Search Results (8,438)

Platelet-rich plasma (PRP) is widely used in cosmetic and topical biomedical applications; however, conventional preparation methods rely heavily on centrifugation, which becomes operationally demanding when processing large blood volumes. In this study, a sedimentation-assisted strategy was investigated as an alternative to the initial centrifugation step for industrial-scale production of porcine PRP lyophilized powder. Whole blood anticoagulated with ACD-A was subjected to gravity sedimentation for 6–12 h, achieving >99.6% erythrocyte removal while maintaining a platelet recovery rate of >64%, comparable to conventional centrifugation. For large-volume batches (e.g., 100 L), this approach significantly reduced operator-intensive handling time. ACD-A outperformed other anticoagulants in preserving platelet integrity and preventing hemolysis during prolonged sedimentation. These findings demonstrate that gravity sedimentation represents a practical, scalable, and cost-effective alternative for the initial separation step in large-scale manufacturing of cosmetic-grade PRP raw material, with quality controlled by TGF-β1 concentration as the key release specification. Full article

In intensive animal production, the overuse of antibiotics has exacerbated bacterial antimicrobial resistance and environmental pollution. Together with gut microbiota dysbiosis and recurrent disease outbreaks, these challenges severely constrain the sector’s high-quality development. Quorum sensing (QS), a cell-density-dependent bacterial communication mechanism, can be modulated through agents that specifically inhibit or activate QS circuitry to regulate microbial community functions. Such QS modulators possess notable advantages, such as environmental benignity and high target specificity, and thus offer innovative strategies to decrease antibiotic reliance, enhance production efficiency, and reduce environmental emissions. This review examines QS modulators sourced from plants, microorganisms, animals, and synthetic processes, while highlighting key challenges such as environmental interference, resistance development, high costs, and the lack of standardized biosafety evaluations. Future research should focus on enhancing specificity, stability, affordability, and safety, with an emphasis on rational design, synergistic systems, improved manufacturing processes, and multi-target modulators. This review may provide a theoretical basis for translating QS-regulation technologies into farm-level applications, thereby advancing sustainable animal production and antibiotic-free husbandry. Full article

Conductive filaments for Material Extrusion Additive Manufacturing (MEX) can enable low-cost fabrication of functional parts with embedded electrical features. However, systematic studies on process-dependent electrical properties like apparent resistivity and repeatability are limited, and the post-printing stability of the electrical response is not commonly addressed. This study evaluates the influence of printing temperature, printing speed and layer height on the apparent resistivity, specimen-to-specimen repeatability and time-dependent drift of a commercial carbon black-filled conductive PLA filament (ProtoPasta). The novelty of the study consists of evaluating not only the initial apparent resistivity, but also the repeatability between specimens and the post-print drift of apparent resistivity over a 0–50 h interval. The filament was investigated using three printing temperatures (210–230 °C), two printing speeds (60–80 mm/s) and three layer heights (0.2–0.4 mm), with three replicates per configuration. Apparent resistivity ranged between 0.156 and 0.205 kΩ·mm at t0 and between 0.162 and 0.222 kΩ·mm at t50. Multifactorial ANOVA and main-effects analyses showed that the printing temperature was the main factor affecting mean apparent resistivity at both t0 and t50. Higher temperature reduced apparent resistivity, most likely due to improved polymer flow, inter-bead/inter-layer bonding and conductive-network continuity. Printing speed had no significant main effect on the mean apparent resistivity or drift within the tested range. Repeatability depended on the parameter configuration and measurement time, with variability increasing after 24 h and then becoming mainly dependent on layer height. Drift analysis showed a significant main effect of layer height and a significant layer height × temperature interaction, with the largest increase at 0.3 mm. These results show that parameter selection for conductive MEX parts should consider both the initial resistivity level and post-print stability over time. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

The rapid growth of prosumer photovoltaic installations has introduced significant supply–demand imbalances in modern power grids, motivating the development of energy management systems that can coordinate distributed resources without sacrificing local control responsiveness. This paper presents qToggleEMS, a distributed architecture that combines cloud-resident receding-horizon planning with edge-resident bounded-override control for prosumer sites equipped with photovoltaic generation, battery storage, and grid interconnection. The contribution is positioned at the systems-engineering level: a documented partitioning of responsibilities between a cloud planner (forecasting, price-aware scheduling) and an edge controller (sub-second actuation, autonomous fallback) that preserves planning quality while remaining operational under cloud–edge disconnection. The cloud component, powerHub, is implemented as a set of microservices communicating via MQTT and TimescaleDB; the edge component runs qToggleOS on an ARM single-board computer and accesses inverters directly via Modbus RTU, bypassing manufacturer-provided cloud APIs. The system was deployed at a commercial prosumer site for approximately two months using the prosumer-oriented optimization strategy. Compared with a within-period counterfactual baseline (the cost the site would have incurred under its previous flat-tariff contract), monthly energy costs decreased by 14–15%. An analytical projection of the producer-oriented strategy using historical day-ahead prices from OPCOM PZU suggests a revenue uplift of approximately 23%, pending field validation. Full article

Ultra-low-expansion (ULE) glass serves as a critical material in high-precision optical devices and semiconductor manufacturing; however, its inherent hardness and brittleness pose significant challenges for machining processes. During the diamond cutting of ULE glass, severe tool wear emerges as the primary factor limiting machined quality, which not only shortens tool life but also prolongs subsequent polishing time, thereby increasing processing costs and hindering sustainable manufacturing. To address this challenge, in situ laser assisted diamond cutting (LADC) has emerged as a promising technique for the sustainable machining of difficult-to-machine materials. In this study, for achieving sustainable machining of ULE glass, the effects of cutting speed on surface roughness and tool wear were systematically investigated. To determine the optimal parameter combination for minimizing surface roughness and tool wear simultaneously, an integrated optimization approach combining artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) was employed. The experimental results indicated that a spindle speed of 2900 rpm and a feed speed of 1.1 mm/min was ascertained as the optimum combination to attain the desired outcomes for in situ LADC of ULE glass. Under the optimum machining parameters, in situ LADC resulted in a 70.08% reduction in surface roughness and 61.24% reduction in tool wear compared to conventional diamond cutting (CDC). This study demonstrates that in situ LADC can be recognized as a promising sustainable machining technique for machining of ULE glass. Full article

The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers only a few parameters and a single objective. This research develops a simulation-based framework that integrates multiple EOQ-based inventory policies and performs multi-objective optimization using the NSGA-II algorithm. The framework optimizes total cost, fill rate, and average inventory level and finally generates a Pareto front as a result. To reduce computational costs, we use a machine learning-based random forest model, which replaces a significant amount of the simulations with predictions. This reduces the simulation cost to approximately one-sixth of the original, while the quality of the simulation changes only minimally, as the hypervolume value decreases by only 4%. The proposed framework can be used as an effective decision-support tool for inventory optimization under stochastic demand conditions. Full article

Digital twin technology is rapidly gaining traction in the semiconductor industry for its ability to model manufacturing processes, including packaging engineering, to monitor and optimise performance cost-effectively. This paper focuses on two key areas of development. The first part explores the potential of digital design and additive manufacturing to produce high-performance, compact thermal management solutions that significantly reduce device junction temperatures and enhance operational efficiency. The second part presents the development of surrogate models to predict junction temperatures of electronic packages under varying operating and geometrical conditions. These models, trained using deep learning, were integrated into a user-friendly COMSOL Multiphysics application builder version 6.3. The proposed digital twin framework enables fast and accurate full-thermal field predictions in comparison to conventional 3D finite element simulations. Full article

This study analyzed online review data to examine user requirements for audiovisual products and to compare requirement salience and satisfaction across traditional and emerging product contexts. We collected 86,213 Chinese-language reviews of Skyworth TVs, Xiaomi TVs, and Xiaomi projectors from JD.com. LDA topic modeling was used to identify major user requirement areas, and Logistic Regression, Random Forest, and Support Vector Machine (SVM) models were compared for sentiment classification, with the tuned SVM model retained for downstream analysis. The results show that user discussions primarily concern audiovisual experience, cost performance, service quality, design aesthetics, and intelligent operation. Skyworth TVs receive particularly strong evaluations for picture and sound quality (97.89% positive sentiment), whereas Xiaomi TVs are more strongly associated with cost-effectiveness and smart features (94.05% positive sentiment). Xiaomi projectors attract attention for portability but receive lower satisfaction ratings on core audiovisual performance and intelligent operation. These findings suggest that traditional manufacturers should continue strengthening core performance while improving service responsiveness, whereas emerging brands should build on their technological advantages while further enhancing their product reliability and user experience. Full article

Against the backdrop of the ongoing advancement of the “dual carbon” goals and the carbon emission trading system, green innovation in small and medium-sized manufacturing enterprises faces multiple practical constraints, including financing constraints, technological commercialization risk, and market recognition costs. To examine the mechanism through which government regulation affects firms’ green innovation behavior, this study develops a four-party evolutionary game model involving government, small and medium-sized manufacturing enterprises, consumers, and investment institutions, and analyzes the strategic interactions and dynamic evolution of these actors. The results show that regulatory intensity, consumer green preference, and financial support from investment institutions all exert significant effects on green innovation decisions in small and medium-sized manufacturing enterprises. Whether firms choose substantive green innovation depends primarily on such key factors as financing uncertainty, technological commercialization risk, the intensity of government penalties, and the level of policy incentives. Further stability analysis and numerical simulations indicate that stronger administrative penalties significantly increase the likelihood that firms adopt substantive green innovation and also promote green consumption among consumers. This effect becomes more pronounced when financing uncertainty declines. At the same time, stronger policy incentives for green investment enhance the willingness of investment institutions to participate in green projects, and this effect is further reinforced when technological commercialization risk is reduced. The findings suggest that green innovation in small and medium-sized manufacturing enterprises is characterized by strong multi-actor interdependence. Its evolutionary outcome is shaped not only by regulatory pressure, but also by green financial support, the conditions for technological commercialization, and market demand. Accordingly, sustained green innovation in small and medium-sized manufacturing enterprises requires coordinated efforts to improve regulatory arrangements, strengthen green finance support systems, reduce the cost of technological commercialization, and cultivate green consumer markets. Full article

Conventional epoxy polymers and their composites are increasingly challenged by environmental concerns, high manufacturing costs, and limited recyclability, necessitating the exploration of sustainable alternatives. Many research groups have sought to develop alternate polymers from various renewable resources, such as lignin, polyphenols, natural resins, saccharides, and plant oils. This new type of polymer has led to the emergence of bio-based polymers, which are often used with different reinforcements as bio-based composites. In this review, the synthesis of different bio-epoxy resins is discussed in detail along with their chemical structures. Subsequently, the enhancements in the properties of these bio-composites with the addition of different nanomaterials such as carbonaceous nanofillers (carbon nanotubes, graphene nanoplatelets, graphene oxide, etc.), cellulose-based nanomaterials, inorganic nano-silica (spherical and mesoporous), and nano-clay is explained. Lastly, the properties of these bio-composites and their applications in civil engineering are highlighted. This review has provided a detailed overview of the developments in bio-composites that can be used as a guide for the development of a new class of bio-composites using other alternate resources. Full article

Telemetric systems are particularly valuable in applications where remote data acquisition and automatic transmission allow effective monitoring of local characteristics. Among the different telemetric approaches, passive wireless systems based on inductive coupling are particularly attractive because they enable sensor interrogation without onboard power storage. Printed electronics (PE) offer several advantages in the realization of such systems, including a wide selection of functional materials, reduced production costs, possibility of rapid prototyping and complete customization. This allows for the development of smart products by embedding sensors and electronics directly into existing objects without significantly altering their geometry or weight. In light of this, the aim of this scoping review is to explore key factors in implementing chipless passive inductively coupled LC telemetric systems via PE. Given the growing interest in smart products, this scoping review serves as a starting point for the design and implementation of smart products specifically on printed passive inductively coupled LC telemetric systems, addressing their development. To better understand the identified solutions, we first outlined the requirements and characteristics of ideal chipless passive LC-based inductively coupled telemetric systems. Then, we provided a comprehensive analysis of conductive materials and substrates, manufacturing technologies, and the design and performance of printed inductors and associated readout architectures. Full article

Energy-intensive processes such as flexographic printing, extrusion coating, slitting, compressed air generation, and chilled water production make liquid carton packaging manufacturing a major electricity consumer, increasing the need for cost-effective and sustainable energy solutions. This study evaluates the real-world performance of a 679 kWp grid-tied solar photovoltaic (PV) system integrated at the 11 kV level in a liquid carton packaging factory in Nairobi, Kenya, operating under regulatory export control constraints that require full on-site consumption of PV generation. Using measured operational data from energy monitoring platforms, including Sunny Portal, 1.31.8 Schneider EcoStruxure, and Sphera Cloud 8.17.2, system performance was assessed in accordance with IEC 61724-1, focusing on final yield, capacity utilization factor, grid offset contribution, and carbon emissions reduction. The results show that the system generated 617 MWh over the assessment period, corresponding to an average daily final yield of 2.49 kWh/kWp·day and a capacity utilization factor of 10.38%. On-site PV generation supplied approximately 17% of the plant’s annual electricity demand and avoided about 277.7 t CO₂ emissions. Performance benchmarking against comparable installations in Kenya, Morocco, Malaysia, Senegal, and Uzbekistan indicates that the lower observed yield is primarily driven by curtailment and industrial load-matching limitations rather than inadequate solar resource or component inefficiency. The findings demonstrate that meaningful electricity cost savings and emissions reductions can be achieved in energy-intensive manufacturing environments despite export restrictions while highlighting the importance of improved load alignment and data-driven operational strategies to enhance PV utilization. Full article

Utility disruptions may stem from insufficient power generation, inferior infrastructure, or secondary weather perils (e.g., tornadoes, floods, snowstorms) that take energy infrastructure offline. The latter present a unique risk that not all existing power options can mitigate. Regardless of their origin, power disruptions have the potential to cripple food supply chains and undermine food system sustainability. To prepare for managing future disruptions, food and beverage manufacturers may couple electrical microgrid and thermal district heating infrastructure with small modular reactors (SMRs) or smaller microreactor systems to form low-carbon power islands. Although SMR technology is a somewhat new source of energy and has not yet achieved commercial viability, it provides the potential to make food and beverage manufacturing more resilient and sustainable when it becomes broadly available. To assess the potential cost–benefit of activating such technology as a sustainability-oriented resilience investment, we conducted a technoeconomic downtime threshold analysis. The case assumes that the technology is the full-time power source and the SMR yields stronger returns as facility downtime or downtime costs rise. The analysis found the breakeven point to range from 12.3 h down to 613.2 h down annually for a 5 MW system, depending on facility scale and assumed downtime costs. At a representative downtime opportunity cost of $10,000/h, SMR adoption requires approximately 61.3 h (5 MW) of annual outages to break even, highlighting scale effects on feasibility. Incorporating a 20% thermal energy credit reduces required outage thresholds by roughly 20%, lowering the breakeven level to 49.1 h. These results highlight the potential role of SMR-enabled power islanding in supporting sustainable food manufacturing through improved energy resilience, low-carbon power, and thermal energy recovery. 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