Search Results (19,793)

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Microplastic (MP) contamination in freshwater systems has emerged as a growing environmental concern. This study investigated the occurrence and seasonal variability of MPs in surface water, the water column, and sediments at selected sites in Lake Alhajuela, Panama. Lake Alhajuela is an artificial reservoir that supplies water to the Panama Canal lock system and to the cities of Panama and Colón, serving more than 50% of the country’s population. MPs were isolated using two digestion protocols followed by density separation, and fragments and films larger than 1 mm were chemically characterized using FTIR–ATR spectroscopy. Mean MP concentrations were 759 ± 536 MPs L⁻¹ in surface water, 328 ± 140 MPs L⁻¹ in the water column, and 109 ± 87 MPs g⁻¹ in sediments. Statistical analyses revealed no significant differences among sampling sites; however, significant seasonal differences were observed (p < 0.01). Smaller MPs (63–249 µm) were more abundant compared to larger MPs (>250 µm). Fragments and fibers were the most predominant type of MP reported. Our results confirm the presence of MPs in the surface and water column, as well as sediments of the Alhajuela Lake. Further studies are needed to elucidate the fate, sources, transport, and distribution of MPs across Lago Alhajuela as well as to assess the lake’s potential contribution of MPs to Gatun Lake and the Panama Canal system. Full article

Intentional and unintentional GNSS interference can greatly affect the performance of precise timing and localization in areas such as automated driving or aviation. Nevertheless, reports show that jamming occurs near many European airports that are located close to a highway or in heavy industry areas due to broadcasting of interfering signals. To assess the impact of such potential risks, we investigated interference occurring on a section of highway located both near to an airport and close to logistics centers as part of the Austrian Security Research Program project CATCH-IN. This section of highway is of particular interest, as the highway runs in parallel to the approach path of aircraft and crosses the approach path 3.7 km before the aircraft touches down (the flight altitude is only 200 m above the ground). For this experiment, we distributed six Septentrio Mosaic x5 GNSS receivers as sensors along the highway and monitored this section for five months. We analyzed the data with AGC monitoring, CN0 monitoring, and baseband sample monitoring to identify interference along the highway that could affect sensors along the descending flight trajectory. During the period of this experiment, we saw events that we believe could cause potential safety risks and problems for aviation safety. In our analysis, we focused on the statistical evaluation of the temporal repetitions, in particular the times of day that see more interference and the frequencies at which more interference occurs. Additionally, we analyzed the performance of different algorithms for dealing with large datasets. The results provide new insight into potential monitoring stations near airports and raise awareness of potential risks and vulnerabilities in aviation safety as well as automated driving along highways. Full article

Background/Objectives: This study aimed to assess the feasibility and acceptability of a 3-month health coaching intervention to promote PNF and healthy diet for men on ADT for PCa. Methods: The study was carried out via a two-armed randomized controlled trial including 40 patients with PCa at a medical center in Philadelphia. During the 3-month period, the intervention group (PNF+) received health coaching utilizing an interactive text message system, and the control group received healthy eating text messages for the same duration. The outcome variables were feasibility and acceptability. Results: The PNF+ group (n = 27) had high adherence to health coaching (82%), picture response (85%) and moderate adherence to the PNF window (69%). The intervention was rated highly acceptable with no reported A/E associated with the intervention, and most participants planning to continue in some capacity. At 3 months, the PNF+ group had numerically lower BMI (29.1) and body weight (195.2 lbs) compared to the control group (n = 13; BMI 31.6, weight 223.3 lbs). Improvements in patient-reported outcomes were observed in both groups. FACIT-F scores (higher scores indicate less fatigue) increased in the PNF+ group (43.6 to 45.2) and in the control group (42.5 to 45.5). FACT-P scores (higher scores indicate better quality of life) increased in the PNF+ group (121.3 to 125.5) but decreased slightly in the control group (121.1 to 119.8). Between-group comparisons of change from baseline showed no statistically significant differences across outcomes (all p > 0.05). Conclusions: The intervention demonstrated partial feasibility and high acceptability. It was associated with numerically lower BMI and body weight and favorable changes in patient-reported outcomes, particularly quality of life; however, no statistically significant differences were observed between groups. These findings should be interpreted cautiously given the small sample size and require confirmation in larger, adequately powered trials. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Digital transformation is increasingly reshaping how tourism destinations enhance service quality and strengthen competitive positioning, particularly in developing economies characterized by heterogeneous digital maturity and structural constraints. This study develops and empirically tests a conceptual model examining the relationship between destination digital transformation, tourism supply quality, and destination competitiveness, with a specific focus on the mediating role of quality-oriented tourism supply. Survey data were collected from 242 tourism stakeholders and analyzed using hierarchical regression and bootstrapped mediation analysis (PROCESS Model 4, 5000 samples). The results show that digital transformation has a significant positive total effect on destination competitiveness (β = 0.48, p < 0.001), explaining 56% of the variance in competitiveness (R² = 0.56). However, a substantial portion of this effect is transmitted indirectly through tourism supply quality. The mediation analysis confirms a statistically significant partial mediation effect, with approximately 41% of the total effect operating through quality-oriented mechanisms. The findings demonstrate that digital transformation enhances competitiveness primarily when embedded within structured quality management, online reputation management, and smart governance practices, rather than through technological adoption alone. The study contributes to the literature by integrating digital transformation and tourism supply quality into a unified competitiveness framework tailored to developing economy contexts and provides practical guidance for policymakers and destination managers seeking inclusive and sustainable growth through quality-oriented digital strategies. Full article

The insulin-like growth factor 1 (IGF-1) gene plays a key role in growth and production traits in livestock. Limited information is available regarding its genetic polymorphisms in Saudi camel breeds. This study aimed to investigate genetic variation in the IGF-1 gene among Saudi camel breeds to provide baseline genetic information for future association studies. A total of 176 camels representing six Saudi breeds were sampled. DNA was extracted and Polymerase chain reaction (PCR) amplification and Sanger sequencing were applied to detect IGF-1 polymorphisms. Genotype and allele frequencies were calculated across breeds, and statistical comparisons were performed based on proportional distributions to account for unequal sample sizes. Two single-nucleotide polymorphisms (SNPs) were identified: c.365G>A in exon 3 and c.435C>T in exon 5. The exon 3 variant resulted in a missense mutation (p. Arg122His) but was detected in heterozygous form in only one camel, and subsequent screening of 109 additional samples confirmed its rarity. The exon 5 variant was synonymous in isoform X1 and located in the 3′ untranslated region of isoform X2. Sequencing of 176 camels revealed that c.435C>T was highly polymorphic across the examined breeds. Significant differences in genotype frequencies were observed within and among breeds (p < 0.001). The CT genotype predominated in Waddah (60%), Shageh (48%), and Sofor (60%), significantly exceeding CC and TT frequencies (p < 0.001). In Majaheem and Saheli, CT (47%) and TT (45%) were nearly equal and both significantly higher than CC (p < 0.001). Shaele exhibited a distinct pattern, with TT being most frequent (57%), significantly higher than CC (7%, p < 0.001) and CT (36%, p < 0.01). These findings indicate directional selection favoring the C allele in the Waddah and Shageh breeds, whereas the T allele is favored in the remaining breeds. This study provides the first baseline characterization of IGF-1 polymorphisms among Saudi camel breeds. Although no phenotypic associations were assessed, the results offer a foundation for future research examining relationships between IGF-1 variants and economically important traits. Full article

Architectural heritage plays a central role in shaping visitors’ emotional experiences within cultural tourism contexts. However, empirical research examining how specific architectural styles evoke emotional responses remains limited, particularly when using objective measurement techniques. This study investigates emotional reactions to architectural heritage by applying the Stimulus–Organism–Response (SOR) theoretical framework. In this model, architectural styles act as environmental stimuli, emotional processing represents the organismic state, and the resulting emotional activation constitutes the response. An experimental protocol was conducted with a sample of 645 participants exposed to a series of standardized architectural heritage images representing different architectural styles and infrastructure types. Emotional reactions were captured in real time through facial emotion recognition technology, enabling the objective measurement of eight basic emotions: neutral, happiness, sadness, surprise, fear, disgust, anger, and contempt. The collected emotional data were statistically analyzed using Analysis of Variance (ANOVA) to identify significant differences in emotional responses across architectural styles, heritage typologies, and gender. When significant differences were detected, Tukey’s HSD post hoc tests were applied to determine specific group contrasts. The findings reveal that different architectural styles generate distinct emotional patterns, highlighting the role of architectural aesthetics as a powerful mediator of affective engagement with heritage environments. From a theoretical perspective, this research contributes to heritage tourism and environmental psychology by integrating the SOR framework with real-time emotion detection technologies, providing a novel methodological approach for analyzing emotional responses to architectural heritage. Full article

Background: Indoleamine 2,3-dioxygenase (IDO) is an immune checkpoint that has been shown to play a key immunomodulatory role in various solid tumors, including breast cancer (BC). Although increased IDO expression has been previously observed in some BC subtypes, mainly triple-negative BC (TNBC), the clinical relevance of this protein across the entire range of BC and its exact correlations with other immune checkpoints remain to be elucidated. We herein aimed to further investigate the differential expression patterns of IDO and programmed death-ligand 1 (PD-L1) in variable BC subtypes and in distinct compartments of breast cancer tissue, and to explore their potential associations with standard patient- and tumor-related clinicopathological parameters as well as prognosis. Methods: This was a retrospective multi-center cohort study of 150 female patients with BC. The clinicopathological parameters analyzed were retrieved from the medical records of patients while sections from archival formalin-fixed, paraffin-embedded (FFPE) tissue blocks were also obtained for the performance of immunohistochemistry. The expression of IDO and PD-L1 was evaluated separately on tumor cells (IDO/CA, PD-L1/CA), lymphocytes (IDO/L, PD-L1/L) and stromal cells (IDO/S, PD-L1/S) and the results were correlated with the remaining clinical and pathological features of patients, as well as with local recurrence, metastasis and survival. Results: The mean age of patients was 59.5 years (SD = 13.4 years). Positive expression of IDO/CA, IDO/L and IDO/S was found in 6%, 93.3% and 90.7% of tissue samples, respectively, while 4%, 11.2% and 6.7% of tumors were positive for PD-L1/CA, PD-L1/L and PD-L1/S, respectively. A significantly higher rate of positive IDO/CA expression was observed in triple-negative BC (TNBC) patients (p = 0.037). Positive expression of IDO-CA was also significantly associated with positivity for PD-L1/L and PD-L1/S (p = 0.001 and p = 0.015, respectively). Multivariable logistic regression analysis showed independent correlations between IDO/CA and IDO/L and the presence of invasive ductal carcinoma (IDC) (OR = 1.10; p = 0.026) and N1 status (OR = 10.93; p = 0.039), respectively, IDO/S and both N1 (OR = 14.64; p = 0.018) and positive HER2 status (OR = 6.11; p = 0.019), PD-L1/L and high Ki67 (OR = 7.96; p = 0.001) as well as negative ER (OR = 0.08; 0.003) and PR status (OR = 0.09; p = 0.002), PD-L1/S and both NST (no special type) histology (OR = 4.68; p = 0.032) and negative ER status (OR = 0.21; p = 0.044). No statistically significant associations were observed between the expression patterns of the examined biomarkers and recurrence, metastasis or survival. Conclusions: In our study, IDO expression on tumor cells was predominantly observed in TNBC and was found to correlate with PD-L1 expression in the lymphocytic and stromal compartments. Furthermore, expression of PD-L1 among lymphocytes was found to independently correlate with unfavorable clinicopathological parameters, including high proliferation rate and negative hormone receptor status. Full article

In clinical research, unilateral data and bilateral data are commonly collected when paired organs or body parts of people receive treatment. Existing models are often inadequate for the research of combined unilateral and bilateral data. Considering population heterogeneity, this paper proposes three statistical tests and sample size estimation methods for the relative risk ratio in stratified unilateral and bilateral data under Dallal’s model. We derive test statistics (i.e., likelihood ratio, Wald-type, and score statistics) and evaluate their performance in terms of type I error rates and powers. Then, sample size determination is performed using an iterative algorithm. Monte Carlo simulations demonstrate that the score test performs well across various parameter configurations. Moreover, the estimated powers for determining sample size based on the score test are closer to the actual empirical powers. Two real examples of otolaryngology and myopathy are provided to illustrate the effectiveness of the proposed methods. Full article

We examine a simple duration-rotation strategy applied to six U.S. Treasury ETFs spanning the full maturity spectrum, using data from 2007 to 2025. At each semi-annual rebalancing date, ETFs are ranked by prior-period return and divided into three equal groups—Winners, Median, and Losers. Contrary to conventional momentum logic, the middle group consistently outperforms. The Median strategy grows USD 100 to USD 199.90 by end-2025, a CAGR of 3.79% against 2.17% for the passive benchmark, with a higher Sharpe ratio (0.606 vs. 0.494) and a shallower maximum drawdown ($-11.6\%$ vs. $-14.4\%$). Newey–West HAC and Lo (2002) tests confirm statistical significance ($p=0.031$ and $p=0.014$), and an expanding-window walk-forward procedure yields $p=0.0005$ across 27 out-of-sample evaluations from 2012 to 2025. The result is robust to calendar alignment, evaluation endpoint, lookback window, and execution timing, and survives transaction costs by a wide margin. The strategy requires no interest rate forecasts, no proprietary data, and is implementable with standard ETF brokerage access. Full article

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy. Full article