Search Results (183)

This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and clutter height—and train Random Forests (RF), Gradient Boosting (GB), Support Vector Regression (SVR), Gaussian Processes (GP), and a shallow neural network (NN). A unified pipeline with 5-fold cross-validation (CV), seeded reproducibility, and Optuna-driven hyperparameter search is adopted; performance is reported as RMSE/MAE/R² (mean ± sd). To contextualize feature reliability, we include Pearson correlation heatmaps and Variance Inflation Factors (VIFs), a systematic ablation of predictors, and TreeSHAP beeswarm analyses on held-out splits. We also evaluate spatially aware validation (blocked CV within route and leave-one-route-out checks) to mitigate optimism due to spatial autocorrelation. Results show that multivariate ML consistently outperforms classical empirical formulas (COST-231, ECC-33) in this campus setting, with RF achieving the lowest errors across routes (RMSE ≈ 2.14/2.16/2.95 dB for X/Y/Z, respectively), while GB ranks second and kernel methods (SVR/GP) and the NN trail closely behind. Ablation confirms that distance plus coordinates drive the largest gains, with terrain/clutter providing route-dependent refinements. SHAP analyses align with these findings, highlighting stable, interpretable contributions of geospatial covariates. Spatial CV increases absolute errors moderately but preserves model ranking, supporting the robustness of conclusions. Overall, scenario-aware, multivariate ML yields material accuracy gains for smart-campus planning at 1.8 GHz. Full article

In safety-critical systems like nuclear power plants, the rapid and accurate perception of visual interface information is vital. This study investigates the relationship between visual attention dispersion measured via heatmap entropy (as a specific measure of gaze entropy) and response time during information search tasks. Sixteen participants viewed a prototype of an accident response support system and answered questions at three difficulty levels while their eye movements were tracked using Tobii Pro Glasses 2. Results showed a significant positive correlation (r = 0.595, p < 0.01) between heatmap entropy and response time, indicating that more dispersed attention leads to longer task completion times. This pattern held consistently across all difficulty levels. These findings suggest that heatmap entropy is a useful metric for evaluating user attention strategies and can inform interface usability assessments in high-stakes environments. Full article

Metaheuristic algorithms are widely utilized as effective tools for solving complex optimization problems. Among them, the Harmony Search (HS) algorithm has garnered significant attention for its simple structure and excellent performance. The efficacy of the HS algorithm is heavily dependent on the configuration of its internal parameters, with the Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR) playing pivotal roles. These parameters determine the probabilities of using the Random Generation (RG), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) operators, thereby controlling the balance between exploration and exploitation. However, a systematic empirical analysis of the interaction between these parameters and the characteristics of the problem at hand remains insufficient. Thus, this study conducts a comprehensive empirical analysis of the performance sensitivity of the HS algorithm to variations in HMCR and PAR values. The analysis is performed on a suite of 23 benchmark functions, encompassing diverse characteristics such as unimodality/multimodality and separability/non-separability, along with 5 real-world optimization problems. Through extensive experimentation, the performance for each parameter combination was evaluated on a rank-based system and visualized using heatmaps. The results experimentally demonstrate that the algorithm’s performance is most sensitive to the HMCR value across all function types, establishing that setting a high HMCR value (≥0.9) is a prerequisite for securing stable performance. Conversely, the optimal PAR value showed a direct correlation with the topographical features of the problem landscape. For unimodal problems, a low PAR value between 0.1 and 0.3 was more effective, whereas for complex multimodal problems with numerous local optima, a relatively higher PAR value between 0.3 and 0.5 proved more efficient in searching for the global optimum. This research provides a guideline into the parameter settings of the HS algorithm and contributes to enhancing its practical applicability by proposing a systematic parameter tuning strategy based on problem characteristics. Full article

CrossFit performance is influenced by physiological, neuromuscular, and perceptual factors, yet the extent to which these determinants vary by sex or training experience in standardized CrossFit Workouts of the Day (WODs) remains unclear. This study examined whether variables such as lactate accumulation, oxygen uptake dynamics, jump performance loss, and ventilatory responses relate differently to performance when stratified by sex and expertise. Fifteen trained athletes (eight males, seven females; overall mean age 27.7 ± 4.6 years) took part. Assessments included body composition, squat (SJ) and countermovement jumps (CMJ), and maximal oxygen consumption [VO₂max]. On a separate day, they performed Fran (21-15-9 thrusters and pull-ups, Rx or scaled) The prescribed (‘Rx’) version used standardized barbell loads (43 kg for men, 29 kg for women), while the scaled version involved reduced loads or pull-up modifications. Respiratory gas exchange and heart rate were continuously monitored, while blood lactate and jump performance were measured pre- and post-WOD. Workout completion time [s] was the primary outcome. Correlation heatmaps explored associations in the overall sample and by sex and expertise. Mean completion time was 422.1 ± 173.2 s (range: 200–840). Faster performance correlated with higher ventilatory responses [ΔVe, r = −0.60, p = 0.018], greater mean VO₂ (r = −0.62, p = 0.014), superior jump power [CMJ pre, r = −0.65, p = 0.009], and higher post-WOD lactate [r = −0.54, p = 0.036]. Sex-stratified analyses showed that males relied on ventilatory efficiency and neuromuscular power, whereas females were more constrained by performance loss and higher resting perceived exertion (RPE). Experts depended on ventilatory and neuromuscular efficiency, while initiates showed stronger associations with decrements in jump performance and higher RPE. These findings highlight subgroup-specific performance profiles and reinforce the need for tailored training strategies in CrossFit athletes. Full article

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

Endometriosis is a chronic, estrogen-dependent inflammatory disease with heterogeneous clinical manifestations and uncertain systemic immune involvement. This study aimed to characterize peripheral immune profiles and circulating tumor markers in women with ovarian endometrioma (OE) and deep infiltrating endometriosis (DIE), and to explore their associations with disease severity, symptom burden, and physical health perception. Peripheral blood leukocyte subsets, plasma cytokines, and tumor markers (CA125, CA19-9, CEA, HE4) were analyzed in 146 patients and 50 healthy controls. OE was associated with increased monocyte counts and reduced neutrophil proportions, while DIE showed elevated levels of IL-8 and Galectin-1. IL-33 levels correlated negatively with the revised American Society for Reproductive Medicine (rASRM) scores and positively with neutrophil proportion, suggesting a role in systemic immune regulation. Tumor marker levels varied by subtype: CA19-9 was higher in OE, and CEA in DIE. CA125 correlated with disease severity, and CEA with monocyte levels. Exploratory heatmaps revealed consistent immune-tumor associations linked to anatomical severity and symptom profiles. Although exploratory, these findings highlight the presence of distinct systemic immune patterns in endometriosis and support the potential of integrative blood-based biomarkers for future diagnostic and stratification strategies. Full article

This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions. Full article

In high-density built environments, perceived density (PD)—shaped by physical, socio-cultural, and perceptual factors—often induces sensations of crowding, stress, and spatial oppression. Although green spaces are recognised for their stress-reducing effects, the influence of built-environment characteristics on public sentiment under stringent mobility restrictions remains inadequately explored. This study takes Chongqing, a representative mountainous metropolis in China, as a case to examine how natural and built environmental elements modulate emotional valence across varying PD levels. Using housing data (n = 4865) and geotagged Weibo posts (n = 120,319) collected during the 2022 lockdown, we constructed a PD-sensitive sentiment dictionary and applied Python’s Jieba package and natural language processing (NLP) techniques to analyse emotional scores related to PD. Spatial and bivariate autocorrelation analyses revealed clustered patterns of sentiment distribution and their association with physical density. Using entropy weighting, building density and floor area ratio were integrated to classify residential built environments (RBEs) into five tiers based on natural breaks. Key factors influencing positive sentiment across PD groups were identified through Pearson correlation heatmaps and OLS regression. Three main findings emerged: (1) Although higher-PD areas yielded a greater volume of positive sentiment expressions, they exhibited lower diversity and intensity compared to low-PD areas, suggesting inferior emotional quality; (2) Environmental and socio-cultural factors showed limited effects on sentiment in low-PD areas, whereas medium- and high-PD areas benefited from a significantly enhanced cumulative effect through the integration of socio-cultural amenities and transportation facilities—however, this positive correlation reversed at the highest level (RBE 5); (3) The model explained 20.3% of the variance in positive sentiment, with spatial autocorrelation effectively controlled. These findings offer nuanced insights into the nonlinear mechanisms linking urban form and emotional well-being in high-density mountainous settings, providing theoretical and practical guidance for emotion-sensitive urban planning. Full article

Total Mixed Ration (TMR) particle size significantly impacts dairy cow health and productivity. This study investigated the effects of TMR particle size tertiles on rumen pH, dry matter intake (DMI), and milk characteristics in Simmental cows by continuous pH monitoring (Moonsyst Ltd., Kilkenny, Republic of Ireland) and particle separation by 19, 8, 4 mm sieves and pad using the Wasserbauer particle separator, along with regular milk and DMI measurements. Data were analyzed by IBM SPSS 26.0 with ANOVA, Pearson correlations and statistically significant differences between tertiles by post hoc Tukey HSD test were performed (p < 0.05). Tertiles by frequency analysis were used to categorize particle size proportions into three groups, each containing an equal number of observations. Principal component analysis (PCA) and heatmaps by SRplot were generated. Moderate particle size distributions (second tertiles of 19 mm, 8 mm, 4 mm sieves, and pad as the fraction of TMR particles that pass through the all sieves and are collected in the bottom pan) optimized rumen pH stability, reducing time below 6.2 (SARA risk) or above 6.8, and correlated with milk β-hydroxybutyrate (BHB), oleic acid, and acetone levels. Moreover, milk production was maximized with a combination of coarser (19 mm and 8 mm, third tertiles) and finer (4 mm, first tertile) particles, milk fat peaked in both the finest pad fraction (third tertile) and coarsest larger sieves (first tertiles), and milk protein in the first tertiles of 19 mm and 8 mm sieves. Similarly, DMI positively correlated with coarser particles, but sometimes negatively with milk quality. In addition, PCA showed fine particle groups clustering with higher milk fat-to-protein ratios, somatic cell counts, and urea. In conclusion, mid-range TMR particle sizes (second tertiles) consistently provided the most benefits across ruminal, metabolic, and production parameters, underscoring TMR structure as a crucial precision feeding tool. Full article

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

Wheat domestication and selection for aboveground traits may have influenced belowground traits, reducing genetic diversity critical for adaptation to stress such as drought. However, the impacts on root system architecture and root–endophytic fungal interactions remain unclear. This study evaluated variation in root traits and associations with arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) among nine diploid Aegilops tauschii accessions (wild progenitor), one tetraploid Triticum turgidum cv. ‘Langdon’ (LNG), and one hexaploid Triticum aestivum cv. ‘Norin 61’ (N61). Root traits and fungal colonization varied significantly among genotypes. All Ae. tauschii accessions showed superior root development and lower DSE colonization compared to LNG and N61. AMF colonization was highest in accessions AT76 and KU-2126 (54% and 53%, respectively), while N61 exhibited the highest specific root length (SRL) and DSE colonization. AMF positively correlated with most root traits (except SRL), while DSE showed the opposite trend. Although Ae. tauschii accessions shared broadly favorable root traits, variation in their fungal interactions were more pronounced. A clustering heatmap incorporating both root and biotic traits clustered the genotypes into four groups, clearly separating the Ae. tauschii accessions into two clusters based on their root characteristics and root-fungal associations. These results highlight the hidden interspecific and intraspecific variations in Ae. tauschii and its potential as a genetic resource for optimizing root–endophytic fungal interactions, and improving wheat resilience to biotic and abiotic stress in a changing climate. Full article

As low temperature is a key factor affecting the growth and development of plants and the utilization of agricultural waste has significant research value, this study explores the effects of 16 agricultural wastes on the growth of P. fraseri under natural low-temperature conditions and evaluates its cold resistance capacity. Soil chemical properties were analyzed and all the wastes were found to exhibit alkalinity. The highest total nitrogen content was found in group A (garden soil/coir/municipal sludge = 7:1:2). In this group, the branch number, branch length, and branch diameter were the largest. Interestingly, the plants in group E (garden soil/coir/pig manure = 7:1:2) had the highest average number of new shoots, with 5.72. Analysis of the physiological indexes of leaves revealed that the proline content, superoxide dismutase activity, fresh weight, and dry weight of plants in group L (garden soil/coir/pear residue = 7:1:2) were the highest. The stomatal conductance and transpiration rate of the leaves of plants in group L were the largest, at 86.23 mmol∙m⁻²∙s⁻¹ and 1.67 mmol∙m⁻²∙s⁻¹, respectively. Furthermore, combined with morphological and physiological indicators for membership function analysis, the results show that plants in group A exhibited optimal growth under natural low temperature. Correlation analysis indicated varying degrees of correlation between 38 pairs of indicators, including branch number and branch length, intercellular CO₂ concentration and stomatal conductance, and leaf fresh weight and dry weight. Heatmap analysis showed that branch number, branch length, and branch diameter were highest in group A plants, while the highest levels of proline occurred in group L plants. In this study, groups A and L are recommended for growth under naturally low-temperature conditions. Full article

Oxidative stress plays a key role in tissue damage during inflammation, highlighting the need for effective antioxidant interventions. This study investigates the antioxidant potential of argan oil (AO)—obtained from Argania spinosa (L.) Skeels almonds—in comparison with olive oil (OO), cactus seed oil (CSO), and colza oil (CO). Quantitative analyses of total polyphenols and pigments—including chlorophylls, carotenoids, and xanthophylls—were conducted alongside antioxidant capacity assessments via DPPH, ABTS, and FRAP assays. The methanolic fraction consistently demonstrated the highest phenolic concentration and antioxidant efficacy across all oils. To establish in vivo relevance, a male C57BL/6J mouse model of acute oxidative stress was induced by lipopolysaccharide (LPS) administration. Pretreatment with oils significantly modulated key oxidative stress biomarkers—SOD, CAT, GPx activities, GSH levels, and lipid peroxidation (MDA)—in both heart and kidney. LPS challenge induced marked oxidative imbalance, notably increasing enzymatic activity and MDA levels, while depleting GSH in the heart and elevating it in the kidney. However, pretreatment with oils effectively restored redox homeostasis, with AO showing particularly potent effects and a stronger regulatory effect observed in the kidney. Hierarchical clustering of z-score-normalized heatmaps revealed distinct oxidative stress signatures, clearly separating LPS-treated heart and kidney tissues from other groups due to heightened oxidative markers. In contrast, oil-treated and oil-combined-with-LPS groups clustered closer to the control, underscoring the protective effect of oils against LPS-induced oxidative stress, with efficiency varying by oil type. Pearson correlation analysis, complemented by multivariate principal component analysis (PCA), further emphasized strong positive associations between antioxidant enzymes (SOD, CAT, GPx) and MDA levels, while GSH exhibited tissue-specific behavior—negatively correlated in the heart but positively in the kidney—highlighting divergent redox regulation between organs. Collectively, AO demonstrated robust cardioprotective and nephroprotective properties, supporting its potential as a natural dietary strategy against inflammation-induced oxidative stress. Full article

This study investigates the certification outcomes of intermediate-level ICs in The National Fire Service Academy in South Korea through a comprehensive quantitative analysis of their evaluated competencies. Using assessment data from 141 candidates collected from 2022 to 2024, we examine how scores on six higher-order competencies—comprising 35 sub-competencies—influence pass or fail results. Descriptive statistics, correlation analysis, logistic regression (a statistical model for binary outcomes), random forest modeling (an ensemble decision-tree machine-learning method), and principal component analysis (PCA; a dimensionality reduction technique) were applied to identify significant predictors of certification success. Visualization techniques, including heatmaps, box plots, and importance bar charts, were used to illustrate performance gaps between successful and unsuccessful candidates. Results indicate that competencies related to decision-making under pressure and crisis leadership most strongly correlate with positive outcomes. Furthermore, unsupervised clustering analysis (a data-driven grouping method) revealed distinctive performance patterns among candidates. These findings suggest that current evaluation frameworks effectively differentiate command readiness but also highlight specific skill domains that may require enhanced instructional focus. The study offers practical implications for fire training academies, policymakers, and certification bodies, particularly in refining curriculum design, competency benchmarks, and evaluation criteria to improve fireground leadership training and assessment standards. Full article

To elucidate the spatial distribution characteristics of algal communities and their correlation with environmental factors in the Heilongjiang River, algal surveys and water quality monitoring were carried out from May to October 2023. The results were as follows: (1) In total, 234 species from 95 genera belonging to seven phyla were detected, mainly Bacillariophyta, Chlorophyta, and Cyanophyta. (2) The most dominant species in the Heilongjiang River in summer and autumn were Pseudanabaena minima (G. S. An) Anagnostidis and Phormidium gelatinosum Woronichin. The dominant species in the middle niche in summer and the dominant species in the broad niche in autumn were Bacillariophyta. (3) Canonical Correlation Analysis results revealed that the environmental factors that significantly affected the distribution of the epiphytic algae during the summer were COD, F-, and WT, while EC, TN, BOD₅, and pH significantly influenced the distribution of epiphytic algae in autumn. (4) Significant correlation heatmaps revealed that the dominant species were significantly correlated with WT and TP in the Greater Khingan Mountains in summer, whereas the dominant species were significantly correlated with COD, NH₃-N, and TP in the Heihe region, Lesser Khingan Mountains, and Sanjiang Plain. There was a significant correlation between the dominant species and TN in the Greater Khingan Mountains in autumn. The spatial distribution characteristics of the algal communities and the correlations between the dominant species and water environmental factors can provide a theoretical reference for the assessment of the water ecological health status. Full article