Search Results (56)

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article

Background: Multiple studies of the role of neurotransmitter systems in the effects of various substances on brain functions under normal conditions and at various brain disorders have demonstrated the relatively high usefulness of the electroencephalogram (EEG). However, little is known about EEG “fingerprints” of direct neurotransmitter–receptor interactions, in particular, for monoamine (MA) systems involved in the main brain functions. Methods: We looked at how the EEG effects of serotonin, dopamine, and norepinephrine receptors activating substances (quipazine, SKF-38393, and clonidine, respectively) injected into the brain’s lateral ventricles were affected by corresponding blockers (cyproheptadine, SCH-23390, and yohimbine) in freely moving rats. We introduced a method for clustering significant changes in the EEG spectra based on specific time intervals and narrow frequency subranges. Results: Stimulating serotonin and dopamine receptors caused specific suppression of EEG activity around 10 Hz and an increase near 18 Hz, respectively. The effects were reduced after pretreatment with the corresponding receptor blockers. Clonidine produced clusters of increased and decreased EEG activity around 6 Hz and 21 Hz, respectively, which were weakened by the blocker, yohimbine. These results demonstrate the “signatures” of different MA systems in EEG time–frequency clustering. Conclusions: We consider the developed approach as a potentially useful tool in clinics for evaluation of MA transmission pathology and its therapy with corresponding substances penetrating the blood–brain barrier. Full article

Extreme low-light image enhancement presents persistent challenges due to compounded degradations involving underexposure, sensor noise, and structural detail loss. Traditional low-light image enhancement methods predominantly employ global adjustment strategies that disregard semantic context, often resulting in incomplete detail recovery or color distortion. To address these limitations, we propose a semantic-aware knowledge-guided framework (SKF) that systematically integrates semantic priors for improved illumination recovery. Our framework introduces three key modules: A Semantic Feature Enhancement Module for integrating hierarchical semantic features, a Semantic-Guided Color Histogram Loss to enforce color consistency, and a Semantic-Guided Adversarial Loss to enhance perceptual realism. Furthermore, we improve the semantic-guided color histogram loss by leveraging multi-color space constraints. Inspired by human visual perception mechanisms, our enhanced loss function calculates color discrepancies across three color spaces—RGB, LAB, and LCH—through three components: $los{s}_{rgb}$, $los{s}_{lab}$ and $los{s}_{lch}$. These components collaboratively optimize image contrast and saturation, thereby simultaneously enhancing contrast preservation and chromatic naturalness. Full article

The problem of estimation the friction torque in operating miniature ball bearings lubricated with oil or grease is a complex one. Generally, in an angular contact ball bearing (ACBB), various types of losses can appear including losses caused by kinematics in ball-race contacts (rolling, sliding and pivoting), losses between the cage and the balls and between the cage and the guiding race and losses generated by lubricant, especially at high speeds. In the miniature ACBB, the applied loads have generally low values, and some losses can be ignored. In these circumstances, the most important contribution to the increase in the losses in miniature ACBB is the presence of the lubricant. In normal rolling bearings, the lubricant has an important contribution to decrease the losses and increase the reliability in miniature ball bearing; the lubricant (oil or grease) leads to the increase in the losses compared to the dry or limit lubrication conditions. The catalogues of various rolling bearing companies have not provided more details referring to the friction losses in miniature ball bearings. In order to evaluate the total friction torque in the rolling bearings, some empirical complex relations are presented via the SKF methodology, which can be applied only to moderate and high loads applied to the rolling bearings. Other empirical relations are presented by the Schaeffler catalogue. Based on previous experiments, the authors determined the friction torque in a 7000C ACBB with the spin-down method. The experimental results were correlated with the results obtained via the theoretical model developed by Houpert for IVR lubrication conditions. The theoretical results evidenced that the hydrodynamic rolling resistance generated by the lubricant is the most important component of the friction torque for 7000C ACBB. The experimental and theoretical results were compared to the results obtained according to the SKF and Schaeffler relations. The experimental results and the results obtained with the Houpert model generally had higher values compared to the results obtained with the SKF and Schaeffler relations. Full article

Background/Objectives: Doxorubicin (DOX) is commonly used as a chemotherapeutic agent for the treatment of breast cancer. Nonetheless, its systemic delivery via intravenous injection and toxicity towards healthy tissues commonly result in a broad range of detrimental side effects. Breast cancer severity was previously shown to be correlated with TRPC1 channel expression that conferred upon it enhanced vulnerability to pulsed electromagnetic field (PEMF) therapy. PEMF therapy was also previously shown to enhance breast cancer cell vulnerability to DOX in vitro and in vivo that correlated with TRPC1 expression and mitochondrial respiratory rates. Methods: DOX uptake was assessed by measuring its innate autofluorescence within murine 4T1 or human MCF7 breast cancer cells following magnetic exposure. Cellular vulnerability to doxorubicin uptake was assessed by monitoring mitochondrial activity and cellular DNA content. Results: Here, we demonstrate that 10 min of PEMF exposure could augment DOX uptake into 4T1 and MCF7 breast cancer cells. DOX uptake could be increased by TRPC1 overexpression, whereas inhibiting the activity of TRPC1 channels with SKF-96356 or genetic knockdown, precluded DOX uptake. PEMF exposure enhances DOX-mediated killing of breast cancer cells, reducing the IC₅₀ value of DOX by half, whereas muscle cells, representative of collateral tissues, were less sensitive to PEMF-enhanced DOX-mediated cytotoxicity. Vesicular loading of DOX correlated with TRPC1 expression. Conclusions: This study presents a novel TRPC1-mediated mechanism through which PEMF therapy may enhance DOX cytotoxicity in breast cancer cells, paving the way for the development of localized non-invasive PEMF platforms to improve cancer outcomes with lower systemic levels of DOX. Full article

The loose integration system of high-rate GNSS and strong-motion records based on Kalman filtering technology is currently a research focus for capturing broadband co-seismic displacements. To address the problem of time-varying system noise variance in the standard Kalman filter (SKF), a variance compensation adaptive Kalman filter (VC-AKF) was adopted in this study to obtain more accurate high-precision broadband co-seismic displacement and provide reliable data support for seismic scientific research and practical applications. The algorithm continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time. To verify the effectiveness and superiority of this method, a numerical simulation and a seismic experiment from the 2017 Ms 7.0 Jiuzhaigou earthquake were carried out for comparative analysis. Based on the simulation results, the precision of the proposed algorithm was 46% higher than that of the SKF. The seismic experiment results indicate that the proposed VC-AKF approach can eliminate the baseline shift of accelerometers and weaken the influence of time-varying system noise variance towards more robust displacement information. Full article

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD) and conditional domain adversarial network with entropy conditioning (CDAN+E) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model. Full article

Background: A major issue in Chronic Myeloid Leukemia (CML) is the persistence of quiescent leukemia stem cells (LSCs) in the hematopoietic niche under tyrosine kinase inhibitor (TKI) treatment. Results: Here, using CFSE sorting, we show that low-proliferating CD34+ cells from CML patients in 3D co-culture hide under HS27A stromal cells during TKI treatment—a behavior less observed in untreated cells. Under the same conditions, Ba/F3p210 cells lose their spontaneous motility. In CML CD34+ and Ba/F3p210 cells, while Rac1 is completely inhibited by TKI, RhoA remains activated but is unable to signal to ROCK. Co-incubation of Ba/F3p210 cells with TKI, SKF-96365 (a calcium channel inhibitor), and EGF restores myosin II activation and amoeboid motility to levels comparable to untreated cells, sustaining the activation of ROCK. In CFSE+ CD34+ cells containing quiescent leukemic stem cells, co-incubation of TKI with SKF-96365 induced the expulsion of these cells from the HS27A niche. Conclusions: This study underscores the role of RhoA in LSC behavior under TKI treatment and suggests that SKF-96365 could remobilize quiescent CML LSCs through reactivation of the RhoA/ROCK pathway. Full article

Body composition assessment helps conducting a healthy life or tracking the effectiveness of a weight management therapy. Ultrasound (US)-based body composition research has gained momentum because of the emergence of portable and inexpensive instruments bundled with user-friendly software. Previously, US-based assessment of body fat percentage (% BF) was found precise, but inaccurate in certain populations. Therefore, this study sought to compute % BF from subcutaneous fat thicknesses (SFs) given by US converting an anthropometric formula that involves skinfold thicknesses (SKFs) measured at the same sites. The symmetry of the body with respect to the central sagittal plane is an underlying assumption in both anthropometry and US-based body composition assessment, so measurements were taken on the right side of the body. Relying on experimental data on skinfold compressibility, we adapted 33 SKF formulas for US use and tested their validity against air displacement plethysmography on a study group of 97 women (BMI = 25.4 ± 6.4 kg/m², mean ± SD) and 107 men (BMI = 26.7 ± 5.7 kg/m²). For both sexes, the best proprietary formula had Lin’s concordance correlation coefficient (CCC) between 0.7 and 0.73, standard error of estimate (SEE) < 3% BF and total error (TE) > 6% BF—mainly because of the underestimation of % BF in overweight and obese subjects. For women (men) the best adapted formula had CCC = 0.85 (0.80), SEE = 3.2% (2.4%) BF, and TE = 4.6% (5.4%) BF. Remarkably, certain adapted formulas were more accurate for overweight and obese people than the proprietary equations. In conclusion, anthropometric equations provide useful starting points in the quest for novel formulas to estimate body fat content from ultrasound measurements. Full article

Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. Full article

Kiwi’s increased popularity as a healthy fruit with several agro-food applications has increased the amount of bio-waste produced like leaf, peel, and seed by-products, usually combined to form a kiwi pomace, which increases the environmental footprint of kiwi fruit and waste management costs. The aim of the present study is to thoroughly review and outline the nutritional content and bioactive components of both kiwi fruit and its by-products, as well as the innovative approaches to obtain and valorize kiwi’s bioactives, phytochemicals, vitamins, and nutrients in several functional food products, nutraceuticals, and cosmetics applications with health-promoting properties. The antioxidant and anti-inflammatory properties and mechanisms of action of the extracted polyphenols, flavonoids, flavones, organic acids, and other bioactive components in both the fruit and in its functional products are also elucidated. Emphasis is given to those bioactive ingredients and extracts from kiwi by-products that can be valorized in various functional foods, supplements, nutraceuticals, nutricosmetics, cosmeceuticals, and cosmetics-related applications, with antioxidant and anti-inflammatory health-promoting properties. Characteristic examples with reported health benefits are the functional kiwi fruit jelly (FKJ),fermented kiwi fruit products like wine, starchy kiwi fruit flour (SKF), and kiwi-derived functional protein bars, cheese and flour, as well as several nutraceuticals and functional cosmetics with kiwi bioactives improving their antioxidant, antiaging, and photoprotective properties, collagen synthesis, skin density, hydration, elasticity, and the wound healing process, while beneficially reducing skin roughness, wrinkles, hyperpigmentation, keratinocyte death, and DNA and cell damage. The limitations and future perspectives for these kiwi bioactive-based applications are also discussed. Full article

Osteoporosis is the most common metabolic bone disorder and is characterized by decreased bone density, which has a relationship with the quality of life among the aging population. Previous research has found that activation of the dopamine D1 receptor can improve bone mass formation. SKF38393 is an agonist of dopamine D1 receptors. However, as a small-molecule drug, SKF38393 is unstable and releases quickly. The aim of this study was to prototype polylactic-co-glycolic acid (PLGA)/SKF38393 microspheres and assess their potential osteogenic effects compared to those under the free administration of SKF38393. The cytocompatibility of PLGA/SKF38393 was determined via CCK-8 and live/dead cell staining; the osteogenic effects in vitro were determined with ALP and alizarin red staining, qRT-PCR, and Western blotting; and the in vivo effects were assessed using 25 Balb/c mice. We also used a PCR array to explore the possible signaling pathway changes after employing PLGA/SKF38393. Our experiments demonstrated that the osteogenic effect of D1Rs activated by the PLGA/SKF38393 microsphere was better than that under free administration, both in vitro and in vivo. According to the PCR array, this result might be associated with six signaling pathways (graphical abstract). Ultimately, in this study, we prototyped PLGA/SKF38393, demonstrated its effectiveness, and preliminarily analyzed its mechanism of action. Full article

Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research trend, but it faces challenges such as heterogeneous data, catastrophic forgetting, and inter-client interference. However, most existing methods enhance model performance at the expense of privacy, such as uploading prototypes or samples, which violates the basic principle of only transmitting models in federated learning. This paper presents a novel selective knowledge fusion (FedSKF) model to address data heterogeneity and inter-client interference without sacrificing any privacy. Specifically, this paper introduces a PIT (projection in turn) module on the server side to indirectly recover client data distribution information through optimal transport. Subsequently, to reduce inter-client interference, knowledge of the global model is selectively absorbed via knowledge distillation and an incomplete synchronization classifier at the client side, namely an SKS (selective knowledge synchronization) module. Furthermore, to mitigate global catastrophic forgetting, a global forgetting loss is proposed to distill knowledge from the old global model. Our framework can easily integrate various CIL methods, allowing it to adapt to application scenarios with varying privacy requirements. We conducted extensive experiments on CIFAR100 and Tiny-ImageNet datasets, and the performance of our method surpasses existing works. Full article

Background: After the increasingly common anterior cruciate ligament reconstruction (ACLR) procedure in competitive athletes, rehabilitation is crucial for facilitating a timely return to sports (RTS) and preventing re-injury. This pilot study investigates the patient-reported outcomes of postoperative rehabilitation in competitive athletes, comparing supervised rehabilitation (SVR) and home-based rehabilitation (HBR). Methods: After ACLR, 60 (out of 74 screened) athletes were recruited and equally divided into HBR and SVR groups using non-probability convenience sampling, with each group comprising 15 males and 15 females. The rehabilitation outcomes in the respective groups were evaluated at 8 months using measures (Tegner Activity Scale [TAS], International Knee Documentation Committee subjective knee form [IKDC-SKF], ACL Return to Sport after Injury [ACL-RSI]) and objective parameters (isometric muscle strength, hamstring/quadricep asymmetry). RTS was evaluated at 9 months, with ACL re-injury rates recorded approximately 6 months post-RTS. Results: Both groups exhibited decreased TAS scores (HBR: 8 to 6, SVR: 8 to 7), with the SVR group demonstrating superior postoperative IKDC-SKF scores (81.82 vs. 68.43) and lower ACL-RSI scores (49.46 vs. 55.25). Isometric and isokinetic muscle strength, along with asymmetry values, was higher in the SVR group 8 months post-ACLR (p < 0.05). The SVR group showed a higher RTS rate to the same level (76.6% vs. 53.3%), while the re-injury rate was the same in both the rehabilitation groups (3.3%). Conclusions: Although both rehabilitation approaches yielded comparable outcomes, SVR may demonstrate some superior biomechanical improvements in athletes, resulting in a higher RTS rate. However, the psychological outcomes and re-injury rates did not significantly differ between the groups, emphasizing the need to address individual psychological needs during rehabilitation. Further investigation is recommended with a larger sample size to address the differences of gender among competitive athletes. Full article

The pursuit of developing healthier pasta products without compromising technological properties involves a strategic approach via the customization of raw material formulations and the integration of grain germination and extrusion processes. This study explores the impact of incorporating sprouts from quinoa (Chenopodium quinoa Willd) and kiwicha (Chenopodium pallidicaule Aellen) on the physicochemical properties of pasta by employing a centroid mixture design. The desirability function was utilized to identify the optimal ingredient proportions necessary to achieve specific objectives. The study identified optimal formulations for two pasta variations: pasta with the substitution of sprouted quinoa and cushuro powder (PQC), and pasta with partial substitution of sprouted kiwicha and cushuro powder (PKC). The optimal formulation for PKC was determined as 70% wheat flour (WF), 15% sprouted kiwicha flour (SKF), and 15% cushuro powder (CuP), with a desirability score of 0.68. Similarly, for PQC, the optimal formulation comprised 79% WF, 13% sprouted quinoa flour (SQF), and 8% CuP, with a desirability of 0.63. The optimized pasta formulation exhibited longer cooking times (10 and 8 min), increased weight gain (235% and 244%), and minimal loss of solids (1.4 and 1.2%) for PQC and PKC, respectively. Notably, firmness (2.8 and 2.6 N) and breaking strength values (2 and 2.7 N) for PQC and PKC pasta formulations, respectively, were comparable to those of the control sample (2.7 N and 2.6 N for firmness and fracturability, respectively). This research underscores the potential of tailored formulations and innovative processes to enhance the nutritional profile of pasta while maintaining key technological attributes. Full article