Search Results (3,506)

The 795 nm wavelength corresponds to the D1 transition of rubidium atoms and is widely used in atomic optical pumping, atomic clocks, magnetometers, and precision spectroscopy. For compact free-space collimation, beam shaping, and efficient fiber coupling, edge-emitting semiconductor lasers with reduced fast-axis (vertical) divergence are highly desirable, yet low-divergence designs at 795 nm remain limited. Here, we propose and demonstrate low-divergence photonic-crystal epitaxy (LD–PC) for 795 nm edge-emitting lasers. By engineering a periodic n-side photonic-crystal stack to place the fundamental vertical mode near the photonic band edge, the vertical mode is expanded while maintaining effective modal discrimination. Narrow-ridge Fabry–Pérot lasers based on GaAsP/AlGaAs single-quantum-well epitaxy were fabricated and characterized. The optimized LD–PC device (3 μm ridge width, 1 mm cavity length) delivers 227 mW at 200 mA with a threshold current of 23 mA, a slope efficiency of 1.28 W/A, and a peak wall-plug efficiency of 55% under continuous-wave operation at 25 °C. The measured far-field divergences (FWHMs) are 7.16° and 18.83° in the lateral and vertical directions, respectively, corresponding to a reduction in the vertical divergence from >40° in the reference structure to <20° with LD–PC. These results validate photonic-crystal epitaxy as an effective route toward compact, high-performance, low-divergence 795 nm semiconductor laser sources for rubidium-based atomic systems. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

To address ammonium nitrogen (NH₄⁺-N) and nitrite accumulation in intensive marine shrimp aquaculture, a marine recirculating aquaculture system (RAS) for Penaeus vannamei centered on a moving bed biofilm reactor (MBBR) was constructed to investigate the microbial basis of nitrogen removal. The results showed that the MBBR contributed most to NH₄⁺-N removal, demonstrating favorable nitrification potential under marine conditions (0.513 mg·L⁻¹·h⁻¹). The biofilm carrier formed a complete attached layer and developed a mature biofilm structure. Microbial community analysis revealed clear differentiation between the biofilm and sediment. The biofilm community was dominated by norank_f__Caldilineaceae (9.89%). Linear discriminant analysis effect size identified the nitrifying genus Nitrospira to be significantly enriched on the biofilm side (α = 0.05, linear discriminant analysis > 2.0). In addition, PICRUSt2-based functional prediction suggested a higher potential in biofilm than in sediment for ammonia oxidation and downstream nitrogen transformation, involving ammonia monooxygenase (EC:1.14.99.39), hydroxylamine dehydrogenase (EC:1.7.2.6), nitrate reductase (EC:1.7.99.4), and nitrite reductase (EC:1.7.2.1). Thus, this study provides a microbial basis and process strategy for P. vannamei RAS. Full article

This study presents a structured narrative review integrating methodological and decision-oriented perspectives. Milk proteins, particularly β-casein, have attracted increasing scientific and commercial attention due to their genetic variability and role in dairy production and product differentiation. Among β-casein variants, the A1 and A2 alleles of the CSN2 gene are of particular relevance, as their single-nucleotide difference has influenced breeding strategies and the expansion of A2-oriented dairy markets. Although multiple validated molecular genotyping approaches are available for CSN2 A1/A2 discrimination, guidance on their context-appropriate deployment in agricultural systems remains largely technique-centric. The present framework integrates analytical performance, sample complexity, and operational constraints to support the selection of fit-for-purpose methods across breeding, diagnostic, and dairy authentication contexts. Classical and advanced approaches, including polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), allele-specific polymerase chain reaction (AS-PCR) and amplification refractory mutation system PCR (ARMS-PCR), high-resolution melting (HRM) analysis, sequencing-based methods, single nucleotide polymorphism (SNP) arrays, and digital polymerase chain reaction (dPCR), are comparatively evaluated not only in terms of sensitivity and throughput but also with respect to scalability, reproducibility, and decision risk. This framework provides a practical decision-support tool for aligning genotyping strategies with application-specific risk profiles, thereby improving reliability, transparency, and regulatory compliance in modern dairy systems. Full article

Background: Adolescent insomnia is a public health concern associated with affective disturbances, poor academic performance, and cardiometabolic risk. In Peru, nighttime screen use, psychosocial stressors, and social inequalities intensify its impact; however, brief, validated screening instruments remain limited. Objective: To translate, culturally adapt, and evaluate the psychometric properties of the Youth Self-rated Insomnia Scale (YSIS) in Peruvian adolescents, examining its internal structure, reliability, and invariance across sex. Methods: An instrumental study was conducted with 300 students aged 13 to 17 years (M = 15.02; SD = 1.07). Descriptive statistics were calculated, and confirmatory factor analysis (CFA) was performed using a robust estimator. Reliability was assessed through Cronbach’s alpha (α), McDonald’s omega (ω), and average variance extracted (AVE). Factorial invariance by sex was examined at the configural, metric, scalar, and strict levels. Results: The unidimensional model demonstrated adequate fit (χ² = 44.55, df = 18, p < 0.001; CFI = 0.97; TLI = 0.95; RMSEA = 0.07; SRMR = 0.04), with factor loadings ranging from 0.47 to 0.76, high internal consistency (α = 0.86; ω = 0.81), and AVE = 0.51. Although the two-factor model showed acceptable global fit indices, it revealed insufficient AVE in one factor (AVE = 0.40) and a high inter-factor correlation (r = 0.93), suggesting a lack of discriminant validity. Factorial invariance across sex was supported at all evaluated levels. Conclusions: The Spanish version of the YSIS demonstrates a unidimensional structure, adequate internal consistency, and factorial invariance across sex, supporting its use as a brief screening tool in clinical and school settings, as well as in epidemiological studies among Peruvian and Latin American adolescents. Full article

Background: Temporomandibular disorders (TMDs) are multifactorial conditions frequently encountered in orthodontic practice, and the independent associations of occlusal and structural variables remain unclear. This case–control study constructed a multivariable model integrating clinical, cephalometric, panoramic, and functional variables to examine their associations with TMD, diagnosed according to the DC/TMD Axis I protocol. Fifty patients with TMD and 50 non-TMD controls were consecutively recruited between October 2024 and December 2025. Occlusal characteristics, lateral cephalometric measurements, and Kjellberg panoramic symmetry indices (SI1/SI2) were assessed using standardized protocols. Candidate variables were initially explored using univariable analyses with false discovery rate adjustment, followed by multivariable Firth penalized logistic regression to reduce small-sample bias and separation. Mandibular deflection (OR = 3.57, 95% CI 1.54–9.09) and deviation (OR = 4.35, 95% CI 1.69–12.50) demonstrated the strongest independent associations with TMD, while SI1 asymmetry (<90%) became significant after multivariable adjustment (OR = 3.57, 95% CI 1.08–14.29). The final model showed apparent discrimination within the study sample (AUC = 0.822; 95% CI: 0.742–0.902). However, this value was calculated using the same dataset and should not be interpreted as validated model performance or compared to other studies. The observed SI1 effect should be interpreted cautiously, as it may reflect model instability due to the relatively small sample size. Within the limitations of this case–control design, functional disturbances showed stronger associations with TMD than static structural variables; however, external validation is required before clinical application. Full article

Jamming pattern identification is a crucial prerequisite for countering jamming. Combined jamming exhibits complex structures and diverse forms, making it difficult for traditional identification methods to extract suitable and stable features for effective discrimination. To address this challenge, this paper proposes a combined jamming identification method based on joint modulation of linear frequency modulation, phase coding and phase coding frequency modulation (LFM-PC-PCFM) waveforms. Building upon the time–frequency entropy features of combined interference, this method enhances the separability of jamming features in the radar-transmitted waveform dimension. The experiment employed the SVM classification algorithm based on particle swarm optimization for validation. Experiments demonstrate that the combined jamming recognition method under LFM-PC-PCFM waveform modulation achieves higher and more stable recognition accuracy than traditional LFM single-waveform modulation under jamming-to-noise ratios ranging from −10 dB to 30 dB. Full article

Background: Women with endometriosis frequently experience mobility limitations that affect daily functioning. A specific tool to assess these restrictions would help clinicians to better understand patients’ functional challenges, facilitating more effective communication and shared decision making. Addressing this gap is essential for strengthening patient–professional dialogue and improving individualized care. Objective: To develop the new instrument MobEndo and to perform initial psychometric testing of the tool. Methods: The initial domains and items were generated through semi-structured interviews with patients and based on experts’ advice. Guided by the International Classification of Functioning, Disability, and Health (ICF) framework, exploratory factor analysis was conducted on data from patients diagnosed with endometriosis. Internal consistency was assessed using Cronbach’s alpha, considering values ≥ 0.70 as acceptable. Test–retest reliability was examined using intraclass correlation coefficients (ICCs), and ICC values were judged as excellent if >0.75. Construct validity was evaluated through concurrent, discriminant, and known-groups validity. For the known-groups validity hypothesis, participants were categorized by baseline pain levels. Results: The final questionnaire included 18 items, developed from responses from 301 women (mean age 38.96 ± 6.85). Factor analysis revealed two components—transitioning between body positions and performing movements requiring stabilization and executing load-bearing tasks involving the upper limbs—with the model explaining 71.78% of the total variance. Reliability was excellent, with a Cronbach’s alpha of 0.977. The ICC for the total score was 0.976 (95% CI 0.949–0.988), with similarly high values for each component. Concurrent validity correlations were significant, while discriminant validity showed no relevant associations. Known-groups analyses showed clear differences across pain-level groups. Conclusions: The questionnaire is a valid and reliable tool for capturing women’s perceived mobility limitations in endometriosis. Full article

Host receptors can detect traces of non-self-pathogenic RNAs within a sea of cellular mRNA molecules. In host cells, mRNA cap methylation occurs in the nucleus, generating Cap1 and Cap2 structures (m⁷GpppNm and m⁷GpppNmNm, respectively). By contrast, alphavirus genomes carry a Cap0 structure (m⁷GpppN), which lacks 2′-O-methylation. This difference in the structure of the host and viral caps serves as a molecular signature that enables discrimination between self and non-self RNAs. Several host immune sensors, such as RIG-I and IFIT1, recognize the alphavirus Cap0 structure and trigger an antiviral response to restrict viral replication. It has been proposed that IFIT1 sequesters aberrant RNAs, preventing their translation by host ribosomes and blocking viral protein synthesis. However, alphaviruses have evolved molecular strategies to circumvent IFIT1-mediated restriction and facilitate infection in mammalian cells. One such strategy involves the folding of a 5′ RNA structure that hides the cap from host immune sensors. This highlights the dynamic interplay between viral evasion tactics and host immune defenses. This review will discuss how specific modifications at the 5′ end of alphavirus RNA modulate host defenses and how a deeper understanding of the virus–host interaction may inform the development of novel vaccine strategies. Full article

Bisphenol A (BPA) has long been used in plastics, resins, and food packaging materials; however, extensive research has demonstrated its reproductive, developmental, and endocrine-disrupting effects. Consequently, BPA has been increasingly restricted and replaced with structural analogues. Among these, tetramethyl bisphenol F (TMBPF) has emerged as one of the most widely used substitutes, particularly in epoxy resins and food-can coatings. Although initially regarded as a safer alternative, accumulating evidence suggests that TMBPF may exert multiple toxicological effects, raising concerns about its potential developmental neurotoxicity. The present study aimed to investigate the neurodevelopmental effects of TMBPF using both in vitro and in vivo approaches. First, a developmental neurotoxicity assay employing Sox1−GFP mouse embryonic stem cells was used to evaluate cytotoxicity using the cell counting kit-8 assay and neural differentiation based on green fluorescent protein (GFP) fluorescence intensity. The results indicated developmental neurotoxic potential according to the established discrimination index. Subsequently, pregnant and lactating mice were exposed to TMBPF daily from gestational day 10.5 to postnatal day 20, and their offspring were assessed for behavioral performance as well as changes in the expression of neurodevelopment-related genes in the brain. Behavioral analyses encompassed multiple domains, including memory and learning, social behavior, anxiety-related responses, and spontaneous locomotor activity, suggesting alterations in these functional outcomes. Molecular analyses further demonstrated changes associated with dopaminergic and cholinergic signaling, synaptic plasticity, neuronal activity markers, neuropeptides, and inflammatory pathways. Collectively, these findings provide the first evidence in a mammalian model that maternal exposure to TMBPF may influence offspring neurodevelopment. These findings suggest potential implications for human exposure to TMBPF, particularly through food-contact materials, and warrant further mechanistic and dose–response studies. Full article

Background: Atrial fibrillation (AF) frequently coexists with heart failure (HF) and worsens clinical outcomes. However, factors associated with AF in HF with preserved (HFpEF) and mildly reduced ejection fraction (HFmrEF) remain poorly defined. This study aimed to identify clinical, laboratory, and echocardiographic determinants of AF in these HF phenotypes. Methods: This retrospective single-center observational study included 700 consecutive patients with HF hospitalized between January 2018 and December 2023. The median age was 74 years (IQR 66–80). Women predominated in the cohort (55.3% vs. 44.7%, p < 0.001). Based on echocardiographically assessed left ventricular ejection fraction, patients were stratified into groups with preserved (≥50%), mildly reduced (41–49%), and reduced (≤40%) ejection fraction. Determinants of AF were evaluated using univariate and multivariate logistic regression analyses, and model discrimination was assessed using ROC analysis. Results: Strongest determinants of AF in our patients with HFpEF and HFmrEF were left atrial size (OR 1.114 per mm increase; 95% CI 1.054–1.177; p < 0.001), moderate and severe tricuspid regurgitation (OR 4.092; 95% CI 1.977–8.466; p < 0.001 and OR 6.957; 95% CI 2.482–19.499; p < 0.001), male gender (OR 1.680; 95% CI 1.076–2.621; p = 0.022) and advanced age (OR 1.070 per year; 95% CI 1.032–1.109; p < 0.001). Conclusions: In patients with HFpEF and HFmrEF, AF is strongly associated with atrial remodeling, with left atrial enlargement as the key structural determinant. The identified associations may contribute to an improved understanding of AF in HFpEF and HFmrEF; however, their potential role in risk stratification requires validation in prospective studies. Full article

As the most prevalent type of existing building in China, masonry structures are susceptible to cracking due to the low tensile strength of the masonry material. In the event of a sudden, strong earthquake, they are highly prone to brittle collapse, leaving occupants little time and space to escape. Based on this, combining the advantages of the elastoplastic mechanical theory and the nonlinear finite element (FE) method, this study adopts different modeling methods: integral modeling (IM), contact element discrete modeling (CEDM), spring element discrete modeling (SEDM), and co-node discrete modeling (CNDM). FE models of unreinforced masonry walls (UMWs) are established, respectively, and a monotonic pushover mechanical performance analysis is carried out. The accuracy of the adopted modeling methods is verified against existing test results for UMW specimens. Through parametric analysis of aspect ratios (0.5, 0.75, 1.0, and 1.25), axial compression ratios (0.1, 0.3, 0.5, 0.7, and 0.8), and mortar strengths (M5, M7.5, and M10), the characteristic mechanical performance factors of UMWs are determined. A novel strength index is proposed to discriminate between failure modes and elucidate the damage mechanism of UMWs. The results indicate that the ultimate load and its corresponding displacement change systematically with variations in aspect ratios, axial compression ratios, and mortar strengths. Furthermore, integrating stress cloud maps with the proposed strength index provides a quantitative basis for discriminating between flexural and shear failure modes in UMWs. All four modeling methods can, to varying degrees, capture the pushover behavior of UMWs, and quantifiable selection schemes are provided to balance analysis accuracy and computational cost. The analytical methods and findings presented in this work can be applied to performance assessment, seismic design, and engineering practice of UMWs. Full article

This study evaluates the factors associated with municipal solid waste management performance under a circular economy approach in the municipalities of Cajamarca, Peru. A hybrid analytical design was applied to 120 municipal observations, combining partial least squares structural equation modeling to estimate the measurement and structural properties of four latent constructs—legal-regulatory framework, institutional capacity, operational management, and perceived performance—and XGBoost with SHAP to explore predictive classification of participation in circular economy training. The structural results indicate that operational management plays the central articulating role in linking regulation and institutional capacity to perceived performance, whereas the predictive component showed only modest out-of-sample discrimination (AUC-ROC = 0.519). Overall, the findings suggest that the proposed hybrid pipeline is more informative for explanatory integration and variable-importance analysis than for strong predictive discrimination under the current specification. Full article

γ-photon tomography, which leverages the high penetration and electrical neutrality of high-energy γ-photons, offers a promising non-contact approach for industrial flow field monitoring. However, γ-photon flow-field images are inherently grayscale and exhibit probabilistic statistical imaging characteristics, leading to color banding artifacts when processed by mainstream colorization algorithms like DeOldify, which compromise structural continuity and visual consistency. To address this issue, this paper proposes a Structure Enhancement Colorization Network (SECN) model for γ-photon flow-field image colorization. A U-Net + GAN framework is employed, with ResNet101 as the generator backbone. It integrates structure-aware enhancement and multi-scale attention modules, while the discriminator incorporates enhanced blocks for improved boundary and texture discrimination. By adaptively fusing global–local features across channel and spatial dimensions, the SECN model effectively suppresses color banding artifacts and enhances structural consistency. To validate the effectiveness of the proposed algorithm, two CFD-simulated γ-photon flow-field image colorization scenarios—namely a large-scale vortex wake and a horizontal wake—are used as evaluation targets. In terms of image quality metrics, the proposed colorization algorithm achieves PSNR, SSIM, FID, and MAE values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, corresponding to improvements over DeOldify of 4.54%, 2.82%, 5.18%, and 11.16%. When considering information entropy, the proposed colorization algorithm achieves an average entropy value of 4.0257, marking a 4.44% increase compared to DeOldify’s 3.8543, demonstrating superior information preservation and reduced uncertainty in reconstructing complex probabilistic structures. Furthermore, from the perspective of parameter inversion, the temperature inversion MAPE is 7.60%, which is a significant reduction of 18.42% compared to that of DeOldify. Full article

In this study, a generalized and advanced form of the near soft set theory (NST) framework is proposed for information aggregation (IA) processes. The primary motivation of the study is to address the lack of similarity-based uncertainty modeling in the literature by integrating the parametric structure of soft sets with the similarity-oriented structure of nearness approximation spaces. Within this framework, the AND-product and OR-product operations are introduced as the main methodological tools, and their algebraic structures are analyzed in detail. It is mathematically demonstrated that these operations satisfy fundamental properties such as idempotency, absorption, distributivity, and De Morgan identities. The principal original contribution of the study is the development of a novel Uni–Int-based decision-making mechanism that enables the systematic distinction between strong and acceptable alternatives. In addition, the boundary frequency indicator ($b_r$), which quantitatively evaluates the reliability of objects under perceptual uncertainty and is introduced for the first time in the literature, is proposed. The applicability of the proposed model is demonstrated through a real-estate selection problem, and a sensitivity analysis is conducted to reveal the determining effect of the nearness parameter r on decision granularity. The obtained findings indicate that the proposed NST framework provides a more flexible, more discriminative, and structurally robust decision-support model than classical approaches, particularly for similarity-based IA problems. Full article