Search Results (2,758)

This study characterizes the multifractal structure of monthly precipitation in the semi-arid state of Guanajuato, Mexico, using Multifractal Detrended Fluctuation Analysis with quadratic detrending (MFDFA-2). We analyze 65 quality-controlled meteorological stations covering the period 1981–2016. All series exhibit multifractality, with generalized Hurst exponents $h\left(2\right)=0.568\pm 0.065$ indicating predominantly persistent dynamics and long-term positive autocorrelation (64.6% of stations). The multifractal spectrum width ($\Delta \alpha$) ranges from 0.15 to 0.72 (mean = 0.2423), revealing substantial spatial variability in scaling complexity. K-means clustering based on multifractal features identifies the following four hydroclimatic groups: one random cluster (29.2% of stations) and three persistence-dominated clusters (70.8%), with coherent spatial organization. These findings provide new insights into the temporal scaling properties of precipitation in semi-arid regions and have important implications for water resource management and regionalized drought-risk assessment. Full article

This study investigates the feasibility of acoustic signal analysis for the assessment of weld bead quality in the shielded metal arc welding (SMAW) process. The work focuses on comparing time-domain acoustic signals and time–frequency spectrogram representations for the classification of welds as accepted or rejected according to standard welding inspection criteria. Two key acoustic descriptors, the fundamental frequency (F₀) and the harmonics-to-noise ratio (HNR), were extracted and analyzed to evaluate statistical differences between the two weld quality classes. Statistical tests, including Anderson–Darling, Levene, ANOVA, and Kruskal–Wallis (α = 0.05), revealed significant differences between accepted and rejected welds. Accepted welds exhibited a bimodal HNR distribution associated with transient arc instability at the beginning and end of the bead, whereas rejected welds showed more uniform acoustic behavior throughout the process. Subsequently, the acoustic data were represented using both audio signals and spectrograms and used as inputs for ten supervised machine learning models, including Support Vector Classifier (SVC), Logistic Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), and Naïve Bayes (NB). The results demonstrate that spectrogram-based representations significantly outperform time-domain signals, achieving accuracies of 0.95–0.96, ROC-AUC values above 0.95, and false positive and false negative rates below 6%. These findings indicate that, while scalar acoustic descriptors provide statistically significant insight into weld quality, time–frequency representations combined with machine learning enable a more robust and reliable framework for automated non-destructive evaluation, particularly in manual SMAW processes under realistic operating conditions. Full article

To enable non-destructive quantitative characterization of constituent content in C/C–SiC ceramic-matrix composites, this study develops a physics-guided framework based on multispectral photon-counting X-ray detection. In practical photon-counting measurements, multispectral attenuation features are jointly distorted by detector-response non-idealities, including charge sharing, K-escape, and finite energy resolution, as well as by beam-hardening effects from the polychromatic X-ray source. To address this coupled problem, a Geant4 11.2-based detector-response model was incorporated into a unified correction workflow together with beam-hardening compensation, so that physically consistent multispectral attenuation vectors could be recovered for subsequent constituent inversion rather than merely for spectrum restoration. On this basis, a fine-grained theoretical database covering different SiC mass fractions was established, and quantitative constituent inversion was achieved by matching the corrected attenuation features to the database. Experimental results show that the proposed framework effectively suppresses thickness-dependent bias in attenuation measurements and yields an average relative error below 3% for pure aluminum. For C/C–SiC composites, the SiC mass fraction can be quantified with an accuracy better than 3 wt%. These results demonstrate that the proposed method provides a practical non-destructive route for constituent-content characterization in heterogeneous ceramic-matrix composites and is valuable for manufacturing quality control and in-service assessment. Full article

Background: Assisted reproductive technology has been linked to an increased risk of pregnancy-related cardiovascular complications, but the long-term cardiovascular outcome is poorly understood. This study aimed to assess whether women who use ART have an elevated long-term risk of adverse cardiovascular outcomes. Methods: We conducted a systematic review and meta-analysis to examine the association between ART and long-term cardiovascular outcomes after pregnancy. We systematically searched MEDLINE, Embase, and the Cochrane Library for studies published by January 2026. We evaluated the methodological quality of included studies using the Newcastle-Ottawa Scale. We used random effects models to calculate pooled adjusted risk ratios (aRR) with 95% confidence intervals (CI) for the association of ART with cardiovascular outcomes. Results: We included thirteen studies comprising 553,331 patients who used ART and 37,826,591 patients who conceived spontaneously. All women achieved a live birth. Mean duration of follow-up after delivery was 8.4 ± 8.3 years. In models adjusted for age, parity, and comorbidity, ART was associated with a small increase in the risk of cardiovascular disease compared with spontaneous conception (aRR 1.18, 95% CI 1.03–1.35), but the association was attenuated when studies that had only 42 days of follow-up were excluded (aRR 1.13, 95% CI 0.99–1.29). ART was not associated with cardiac complications (aRR 0.94, 95% CI 0.82–1.08), stroke (aRR 1.20; 95% CI 0.93–1.55), hypertension (aRR 1.02; 95% CI 0.72–1.44), or venous thrombosis (aRR 1.27, 95% CI 0.97–1.67). Conclusions: Our findings suggest that women who achieve a live birth following ART do not appear to have an increased long-term risk of adverse cardiovascular outcomes. These results provide reassuring evidence for patient counseling regarding the long-term cardiovascular safety of ART among women with successful pregnancies. Further research that includes women who do not achieve a live birth is warranted to more fully characterize the potential long-term cardiovascular effects of ART across the entire spectrum of treatment outcomes. Full article

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Eggshell thickness is a critical indicator for evaluating egg breakage resistance and hatchability, yet traditional measurement methods remain destructive and inefficient. To address this, this study proposes a robust prediction approach by integrating Gradient Boosting Decision Tree (GBDT) feature optimization with an improved CatBoost algorithm. First, a joint strategy of Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) was employed to eliminate spectral scattering noise and enhance organic matrix fingerprint information. Subsequently, GBDT was introduced for nonlinear feature evaluation to adaptively screen the top 50 wavelengths, effectively mitigating the “curse of dimensionality” and multicollinearity in full-spectrum data. A CatBoost regression model was then constructed using an Ordered Boosting mechanism, supported by a dual anti-overfitting strategy that merged 10-fold nested cross-validation with Bootstrap resampling. Experimental results demonstrate that this method significantly outperforms traditional algorithms in both prediction accuracy and generalization. The coefficients of determination (R²) for the calibration and prediction sets reached 0.930 and 0.918, respectively, with a root mean square error of prediction (RMSEP) of 0.008 mm. Residual analysis confirms that prediction errors follow a zero-mean Gaussian distribution, indicating that systematic bias was effectively eliminated. This research provides a reliable theoretical foundation and technical support for the intelligent grading of poultry egg quality. Full article

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques. Full article

Background/Objectives: Amelogenesis imperfecta (AI) is a heterogeneous group of rare hereditary conditions mainly affecting the quantity and/or quality of tooth enamel. Its phenotypic expression is diverse, as is the mutational spectrum of the AI-causing genes and mutations. Integrins are cell-surface receptors that mediate adhesion between cells and between cells and the extracellular matrix. Among these, mutations in integrin αvβ6 have been shown to cause AI; however, phenotypic variation exists between the knockout mouse model and human cases, as well as among different human AI families. Methods: We recruited AI families and performed mutational analysis using whole exome sequencing. Results: We identified compound heterozygous ITGB6 mutations in two families. In Family 1, a paternally transmitted nonsense mutation (NM_000888.5: c.1060C>T, p.(Gln354*)) and a maternally transmitted missense mutation (NM_000888.5: c.2312A>G, p.(Asn771Ser)) were identified; in Family 2, a paternal missense mutation (NM_000888.5: c.1693T>C, p.(Cys565Arg)) and a maternal frameshift mutation (NM_000888.5: c.2091delC, p.(Asn698Metfs*13)) were identified, each causing AI in the respective proband. Both probands exhibited generalized hypoplastic and hypomineralized AI, but no other extraoral symptoms. Conclusions: This report will not only expand the known mutational spectrum of the ITGB6 gene but also provide evidence for the genotype–phenotype correlations, thereby improving our understanding of the functional role of ITGB6 during amelogenesis. Full article

Mesenchymal stem cells (MSCs) are multipotent cells that have the ability to mediate cellular repair through a combination of soluble paracrine factors, as well as bioactive cargo packaged within extracellular vesicles (EVs). Although MSC-derived EVs have been widely investigated for their regenerative potential, progress toward translational evaluation has been limited in part by challenges in scalable and reproducible manufacturing. We recently reported that human telomerase reverse transcriptase (hTERT)-immortalized MSCs reproducibly produce EVs that retain key characteristics of EVs derived from primary MSCs. Building on this work, three-dimensional (3D) culture systems have emerged as promising platforms for large-scale manufacturing. In this study, we compared the yield, molecular composition, and functional activity of EVs produced from hTERT-immortalized MSCs cultured in either a fixed-bed bioreactor or conventional two-dimensional (2D) flasks. Our data demonstrate that bioreactor culture results in increased EV yield as compared to an equivalent production from 2D cultures. Molecular analyses indicated that bioreactor-derived EVs were associated with a broader spectrum of cargo and were enriched with molecules that may contribute to enhanced reparative function. Importantly, bioreactor-derived EVs also exerted a more pronounced effect in cellular repair assays in vitro. Collectively, these results highlight the potential of fixed-bed bioreactors as scalable platforms for EV production, offering higher yields while preserving molecular composition and functional activity. This approach represents an important step toward achieving the reproducible, high-quality EV production required for research and future translational applications. Full article

Light quality plays a decisive role in controlled-environment agriculture, shaping plant morphology, physiology, and productivity. This study investigated the impact of far-red (FR) light on Cannabis sativa L. by comparing two different application strategies: continuous FR supplementation throughout 12 h of the photoperiod and end-of-day (EOD) FR exposure applied only at the end of the light period. In both treatments, FR was added to a background spectrum of red and blue (RB) light, while a control group grown under RB light alone was included to assess the specific effects of FR on plant growth, physiological responses, and flowering. Continuous FR exposure induced pronounced shade-avoidance traits, increasing plant height by 9% and petiole length by 17% relative to the control, and raised leaf dry weight to 12.9 g, 9% higher than under EOD (11.7 g) and 16.3% higher than under RB alone (10.8 g). Besides plant height and petiole length, both FR and EOD treatment induced limited morphological adjustments but increased chlorophyll content by 9%, resulting in greater canopy expansion and photosynthetic potential. However, flowering time was unaffected by spectral treatment, confirming that Cannabis floral induction is tightly regulated by photoperiod rather than light quality. Energy-use analysis revealed that EOD supplementation achieved many of the benefits of continuous FR while reducing overall consumption, but energy-use efficiency analysis proved FR as the more efficient treatment. These findings highlight the potential of FR light, particularly when applied continuously, to optimize vegetative growth and canopy physiology in controlled-environment Cannabis cultivation, while EOD strategies offer a practical compromise between cost savings and physiological benefits. Full article

Molecular subtype–guided therapy for breast cancer (BC) remains limited in a subset of patients by suboptimal efficacy, acquired resistance, and the presence of “undruggable” targets. Proteolysis-targeting chimeras (PROTACs) represent a targeted protein degradation (TPD) strategy that differs fundamentally from conventional occupancy-driven inhibition. By inducing ubiquitination of a protein of interest and subsequent proteasomal degradation, PROTACs can directly reduce pathogenic protein abundance and potentially abrogate non-catalytic or scaffolding functions, thereby enabling more durable pathway suppression in selected resistance contexts. This review comprehensively summarizes the mechanisms of action, key molecular design elements, and the developmental landscape of PROTACs, and maps target selection and research progress across BC molecular subtypes. In hormone receptor–positive/HER2-negative BC, clinical translation is most advanced for estrogen receptor alpha-directed PROTACs; Phase III evidence indicates biomarker-dependent efficacy, with clearer benefit signals in resistant subgroups such as estrogen receptor 1 mutations, suggesting that the net clinical benefit of TPD is more likely to be realized through precision stratification. In contrast, in solid-tumor settings, including human epidermal growth factor receptor 2 (HER2)-positive BC and triple-negative breast cancer, PROTAC translation is more frequently constrained by an “exposure–selectivity–therapeutic window” trade-off driven by physicochemical liabilities, insufficient tumor penetration, and broad target expression. Accordingly, engineering strategies—such as antibody/aptamer-mediated targeted delivery, stimulus-responsive prodrugs, nanocarriers, and local administration—are emerging as decisive approaches to enable safe and effective clinical implementation. Looking forward, further progress of PROTACs in BC will depend on expanding the spectrum of E3 ubiquitin ligases and recruitment modalities, establishing predictable and dynamically monitorable biomarker systems, optimizing rational combination/sequencing regimens with exposure- and schedule-guided dosing, and advancing scalable manufacturing and quality control capabilities, thereby translating mechanistic advantages of TPD into verifiable precision-therapy applications. Full article

Suspended sediment plays a critical role in river ecosystem health, nutrient transport, and water quality, while also affecting navigation infrastructure and reservoir sedimentation in regulated rivers. A sound understanding of sediment dynamics in complex river systems consisting of natural lakes and engineered navigation structures remains a critical challenge for river management and water quality assessment. This study investigates the longitudinal patterns of suspended sediment concentration (SSC) along a ~500-km reach of the Upper Mississippi River containing Lake Pepin and multiple lock-and-dam structures. In this study, we analyze remotely sensed SSC estimates from the RivSED database (2001–2019). The SSC datasets were then integrated with in situ streamflow measurements and potential soil erosion to characterize sediment supply and transport dynamics and relate with upstream contributing watershed’s attributes. Results reveal distinct sediment behavior patterns: (1) Lake Pepin functions as a significant sediment trap, creating a clear discontinuity in SSC with mean concentrations decreasing from ~25 mg/L upstream to ~13 mg/L within the lake; (2) longitudinal SSC profiles show re-establishment patterns downstream of the lake, reaching ~23 mg/L approximately 100 km below the outlet; (3) strong positive correlation (r = 0.80, R² = 0.64, p < 0.001) exists between watershed sediment export and river-reach-scale sediment fluxes. Temporal analysis across these upstream monitoring stations shows sediment export rates ranging from 10,000 to 200,000 tons/year, with notable inter-annual variability driven by discharge patterns. This research demonstrates the utility of combining a spectrum of datasets for exploring sediment dynamics in complex riverine systems. Though the current study is a case study, the study results provide crucial insights for navigation management, ecosystem health assessment, and watershed management strategies in similar settings. Full article

X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility because strong attenuation lowers contrast, while conventional rhodium (Rh) K-edge filtering suppresses part of the high-energy spectral tail. This study presents a Monte Carlo framework for spectroscopic mammography using a voxelated 1 mm thick cadmium telluride (CdTe) sensor and a first-order detector interaction model to evaluate energy-dependent image quality. The model reproduces fluorescence and inter-voxel energy redistribution in CdTe, but not the full detector chain, and remains idealized with respect to charge transport, carrier collection, threshold dispersion, and pile-up. Energy-resolved simulations in the 10–50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and to form integrated-spectrum (IS) images for four tested spectra. For the dense-breast calcium hydroxyapatite (HA) speck detection task considered here, and under the present simulation assumptions, replacing the standard 28 kVp + 50 μm Rh spectrum with 28 kVp + 1 mm Al increased the simulated IS image CNR by 23.11%, with an approximately 5% increase in estimated primary-incident air kerma at the phantom entrance plane. Preliminary experimental implant-phantom images were included as a qualitative feasibility check, showing a trend consistent with simulations. Within the limits of this task-specific simulation, the results suggest that preserving the transmitted high-energy tail can improve HA speck visibility for the present 1 mm CdTe photon-counting detector, with the 28 kVp + 1 mm Al spectrum outperforming the other tested cases. Full article

To accurately quantify the intrinsic absorption efficiency of bamboo leaves to the solar spectrum, we measured the reflectance and transmittance of leaves from 55 bamboo species cultivated at the same site, and developed a mathematical model to calculate the annual cumulative photon absorption of photosynthetically active radiation (PAR) per leaf. The results showed the following: (1) Bamboo leaf optical properties exhibited high instrumental and spatial measurement consistency, with transmittance not significantly fluctuating with changes in incident light intensity or quality. (2) Bamboo leaves exhibited significant spectral selective absorption characteristics, with stronger absorption of blue and red light and weaker absorption of green light; Phyllostachys vivax had the highest mean absorptance per unit area, while Chimonobambusa tumidinoda had the lowest. (3) The annual photon absorption per unit leaf area ranged from 1.83 × 10⁵ to 9.86 × 10⁵ μmol, with Phyllostachys iridescens being the lowest and Chimonobambusa marmorea the highest. The annual photon absorption per single leaf ranged from 1.84 × 10⁶ to 5.13 × 10⁷ μmol, with Indocalamus decorus achieving the highest total absorption due to its largest leaf area (114.9 cm²), while Bambusa multiplex var. riviereorum was the lowest. (4) All tested bamboo species showed consistent seasonal dynamics in photon absorption, with the highest in summer and lowest in winter. Although unit-area absorptance reflects the intrinsic light interception efficiency, leaf morphology has a substantial influence (explaining 99.56% of the variance) in determining total light acquisition per leaf. Full article

Adenine nucleotide translocase (ANT) has traditionally been defined as the ADP/ATP exchanger of the inner mitochondrial membrane. However, accumulating mechanistic evidence reveals a substantially broader functional spectrum that extends beyond nucleotide transport. In this review, we integrate these advances into a unified conceptual framework that positions ANT isoforms as modulators of mitochondrial bioenergetics, quality control, and cellular communication. Beyond its canonical exchange activity, ANT influences permeability transition thresholds and membrane potential stability, participates in regulated uncoupling and redox control, and contributes to inner membrane organization and cristae integrity. ANT further modulates TIMM23-dependent protein import and PINK1–Parkin-mediated mitophagy, thereby shaping mitochondrial quality control decisions. In addition, ANT regulates mitochondrial nucleic acid release and inflammasome activation, linking bioenergetic imbalance to innate immune signaling. Emerging evidence for alternative subcellular localizations suggests that ANT-dependent signaling extends mitochondrial state information to extracellular and intercellular contexts. Collectively, these findings support an expanded view of ANT as a multifunctional modulator linking mitochondrial energetic state to stress adaptation, inflammatory signaling, and tissue-level communication. Full article