Search Results (109,023)

We describe a modification of a previously described measurement–analysis protocol to determine the intrinsic properties of triaxial accelerometers by using a measurement protocol based on angular stepwise rotation in the Earth’s gravitational field. This study was conducted with MEMS triaxial accelerometers that were co-integrated in four consumer-grade wireless microsensors. The measurements were carried out on low-cost rotation tables in different laboratories in different countries to simulate the reproducibility environment encountered in inter-comparisons of calibration capabilities. We used a previously described calibration–uncertainty metric to independently characterize the overall uncertainty of the calibration and analysis process. The intrinsic property analysis suggested, and the uncertainty metric confirmed, an unacceptably large error in one combination of microsystem and low-cost rotation table. A simple modification of the analysis protocol provided a substantial improvement in the reproducibility of the protocol with all combinations of microsystem and rotation table. Later, measurements with a high-performance triaxial accelerometer using a significantly more expensive rotation table carried out at one location further validated the usefulness of this modification. The results reported here also demonstrate the existence of unidentified defects in one microsystem and one low-cost rotation table that interact with each other in ways not currently understood to produce anomalously large errors with the old protocol but not with the new protocol. Full article

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics. The aim of the present study was to identify biomarkers with the potential to improve the accuracy of prognosis assessment in MM. Initially, miRNA sequencing was conducted on bone marrow (BM) samples from patients with MM. Subsequently, the expression levels of 27 microRNAs (miRNA) and the gene expression levels of ASF1B, CD82B, CRISP3, FN1, MEF2B, PD-L1, PPARγ, TERT, TIMP1, TOP2A, and TP53 were evaluated via real-time reverse transcription polymerase chain reaction in BM samples from patients with MM exhibiting favorable and unfavorable prognoses. Additionally, the analysis involved the bone marrow samples from patients undergoing examinations for non-cancerous blood diseases (NCBD). The findings indicate a statistically significant increase in the expression levels of miRNA-124, -138, -10a, -126, -143, -146b, -20a, -21, -29b, and let-7a and a decrease in the expression level of miRNA-96 in the MM group compared with NCBD (p < 0.05). No statistically significant differences were detected in the expression levels of the selected miRNAs between the unfavorable and favorable prognoses in MM groups. The expression levels of ASF1B, CD82B, and CRISP3 were significantly decreased, while those of FN1, MEF2B, PDL1, PPARγ, and TERT were significantly increased in the MM group compared to the NCBD group (p < 0.05). The MM group with a favorable prognosis demonstrated a statistically significant decline in TIMP1 expression and a significant increase in CD82B and CRISP3 expression compared to the MM group with an unfavorable prognosis (p < 0.05). From an empirical point of view, we have established that the complex biomarker encompassing the CRISP3/TIMP1 expression ratio holds promise as a prognostic marker in MM. From a fundamental point of view, we have demonstrated that the development of MM is rooted in a cascade of complex molecular pathways, demonstrating the interplay of genetic and epigenetic factors. Full article

Among the existing sensorless control algorithms for permanent magnet synchronous motors (PMSMs), the model reference adaptive system (MRAS) is widely utilized due to its merits of good robustness, high stability, and rapid response. Nevertheless, the performance of the sensorless control is largely determined by the identified inductance parameters. The virtual-rotary-axis high-frequency injection (VHFSI) method is an online identification strategy for PMSM inductance parameters which is not affected by the observed rotor position errors among inductance identification algorithms. However, the existing PMSM inductance online identification algorithm based on VHFSI has ignored the influence of the error components, resulting in low accuracy and the weak dynamic performance of the inductance identification algorithm. In response to the problems existing in the original method, this paper improves VHFSI by designing a feedforward decoupling algorithm to compensate for the error components. Theoretical verification and simulation results indicate that the improved identification algorithm enhances the accuracy of inductance parameter identification, significantly improves the dynamic tracking performance of inductance identification, and combines it with the MRAS sensorless control method to constitute a sensorless control system for the motor, thereby enhancing control performance and system stability. Full article

With generative Artificial Intelligence (AI) capturing public attention, the appetite of the technology sector for larger and more complex Deep Learning (DL) models is continuously growing. Traditionally, the focus in DL model development has been on scaling the neural network’s foundational structure to increase computational complexity and enhance the representational expressiveness of the model. However, with recent advancements in edge computing and 5G networks, DL models are now aggressively being deployed and utilized across the cloud–edge–IoT continuum for the realization of in situ intelligent IoT services. This paradigm shift introduces a growing need for AI practitioners, as a focus on inference costs, including latency, computational overhead, and energy efficiency, is long overdue. This work presents a benchmarking framework designed to assess DL model scaling across three key performance axes during model inference: classification accuracy, computational overhead, and latency. The framework’s utility is demonstrated through an empirical study involving various model structures and variants, as well as publicly available datasets for three popular DL use cases covering natural language understanding, object detection, and regression analysis. Full article

To better process irregular sample images for their image feature extraction and recognition, this essay proposes asymmetric adaptive neural network (AACNN) structures, including dual structures of an adaptive image feature extraction network (AT-CNN) and adaptive image recognition network (AT-ACNN). They both comprise an Adaptive Transform (AT) module and a deep learning network, but the ACNN comprises pixel-adaptive convolutional (PAC) kernels that CNN does not have, reflecting the asymmetry of these network structures. Structural analysis and comparative testing experiments indicated that the proposed method is more appropriate and effective for dealing with irregular sample images with different sizes and views, mainly focusing on their feature extraction accuracy and image recognition efficiency. The proposed method constructs the interaction between asymmetric dual network structures, essential in improving model performance and efficiency. It specifically manifests that the PAC kernels in an ACNN resolves the problem of content-agnostic convolution in image recognition by learning image features from a pre-trained CNN. On the other hand, it improves image recognition efficiency by using feature maps extracted from the pre-trained CNN to train the classifiers in the ACNN. We also found that parameter balance was essential in adaptive neural network structure for better performance in further testing experiments. When setting the Dropout layer parameter at 0.5 and the iteration number was 32, the proposed model achieved adequate recognition accuracy and efficiency. Smaller parameters affect model performance, but more extensive parameters significantly increase computational burden and loss. Comparative testing experiments fully validated its superiority compared with traditional methods based on CNNs. Using traditional carving patterns from Anhui Province as an example, we conducted image recognition and feature graphic application under ideal parameter balance conditions and thereby demonstrated the practicality and value of the proposed method. Full article

Global population aging creates distinct healthcare needs, particularly for older adults and those with serious illnesses. There are several gaps in current models for monitoring elderly individuals. These include the limited application of advanced deep learning techniques in elderly health monitoring, the lack of real-time anomaly detection for vital signs, the absence of robust evaluations using real-world data, and the failure to tailor monitoring systems specifically for the unique needs of elderly individuals. This study addresses these gaps by proposing a Hierarchical Attention-based Temporal Convolutional Network (HATCN) model, which enhances anomaly detection accuracy and validates effectiveness using real-world datasets. While the HATCN approach has been used in other fields, it has not yet been applied to elderly healthcare monitoring, making this contribution novel. Specifically, this study introduces a Hierarchical Attention-based Temporal Convolutional Network with Anomaly Detection (HATCN-AD) model, based on the real-world MIMIC-II dataset. The model was validated using two subjects from the MIMIC-II dataset: Subject 330 (Dataset 1) and Subject 441 (Dataset 2). For Dataset 1 (Subject 330), the model achieved an accuracy of 99.15% and precision of 99.47%, with stable recall (99.45%) and F1-score (99.46%). Similarly, for Dataset 2 (Subject 441), the model achieved 99.11% accuracy, 99.35% precision, and an F1-score of 99.44% at 100 epochs. The results show that the HATCN-AD model outperformed similar models, achieving high recall and precision with low false positives and negatives. This ensures accurate anomaly detection for real-time healthcare monitoring. By combining Temporal Convolutional Networks and attention mechanisms, the HATCN-AD model effectively monitors elderly patients’ vital signs. Full article

A multi-strategy improved honey badger algorithm (MIHBA) is proposed to address the problem that the honey badger algorithm may fall into local optimum and premature convergence when dealing with complex optimization problems. By introducing Halton sequences to initialize the population, the diversity of the population is enhanced, and premature convergence is effectively avoided. The dynamic density factor of water waves is added to improve the search efficiency of the algorithm in the solution space. Lens opposition learning based on the principle of lens imaging is also introduced to enhance the ability of the algorithm to get rid of local optimums. MIHBA achieves the best ranking in 23 test functions and 4 engineering design problems. The improvement of this paper improves the convergence speed and accuracy of the algorithm, enhances the adaptability and solving ability of the algorithm to complex functions, and provides new ideas for solving complex engineering design problems. Full article

Background: Pulmonary hypertension (PH) can cause characteristic electrocardiographic (ECG) changes due to right ventricular hypertrophy and/or strain. The aims of the present study were to explore the diagnostic accuracy of ECG parameters for the diagnosis of PH, applying the recently adjusted mean pulmonary artery pressure (mPAP) threshold of >20 mmHg, and to determine the role of “R V1, V2 + S I, aVL − S V1”. Methods: Between July 2012 and November 2023, 100 patients without PH, with pulmonary arterial hypertension, or with chronic thromboembolic pulmonary hypertension were retrospectively enrolled. Results: The sensitivity and specificity of the ECG parameters for the diagnosis of PH varied from 3 to 98% and from 3 to 100% (means: 39% and 87%). After optimising the parameters’ cut-offs, the mean sensitivity (39% to 66%) increased significantly but the mean specificity (87% to 74%) slightly decreased. “R V1, V2 + S I, aVL − S V1” was able to predict an mPAP >20 mmHg (OR: 34.33; p < 0.001) and a pulmonary vascular resistance >5 WU (OR: 17.14, p < 0.001) but could not predict all-cause mortality. Conclusions: Even with improved cut-offs, ECG parameters alone are not able to reliably diagnose or exclude PH because of their low sensitivity. However, they still might be helpful to reveal a suspicion of PH, especially in early diagnostic stages, e.g., in primary care with general practitioners or non-specialised cardiologists and pulmonologists. “R V1, V2 + S I, aVL − S V1” was able to predict the diagnosis of (severe) PH but could not predict all-cause mortality. Nevertheless, it can still be useful in risk stratification. Full article

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes. Full article

(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-based Multi-Spatiotemporal Features for Travel Time Prediction (GMTP) model, which integrates an enhanced graph attention network (GATv2) and Bidirectional Encoder Representations from Transformers (BERT) to analyze dynamic correlations across spatial and temporal dimensions. The pre-training process consists of two blocks: the Road Segment Interaction Pattern to Enhance GATv2, which generates road segment representation vectors, and a traffic congestion-aware trajectory encoder by incorporating a shared attention mechanism for high computational efficiency. Additionally, two self-supervised tasks are designed for improved model accuracy and robustness. (3) Results: The fine-tuned model had comparatively optimal performance metrics with significant reductions in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). (4) Conclusions: Ultimately, the integration of this model into travel time prediction, based on two large-scale real-world trajectory datasets, demonstrates enhanced performance and computational efficiency. Full article

Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), and linear-frequency cepstral coefficients (LFCCs). These features are input into convolutional neural network (CNN) classifiers to evaluate performance. Experimental results demonstrate the effectiveness of different CNN models and feature extraction methods, with AlexNet, DenseNet, EfficientNet, ResNet50, and ResNet152 being evaluated. The system achieves high accuracy in classifying vocal behaviors, such as barking and howling in dogs, providing a robust tool for behavioral analysis. The study highlights the importance of automated systems in bioacoustics research and suggests future improvements using deep learning-based methods for enhanced classification performance. Full article

Background/Objectives: The nine-step inflation/deflation tympanometric Eustachian tube function test (commonly referred to as the nine-step test) is a widely utilized method for evaluating Eustachian tube function (ETF). This study aimed to establish normative values for the nine-step test to facilitate the diagnosis of Eustachian tube dysfunction (ETD). Methods: A total of 160 adults, including 70 healthy volunteers and 90 patients with chronic rhinosinusitis (CRS), were recruited for this study. Participants were further categorized into “fair ETF” and “poor ETF” groups based on their scores on the Eustachian Tube Dysfunction Questionnaire (ETDQ-7). Eustachian tube function was assessed using both the nine-step test and the ETDQ-7. The diagnostic accuracy of the maximal peak pressure difference (MPD) from the nine-step test was evaluated, using an ETDQ-7 score of ≥14 as the reference standard. Discriminative ability was analyzed using receiver operating characteristic (ROC) curves. Results: An MPD value of ≤4 yielded an area under the ROC curve (AUC) of 0.619, indicating moderate discriminative ability in the Taiwanese population. The median MPD value on the nine-step test was 9.5 (interquartile range [IQR]: 4.5–14.0) in participants with an ETDQ-7 score of <14, compared to a median MPD value of 7.5 (IQR: 2.5–12.0) in those with an ETDQ-7 score of ≥14 (p = 0.033). This finding suggests a potential association between MPD values and ETDQ-7 scores. Conclusions: This study identified an MPD value of 4 as a normative cutoff for screening ETD in a Taiwanese population. However, the diagnostic discriminative power of this parameter was moderate. Full article

Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. However, traditional methods often lack the ability to address fairness and transparency, leading to potential biases and inequities in the decision-making process. This paper explores the development and evaluation of machine learning models designed to predict student admissions while prioritizing fairness and interpretability. We employ a diverse set of algorithms, including Logistic Regression, Decision Trees, and ensemble methods, to forecast admission outcomes based on academic, demographic, and extracurricular features. Experimental results on real-world datasets highlight the effectiveness of the proposed models in achieving competitive predictive performance while adhering to fairness metrics such as demographic parity and equalized odds. Our findings demonstrate that machine learning can not only enhance the accuracy of admission predictions but also support equitable access to education by promoting transparency and accountability in automated systems. Full article

One of the key areas of contemporary marketing is the formulation of a pricing strategy, which is one of the four pillars of the traditional marketing mix. One way to implement this strategy is through dynamic pricing. It is currently gaining popularity in many industries for two reasons. Firstly, it is possible, easy, and cheap to collect information about transactions and customers. Secondly, machine learning mechanisms, for which these data are essential, are becoming widely available. The aim of this article is to propose a dynamic pricing method for the e-commerce industry. To achieve this goal, machine learning methods such as the Naive Bayes classifier, support vector machines (linear and nonlinear), decision trees, and the k-nearest neighbor algorithm were used. The empirical results indicate that the linear support vector machine achieved the highest accuracy (86.92%), demonstrating the model’s effectiveness in classifying pricing decisions. This article aligns with two leading research trends in dynamic pricing: personalized dynamic pricing (the target model considers customer-related criteria) and the development of systems to assist managers in optimizing pricing strategies to increase revenues (using machine learning methods). This article presents a literature review on dynamic pricing and then discusses the machine learning methods applied. In the final part of this article, verification of the developed dynamic pricing method using real-world conditions is presented. Full article

This paper examines the modeling approaches used to analyze the electric field distribution in high-voltage direct-current gas-insulated systems (HVDC-GISs) used for the acceleration grid power supply (AGPS) of neutral beam injectors (NBIs). A key challenge in this context is the degradation of dielectric performance due to radiation-induced conductivity (RIC), a phenomenon specific to the harsh radioactive environments near fusion reactors. Traditional models for gas conductivity in HVDC-GISs often rely on constant or nonlinear conductivity formulations, which are based on experimental data but fail to capture the effects of external ionizing radiation that triggers RIC. To address this limitation, a more advanced approach, the drift–diffusion recombination (DDR) model, is used, as it more accurately represents gas ionization and the influence of radiation fields. However, this increased accuracy comes at the cost of higher computational complexity. This paper compares the different modeling strategies, discussing their strengths and weaknesses, with a focus on the capabilities in evaluating the charge accumulation and the RIC phenomenon. Full article