Search Results (40,779)

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

For the first time, red LED irradiation was applied at pilot scale in the table olive industry to evaluate its influence on Negrinha de Freixo cultivar natural fermentation. Physicochemical parameters, microbial dynamics, and sensory attributes were evaluated between 60 and 95 days, with two irradiation periods (60–70 and 85–95 days). Three conditions were examined: control-static, pumping-brine recirculation, and LED-brine recirculation + red light exposure. Color or texture was not affected. The lowest pH values were consistently observed in the LED-treated samples. Total phenolic compounds in olives showed a slight decrease from 60 to day 95; however, significant differences were only detected between the pumping treatment and the other two conditions. At the end of the first LED irradiation period, a growth of lactic acid bacteria and aerobic mesophilic bacteria was observed in the order of log 1.0 CFU/mL in the brine, and the yeast count (log 1.4 CFU/g) and LAB (log 1.2 CFU/g) in the olives relative to the control, while the second irradiation period did not show a significant effect. Sensory analysis revealed that LED- irradiated olives exhibited the highest hardness (5.6) values, whereas control samples presented the highest perception of putrid defect. Overall, the results demonstrate that red LED photostimulation may be promising for application in the table olive industry. Full article

Welfare assessment for the endangered red panda (Ailurus fulgens) in captivity requires systematic behaviour monitoring, yet traditional direct observation is often limited by observer subjectivity and time constraints. This study evaluates the feasibility of employing machine learning (ML) to automate behavioural monitoring of a red panda in a complex, mixed-species enclosure at Aalborg Zoo, Denmark. Using video data from cameras in the enclosure of the red panda, and the ML model LabGym for animal detection and behavioural categorisation, models were trained to analyse activity patterns of the red panda. The results demonstrate that, while the behaviour categorizer is a promising tool with high classification confidence, the overall system effectiveness is currently limited by the object detector’s performance in a naturalistic environment. Challenges such as environmental obstructions (e.g., rocks, foliage, and trees) and the animal’s camouflage contributed to a significant amount of unclassified time, which may affect the overall assessment of behavioural distribution. We conclude that, while ML holds potential for non-invasive behaviour monitoring, its application in complex zoo settings requires improved detection capabilities to be fully reliable. Future iterations of this system could be enhanced by complementing standard object detection with pose estimation frameworks. Implementing alternative labelling strategies or background subtraction methods could additionally mitigate the detection challenges posed by environmental obstruction. Full article

Naked oat (Avena nuda L.) is an important dual-purpose crop for grain and forage in cold regions; however, its high fatty acid content renders seeds prone to deterioration during storage. This study aimed to investigate the protective effects of zinc oxide nanoparticles (ZnO NPs) on artificially aged naked oat seeds and elucidate the underlying molecular mechanisms. Non-aged seeds (Naged) were subjected to artificial aging at 45 °C and 100% relative humidity for 24 h (Aged), followed by priming with 30 mg L⁻¹ ZnO NPs for 6 h (Daged). Antioxidant enzyme activities were determined spectrophotometrically, and transcriptome sequencing was performed on an Illumina platform to identify differentially expressed genes (DEGs) and enriched pathways. We found that ZnO NPs increased catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) activities by 3–4-fold, restored germination rate from 75% to 98%, and enhanced seed vigor index. A total of 21,403 DEGs were detected, with 15,841 stably expressed in response to nano-priming. Reactive oxygen species (ROS) burst rapidly induced up-regulation of AP2/EREBP transcription factor family members, which subsequently activated antioxidant enzyme genes to maintain cellular redox homeostasis. Metabolic pathway analysis demonstrated that the phenylpropanoid pathway was reprogrammed, characterized by down-regulated lignin biosynthesis and up-regulated flavonoid production, thereby enhancing ROS scavenging capacity. Additionally, the pentose phosphate pathway was activated to provide additional NADPH for antioxidant defense, and up-regulated ADP-glucose pyrophosphorylase (AGPase) facilitated starch accumulation. Notably, the 40S ribosomal protein S13 exhibited the highest connectivity in protein–protein interaction networks, was up-regulated 2.1-fold, and was enriched in post-translational modification processes. These findings suggest that nano-priming with ZnO NPs represents a promising biotechnological strategy for enhancing seed vigor and storability in naked oat, with potential applications in sustainable agriculture and the seed industry. Full article

The rapid detection of organophosphorus (OP) compounds is crucial for safeguarding human health and ensuring food safety. This study presents a novel wearable electrochemical biosensor that integrates miniaturized screen-printed electrodes with wearable devices to achieve real-time, on-site OP detection. The biosensor was fabricated by constructing a screen-printed carbon electrode (SPCE) on a thermoplastic polyurethane (TPU) substrate, sequentially modified with graphene (GR), gold nanoparticles (AuNPs), and organophosphorus hydrolase (OPH), and finally encapsulated with Nafion. This SPCE/GR/AuNPs/OPH/Nafion configuration yields a highly flexible and portable device. The detection principle relies on the enzymatic hydrolysis of methyl paraoxon (MPOX) by OPH, generating p-nitrophenol (PNP), which is quantitatively measured via square wave voltammetry (SWV). The sensor exhibits a broad linear detection range (30–400 μM) with a strong linear correlation (R² = 0.995) and a low detection limit (0.321 μM). It demonstrates excellent selectivity against common interfering substances, including urea, sucrose, and various metal ions. Application to real-world samples such as cabbage and tap water yielded high recoveries (107.2% for cabbage and 101.2% for tap water), with relative standard deviations (RSDs) below 8%. Furthermore, the biosensor maintains robust flexibility and mechanical resilience, with less than 5% signal loss after 100 bending cycles, confirming its suitability for wearable applications and reliable operation under mechanical stress. This innovative, flexible electrochemical biosensor provides a powerful and reliable platform for rapid OP detection, particularly in complex testing environments. Full article

Overhauser magnetometer (OVM) is a proton precession magnetometer (PM) enhanced by electron resonance, and it is widely used in earthquake prediction, UXO detection, geological exploration, etc. For fast measurement, high cycling rate is necessary for OVM to enhance spatial resolution. Due to the impossibility to receive Larmor signal during the polarization process, traditional intermittent measurement is limited in fast mobile measurement applications owing to the long polarization time. Since it is difficult for proton magnetization to align rapidly for the long longitudinal relaxation time of liquid proton, we combined RF continuous excitation with a series 90° pulse to achieve fast measurement. To achieve the best alignment, a dynamic equation of Larmor precession is constructed and calculated, and the influences such as pulse waveform, pulse strength, and pulse duration on the proton magnetization alignment were investigated. The influence of different waveform pulses on the Larmor signal was studied experimentally, and the experimental results verified that the polarization time can be significantly shortened and fast measurement can be achieved by optimizing the waveform, strength, and duration of the 90° pulse. By using the optimized 90° pulse, the proton magnetization can be saturated within 3 ms, and 0.02 nT sensitivity was observed at 1 Hz cycling rate. Consistency between theory and the experiment indicates that the dynamic equation of Larmor motion can provide theoretical guidance for the investigation of fast measurement. Full article

Background/Objectives: Citrus peels are widely utilized as functional ingredients in health foods; however, their functional value is often assumed based on botanical classification rather than verified chemical composition. Bushukan (Citrus medica var. sarcodactylis) was selected as it lacks developed edible pulp; consequently, the usable portion consists almost entirely of peel tissue, making it a suitable model for evaluating peel-specific functional components. This commentary highlights the importance of species- and origin-specific evaluation through a case study of Bushukan (Citrus medica var. sarcodactylis) whole fruit powder cultivated in Japan. Methods: Dried whole-fruit powder samples of bushukan, prepared by freeze-drying and hot-air drying at 50 °C, were analyzed, and the contents of hesperidin and nobiletin were quantified using high-performance liquid chromatography (HPLC) following methanol reflux extraction. Results: Hesperidin was detected at 75 mg/100 g under both drying conditions, whereas nobiletin was below the practical limit of quantification (approximately 1 mg/100 g). No reduction in hesperidin content was observed after drying at 50 °C. These levels were markedly lower than those reported for commonly used citrus peels, such as satsuma mandarin, in previous studies. Conclusions: This commentary demonstrates that Japanese-grown bushukan samples do not necessarily provide substantial levels of commonly expected citrus flavonoids. These findings underscore the need for species- and origin-specific compositional verification before the use of citrus peels as raw materials for health food applications, illustrating this need through a practical, cautionary case study. Full article

Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study focuses on object detection in handball and targets key entities, such as players, referees, goalkeepers, and the ball. A comprehensive dataset was created through a collaborative annotation process, consisting of annotated images extracted from real handball games. The YOLOv8 model was then trained and evaluated on this dataset to assess its effectiveness in entity recognition. The proposed approach achieved an object detection accuracy of 86.8% on a relatively small held-out test set, providing an indicative first benchmark for the application of state-of-the-art machine learning models to handball. To the best of our knowledge, the dataset generated in this study is the first comprehensive collection of annotated handball images, providing a valuable resource for further research. By bridging sports analytics and computer vision, this study contributes to the advancement of performance assessment in handball. These exploratory results suggest potential directions for future real-time systems and practical applications, such as improved understanding of player performance, team dynamics, and strategic decision-making. Full article

Extracellular vesicles (EVs) mediate intercellular communication by delivering proteins and RNAs, with their molecular cargo often reflecting the biological context of their source. Perinatal tissues are promising sources of EV-related biomaterials with potential dermatologic applications. In this study, we compared EV-related molecular cargo from two umbilical cord-associated sources, umbilical cord mesenchymal stem cell (UCMSC)-derived EVs and cord blood plasma (CBP), to investigate whether these materials exhibit distinct functional effects on melanogenesis. UCMSC-derived EVs were isolated from conditioned culture medium and characterized using nanoparticle tracking analysis (NTA), cryo-electron microscopy (cryo-EM), and canonical EV marker detection, while cord blood samples were processed to obtain plasma following centrifugation and filtration, containing EVs together with soluble plasma components. Functional assays in the murine melanocyte cell line B16F10 demonstrated that UCMSC-derived EVs suppressed melanin production, whereas CBP treatment enhanced melanogenesis. Integrative omics analyses combining microRNAs (miRNAs) microarray profiling and proteomic characterization revealed distinct molecular signatures between UCMSC-derived EVs and CBP samples. Functional validation using miRNA mimic assays showed that selected miRNAs, including miR-6862-5p, miR-3622b-5p, miR-7847-3p, miR-6774-5p, and miR-4685-5p, reduced melanin production, whereas others, including miR-203a-3p, miR-126-3p, miR-139-5p, and miR-15b-5p, increased melanin levels. Pathway analysis using Ingenuity Pathway Analysis (IPA) (QIAGEN Inc.) associated these miRNA subsets with signaling pathways involved in melanogenesis. Together, these findings indicate that UCMSC-derived EVs and CBP exhibit opposite functional effects on melanogenesis and possess distinct miRNA and protein cargo profiles, providing potential molecular targets for modulating pigmentation and supporting the development of EV-related therapeutic strategies for pigmentation disorders. Full article

The impact of skin pigmentation on the accuracy of optical biomedical devices has gained increased attention since the COVID-19 pandemic, particularly following evidence of oximetry measurement bias in dark-skinned individuals. Meanwhile, many computational models utilising the Monte Carlo (MC) technique have been developed as a cost-effective and scalable method for investigating these effects. Hence, this review explores the application of the MC technique in modelling skin pigmentation, focusing specifically on how melanin in the epidermis is represented across different studies. First, the biological mechanisms of pigmentation and current stratification methods are outlined to contextualise the variability in skin tone, followed by the principles of MC modelling, including photon scattering, absorption, reflection, and detection. Following a screening and exclusion process, 50 studies were evaluated in terms of how melanin concentration and distribution are incorporated into MC models and their applications, revealing a range of approaches that include analytical equations, experimental optical property measurements, or hybrid methods. The benefits and limitations of each approach is discussed, in addition to emerging advancements such as heterogeneous melanin distribution and the relation between optical properties and skin colour classification scales. Overall, the review outlines the current methodological approaches utilised for skin pigmentation modelling and offers a reference framework for researchers seeking to improve the representation of skin pigmentation in MC-based optical simulations. Full article

The detection of sulfur hexafluoride (SF₆) decomposition products is critical for diagnosing insulation faults in gas-insulated switchgear (GIS). In this study, a vanadium-doping strategy was incorporated into the graphene/MoS₂ (GM) heterojunction to design a vanadium-doped graphene/MoS₂ (GMV) heterojunction material. Leveraging first-principles density functional theory (DFT), the adsorption behaviors of five characteristic SF₆ and its decomposition gases (H₂S, SO₂, SOF₂, SO₂F₂) on intrinsic GM and GMV were systematically investigated to evaluate their potential for gas sensing applications. Computational results reveal that intrinsic GM exhibits only weak physical adsorption toward all target molecules, with low adsorption energies and negligible charge transfer, which fails to meet practical application requirements. In contrast, GMV demonstrates significantly enhanced adsorption energies for H₂S, SO₂, and SOF₂ at vanadium sites (with a maximum value of −0.388 eV for SO₂) and shorter adsorption distances, while SO₂F₂ and SF₆ preferentially adsorb near electron-deficient carbon regions. Intrinsic GMV displays semimetallic properties, with a Fermi level at 0.126 eV and a band gap of 0.0017 eV. Upon adsorption of H₂S, SOF₂, SO₂F₂, or SF₆, the Fermi level undergoes a moderate shift (ranging from −1.083 eV to +0.349 eV), with minimal changes in the band gap. Conversely, SO₂ adsorption induces a substantial downward shift of the Fermi level to −1.732 eV, accompanied by the emergence of a sharp partial density of states (PDOS) peak near the Fermi level (0–1.5 eV), indicating strong orbital coupling and significant charge transfer. Furthermore, recovery times calculated using classical formulas show that at room temperature and a frequency of 1 × 10⁶ Hz, the recovery time of GMV for SO₂ is 2.43 s, outperforming the other four gases and satisfying practical gas sensing requirements. Through comprehensive analysis of adsorption distances, electronic structure changes, and recovery times, GMV exhibits higher selectivity toward SO₂. Thus, GMV can serve as a sensing material for detecting GIS insulation faults associated with elevated SO₂ concentrations, offering a viable strategy for advancing online monitoring technologies in power systems. Full article

We developed a dual-arm motion recognition system designed for real-time upper-limb movement analysis using video input. The system integrates MediaPipe Hands for skeletal critical point detection, a feature extraction pipeline that encodes spatial and temporal characteristics from upper-limb joints, and a three-layer long short-term memory network for temporal modeling and classification. By computing directional vectors from the shoulder to the elbow and wrist, a 168-dimensional feature vector is generated per frame. Sequences of 90 frames are used to capture full motion patterns. The system effectively supports multi-class recognition of coordinated dual-arm gestures, offering applications in rehabilitation, gesture control, and human–computer interaction. Full article

A precise understanding of the electrical properties of bearings is of great interest in many areas of application, especially in the context of electrification. The understanding of electrical properties allows for damage detection and sensory utilization of bearings. Previous research into the capacitive properties of rolling bearings has been limited to ball bearings. Cylindrical roller bearings, which are predominantly used in applications with large radial loads, have not been investigated so far. This paper develops a method to calculate the capacitance of cylindrical roller bearings. The calculation of the raceway–surface contact capacitance is adapted from ball bearings. In addition, a calculation method for the electrical capacitance in the flange contact is derived. Both calculation methods account for the geometric and operating conditions of the bearing and do not include any correction factors. To validate the calculation model, the capacitance of NU-208 and NJ-208 cylindrical roller bearings is measured and compared with the model results. Full article

Neurofilament light chain (NfL) is a promising biomarker of axonal injury across acute and chronic neurodegeneration, which can improve drug discovery and disease monitoring models. Traditional in vivo animal models cannot fully mimic human pathophysiology of neurodegenerative diseases (NDDs), but in vitro models based on human cells solve this problem, reducing the time and cost of drug testing. We developed an electrochemical immunosensor for NfL detection in cell culture media to monitor acute neuronal injury in in vitro models. The biosensor was designed in two configurations: the label-free system, which directly detects NfL in the sample via the antibody–antigen interaction, and the sandwich configuration, which incorporates two additional antibodies. Detection was examined using electrochemical techniques, including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA). The sensor demonstrated a detection limit of 3–9 pg mL⁻¹, and a dynamic working range spanning from 10 up to 10⁷ pg mL⁻¹. Importantly, NfL was successfully detected in physiological media collected from cultured neurons that were differentiated from the long-term human neuroepithelial-like stem cells. This discovery highlights the platform’s applicability for in vitro neurodegenerative models. The immunosensor offers a sensitive, scalable, and cost-effective alternative for neurodegeneration detection in drug testing applications. Full article