Search Results (3,341)

Marine mammal sound classification plays an important role in understanding species behavior, communication, and ecology. Automated classification methods have received increasing attention due to their ability to efficiently process and analyze large volumes of acoustic data. Traditional classification approaches often rely on frequency-domain representations, such as spectrograms, and image-based classifiers, which can be highly influenced by user-defined parameters. In this study, we investigate a classification method for marine mammal vocalizations using a one-dimensional convolutional neural network (1D CNN) that directly processes raw audio signals. The approach can handle signals of varying durations through a random cropping technique, minimizing signal distortion that is commonly introduced by conventional methods. The model was evaluated using marine mammal vocalization recordings obtained from the Watkins Marine Mammal Sound Database under three experimental scenarios. The results demonstrate the feasibility of using raw audio inputs with a 1D CNN for classifying marine mammal vocalizations with variable signal durations. Full article

In this study, an active vibration control (AVC) strategy is effectively used on a system made of a honeycomb polymer–paper core and carbon fiber-reinforced polymer (CFRP) plates. A cost-effective and practical solution based on an AVC system has been developed and tested using a microcontroller unit (MCU) from Texas Instruments. The control system is studied by applying out-of-plane disturbances to the composite panel via an electrodynamic shaker, by exciting the identified mode shapes obtained through experimental modal analysis, i.e., impact tests. The actuator chosen for the AVC system is a Macro Fiber Composite (MFC) patch. Multiple analog signal processing circuits were developed to scale and shift the signal at the input and output of the MCU. The proposed control algorithm is based on a positive position feedback (PPF) technique. Modal analysis was performed to identify the natural frequencies and mode shapes of the structure, which are essential for the design and tuning of the modal-based PPF controller. This analysis also enabled optimal sensor and actuator placement, ensuring effective targeting and control of the dominant vibration modes. Then, a series of tests were performed using pure sine excitations at frequencies of interest, close to the 2nd and 8th mode at 25.13 Hz and 129 Hz, respectively. The results of the experiments revealed a velocity attenuation of 55.8% to 76.9% and a Power Spectral Density (PSD) attenuation of 5.8 dB to 12.8 dB, depending on the mode under study. Owing to the size and mass properties of the Macro Fiber Composite (MFC) patches, the control system is very much suitable for automobile and aerospace applications. Full article

With the wide use of nonlinear loads, power grids’ power quality problems are becoming more prominent, threatening the stability of power systems seriously. To address these problems, this paper proposes a power quality disturbances (PQD)classification method based on the Kaiser window S-transform (KST) and deep neural networks. Firstly, the Kaiser window S-transform is applied to the processing of PQD signals. Secondly, the Kaiser window control function is modified to adjust the shape of the window, and the window function parameters are automatically optimized according to the maximum energy density to achieve a better time-frequency resolution. Then, deep neural networks are utilized to perform deep feature extraction on the feature vectors of the Kaiser window S-transform. Lastly, a Softmax layer is applied to the extracted features. The results show that the method has excellent classification accuracy and noise resistance: the 99.1% accuracy achieved under 20 dB noise. Full article

In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability. Full article

Background/Objectives: Epigenetic modifications, particularly DNA methylation, contribute to tumor progression and therapy resistance. Guadecitabine, a hypomethylating agent (HMA), has shown promising clinical activity when combined with carboplatin in preclinical models. We evaluated the combination of guadecitabine with carboplatin as a second-line treatment for extensive-stage small-cell lung cancer (SCLC; NCT03913455), one of the deadliest malignancies. Here, we report methylome changes in peripheral blood mononuclear cells (PBMCs) collected at baseline and during treatment from patients on the trial. Methods: PMBC DNA was analyzed using Infinium HumanMethylationEPIC v1.0 bead chips. Data were processed, and differentially methylated positions (DMPs) were identified and analyzed for pathway enrichment using bioinformatic approaches, and immune deconvolution analyses were conducted to investigate the impact on immune cell composition. Results: Direct comparison of PBMCs between cycle 2 day 5 (C2D5; post-treatment) vs. cycle 1 day 1 (C1D1; pre-treatment) revealed a greater number of hypomethylated DMPs (380 DMPs in C2D5 vs. C1D1 PBMCs; p < 0.05, |β| > 20%). Moreover, when first compared with normal PBMCs from cancer-free controls, the number of hypomethylated DMPs was even greater in C2D5 than in C1D1 (1771 vs. 237 DMPs, respectively; p < 0.05, |β| > 20%). Long interspersed nucleotide elements-1 (LINE-1) were significantly hypomethylated in PBMCs after HMA treatment (C2D5 vs. C1D1). Pathway analysis of hypomethylated DMPs revealed significant alterations in key signaling pathways, including NF-κB, Rho GTPase, and pulmonary fibrosis in C1D1 vs. C2D5. Normal PBMCs to C1D1 PBMCs revealed changes in IL-3 signaling, Fcγ receptor-mediated phagocytosis, and molecular mechanisms of cancer. Deconvolution analysis revealed a greater percentage of monocytes in C1D1 vs. normal PBMCs; after HMA treatment, percentages of monocytes and B cells decreased, while the eosinophil percentage increased in C1D1 vs. C2D5. Conclusions: HMA treatment has a global impact on PBMC methylomes in cancer patients. DNA methylation changes were associated with biological pathways related to PBMC function, and shifts in distinct immune cell populations were observed. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an action potential since it can determine both the positive peak and the negative peak of the action potential by means of the HONEO and two threshold generators. In addition, the signal-to-noise ratio (SNR) of the APD can also be improved due to the two adaptive threshold generators. The circuit is designed in a standard 0.18 $\mathsf{\mu }$m CMOS process with a 1.8 V supply voltage. Pre-layout simulations are performed under typical conditions (TT process corner, 1.8 V supply, 27 ${}^{\circ }$C). The results show that the output amplitudes of the HONEO remain almost constant ($\pm 100$ mV) when the amplitude of the source signal varies from −10 mV to 30 mV at 1 kHz. Across temperature variations from $-20$${}^{\circ }$C to 80 ${}^{\circ }$C, the output amplitude remains within $\pm 12\%$ of the nominal value, demonstrating acceptable stability for the target implantable application. Compared to the conventional NEO, the APD achieves 14–$20\mathrm{dB}$ SNR improvement, a detection accuracy of $97\%$. The power consumption of the APD is approximately $62\mathsf{\mu }\mathrm{W}$. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

This study presents an integrated optimization framework that combines the Design of Experiments (DOE) approach with Machine Learning (ML) techniques to minimize defects in the injection molding of Fuel Vapor Charcoal Canisters. The research focuses on five critical process parameters—melt temperature, mold temperature, filling time, pressure holding time, and pure cooling time—whose combined influence on major molding defects (warpage, shrinkage, shear stress, residual stress, and short shots) was systematically investigated. A Taguchi L25 orthogonal array was employed to structure the experiments and identify the optimal parameter levels through signal-to-noise (S/N) ratio analysis using the “smaller-the-better” quality criterion. The Taguchi results revealed that pressure holding time was the most influential factor, followed by mold temperature and melt temperature. Simulation results from SolidWorks Plastics confirmed the reduction in major defects under the optimized settings. To further validate and generalize the DOE findings, a Random Forest regression model was trained on the same dataset to capture nonlinear interactions between parameters. The model achieved an average RMSE of 2.451 ± 0.591 in five-fold cross-validation, demonstrating strong predictive accuracy. Feature importance analysis indicated that pressure holding time accounted for approximately 77.5% of the variance in the defect index, reaffirming its dominant role. A 3D response surface of the global parameter space (mold temperature vs. pressure holding time) revealed a distinct minimum defect region, consistent with the DOE-optimized settings. The Taguchi analysis identified the optimal parameter settings as Melt Temperature at Level 2, Mould Temperature at Level 3, Filling Time at Level 4, Pressure Holding Time at Level 5, and Pure Cooling Time at Level 4, which collectively produced the highest S/N ratios and the lowest overall defect index. The overall discrepancy between DOE and ML predictions was only 12.5%, confirming methodological consistency. The integration of DOE and ML not only enhances parameter interpretability and defect prediction accuracy but also provides a scalable, data-driven approach for intelligent process control and quality assurance in automotive injection molding. Full article

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean $-75.51$ dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), $4.98\%$ (humidity), and a stable pressure offset of $-9.58$ hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications. Full article

Programmed cell death (PCD) is a pivotal biological process that occurs at various stages of plant development, including embryogenesis and seed germination. This study investigated whether the absence of PCD in endosperm cells is connected to the poor germination of Viola odorata seeds. Seeds of poorly germinating V. odorata and well-germinating V. × wittrockiana were either cold-stratified for 10 days or left untreated. Germination tests, tetrazolium viability tests, Western blot analyses for caspase-like proteases, and Terminal deoxynucleotidyl transferase (TdT) dUTP nick end labeling (TUNEL) assays for DNA strand break detection were performed. The results revealed that V. odorata seeds did not germinate, regardless of stratification or lack thereof, whereas in V. × wittrockiana, stratification significantly increased their germination capacity (34 ± 6.5% vs. 56.5 ± 9.8% in non-stratified and stratified seeds, respectively). The tetrazolium viability test revealed that V. odorata seeds were nonviable (100% nonviable endosperm and 96% nonviable embryos in total), whereas the seeds of V. × wittrockiana were highly viable (63% and 59% endosperm and embryos in total, respectively). Western blot analysis revealed that in the germinating seeds of V. × wittrockiana, caspase-like activity was detected in the endosperm but not in the embryos, whereas in seeds that failed to germinate, the PCD signal in the endosperm was very weak. In the seeds of V. odorata, caspase-like activity was detected in the embryos and endosperm collected directly after 10 days of stratification, but no signal was detected in the seeds left to germinate for one month after cold stratification. TUNEL assays revealed DNA strand breaks in the peripheral part of the endosperm in V. odorata and in non-germinating V. × wittrockiana, whereas in the germinating seeds of V. × wittrockiana, DNA strand breaks were detected in the endosperm cells adjacent to the embryo. These findings indicate that endosperm-localized PCD facilitates nutrient mobilization to the embryo and seems crucial for successful germination. Overall, these results suggest that PCD contributes to the regulation of seed germination in Viola spp. Full article

Congenital anomalies of the kidney and urinary tract (CAKUT) constitute the most common underlying cause of chronic kidney disease in pediatric populations. Maternal hypovitaminosis D links to mesoderm-related birth defects, leading to our hypothesis that maternal vitamin D deficiency (VDD) impairs renal development (a mesoderm-derived process) and induces offspring CAKUT. To investigate whether a low-vitamin D level can cause CAKUT, we used vitamin D-free diets to induce a maternal vitamin D deficiency mice model. The maternal vitamin D deficiency (VDD) mice models and normal vitamin D status (CON) were successfully established by administering a vitamin D-free or vitamin D-sufficient diet for 4 weeks prior to pregnancy. The overall incidence of CAKUT was significantly increased in VDD neonatal mice (19.4% vs. 2.44%; p = 0.0006), with a higher incidence of early duplicated budding in E11.5. E11.5 ureteric bud tissue revealed significantly increased activity of Gdnf-Ret-p-Erk1/2 signaling in the VDD group. In vivo intervention with the p-Erk1/2 antagonist U0126 in the pregnant VDD mice model at E10.5 improved CAKUT occurrence in offspring with p-Erk1/2 expression decreasing toward normal levels. Early metanephric ureteric bud H3K4me3 CUT&TAG analysis at E12.5 revealed chromatin activation patterns, which revealed that the downregulation of Hnf1β promoter region peaks was accompanied by reduced Hnf1β expression, and Robo2 promoter region peak was upregulated with increased Robo2 expression in the VDD group. Maternal vitamin D deficiency in mice significantly increased offspring CAKUT incidence. This phenotype was mediated by enhanced Gdnf-Ret-p-Erk1/2 signaling and reversed by p-Erk1/2 inhibition, with VDD inducing epigenetic remodeling of Hnf1β and Robo2 promoters. Full article

The development of biomimetic scaffolds requires balancing structural integrity with biological signaling. This study evaluates kefiran, a microbial exopolysaccharide, as a bioactive component in establishing printing feasibility of 3D composite constructs. Kefiran from Romanian artisanal cultures was characterized via ¹H-NMR, HPLC, and SEM/TEM, confirming a high-quality hexasaccharide repeating unit. Three composite inks (K100, K70, and K50) were developed by integrating kefiran, chondroitin sulfate, and Si-substituted hydroxyapatite into an alginate matrix and processed using a Bio X 3D-printer. Results showed that higher kefiran concentrations improved printing feasibility, providing enhanced structural fidelity and stability during the layer-by-layer deposition process. All bioprinted scaffolds demonstrated high cytocompatibility with L929 fibroblasts, maintaining viability above 70%. Notably, kefiran exhibited dual-functional therapeutic potential: concentrations above 500 mg/L showed a concentration-dependent antiproliferative effect against HT-29 cells at 72 h while remaining safe for normal cells. These findings establish kefiran-based biomaterial inks as robust, bioactive platforms for regenerative medicine. By enhancing both the mechanical printability of alginate composites and the biological response of cultured cells, kefiran proves to be a versatile component for advanced tissue engineering and potential biological activity applications. Full article

Geiger-mode avalanche photodiode (GM-APD) array light detection and ranging (LiDAR) has significant advantages in low-light scenes due to its single-photon-level detection sensitivity. However, it is susceptible to noise, which leads to a decrease in target localization accuracy. Traditional methods rely on long-term accumulation to distinguish signal photons from noise photons, making it difficult to achieve efficient processing, especially in scenarios with sparse echo photons and low signal-to-noise ratio (SNR), where performance is limited. To quickly and accurately obtain three-dimensional (3D) information of the target under such extreme conditions, this paper proposes a method for target detection and temporal window depth estimation based on intensity information guidance. First, noise suppression is performed on the intensity image according to its statistical characteristics, and an outlier detection mechanism based on neighborhood sparsity is introduced to remove outliers, thereby completing the target detection. Next, by exploiting the spatial continuity and reflectivity similarity of the target, local fusion of photon data within the target neighborhood is performed to construct highly consistent “superpixels”. Finally, according to the distribution difference between signal photons and noise photons on the time axis, temporal window screening is applied to the superpixels to extract depth information, and empty pixels are filled using a convex segmentation method to achieve depth estimation of the target. The experimental results demonstrate that under conditions of low photon counts and strong noise, the proposed method significantly outperforms traditional and existing methods in target recovery and depth estimation by effectively integrating target intensity information. Furthermore, this method achieves faster reconstruction speed, enabling high-precision and high-efficiency 3D target reconstruction. Full article

The vitamin D receptor (VDR) acts as both a nuclear transcription factor and a non-genomic mediator that regulates a broad spectrum of physiological processes beyond calcium and phosphate homeostasis. VDR plays an important role in the modulation of ion channels across multiple tissues, including osteoblasts, renal and intestinal epithelial cells, neurons, and vascular smooth muscle. These regulatory mechanisms encompass genomic actions through vitamin D response elements in target genes—such as TRPV5, TRPV6, KCNK3, and KCNH1—as well as rapid, non-genomic actions at the plasma membrane involving protein disulfide isomerase A3 and associated signaling cascades. VDR-mediated transcriptional control of calcium, potassium, and chloride channels contributes to the fine-tuning of cellular excitability, calcium transport, and mitochondrial function. Evidence also implicates VDR–ion channel crosstalk in various pathological contexts, including renal cell carcinoma, breast and cervical cancers, pulmonary arterial hypertension, and osteoporosis. Understanding the molecular interplay between VDR and ion channels provides new perspectives on the pleiotropic effects of vitamin D and offers promising therapeutic opportunities in oncology, cardiovascular disease, and skeletal disorders. This review synthesizes previous and current evidence on the genomic and non-genomic mechanisms underlying VDR–ion channel regulation and highlights novel frontiers in vitamin D signaling relevant to human health and disease. Full article