Reduction Techniques Research Articles

Application of Sensitivity & Principal Component Analyses for Modelling of Safety Parameters for Oil & Gas Companies in Niger Delta

This study assesses the sensitivity analysis in classical multiple regression models as well as those of the principal component regression, PCR models. The study is a cross-sectional research design and data collection was through questionnaire distribution to employees of Oil & Gas indigenous (OGI) and multinational (OGM) companies in Niger Delta. The questionnaire was designed to obtain information from respondents on 7 safety constructs as independent variables against employee productivity as dependent variable. The questionnaire was designed using 5-point Likert scale, strongly agree, agree, disagree, strongly disagree and undecided with corresponding weighting of 5 – 1. The descriptive statistics featured the calculation of weighted arithmetic mean which served as input data for the calibration of both simple linear regression models, multiple principal component regression models and classical multiple regression models, respectively. SPSS was employed to calculate Cronbach's alpha coefficients, assessing the internal consistency of the research constructs; while XLSTAT, was used for principal component analysis, PCA & principal component regression, PCR. The relationship between the slope of simple linear regression equation for 7 independent variables/constructs were examined with respect to the corresponding goodness of fit. The result showed that the simple linear regression models of the Employee Productivity as dependent variable against each of the 7 independent variables (Employee Involvement, Management Commitment, Safety Compliance, Safety Knowledge, Safety Participation, Safety Promotional Policies & Safety Training) yielded positive slope values for both OGI & OGM. In contrast to the classical multiple regression model, for which the total goodness of fit value for the 7 constructs for OGI = 44.9 & 48.4% for OGM; the simple linear models for the same 7 construct yielded total goodness of fit of 81.2 & 171.1% for OGI & OGM, respectively. The total goodness of fit values for the simple linear regression were not only greater than those of classical multiple regression models, but exceeded 100% limit in classical multiple regression models. The principal component regression models established in this study is a variable reduction technique. Given that all the R2 values of PCR models (i.e. 3 & 4 constructs) are superior to the equivalent R2 values extracted from sensitivity values of classical multiple regression models; the reason is on the manner of solving the resulting simultaneous equations arising from those of PCR and classical multiple regression. The PCR modelling adopts the optimization technique, using XLSTAT version 16 in this study; while the classical multiple regression modelling utilizes the Gaussian method of solving the simultaneous linear equations. Therefore, the optimization method of solution of simultaneous equations using XLSTAT software is recommended.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

Dependency Reduction Techniques for Performance Improvement of Hyperledger Fabric Blockchain

Dependency Reduction Techniques for Performance Improvement of Hyperledger Fabric Blockchain

Adapting a Mobile Health App for Smoking Cessation in Black Adults With Anxiety Through an Analysis of the Mobile Anxiety Sensitivity Program Proof-of-Concept Trial: Qualitative Study.

At least half of smokers make a serious quit attempt each year, but Black adults who smoke are less likely than White adults who smoke to quit smoking successfully. Black adults who smoke and have high anxiety sensitivity (an individual difference factor implicated in smoking relapse and culturally relevant to Black adults) are even less successful. The Mobile Anxiety Sensitivity Program for Smoking (MASP) is a smoking cessation smartphone app culturally tailored to Black adults who smoke to increase smoking cessation rates by targeting anxiety sensitivity. This study examined the acceptability and feasibility of the MASP smartphone app following a 6-week pilot test through postintervention qualitative interviews. The MASP smoking cessation app was adapted from an evidence-based app by adding culturally tailored narration and images specific to the Black community, educational content on tobacco use in the Black community and the role of menthol, culturally tailored messages, and addressing tobacco use and racial discrimination. The MASP app was piloted with 24 adults with high anxiety sensitivity who identified as Black, smoked daily, and were not currently using medications or psychotherapy for smoking cessation. At the end of the 6-week pilot test, 21/24 participants (67% female; 95.2% non-Hispanic; mean age=47.3 years; 43% college educated; 86% single or separated) completed an audio-recorded semistructured interview assessing the acceptability and utility of the app, individual experiences, barriers to use, the cultural fit for Black adults who wanted to quit smoking, and identified areas for improvement. Transcribed interviews were coded using NVivo (Lumivero), and then analyzed for themes using an inductive, use-focused process. Most participants (17/21, 81%) had smoked for more than 20 years and 29% (6/21) of them smoked more than 20 cigarettes daily. Participants felt the MASP app was helpful in quitting smoking (20/21, 95%) and made them more aware of smoking thoughts, feelings, and behaviors (16/19, 84%). Half of the participants (11/21, 52%) thought the combination of medication and smartphone app gave them the best chance of quitting smoking. Themes related to participant experiences using the app included establishing trust and credibility through the recruitment experience, providing personally tailored content linked to evidence-based stress reduction techniques, and self-reflection through daily surveys. The culturally tailored material increased app relevance, engagement, and acceptability. Suggested improvements included opportunities to engage with other participants, more control over app functions, and additional self-monitoring functions. Adding culturally tailored material to an evidence-based mobile health (mHealth) intervention could increase the use of smoking cessation interventions among Black adults who want to quit smoking. Qualitative interviews provide mHealth app developers important insights into how apps can be improved before full study implementation and emphasize the importance of getting feedback from the target population throughout the development process of mHealth interventions. ClinicalTrials.gov NCT04838236; https://clinicaltrials.gov/ct2/show/NCT04838236.

Three-dimensional inversion algorithm of magnetotelluric data based on solution space dimensionality reduction technique

The magnetotelluric (MT) method is highly advantageous in geothermal resource exploration because of its deep probing capability, low cost, and sensitivity to key geothermal system elements such as heat sources, reservoirs, and caprocks. To address the challenge of balancing the MT inversion resolution and computational efficiency, we propose a Gauss–Newton optimization inversion algorithm based on the solution space dimensionality reduction technique. This algorithm uses solution space dimensionality reduction to establish algebraic mapping relationships between the edges of grid cells, effectively reducing the degrees of freedom in the linear equation system required for forward and pseudo-forward modeling, thereby saving computational costs. In addition, during the inversion process, a multiple right-hand direct–iterative hybrid solver is employed to solve the dimensionally reduced linear equation s, further enhancing the computational efficiency of the inversion. We then validate and analyze the accuracy and efficiency of the forward and inversion algorithms using various synthetic models. Finally, we apply this inversion strategy to field MT data from the Dabie Mountain geothermal area in eastern China. On the basis of the inversion results, we then analyze the thermal genesis model of the region, providing technical support for the exploration and development of deep geothermal resources.

Fast and accurate peak skin dose estimation method for interventional fluoroscopy patients.

In fluoroscopy, particularly fluoroscopically guided interventions (FGIs), accurate estimation of peak skin dose (PSD) is crucial for identifying potential radiation-induced skin injuries. Most current methodologies for PSD calculation methods rely on analytical methods, which may introduce uncertainty due to their limited consideration of the complexities of x-ray beam conditions, patient geometry, and positioning. Methods based on full Monte Carlo (MC) simulations can enhance accuracy, but their practical application is limited due to the intensive requirement of computational resources and time. We aimed to develop a novel method that combines MC simulation with a noise reduction technique to calculate PSD, as well as skin dose distributions, more efficiently and accurately. The goal was to overcome the limitations of current methods, providing a more practical solution for clinical and academic use. Our method to calculate the PSD and skin dose distributions consists of two steps of rough MC simulation and iterative noise reduction. The performance of the methodology was demonstrated for six fluoroscopy scenarios, with results compared against those from full MC simulation with high particle history, which is considered a gold standard for radiation dosimetry relative to conventional analytical methods. Our method was demonstrated for various fluoroscopy scenarios, and the result showed that the iterative noise reduction procedure successfully estimates PSD and skin dose distribution for rough MC simulations with a maximum dose statistical error of up to 20%. For successful dose estimations, PSD discrepancies from the values obtained by full MC simulation were within 3%, and voxel-wise dose differences in skin dose distributions were less than 10% of the average skin dose. The computation time of our method was on the order of a few seconds on a personal computer, which is estimated to be at least 104 times faster than full MC simulation when using the same computing resources. Our method rapidly and accurately calculates PSD and skin dose distribution, making it a useful tool for research and clinical applications. The planned integration of our method into the National Cancer Institute Dosimetry System for Radiography and Fluoroscopy (NCIRF) will enhance accessibility. Additionally, future upgrades of NCIRF will include a comprehensive phantom library and pregnant phantoms that will enable our method to account for patient-specific body shapes, further improving the accuracy and personalization in dose assessments.

Evaluating Dimensionality Reduction Techniques in Bitcoin Ransomware Detection: Comparative Analysis of Incremental PCA and UMAP

ABSTRACTThis study investigates the classification performance of various machine learning algorithms on the Bitcoin Heist ransomware dataset, focusing on the effects of dimensionality reduction techniques. The primary objective was to evaluate the classifiers' effectiveness in distinguishing between malicious and benign transactions under three experimental scenarios: without dimensionality reduction, utilizing Incremental Principal Component Analysis (IPCA), and applying Uniform Manifold Approximation and Projection (UMAP). The methodology involved rigorous experimentation with four classifiers: K‐Nearest Neighbors (KNN), XGBoost, Decision Tree, and Multi‐Layer Perceptron (MLP). The results demonstrated that dimensionality reduction techniques, particularly UMAP, improved the performance of KNN and Decision Tree classifiers while adversely affecting the performance of XGBoost and MLP. Notably, KNN consistently outperformed the other classifiers across different scenarios, indicating its robustness in handling reduced feature spaces. This study concludes that the effectiveness of dimensionality reduction is contingent upon the specific characteristics of the classifiers employed.

Embedding Antimony Nanoparticles into a Nitrogen-Doped Porous Carbon Matrix for High-Performance Potassium-Ion Battery Anode.

Antimony (Sb) has the advantages of high theoretical K+ storage capacity, low alloying potential, and excellent electrical conductivity, and it is a promising active anode material for potassium-ion batteries (PIBs). In this work, a series of Sb/NC nanocomposites were prepared by the sol-gel method and carbothermic reduction technique, encapsulating Sb particles in a nitrogen (N)-doped three-dimensional porous carbon matrix. The results show that Sb/NC nanocomposites with a unique porous structure and optimal Sb content can be obtained by simply adjusting the amount of organic carbon source during the synthesis process. When used as anodes for PIBs, they exhibit high capacity, good cycling stability, and excellent rate performance (at a current density of 100 mA/g, the initial reversible discharge specific capacity reached 476.9 mAh/g; it remained at 418.0 mAh/g after 50 cycles, corresponding to a capacity retention of approximately 87.6%. At a higher current density of 500 mA/g, the discharge capacity was 310.1 mAh/g). This can be attributed to the porous carbon matrix and uniformly dispersed Sb nanoparticles in Sb/NC, which together alleviate the stress during the K+ alloying process, enhancing its cycling stability. In addition, the N-doped carbon matrix not only enhances the diffusion rate of K+ but also significantly increases the contact area between active electrode material and electrolyte, thereby improving the K+ storage performance of Sb/NC at high current densities.

Advancing Security and Efficiency in IoT Healthcare Applications with Challenges Benefits and Latency Reduction Techniques

With the expansion of the Internet of Things (IoT) and cloud computing, conventional reliance on centralized cloud data centres for the storage, analysis, and real-time processing of vast data volumes presents significant challenges. This is particularly applicable to the elevated latency and stringent security demands necessary for real-time IoT healthcare applications. Critical and time-sensitive applications, including e-healthcare, telemedicine, and robotic surgery, necessitate ultra-low latency and stringent security measures. Suboptimal processing, connectivity, and networks impede the performance of these applications. Moreover, conventional cloud designs frequently fail to provide the necessary Quality of Service (QoS) for IoT healthcare systems. Consequently, this essay also explores latency reduction and security improvement techniques in IoT healthcare focusing on the need for information transmission in the respective areas. It wishes to list the first principles for approaches that reduce latency and protect communications, and computational architectures that can operate with such systems. It also explores the features that are crucial for understanding latency and security, as well as comparing several methods of addressing latency reduction and improvement in security alongside their effectiveness. It critically assesses previous approaches, identifies gaps in the literature and emphasizes unanswered questions in the study that may be useful in other works in this field. This research incorporates these findings into propelling concepts relative to IoT device connection and enhancing general paradigms relating to the better employment of healthcare applications. These findings support the need to design future IoT healthcare systems that will be latent sensitive, security-enhanced, and high-performance systems due to emerging characteristics of IoT in healthcare.

Generalizable Metamaterials Design Techniques Inspire Efficient Mycelial Materials Inverse Design.

Fungal mycelial materials can mimic numerous nonrenewable materials; they are even capable of outperforming certain materials at their own applications. Fungi's versatility makes mock leather, bricks, wood, foam, meats, and many other products possible. That said, there is currently a critical need to develop efficient mycelial materials design techniques. In mycelial materials, and the wider field of biomaterials, design is primarily limited to costly forward techniques. New mycelial materials could be developed faster and cheaper with robust inverse design techniques, which are not currently used within the field. However, computational inverse design techniques will not be tractable unless clear and concrete design parameters are defined for fungi, derived from genotype and bulk phenotype characteristics. Through mycelial materials case studies and a comprehensive review of metamaterials design techniques, we identify three critical needs that must be addressed to implement computational inverse design in mycelial materials. These critical needs are the following: 1) heuristic search/optimization algorithms, 2) efficient mathematical modeling, and 3) dimensionality reduction techniques. Metamaterials researchers already use many of these computational techniques that can be adapted for mycelial materials inverse design. Then, we suggest mycelium-specific parameters as well as how to measure and use them. Ultimately, based on a review of metamaterials research and the current state of mycelial materials design, we synthesize a generalizable inverse design paradigm that can be applied to mycelial materials or related design fields.

Preliminary Feasibility and Acceptability of a Culturally Specific Intervention for Reducing Sexual Revictimization of College Women.

More than 30% of women who experience sexual assault (SA) during college experience sexual revictimization (SRV) prior to graduating. Current SA interventions have been developed with predominantly White samples and most do not focus on reducing SRV or include effective alcohol reduction techniques. The purpose of the current study was to conduct a pilot randomized controlled trial to assess the feasibility and acceptability of a new intervention designed to reduce SRV in Black and White college women. A sample of 59 women (n = 43, White; n = 16, Black) were randomly assigned to either the intervention or time and attention control condition. Both conditions consisted of two, 90-minute in-person group sessions and two, 30-minute online self-administered learning units. During the intervention, participants watched culturally specific videos (e.g., created in partnership with the cultural group, culturally congruent with regard to race of actors, vernacular, dress, and social situations) embedded with SA risk cues. Women participated in discussions designed to improve risk recognition and assertive responding to SA threat. All groups were racially homogeneous with same race facilitators. Online intervention units included alcohol reduction strategies (e.g., personalized normative feedback) and safe dating practices. Acceptability and feasibility of the intervention were good and suggested that cultural specificity was important for Black women. However, attrition was higher than expected and barriers to participating were noted for Black women. These preliminary findings highlight the potential importance as well as the challenges in developing culturally specific SA interventions for college women.

Тестирование эффективности гибридной методики шумоподавления в речевом сигнале для системы видеоконференций

The issue of audio signal quality during video conferences is considered. The effect of noise on the quality and intelligibility of the speech signal is described. The analysis of the noise reduction process in the audio signal in real time is carried out. The main approaches to solving the problem of noise reduction in real time are highlighted, namely the approach with recognition and elimination of noise and the approach with voice recognition and elimination of sounds different from the speech signal. An algorithm for intelligent noise reduction using artificial intelligence methods has been developed, characterized by the presence of mechanisms for adjusting the “intensity” of the noise reduction effect on the speech signal, as well as adjusting the “sensitivity” to noise, allowing to influence the efficiency of the algorithm for specific operating conditions without the need to retrain the neural network model of the algorithm. Software has been developed that implements a hybrid noise reduction technique in the form of a module that was implemented in a previously developed software package for organizing and conducting video conferences. Three typical scenarios have been identified, for each of which a reference (noiseless) speech signal has been selected, as well as a set of noises for these scenarios for conducting two types of tests in order to test the effectiveness of the noise reduction module in various conditions of its application. Experiments were carried out with a test of the effectiveness of noise reduction of noise segments during speech pauses using the methods of RMS signal volume and visual spectrogram analysis with calculation of the percentage of the number of samples of rhythms in the output signal. Experiments were carried out with a test of the effectiveness of noise reduction of a noisy speech signal using objective methods for evaluating the quality of the speech signal ViSQOL and NISQA. The test results are presented in the form of tables.

Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.

Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1). EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes. We compared the performance of logistic regression, XGBoost, and random forest models utilizing each feature set. XGBoost-based predictive models were most successful at predicting NF1 sub-phenotypes. Overall, features based on domain knowledge-informed mapping schema performed better than unsupervised feature reduction methods. High-level features exhibited the worst performance across models and outcomes, suggesting excessive information loss with over-aggregation of features. Model performance is significantly impacted by dimensionality reduction techniques and varies by specific ML algorithm and outcome being predicted. Automated methods using existing knowledge and ontology databases can effectively aggregate features extracted from EHRs. Dimensionality reduction through feature aggregation can enhance the performance of ML models, particularly in high-dimensional datasets with small sample sizes, commonly found in EHRs health applications. However, if not carefully optimized, it can lead to information loss and data oversimplification, potentially adversely affecting model performance.

Stochastic automatic differentiation for Monte Carlo processes

Monte Carlo methods represent a cornerstone of computer science. They allow sampling high dimensional distribution functions in an efficient way. In this paper we consider the extension of Automatic Differentiation (AD) techniques to Monte Carlo processes, addressing the problem of obtaining derivatives (and in general, the Taylor series) of expectation values. Borrowing ideas from the lattice field theory community, we examine two approaches. One is based on reweighting while the other represents an extension of the Hamiltonian approach typically used by the Hybrid Monte Carlo (HMC) and similar algorithms. We show that the Hamiltonian approach can be understood as a change of variables of the reweighting approach, resulting in much reduced variances of the coefficients of the Taylor series. This work opens the door to finding other variance reduction techniques for derivatives of expectation values.

Barriers to uptake of harm reduction techniques for GBMSM who use drugs in night-clubs and sex-on-premises venues in London and the Southeast: a mixed-methods, qualitative study

BackgroundDrug-related harm is a significant public health concern in the UK, particularly among underserved groups such as gay, bisexual, and other men who have sex with men (GBMSM). This study explores the role of night-time venues (for example night clubs or sex-on-premises venues) in promoting harm reduction strategies for GBMSM who use drugs, highlighting unique challenges within these spaces.MethodsThe study used a mixed-methods approach, including an online survey (n = 53) and semi-structured interviews (n = 8). Participants included GBMSM with lived experience of substance use in night-time venues, as well as those providing support to this population. Data was collected through a Likert-scale survey and thematic analysis of qualitative responses.ResultsFindings reveal dissatisfaction among survey respondents about the level of support for harm reduction provided by night-time venues, which are perceived as inconsistent in their approach towards substance use. The study also identifies economic and legal barriers faced by venues that prevent the endorsement of harm reduction techniques.ConclusionsAddressing these barriers could transform night-time venues into effective sites for harm reduction, particularly by targeting “afters” culture (the phenomenon where club-goers will return to a residential setting and continue substance use for prolonged periods ‘after’ the night-time venue closes or the event ends) and promoting safer practices. This research suggests that coordinated efforts with local government and policy reform are crucial to fostering safer environments for GBMSM.

Expanded Approaches for Mastopexy in Aesthetic and Reconstructive Breast Surgery.

After studying this article, the participant should be able to: (1) Understand the unique differences between mastopexy in aesthetic and reconstructive breast surgery. (2) Describe the approach to performing mastopexy with autoaugmentation or after explantation. (3) Have insight into the approach and decision-making process for performing mastopexy with nipple-sparing mastectomy. (4) Apply the mastopexy skin reduction techniques to ptotic women undergoing postmastectomy breast reconstruction. Techniques and indications for mastopexy are expanding. In aesthetic surgery, access to supportive meshes and higher rates of breast implant explantation without replacement contribute to this. In breast reconstruction, higher rates of nipple-sparing mastectomy and higher expectations for cosmetically favorable outcomes with ptotic breasts are key drivers. A keen understanding of the available blood supply and how it may change over time with a staged approach, thoughtful use of otherwise-discarded tissues, and judicious application of mesh support are important new considerations for plastic surgeons to apply to traditional mastopexy. This evidence-based article focuses on specific aesthetic and reconstructive mastopexy techniques associated with autoaugmentation, explantation, and nipple-sparing mastectomy.

Parametric model order reduction for a wildland fire model via the shifted POD-based deep learning method

Parametric model order reduction techniques often struggle to accurately represent transport-dominated phenomena due to a slowly decaying Kolmogorov n-width. To address this challenge, we propose a non-intrusive, data-driven methodology that combines the shifted proper orthogonal decomposition (POD) with deep learning. Specifically, the shifted POD technique is utilized to derive a high-fidelity, low-dimensional model of the flow, which is subsequently utilized as input to a deep learning framework to forecast the flow dynamics under various temporal and parameter conditions. The efficacy of the proposed approach is demonstrated through the analysis of one- and two-dimensional wildland fire models with varying reaction rates, and its error is compared with the error of other similar methods. The results indicate that the proposed approach yields reliable results within the percent range, while also enabling rapid prediction of system states within seconds.

The Role of Lifestyle in the Prevention and Treatment of Insulin Resistance: A Review of Clinical and Intervention Studies

Introduction Insulin resistance (IR) is a metabolic disorder characterized by reduced tissue sensitivity to insulin, resulting in impaired glucose homeostasis. IR plays a pivotal role in the development of type 2 diabetes, metabolic syndrome, and associated cardiovascular complications. Effective strategies for the prevention and treatment of IR focus on lifestyle modification, including physical activity and stress management. Aim of the Study The aim of this study is to evaluate the role of lifestyle factors in the prevention and treatment of insulin resistance (IR) based on the analysis of clinical and interventional studies, with a particular focus on dietary interventions, physical activity programs, stress reduction techniques, and sleep quality improvement recommendations. Materials and Methods A review of clinical and interventional studies was conducted using databases such as PubMed. The analysis included studies evaluating the impact of specific lifestyle interventions on markers of insulin resistance (IR), such as fasting insulin levels, HOMA-IR indices and glucose tolerance. Particular attention was paid to randomized controlled trials (RCTs) and meta-analyses published between 2020 and 2024. Summary Studies have shown that diets such as the Mediterranean and DASH, combined with regular aerobic and resistance exercises, significantly improve insulin sensitivity. Additionally, stress-reduction techniques like mindfulness and yoga, along with adequate sleep quality, enhance positive metabolic effects. These findings highlight the need for a multifaceted approach to treating insulin resistance, incorporating dietary and behavioral changes. The review also points out research gaps and emphasizes the necessity of long-term studies to refine lifestyle recommendations for diverse populations

Reduction of nonlinear distortion in condenser microphones using a simple post-processing technique.

In this paper, an approach for effectively reducing nonlinear distortion in single-backplate condenser microphones is introduced, i.e., most microelectromechanical systems (MEMS) microphones, studio recording condenser microphones, and laboratory measurement microphones. This simple post-processing technique can be easily integrated on external hardware such as an analog circuit, microcontroller, audio codec, digital signal processing unit, or within the Application Specific Integrated Circuit chip in a case of MEMS microphones. It effectively reduces microphone distortion across its frequency and dynamic range, and relies on a single parameter, which can be derived from either the microphone's physical parameters or a straightforward measurement presented in this paper. An optimal estimate of this parameter achieves the best distortion reduction, whereas overestimating it never increases distortion beyond the original level. The technique was tested on a MEMS microphone. The findings indicate that for harmonic excitation, the proposed technique reduces the second harmonic by approximately 40 dB, leading to an effective reduction in the total harmonic distortion. The efficiency of the distortion reduction technique for more complex signals is demonstrated through two-tone and multitone experiments, where second-order intermodulation products are reduced by at least 20 dB.

A Novel Rolling Bearing Fault Diagnosis Method Based on the NEITD-ADTL-JS Algorithm

This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase sine wave signals to effectively extract the geometric mean of the intrinsic rotational component, and selects the optimal decomposition result based on the orthogonality index, significantly improving the quality and reliability of the signals. In addition, fault diagnosis parameters are adaptively optimized using an improved adaptive deep transfer learning (ADTL) network combined with the Jellyfish Search (JS) algorithm, further enhancing diagnostic performance. By innovatively combining signal noise reduction, feature extraction, and deep learning optimization techniques, this method significantly improves fault diagnosis accuracy and robustness. Comparative simulations and experimental analyses show that the NEITD algorithm outperforms traditional methods in both signal decomposition performance and diagnostic accuracy. Furthermore, the NEITD-ADTL-JS method demonstrates stronger sensitivity and recognition capabilities across various fault types, achieving a 5.29% improvement in accuracy.