Search Results (32,523)

Background: The study evaluates the immunomodulatory potential of secukinumab (SECU) and honokiol (HONK) in a murine model of allergic asthma complicated by acute lung injury (ALI), with an emphasis on modulating key inflammatory pathways. The rationale is driven by the necessity to attenuate Th17-mediated cytokine cascades, wherein IL-17 plays a critical role, as well as to explore the adjunctive anti-inflammatory effects of HONK on Th1 cytokine production, including IL-6, TNF-α, and Th2 cytokines. Methods: Mice were sensitized and challenged with ovalbumin (OVA) and lipopolysaccharide (LPS) was administrated to exacerbate pulmonary pathology, followed by administration of SECU, HONK (98% purity, C₁₈H₁₈O₂), or their combination. Quantitative analyses incorporated OVA-specific IgE measurements, differential cell counts in bronchoalveolar lavage fluid (BALF), and extensive cytokine profiling in both BALF and lung tissue homogenates, utilizing precise immunoassays and histopathological scoring systems. Results: Both SECU and HONK, when used alone or in combination, display significant immunomodulatory effects in a murine model of allergic asthma concomitant with ALI. The combined therapy synergistically reduced pro-inflammatory mediators, notably Th1 cytokines, such as TNF-α and IL-6, as measured in both BALF and lung tissue homogenates. Conclusions: The combined therapy showed a synergistic attenuation of pro-inflammatory mediators, a reduction in goblet cell hyperplasia, and an overall improvement in lung histoarchitecture. While the data robustly support the merit of a combinatorial approach targeting multiple inflammatory mediators, the study acknowledges limitations in cytokine diffusion and the murine model’s translational fidelity, thereby underscoring the need for further research to optimize clinical protocols for severe respiratory inflammatory disorders. Full article

A novel cellulose-degrading bacterial strain, D3-1, capable of degrading cellulose under medium- to high-temperature conditions, was isolated from soil samples and identified as Staphylococcus caprae through 16SrRNA gene sequencing. The strain’s cellulase production was optimized by controlling different factors, such as pH, temperature, incubation period, substrate concentration, nitrogen and carbon sources, and response surface methods. The results indicated that the optimal conditions for maximum cellulase activity were an incubation time of 91.7 h, a temperature of 41.8 °C, and a pH of 4.9, which resulted in a maximum cellulase activity of 16.67 U/mL, representing a 165% increase compared to pre-optimization levels. The above experiment showed that, when maize straw flour was utilized as a natural carbon source, strain D3-1 exhibited relatively high cellulase production. Furthermore, gas chromatography–mass spectrometry (GC-MS) analysis of products in the degradation liquid revealed the presence of primary sugars. The results indicated that, in the denitrification of simulated sewage, supplying maize straw flour degradation liquid (MSFDL) as the carbon source resulted in a carbon/nitrogen (C/N) ratio of 6:1 after a 24 h reaction with the denitrifying strain WH-01. The total nitrogen (TN) reduction was approximately 70 mg/L, which is equivalent to the removal efficiency observed in the glucose-fed denitrification process. Meanwhile, during a 4 h denitrification reaction in urban sewage without any denitrifying bacteria, but with MSFDL supplied as the carbon source, the TN removal efficiency reached 11 mg/L, which is approximately 70% of the efficiency of the glucose-fed denitrification process. Furthermore, experimental results revealed that strain D3-1 exhibits some capacity for nitrogen removal; when the cellulose-degrading strain D3-1 is combined with the denitrifying strain WH-01, the resulting TN removal rate surpasses that of a single denitrifying bacterium. In conclusion, as a carbon source in municipal sewage treatment, the degraded maize straw flour produced by strain D3-1 holds potential as a substitute for the glucose carbon source, and strain D3-1 has a synergistic effect with the denitrifying strain WH-01 on TN elimination. Thus, this research offers new insights and directions for advancement in environmental sewage treatment. Full article

Esterification is a key transformation in the production of lubricants, pharmaceuticals, and fine chemicals. Conventional processes employing homogeneous acid catalysts suffer from limitations such as corrosive byproducts, energy-intensive separation, and poor catalyst reusability. This review provides a comprehensive overview of heterogeneous catalytic systems, including ion exchange resins, zeolites, metal oxides, mesoporous materials, and others, for improved ester synthesis. Recent advances in membrane-integrated reactors, such as pervaporation and nanofiltration, which enable continuous water removal, shifting equilibrium and increasing conversion under milder conditions, are reviewed. Dual-functional membranes that combine catalytic activity with selective separation further enhance process efficiency and reduce energy consumption. Enzymatic systems using immobilized lipases present additional opportunities for mild and selective reactions. Future directions emphasize the integration of pervaporation membranes, hybrid catalyst systems combining biocatalysts and metals, and real-time optimization through artificial intelligence. Modular plug-and-play reactor designs are identified as a promising approach to flexible, scalable, and sustainable esterification. Overall, the interaction of catalyst development, membrane technology, and digital process control offers a transformative platform for next-generation ester synthesis aligned with green chemistry and industrial scalability. Full article

Polymer flooding is a widely used enhanced oil recovery (EOR) method for improving energy efficiency and reducing the carbon footprint of oil production. Optimizing polymer concentration is critical for maximizing recovery while minimizing economic and environmental costs. Here, we present a systematic experimental study which shows that even very low concentrations of polymers yield relatively high recovery rates at adverse mobility ratios (230 cP oil). A series of core flood experiments were conducted on Bentheimer sandstone rock, with polymer concentrations ranging from 40 ppm (1.35 cP) to 600 ppm (10.0 cP). Beyond a mobility ratio threshold, increasing polymer concentration did not significantly enhance recovery. This plateau in performance was attributed to the persistence of viscous fingering and oil crossflow into pre-established water channels. The study suggests that low concentrations of polymer may mobilize oil at high mobility ratios by making use of the pre-established water channels as transport paths for the oil and that the rheology of the polymer enhances this effect. These findings enable reductions in the polymer concentration in fields with adverse mobility ratios, leading to substantial reductions in chemical usage, energy consumption, and environmental impact of the extraction process. Full article

The sustainable production of plant bioactive compounds is increasingly important as natural habitats decline. This study investigates whether in vitro cell cultures of Eryngium planum, Lychnis flos-cuculi, and Kickxia elatine can serve as alternative sources of biologically active biomass with antimicrobial and anti-Acanthamoeba properties. Callus cultures were established under optimized and controlled conditions, and metabolomic profiling was completed using UPLC-HRMS/MS. In silico analysis, using a molecular docking approach, was applied to understand the interaction between target compounds and Acanthamoeba profilin and identify possible targets for antimicrobial properties. Untargeted metabolomic analysis confirmed the presence of valuable compounds in the callus cultures of the studied species. Biological activity was assessed through anti-Acanthamoeba and antimicrobial assays. Lychnis flos-cuculi and Kickxia elatine callus extracts showed significant inhibitory effects on Acanthamoeba trophozoites, with 87.5% and 80.1% inhibition at 10 mg/mL, respectively. In contrast, E. planum extract stimulated amoebic growth. The anti-Acanthamoeba activity correlated with the presence of ferulic acid and p-coumaric acid in L. flos-cuculi extract, and acteoside in K. elatine extract. Antibacterial testing revealed moderate activity of E. planum and K. elatine extracts against Staphylococcus spp., while Gram-negative bacteria and fungi were largely resistant. These findings highlight the potential of in vitro cultures—particularly those from L. flos-cuculi and K. elatine—as promising, sustainable sources of anti-Acanthamoeba and antimicrobial agents, warranting further investigation into their pharmacologically active constituents. Full article

This paper analyzes the concept of packaging redesign, with the primary objective of improving material utilization. It further examines the potential environmental and economic benefits that may result from effective packaging redesign for both industry and society. The research is based on a specific case study comparing two alternative bottle designs with identical capacity, focusing on shape, material usage, and space efficiency. A detailed numerical comparison highlights the advantages and disadvantages of each option. The analysis demonstrates that an optimized bottle design can lead to substantial material savings and waste reduction. For example, an 8% reduction in bottle weight could eliminate approximately 1.6 million tons of material annually, potentially translating into economic savings exceeding 3 billion U.S. dollars per year. The study underscores how strategic packaging redesign can yield significant benefits in terms of material efficiency and cost savings for companies. It also contributes to the field of Life Cycle Analysis by linking packaging design innovation to key environmental and economic outcomes, while ensuring that packaging continues to protect products and meet the needs of the end consumer. Full article

Machine learning models that only use data for training and forecasting oilfield production have a sense of disconnection from the physical background, while embedding development patterns in them can enhance interpretability and even improve accuracy. In this paper, a novel multi-well production forecasting model embedded with decline curve analysis (DCA) is proposed, enabling the machine learning model to incorporate physical information. Moreover, an improved particle swarm optimization algorithm is proposed to optimize the hyperparameters in the loss function of the model. These hyperparameters determine the importance of the overall DCA and each module in training, which traditionally requires expert knowledge to determine. Simulation results based on the benchmark reservoir model show that the model has better forecasting ability and generalization performance compared to typical machine learning methods. Full article

This study evaluates the agronomic potential of two types of vermicompost—one produced solely from wine industry residues (WIR) and one incorporating sewage sludge (WIR + SS)—under rainfed and deficit irrigation conditions in Mediterranean vineyards. The vermicompost was obtained through a two-phase process involving initial thermophilic pre-composting, followed by vermicomposting using Eisenia fetida for 90 days. The conditions were optimized to ensure aerobic decomposition and maintain proper moisture levels (70–85%) and temperature control. This resulted in end products that met the legal standards required for agricultural use. However, population dynamics revealed significantly higher worm reproduction and biomass in the WIR treatment, suggesting superior substrate quality. When applied to grapevines, WIR vermicompost increased soil organic matter, nitrogen availability, and overall fertility. Under rainfed conditions, it improved vegetative growth, yield, and must quality, with increases in yeast assimilable nitrogen (YAN), sugar content, and amino acid levels comparable to those achieved using chemical fertilizers, as opposed to the no-fertilizer trial. Foliar analyses at veraison revealed stronger nutrient uptake, particularly of nitrogen and potassium, which was correlated with improved oenological parameters compared to the no-fertilizer trial. In contrast, WIR + SS compost was less favorable due to lower worm activity and elevated trace elements, despite remaining within legal limits. These results support the use of vermicompost derived solely from wine residues as a sustainable alternative to chemical fertilizers, in line with the goals of the circular economy in viticulture. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

Biomass torrefaction must be self-sustaining and continuous to be commercially viable, eliminating dependence on additional fuels while achieving industrial-scale production. This study presents a predictive model of a full-scale continuous biomass torrefaction process that explicitly incorporates the radiation absorption properties of torrefaction gas, with a focus on water vapor. Previous research, primarily based on lab-scale batch processes, has not adequately addressed scale-up challenges or the dynamic evolution of torrefaction gas. Industrial insights from Perpetual Next confirm that water vapor significantly impacts reactor performance by absorbing heat and reducing radiative flux to the biomass. Simulations show that neglecting water vapor absorption in reactor design can lead to throughput deviations of 10–20%, affecting process stability and efficiency. Industrial-scale validation demonstrates that the model accurately predicts this effect, ensuring realistic energy demand and throughput expectations. By explicitly incorporating water vapor absorption into the radiation balance, the model provides a validated framework for optimizing reactor design and process scale-up. It demonstrates that failing to consider this effect can lead to operational instability and deviations from the intended torrefaction severity, ultimately affecting industrial-scale performance and self-sustaining operation. Full article

This study focuses on the optimization of valve assemblies in downhole rod pumping units (DRPUs), which remain the predominant artificial lift technology in oil production worldwide. The research addresses the critical issue of premature failures in DRPUs caused by leakage in valve pairs, i.e., a problem that accounts for approximately 15% of all failures, as identified in a statistical analysis of the 2022 operational data from the Uzen oilfield in Kazakhstan. The leakage is primarily attributed to the accumulation of mechanical impurities and paraffin deposits between the valve ball and seat, leading to concentrated surface wear and compromised sealing. To mitigate this issue, a novel valve assembly design was developed featuring a flow turbulizer positioned beneath the valve seat. The turbulizer generates controlled vortex motion in the fluid flow, which increases the rotational frequency of the valve ball during operation. This motion promotes more uniform wear across the contact surfaces and reduces the risk of localized degradation. The turbulizers were manufactured using additive FDM technology, and several design variants were tested in a full-scale laboratory setup simulating downhole conditions. Experimental results revealed that the most effective configuration was a spiral plate turbulizer with a 7.5 mm width, installed without axis deviation from the vertical, which achieved the highest ball rotation frequency and enhanced lapping effect between the ball and the seat. Subsequent field trials using valves with duralumin-based turbulizers demonstrated increased operational lifespans compared to standard valves, confirming the viability of the proposed solution. However, cases of abrasive wear were observed under conditions of high mechanical impurity concentration, indicating the need for more durable materials. To address this, the study recommends transitioning to 316 L stainless steel for turbulizer fabrication due to its superior tensile strength, corrosion resistance, and wear resistance. Implementing this design improvement can significantly reduce maintenance intervals, improve pump reliability, and lower operating costs in mature oilfields with high water cut and solid content. The findings of this research contribute to the broader efforts in petroleum engineering to enhance the longevity and performance of artificial lift systems through targeted mechanical design improvements and material innovation. Full article

Lithium slag (LS), a by-product of lithium extraction processes, poses a significant disposal challenge during the rapid development of new energy technologies. In this study, LS was used to replace partially washed sand in the process of mortar production to compensate for the content of stone powder in sand. Five mortar mixes containing varying proportions of LS were prepared, and the macroscopic performance was evaluated. A comprehensive microscopic analysis, including microstructure observations, hydration product identification, and pore structure analysis, was conducted. The impact of LS on the chloride ion permeability of mortar was also investigated in this study. The results indicate that an increase in LS content gradually reduces the workability of the mortar, with a 39.29% decrease in fluidity when 40% of the sand is replaced with LS. Moreover, the compressive and flexural strengths of the mortar initially increase and then decrease with higher LS content. Microscopic tests reveal that 20% LS substitution significantly optimizes the pore structure of the mortar, resulting in a lower chloride ion permeability coefficient. Consequently, 20% LS substitution is recommended as the optimal dosage for use as fine aggregate in mortar. Full article

This study introduces the concept of DEEPEIA, a novel deep learning (DL) platform designed to recommend the optimal export market, and its ideal foreign champion, for any product or service offered by a small and medium-sized enterprise (SME). Drawing on expertise in SME internationalization and leveraging recent advances in generative artificial intelligence (AI), this research addresses key challenges faced by SMEs in global expansion. A systematic review of existing platforms was conducted to identify current gaps and inform the conceptualization of an advanced generative DL recommender system. The Discussion section proposes the conceptual framework for such a decision optimizer within the context of contemporary technological advancements and actionable insights. The conclusion outlines future research directions, practical implementation strategies, and expected obstacles. By mapping the current landscape and presenting an original forecasting tool, this work advances the field of AI-enabled SME internationalization while still acknowledging that more empirical validation remains a necessary next step. Full article

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain. Full article

Chinese hamster ovary (CHO) cells are the most common protein production platform for glycosylated biopharmaceuticals due to their relatively efficient secretion systems, post-translational modification (PTM) machinery, and quality control mechanisms. However, high productivity and titer demands can overburden these processes. In particular, the endoplasmic reticulum (ER) can become overwhelmed with misfolded proteins, triggering the unfolded protein response (UPR) as evidence of ER stress. The UPR increases the expression of multiple genes/proteins, which are beneficial to protein folding and secretion. However, if the stressed ER cannot return to a state of homeostasis, a prolonged UPR results in apoptosis. Because ER stress poses a substantial bottleneck for secreting protein therapeutics, CHO cells are both selected for and engineered to improve high-quality protein production through optimized UPR and ER stress management. This is vital for optimizing industrial CHO cell fermentation. This review begins with an overview of common ER-stress related markers. Next, the optimal UPR profile of high-producing CHO cells is discussed followed by the context-dependency of a UPR profile for any given recombinant CHO cell line. Recent efforts to control and engineer ER stress-related responses in CHO cell lines through the use of various bioprocess operations and activation/inhibition strategies are elucidated. Finally, this review concludes with a discussion on future directions for engineering the CHO cell UPR. Full article