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Background: Limited information is available on factors that affect the burden tinnitus. The aim of this study is to investigate the association between tinnitus burden and demographic, patient-specific and tinnitus characteristics. Secondly, it was examined which variables could predict a change in tinnitus burden after 12 months. Method: In a prospective Dutch cohort of 383 tinnitus patients seeking medical help, tinnitus complaints, demographics, tinnitus characteristics, psychological wellbeing and quality of life were assessed using an online self-report survey at three timepoints (start, 6 months, 12 months). The main outcome variables for tinnitus burden are the Tinnitus Questionnaire (TQ) and Visual Analog Scale (VAS) for tinnitus burden and loudness. Results: Several variables (time, sex, education level, life events, anxiety and depression, sleep issues, tinnitus loudness, hearing impairment and treatment) were significantly associated with tinnitus burden. Additionally, tinnitus burden after 12 months was associated with anxiety, following treatment, sleep issues, negative life events and hearing impairment (increase) and anxiety, total of life events and environmental quality of life (decrease) predicted the tinnitus burden after 12 months. Conclusions: Several factors, such as education level, life events, psychological factors and sleep quality, are related to tinnitus burden and can predict tinnitus burden over time. Full article

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22 pages, 1155 KiB

Open AccessArticle

Advanced Comparative Analysis of Machine Learning and Transformer Models for Depression and Suicide Detection in Social Media Texts

by Biodoumoye George Bokolo and Qingzhong Liu

Electronics 2024, 13(20), 3980; https://doi.org/10.3390/electronics13203980 (registering DOI) - 10 Oct 2024

Abstract

Depression detection through social media analysis has emerged as a promising approach for early intervention and mental health support. This study evaluates the performance of various machine learning and transformer models in identifying depressive content from tweets on X. Utilizing the Sentiment140 and the Suicide-Watch dataset, we built several models which include logistic regression, Bernoulli Naive Bayes, Random Forest, and transformer models such as RoBERTa, DeBERTa, DistilBERT, and SqueezeBERT to detect this content. Our findings indicate that transformer models outperform traditional machine learning algorithms, with RoBERTa and DeBERTa, when predicting depression and suicide rates. This performance is attributed to the transformers’ ability to capture contextual nuances in language. On the other hand, logistic regression models outperform transformers in another dataset with more accurate information. This is attributed to the traditional model’s ability to understand simple patterns especially when the classes are straighforward. We employed a comprehensive cross-validation approach to ensure robustness, with transformers demonstrating higher stability and reliability across splits. Despite limitations like dataset scope and computational constraints, the findings contribute significantly to mental health monitoring and suggest promising directions for future research and real-world applications in early depression detection and mental health screening tools. The various models used performed outstandingly. Full article

(This article belongs to the Special Issue Information Retrieval and Cyber Forensics with Data Science)

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15 pages, 6728 KiB

Open AccessArticle

Flexural Analysis of Additively Manufactured Continuous Fiber-Reinforced Honeycomb Sandwich Structures

by Rafael Guerra Silva, Esteban Gonzalez, Andres Inostroza and Gustavo Morales Pavez

J. Manuf. Mater. Process. 2024, 8(5), 226; https://doi.org/10.3390/jmmp8050226 (registering DOI) - 10 Oct 2024

Abstract

This study explores the flexural behavior of continuous fiber-reinforced composite sandwich structures built entirely using material extrusion additive manufacturing. The continuous fiber additive manufacturing system used in this study works sequentially, thus enabling the addition of fiber reinforcement just in the face sheets, where it is most effective. Three-point bending tests were carried out on sandwich panel specimens built using thermoplastic reinforced with continuous glass fiber to quantify the effect of fiber reinforcement and infill density in the flexural properties and failure mode. Sandwich structures containing continuous fiber reinforcement had higher flexural strength and rigidity than unreinforced sandwiches. On the other hand, an increase in the lattice core density did not improve the flexural strength and rigidity. The elastic modulus of fiber-reinforced 3D-printed sandwich panels exceeded the predictions of the analytical models; the equivalent homogeneous model had the best performance, with a 15% relative error. However, analytical models could not correctly predict the failure mode: wrinkle failure occurs at 75% and 30% of the critical load in fiber-reinforced sandwiches with low- and high-density cores, respectively. Furthermore, no model is currently available to predict interlayer debonding between the matrix and the thermoplastic coating of fiber layers. Divergences between analytical models and experimental results could be attributed to the simplifications in the models that do not consider defects inherent to additive manufacturing, such as air gaps and poor interlaminar bonding. Full article

(This article belongs to the Special Issue Advances in Additive Manufacturing and Material Characterization Techniques)

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23 pages, 14291 KiB

Open AccessArticle

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process

by Xuguo Yan, Shiyang Zhou, Huan Zhang and Cancan Yi

Materials 2024, 17(20), 4943; https://doi.org/10.3390/ma17204943 (registering DOI) - 10 Oct 2024

Abstract

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model’s adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model’s potential as a reliable tool for real-time RUL prediction in industrial applications. Full article

(This article belongs to the Section Materials Physics)

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20 pages, 4388 KiB

Open AccessArticle

A Software Defect Prediction Method That Simultaneously Addresses Class Overlap and Noise Issues after Oversampling

by Renliang Wang, Feng Liu and Yanhui Bai

Electronics 2024, 13(20), 3976; https://doi.org/10.3390/electronics13203976 (registering DOI) - 10 Oct 2024

Abstract

Software defect prediction datasets often suffer from issues such as class imbalance, noise, and class overlap, making it difficult for classifiers to identify instances of defects. In response, researchers have proposed various techniques to mitigate the impact of these issues on classifier performance. Oversampling is a widely used method to address class imbalance. However, in addition to inherent noise and class overlap in the datasets themselves, oversampling methods can introduce new noise and class overlap while addressing class imbalance. To tackle these challenges, we propose a software defect prediction method called AS-KDENN, which simultaneously improves the effects of class imbalance, noise, and class overlap on classification models. AS-KDENN first performs oversampling using the Adaptive Synthetic Sampling Method (ADASYN), followed by our proposed KDENN method to address noise and class overlap. Unlike traditional methods, KDENN takes into account both the distance and local density information of overlapping samples, allowing for a more reasonable elimination of noise and instances of overlapping. To demonstrate the effectiveness of the AS-KDENN method, we conducted extensive experiments on 19 publicly available software defect prediction datasets. Compared to four commonly used oversampling techniques that also address class overlap or noise, the AS-KDENN method effectively alleviates issues of class imbalance, noise, and class overlap, subsequently improving the performance of the classifier models. Full article

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25 pages, 4658 KiB

Open AccessArticle

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles

by Mehdi Shirzadi, Mónica Rojas Martínez, Joan Francesc Alonso, Leidy Yanet Serna, Joaquim Chaler, Miguel Angel Mañanas and Hamid Reza Marateb

Diagnostics 2024, 14(20), 2255; https://doi.org/10.3390/diagnostics14202255 (registering DOI) - 10 Oct 2024

Abstract

Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders. Full article

(This article belongs to the Special Issue Advanced Diagnosis and Management of Chronic Musculoskeletal Disorders)

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22 pages, 11975 KiB

Open AccessArticle

Fall Risk Classification Using Trunk Movement Patterns from Inertial Measurement Units and Mini-BESTest in Community-Dwelling Older Adults: A Deep Learning Approach

by Diego Robles Cruz, Sebastián Puebla Quiñones, Andrea Lira Belmar, Denisse Quintana Figueroa, María Reyes Hidalgo and Carla Taramasco Toro

Appl. Sci. 2024, 14(20), 9170; https://doi.org/10.3390/app14209170 (registering DOI) - 10 Oct 2024

Abstract

Falls among older adults represent a critical global public health problem, as they are one of the main causes of disability in this age group. We have developed an automated approach to identifying fall risk using low-cost, accessible technology. Trunk movement patterns were collected from 181 older people, with and without a history of falls, during the execution of the Mini-BESTest. Data were captured using smartphone sensors (an accelerometer, a gyroscope, and a magnetometer) and classified based on fall history using deep learning algorithms (LSTM). The classification model achieved an overall accuracy of 88.55% a precision of 90.14%, a recall of 87.93%, and an F1 score of 89.02% by combining all signals from the Mini-BESTest tasks. The performance outperformed the metrics we obtained from individual tasks, demonstrating that aggregating all cues provides a more complete and robust assessment of fall risk in older adults. The results suggest that combining signals from multiple tasks allowed the model to better capture the complexities of postural control and dynamic gait, leading to better prediction of falls. This highlights the potential of integrating multiple assessment modalities for more effective fall risk monitoring. Full article

(This article belongs to the Special Issue Falls: Risk, Prevention and Rehabilitation (2nd Edition))

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17 pages, 1944 KiB

Open AccessArticle

A Water Level Forecasting Method Based on an Improved Jellyfish Search Algorithm Optimized with an Inverse-Free Extreme Learning Machine and Error Correction

by Qiwei Zhang, Weiwei Shou, Xuefeng Wang, Rongkai Zhao, Rui He and Chu Zhang

Water 2024, 16(20), 2871; https://doi.org/10.3390/w16202871 (registering DOI) - 10 Oct 2024

Abstract

Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, reducing flood risks, and promoting ecological and environmental protection, which is crucial for the sustainable development of society. This study proposes a hybrid water level forecasting model based on Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Inverse-Free Extreme Learning Machine (IFELM), and error correction. Firstly, historical water level data are decomposed into different modes using TVFEMD; secondly, the Improved Jellyfish Search (IJS) algorithm is employed to optimize the IFELM, and subsequently, the optimized IFELM independently forecasts each sub-sequence and obtains the predictive results of each sub-sequence; thirdly, an Online Sequential Extreme Learning Machine (OSELM) model is used to correct data errors, and the initial predictive results and error prediction results are added together to obtain the final prediction for the sub-sequence; and finally, the final prediction for the sub-sequences are added to obtain the prediction results of the entire water level sequence. Taking the daily water level data from 2006 to 2018 in Taihu, China as the research object, this paper compares the proposed model with the ELM, BP, LSTM, IFELM, TVFEMD-IFELM, and TVFEMD-IFELM-OSELM models. The results show that the TVFEMD-IJS-IFELM-OSELM model established in this study has high prediction accuracy and strong stability and is suitable for water level forecasting. Full article

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17 pages, 4158 KiB

Open AccessArticle

Investigations of the Windage Losses of a High-Speed Shrouded Gear via the Lattice Boltzmann Method

by Yu Dai, Caihua Yang and Xiang Zhu

Appl. Sci. 2024, 14(20), 9174; https://doi.org/10.3390/app14209174 (registering DOI) - 10 Oct 2024

Abstract

To suppress the adverse effect of the gear windage phenomenon in the high-speed aeronautic industry, a shroud as an effective alternative strategy is usually to enclose gears to reduce the windage behaviors of high-speed gears. To deeply understand these no-load power losses, this paper proposes a new simulation methodology based on the Lattice Boltzmann method to study the windage losses of a shrouded spur gear and conducts a series of numerical studies. The models reproduce a shroud spur gear varying radial and axial clearances to evaluate the influence of casing walls on windage losses. The simulation results were then compared with experimental values, showing a satisfactory agreement. Furthermore, a torque containment factor integrating the air compressibility at high Mach numbers is introduced to represent the reduction in torque (windage power losses) for the shrouded gear compared to the free case, and the theoretical formulae for predicting windage power losses are further improved for better applicability as the tight shroud approaches the gear during the preliminary design stage. Full article

(This article belongs to the Special Issue Mathematical Methods and Simulations in Mechanics and Engineering)

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40 pages, 6085 KiB

Open AccessReview

Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites

by K. Rooney, Y. Dong, A. K. Basak and A. Pramanik

J. Compos. Sci. 2024, 8(10), 416; https://doi.org/10.3390/jcs8100416 (registering DOI) - 10 Oct 2024

Abstract

This review explores fundamental analytical modelling approaches using conventional composite theory and artificial intelligence (AI) to predict mechanical properties of 3D printed particle-reinforced resin composites via digital light processing (DLP). Their mechanisms, advancement, limitations, validity, drawbacks and feasibility are critically investigated. It has been found that conventional Halpin-Tsai model with a percolation threshold enables the capture of nonlinear effect of particle reinforcement to effectively predict mechanical properties of DLP-based resin composites reinforced with various particles. The paper further explores how AI techniques, such as machine learning and Bayesian neural networks (BNNs), enhance prediction accuracy by extracting patterns from extensive datasets and providing probabilistic predictions with confidence intervals. This review aims to advance a better understanding of material behaviour in additive manufacturing (AM). It demonstrates exciting potential for performance enhancement of 3D printed particle-reinforced resin composites, employing the optimisation of both material selection and processing parameters. It also demonstrates the benefit of combining empirical models with AI-driven analytics to optimise material selection and processing parameters, thereby advancing material behaviour understanding and performance enhancement in AM applications. Full article

(This article belongs to the Special Issue Feature Papers in Journal of Composites Science in 2024)

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11 pages, 687 KiB

Open AccessArticle

Prediction of Successful Liberation from Continuous Renal Replacement Therapy Using a Novel Biomarker in Patients with Acute Kidney Injury after Cardiac Surgery—An Observational Trial

by Johanna Tichy, Andrea Hausmann, Johannes Lanzerstorfer, Sylvia Ryz, Ludwig Wagner, Andrea Lassnigg and Martin H. Bernardi

Int. J. Mol. Sci. 2024, 25(20), 10873; https://doi.org/10.3390/ijms252010873 (registering DOI) - 10 Oct 2024

Abstract

An acute kidney injury (AKI) is the most common complication following cardiac surgery, and can lead to the initiation of continuous renal replacement therapy (CRRT). However, there is still insufficient evidence for when patients should be liberated from CRRT. Proenkephalin A 119–159 (PENK) is a novel biomarker that reflects kidney function independently of other factors. This study investigated whether PENK could guide successful liberation from CRRT. Therefore, we performed a prospective, observational, single-center study at the Medical University of Vienna between July 2022 and May 2023, which included adult patients who underwent cardiac surgery for a cardiopulmonary bypass; patients on preoperative RRT were excluded. The PENK levels were measured at the time of AKI diagnosis and at the initiation of and liberation from CRRT, and were subsequently compared to determine whether the patients were successfully liberated from CRRT. We screened 61 patients with postoperative AKI; 20 patients experienced a progression of AKI requiring CRRT. The patients who were successfully liberated from CRRT had mean PENK levels of 113 ± 95.4 pmol/L, while the patients who were unsuccessfully liberated from CRRT had mean PENK levels of 290 ± 175 pmol/L (p = 0.018). For the prediction of the successful liberation from CRRT, we found an area under the curve of 0.798 (95% CI, 0.599–0.997) with an optimal threshold value of 126.7 pmol/L for PENK (Youden Index = 0.53, 95% CI, 0.10–0.76) at the time of CRRT liberation (sensitivity = 0.64, specificity = 0.89). In conclusion, PENK is a novel biomarker that has the potential to predict the successful liberation from CRRT for patients with AKI after cardiac surgery. Full article

(This article belongs to the Special Issue Kidney Diseases: Molecular Pathogenesis and Therapeutic Strategies—2nd Edition)

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17 pages, 5872 KiB

Open AccessArticle

Prediction Models and Feature Importance Analysis for Service State of Tunnel Sections Based on Machine Learning

by Debo Zhao, Yujia Yang, Chengyong Cao and Bin Liu

Appl. Sci. 2024, 14(20), 9167; https://doi.org/10.3390/app14209167 (registering DOI) - 10 Oct 2024

Abstract

The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the United States to predict the service states of the structural, civil, and non-structural sections of a tunnel, respectively. The results indicate that ensemble learning algorithms such as Light Gradient Boosting Machine (LGBM) and Random Forest outperform Support Vector Machine, Multi-Layer Perceptron, Decision Tree, and K-Nearest Neighbor in solving imbalanced classification problems presented in the NTI database. The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. The importance sorting of features influencing the tunnel’s service state was then performed based on the LGBM model, revealing that the features with a significant impact on the service states of the structural, civil, and non-structural sections are service time, tunnel length and width, geographic position (longitude and latitude), minimum vertical clearance, annual average daily traffic (AADT), and annual average daily truck traffic (AADTT). Data-driven LGBM models identified human factors such as AADT and AADTT as key features influencing the service states of tunnels’ structural sections, and these factors should be taken into consideration in further research to elucidate the potential physical mechanisms. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering, 2nd Edition)

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10 pages, 3009 KiB

Open AccessArticle

Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution

by Woonbae Sohn, Taekyung Kim, Cheon Woo Moon, Dongbin Shin, Yeji Park, Haneul Jin and Hionsuck Baik

Nanomaterials 2024, 14(20), 1614; https://doi.org/10.3390/nano14201614 (registering DOI) - 10 Oct 2024

Abstract

Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure–property relationships. Full article

(This article belongs to the Special Issue Exploring Nanomaterials through Electron Microscopy and Spectroscopy)

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41 pages, 1903 KiB

Open AccessReview

Acute Sarcopenia: Mechanisms and Management

by Sarah Damanti, Eleonora Senini, Rebecca De Lorenzo, Aurora Merolla, Simona Santoro, Costanza Festorazzi, Marco Messina, Giordano Vitali, Clara Sciorati and Patrizia Rovere-Querini

Nutrients 2024, 16(20), 3428; https://doi.org/10.3390/nu16203428 (registering DOI) - 10 Oct 2024

Abstract

Background: Acute sarcopenia refers to the swift decline in muscle function and mass following acute events such as illness, surgery, trauma, or burns that presents significant challenges in hospitalized older adults. Methods: narrative review to describe the mechanisms and management of acute sarcopenia. Results: The prevalence of acute sarcopenia ranges from 28% to 69%, likely underdiagnosed due to the absence of muscle mass and function assessments in most clinical settings. Systemic inflammation, immune–endocrine dysregulation, and anabolic resistance are identified as key pathophysiological factors. Interventions include early mobilization, resistance exercise, neuromuscular electrical stimulation, and nutritional strategies such as protein supplementation, leucine, β-hydroxy-β-methyl-butyrate, omega-3 fatty acids, and creatine monohydrate. Pharmaceuticals show variable efficacy. Conclusions: Future research should prioritize serial monitoring of muscle parameters, identification of predictive biomarkers, and the involvement of multidisciplinary teams from hospital admission to address sarcopenia. Early and targeted interventions are crucial to improve outcomes and prevent long-term disability associated with acute sarcopenia. Full article

(This article belongs to the Special Issue Malnutrition, Acute Sarcopenia and Wasting Syndromes during Hospital Stay)

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11 pages, 595 KiB

Open AccessArticle

A Longitudinal Examination between Chronotype and Insomnia in Youths: A Cross-Lagged Panel Analysis

by Forrest Tin Wai Cheung, Hao Fong Sit, Xiao Li, Joey Wing Yan Chan, Ngan Yin Chan, Yun Kwok Wing and Shirley Xin Li

Clocks & Sleep 2024, 6(4), 557-567; https://doi.org/10.3390/clockssleep6040037 (registering DOI) - 10 Oct 2024

Abstract

Adolescence and young adulthood are transitional periods associated with significant changes and challenges, leading to a heightened vulnerability to sleep disturbances and mental health difficulties. This stage is often associated with an increased preference for eveningness, manifested as a later chronotype. The current study aimed to investigate the directionality of the association between chronotype, based on an individual’s sleep–wake behaviour, and insomnia in young people using a two-wave panel design with a 12-month interval. A total of 370 participants aged 15–24 (mean age: 21.0 ± 2.0, 72.7% female) were recruited from local secondary schools and universities. Insomnia symptoms were assessed using the Insomnia Severity Index, while chronotype was measured using the Munich Chronotype Questionnaire. Temporal associations were analysed using a series of cross-lagged panel models. The best fitting and most parsimonious model indicated that a later chronotype at baseline predicts more severe insomnia symptoms at the 12-month follow-up after accounting for autoregressive effects. However, the opposite causal model, where baseline insomnia symptoms predicted the chronotype at the 12-month follow-up, was not supported. These findings suggest that a late chronotype may be a potential risk factor for the development of insomnia in young people, emphasising the importance of considering circadian factors in the prevention and treatment of sleep disturbances among this population. Full article

(This article belongs to the Section Disorders)

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