Search Results (1,174)

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

To obtain radar images of a group of small space objects or to resolve individual elements of complex space objects in near-Earth orbit, a radar system must have high spatial resolution. High range resolution is achieved by using complex probing signals with a wide spectrum bandwidth. Achieving high angular resolution for small or complex space objects is based on the inverse synthetic aperture antenna effect. Among the various classes of complex signals, only two have found practical application in Inverse Synthetic Aperture Radar (ISAR) systems so far: the Linear Frequency-Modulated signal (chirp) and the Stepped-Frequency signal. Over the coherent integration interval of the echo signals, which corresponds to the ISAR aperture synthesis time, the combined correlation characteristics of the signal ensemble are analyzed. A high level of integral correlation noise in the ensemble of probing signals degrades the quality of the radar image. Therefore, a probing signal with a Zero Autocorrelation Zone (ZACZ) is highly relevant for ISAR applications. In this work, through simulation, radar images of a complex space object were obtained using both chirp and ZACZ probing signals. A comparative analysis of the correlation characteristics of the echo signals and the resulting radar images of the complex space object was performed. Full article

Two-dimensional carbide crystals (MXenes) are emerging as a promising platform for the development of novel gas sensors, offering advantages in energy efficiency and tunable analyte selectivity. One of the most effective strategies to enhance and tailor their functional performance involves forming hetero-structured composites with metal oxides. In this work, we explore a chemiresistive effect in double-metal MXene of Ti_0.2V_1.8C and its composites with 2 mol. % SnO₂ and Co₃O₄ nanocrystalline oxides toward feasibility tests with alcohol and ammonia vapor probes. The materials were characterized by simultaneous thermal analysis, X-ray diffraction analysis, Raman spectroscopy, and scanning/transmission electron microscopy. Gas-sensing experiments were carried out on composite layers deposited on multi-electrode substrates to be exposed to the test gases, 200–2000 ppm concentrations, at an operating temperature of 370 °C. The developed sensor array demonstrated clear analyte discrimination. The distinct sensor responses enabled a selective identification of vapors through linear discriminant analysis, demonstrating the further potential of MXene-based materials for integrated electronic nose applications. Full article

Highly accurate quantitative detection of heavy metals is crucial for preventing environmental pollution and safeguarding public health. To address the demand for sensitive and specific detection of Cu²⁺ ions, we have developed carbon dots using a simple hydrothermal process. The synthesized carbon dots are highly stable in aqueous media, environmentally friendly, and exhibit strong blue photoluminescence at 440 nm when excited at 352 nm, with a quantum yield of 5.73%. Additionally, the size distribution of the carbon dots ranges from 2.0 to 20 nm, and they feature excitation-dependent emission. They retain consistent optical properties across a wide pH range and under high ionic strength. The photoluminescent probes are selectively quenched by Cu²⁺ ions, with no interference observed from other metal cations such as Ag⁺, Ca²⁺, Cr³⁺, Fe²⁺, Fe³⁺, Hg²⁺, K⁺, Mg²⁺, Sn²⁺, Pb²⁺, Sr²⁺, and Zn²⁺. The emission of carbon dots exhibits a strong linear correlation with Cu²⁺ concentration in the range of 0–14 μM via a static quenching mechanism, with a detection limit (LOD) of 4.77 μM in water. The proposed carbon dot sensor is low cost and has been successfully tested for detecting Cu²⁺ ions in general water samples collected from rivers in Taiwan. Full article

Potato late blight is a devastating disease and threatening global potato production, necessitating accurate early prediction for effective management and yield enhancement.This paper presents the PLB-GPT, a novel generative pre-trained transformer-based model built on GPT-2 architecture, designed to forecast late blight outbreaks using meteorological data. Our method is trained and evaluated on a real-world dataset encompassing temperature, humidity, atmospheric pressure, and other climatic variables from diverse regions of China; PLB-GPT demonstrates state-of-the-art performance. The framework of PLB-GPT employs advanced fine-tuning strategies, including Linear Probing, Full Fine-Tuning, and a novel two-stage method, effectively applied across different time windows (1-day, 3-day, 5-day, 7-day). The model achieves an accuracy of 0.8746, a precision of 0.8915, and an F1 score of 0.8472 in the 5-day prediction window, surpassing baseline methods such as CARAH, ARIMA, LSTM, and Informer. These results highlight PLB-GPT as a robust tool for early disease outbreak prediction, with significant implications for agricultural disease management. Full article

Carpione rhabdovirus (CAPRV) is an emerging virus within the family Rhabdoviridae, posing potential threats to aquaculture species such as golden pompano (Trachinotus anak). However, since the 21st century, and for CAPRV strains isolated from marine fish, only a single CAPRV2023 sequence has previously been available in public databases, with no additional sequences reported. Because the virus undergoes genetic variation, relying on this single sequence likely introduced mismatches or off-target risks in earlier detection assay designs. Notably, the previously developed two-step N-targeting detection assay was designed based solely on that single CAPRV2023 sequence. Consequently, this study involved determining and analyzing the N gene sequences from CAPRV isolates gathered from 2023 to 2025, with the aim of pinpointing conserved regions for assay development, and sequence comparisons subsequently verified the existence of mismatches in the primer–probe binding sites of the previous assay. Since quantitative assays in aquatic virology often define copy numbers utilizing either plasmid DNA templates or RNA templates produced via in vitro transcription, which may lead to variations in amplification kinetics and sensitivity, this study compared both standards to ensure reliable quantification across different nucleic acid types. Based on these findings, a one-step TaqMan quantitative PCR (qPCR) assay was developed and validated using dual nucleic acid standards, namely plasmid DNA and in vitro–transcribed RNA. Compared with conventional two-step qPCR, the one-step format combines cDNA synthesis and subsequent DNA amplification in a single sealed tube, thereby effectively preventing cross-contamination, simplifying the workflow, and improving detection efficiency. The assay exhibited strong linearity (R² > 0.99) and consistent amplification efficiencies between 90% and 110%, demonstrating excellent quantitative performance. The detection limits were 2 copies per reaction for plasmid DNA and 20 copies for in vitro–transcribed RNA templates. No cross-reactivity was observed with other aquatic pathogens, and the assay showed strong repeatability and reproducibility (coefficients of variation below 2.0%), providing a sensitive and reliable tool for epidemiological surveillance and the analysis of CAPRV distribution in marine aquaculture systems of southern China. Full article

Background: Accurate intraoperative assessment of sentinel lymph node (SLN) status is critical for staging and guiding surgical management in gynaecological malignancies. Frozen-section histopathology remains the gold standard, but it is time-consuming and resource-intensive. Shear-wave elastography (SWE) quantifies tissue stiffness in real time and may offer a rapid alternative. Methods: In this prospective single-centre study, 63 women (median age 62 years) undergoing primary surgery with sentinel lymph node biopsy (SLNB) for endometrial, cervical, vulvar, or early ovarian carcinoma were enrolled. A total of 172 SLNs were excised, submerged in coupling gel, and scanned ex vivo using a 9 MHz linear probe. Results: A total of 172 SLNs underwent SWE (mean 2.7 nodes/patient). Endometrial primaries accounted for 58% of nodes, mostly retrieved by robotic-assisted surgery (71.8%). Node dimensions were significantly larger in malignant lesions for sonographic (long-axis: 13.02 ± 3.31 mm vs. 10.80 ± 3.28 mm; p = 0.002) and pathological long-axis measurements (11.45 ± 2.83 mm vs. 9.75 ± 2.61 mm; p = 0.004). Mean SWE velocities were similar between groups (1.381 ± 0.307 vs. 1.343 ± 0.236 m/s; p = 0.541). Histopathology identified metastases in 18% of SLNs, comprising macrometastases (7%), micrometastases (5%), and isolated tumour cells (6%). Conclusions: Although ex vivo SWE is rapid, reproducible, and integrates seamlessly into the sterile field, stiffness measurements alone lack sufficient discriminatory power for SLN staging in gynaecological cancers. Future research should focus on three-dimensional SWE, advanced radiomic analyses, and machine-learning algorithms to improve the detection of low-volume metastatic disease. Full article

We confront two future-singularity dark-energy templates—sudden future singularities (SFSs) and finite scale factor singularities (FSFSs)—with late-time geometric probes and redshift-space distortion growth data. We compute the observable growth $f{\sigma }_8\left(z\right)$ by solving the full linear perturbation system (including the standard fiducial cosmology rescaling of RSD measurements) and build a joint ${\chi }^2$ from Pantheon+SH0ES SNe Ia, $H\left(z\right)$, DESI AP-only BAO, and $f{\sigma }_8$. Parameter constraints are obtained via grid-based profiling over nuisance parameters and the singularity time location parameter. We compare the viability and goodness of fit of the singularity scenarios to the $\mathrm{\Lambda }$CDM reference. Full article

Background: This study evaluates the increase in gingival thickness following the administration of injectable atelocollagen. Materials and Methods: A retrospective analysis was conducted using the medical records of 60 patients with a thin gingival phenotype at baseline, treated between 2017 and 2025. All patients received a standardised protocol for soft tissue thickness modification using atelocollagen injections. Based on the continuation of maintenance therapy, patients were divided into Group A (n = 30), consisting of patients who received booster doses at six-month intervals following completion of the full treatment protocol, and Group B (n = 30), consisting of patients who did not continue maintenance therapy. The observation period for all patients was five years. Gingival thickness was assessed by periodontal probe transparency using a standard WHO probe (WHO 621) and the Hu-Friedy Colorvue Biotype Probe. Longitudinal changes were analysed using linear mixed-effects models (LMMs) for continuous outcomes and generalised linear mixed-effects models (GLMMs) with a binomial distribution and logit link for binary outcomes, accounting for repeated measurements at the patient level. Results: Significant effects of Group and Time, as well as their interaction, were observed for the proportion of sites with a thick gingiva (Group effect: F (1,93.14) = 57.94, p < 0.001; Group × Time interaction: p < 0.001). GLMM analysis confirmed a significant Group × Time interaction (χ² = 23.11, p < 0.001), indicating sustained gingival thickness improvement in Group A and a gradual decrease in effectiveness in Group B. Conclusions: Injectable atelocollagen represents a reliable, effective, and user-friendly method for long-term modification of gingival thickness, particularly when supported by maintenance therapy. Full article

Background: To assess sex-related differences in periodontal inflammation and bone loss and identify sex-specific associations with systemic and local risk factors. Methods: This cross-sectional study analyzed records from a university setting. Outcomes were bleeding on probing (BOP) and bone loss index (BLI). Predictors included smoking, diabetes mellitus, age, plaque control record (PCR), the proportion of sites with pocket depth (PD) ≥ 5 mm, and number of teeth. Sex-stratified generalized linear models were applied. Results: A total of 232 participants were included (114 men, 118 women; mean age 55.6 ± 11.6 years). Men showed higher PD ≥ 5 mm (p = 0.030), with no sex difference in mean BOP or BLI. PD ≥ 5 mm predicted higher BOP in both sexes (men p < 0.001; women p = 0.002). Smoking was associated with higher BOP in men and with increased BLI in women (p = 0.010). PCR was positively associated with BOP in women and inversely with BLI in men (p = 0.042). Conclusions: In this study, sex-specific associations between behavioral/clinical factors and periodontal outcomes were observed. PD ≥ 5 mm was related to BOP in both sexes, while smoking and plaque control showed sex-divergent patterns. These exploratory findings warrant confirmation in prospective studies. Full article

Lately the nonlinear optical third-harmonic generation (THG) microscopy is starting to emerge as a laboratory standard for label-free studies in biological samples. In this study, the THG signals produced from corn starch granules are investigated. In particular, the polarization-dependent THG (P-THG) signals emerging from the outer layer (shell) of the starch granules are compared with the P-THG signals originating from their inner portion (core). By rotating the linear polarization of the excitation beam, two distinct P-THG modulation patterns are revealed within single granules, corresponding to their shells and to their structurally different cores. These patterns are analyzed using a theoretical framework that describes THG from an orthorhombic crystal symmetry, characteristic of corn starch. This allows us to extract point-by-point in the granules the ratios of the ${\chi }^{\left(3\right)}$ susceptibility tensor elements and the average molecular orientations. Then, the anisotropy ratio (AR = ${\chi }_{xxxx}^{\left(3\right)}$/${\chi }_{yyyy}^{\left(3\right)}$) is defined and used as a quantitative descriptor of the local molecular arrangements. Our results show that the shells and cores exhibit distinct AR values, probing the anisotropy in the molecular arrangements between the two regions. This study establishes P-THG as a powerful contrast mechanism for probing structural anisotropy in biological samples beyond conventional THG intensity-only microscopy. Full article

Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator with corner-mounted SiPMs. Previous work has demonstrated that analytic algorithms based on a physical model of light propagation can reconstruct particle impinging positions and tracks and estimate deposited energy and Linear Energy Transfer (LET) with moderate accuracy. In this study, we enhance this approach by applying machine learning (ML) methods, specifically gradient boosting techniques, to improve the accuracy of spatial location and energy deposition estimation. Using the GEANT4 simulation toolkit, we simulated cosmic muons and energetic oxygen ions traversing an SSPD, and we trained XGBoost and LightGBM models to predict particle impinging positions and deposited energy. Both algorithms outperformed the analytic baseline. We further investigated hybrid strategies, including hybrid boosting and probing. While hybrid boosting provided no significant improvement, probing yielded measurable gains in both position and LET estimation. These results suggest that ML-driven reconstruction provides a powerful enhancement to SSPD performance. Full article

Singlet oxygen is a type of reactive oxygen species that is typically generated via type II photosensitization reactions. Since 1,3-diphenylisobenzofuran (DPBF), a commonly used chromogenic probe, exhibits peak absorbance at 410 nm for singlet oxygen detection, it severely interferes with blue light irradiation and compounds that absorb in this wavelength region. This study investigated developing and validating a fluorescence-based method using DPBF to quantitatively analyze the type II photosensitizing property of riboflavin (RF) and its heterocyclic flavin derivatives. DPBF fluorescence-based analysis provided more sensitive and practical results than traditional colorimetric methods. It effectively overcomes spectral interference from colored photosensitizers, such as RF and its derivatives, under blue light irradiation (λ peak 447 nm). This method permitted more effective measurement of their activity without interference from their intrinsic color and maintained high linearity and low variation across different sample concentrations, even with short irradiation times. The type II photosensitizing potency of the tested compounds under blue light was consistently ranked as follows: RF > flavin mononucleotide > flavin adenine dinucleotide > lumiflavin > lumichrome. The results suggest that the DPBF fluorescence-based assay is a more effective approach than colorimetric analysis, making it a practical and reproducible tool for assessing the type II photosensitizing properties of diverse compounds. This study also provides a refinement of an existing probe-based assay for relative comparisons under visible light conditions. Full article

The strategic engineering of metal–organic frameworks (MOFs) through integration with clay minerals offers a promising route to tailor their functional properties and expand their application scope. In this study, a series of clay-MOF composites was constructed by introducing MOFs onto the surfaces of different clay minerals. By varying the type of clay mineral, the nature and strength of surface-active sites could be effectively modulated. Notably, the Kaolinite-based MOFs (Ka-MOF) composite exhibited superior sensitivity for the detection of aspartic acid (AA), outperforming other composite nanozymes using o-phenylenediamine (OPD) and hydrogen peroxide (H₂O₂) as substrates, with a linear detection range of 0–37.56 μM and a low detection limit of 55.7 nM. The enhanced peroxidase-like activity is attributed to the substitution of silicon in the kaolinite structure by MOF components, which increases the density of Lewis acid–base sites. These sites facilitate H₂O₂ adsorption and promote its decomposition to generate singlet oxygen (¹O₂), thereby enhancing the catalytic oxidation process. Furthermore, the probe yielded satisfactory recoveries of aspartic acid (94.2% to 98.5%) in different real water samples through spiking recovery experiments. This work not only elucidates the influence of crystal surface engineering on the optical and catalytic properties of nanozymes but also provides a robust platform for tracing amino acids and studying their environmental chemical behaviors. Full article

A new rhodamine-based fluorescent probe (RDC, rhodamine-based derivative) was rationally designed and synthesized for the highly selective, sensitive, and quantitative detection of Cu²⁺. The probe demonstrated outstanding specificity toward Cu²⁺, even in the presence of competing metal ions (e.g., Al³⁺, Fe³⁺, Cr³⁺, Na⁺, and K⁺), exhibiting negligible interference and confirming its robust anti-interference capability. A spectroscopic analysis revealed that Cu²⁺ induced spirocyclic ring cleavage, resulting in a colorless-to-pink colorimetric transition and enhancement of the yellow–green fluorescence at 590 nm. Upon addition of Cu²⁺, the fluorescence spectrum showed a linear response in the concentration range of 0.4–20 μM, with a correlation coefficient (R²) of 0.9907 and the limit of detection (LOD) calculated to be 0.12 μM. Meanwhile, Job’s plot analysis verified that the binding stoichiometry between RDC and Cu²⁺ was 1:1. The probe exhibits rapid response kinetics (<5 min) and non-destructiveness properties, enabling in vivo imaging. Under stress conditions, Cu²⁺ accumulated predominantly in root tips (its primary target tissue), with the following distribution hierarchy: root tips > maturation zone epidermis > xylem vessels > cortical cell walls. In conclusion, RDC is a well-characterized, high-performance tool with high accuracy, excellent selectivity, and superior sensitivity for plant Cu²⁺ studies, and this work opens new technical avenues for rhodamine-based probes in plant physiology, environmental toxicity monitoring, and rational design of phytoremediation strategies. Full article