Search Results (5,181)

Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug from multiple candidates. We present SEATS (Systematic Efficacy Assignment with Treatment Seats), which adapts conventional models like Random Forest and XGBoost for multiclass drug assignment by allocating probabilistic “treatment seats” to drugs based on efficacy. This approach helps models learn clinically relevant distinctions. Additionally, we assess an interpretable Optimal Decision Tree (ODT) model designed specifically for drug assignment. Trained on the BeatAML2 cohort and validated on the GDSC AML cell line dataset, integrating SEATS with Random Forest and XGBoost improved prediction accuracy and consistency. The ODT model offered competitive performance with clear, interpretable decision paths and minimal feature requirements, facilitating clinical use. SEATS reorients standard models towards personalized drug selection. Combined with the ODT framework it provides effective, interpretable strategies for precision oncology, underscoring the potential of tailored machine learning solutions in supporting real-world treatment decisions. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article

The traditional Mahalanobis–Taguchi System (MTS) employs two main strategies for multi-class classification: the partial binary tree MTS (PBT-MTS) and the multi-tree MTS (MT-MTS). The PBT-MTS relies on a fixed binary tree structure, resulting in limited model flexibility, while the MT-MTS suffers from its dependence on pre-defined category partitioning. Both methods exhibit constraints in adaptability and classification efficiency within complex data environments. To overcome these limitations, this paper proposes an innovative Adaptive Tree-structured Mahalanobis–Taguchi System (ATMTS). Its core breakthrough lies in the ability to autonomously construct an optimal multi-layer classification tree structure. Unlike conventional PBT-MTS, which establishes a Mahalanobis Space (MS) containing only a single category per node, ATMTS dynamically generates the MS that incorporates multiple categories, substantially enhancing discriminative power and structural adaptability. Furthermore, compared to MT-MTS, which depends on prior label information, ATMTS operates without predefined categorical assumptions, uncovering discriminative relationships solely through data-driven learning. This enables broader applicability and stronger generalization capability. By introducing a unified multi-objective joint optimization model, our method simultaneously optimizes structure construction, feature selection, and threshold determination, effectively overcoming the drawbacks of conventional phased optimization approaches. Experimental results demonstrate that ATMTS outperforms PBT-MTS, MT-MTS, and other mainstream classification methods across multiple benchmark datasets, achieving significant improvements in the accuracy and robustness of multi-class classification tasks. Full article

Background/Objectives: Male breast cancer (MBC) is a rare malignancy representing less than 1% of all breast cancer cases, with rising incidence worldwide. Current treatment strategies largely rely on extrapolation from female breast cancer, despite clear biological and clinical distinctions. This review aims to summarize current knowledge on non-metastatic MBC, with a particular focus on genetic predisposition, tumor biology, and recent therapeutic advances. Methods: A systematic literature search was conducted using PubMed and PMC databases to identify clinical trials, observational studies and systematic reviews related to MBC published up to 1st June, 2025. Studies were selected for their relevance to genetic and molecular features, as well as treatment outcomes in non-metastatic disease. Results: Fifty-one studies were included in the review. Findings confirm the predominance of hormone receptor–positive tumors in MBC and underscore the central role of BRCA2 mutations. Germline mutations in BRCA2 and BRCA1 were reported in approximately 1 and 2% of male cases, respectively. Additional germline alterations were identified in PALB2, CHEK2, and other DNA repair genes. Comparative analyses of surgical approaches showed no significant difference in survival between breast-conserving surgery and mastectomy. Postmastectomy radiotherapy improved overall survival compared to surgery alone. Adjuvant tamoxifen therapy was independently associated with significant survival benefits, although adherence remains a challenge. Conclusions: MBC is a biologically distinct and molecularly heterogeneous disease. Breast-conserving surgery appears safe and effective in selected patients. Adjuvant radiotherapy and tamoxifen confer clear survival advantages. The lack of male-specific clinical trials remains a major limitation in optimizing evidence-based care for MBC. Full article

Selective laser melting (SLM) builds metal parts layer by layer by locally melting powder with a fine laser beam, generating complex, geometry-dependent temperature gradients that govern density, microstructure, defects, and residual stresses. Resolving these gradients with high-fidelity finite-element (FE) models is prohibitively slow because the temperature field must be evaluated at dense points along every scan track across multiple layers, while the laser spot is orders of magnitude smaller than typical layer dimensions. This study replaces FE analysis with a deep neural network that predicts the end-of-build temperature field orders of magnitude faster. A benchmark part containing characteristic shape features is introduced to supply diverse training cases, and a novel control-volume-based geometry-abstraction scheme encodes arbitrary workpiece shapes into compact, learnable descriptors. Thermal simulation data from the benchmark train the network, which then predicts the residual temperature field of an unseen, geometrically dissimilar part with a mean absolute error of ~10 K and a mean relative error of ~1% across 500–1300 K. The approach thus offers a rapid, accurate surrogate for FE simulations, enabling efficient temperature-driven optimization of SLM process parameters and part designs. Full article

Accurately predicting aircraft fuel consumption is vital for aviation safety, operational efficiency, and resource optimization, yet existing models face key limitations. Traditional physical models rely on prior assumptions, while mainstream deep learning models use fixed architectures and time-slice tokens—failing to adapt to distinct flight phases and losing long-range temporal features critical for cross-phase dependency capture. This paper proposes Gate-iInformer, an adaptive framework centered on iInformer with a gating network. It treats flight parameters as independent tokens, integrates Informer to handle long-range dependencies, and uses the gating network to dynamically select pre-trained phase-specific sub-models. Validated on 21,000 Air China 2023 medium-aircraft flights, it reduces MAE and RMSE by up to 53.38% and 44.51%, achieves 0.068 MAE in landing, and outperforms benchmarks. Its prediction latency is under 0.5 s, meeting ADS-B needs. Future work will expand data sources to enhance generalization, boosting aviation intelligent operation. Full article

Migraine is a common neurological illness that has a major influence on the quality of life; yet, precise categorization and prediction remain difficult because of its complicated symptoms and multiple triggers. This work investigates the use of advanced machine learning (ML) algorithms to improve migraine diagnosis and prediction, drawing on a large dataset that includes clinical, lifestyle, and environmental aspects. Various machine learning models, such as ensemble methods, deep learning, and hybrid approaches, are tested to see how well they discriminate migraine from other headache conditions and predict migraine episodes. Feature selection approaches are used to identify the most important predictors, which improve model interpretability and performance. Experimental results show that the proposed machine learning framework outperforms established diagnostic methods in terms of classification accuracy, sensitivity, and specificity. The study demonstrates how ML-driven solutions may be used to manage migraines in a tailored way, helping medical practitioners with early diagnosis and intervention techniques. My suggested framework, NeuroVote(ensemble model), offers a remarkable 99.99% classification accuracy for migraines. Future studies will concentrate on optimizing models for clinical deployment and incorporating real-time data from wearable technology. Full article

This paper proposes TWISS (TOPSIS + Wrapper Incremental Subset Selection), a novel hybrid feature selection framework designed for electromyographic (EMG) pattern recognition in upper-limb prosthetic control. TWISS integrates the multi-criteria decision-making method TOPSIS with a forward wrapper search strategy, enabling subject-specific feature optimization based on a ranking that combines filter metrics, including Chi-squared, ANOVA, and Mutual Information. Unlike conventional static feature sets, such as the Hudgins configuration (48 features: four per channel, 12 channels) or All Features (192 features: 16 per channel, 12 channels), TWISS dynamically adapts feature subsets to each subject, addressing inter-subject variability and classification robustness challenges in EMG systems. The proposed algorithm was evaluated on the publicly available Ninapro DB7 dataset, comprising both intact and transradial amputee participants, and implemented in an open-source, fully reproducible environment. Two Google Colab tools were developed to support diverse workflows: one for end-to-end feature extraction and selection, and another for selection on precomputed feature sets. Experimental results demonstrated that TWISS achieved a median F1-macro score of 0.6614 with Logistic Regression, outperforming the All Features set (0.6536) and significantly surpassing the Hudgins set (0.5626) while reducing feature dimensionality. TWISS offers a scalable and computationally efficient solution for feature selection in biomedical signal processing and beyond, promoting the development of personalized, low-cost prosthetic control systems and other resource-constrained applications. Full article

Student exam pass prediction (EPP) is a key task in educational assessment and can help teachers identify students’ learning obstacles in a timely manner and optimize teaching strategies. However, existing EPP models, although capable of providing quantitative analysis, suffer from issues such as complex algorithms, poor interpretability, and unstable accuracy. Moreover, the evaluation process is opaque, making it difficult for teachers to understand the basis for scoring. To address this, this paper proposes an approximate belief rule base (ABRB-a) student examination passing prediction method based on adaptive reference point selection using symmetry. Firstly, a random forest method based on cross-validation is adopted, introducing intelligent preprocessing and adaptive tuning to achieve precise screening of multi-attribute features. Secondly, reference points are automatically generated through hierarchical clustering algorithms, overcoming the limitations of traditional methods that rely on prior expert knowledge. By organically combining IF-THEN rules with evidential reasoning (ER), a traceable decision-making chain is constructed. Finally, a projection covariance matrix adaptive evolution strategy (P-CMA-ES-M) with Mahalanobis distance constraints is introduced, significantly improving the stability and accuracy of parameter optimization. Through experimental analysis, the ABRB-a model demonstrates significant advantages over existing models in terms of accuracy and interpretability. Full article

The aim of this work was to prepare new heterodimeric molecules containing pharmacophoric fragments of 3-cyanoquinoline/3-aminothieno[2,3-b]pyridine/3-aminothieno[2,3-b]quinoline on one side and phenothiazine on the other. The products were synthesized via selective S-alkylation of readily available 2-thioxo-3-cyanopyridines or -quinolines with N-(chloroacetyl)phenothiazines, followed by base-promoted Thorpe–Ziegler isomerization of the resulting N-[(3-cyanopyridin-2-ylthio)acetyl]phenothiazines. We found that both the S-alkylation and the Thorpe–Ziegler cyclization reactions, when conducted with KOH under heating, were accompanied to a significant extent by a side reaction involving the elimination of phenothiazine. Optimization of the conditions (0–5 °C, anhydrous N,N-dimethylacetamide and NaH or t-BuONa as non-nucleophilic bases) minimized the side reaction and increased the yields of the target heterodimers. The structures of the products were confirmed by IR spectroscopy, ¹H, and ¹³C DEPTQ NMR studies. It was demonstrated that the synthesized 3-aminothieno[2,3-b]pyridines can be acylated with chloroacetyl chloride in hot chloroform. The resulting chloroacetamide derivative reacts with potassium thiocyanate in DMF to form the corresponding 2-iminothiazolidin-4-one; in this process, phenothiazine elimination does not occur, and the Gruner–Gewald rearrangement product was not observed. The structural features and spectral characteristics of the synthesized 2-iminothiazolidin-4-one derivative were investigated by quantum chemical methods at the B3LYP-D4/def2-TZVP level. A range of drug-relevant properties was also evaluated using in silico methods, and ADMET parameters were calculated. A molecular docking study identified a number of potential protein targets for the new heterodimers, indicating the promise of these compounds for the development of novel antitumor agents. Full article

To address the difficulty of extracting thermal bending failure and centerline horizontal displacement fault feature signals when judging the operating status of cement rotary kilns, we propose a method for extracting fault features based on improved secretary bird optimization algorithm (ISBOA) and variational modal decomposition (VMD). First, a strategy of randomly consuming prey with inertial weights is proposed to enhance the randomness of search results and avoid local optima. Then, the whale algorithm’s encirclement strategy is introduced into the secretary bird’s camouflage strategy to coordinate the capabilities of local search and global exploration. Finally, ISBOA demonstrated superior performance to other optimization algorithms in VMD parameter selection, achieving a 75% improvement in convergence speed compared to pre-optimization. Through validation with experimental and simulation data, this method demonstrates good feasibility. By decomposing actual signals and comparing the mean energy of their characteristic signals, the severity of thermal bending faults in the cylinder and centerline horizontal displacement faults in cement rotary kilns is diagnosed. Verified against actual measurement results, the accuracy reached 96.7%. Full article

An Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for paediatric wrist injury classification (fracture versus sprain) was developed utilising infrared thermography (IRT). ANFIS combines artificial neural network (ANN) learning with interpretable fuzzy rules, mitigating the “black-box” limitation of conventional ANNs through explicit membership functions and Takagi–Sugeno rule consequents. Forty children (19 fractures, 21 sprains, confirmed by X-ray radiograph) provided thermal image sequences from which three statistically discriminative temperature distribution features namely standard deviation, inter-quartile range (IQR) and kurtosis were selected. A five-layer Sugeno ANFIS with Gaussian membership functions were trained using a hybrid least-squares/gradient descent optimisation and evaluated under three premise-parameter initialisation strategies: random seeding, K-means clustering, and fuzzy C-means (FCM) data partitioning. Five-fold cross-validation guided the selection of membership functions standard deviation (σ) and rule count, yielding an optimal nine-rule model. Comparative experiments show K-means initialisation achieved the best balance between convergence speed and generalisation versus slower but highly precise random initialisation and rapidly convergent yet unstable FCM. The proposed K-means–driven ANFIS offered data-efficient decision support, highlighting the potential of thermal feature fusion with neuro-fuzzy modelling to reduce unnecessary radiographs in emergency bone fracture triage. Full article

Metatropic dysplasia is an autosomal dominant skeletal disorder characterized by progressive kyphoscoliosis, severe platyspondyly, pronounced metaphyseal enlargement, and shortening of the long bones. This condition is caused by pathogenic variants in the TRPV4 (Transient Receptor Potential Vanilloid 4) gene, which encodes a non-selective calcium channel involved in bone homeostasis. Variants in TRPV4 have been associated with two major disease groups: skeletal dysplasias and neuropathies, with recent findings indicating an overlap in their clinical features. We report three patients with metatropic dysplasia, each presenting a distinct severity profile. All exhibited a bell-shaped thorax, significant platyspondyly, and shortened long bones with broad metaphyses. Notably, patients 1 and 3 had more complex clinical courses, including seizures and global developmental delay. Genetic analysis revealed two different TRPV4 variants: p.Asn796del (patient 1) and p.Pro799Leu (patients 2 and 3). These cases illustrate variability in extra-skeletal manifestations, complications, and prognosis. In our patients with TRPV4-related disorders, the co-occurrence of neurological symptoms and skeletal abnormalities suggests a clinically heterogeneous spectrum consistent with a single disease rather than distinct entities. A comprehensive, multidisciplinary approach is essential to optimize management and improve the quality of life for patients. Full article

Bolivian hemorrhagic fever (BHF) is a zoonotic disease caused by Mammarenavirus machupoense (MACV) featuring severe neurological and hemorrhagic symptoms and a high mortality rate. BHF is usually diagnosed by serological tests or real-time polymerase chain reaction (RT-PCR); these methods are often inaccessible in endemic regions due to a lack of laboratory infrastructure, creating a demand for sensitive and rapid equipment-free alternatives. Here, we present an isothermal method for MACV nucleic acid detection based on the Cas12a-based DETECTR system combined with recombinase polymerase amplification (RPA) in a single tube: the RT-RPA/DETECTR assay. We demonstrate the possibility of using more than one primer set for the simultaneous detection of MACV genetic variants containing multiple point mutations. The method was optimized and tested using specially developed virus-like armored particles containing the target sequence. The multiplex RT-RPA/DETECTR method achieved a limit of detection of approximately 5 × 10⁴ copies/ mL (80 aM) of armored particles. The method was validated using clinical samples spiked with virus-like particles. The assay proved to be selective and reliable in detecting certain nucleotide substitutions simultaneously. Full article

Oxide semiconductor gas sensors are widely used due to their low cost, rapid response, small footprint, and ease of integration. However, in complex gas mixtures their selectivity is often limited by inherent cross-sensitivity. To address this, we developed a reconfigurable sensor-array system that supports up to 12 chemiresistive sensors with four- or six-electrode configurations, independent thermal control, and programmable gas paths. As a representative case study, we designed a customized array for fish-spoilage biomarkers, intentionally leveraging the cross-sensitivity and broad-spectrum responses of metal-oxide sensors. Following principal component analysis (PCA) preprocessing, we evaluated convolutional neural network (CNN), random forest (RF), and particle swarm optimization–tuned support vector machine (PSO-SVM) classifiers. The RF model achieved 94% classification accuracy. Subsequent channel optimization (correlation analysis and feature-importance assessment) reduced the array from 12 to 8 sensors and improved accuracy to 96%, while simplifying the system. These results demonstrate that deliberately leveraging cross-sensitivity within a carefully selected array yields an information-rich odor fingerprint, providing a practical platform for complex gas-mixture identification and food-freshness assessment. Full article