Selection Method Research Articles

Development and Validation of Stability Indicating RP-HPLC Method for the Estimation of Pirtobrutinib and Characterization of its Degradants by LC - MS

Background: Pirtobrutinib is a novel non-covalent BTK inhibitor used to treat adult patients with relapsed/refractory mantle cell lymphoma and B-cell leukemias. The mechanism of action involves binding and inhibition of Bruton's tyrosine kinase (BTK). Objective: The main goal of the current work was to create a selective liquid chromatographic method (RP-HPLC) that is easy to use, accurate and exact for quantifying Pirtobrutinib and its degradation products, thereby seeking insight into the drug’s degradation behaviour. Methods: Using an isocratic mode and a 50:50 mixture of acetonitrile and buffer solution (0.1% orthophosphoric acid) as the mobile phase, a High Performance Liquid Chromatographic System with a PDA detector and an X-Bridge Phenyl column (150 x 4.6mm, 3.5μm) at a flow rate of 1.0 ml/min was able to achieve good chromatographic separation. At 219 nm, the detection was carried out. A retention time of 2.271 minutes was discovered. To ascertain the drug's degrading properties and stability, forced degradation tests were carried out, leading to the development of the RPHPLC method. The chemical structures of the degradation products were clarified and their fragmentation mechanisms were suggested using LC-MS. Results: The suggested approach demonstrated a linearity within the 25-150% (2.5 to 15μg/mL) concentration range, with a correlation coefficient of 0.9999. The precision of the system (measured by % RSD=0.49) and the method (measured by % RSD=0.86) were all within the acceptable limits set by ICH guidelines, with % RSDs less than 1% and less than 2% for system precision and method precision, respectively. The LOD was 0.3μg/mL, and the LOQ was1μg/mL. Pirtobrutinib underwent rigorous tests for forced degradation under the specified conditions outlined in ICH Q1 (R2) guidelines. Pirtobrutinib was primarily broken down in acidic, alkaline, peroxide, and thermal conditions. It was found to remain stable under reduction, photolytic, and hydrolytic conditions. Through LC-MS analysis, the chemical structures of the resulting degradation by-products were identified, along with the proposed pathways of their fragmentation. Conclusion: The current study gives insight into Pirtobrutinib’s degradation behaviour. Pirtobrutinib was stable in reduction, photolytic, and hydrolytic conditions but degraded more readily in acidic, alkaline, peroxide, and thermal environments. The degradation products were characterized as 4-carbamoyl- 5-chloro-3(4-((5-fluoro-2-methoxybenzamido)methyl)phenyl)-4,5-dihydro-1H-pyrazol-5-aminium (acid impurity, DP1), N-(4-(4,5-diamino-1H-pyrazol-3-yl)benzyl)-5-fluoro-2-hydroxybenzamide (alkali impurity, DP2), 5-amino-3-(4-((s-fluro-2-methoxybenzamido)methyl))-4,5dihydro-1H-pyrazol-5-aminium (peroxide impurity, DP3) and N-(4-(4-acetyl-5-amino-4,5-dihydro-1H-pyrazol-3-yl)benzyl)-5-fluoro-2- methoxybenzamide compound with λ1-oxidane (1:1) (thermal impurity, DP4) and their fragmentation pathways were proposed. This study presents the first ever reported method for the quantification of Pirtobrutinib. It ensures precise quantitation of Pirtobrutinib, a new Bruton's Tyrosine Kinase inhibitor, and its degradation products. The method's enhanced sensitivity and regulatory compliance ensure its consistent use in the quantification of Pirtobrutinib.

Ethylendiaminetetrakis(1,10-phenanthroline)dicopper(II) chloride: Synthesis and application for voltammetric determination of doxycycline in real samples

The study describes on the preparation of a new homobinuclear mixed ligand complex µ-ethylendiaminetetrakis(1,10-phenanthroline)dicopper(II) chloridedihydrate (EP4Cu2CH2) from a previously reported complex chlorobis(1,10-phenanthroline)copper(II) chloridemonohydrate (CP2CuCH). The synthesis was checked by spectroscopy (UV–Vis, FT IR), CHN elemental analysis, Cu determination using ICP-OES, electrolytic conductivity, qualitative and quantitative chloride estimation, and melting point measurements. The new complex was applied for the modification of a glassy carbon electrode (poly(EP4Cu2CH2)/GCE) for determination of doxycycline (Doxy), (which belongs to a tetracycline antibiotic, mainly used for the treatment of a wide bacterial infections) by selective, accurate, and precise differential pulse voltammetric method. Electrode characterization revealed modification of the electrode surface by a conductive, and electroactive polymer film (poly(EP4Cu2CH2)/GCE) leading to improved effective electrode surface area. In contrast to the bare electrode, the appearance of two well-resolved irreversible oxidative peaks at much reduced potential with sevenfold current enhancement at poly(EP4Cu2CH2)/GCE showed the catalytic effect of the modifier towards Doxy. Differential pulse voltammetric current response of poly(EP4Cu2CH2)/GCE showed a linear dependence on the concentration of Doxy in the range 5 × 10−8 – 3.5 × 10−4 M with a detection limit of 3.5 × 10−9. The Doxy levels in four tablet brands were in the range 96.00–101.30 % of their labeled values. Spike recovery results in tablet, cow milk, and human urine samples were 98.90–100.73 %, 98.30–100.00 %, and 98.50–100.54 %, respectively. Based on the innovative mixed ligand poly(EP4Cu2CH2)/GCE, the interference recovery results about 4.79 % error, lesser LoD, and a broader concentrationrange than most previously published methods validated the present method’s potential applicability with excellent accuracy and sensitivity for Doxy determination in various real samples with complex matrix.

Intrapersonal conflicts experienced by vocational school students and ways to resolve them

Introduction. The use of effective ways to resolve intrapersonal conflicts by vocational school students in the process of their vocational training is an important component of developing students' knowledge about sensory and emotional spheres and forming their emotional self-regulation skills. Aim: theoretical substantiation of the features of intrapersonal conflicts and ways to resolve them by vocational school students. Methods. Analysis, synthesis, generalization of scientific information, as well as the selection method to develop a set of psychodiagnostic tools. Results. The essence of the concept of conflict was clarified. Scientific approaches to the study of intrapersonal conflicts were analyzed and the sources, consequences and types of intrapersonal conflicts experienced by vocational school students were described. It was established that the main type of intrapersonal conflicts was a value conflict, which at the level of individual consciousness was caused by a clash between the real self and ideal self. A complex of psychodiagnostic instruments and techniques was proposed for studying intrapersonal conflicts experienced by vocational school students. Conclusions. In order to resolve intrapersonal conflicts experienced by vocational school students, it is advisable to use psychotherapy to improve students' moral and psychological condition and resolve their crisis situations and promote success.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data.

Most epithelial ovarian cancer (EOC) eventually develops recurrence. Identification of high-risk patients can prompt earlier intervention and improve long-term outcomes. We used laboratory and clinical data to create models based on machine learning for EOC platinum resistance recurrence identification. This study was designed as a retrospective cohort analysis. Initially, we identified 1,392 patients diagnosed with epithelial ovarian cancer who underwent platinum-based chemotherapy at Yunnan Cancer Hospital between January 1, 2012, and June 30, 2022. We collected data on the patients' clinicopathologic characteristics, routine laboratory results, surgical information, details of chemotherapy regimens, and survival outcomes. Subsequently, to identify relevant variables influencing the recurrence of platinum resistance, we screened thirty potential factors using two distinct variable selection methods: Lasso regression and multiple logistic regression analysis. Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. These included decision tree analysis (DTA), K-Nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost). The performance of these models was compared against that of traditional logistic regression. To ensure robust internal validation and facilitate comparison among model performance metrics, a five-fold cross-validation method was implemented. Key performance indicators for the models included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and average accuracy. Finally, we will visualize these models through nomograms, decision tree diagrams, variable importance plots, etc., to assist clinicians in their practice. Multiple logistic regression analysis identified eight variables associated with platinum resistance recurrence. In the lasso regression, seven variables were selected. Based on the findings from both Lasso regression and multiple logistic regression analysis, models were developed using these 7 and 8 factors. Among these, the XGBoost model derived from multiple logistic regression exhibited superior performance and demonstrated good discrimination during internal validation, achieving an AUC of 0.784, a sensitivity of 0.735, a specificity of 0.713, an average accuracy of 80.4%, with a cut-off value set at 0.240. Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development. We have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model-derived from variables selected through multiple logistic regression-demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation.

WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for enhanced autism spectrum disorder diagnosis

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6 %, 96.2 %, and 96.4 % on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.

Using UAV-based multispectral and RGB imagery to monitor above-ground biomass of oat-based diversified cropping

Timely access to crop above-ground biomass (AGB) information is crucial for estimating crop yields and managing water and fertilizer efficiently. Unmanned aerial vehicle (UAV) imagery offers promising avenues for AGB estimation due to its high efficiency and flexibility. However, the accuracy of these estimations can be influenced by various factors, including crop growth stages, the spectral resolution of UAV sensors, and flight altitudes. These factors need thorough investigation, especially in diversified cropping systems where crop diversity and growth stages interplay complexly, challenging the accuracy of AGB estimation. This study aims to estimate AGB of oats planted under different agricultural regimes—monoculture, crop rotation, and intercropping—at various growth stages (jointing, flowering, and grain-filling) and across all stages combined, using multispectral and RGB UAV images collected at different flight altitudes (25 m, 50 m, and 100 m). Three feature selection methods—maximal information coefficient (MIC), least absolute shrinkage and selection operator (LAS), and recursive feature elimination (RFE)—were tested. Four machine learning models—ridge regression (RR), multilayer perceptron (MLP), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—were used for estimating AGB. Each feature selection method was combined with each machine learning model (e.g., MIC-RR, MIC-MLP, MIC-LGBM, MIC-XGBoost, LAS-RR) to evaluate their performance. Results revealed that the highest accuracy in AGB estimation was achieved with images acquired at a flight altitude of 25 m. The RFE-MLP model demonstrated superior results during the jointing stage (R² = 0.84, root mean squared error (RMSE) = 217.45 kg/ha, root mean squared logarithmic error (RMSLE) = 0.16, mean absolute percentage error (MAPE) = 4.15 %), the LAS-RR model excelled in the flowering stage (R² = 0.85, RMSE = 263.03 kg/ha, RMSLE = 0.05, MAPE = 14.44 %), and the RFE-XGBoost model was most effective during the grain-filling stage (R² = 0.68, RMSE = 865.03 kg/ha, RMSLE = 0.12, MAPE = 8.88 %). For cross-stage modelling, the RFE-MLP achieved remarkable results (R² = 0.93, RMSE = 680.44 kg/ha, RMSLE = 0.16, MAPE = 12.12 %). This study demonstrates the efficacy of combining feature selection methods with machine learning algorithms to enhance the accuracy of oat AGB estimations. The involvement of multiple cropping patterns enhances the generalizability of our findings, facilitating real-time and rapid monitoring of crop growth in future diversified cropping systems.

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

Method for Constructing a Compact Component Mode Synthesis Model for Analyzing Nonlinear Normal Modes of Structures with Localized Nonlinearities

Abstract Nonlinear dynamics analysis is a crucial topic in mechanical and aerospace engineering. The analysis of nonlinear normal modes (NNMs) provides an effective mathematical tool for interpreting complex nonlinear vibration phenomena. Unlike the invariant normal modes of linear systems, NNMs often exhibit frequency-energy dependence and cannot be computed using the traditional eigen-decomposition method. Calculating NNMs relies on numerical methods that involve expensive iterative computations, especially for systems with numerous degrees-of-freedom. To relieve computational costs, this paper proposes a mode selection method for the component mode synthesis (CMS) technique to enable compact reduced-order modeling of structures with localized nonlinearities. The reduced-order model is then combined with a numerical continuation scheme to establish a low-cost NNM analysis framework. This framework can efficiently predict the NNMs of high-dimensional finite element models. The proposed framework is demonstrated on an I-shaped cantilever beam with localized nonlinear stiffness. The results show that the proposed approach can identify the key modes in the CMS modeling procedure. The NNMs of the nonlinear I-shaped beam structure can then be analyzed with a significant reduction in computational costs.

Repurposing as a Decommissioning Strategy for Complex Systems: A Systematic Review

Abstract Managing the waste of decommissioned complex systems (e.g., aircraft, wind turbines, etc.) is a growing issue. In this review, we investigate repurposing as a potential solution. The objectives are to identify strategies that can enable repurposing and identify the research gaps hindering those strategies. We analyzed 104 journal articles published in the last decade. We identified four proactive strategies that can be applied before the decommissioning stage and three reactive strategies that can be applied after the decommissioning stage. The proactive strategies are local ecosystem-focused repurposing, modular design, efficient disassembly methods, and component embedded design and health information. The reactive strategies are decision support methods for repurposable component selection, function and context based repurposing opportunities, and business models for repurposing. Six research gaps were identified, hindering the strategies due to the lack of support methods for repurposing, strategy scope limitations, and repurposing opportunity limitations. We identified that two repurposing examples were most commonly studied (wind turbine blades and electric vehicle batteries). Addressing the research gaps through design could uncover new repurposing opportunities. The resulting opportunities could follow similar processes addressed under the two well-researched examples, enabling repurposing as an advantageous and sustainable decommissioning strategy for complex systems. Keywords: circular economy, sustainability, decommissioning, complex systems, repurposing, literature review

Exploring alternative polymer materials for joint liners: a software-guided material selection

This study explores the alternative polymer materials and selection process for joint implant liners, focusing on applying CES Selector software to identify suitable polymer materials. CES Selector provides an easy-to-use interface. It offers multiple selection methods, including boundary values and property constraints. Seven materials were excluded from the analysis, resulting in 19 potential candidates, including unconventional options like EVOH, PCTA, PESU, PI, PPA, PPC, PPSU, and PSU. The materials underwent evaluation based on key criteria, including tensile strength, Young’s modulus, compressive strength, fatigue strength, and fracture toughness. Overall, TPU exhibited a remarkable combination of high mechanical strength and adaptable Young’s modulus, making it a top contender. However, in other evaluation criteria, PI surpassed TPU, solidifying its potential as a superior choice. This systematic approach provides valuable insights for engineers and designers seeking innovative materials for joint implant liners. The study results broaden the range of materials used in implant manufacturing, providing potential alternatives that offer better long-term durability and performance.

Catiknifap, new native variety of Xcat ik pepper (Capsicum annuum L.)

O Objective: To describe the morphological and agronomic characteristics of Catiknifap, a native variety of the Xcat ik pepper. Design/Methodology/Approach: Catiknifap was formed by mass selection method from a P108 accession collected in 2013 in Muna, Yucatan. Characterization and yield trails were carried out in greenhouse during three cycles from 2021 to 2022. Forty-five characteristics were evaluated based on UPOV guidelines. Results: Catiknifap is an early-ripening variety. Its fruits ripen between 77 and 81 days after transplanting. Its main morphological characteristic is the rounded shape at the junction with the peduncle. The yield depends on the growing season and ranges from 32.3 to 42.3 t ha-1. Study Limitations/Implications: Assays were carried out in greenhouses. Pests and diseases may cause differences in open field yield. Findings/Conclusions: Catiknifap is the first morphologically characterized variety of Xcat ik pepper. The fruits of this homogenous and stable variety have a high yield potential in Yucatan.

A robust multi-label feature selection based on label significance and fuzzy entropy

Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.

A ratiometric and colorimetric dual-mode fluorescence method for the sensitive determination of copper ion

The present work describes the fabrication of a low-cost, simple, and highly selective analytical method based on the combination of two fluorescent carbon quantum dots (CQDs) for visual or quantitative detection of copper ions at trace levels. Folium cycas and o-phenylenediamine are used as carbon precursors for the preparation of blue fluorescence CQDs (B-CQDs) and yellow fluorescence CQDs (Y-CQDs), respectively. The dual emission ratio fluorescence sensor (B@Y-CQDs), which is obtained through a physical blending method, demonstrates the dual-mode detection of copper ions, including colorimetric detection (visual detection) and ratiometric detection (quantitative detection). Our study reports a portable fluorescent paper sensing platform based on smartphones and computer software that can visually quantify Cu2+ without the need for expensive and bulky laboratory instruments. Upon addition of Cu2+, the simple B@Y-CQDs solution or B@Y-CQDs modified paper-based strip shows a visible color change from blue to yellow under 365 nm ultraviolet light, which achieves visual detection coupled with computer software and smartphones. For the accurate quantitative detection of trace Cu2+, the changes in the fluorescence intensity ratio of F450/550 are recorded, in which B@Y-CQDs exhibited remarkable selectivity and sensitivity, with a low limit of detection (0.73 μM) within the linear ranges of 7–15 μM. The applicability of the sensor is explored by the successful detection of Cu2+ in tap water and rice samples. These results provide a novel strategy for the quantitative and visual detection of various metal ions and hold significant potential for applications in food safety assurance and water pollution control.

Genomic signal selection analysis reveals genes related to the lambing trait of Hotan sheep.

Lambing in ewes is a complex and crucial aspect of sheep production that directly influences economic viability and production efficiency. In the present study, we analyzed the genomes of single lamb (SLE) and twin lamb (TLE) Hotan sheep to elucidate the genetic mechanisms underlying lamb production in Hotan sheep. In this study, we used genome-wide resequencing to analyze the genomes of Hotan sheep exhibiting SLE and TLE traits. To identify the population genetic structure and linkage disequilibrium associated with SLE and TLE traits, we employed two complementary genome selection signals: the interpopulation genetic differentiation index (FST) and nucleotide diversity (Pi). Subsequently, we performed gene annotation and enrichment analyses of the selected regions of the obtained genome. Our analysis generated 801 Gb of sequence data, from which 31,864,651 high-quality single nucleotide polymorphic loci were identified. We identified 290 selected regions and 332 genes across the Hotan sheep genome by using two widely adopted selective scanning detection methods (FST statistics and Piratio). Functional annotation and enrichment analysis of these genes identified 13 genes associated with the lambing rate, which were enriched in pathways such as the transforming growth factor-β (TGF-β) signaling pathway (BMPR2, ID2, SMAD7, THBS1, and RBX1), renal cell carcinoma (PAK1, ELOC), inositol phosphate metabolism (PLCZ), non-homologous terminal junction (RAD50), ABC transporters (ABCC4), and the NET pathway (H2B, H4, and H2A). This study employed selective elimination analysis to identify candidate genes involved in the regulation of lambing trait in Hotan sheep. By investigating the molecular mechanisms underlying lambing rate in Hotan sheep, we developed molecular markers for twin lambing to enhance reproductive performance and promote the conservation and development of outstanding genetic resources in local Xinjiang sheep.

Enhanced Output Performance of Two-Level Voltage Source Inverters Using Simplified Model Predictive Control with Multi-Virtual-Voltage Vectors

Interest in electric propulsion ships has garnered attention to reduce ship exhaust emissions. This has sparked extensive research on inverters. While two-level voltage source inverters are commonly utilized in small- and medium-sized ships owing to their simple structure and cost-effectiveness, they have limitations, such as high switching losses and reduced output performance. To address these issues, a model predictive control technique based on virtual voltage vectors is proposed in this study. Conventional two-level voltage source inverters are restricted to using only eight voltage vectors, which limits their output performance. By incorporating virtual voltage vectors, similar performance to multilevel converters can be achieved. The proposed technique involves a pre-voltage selection method that enhances output performance without increasing computational load. Through simulation and experiments, improved output current THD and current error were observed under various load conditions. This showcases the potential for enhancing the efficiency and performance of electric propulsion ships.

HFS-CSR: A hierarchical feature selection method based on correlation and structural redundancy

Feature selection (FS) is a crucial step in data engineering, as it not only saves time and resources for model training but also improves the prediction performance. In many realistic data sets, the feature space is formed by binary features which are related via generalization-specialization relationships (a.k.a., hierarchical feature space). These interdependencies among features, which can be described via a directed acyclic graph, present new challenges for classical FS methods. In this paper, a Hierarchical Feature Selection method based on Correlation and Structural Redundancy (HFS-CSR) is proposed to address it. In HFS-CSR, the correlation between features and the class label is measured based on Fisher Score whereas the structural redundancy among features are assessed quantitatively via hierarchical redundancy and depth penalties. The importance of features are determined by combining them adaptively for feature filtering. Experiments are conducted based on two kinds of typical data sets with hierarchical feature space: aging gene data sets and NLP data sets. The results demonstrate that HFS-CSR can achieve better performance compared with seven existing FS methods.

COVID-19 pandemic: mental health (distress as depression) in orthodontists

ABSTRACT Objectives For health professionals, pandemic conditions impacted all societal aspects, including economic, wellness, and mental health. This study analysed sociodemographic factors, post-traumatic stress disorder symptoms (PTSD), and depression levels caused by the coronavirus disease 2019 (COVID-19) pandemic in orthodontists. Materials and Methods A sociodemographic questionnaire, the Impact of Event Scale (IES), and the Patient Health Questionnaire (PHQ-9) were used. Following data collection, multiple statistical analyses were applied: scores were compared using the Mann-Whitney and Kruskal-Wallis tests, followed by Dunnett's multiple comparisons. The association between variables was verified using Chi-square or likelihood ratio tests. Multiple logistic regression models were estimated after bivariate logistic regression tests (p < 0.10). The final models using the Stepwise backward selection method were assessed for variables at 5%. Results Of the respondents, 69.3% were women, and their mental health questionnaire scores were significantly higher (p < 0.001). The IES avoidance and PHQ-9 scores were statistically different between the age groups (p = 0.040 and p < 0.001, respectively). Subjective distress was associated with years of practice, monthly income, fear of contracting COVID-19 during dental care, transmitting COVID-19 to patients, and thinking about the future (p < 0.05). Depression was significantly associated with orthodontic practice type, self-confidence in providing dental care, pandemic impact on dental practice, remote care, monthly income, changes in financial expenses, fear of contracting COVID-19 and thinking about the future (p < 0.05). Conclusion Over half of the orthodontists experienced moderate-to-severe mental distress, and the same number suffered from mild-to-severe depression during the pandemic. Registration: Ethics and Research Committee (CEP) of the Faculty of Dentistry of the University of São Paulo (FOUSP): CAAE: 34223720.1.0000.0075 Protocol: 4.191.353

Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic–percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis.

Multi-label feature selection based on adaptive label enhancement and class-imbalance-aware fuzzy information entropy

Multi-label feature selection can select representative features to reduce the dimension of data. Since existing multi-label feature selection methods usually suppose that the significance of all labels is consistent, the relationships between samples in the entire label space are generated straightforwardly such that the shape of label distribution and the property of class-imbalance are ignored. To address these issues, we propose a novel multi-label feature selection approach. Based on non-negative matrix factorization (NMF), the similarities between the logical label and label distribution are constrained, which ensures that the shape of label distribution does not deviate from the underlying actual shape to some extent. Further, the relationships between samples in label space and feature space are restricted by graph embedding. Finally, we leverage the properties of label distribution and class-imbalance to generate the relationships between samples in label space and propose a multi-label feature selection approach based on fuzzy information entropy. Eight state-of-the-art methods are compared with the proposed method to validate the effectiveness of our method.