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Extracellular vesicle (EV)‐based therapies and vaccines are emerging. However, employment at the scale for population‐based dose development is always a huge bottleneck. In order to overcome such a roadblock, we introduce a simple and straightforward approach for promoting cellular production of dendritic cell derived EVs (DEVs) by leveraging phototherapy based light induction. Under the optimization of light wavelengths, intensities, and exposure times, we achieved more than 13‐fold enhancement in DEV production rate, while maintaining good integral quality and immune function from produced EVs. The LED light at 365 nm is optimal to reliably trigger enhanced cellular production of EVs no matter cell line types. Our observation and other reported studies support longer near UV wavelength does not impair cell growth. We conducted a series of investigations in terms of size, zeta potential, morphology, immune surface markers and cytokines, biocompatibility, cellular uptake behaviour, and immune‐modulation ability on eliciting cellular responses in vitro. We also validated the biodistribution, immunogenicity, and administration safety using light‐promoted DEVs in mice models from both male and female genders. Overall data supports that light promoted DEVs are highly immune functional with great biocompatibility for serving as good therapeutic platforms. The in vivo animal study also demonstrated light‐promoted DEVs are as well tolerated as native DEVs, with no safety concerns. Taken together, the data supports that light promoted DEVs are in excellent quality, high biocompatibility, in vivo tolerant, and viable for serving as an ideal therapeutic platform in scalable production.

Calculations are providing a molecular picture of hydrogen production on catalytic surfaces and within enzymes. This knowledge may guide the design of new, more efficient catalysts for the hydrogen economy.

The idea of a circular economy (CE) has gained ground over the past ten years as a means of addressing sustainable development and getting around the limitations of the current and linear dominant production and consumption patterns. The primary goal of a CE is to encourage the adoption of closing-the-loop production methods to improve resource use efficiency, modify chemical processes, and increase product and material lifespan. According to the 2030 Agenda for Sustainable Development, which focuses on 17 Sustainable Development Goals, 14 of which call for the appropriate application of green chemistry (GC) concepts and patterns, the role that chemistry may play in the shift toward more sustainable models is critical. By serving as the foundation for novel products made from renewable feedstocks and designed to be reused, recycled, or recovered with the associated minimum energy requirements, green and sustainable chemistry could be the key to unlocking the economic potential of the CE toward new product design and ultimately solving waste management problems. The aim of this perspective paper, while using a variety of literature sources, is to essentially capture the main issues associated with the CE and GC paradigms and how these two approaches can merge toward sustainable business models and the production of new materials. This integration focuses on reducing waste, conserving resources, and minimizing negative environmental impacts, while also considering economic viability. However, the obstacles to achieving implementation of the CE and GC principles are investment, environmental education, and legislation. To advance toward the circular economy and green chemistry, international agreements should be reconsidered to provide an appropriate framework, including the creation of incentives for businesses and individuals to adopt circular practices, the establishment of education programs to promote the benefits of circular practices, and the development of regulations to support the transition to sustainable production and consumption patterns.

Greenhouse vegetable production faces significant challenges due to the non-stationary and nonlinear dynamics of the cultivation environment, which demand adaptive and intelligent control strategies. This study presents an intelligent control system for greenhouse complexes based on artificial neural networks and fuzzy logic, optimized using genetic algorithms. The proposed system dynamically adjusts PI controller parameters to maintain optimal microclimatic conditions, including temperature and humidity, enhancing resource efficiency. Comparative analyses demonstrate that the genetic algorithm-based tuning outperforms traditional and fuzzy adaptation methods, achieving superior transient response with reduced overshoot and settling time. Implementation of the intelligent control system results in energy savings of 10–12% compared to conventional stabilization algorithms, while improving decision-making efficiency for electrotechnical subsystems such as heating and ventilation. These findings support the development of resource-efficient cultivation regimes that reduce energy consumption, stabilize agrotechnical parameters, and increase profitability in greenhouse vegetable production. The approach offers a scalable and adaptable solution for modern greenhouse automation under varying environmental conditions.

Ultrafine Polyvinyl alcohol (PVA) fibers have an outstanding potential in various applications, especially in absorbing fields. In this manuscript, an electrostatic-field-assisted centrifugal spinning system was designed to improve the production efficiency of ultrafine PVA fibers from PVA aqueous solution for NH3 adsorption. It was established that the fiber production efficiency using this self-designed system could be about 1000 times higher over traditional electrospinning system. The produced PVA fibers establish high morphology homogeneity. The impact of processing variables of the constructed spinning system including rotation speed, needle size, liquid feeding rate, and voltage on fiber morphology and diameter was systematically investigated by SEM studies. To acquire homogeneous ultrafine PVA fiber membranes, the orthogonal experiment was also conducted to optimize the spinning process parameters. The impact weight of different studied parameters on the spinning performance was thus provided. The experimental results showed that the morphology of micro/nano-fibers can be well controlled by adjusting the spinning process parameters. Ultrafine PVA fibers with the diameter of 2.55 μm were successfully obtained applying the parameters, including rotation speed (6500 rpm), needle size (0.51 mm), feeding rate (3000 mL h−1), and voltage (20 kV). Furthermore, the obtained ultrafine PVA fiber mat was demonstrated to be capable of selectively adsorbing NH3 gas relative to CO2, thus making it promising for NH3 storage and other environmental purification applications.

Energy-efficient operation of manufacturing systems is critical for industrial enterprises in current environmentally conscious society. Decreasing the idle time of a machine is one of the main methods to achieve energy-efficient production. From the system level, when and how long a machine can be turned into standby state with lower energy consumption is still a difficult problem for unreliable manufacturing systems considering less throughput loss. In this paper, a novel multi-state decision method based on fuzzy logic is proposed to switch a machine into different sleep states considering real-time work in process inventory of buffers. Three basic modules and their corresponding fuzzy controllers are presented to construct complex manufacturing systems with disassembly and assembly workstations. The fuzzy rules for machine state decision are generated based on the expert/production knowledge. By means of simulation experiments, the effectiveness of the proposed method is illustrated for an unreliable complex manufacturing system.

In this study, the policy analysis matrix (PAM) and data envelopment analysis (DEA) approaches were used to assess lemon producers’ productivity in Mersin, Turkey, as well as the international competitiveness of lemon cultivation within the scope of its production plan. According to the findings, most enterprises are inefficient, and the profitability of lemons improved from 2863.5 USD ha-1 to 6606.0 USD ha-1 with special prices within the framework of profit-maximising production plans. Regarding social prices, an increment from 3500.8 USD ha-1 to 8071.5 USD ha-1 was proposed to create a more sustainable production plan. To retain competitiveness in the Turkish lemon trade, it has been established that enterprises should transition to a more efficient production structure. For this reason, it has been concluded that inefficiencies in using inputs should be eliminated, and the dissemination of technology and advanced applications will make producers more competitive. RESUMO: Neste estudo, as abordagens da matriz de análise de políticas (PAM) e da análise envoltória de dados (DEA) foram usadas para avaliar a produtividade dos produtores de limão em Mersin, Turquia, bem como a competitividade internacional do cultivo de limão no âmbito de seu plano de produção. De acordo com os resultados, a maioria das empresas é ineficiente e a lucratividade dos limões melhorou de 2863,5 USD ha-1 para 6606,0 USD ha-1 com preços especiais no âmbito dos planos de produção para maximizar o lucro. Com relação aos preços sociais, foi proposto um incremento de 3.500,8 USD ha-1 para 8.071,5 USD ha-1 para criar um plano de produção mais sustentável. Para manter a competitividade no comércio de limão turco, foi estabelecido que as empresas devem fazer a transição para uma estrutura de produção mais eficiente. Por isso, concluiu-se que as ineficiências no uso de insumos devem ser eliminadas, e a disseminação de tecnologia e aplicações avançadas tornará os produtores mais competitivos.

Cyclodextrin glycosyltransferases (CGTases) are widely used in starch deep processing, so reducing their cost by improving their production is of significant industrial interest. The CGTase from Bacillus stearothermophilus NO2 possesses excellent catalytic properties but suffers from low production in E. coli. In this study, directed evolution was used to create three point mutants (I631T, I641T and K647E) that were produced in E. coli with shake-flask yields 1.7-, 2.1-, and 2.2-fold higher than that of wild-type, respectively. The wild-type and K647E were then produced in a 3-L fermenter. The CGTase activity of the K647E (1904 U mL-1) was 2.0-fold higher than that of the wild-type. The K647E fermentation supernatant could be used directly to prepare 2-O-α-d-glucopyranosyl-l-ascorbic acid, reducing the costs associated with its production. Structural modeling of the three mutants suggested that hydrophilicity, hydrogen bonding, and negative charge may be responsible for their improved production. Since K647 is conserved in the CGTase family, the corresponding residues in the CGTases from Bacillus circulans 251, Paenibacillus macerans, and Anaerobranca gottschalkii were changed to glutamic acid. Productions of the resulting K647E mutants were 2.0-, 1.5-, and 1.0-fold higher than those of their respective wild-types. Electrostatic protein surface analysis suggested that mutations occurring at low negative surface charge may increase CGTase production.

Mannosylerythritol lipids (MELs) are a class of glycolipids that have been receiving increasing attention in recent years due to their diverse biological activities. MELs are produced by certain fungi and display a range of bioactivities, making them attractive candidates for various applications in medicine, agriculture, and biotechnology. Despite their remarkable qualities, industrial-scale production of MELs remains a challenge for fungal strains. Excellent fungal strains and fermentation processes are essential for the efficient production of MELs, so efforts have been made to improve the fermentation yield by screening high-yielding strains, optimizing fermentation conditions, and improving product purification processes. The availability of the genome sequence is pivotal for elucidating the genetic basis of fungal MEL biosynthesis. This review aims to shed light on the applications of MELs and provide insights into the genetic basis for efficient MEL production. Additionally, this review offers new perspectives on optimizing MEL production, contributing to the advancement of sustainable biosurfactant technologies.

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.