Efficient Process Research Articles

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.

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.

The Overlooked Potential of Sulfated Zirconia: Reexamining Solid Superacidity Toward the Controlled Depolymerization of Polyolefins.

Closed-loop recycling via an efficient chemical process can help alleviate the global plastic waste crisis. However, conventional depolymerization methods for polyolefins, which compose more than 50% of plastics, demand high temperatures and pressures, employ precious noble metals, and/or yield complex mixtures of products limited to single-use fuels or oils. Superacidic forms of sulfated zirconia (SZrO) with Hammet Acidity Functions (H0) ≤ - 12 (i.e., stronger than 100% H2SO4) are industrially deployed heterogeneous catalysts capable of activating hydrocarbons under mild conditions and are shown to decompose polyolefins at temperatures near 200 °C and ambient pressure. Additionally, confinement of active sites in porous supports is known to radically increase selectivity, coking and sintering resistance, and acid site activity, presenting a possible approach to low-energy polyolefin depolymerization. However, a critical examination of the literature on SZrO led us to a surprising conclusion: despite 40 years of catalytic study, engineering, and industrial use, the surface chemistry of SZrO is poorly understood. Ostensibly spurred by SZrO's impressive catalytic activity, the application-driven study of SZrO has resulted in deleterious ambiguity in requisite synthetic conditions for superacidity and insufficient characterization of acidity, porosity, and active site structure. This ambiguity has produced significant knowledge gaps surrounding the synthesis, structure, and mechanisms of hydrocarbon activation for optimized SZrO, stunting the potential of this catalyst in olefin cracking and other industrially relevant reactions, such as isomerization, esterification, and alkylation. Toward mitigating these long extant issues, we herein identify and highlight these current shortcomings and knowledge gaps, propose explicit guidelines for characterization of and reporting on characterization of solid acidity, and discuss the potential of pore-confined superacids in the efficient and selective depolymerization of polyolefins.

Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

Through-Space Charge-Transfer Organogold(III) Complexes Enable High-Performance X-ray Scintillation and Imaging.

Organic scintillators have recently attracted growing attention for X-ray detection in industrial and medical applications. However, these materials still face critical obstacles of low attenuation efficiency and/or inefficient triplet exciton utilization. Here we developed a new category of organogold(III) complexes, Tp-Au-1 and Tp-Au-2, through adopting a through-space interaction motif to realize high X-ray attenuation efficiency and efficient harvesting of triplet excitons for emission. Thanks to the efficient through-space charge transfer process, this panel of complexes achieved higher photoluminescence quantum yield and shorter radiative lifetimes compared with the through-bond reference complexes. Inspiringly, these organogold(III) complexes exhibited polarity-dependent emission origins: thermally activated delayed fluorescence and/or phosphorescence. Under X-ray irradiation, Tp-Au-2 manifested intense radioluminescence together with a record-high scintillation light yield of 77,600 photons MeV-1 for organic scintillators. The resulting scintillator screens demonstrated high-quality X-ray imaging with >16.0 line pairs mm-1 spatial resolution, outstripping most organic and inorganic scintillators. This finding provides a feasible strategy for the design of superior organic X-ray scintillators.

Data Communication Algorithm of HPDB Parallel Database System Based on Computer Network

High-Performance Database (HPDB) is designed to meet the demands of today’s modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system’s performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query’s conditions and the data’s placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis.

Purification of Anti-Mycobacterium tuberculosis MPT64 Immunoglobulin-Y from Egg-Yolk Supernatant Using Thiophilic Adsorption Chromatography

The significance of immunoglobulin Y (IgY) as a particular antibody equal to mammalian IgG is well understood. However, due to a lack of reliable purification procedures, producing highly pure IgY remains problematic. In this study, we aimed to optimize the recovery of pure IgY anti-MPT64 using thiophilic adsorption chromatography. The purification of IgY anti-MPT64 was achieved by initial PEG lipid precipitation, then an optimized purification by varying the gradient concentration of elution buffer into five steps gradient (0-20, 20-40, 40-60, 60-80, and 80-100%v/v) for three injection column volume (CV) each and two steps gradient (0-50 and 50-100%v/v) in eight CV for each concentration. The obtained IgY was characterized by SDS-PAGE and dot-blot then determined its content levels using the Lowry method. The results showed that the five steps gradient purification was found to provide a better purity level of IgY than the two steps gradient. However, the IgY content obtained in the two steps gradient purification (2.2632± 0.011 mg/mL) was higher than the five steps gradient purification (1.35482 ± 0.023 mg/mL). Nevertheless, both purified IgY results can recognize MPT64 protein through a dot blot test. Therefore, it can be summarized that thiophilic adsorption chromatography with five steps gradient of purification was an efficient process to obtain a higher purity of IgY anti-MPT64, especially to be targeted as a diagnostic kit component for MPT64 detection.

Metal‐Oxo Electronic Tuning via In Situ CO Decoration for Promoting Methane Conversion to Oxygenates over Single‐Atom Catalysts

AbstractDirect methane conversion (DMC) to oxygenates at low temperature is of great value but remains challenging due to the high energy barrier for C−H bond activation. Here, we report that in situ decoration of Pd1‐ZSM‐5 single atom catalyst (SAC) by CO molecules significantly promoted the DMC reaction, giving the highest turnover frequency of 207 h−1 ever reported at room temperature and ~100 % oxygenates selectivity with H2O2 as oxidant. Combined characterizations and DFT calculations illustrate that the C‐atom of CO prefers to coordinate with Pd1, which donates electrons to the Pd1−O active center (L−Pd1−O, L=CO) generated by H2O2 oxidation. The correspondingly improved electron density over Pd−O pair renders a favorable heterolytic dissociation of C−H bond with low energy barrier of 0.48 eV. Applying CO decoration strategy to M1‐ZSM‐5 (M=Pd, Rh, Ru, Fe) enables improvement of oxygenates productivity by 3.2–11.3 times, highlighting the generalizability of this method in tuning metal‐oxo electronic structure of SACs for efficient DMC process.

Augmenting roadway safety with machine learning and deep learning: Pothole detection and dimension estimation using in-vehicle technologies

Detection and estimation of pothole dimensions is an essential step in road maintenance. Aging, heavy rainfall, traffic, and weak underlying layers may cause pavement potholes. Potholes can cause accidents when drivers lose control after hitting or swerving to avoid them, which may lead to injuries or fatal crashes. Also, potholes may result in property damages, such as flat tires, scrapes, dents, and leaks. Additionally, potholes are costly; for example, in the United States, potholes cost drivers about $3 Billion annually. Traditional ways of attending to potholes involve field surveys carried out by skilled personnel to determine their sizes for quantity and cost estimates. This process is expensive, prone to errors, subjectivity, unsafe, and time-consuming. Some authorities use sensor vehicles to carry out the surveys, a method that is accurate, safer, and faster than the traditional approach but much more expensive; therefore, not all authorities can afford them. To avoid these challenges, a modern, real-time, cost-effective approach is proposed to ensure the efficient and fast process of pothole maintenance. This paper presents a Deep Learning model trained using the You Only Look Once (YOLO) algorithm to capture potholes and estimate their dimensions and locations using only built-in vehicle technologies. The model attained 93.0 % precision, 91.6 % recall, 87.0 % F1-score, and 96.3 % mAP. A statistical analysis of the on-site test results indicates that the results are significant at a 5 % level, with a p-value of 0.037. This approach provides an economical and faster way of monitoring road surface conditions.

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring.

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800ppb) to people at high risk of DKA (>75ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations insimulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

Combined Strategies Enable Highly Selective Light Olefins and para-Xylene Production on Single Catalyst Bed.

Achieving multiple high-value-added chemical production through novel reaction processes and shape-selective catalytic strategy is the key to realizing efficient low-carbon catalytic processes. In this work, a methanol-toluene coreaction system was developed, and combined control strategies of reaction pathway guidance and shape-selective catalysis were applied for the successful production of light olefins and para-xylene on single HZSM-5 catalyst bed. Cofeeding toluene additionally provides reactive and flowing aromatic hydrocarbon pool species that change the dominant reaction pathway in the complex network of the methanol reaction on HZSM-5 and promote the formation of ethylene. For the first time, the key reaction intermediates methylmethylenecyclodiene are directly captured and identified by experimental and theoretical techniques. This helps to propose the catalytic cycle for the dominant generation of ethylene and, more importantly, enriches the methanol-to-hydrocarbons (MTH) chemistry and hydrocarbon pool mechanism. Furthermore, 0.4HZSM-5@S-1-CLD, an optimized HZSM-5 catalyst modified by the silicalite-1 epitaxial growth followed by silanization approach, realizes highly selective production of light olefins (especially ethylene) and para-xylene, while excellent reactant activity is maintained. This highly efficient coreaction route gives an important leading significance in synthesizing the raw materials for the polyolefin and polyester industries. The establishment of the combined control strategies provides a model for the joint production of multiple target chemicals in complex catalytic processes.