Efficient Process Research Articles

From pollutants to products: Microbial cell factories driving sustainable biomanufacturing and environmental conservation

Microbial cell factories are emerging as powerful tools in addressing pressing environmental challenges and promoting sustainable biomanufacturing. This paper highlights the key role of engineered microorganisms in mitigating pollution, converting waste and pollutants into valuable products, and reducing reliance on fossil fuels. Through advancements in metabolic engineering, gene editing technologies, and synthetic biology, microbial cell factories are being optimized to enhance their efficiency in breaking down pollutants and producing renewable chemicals, such as biofuels, bioplastics, and specialty chemicals. These processes contribute to environmental conservation, waste valorization, and the establishment of a circular economy.The study focuses on overcoming key barriers in microbial biotechnology, such as limited scalability, process inefficiency, and economic viability, by employing strategies like metabolic pathway optimization, enzyme overexpression, and tolerance enhancement. These strategies are applied to various microbial species, demonstrating how their metabolic capabilities can be fine-tuned for industrial applications. Detailed case studies illustrate successful implementations, such as the conversion of lignocellulosic biomass, CO2, and industrial waste into high-value products, underscoring the practical impact of microbial cell factories in diverse sectors, including energy, materials, and chemicals.Furthermore, this research addresses the challenges faced by microbial cell factories in industrial-scale operations, such as maintaining genetic stability and optimizing growth conditions, and offers insight into emerging technological solutions to these obstacles. By providing a comprehensive overview of recent developments and identifying future research directions, this paper offers actionable recommendations for unlocking the full potential of microbial biotechnology. These efforts aim to further integrate microbial processes into industrial systems, contributing to a more sustainable and resilient global economy, with the ultimate goal of fostering a circular bioeconomy.

Auto-transduction in lentiviral vector bioprocessing: A quantitative assessment and a novel inhibition strategy.

Lentiviral vectors are highly efficient gene delivery vehicles used extensively in the rapidly growing field of cell and gene therapy. Demand for efficient, large-scale, lentiviral vector bioprocessing is growing as more therapies reach late-stage clinical trials and are commercialized. However, despite substantial progress, several process inefficiencies remain. The unintended auto-transduction of viral vector-producing cells by newly synthesized lentiviral vector particles during manufacturing processes constitutes one such inefficiency which remains largely unaddressed. In this study, we determined that over 60% of functional lentiviral vector particles produced during an upstream production process were lost to auto-transduction, highlighting a major process inefficiency likely widespread within the industry. Auto-transduction of cells by particles pseudotyped with the widely used vesicular stomatitis virus G protein was inhibited via the adoption of a reduced extracellular pH during vector production, impairing the ability of the vector to interact with its target receptor. Employing a posttransfection pH shift to pH 6.7-6.8 resulted in a sevenfold reduction in vector genome integration events, arising from lentiviral vector-mediated transduction, within viral vector-producing cell populations and ultimately resulted in improved lentiviral vector production kinetics. The proposed strategy is scalable and cost-effective, providing an industrially relevant approach to improve lentiviral vector production efficiencies.

Development of a reliable and efficient combined grinding and separation process for PVC-coated fabric waste

Development of a reliable and efficient combined grinding and separation process for PVC-coated fabric waste

Neural dysfunction underlying working memory processing at different stages of the illness course in schizophrenia: a comparative meta-analysis.

Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

Mesh Neural Networks Based on Dual Graph Pyramids.

Deep neural networks (DNNs) have been widely used for mesh processing in recent years. However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most DNNs expect 2-manifold, watertight meshes, but many meshes, whether manually designed or automatically generated, may have gaps, non-manifold geometry, or other defects. On the other hand, the irregular structure of meshes also brings challenges to building hierarchical structures and aggregating local geometric information, which is critical to conduct DNNs. In this paper, we present DGNet, an efficient, effective and generic deep neural mesh processing network based on dual graph pyramids; it can handle arbitrary meshes. First, we construct dual graph pyramids for meshes to guide feature propagation between hierarchical levels for both downsampling and upsampling. Second, we propose a novel convolution to aggregate local features on the proposed hierarchical graphs. By utilizing both geodesic neighbors and euclidean neighbors, the network enables feature aggregation both within local surface patches and between isolated mesh components. Experimental results demonstrate that DGNet can be applied to both shape analysis and large-scale scene understanding. Furthermore, it achieves superior performance on various benchmarks, including ShapeNetCore, HumanBody, ScanNet and Matterport3D. Code and models will be available at https://github.com/li-xl/DGNet.

Fast Filter Pruning via Coarse-to-Fine Neural Architecture Search and Contrastive Knowledge Transfer.

Filter pruning is the most representative technique for lightweighting convolutional neural networks (CNNs). In general, filter pruning consists of the pruning and fine-tuning phases, and both still require a considerable computational cost. So, to increase the usability of CNNs, filter pruning itself needs to be lightweighted. For this purpose, we propose a coarse-to-fine neural architecture search (NAS) algorithm and a fine-tuning structure based on contrastive knowledge transfer (CKT). First, candidates of subnetworks are coarsely searched by a filter importance scoring (FIS) technique, and then the best subnetwork is obtained by a fine search based on NAS-based pruning. The proposed pruning algorithm does not require a supernet and adopts a computationally efficient search process, so it can create a pruned network with higher performance at a lower cost than the existing NAS-based search algorithms. Next, a memory bank is configured to store the information of interim subnetworks, i.e., by-products of the above-mentioned subnetwork search phase. Finally, the fine-tuning phase delivers the information of the memory bank through a CKT algorithm. Thanks to the proposed fine-tuning algorithm, the pruned network accomplishes high performance and fast convergence speed because it can take clear guidance from the memory bank. Experiments on various datasets and models prove that the proposed method has a significant speed efficiency with reasonable performance leakage over the state-of-the-art (SOTA) models. For example, the proposed method pruned the ResNet-50 trained on Imagenet-2012 up to 40.01% with no accuracy loss. Also, since the computational cost amounts to only 210 GPU hours, the proposed method is computationally more efficient than SOTA techniques. The source code is publicly available at https://github.com/sseung0703/FFP.

Novel approach to CO2 capture: Improving the hybridization between membranes and calcium-looping

A 70 MWe Waste-to-Energy (WtE) plant is modelled to design and evaluate the performance of two CO2 capture strategies: a standalone Calcium-Looping (CaL) process (Case 1) and an innovative hybrid system combining CO2-selective polymeric membranes with CaL (Case 2). The latter involves a CO2 pre-enrichment process facilitated by a pressure gradient within the membrane module, which enhances the CaL performance. The heat released in the CaL process is recovered within a Combined Cycle configuration, taking advantage of the pressurized stream exiting the membrane modules. Comparative analysis reveals that the hybrid system (Case 2) significantly outperforms the standalone CaL process (Case 1) in efficiency and economic viability. With a baseline LCOE for a standard WtE plant without CO2 capture at 150 €/MWh, the LCOE for Cases 1 and 2 are 294 €/MWh and 243.21 €/MWh, respectively. The hybrid system achieves a thermoelectric efficiency of 25.25 %, reducing the energy penalty to 3.32 %points compared to Case 1, which results in 9.49 %points. Furthermore, the cost of avoided carbon emissions is significantly lower in Case 2 (83.82 €/ton CO2) than in Case 1 (155.12 €/ton CO2), underscoring the hybrid system’s superior potential for efficient CO2 capture in WtE applications.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

Extraction Optimization, Structure Analysis and Antioxidant Activity of Polysaccharide from Sanghuangporus baumii

Introduction: In this study, we optimized the extraction process, analyzed the structure and assessed the antioxidant activity of Sanghuangporus baumii polysaccharide (SBP). The present results provide important information for the Sanghuangporus baumii polysaccharides in potential natural antioxidant effects. Background: The extraction and structural analysis of polysaccharides from Sanghuangporus has gained significant attention in the fields of chemistry, medicine, and life sciences. There is great significance in maximizing the extraction of polysaccharides from Sanghuangporus and developing their potential products in a scientific and rational manner. Objective: The study was designed to establish an efficient and practical extraction process for SBP, and then investigated the structure and the antioxidant activity. Methods: The Response Surface Methodology (RSM) based on Box-Behnken design was used to explore the ultrasound-assisted extraction of SBP, and the structure of SBP was studied by ultraviolet spectroscopy, infrared spectroscopy and other instrumental analysis methods. The total antioxidant capacity of SBP was studied by the Ferric Reducing Ability of Plasma (FRAP) method, and the scavenging capacity of ABTS+·, DPPH· and OH· was carried out as the index to investigate its antioxidant activity in vitro. Results: The statistical analysis results showed that the optimal conditions for extracting SBP were an ultrasound time of 20.74 min, ultrasound power of 268.40 W and material-liquid ratio of 1:25.71. Under optimal conditions, the experimental yield of SBP was 3.36 ± 0.01%. The RSM optimization process was applied to the experiment of complex enzyme extraction of SBP, and the yield increased to 4.72 ± 0.03%. Structural analysis showed that SBP mainly consisted of glucose, a small amount of mannose and galactose, and the molecular weight distribution was uneven, mainly concentrating in the three parts of 24.5,6.4,2.5 kDa. Moreover, SBP exhibited dose-dependent and strong reducing power and radical scavenging activity. For DPPH·, ABTS+· and OH· radical scavenging assays, IC50 values were 1.505 ± 0.050 mg/ml, 0.065 ± 0.036 mg/ml and 0.442 ± 0.007 mg/ml, respectively. Conclusion: In the present study, a β-linked heteroglucan (SBP) was extracted using the optimized process combining enzymes from the fruiting bodies of Sanghuangporus baumii. SBP exhibited effective and dose-dependent antioxidant activities. Our findings were of great value in terms of developing polysaccharides with potential natural antioxidants.

Pemanfaatan Sistem Informasi Pengelolaan Data Prestasi Siswa Bagi Sekolah Menengah Atas

SMAN 1 Waru is experiencing rapid development, particularly in this digital era, with its students achieving notable success in sports, science, and arts. However, despite these achievements, the management of such accomplishments remains inadequate, rely-ing on manual systems. Consequently, the collection and recording of student achieve-ment data at SMAN 1 Waru are disorganized and often not up to date. Recognizing this issue, the community service team has developed an information system to manage stu-dent achievement data. This system includes comprehensive training to facilitate the ef-ficient processing of student achievements for both internal and external purposes, such as accreditation. The training session took place on March 28, 2024, at SMAN 1 Waru's computer laboratory and was attended by 35 students, website administrators, and three teachers. The training employed various methods, including lectures, practical exercises, and discussions. All participants demonstrated a clear understanding of the application's usage, as evidenced by their improved pre-test and post-test scores. Statistical analysis using the t-test confirmed the significance of this improvement, indicating that the train-ing effectively enhanced participants' understanding of using the student achievement website. Furthermore, the success of the training is reflected in the records of achieve-ment data inputted by the participants. Keywords: achievement data; information system; website.

Construction of a network intelligence platform for privacy protection and integrated big data mining

MOTIVATION: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy.

Concentration-Gradient Structural LiFe0.5Mn0.5PO4/C Prepared via Co-precipitation Reaction for Advanced Lithium-Ion Batteries.

The intrinsically low electronic conductivity and slow ion diffusion kinetics limit further development of olivine LiFexMn1-xPO4 cathode materials. In this paper, with the aim of improving the performance of such materials and alleviating the Jahn-Taller effect of Mn3+ ion, a bimetallic oxalate precursor with gradient distribution of elemental concentration followed with an efficient process is applied to synthesize LiFe0.5Mn0.5PO4 nanocomposite. The results shown that with certain structural modulation of the precursor, the discharge capacity of synthesized LiFe0.5Mn0.5PO4 increased from 149 mAh g-1 to 156 mAh g-1 at 0.1 C, the cycling capacity was also remarkably improved. the Fe0.5Mn0.5C2O4 ⋅ 2H2O-1 precursor with gradient distribution of elemental concentration effectively restricts the reaction between electrode material and electrolyte, thereby alleviates the dissolution of Mn3+ ion, reduces the decay of capacity and improves the stability of the material.

An advanced ensemble load balancing approach for fog computing applications

Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.

Live Supplier Catalog and Pricing for Omnichannel

This paper proposes a technical architecture for an Omni-channel Supplier Catalog that provides real-time price updates. This system leverages a combination of open-source technologies, including Apache Camel, Spark Connector, Kafka Topics, Cassandra database, and Kubernetes, deployed on a resilient and elastic cloud platform. This paper details the data pipeline design, outlining how each component contributes to efficient data ingestion, processing, and dissemination. Additionally, the paper highlights the benefits of using this architecture, including improved decision-making, enhanced operational efficiency, and a seamless omnichannel experience for customers.

Sulfonamide derivatives: Synthesis and applications

Sulfonamides belong to an important class of compounds which showed wide ranges of biological activities like antibacterial, antiviral, diuretic, hypo-glycemic, anticancer, anti-inflammatory and recently anti-covid-19. Over the last few decades, various pharmacological activities of sulfonamide conjugates were published. Moreover, many lead compounds with sulfonamide functionality are also in clinical trial for the treatment of various medical conditions. For these reasons, development of an efficient process for the synthesis of sulfonamides has always been in the focus for research in organic field synthesis. The most typical method for the synthesis involves reaction between primary or secondary amines and sulfonyl chloride in presence of organic or inorganic bases. Although this method is effective, but the nucleophilicity of amines may vary depending on the groups attached to it. In general, primary amines are highly reactive, whereas secondary amines show very low to almost nil reactivity. In this study, we have reviewed past and recent biological effects of some sulfonamide derivatives and some advances efficient synthetic procedures for some types of sulfonamides.

Personalized federated learning for dealing with computational latency and non-IID data scenarios

Federated learning (FL) is widely used because it is effective at enhancing data privacy. However, there will be many problems in the FL training process, such as poor performance of training models and the model converging too slowly, as the data is typically heterogeneous and the computing capabilities of the participants device are different. Here, we proposed an optimized FL model paradigm, that applies model arithmetic prediction to prevent the training process's inefficiency due to the participants' limited computational resources. The proposed formula for participant selection is based on posterior probabilities and correlation coefficients, which have been validated to reduce data noise and enhance the effect of central model aggregation. In addition, high-quality participant models are selected based on posterior probability, combined with correlation coefficients, which allows the server model to aggregate as many better-performing participant models as possible, meanwhile avoiding the impact of participants with too much data noise. During the aggregation step, the model loss values and the participant training delay are used to weight factors for participant devices, which accelerates FL convergence and improves model performance. Data heterogeneity and non-IID are fully taken into consideration in the method we proposed. Finally, these results have been verified by extensive experimental, we demonstrate better performance in the presence of non-IID data, especially affective computing. Compared with previous research, reduces training latency by 4 seconds, and the model accuracy is increased by 10% on average.

Investigation of soybean oil epoxidation process with phase transfer catalyst: Risk of thermal runaway

This paper explores an efficient and eco-friendly epoxidation process using the phase transfer catalyst ([(C18H37)2(CH3)2N]3{PO4[W(O)(O2)2]4}), which offers more advantages over the use of carboxylic and inorganic acids as catalysts in the Prileschajew epoxidation process. Consequently, a study of the process's thermal hazards is imperative. The paper conducts a comprehensive analysis of the process, employing a combination of calorimetric techniques. The critical runaway temperature, stabilization temperature, and required heat dissipation rate to prevent thermal runaway reactions were calculated using the Semenov model. On-line Fourier transform infrared spectroscopy and reaction calorimetry were used to relate the reaction mechanism and exothermic behavior of the actual production process, and a reliable model was developed for the calculation of reaction enthalpy. The findings indicate that the thermal risk depends on the rate of double bond epoxidation, which provides offering valuable insights for safe industrial-scale ESO production.

Study on the Microstructure, Corrosion Resistance and Dielectric Properties of Atmospheric Plasma-Sprayed Y2O3 Ceramic Coatings

Atmospheric plasma spraying (APS) is one of the most efficient processes for the preparation of yttrium oxide (Y2O3) ceramic coatings. Changing the spraying process parameters can significantly improve the microstructure and enhance the coating properties. In this study, the combination of plasma-spraying process parameters (current, spraying distance, and argon (Ar) flow) was varied by Response Surface Methodology (RSM) with the help of Minitab 19 software. Applied to the design of experiments, improvement of errors, and prediction of microstructure property results, the optimization and validation of experimental parameters for attaining the desired microstructure of Y2O3 coatings, especially porosity, was achieved. Process parameters were optimized by RSM: current 613.64 A, Ar flow rate 46.92 L/min, spray distance 15.38 cm, and optimum porosity 1.8% after optimization. Electrochemical corrosion experiments and breakdown voltage experiments revealed that the corrosion resistance and dielectric properties increased significantly as the porosity of the coatings decreased. Therefore, by optimizing the plasma-spraying process parameters, the porosity of the coatings can be significantly reduced and the corrosion resistance and dielectric properties of Y2O3 coatings can be effectively improved.

Natural air diffusion electrode modified with metal-organic frameworks derived Fe doped porous carbon for sulfamethazine degradation by heterogeneous electro-Fenton

A novel and efficient heterogeneous electro-Fenton process has been developed, utilizing a modified natural air diffusion electrode (NADE) as a bifunctional cathode. This cathode is based on Fe-doped porous carbon (Fe-PC(800)), which is derived from metal-organic frameworks (MOFs). This cathode has the unique ability to simultaneously generate and activate H2O2 in-situ without the need for aeration. Fe-PC(800) synthesized at a pyrolysis temperature of 800 °C exhibited the optimal catalytic efficiency for sulfamethazine (SMT) degradation over a wide pH between 3 and 11, attributed to the production of Fe0 and Fe3C which improved electron transfer efficiency. The presence of Fe0 facilitated the regeneration of Fe2+ and thus increased the activity of the bifunctional cathode in the Hetero-EF process. The co-action mechanism of •OH and 1O2 was confirmed by the radical quenching and electron paramagnetic resonance (EPR) investigations. NADE modified with Fe-PC(800) demonstrated broad applicability towards diverse contaminants and exceptional stability in ten consecutive cycling experiments. This study shed light on the viability of a straightforward and economically viable cathode utilizing NADE without the need of aeration in a Hetero-EF system, offering novel perspectives on bifunctional electrode design and the activation mechanism involving in-situ production and catalysis of H2O2.

Business Management in the Information Age: Use of Systems, Data Processing and Scalability for Organizational Efficiency

Abstract: This article reviews the challenges and opportunities facing companies in business management in the era of information. Challenges in managing large volumes of data, emerging trends in cybersecurity, and companies' ability to adapt to the digitalized environment are analyzed. The methodology used includes an exhaustive search of articles in indexed journals and the application of inclusion criteria to select 50 relevant articles. Key findings include obstacles in data management, the increasing sophistication of cyber threats, and business adaptation strategies such as digital transformation and the integration of emerging technologies. In conclusion, the importance of addressing these challenges and leveraging the opportunities presented by technology to enhance business efficiency and competitiveness is highlighted.