Search Results (388)

We observed vibrational eigenmodes for a variety of millimeter-scale objects, including glass and sapphire lenses, by placing them on a piezoelectric ‘shaker’ driven by a broadband noise or frequency sweep signal, and using an optomechanical microphone to pick up their vibrational signatures emitted into the surrounding air. High-quality vibrational modes were detected over the ~0–8 MHz range for a typical object–microphone spacing of 1–10 mm. The observed eigenfrequencies are shown to be in excellent agreement with numerical predictions. Non-contact detection of resonant vibrational eigenmodes in the MHz ultrasound range could find application in the quality control of numerous industrial parts, such as ball bearings and lenses. Full article

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. Full article

Mechanical damage like bruises produced during postharvest handling can lower market value, affect nutritional value, and pose food safety risks. The study evaluated bruises on apples using image processing. This research focuses on using computer vision for apple fruit damage detection. The fruits were subjected to three levels of impact using three ball weights (66, 98, and 110 g) dropped from 50 cm height and stored at 22 °C. The overall impact energies generated were 0.323 J (low), 0.480 J (medium), and 0.539 J (high). The bruise area and susceptibility of the damage, surface area of the fruit, and color were measured manually (colorimeter) and by image processing. The study found that the bruise area was significantly affected by impact force, where 110 g (0.539 J) damaged apples showed a bruise area of 4.24 cm² after 21 days of storage at 22 °C. The images showed a significant change in the RGB values (Red, Green, Blue) over 21 days of storage when impacted at 0.539 J. The study showed that the greater the impact energy effect, the higher the weight loss under constant conditions of storage. After 21 days of storage, the 110 g mechanically damaged apples recorded the highest percentage of weight loss (6.362%). The study found a significant decrease in the surface area of 110 g bruised apples, with a smaller decrease in surface area for 66 g bruised fruit. The use of computer vision to detect bruise damage and other quality attributes of Granny Smith apples can be highly recommended to detect their losses. Full article

Background: Pediatric sepsis is a life-threatening disease that is associated with the progression of acute lymphoblastic leukemia (ALL) and the recurrence of B-cell ALL (B-ALL). Although previous studies have reported a partial association between sepsis and ALL, there is limited research on the shared genes between pediatric sepsis and relapsed B-ALL. This study aims to further elucidate the more comprehensive and novel common genetic factors and molecular pathways between the two diseases. Methods: Gene expression datasets pertaining to pediatric sepsis (GSE13904, GSE80496) and relapsed B-ALL (GSE3910, GSE28460) were retrieved from the Gene Expression Omnibus database for this retrospective analysis. The initial analysis identified differentially expressed genes common to both pediatric sepsis and relapsed B-ALL. Subsequent investigations employed three complementary approaches: protein–protein interaction networks, molecular complex detection (MCODE) clustering functions, and support vector machine recursive feature elimination model to separately identify the diagnostic biomarkers for each condition. Importantly, key common genes were identified by overlapping the diagnostic genes for pediatric sepsis and relapsed B-ALL. Further characterization involved comprehensive functional analysis through the Metascape platform, construction of transcription factor (TF)-mRNA-microRNA (miRNA) networks, drug prediction, and molecular docking to explore their biological significance and potential therapeutic targets. Results: Comparative analysis of pediatric sepsis-related and relapsed B-ALL-related datasets revealed two shared genetic markers, lactotransferrin (LTF) and matrix metallopeptidase 9 (MMP9), exhibiting diagnostic significance and consistent upregulation in both disease groups. Transcriptional regulatory network analysis identified specificity protein 1 (SP1) as the principal transcription factor capable of coregulating LTF and MMP9 expression. In addition, molecular docking demonstrated high-affinity interactions between curcumin and MMP9 (−7.18 kcal/mol) as well as reserpine and LTF (−5.4 kcal/mol), suggesting their potential therapeutic utility for clinical evaluation. Conclusions: These findings elucidate the molecular pathogenesis involving LTF and MMP9 in pediatric sepsis and relapsed B-ALL, providing novel insights for clinical diagnosis and therapeutic development. Full article

Lidar point clouds are crucial for autonomous driving, but their sparsity and scale variations pose challenges for instance segmentation. In this paper, we propose LCPSNet, a Light Detection and Ranging (LiDAR) channel-aware point segmentation network designed to handle distance-dependent sparsity and scale variation in point clouds. A top-down FPN is adopted, where high-level features are progressively upsampled and fused with shallow layers. The fused features at 1/16, 1/8, and 1/4 are further aligned to a common BEV/polar grid and processed by the Local Perception Module (LPM), which applies cross-scale, position-dependent weighting to enhance intra-object coherence and suppress interference. The Inter-Channel Correlation Module (ICCM) employs ball queries to model spatial and channel correlations, computing an inter-channel similarity matrix to reduce redundancy and highlight valid features. Experiments on SemanticKITTI and Waymo show that LPM and ICCM effectively improve local feature refinement and global semantic consistency. LCPSNet achieves 70.9 PQ and 77.1 mIoU on SemanticKITTI, surpassing mainstream methods and reaching state-of-the-art performance. Full article

Yerba mate (Ilex paraguariensis) is widely consumed in South America and is valued for its bioactive compounds, such as polyphenols and methylxanthines. However, during traditional processing, mainly in the fire-based scorch and drying steps, polycyclic aromatic hydrocarbons (PAHs) and anthraquinone (AQ), substances with carcinogenic potential, may be formed. This study aimed to develop and validate an analytical method based on the balls-in-tube matrix solid-phase dispersion technique (BiT-MSPD) and analysis by gas chromatography with mass spectrometry (GC-MS) for the simultaneous determination of 16 priority PAHs and AQ in yerba mate. Parameters such as sorbent type, solvent, sample-to-sorbent ratio, and extraction time were optimized. The method showed good linearity (r² > 0.99), detection limits between 1.8 and 3.6 µg·kg⁻¹, recoveries ranging from 70 to 120%, and acceptable precision (RSD ≤ 20%). The method was applied to 31 yerba mate samples, including 20 commercial samples and 11 collected at different stages of processing. Most commercial samples showed detectable levels of PAHs, with some exceeding the limits established by the European Union. AQ was detected in 40% of the samples, with some values above the permitted limit of 20 µg·kg⁻¹. The results confirm that scorch (sapeco) and drying contribute to contaminant formation, highlighting the need to modernize industrial processing practices. The proposed method proved to be effective, rapid, and sustainable, representing a promising tool for the quality control and food safety monitoring of yerba mate. Full article

Flexible pressure sensors have shown promise applications in scenarios such as robotic tactile sensing due to their excellent sensitivity and linearity. However, the realization of flexible pressure sensors with both static and dynamic response capabilities still face significant challenges due to the properties of the sensing materials themselves. In this study, we propose a flexible pressure sensor that integrates piezoelectric and ionic capacitance mechanisms for full-domain response detection of dynamic and static forces: a “sandwich” sensing structure is constructed by printing a mixture of multi-walled carbon nanotubes (MWCNTs) onto the surface of the upper and lower electrodes, and sandwiching a polyvinylidene fluoride (PVDF) thin film between the electrodes. The device exhibits a sensitivity of 0.13 kPa⁻¹ in the pressure range of 0–150 kPa. The sensor has a rapid dynamic response (response time 19 ms/12 ms) with a sensitivity of 0.49 mV kPa⁻¹ based on the piezoelectric mechanism and a linearity of 0.9981 based on the ionic capacitance mechanism. The device maintains good response stability under the ball impact test, further validating its potential application in static/dynamic composite force monitoring scenarios. Full article

Foodborne pathogenic biofilms pose significant challenges to food safety due to their enhanced resistance to conventional antimicrobial agents. In this study, we evaluated the synergistic antibiofilm activity of oregano essential oil (OEO) from Lippia graveolens and the lytic bacteriophage phiLLS against six foodborne bacteria. GC–MS analysis achieved a 100% identification ratio, revealing that OEO was mainly composed of carvacrol (58.9%), p-cymene (28.6%), γ-terpinene (2.9%), and caryophyllene (2.6%). The MIC and MBC of OEO were 1 and 2 mg/mL, respectively, for all strains except E. coli BALL1119 (MIC = 2 mg/mL). We assessed biofilm biomass by crystal violet (CV) staining and metabolic activity using the TTC assay under both individual and combined treatments, monitored 9-hour planktonic growth kinetics to calculate Bliss and HSA synergy indexes, and employed atomic force microscopy (AFM) to visualize nanoscale alterations in Staphylococcus aureus and Escherichia coli BALL1119 biofilms. Combined OEO (2 mg/mL) and phiLLS (MOI 1) treatments achieved significantly greater biofilm biomass reduction than single agents, notably yielding >70% inhibition of S. aureus biofilms (p < 0.05) and a Bliss synergy index of 10.8% in E. coli BALL1119 growth kinetics, whereas other strains were additive. In biofilm assays, S. aureus and Salmonella spp. showed the highest reductions in biomass (CV) (71.0% and 67.8%, ΔHSA = 27.0% and 17.4%; ΔBliss = 21.1% and 13.8%) and metabolic activity (TTC) (68.6% and 48.5%). AFM revealed that OEO alone smoothed the extracellular matrix (averaging a 35% reduction in roughness), whereas the combined treatment caused fracturing (≈68 nm roughness) and prominent lytic pits. Although variability in S. aureus biofilm architecture precluded statistically significant pairwise comparisons, AFM topography and consistent trends in Ra/Rz parameters provided clear visual corroboration of the significant reductions detected by CV and TTC assays. These complementary data indicate that OEO primes the biofilm matrix for enhanced phage-mediated collapse, offering a green, two-step strategy for controlling resilient foodborne biofilms. Full article

The automated analysis of pool game videos presents significant challenges due to complex object interactions, precise rule requirements, and event-driven game dynamics that traditional computer vision approaches struggle to address effectively. This research introduces TCGA-Pool, a novel video analytics framework specifically designed for comprehensive 9-ball pool game understanding through advanced object attention mechanisms and temporal context modeling. Our approach addresses the critical gap in automated cue sports analysis by focusing on three essential classification tasks: Clear shot detection (successful ball potting without fouls), win condition identification (game-ending scenarios), and potted balls counting (accurate enumeration of successfully pocketed balls). The proposed framework leverages a Temporal Context Gated Attention (TCGA) mechanism that dynamically focuses on salient game elements while incorporating sequential dependencies inherent in pool game sequences. Through comprehensive evaluation on a dataset comprising 58,078 annotated video frames from diverse 9-ball pool scenarios, our TCGA-Pool framework demonstrates substantial improvements over existing video analysis methods, achieving accuracy gains of 4.7%, 3.2%, and 6.2% for clear shot detection, win condition identification, and potted ball counting tasks, respectively. The framework maintains computational efficiency with only 27.3 M parameters and 13.9 G FLOPs, making it suitable for real-time applications. Our contributions include the introduction of domain-specific object attention mechanisms, the development of adaptive temporal modeling strategies for cue sports, and the implementation of a practical real-time system for automated pool game monitoring. This work establishes a foundation for intelligent sports analytics in precision-based games and demonstrates the effectiveness of specialized deep learning approaches for complex temporal video understanding tasks. Full article

Mechanical alloying (MA) has been proven to be an energy-efficient synthetic route for the development of high-performance thermoelectric (TE) materials. Higher Manganese Silicide (HMS) phases of the general formula Mn(Si_1−xAl_x)_1.75 (0 ≤ x ≤ 0.05) were prepared by MA implementing a short-time ball-milling process. Powder XRD and SEM analysis were carried out to validate the HMS phases, while small amounts of the secondary phase, MnSi, were also identified, especially for the Al-doped products. Electrical transport properties measurements showed that Al substitution causes an effective hole doping. A remarkable increase in electrical conductivity is observed for the Al-doped phases, while the corresponding reduction in the Seebeck coefficient is indicative of the increase in carrier density. Despite the small percentages of MnSi detected in Al-doped phases, an improvement in TE efficiency is achieved in the series Mn(Si_1−xAl_x)_1.75 (0 ≤ x ≤ 0.05). The 2.5% Al-doped phase exhibits a maximum figure-of-merit (ZT) of 0.43 at 773 K. Moreover, in an effort to utilize recycled silicon byproducts from photovoltaic (PV) manufacturing, Al-doped phases are developed by MA using two types of Si kerf. The two kerf-based products exhibit lower TE efficiencies, due to the increased amounts of the metallic MnSi phase. Full article

Mitochondria are critical for cellular energy, and while large deletions in their genome (mtDNA) are linked to primary mitochondrial diseases, their significance in cancer is less understood. Given cancer’s metabolic nature, investigating mtDNA deletions in tumors at various stages could provide insights into disease origins and treatment responses. In this study, we analyzed 148 bone marrow samples from 129 pediatric patients with B-cell (B-ALL) and T-cell (T-ALL) acute lymphoblastic leukemia at diagnosis, remission, and relapse using long-range PCR, next-generation sequencing, and the Splice-Break2 pipeline. Both T-ALL and B-ALL exhibited significantly more mtDNA deletions than did the controls, with T-ALL showing a ~100-fold increase and B-ALL a ~15-fold increase. The T-ALL samples also exhibited larger deletions (median size > 2000 bp) and greater heterogeneity, suggesting increased mitochondrial instability. Clustering analysis revealed distinct deletion profiles between ALL subtypes and across disease stages. Notably, large clonal deletions were detected in some B-ALL remission samples, including one affecting up to 88% of mtDNA molecules, which points toward treatment-driven selection or toxicity. A multivariate analysis confirmed that disease type, timepoint, and WHO subtype significantly influenced mtDNA deletion metrics, while age and gender did not. These findings suggest that mtDNA deletion profiling could serve as a biomarker for pediatric ALL and may indicate mitochondrial toxicity contributing to late effects in survivors. Full article

B-lineage acute lymphoblastic leukemia (B-ALL) is classified into more than 20 molecular subtypes, and next-generation sequencing has facilitated the identification of these with high sensitivity. Bulk RNA-seq analysis of bone marrow was realized to identify molecular subtypes in Mexican pediatric patients with B-ALL. High hyperdiploidy (27.3%) was the most frequent molecular subtype, followed by DUX4 (13.6%), TCF3::PBX1 (9.1%), ETV6::RUNX1 (9.1%), Ph-like (9.1%), ETV6::RUNX1-like (9.1%), PAX5alt (4.5%), Ph (4.5%), KMT2A (4.5%), and ZNF384 (4.5%), with one patient presenting both the PAX5alt and low hypodiploidy subtypes (4.5%). The genes TYK2, SEMA6A, FLT3, NRAS, SETD2, JAK2, NT5C2, RAG1, and SPATS2L harbor deleterious missense variants across different B-ALL molecular subtypes. The Ph-like subtype exhibited mutations in STAT2, ADGRF1, TCF3, BCR, JAK2, and NRAS with overexpression of the CRLF2 gene. The DUX4 subtype showed mutually exclusive missense variants in the PDGRFA gene. Here, we have demonstrated the importance of using RNA-seq to facilitate the differential diagnosis of B-ALL with successful detection of gene fusions and mutations. This will aid both patient risk stratification and precision medicine. Full article

This research aims to develop an unsupervised machine learning technique for ball bearing fault detection and fault diagnosis. It evaluates vibration signals through Autoregression (AR) models and Fuzzy C-means (FCM) clustering for experimental data from bearings under degradation and fault conditions. FCM clustering attained almost 97.7% accuracy in fault classification, with robustness validated across the sensor orientations (vertical/horizontal accelerometers) and with rotational speeds of 40 and 50 Hz. The major outcomes indicate the significance of the training data size on order evaluation and the stability of anomaly detection using three-sigma thresholds. Comparative analyses are made that showcase effective performance of the AR+FCM approach over conventional methods (e.g., K-means with statistical features) in clustering metrics (Silhouette Score and Dunn Index). Some of the challenges observed include reduced accuracy under speed variations and transient loads. The system’s unsupervised nature and generalization capability make it appropriate for real-time industrial applications, offering a credible setting for early-stage fault diagnosis and health monitoring. Full article

This article describes a machine vision-based method for measuring the angular velocity of a rotating disk to characterize the viscous resistance of a ball bearing. A bright marker was attached to a disk connected to a shaft supported by two ball bearings. Rotation of the marker was recorded with a digital camera. A simple algorithm was developed to track the trajectory of the marker and calculate angular displacement of the disk. For accurate detection of the rotating marker, the algorithm employed Multi-Otsu thresholding and the Least Squares Method (LSM). Verification of the proposed method was carried out through a direct comparison between the predicted rotational speeds and measured ones by a commercial tachometer. It was demonstrated that the percentage error of the proposed method was less than 1.75 percent. The evolution of angular velocity after motor power-off was measured and found to follow an exponential decay law. The exponent was found to remain consistent regardless of the induced rotational speed. This proposed measurement method will offer a simple and accurate non-contact solution for monitoring angular velocity and characterizing the resistance of a bearing. Full article

B-cell precursor acute lymphoblastic leukemia (B-ALL) is characterized by a constellation of somatic pathogenic variants associated with malignant transformation. These variants have implications for clinical management by providing clinical biomarkers. Most B-ALL cases have a sporadic presentation. However, some patients may present the disease as the neoplastic manifestation of cancer predisposition syndromes caused by germline pathogenic variants. In these cases, genetic counseling and personalized oncologic management is mandatory, considering the patient’s sensitivity to conventional therapies. In this review, we have summarized current knowledge on the biological role and clinical relevance of somatic and germline pathogenic variants associated with B-ALL, and discuss three aspects of their application as biomarkers: (1) their usefulness to determine specific molecular subtypes, predicting prognosis and response to specific therapies, (2) their influence in genetic counseling and therapy adaptation for B-ALL in the context of underlying cancer predisposition syndromes, and (3) their detection and interpretation through methodologies. We also included a brief discussion on the need to reclassify variants of uncertain significance to clarify their clinical relevance. Finally, we discuss cases illustrating the impact of somatic and germline pathogenic variants in personalized medicine. Full article