Reduction Method Research Articles

A Local Model Reduction Method Based on $k$-Nearest-Neighbors for Parametrized Nonlocal Problems

A Local Model Reduction Method Based on $k$-Nearest-Neighbors for Parametrized Nonlocal Problems

Confined growth of ultrasmall monodisperse gold nanoparticles in amino functionalized cellulose beads as effective catalysts for 4-nitrophenol reduction.

This study aims to prepare a monodisperse catalyst with a space-confined strategy, employing cellulose beads (CBs) as a carrier. Amino functionalized cellulose beads (ACBs) were prepared by grafting polyethyleneimine into the space of CBs via glutaraldehyde cross-linking. The in-situ reduction method was successfully employed to confine monodisperse ultrasmall gold nanoparticles (AuNPs) within the amino functionalized cellulose beads (AuNPs@ACBs) matrix. The AuCl4- ions were first immobilized on the amino substance (such as -NH3+/-NH2+, CN and CN) by chelation and electrostatic interaction. Then the Au(III) was reduced to Au(0) by electron transfer. The presence of amino groups in ACBs controls the size and dispersion of AuNPs. AuNPs@ACBs was used as a catalyst for the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP), it reveals excellent catalytic activity, which can be ascribed to the smaller particle size of AuNPs (TOF = 7.86 min-1). The spherical structure of AuNPs@ACBs permits straightforward recovery from the aqueous solution after the reaction is completed, thus improving the reusability and catalytic stability. The catalytic efficiency of AuNPs@ACBs can still reach 64.7 % after 10 cycles. After more than 3000 min, the column continues to run without detecting 4-NP in the eluent, which is still working.

Catalytic transfer hydrogenolysis of lignin derived aromatic ethers over MOF derived porous carbon spheres anchored by Ni species

The efficient hydrogenolysis of CO ether bonds in lignin is the key for producing bio-oil and high-value chemicals. In this work, we synthesized a series of Ni-MOF-derived porous carbon spheres anchored Ni catalysts (Ni/C-x-T) with different metal/ligand molar ratios and calcination temperatures through solvothermal and carbothermal reduction method, and evaluated their catalytic transfer hydrogenolysis (CTH) performance for lignin model compounds using isopropanol as H-donor. The Ni/C-2-400 catalyst exhibited the excellent CTH performance, affording almost 100 % conversion of 2-phenoxy-1-phenylethanol even at a low reaction temperature of 120 °C. It was worth noting that the further hydrogenation of hydrogenolysis products phenol and ethylbenzene could be controlled by adjusting the reaction conditions, achieving phenol and ethylbenzene as main products at 120 °C, cyclohexanol and ethylbenzene at 140 °C, and cyclohexanol and ethylcyclohexane at 200 °C for 4 h. Based on the characterization results, the high catalytic activity of Ni/C-2-400 was attributed to the good dispersion and small particle size of metal Ni particles. Mechanistic studies showed that the cleavage of CO ether bonds was the main reaction pathway, and high temperature helped accelerate hydrogenolysis and subsequent hydrogenation. Moreover, the Ni/C-2-400 catalyst had good stability and applicability to other model compounds. This work could provide some help for the upgrading of lignin and its derivative.

Mesoporous La-substituted nickel-cobalt ferrites synthesized via reduction method resulting in significantly enhanced adsorption properties

Mesoporous La-substituted nickel-cobalt ferrites synthesized via reduction method resulting in significantly enhanced adsorption properties

Janus Cobalt Sites on Carbon Nitride for Efficient Photocatalytic Overall Water Splitting

Regulating the dual active sites is crucial for enhancing the carrier-directed migration efficiency and shortening mass transfer distance of intermediates, particularly in photocatalytic overall water splitting. In this paper, we adopt in situ hydrothermal coupled gas phase chemical reduction methods to synthesize janus cobalt cocatalysts on g-C3N4. Experimental measurement and density functional theory calculations show that the janus cobalt cocatalysts fine-tunes the local electronic structure of g-C3N4, which can greatly reduce energy barriers and shorten mass transfer distance of intermediates for reactions. And while the built-in electric field of CoP and CoO also further efficiently facilitates rapid directional separation of interface carriers of the cocatalysts. This study elucidates atom-level mechanisms underlying overall water splitting and offers valuable insights for rational design of high-performance catalysts for overall water splitting. As a result, the CoP/CoO@g-C3N4 samples exhibit a remarkable enhancement in overall water splitting activity (133.2 μmol g−1 h−1 H2 and 67.2 μmol g−1 h−1 O2), surpassing that of the CoP@g-C3N4 and CoO@g-C3N4 samples by 1.4 and 3.8 times, respectively.

Identification and validation of an immune-related miRNA signature for predicting prognosis of hepatocellular carcinoma.

MicroRNAs play a significant role in the initiation and progression of hepatocellular carcinoma (HCC); however, their roles in immune regulation of HCC remain unclear. Our study aimed to identify an immune-related miRNA signature and explore its impact on the prognosis and tumor immune microenvironment HCC. Initially, we identified 48 differentially expressed immune-related miRNAs. Using the LASSO regression dimensionality reduction method, we constructed an immune-related miRNA signature from 12 of these miRNAs. This signature has emerged as an independent prognostic marker and is associated with the clinical stage of HCC. To elucidate the roles of the twelve-microRNA signature, we predicted their target genes. Enrichment analysis indicated that these target genes were involved in immune cell infiltration. Notably, the target genes regulated by hsa-miR-139-5p, hsa-miR-551a, and hsa-miR-7-5p showed a partial overlap. We further confirmed the differential expression of miR-7, miR-551a, miR-139-5p, and some of their overlapping target genes in tumor and non-tumor tissues derived from patients with HCC using RT-qPCR. Overall, we identified an immune-related miRNA signature that is strongly correlated with the prognosis and immune microenvironment of HCC; and confirmed the differential expression of the three most important microRNAs and their overlapping target genes in tumor and non-tumor tissues derived from HCC patients.

Research Progress on Signal Noise Reduction Methods in Distributed Fiber Optic Sensing Technology Based on Brillouin Scattering

Research Progress on Signal Noise Reduction Methods in Distributed Fiber Optic Sensing Technology Based on Brillouin Scattering

Localized surface plasmon resonance sensing based on monometallic gold nanoparticles: from material preparation to detection of bioanalytes.

The tunable geometrical properties of gold nanoparticles (AuNPs) endow them with the capacity to exhibit distinct behaviors with respect to both macroscopic (color) and microscopic (resonance wavelength) aspects, which has been extensively utilized in localized surface plasmon resonance (LSPR) sensing platforms. Additionally, functionalizing AuNP surfaces enhances the platforms' capabilities, allowing for the detection of a wide range of molecules related to various aspects of human health. In this review, we comprehensively elucidate the fundamental principles of LSPR biosensing and provide an in-depth survey of the preparation processes for metal nanoparticles, encompassing deposition technology for large-scale particle production as well as ion reduction methods that afford superior control over the particles' physical and chemical attributes. The sensing strategies based on adjustment of the dielectric environment and particle dispersion-aggregation levels are thoroughly reviewed and discussed. The discussion focused on a specific class of nanoparticles, characterized by their uniform shape and size, with each section bifurcated into two parts: a summary of the salient features and recent discoveries pertaining to the sensing strategy, as well as illustrations of representative, cutting-edge applications employing the strategy. We specifically aim to scrutinize analytes commonly encountered in the biomedical realm, encompassing biomarkers that serve as indicators of a wide range of diseases and microbial pathogens, while also prognosticating the future development trends of LSPR optical biosensor platforms within the biomedical field.

Environmentally friendly green synthesis of reduced graphene oxide using Laurus nobilis, characterization and evaluation of in vitro anticancer activity

Abstract Graphene, due to its remarkable electrical, mechanical, thermal, and optical characteristics, has become a highly desirable material for scientific investigation. A promising method for large-scale production involves reduction of graphene oxide (GO), derived from graphite through various chemical processes. Although chemical reduction remains the most effective technique for producing reduced graphene oxide (rGO), its environmental impact raises concerns. To address this, researchers are increasingly exploring reduction methods that utilize natural ingredients as a more sustainable alternative.

Second Generation Antipsychotics and Cardiovascular Adverse Effects: Developing Evidence-Based Recommendations for Primary Care Medicine.

Second-generation antipsychotic medications (SGAs) are often used by primary care physicians (PCPs) to treat multiple psychiatric diagnoses. SGAs have been connected to a number of adverse effects, including cardiovascular disease. Currently, there are no published evidence-based recommendations addressing SGAs and cardiotoxicity that are directed toward PCPs. This project aims to fill this gap. Relevant search terms related to SGAs and cardiovascular disease were identified and then used to search databases (PubMed, PubMed Central, and AccessMedicine). Research studies obtained from the searches were narrowed to include systematic reviews and meta-analyses. The Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) tool was utilized by 2 investigators to evaluate the quality of the research independently. Research rated by both investigators to be of high and moderate quality using the AMSTAR-2 system was employed to develop recommendations for PCPs when using SGAs with their patients. Seven systematic reviews and meta-analyses were found to be of high or moderate quality by AMSTAR-2 evaluations. The studies identified associations between SGAs and a number of adverse cardiac conditions, including corrected QT interval (QTc) prolongation, arrhythmias, myocardial infarction, tachycardia, and other cardio-metabolic associated morbidities. Our project indicated that cardiovascular risks can be associated with SGAs and that screening and monitoring for cardiac toxicities are indicated when treating patients with this class of medications. These risks, however, do not appear to override benefits in most patients. Furthermore, it appears that when using SGAs, methods of risk reduction can be applied effectively in the primary care setting.

Analysis of Flood Damage Mitigation Effects Based on the Presence of a Rainwater-Retaining Facility Through Flood-Depth Distribution Analysis by Grid

In recent years, localized heavy rainfall due to climate change has caused increased flood damage in urban areas. To address this issue, efforts are being made to mitigate damage through the installation of a rainwater-retaining facility and improved sewer systems. Various methods have been proposed to analyze the flood mitigation effects of these measures, with commonly used approaches including the analysis of overflow volume, peak flood discharge, and flood maps. However, these methods have limited capacity to accurately assess the reduction in flood depth, which directly affects human safety and property. Additionally, the use of flood maps requires visual analysis, which makes it difficult to evaluate the reduction effects accurately. Therefore, this study proposes a new method for flood reduction using grid-based flood-depth distribution analysis. The maximum flood depth for each grid was determined using the XP-SWMM urban runoff model, and the number of grids for each 0.1-meter flood-depth interval was analyzed using a Python program. Consequently, under various rainfall scenarios and boundary conditions, the proposed analysis method enables a more quantitative assessment of flood mitigation effects by comparing the number of reduced flood-depth grids before and after the installation of the rainwater retention facility. This approach also allows for a more precise assessment of the reconstruction of flood-depth grids, which directly affects human safety and property.

An incremental attribute reduction algorithm based on the matrix dimension reduction method

In view of the dynamic change of decision system data in practical problems, the concept of attribute importance function is proposed, which eff ectively avoids the defi ciency of considering only each attribute based on importance reduction algorithm. In the calculation process of matrix reduction algorithm, the sample combination explosion between big data causes huge time consumption. The matrix dimension reduction processing method is proposed, which greatly improves the effi ciency and accuracy of calculation. Finally, an incremental reduction algorithm based on matrix dimensionality reduction is presented, and the correctness of the algorithm is tested. Meanwhile, the results of the four algorithms are compared to verify the eff ectiveness of the algorithm.

Bearing fault signal detection based on non-negative matrix factorization and dual-feedback segmented unsaturated tristable stochastic resonance system

Abstract Stochastic resonance is widely used in bearing fault detection due to its ability to enhance weak signals. This paper proposes a fault detection method that combines noise reduction with stochastic resonance. Firstly, a segmented unsaturated potential function based on the classic potential function is constructed, and a dual-feedback structure is introduced to feed the system output back to the input, thereby enhancing system performance. Secondly, the theoretical expressions for the Mean First Passage Time (MFPT) and the Spectral Amplification (SA) of the Dual-feedback Segmented Unsaturated Tristable Stochastic Resonance (DSUTSR) system are derived and analyzed. Additionally, a numerical simulation comparison using the fourth-order Runge-Kutta method is performed between the DSUTSR system and its predecessor systems to verify the improvements brought by the dual-feedback structure. Subsequently, Non-negative Matrix Factorization (NMF) is introduced as a noise reduction method, with cross-validation used to determine the decomposition rank of NMF to guide the decomposition of the fault signal matrix. Finally, the combination of NMF and the DSUTSR system is used to detect bearing fault frequencies under white noise and Lévy noise backgrounds. Experimental results demonstrate the superiority and effectiveness of the proposed method in fault signal detection. This system holds significant potential for future weak signal detection, effectively enhancing and identifying fault signals hidden within noisy backgrounds.

First Basic Problem of Elasticity Theory for a Composite Layer with Two Thick-Walled Tubes

The spatial problem of elasticity theory for a fibrous composite in the form of a layer with two thick-walled cylindrical tubes is solved. Stresses are given on the flat surfaces of the layer and on the inner surface of the tubes. The solution to the problem is presented in the form of Lamé equations in different coordinate systems, where the layer is considered in a Cartesian system and the tubes – in local cylindrical ones. To combine the basic solutions in different coordinate systems, the generalized Fourier method is used. Satisfying the boundary conditions and conjugation conditions between the layer and the tubes, an infinite system of integro-algebraic equations is formed, which is reduced to linear algebraic equations of the second kind, and the reduction method is applied. After finding the unknowns, it is possible to obtain the stress-strain state at any point of the elastic combined bodies using the generalized Fourier method to the basic solutions of the problem. According to the results of numerical studies, it can be stated that the problem can be solved with a given accuracy, which depends on the order of the system of equations and has a rapid convergence of solutions to the exact one. Numerical analysis of the stressed state was considered with a variation of the distance between the tubes. The graphs of the distribution of internal stresses in the tubes and the layer are obtained. The results show an inverse relationship between the magnitude of stresses and the distance between the tubes. In addition to the absolute value of stresses, changes in the character of the diagrams and the sign are possible. The proposed method of solution can be applied in the design of a layer with tubes. The obtained stress-strain state makes it possible to preliminarily evaluate the geometric parameters of the structure. Further development of the research topic is necessary for a model where tubes are combined with other types of inhomogeneities.

Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment.

In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk remains underexplored. The current practice of failure risk diagnosis is impeded by the manual intervention of managers, leading to varying evaluations for identical products or similar facilities. This study aims to bridge this gap by implementing advanced data analysis techniques on maintenance data from an aluminum extruder. We have employed text embedding, dimensionality reduction, and feature extraction methods, integrating the K-means algorithm with the Silhouette Score for risk level classification. Our findings reveal that the combination of Word2Vec for embedding and Contractive Auto Encoder for dimensionality reduction and feature extraction yields high-performance results. The optimal cluster count, identified as three, achieved the highest Silhouette Score. Statistical analysis using one-way ANOVA confirmed the significance of these findings with a p-value of 5.3213 × e-6, well within the 5% significance threshold. Furthermore, this study utilized BERTopic for topic modeling to extract principal topics from each cluster, facilitating an in-depth analysis of the clusters in relation to the extruder's characteristics. The outcome of this research offers a novel methodology for facility managers to objectively diagnose equipment failures. By minimizing subjective judgment, this approach is poised to significantly enhance the efficacy of quality assurance systems in manufacturing, leveraging the robust capabilities of artificial intelligence.

A Review on Troubleshooting Effective Factors in Raw Milk Off-Flavor Production: Its Control, Reduction, and Elimination Methods

Milk, a vital product derived from mammalian mammary glands, is essential for newborn nourishment and widely consumed as a food source. However, various internal and external factors can negatively impact milk’s quality, particularly its odor and flavor, which can limit its marketability and cause significant economic losses for farmers. The olfactory sense, being much more sensitive than the sense of taste, plays a key role in the perception of milk’s freshness. This review aims to evaluate the factors contributing to off-flavors and odors in raw milk and the methods for controlling, reducing, and eliminating these undesirable characteristics. Through the examination of published literature, preventive strategies and treatment options are explored, including heating, vapor treatment, Vitamin E supplementation, food additives, and flavoring agents. Additionally, proper management of nutrition, mastitis, and other influencing factors are discussed as effective ways to prevent off-flavors. By addressing these challenges, the economic situation of farmers can be improved, leading to enhanced milk quality, greater consumer acceptance, and increased profitability in the dairy sector.

Wavelet as a feature vector reduction method used in the recognition of urban buildings

Building recognition is a very difficult task to achieve, since their images have different angles and lighting conditions and partial obstructions from trees, moving vehicles or other buildings. Thus, due to all these difficulties, this work proposes a recognition system that uses the Gabor filter representation for feature extraction and the Wavelet transform for dimensionality reduction, and the Multilayer Perceptron artificial neural network for recognition. To verify the efficiency of this system, a database of images of new and old buildings was used. The performance of the method stands out significantly in relation to other methods found in other published works.

Comparative evaluation of feature reduction methods for drug response prediction

Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability. This study presents the first comparative evaluation of nine different knowledge-based and data-driven feature reduction methods on cell line and tumor data. Our analysis employs six distinct machine learning models, with a total of more than 6,000 runs to ensure a robust evaluation. Our findings indicate that transcription factor activities outperform other methods in predicting drug responses, effectively distinguishing between sensitive and resistant tumors for seven of the 20 drugs evaluated.

IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.

Galerkin-Type Solution of the Föppl–von Kármán Equations for Square Plates

The solution of the non-linear Föppl–von Kármán equations for square plates in the form of expansion over a system of eigenfunctions, generated by a linear self-adjoint operator, is obtained. The coefficients of the expansion are determined via the reduction method from the infinite-dimensional system of cubic equations. This allows the proposed solution to be considered as a non-linear generalization of the classical Galerkin approach. The novelty of the study is in the strict formulation of the auxiliary boundary problem, which makes it possible to take into account a rigid fixation against any displacements along the boundary. To verify the proposed solution, it is compared with experimental data. The latter is obtained by the holographic interferometry of small deflection increments superimposed on the large deflection caused by initial pressure. Experiment and theory show a good agreement.