, sterilization-compatibility studies, mechanical/biocompatibility verification to relevant standards, and a techno-economic/circularity analysis with Monte Carlo uncertainty quantification. Three case studies are explored using bio-based PA11 (selective laser sintering), recycled polyethylene terephthalate glycol (fused deposition modeling), and low-volatile organic carbon biocompatible photopolymer (stereolithography): (1) a patient-specific wrist orthosis, (2) a dental surgical guide, and (3) a single-use catheter Y-connector. Results indicate 38–68% reductions in embodied greenhouse-gas emissions, 22–54% energy savings per functional unit, and up to 80% mass recapture through in-process powder/runner reuse while maintaining clinical performance and regulatory conformity. Design-for-circularity patterns (DfC) were created for DfDisassembly, DfSter, DfTraceability, DfUpgrade, and DfPowder-Loop and provide a governance architecture combining ISO 13485 QMS, ISO 10993 biological evaluation, the European Union’s Medical Device Regulation (Regulation (EU) 2017/745), and the United States Food and Drug Administration’s guidance on Additive Manufactured (3D-printed) medical devices, guidance with unique device identification for closed-loop returns. The paper concludes with an Industry 5.0 roadmap for hospital-proximate micro-factories, materials passports, and digital product passports enabling verified circular flows at scale. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

This bibliometric review aimed to map recent scientific production (2020–2026) on autochthonous bacterial strains applied to the food industry, focusing on experimental studies retrieved from the Scopus^® database. Boolean operators and truncation were applied to refine searches and exclude yeast-related terms, and keyword co-occurrence analysis was performed using VOSviewer (v1.6.20). A total of 44,095 experimental articles were analyzed. Results revealed a stable annual output exceeding 8000 papers between 2021 and 2024, indicating sustained scientific interest in the topic. China and the United States accounted for over 55% of total publications, with Chinese institutions and funding agencies showing predominant activity. Research was mainly distributed across Biochemistry, Genetics, Molecular Biology, Medicine, and Microbiology, reflecting applied and mechanistic approaches. Two major thematic clusters were identified: one focused on gastrointestinal health and microbiota modulation and another centered on microbial metabolism, probiotic functionality, and biochemical characterization. The findings confirm the growing scientific and technological relevance of autochthonous strains in improving food quality, safety, and functionality, especially in fermented products, and provide valuable insights for guiding future research and innovation in food microbiology and biotechnology. Full article

Quantum computing threatens widely deployed public-key cryptosystems, accelerating the adoption of Post-Quantum Cryptography (PQC) in practical systems. Beyond asymptotic security, the feasibility of PQC deployments depends on measured performance on real hardware and on implementation-level overheads. This paper presents an experimental evaluation of five post-quantum digital signature schemes (CRYSTALS-Dilithium, HAWK, SQISign, SNOVA, and SPHINCS+) and three key encapsulation mechanisms (Kyber, HQC, and BIKE) selected to cover multiple PQC design families and parameterizations used in practice. We implement a TCP client–server testbed in Python that invokes C implementations for each primitive—via standalone executables and, where provided, in-process dynamic libraries—and benchmarks key generation, encapsulation/decapsulation, and signature generation/verification on two Windows 11 commodity processors: an AMD Ryzen 7 4000 (8 cores, 16 threads, 1.8 GHz) and an Intel Core i5-1035G1 (4 cores, 8 threads, 1.0 GHz). Each operation is repeated ten times under a low-interference setup, and results are aggregated as mean (with 95% confidence intervals) timings over repeated runs. Across the evaluated configurations, lattice-based schemes (Kyber, Dilithium, HAWK) show the lowest computational cost, while code-based KEMs (HQC, BIKE), isogeny-based (SQISign), and multivariate (SNOVA) signatures incur higher overhead. Hash-based SPHINCS+ exhibits larger artifacts and higher signing latency depending on the parameterization. The AMD platform consistently outperforms the Intel platform, illustrating the impact of CPU characteristics on observed PQC overheads. These results provide comparative evidence to support primitive selection and capacity planning for quantum-resistant deployments, while motivating future end-to-end validation in protocol and web service settings. Full article

Low-processed ready-to-eat (RTE) meat products are highly vulnerable to microbial contamination, yet data on in-process dynamics remain limited. This study investigated microbial dynamics and environmental hygiene during the production of vacuum-packed RTE pork bars containing dried plasma, with a focus on identifying process-inherent contamination risks. Samples were collected at successive processing stages and from food-contact and non-food-contact surfaces. Process hygiene was assessed using indicator organisms (Aerobic Plate Count, Enterobacteriaceae, lactic acid bacteria, yeast and mold, E. coli, S. aureus counts), while food safety relevance was addressed by monitoring Listeria monocytogenes and Salmonella spp. Microbial counts increased by approximately 1.5–2.3 log CFU/g between early processing steps, indicating that these operations are critical contamination-prone steps. Environmental monitoring revealed contamination hotspots on frequently handled surfaces, highlighting the vulnerability of pre- and post-lethality stages. Despite the baking achieving a mean microbial reduction of ~3 log CFU/g, consistent with effective thermal processing, low-level microbial reappearance during packaging and maturation indicated the potential for post-process contamination. The results demonstrate that production-inherent factors largely drive microbial contamination patterns and may persist even in facilities operating under implemented GHP, GMP, and HACCP-based procedures, highlighting step-specific limitations rather than system failure. By providing empirical data on in-process microbial dynamics, this study supports both scientifically based and risk-based approaches within Food Safety Management Systems, offering transferable insights applicable to similar RTE meat production environments. The findings may assist food business operators in optimising targeted control measures and strengthening risk-based decision-making in low-processed RTE meat production. Full article

Laser Metal Deposition (LMD) is an advanced Additive Manufacturing (AM) technology widely used for metal component fabrication, cladding, and repair. Despite its potential, issues such as geometrical inaccuracies and deposition flaws can significantly affect part quality and process efficiency. Existing optical monitoring approaches mainly focus on geometric features and generally do not provide real-time estimates of deposition efficiency, which is critical for both product performance and resource utilization. Furthermore, evaluating deposition efficiency in industrial settings is often time-consuming and difficult to implement. This preliminary study introduces an innovative in-process methodology for assessing deposition efficiency during multi-track deposition. The approach exploits end-track scan data acquired by a laser line scanning system to estimate the deposited volume and the corresponding deposition efficiency for each track. A validation test on a two-layer sample demonstrates the capability of the method to detect defects induced by partially clogged and non-clogged nozzle conditions. Comparison with metallographic measurements shows an average deviation of 4.3%. By enabling timely identification of powder feeding anomalies and supporting improved powder utilization, the proposed methodology contributes to waste reduction, enhanced process stability, and more sustainable industrial implementation of LMD. Full article

The digitalization of machining processes is increasingly recognized as essential for achieving higher productivity, reliability, and traceability. However, access to reliable in-process sensor data remains limited, particularly in multi-axis CNC machining, where dimensional accuracy and surface integrity strongly depend on stable and optimized process conditions. This study investigates sensor-based monitoring as a practical approach for evaluating process performance in five-axis CNC milling. Electric current and vibration signals were acquired during three machining operations, under distinct cutting parameters, using current clamps and a plug-and-play MEMS accelerometer. The signals were processed using the root mean square method to assess the correlation between sensor data and machining conditions. Dimensional inspection of each workpiece was carried out to verify geometric conformity. The results show that spindle current measurements exhibit a strong linear correlation with material removal rate and cutting power, supporting their use as indicators of cutting forces and energy consumption. Vibration signals revealed pronounced dynamic behaviour for specific tool orientations, particularly in transverse to tool axis direction. The proposed methodology provides a simple and low-cost framework for integrating sensor-based monitoring into five-axis CNC milling, particularly relevant for semi-roughing operations, and offers a basis for future studies on process optimization and real-time condition monitoring. Full article

Tyrosinase (EC 1.14.18.1) is a binuclear copper enzyme responsible for the rate-limiting steps of melanogenesis, catalyzing the hydroxylation of L-tyrosine and oxidation of L-DOPA into o-quinones that polymerize melanin. Beyond its physiological role in pigmentation, tyrosinase is also implicated in food browning and oxidative stress–related disorders, making it a key target in cosmetic, food, and biomedical industries. This systematic review, conducted following PRISMA guidelines, aimed to identify and analyze microbial metabolites with tyrosinase inhibitory potential as sustainable alternatives to conventional inhibitors such as hydroquinone and kojic acid. Literature searches in Scopus and Web of Science (March 2025) yielded 156 records; after screening and applying inclusion criteria, 11 studies were retained for analysis. The inhibitors identified include indole derivatives, phenolic acids, peptides, and triterpenoids, mainly produced by fungi (e.g., Ganoderma lucidum, Trichoderma sp.), actinobacteria (Streptomyces, Massilia), and microalgae (Spirulina, Synechococcus). Reported IC₅₀ values ranged from micromolar to milli-molar levels, with methyl lucidenate F (32.23 µM) and p-coumaric acid (52.71 mM). Mechanisms involved competitive and non-competitive inhibition, as well as gene-level regulation. However, methodological heterogeneity, the predominance of mushroom tyrosinase assays, and limited human enzyme validation constrain translational relevance. Computational modeling, site-directed mutagenesis, and molecular dynamics are proposed to overcome these limitations. Overall, microbial metabolites exhibit promising efficacy, stability, and biocompatibility, positioning them as emerging preclinical candidates for the development of safer and more sustainable tyrosinase inhibitors. Full article

Additive manufacturing (AM) has emerged as a pivotal technology in component fabrication, renowned for its capabilities in freeform fabrication, material efficiency, and integrated design-to-manufacturing processes. As a critical branch of AM, metal additive manufacturing (MAM) has garnered significant attention for producing metal parts. However, process anomalies during MAM can pose safety risks, while internal defects in as-built parts detrimentally affect their service performance. These concerns underscore the necessity for robust in-process monitoring of both the MAM process and the quality of the resulting components. This review first delineates common MAM techniques and popular in-process monitoring methods. It then elaborates on the fundamental principles of acoustic emission (AE), including the configuration of AE systems and methods for extracting characteristic AE parameters. The core of the review synthesizes applications of AE technology in MAM, categorizing them into three key aspects: (1) hardware setup, which involves a comparative analysis of sensor selection, mounting strategies, and noise suppression techniques; (2) parametric characterization, which establishes correlations between AE features and process dynamics (e.g., process parameter deviations, spattering, melting/pool stability) as well as defect formation (e.g., porosity and cracking); and (3) intelligent monitoring, which focuses on the development of classification models and the integration of feedback control systems. By providing a systematic overview, this review aims to highlight the potential of AE as a powerful tool for real-time quality assurance in MAM. Full article

Ensuring friction stir welding (FSW) joint quality typically relies on ultrasonic testing (UT) and radiographic testing (RT), but achieving complete coverage is challenging, and echo-based defect discrimination becomes difficult in dissimilar joints. Laser ultrasonics is a promising non-contact technique that remotely assesses weld quality and provides high spatial resolution at the generation and detection points. This study establishes a laser-ultrasonic method for defect detection in dissimilar Cu–Al FSW joints. Slit-like artificial defects (0.1–2.5 mm deep in 5 mm thick plates) were introduced at the Al-side interface of specimens fabricated with an Al-offset tool. Experiments and numerical simulations were used to evaluate wave modes and irradiation configurations, focusing on intensity-attenuation ratios of specific wave types, including longitudinal and Rayleigh waves. On the non-slit surface, attenuation of reflected longitudinal waves enabled detection of defects ≥0.5 mm deep. On the slit surface, Rayleigh-wave attenuation allowed identification of defects as shallow as 0.1 mm, although slit-side irradiation may be less practical during joining. These results demonstrate that defect identification in dissimilar materials can be achieved by evaluating wave-intensity attenuation rather than relying solely on the presence of reflected echoes, suggesting potential for implementing laser ultrasonics in in-process monitoring of FSW joints. Full article

Understanding drop dispersion behavior is significant to the optimization of liquid dispersion devices. In this work, the drop dispersion behavior in the combined trapezoid spray tray was directly observed and analyzed with a high-speed camera. It was found that the fracture of the liquid neck is the main mode for the liquid column to generate drops. The dispersion behavior of the drops was simulated by CFD, and it was found that the liquid neck is caused by the surrounding vortex field and the uneven pressure distribution inside the liquid column. At the same time, the dispersion time of the drops was counted, and it was found that the drop dispersion time ranges from 5 to 60 ms, depending on the drop diameter and the gas kinetic energy factor in plate hole F₀. Full article

Conventional polyurethane (PU) synthesis is associated with environmental and health concerns due to the use of toxic isocyanates. In recent years, the development of non-isocyanate polyurethanes (NIPUs) has emerged as a sustainable alternative to conventional polyurethanes. However, these materials still exhibit inconsistencies in their physicomechanical and biological properties. This systematic review was conducted following the PRISMA methodology. A total of sixteen studies published between 2015 and 2025 were analyzed, focusing on functionalization techniques developed for non-isocyanate polyurethanes to evaluate their influence on physicomechanical and biological performance. The results reveal that functionalization can be achieved through the incorporation of inorganic additives, polar or ionic groups, and polymeric modifiers. Among the analyzed systems, those functionalized with azetidinium and Polyethylene glycol diacrylate (PEGDA) exhibited the most balanced performance, combining high mechanical strength, low cytotoxicity, and effective antibacterial activity. Overall, these functionalizations have demonstrated significant improvements in tensile strength, thermal stability, hydrophilicity, and antimicrobial activity, facilitating broader industrial and biomedical applications. Consequently, this review concludes that functionalization plays a pivotal role in improving the overall performance of non-isocyanate polyurethanes. It represents an effective and sustainable strategy to enhance the physicomechanical and biological behavior of these materials, supporting their development for advanced applications such as bioactive coatings, membranes, and wound dressings. Full article

MCSGP (Multicolumn Countercurrent Solvent Gradient Purification) with AutoPeak control is increasingly used for production of synthetic peptides and oligonucleotides at scale, requiring guidance on how to perform regulatory-compliant Process Validation. This work, for the first time, presents a Process Characterization and Process Performance Qualification approach to support regulatory filings of therapeutics produced using MCSGP, based on the relevant Process Validation guidelines. The approach was demonstrated for the purification of synthetic Bivalirudin. During Process Characterization, MCSGP process parameter criticality was investigated, and the gradient slope was classified as a critical process parameter to be controlled within tighter limits. As a further outcome of Process Characterization, a supervision strategy was developed and verified in four Process Performance Qualification MCSGP runs. The strategy was backed by AutoPeak, a UV-based Process Analytical Technology. The Process Validation/Process Performance Qualification (PPQ) runs not only confirmed the selected control and supervision strategy but also the advantages of MCSGP/AutoPeak as a continuous manufacturing technology, including the fully automatic operation and the reduction in in-process control sampling and Process Mass Intensity (PMI). In the presented case, the PMI was reduced from around 5200 to 1400 kg/kg, the number of in-process controls (IPCs) was reduced from 81 IPCs (60 cm i.D. column batch) per kg to 3.2 IPCs per kg (2 × 30 cm i.D. column MCSGP), while yield (gross-to-gross) increased from 57% to 62%, comparing MCSGP/AutoPeak to a process with extensive side-cut recycling. Full article

Robust algorithms have been widely used and intensively studied in the communities of engineering, statistics, and machine learning since such algorithms are less sensitive to outliers and effective in addressing the issue of non-Gaussian noise during the learning process. In this paper we study the learning performance of a distributed robust algorithm with mixing dependent samples, where big data are collected distributively and have a dependence structure. Learning rates are derived by means of an integral operator decomposition technique and probability inequalities in Hilbert spaces. The results show that with a suitable robustification parameter, the performance of the distributed robust algorithm is comparable with that of its non-distributed counterpart, even if the dependent feature restricts the availability and the effective amount of data. Full article

Background/Objectives: Loneliness and social isolation are common among older adults and linked to adverse health outcomes. Videoconferencing can support social connections, but the role of aging-in-place organizations (AIPOs), such as senior centers and Area Agencies on Aging, in facilitating adoption is poorly understood. This review examined how AIPOs use relational videoconferencing to promote social engagement among older adults. Methods: We applied a hybrid methodology combining a scoping review with latent topic modeling to contextualize and analyze the evidence base. Exploratory searches revealed limited literature specifically addressing AIPO involvement; therefore, we first conducted latent topic modeling of the broader literature on social videoconferencing among older adults to establish a thematic foundation for the subsequent PRISMA-guided scoping review. Thematic analysis of this broader corpus, identified through 2021 database searches, applied Latent Dirichlet Allocation (LDA) to a collection of peer-reviewed articles. Subsequent refinement of this corpus by removing non-primary research and non-AIPO records produced the narrower PRISMA subset used for the scoping review. The scoping review followed JBI guidelines and was based on database searches (EBSCOhost: MEDLINE, AgeLine, SocINDEX, Health Source: Nursing/Academic Edition, and Family & Society Studies Worldwide; ProQuest Social Science Premium Collection; and PubMed, including MEDLINE, PMC, and in-process content) for peer-reviewed studies published between 2011 and 2025. Inclusion criteria required primary research involving adults aged 65 years or older, use of videoconferencing technology for social engagement, and reference to AIPOs or analogous community-based aging services. The protocol was post-registered with the Open Science Framework. Results: The LDA analysis of 101 peer-reviewed articles identified six latent themes describing the broader research landscape: problem of isolation, character of socialization, physical health, technology as intervention, technology as social medium, and supportive environments. This thematic framework informed the scoping review, which screened 1908 records and retained 25 publications (representing 24 unique studies) explicitly referencing AIPO involvement in relational videoconferencing. Only one study predated COVID-19. Mapping these studies to the LDA-derived themes revealed the least consistent coverage to be in supportive environments and physical health, particularly among AIPOs other than senior or community centers. Conclusions: Relational videoconferencing has potential to sustain and expand older adults’ social connections, but evidence mapped through the scoping review shows that documentation of how AIPOs support adoption is sparse. The hybrid approach advances understanding of videoconferencing in aging contexts and identifies priorities for documenting, comparing, and refining AIPO practices to inform future interventions and policy. Full article