Search Results (1,334)

This study aimed to establish the concurrent validity and agreement of the K-power (KINVENT Biomecanique, Montpellier, France) hybrid sensor system that combines Ultra-Wideband and Inertial Measurement Unit measures against criterion timing gates for recording 20-m sprint performance in adolescent athletes. Fifteen trained adolescent track and field sprinters (age: 15.2 ± 2.4 years) performed two maximal 20-m sprints. Sprint times were simultaneously recorded using timing gates and the K-power sensor. Validity and agreement were assessed using paired-samples t-tests, Intraclass Correlation Coefficients (ICCs), Coefficient of Variation (CV), and Bland–Altman analysis. Sensitivity was determined by comparing the Typical Error (TE) to the Smallest Worthwhile Change (SWC). No significant systematic bias was observed between the devices (p > 0.05). The K-power sensor demonstrated excellent absolute agreement (ICC = 0.96, [95% CI = 0.94–0.98) and a low relative error (CV = 1.07%). The device displayed high sensitivity, with a TE (0.034 s) smaller than SWC (0.040 s). In conclusion, the K-power sensor is a valid and reliable instrument for measuring 20-m sprint times, being a practical alternative to timing gates. While the system is sensitive (TE < SWC), the Minimal Detectable Change of 0.094 s likely reflects the inherent biological variability of adolescent mechanics; thus, coaches should view changes exceeding 0.09 s as meaningful for individual athletes. Full article

To address the severe geological hazards (e.g., high ground stress and rock burst) that threaten safety and efficiency in ultra-deep mining, this study develops a comprehensive microseismic monitoring system tailored for the Sishanling Iron Mine—a typical ultra-large-scale, ultra-deep mine with an extraction depth exceeding 1500 m. The system integrates high-sensitivity sensors, real-time data transmission, and intelligent processing algorithms. A scientifically designed sensor deployment plan achieves full-coverage of key mining areas, while a multi-level data processing framework encompassing signal acquisition, event detection, location inversion, and magnitude calculation enhances result accuracy. Applied in actual operations, the system effectively captures microseismic events with magnitudes from −2.14 to −1.96, achieving optimal planar and spatial positioning errors of 6.75 m and 9.66 m, respectively. It provides real-time early warning for hazards like rock burst, thereby mitigating risks and ensuring operational continuity. This work offers a practical reference for constructing microseismic systems in similar “double super” mines and enriches the theoretical and technical framework for safety monitoring in deep mining. Full article

Background/Objectives: Ultra-low-dose computed tomography (ULD-CT) enables substantial radiation reduction compared with routine low-dose computed tomography (LD-CT), but its quantitative reliability across lung imaging biomarkers remains insufficiently characterized. This study aimed to assess the agreement and reliability of quantitative pulmonary imaging biomarkers between paired ULD-CT and LD-CT examinations. Methods: In this prospective study, 48 patients who underwent paired LD-CT and ULD-CT on the same day were analyzed. Whole-lung quantitative biomarkers were categorized into density-derived indices, airway structural metrics, and voxel-based functional biomarkers. Agreement between LD-CT and ULD-CT was evaluated using Bland–Altman analysis and Pearson correlation. Results: Density-based biomarkers demonstrated high concordance, strong correlations, and small systematic biases, indicating robust dose stability. Airway structural metrics showed clinically acceptable agreement with near-perfect reproducibility for cluster-based indices. Voxel-based functional biomarkers exhibited greater dose sensitivity but preserved consistent directional bias. Total lung volume showed excellent reproducibility with minimal bias. Conclusions: ULD-CT enables reliable quantitative lung imaging with clinically acceptable agreement across major biomarker domains, supporting its use as a dose-efficient platform for longitudinal and screening applications. Full article

Background: This study examines how contextual factors influence the match load experienced by U14 athletes. Methods: Ninety-six male players from eight Portuguese regional selection teams were monitored during three official matches each, using WIMU Pro™ inertial devices with ultra-wideband (UWB) tracking systems. Fifteen internal and external load variables were analyzed, including player load/min, high-speed running (HSR), maximum heart rate (HRmax), and high impacts/min. Mixed linear models revealed significant inter-individual variability in all variables, showing sensitivity to match context. Results: Losing teams exhibited higher player load/min. Balanced matches provoked greater cardiovascular and locomotor demands, particularly in HRmax and HSR metrics. Cluster analysis identified three match typologies based on score margin. Team level was strongly associated with final outcomes and quarter performance, reinforcing the predictive value of intra-match consistency. In contrast, match type (score margin) showed limited correlation with team quality or load distribution. Conclusions: These findings demonstrate the multifactorial nature of match load in youth basketball, supporting the implementation of individualized, context-aware training and recovery strategies while guiding long-term athlete development. Full article

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed in collaboration between the National Laboratories of Frascati (LNF-INFN) and the University of Rome II Tor Vergata. The statistical analyses of the collected data revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatus. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects that may be related to intercellular signaling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences revealed by applying the Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the groundwork for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and the study of cellular communication. Full article

Flow sensing is essential in biomedical engineering, industrial process control, and environmental monitoring. Conventional sensors, while accurate, are often constrained by high fabrication costs, complex processes, and limited design flexibility, restricting their use in disposable or rapidly customizable applications. This paper presents a novel ultra-low-cost airflow sensor fabricated entirely through fused deposition modeling 3D printing. The device employs a cantilever-based structure printed with PETg filament, followed by the deposition of a conductive ABS piezoresistive layer in a two-step process requiring no curing or post-processing. Experimental characterization reveals that the sensor operates in an ultra-low pressure range of 0.88–26.68 Pa, corresponding to flow velocities of 1.2–6.6 m/s. The sensor achieves a sensitivity of 967 Ω/Pa, a resolution of 9.27 Pa, and a detection limit of 83.27 Pa, with a total resistance change of approximately 51.5 kΩ. This kilo-ohm-scale response enables direct readout via a digital multimeter without requiring Wheatstone bridges or instrumentation amplifiers. The minimalist design, combined with sub-5 min fabrication time and material cost below $0.05, positions this sensor as an accessible platform for disposable, scalable, and resource-constrained flow monitoring applications in both biomedical and industrial contexts. Full article

In soils, it is key to not simply determine the behavior of the major elements but also understand the fate of trace and ultra-trace elements that can often have disproportionate effects on these complex systems. Soils, including agricultural soils, constitute a reservoir of nanoparticles and natural colloids of multiple origins. Nonetheless, only limited information is available on the concentrations and fate of nanoparticles in soils, due largely to the difficulty of distinguishing anthropogenically generated particles from the complex soil matrices in which they are found. Bulk measurements are often unable to quantify the key contributions of trace pollutants (i.e., needle in a haystack); however, single particle techniques have recently become available for studying complex agricultural systems, including soils. For example, the characterization of engineered nanoparticles or incidentally generated particulate pollutants within a natural soil or sediment is now possible using techniques such as single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). Nonetheless, in order to exploit the single particle techniques, it is first necessary to representatively sample the soils. The approach presented here has been designed to help better understand the impact of incidental and engineered nanoparticles on agricultural soils. In this study, we examine two approaches for extracting colloidal particles (CP) from soils in order to facilitate their characterization by single particle inductively coupled plasma mass spectrometry using a sector field- (SP-ICP-SF-MS) and time-of-flight- (SP-ICP-ToF-MS) based instruments. A novel sampling methodology consisting of an ultrasound-assisted continuous-flow extraction (USCFE) was developed and compared to a commonly used batch extraction procedure. Metal containing colloidal particles (M–CP) were quantified and characterized following their extraction in ultrapure water and tetrasodium pyrophosphate (TSPP). At least five successive extraction cycles of 18 h each were required to optimally extract Si–CP (ca. 6 × 10¹⁵ kg⁻¹) using the batch extraction approach, whereas similarly high numbers of CP could be extracted by USCFE in about 3 h. The combined use of continuous flow, ultrasound and TSPP improved the sampling of colloidal particles and nanoparticles from an agricultural soil. Due to its higher sensitivity, SP-ICP-SF-MS was used to measure the smallest detectable M–CP in the soil extracts. SP-ICP-ToF-MS was used to determine the multi-elemental composition of the extracted colloidal particles. Full article

The pharmaceutical industry faces a broken drug development pipeline, characterized by high costs, slow timelines and is prone to high failure rates. The convergence of Artificial Intelligence (AI) and quantum technologies is poised to fundamentally transform this landscape. AI excels in interpreting complex data, optimizing processes and designing drug candidates, while quantum systems enable unprecedented molecular simulation, ultra-sensitive sensing and precise physical control. This convergence establishes an integrated, self-learning ecosystem for the discovery, development, and delivery of therapeutics. This framework co-designs strategies from molecular targeting to formulation stability, compressing timelines and enhancing precision, which may enable safer, faster, and more adaptive medicines. Full article

Maca (Lepidium meyenii Walp.), a Brassicaceae species native to the high Andes of Peru, has gained global attention as a functional food and herbal medicinal product due to its endocrine-modulating, fertility-enhancing, and neuroprotective properties. Although numerous studies have addressed its biological effects, a systematic and up-to-date summary of its chemical constituents and analytical methodologies is lacking. This review aims to provide a critical overview of the chemical constituents of L. meyenii and to evaluate analytical studies published between 2000 and 2025, focusing on recent advances in extraction strategies and qualitative and quantitative analytical techniques for quality control. Major compound classes include macamides, macaenes, glucosinolates, and alkaloids, each contributing to maca’s multifaceted activity. Ultra-(high-)performance liquid chromatography (U(H)PLC), often coupled with ultraviolet, diode array, or mass spectrometric detection, is the primary and most robust analytical platform due to its sensitivity, selectivity, and throughput, while ultrasound-assisted extraction improves efficiency and reproducibility. Emerging techniques such as metabolomics and chemometric approaches enhance quality control by enabling holistic, multivariate assessment of complex systems and early detection of variations not captured by traditional univariate methods. As such, they provide complementary, predictive, and more representative insights into maca’s phytochemical complexity. The novelty of this review lies in its integration of conventional targeted analysis with emerging approaches, comprehensive comparison of analytical workflows, and critical discussion of variability related to phenotype, geographic origin, and post-harvest processing. By emphasizing analytical standardization and quality assessment rather than biological activity alone, this review provides a framework for quality control, authentication, and safety evaluation of L. meyenii as a functional food and dietary supplement. Full article

Metal–organic framework (MOF)-based surface-enhanced Raman scattering (SERS) sensors have emerged as a versatile platform for high-sensitivity and selective detection in agricultural, environmental, and biomedical applications. By integrating plasmonic nanostructures with tunable MOF architectures, these hybrid systems combine ultrahigh signal enhancement with molecular recognition, analyte preconcentration, and controlled hotspot distribution. This review provides a comprehensive overview of the fundamental principles underpinning MOF–SERS performance, including EM and chemical enhancement mechanisms, and highlights strategies for substrate design, such as metal–MOF composites, plasmon-free frameworks, ligand functionalization, and hierarchical or core–shell architectures. We further examine their applications in environmental monitoring, pesticide and contaminant detection, pathogen identification, biomarker analysis, and theranostics, emphasizing real-sample performance, molecular selectivity, and emerging integration with portable Raman devices and AI-assisted data analysis. Despite notable advances, challenges remain in reproducibility, quantitative reliability, matrix interference, scalability, and biocompatibility. Future developments are likely to focus on rational MOF design, sustainable fabrication, intelligent spectral interpretation, and multifunctional integration to enable robust, field-deployable sensors. Overall, MOF-based SERS platforms represent a promising next-generation analytical tool poised to bridge laboratory innovation and practical, real-world applications. Full article

Oral squamous cell carcinoma (OSCC) is an aggressive disease with a glycoproteomically unmapped progression and a low five-year survival rate. Thus, the aim of this pilot study was to explore the N-glycosylation pattern differences in malignant, adjacent mucosal and healthy tissues in the context of OSCC. Oral mucosal soft tissue samples was obtained by incisional biopsy from five patients with OSCC, both from the malignant and the opposite healthy gingival sides, and from seven age-sex-matched healthy controls. The collected tissues were homogenized, followed by N-glycan profiling of the endoglycosidase-released and fluorophore-labeled carbohydrates using capillary electrophoresis with ultra-sensitive laser-induced fluorescent detection (CE-LIF). Six out of the twenty-two identified N-glycan structures, including glycogens, showed significant (p < 0.05) differences between the malignant tissue samples of the OSCC patients and the healthy controls. Comparing the healthy and the positive control oral mucosal samples, differences in four N-glycan structures were revealed, while only one alteration was observed between the N-glycan profiles of the malignant tumor and positive control samples. However, the results are presented descriptively, reflecting the limited sample size of the pilot study, it shows the potential of high-resolution CE-LIF-based glyocoanalytical protocol to be highly efficient and sensitive for glycobiomarker-based molecular diagnostics of oral malignant lesions. Full article

This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), yeast protein localization (1.35%), and ozone level detection (2.9%), we compare ROC-AUC with Matthews Correlation Coefficient, F2-score, H-measure, and PR-AUC. Our empirical analyses span 20 classifier–sampler configurations per dataset, combined with four classifiers (Logistic Regression, Random Forest, XGBoost, and CatBoost) and four oversampling methods plus a no-resampling baseline (no resampling, SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN). ROC-AUC exhibits pronounced ceiling effects, yielding high scores even for underperforming models. In contrast, MCC and F2 align more closely with deployment-relevant costs and achieve the highest Kendall’s τ rank concordance across datasets; PR-AUC provides threshold-independent ranking, and H-measure integrates cost sensitivity. We quantify uncertainty and differences using stratified bootstrap confidence intervals, DeLong’s test for ROC-AUC, and Friedman–Nemenyi critical-difference diagrams, which collectively underscore the limited discriminative value of ROC-AUC in rare-event settings. The findings recommend a shift to a multi-metric evaluation framework: ROC-AUC should not be used as the primary metric in ultra-imbalanced settings; instead, MCC and F₂ are recommended as primary indicators, supplemented by PR-AUC and H-measure where ranking granularity and principled cost integration are required. This evidence encourages researchers and practitioners to move beyond sole reliance on ROC-AUC when evaluating classifiers in highly imbalanced data. Full article

This study investigates the energy performance and preliminary turbomachinery design of post-combustion CO₂ compression systems integrated into an ultra-supercritical coal-fired power plant with carbon capture and storage (CCS). To enable pipeline transport, CO₂ must be delivered at 150 bar and 15 °C, i.e., in liquid phase. Unlike conventional configurations that compress CO₂ entirely in the gaseous/supercritical phase before final cooling, two alternative layouts are proposed, introducing an intermediate liquefaction step prior to liquid-phase compression. Each layout uses a chiller system that operates at CO₂ condensation temperatures of 10 °C and 20 °C. The energy performance and the system layout architecture are evaluated and compared with the conventional gaseous-phase compression configuration. An in-depth sensitivity analysis, which varies the flow coefficient, the working coefficient, and the degree of reaction, confirms that the turbomachinery preliminary design, based on input parameters related to the specific speed, is a high-efficiency design. The results indicate that the 10 °C liquefaction layout requires the least compression power (60 MW), followed by the 20 °C layout (62.5 MW) and the conventional system (67 MW). Including the consumption of the chiller, the proposed systems require an additional power of 11–12 MW, compared to just over 1 MW for the conventional layout with simple CO₂ cooling. These results highlight the significant influence of the integration of the chiller on the overall power requirement of the system. Although the proposed configurations result in a larger equipment footprint, the integrated capture and compression/liquefaction system allows for very low CO₂ emissions, making the power plant more sustainable. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

The ultra-high frequency (UHF)-based partial discharge (PD) detection technology for gas-insulated switchgear (GIS) has achieved large-scale applications due to its high sensitivity and real-time monitoring capabilities. However, long-term service-induced antenna corrosion in UHF sensors may lead to degraded reception characteristics. To ensure the credibility of monitoring data, on-site sensor calibration under ambient noise conditions is required. This study first analyzes the time–frequency domain characteristics of white noise received by UHF sensors in GIS environments. Leveraging the transceiver reciprocity principle of sensors, a noise reconstruction method based on external sensors is proposed to simulate on-site white noise. Subsequently, CST simulation models are established for both standard and degraded sensors, quantifying the impact of factors like antenna corrosion on performance parameters such as echo impedance S11 and voltage standing wave ratio (VSWR). Finally, the two sensor models are coupled into GIS handholes for comparative simulation analysis. Results show that antenna corrosion causes resonant frequency shifts in sensors, reducing PD signal power by 55.27% and increasing noise power by 64.11%. The signal-to-noise ratio (SNR) decreases from −9.70 dB to −15.34 dB, with evident waveform distortion in the double-exponential PD pulses. These conclusions provide theoretical references for on-site UHF sensor calibration in noisy environments. Full article