Search Results (6,587)

Primula vulgaris possesses considerable edible, medicinal, and ornamental value. It is widely applied in food and pharmaceutical development and, as an early-spring flowering plant, is used in landscaping. However, its range of applications and scope are significantly limited due to its inability to withstand high temperatures. This study aimed to investigate the heat tolerance of P. vulgaris under natural high temperatures during summer, identify the most heat-resistant varieties, and determine the optimal conditions for summer outdoor cultivation. Eight P. vulgaris varieties were selected and placed under forest shade with three different shading rates during the summer high-temperature period. Additionally, the heat damage index and the following six physiological indicators were measured: malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, peroxidase (POD) activity, soluble sugar content, soluble protein content, and relative conductivity. Furthermore, a correlation analysis of the physiological indicators was conducted, and a heat tolerance evaluation was performed using the membership function method. Simultaneously, qRT-PCR was employed to analyze the expression patterns of three heat stress-related genes (PvHSP70, PvNCED6, and PvHSF24) across the different cultivars and experimental sites. Under heat stress conditions, leaf area was found to be positively and highly significantly correlated with stomatal density (p < 0.01). The heat damage index, MDA content, and relative conductivity increased significantly with prolonged stress, and they showed highly significant positive correlations. SOD activity, soluble sugar content, and soluble protein content increased to resist heat damage, while POD activity exhibited no consistent trend. Highly significant positive correlations were observed among protective enzyme activities and osmotic regulatory substances. After a comprehensive evaluation, the eight varieties were ranked according to heat tolerance as follows: “Early Punas Yellow” > “Danova Red” > “Middle Punas Rose Red” > “Middle Punas Blue” > “Middle Punas Red” > “Danova Rose White” > “Middle Punas Crimson” > “Middle Punas Scarlet”. Conclusions: “Early Punas Yellow”, “Danova Red”, and “Middle Punas Rose Red” demonstrated strong heat tolerance. In addition, the expression of PvHSP70 and PvHSF24 was significantly upregulated in heat-tolerant cultivars, while that of PvNCED6 showed a sustained increasing trend with rising temperatures. The results of a three-way ANOVA suggested that P. vulgaris exhibited different regulatory patterns among various traits under natural high-temperature stress. Morphological and integrative damage-related indicators, including leaf area, stomatal density, and the heat damage index, all presented significant “site × time” interaction effects. Meanwhile, some physiological regulatory indicators displayed more complex and inconsistent response patterns. These findings further confirm that a dense forest understory grassland is an ideal environment for the summer outdoor cultivation of P. vulgaris. Full article

Accurate segmentation of renal anatomical structures is essential for informed clinical decision-making in nephropathology, supporting precise diagnosis, treatment planning, and longitudinal monitoring of kidney diseases. In this work, we propose an Edge-Aware U-Net architecture with Boundary-Sensitive Optimization, specifically designed to address the challenges of fine anatomical boundary delineation in histopathological images. Comprehensive benchmarking against state-of-the-art models including U-Net, Attention U-Net, and ResUNet demonstrates robust quantitative performance alongside strong potential for clinical deployment. The proposed model achieves superior boundary preservation, reflected by a high structural similarity index (SSIM: 0.9473), while maintaining computational efficiency with an average inference time of 52 ms per image. It further outperforms existing methods across key image quality metrics, including PSNR (17.269 dB), MAE (0.0266), and RMSE (0.0321). Clinical validation indicates statistically significant improvements in glomerular detection (p < 0.01) and effective tubulointerstitial differentiation (F1-score: 0.891), with consistent performance observed across multiple staining protocols. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

The high energy intensity of public buildings, especially those with HVAC systems, calls for advanced control strategies such as Model Predictive Control (MPC) to balance energy efficiency and thermal comfort. However, the performance of MPC relies critically on the accuracy and robustness of building cooling and heating load calculations, which remain challenging, particularly for buildings with complex dynamic characteristics. This study proposes a simplified modeling-based MPC approach and investigates the influence of three different load calculation methods on controller performance: a physics-driven white-box model, a data-driven black-box model, and a novel Closed-Loop Load Grey Model (CLLGM). Under identical outdoor conditions during summer cooling operation, the three controllers exhibit distinct performance disparities: although the proposed CLLGM-based controller only reduces the load prediction MAPE by 0.63% compared with the black-box model, it improves the temperature control stability index (TDI) by 80.43% and increases the comprehensive score from the MPC multi-objective optimization function by 16.55%. Its key advantage is that it can use on-site temperature measurements as feedback to correct the cooling load, making it better suited for simulation and computation in MPC. Full article

One of the main problems faced by database administrators for optimizing analytic workloads is fragmentation. Therefore, in recent decades, several fragmentation methods for analytical platforms have been proposed because this technique is able to improve the performance of OLAP (Online Analytical Processing) queries. In this study, we conducted an exploratory review of horizontal fragmentation methods for analytical repositories such as data warehouses, data lakes, and data lakehouses. This study presents a scoping review conducted using Arksey and O’Malley’s methodological framework and reported according to the PRISMA guidelines, covering 58 primary studies on horizontal fragmentation published from 2015 to 2025. Our analysis focuses on five aspects: (1) determining the main techniques used in horizontal fragmentation works for analytical repositories, (2) the classification of these studies, (3) the performance metrics considered when evaluating the horizontal fragmentation scheme, (4) the information type indexed by the repositories, and (5) the technologies most used by the approaches. Our findings suggest that horizontal fragmentation is a good opportunity to improve the performance of analytical workloads in most cases. The results of this scoping review will provide important guidelines for future research on horizontal fragmentation methods. In addition, the results will provide clues about the use of OLAP technologies for professionals and academics considering future directions. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

Humans face environmental deterioration. Scholars have identified carbon dioxide as one of the culprits, and they emphasize carbon offset. Researchers are investigating carbon offset investments. Some researchers have encouragingly deployed multivariate variational mode decomposition methods, but they have not fully optimized them. Some researchers have opportunely assessed capital asset pricing models, but they have not fully justified them. We devise multiple-objective portfolio selection models, fully optimize them, and dominate carbon offset indexes. We extend the classical methodology of advancing from portfolio selection to capital asset pricing models into the methodology of advancing from multiple-objective portfolio selection to multiple-objective capital asset pricing models. Specifically, we explore multiple-objective capital asset pricing models by numerically verifying many tangent lines (instead of the traditionally singular tangent line) and suggesting a tangent plane (instead of tangent lines). For multiple-objective zero-covariance capital asset pricing models, we numerically compute a set of zero-covariance portfolios (instead of the traditionally singular zero-covariance portfolio) and suggest picking an advantageous zero-covariance portfolio. We consider the second-level indicators of carbon offset and generalize three-objective portfolio selection to k-objective portfolio selection. As for contributions, first, this paper’s methodology is to logically advance from multiple-objective portfolio selection to multiple-objective capital asset pricing models, whereas the literature typically covers multiple-objective portfolio selection alone and barely covers multiple-objective capital asset pricing models. Second, this paper numerically demonstrates some difficulties and proposes hypothetical solutions in the process of obtaining multiple-objective capital asset pricing models. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article

Selenium nanoparticles (SeNPs) synthesized through green routes have emerged as promising nanobiostimulants in sustainable agriculture due to their ability to enhance plant growth and antioxidant defense. The aim of this study was to evaluate the biostimulant effect of SeNPs on Capsicum annuum at two stages of crop development to characterize the response to SeNP exposure and identify concentration-dependent effects and application methods. Physiological indicators, including growth, photosynthetic pigment content, and antioxidant activity, were evaluated. Different concentrations of SeNPs were tested during germination, and dosage and two types of application were compared during the vegetative phase in a hydroponic experiment. SeNPs at concentrations of 1.25, 2.5, 5, 10, 20, 40, and 80 µM were applied to chili seeds for 20 days. The plants were exposed to SeNPs concentrations ranging from 1.25 to 80 µM, applied through the roots and leaves. Germination parameters were not significantly affected except for the seed vigor index, which increased at all concentrations, particularly at 20 µM. Low to moderate doses (1.25–20 µM) acted as biostimulants, enhancing plant height, root length, biomass accumulation, photosynthetic pigment content, and phenolic and flavonoid compound synthesis. Conversely, high doses (80 µM) induced phytotoxic effects, especially via root exposure, reflected by growth inhibition, and reduced chlorophyll content. Foliar application demonstrated a systemic biostimulant response, improving root growth and photosynthetic activity without toxicity symptoms. Antioxidant assays (DPPH and ABTS) revealed dose-dependent modulation of redox balance, suggesting adaptive responses to SeNP-induced oxidative conditions. These findings highlight the potential of SeNPs as biostimulants that improve physiological performance in chili plants, while emphasizing the importance of an optimal dosing and application method for sustainable nanotechnology-based crop management. Full article

Background/Objectives: Recent guidelines have emphasized the importance of early mobilization and rehabilitation of patients following cardiac surgery. However, studies on the optimal targets and prescription methods for phase I cardiac rehabilitation (CR) are lacking. This study aimed to evaluate the feasibility and utility of an early phase 1 submaximal cardiopulmonary exercise test (CPET) using a recumbent ergometer in patients who have undergone cardiac surgery. Methods: Twenty ambulatory patients who underwent cardiac surgery between December 2021 and February 2023 were referred to the CR department on the fifth postoperative day, and a CR program was initiated. The program was conducted five times a week, with hour-long sessions consisting of warm-up exercises, resistance training, aerobic exercises, and a cool-down period. A recumbent ergometer-based submaximal CPET was performed approximately nine days after the surgery, prior to discharge. Participants initiated the test at 0 W, and the workload was increased by 20 W after 2 min. During the test, researchers evaluated parameters including submaximal peak values of oxygen consumption (VO₂), metabolic equivalents of task, respiratory exchange ratio (RER), blood pressure, heart rate (HR), and rating of perceived exertion (RPE). The grip strength test, 6 min walk test (6MWT), Korean Activity Scale/Index (KASI), EuroQol-5 dimension (EQ-5D), and short-form 36-item health survey (SF-36) values were also measured prior to discharge. Results: Twenty patients (75% male, average age 62.50 ± 1.99 years) underwent CPET at a median of 9.0 (8.0; 12.5) days postoperative. The average exercise duration of the CPET was 411.75 ± 168.25 s. During the test, their submaximal peak VO₂ was 12.32 ± 0.75 mL/kg/min (corresponding to 46.65 ± 2.08% of VO₂ max). The submaximal peak RER was 1.01 (0.98–1.12), and the submaximal peak RPE was 15.00 ± 0.51. Furthermore, the submaximal peak HR was 111.8 ± 3.76 beats/min (equivalent to 70.95 ± 2.09% of age-predicted maximal HR). After adjustment for age and sex, statistically significant positive correlations were observed between the submaximal peak VO₂ and 6MWT, squat endurance test, KASI, EQ-5D, and the physical component summary (PCS) of the SF-36 questionnaire. The 6MWT, squat endurance test, KASI, and PCS of SF-36 showed a correlation coefficient (r) of 0.522 (p = 0.026), 0.628 (p = 0.005), 0.586 (p = 0.011), and 0.546 (p = 0.019), respectively. No significant cardiac events, such as ST elevation/depression or hemodynamic instability, were observed during the test. Conclusions: Our findings suggest that performing recumbent ergometer-based CPET during early phase 1 CR is safe and feasible. These results highlight the potential of recumbent ergometer-based CPET as a valuable tool for guiding the appropriate prescription of early CR programs following hospital discharge in patients undergoing cardiac surgery. Full article

Background: We evaluated the association between admission serum glucose-to-potassium ratio (GPR) and injury severity as well as early transfusion requirements in patients with traumatic pelvic fractures. Methods: This single-center, retrospective cohort study included 84 adult patients with isolated or predominantly pelvic fractures admitted between January 2020 and December 2024. Patients with concomitant non-pelvic skeletal fractures were excluded to isolate the metabolic response attributable to pelvic injury. GPR was calculated from admission serum glucose and potassium levels. Higher transfusion requirement (HT) was defined as ≥4 units of packed red blood cells within 24 h. Receiver operating characteristic (ROC) analysis identified the optimal GPR cut-off using the Youden index. Internal validation was performed using bootstrap resampling (1000 iterations), and model calibration was assessed with the Hosmer–Lemeshow test. The incremental discriminatory value of GPR beyond the Injury Severity Score (ISS) was evaluated by comparing AUC values using the DeLong test, and reclassification metrics including the category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. Sensitivity analyses were conducted using alternative transfusion thresholds (≥6 and ≥10 units). Results: The optimal GPR cut-off was 34 (area under the curve (AUC) = 0.730; 95% CI: 0.593–0.853; sensitivity 78.8%; specificity 59.0%). Patients with GPR ≥ 34 (n = 43) had significantly higher ISS values (median 25 [IQR: 16–34] vs. 9 [5–17]; p < 0.001), greater transfusion volumes (median 3 [0–6] vs. 0 [0–1] units; p < 0.001), and longer intensive care unit (ICU) stays (3 (0–6) vs. 0 (0–1) days; p < 0.001). In univariable logistic regression, GPR was significantly associated with HT (OR = 1.059 per unit increase; 95% CI: 1.015–1.104; p = 0.008); however, significance was not retained in the multivariable model after adjustment for ISS (p = 0.194). ISS remained the sole independent predictor (OR = 1.128; p < 0.001). The combined ISS + GPR model yielded an AUC of 0.857, representing a modest increment over ISS alone (AUC = 0.849; ΔAUC = 0.009; DeLong p = 0.566). Bootstrap-corrected AUCs confirmed minimal optimism (GPR alone: 0.726; ISS + GPR: 0.847). The Hosmer–Lemeshow test indicated adequate calibration for all models (p > 0.05). The category-free NRI was 0.627 (p = 0.009), whereas the IDI did not reach significance (0.017; p = 0.290). Sensitivity analysis at the ≥6-unit threshold yielded consistent results (GPR AUC = 0.709). Conclusions: Admission GPR is significantly associated with injury severity, hemorrhagic burden, and transfusion requirements in patients with traumatic pelvic fractures. Although GPR does not independently predict transfusion needs beyond ISS, it yields significant reclassification improvement and may serve as a practical, rapidly obtainable adjunct for early risk stratification in the acute trauma setting. Level of Evidence: III (retrospective prognostic study). Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Precision medicine tailored to individual patient characteristics is crucial for improving long-term health outcomes. In survival analysis, a significant challenge for learning an optimal treatment regimen is to handle censoring, which is ubiquitous due to insufficient follow-up or other reasons. While there exist some ready-made methods under right censoring, learning an optimal treatment regimen with the more complicated interval censoring mechanism is still unexplored. To address this significant gap, this work proposes a novel semiparametric single-index modeling method, in which the interaction between the treatment and a single-index combination of covariates is linked through an unknown monotonic function. The proposed approach can capture complex, nonlinear treatment–covariate relationships while maintaining interpretability for clinical decision-making. Our estimation strategy employs sieve maximum likelihood, utilizing monotone splines to approximate the cumulative baseline hazard and B-splines for the unknown link function. To tackle the challenge of maximizing the complicated likelihood, we develop a stable and computationally efficient EM algorithm. The consistency and asymptotic distribution of the resultant estimators are established through the empirical process theory. Simulation studies demonstrate that the proposed approach performs well in finite samples. An application to a clinical trial data set on AIDS highlights the practical utility of the proposed method. Full article

The accurate measurement and optimization of spatial vitality inside commercial complexes has become crucial for sophisticated urban governance as urban growth moves from rapid expansion to quality-oriented stock augmentation. This research creates a multifaceted assessment methodology that incorporates systemic connectedness (transportation synergy), spatial performance (public activity and social efficacy), and spatial supply (human–land linkages and arrangement). We used a stratified purposive sample of 20 business complexes spread across eight districts in Guangzhou, a typical high-density megacity. In order to understand the underlying mechanisms of spatial vitality, we measured important indicators including the Polycentricity Index (α) and the Spatial Performance Index (β) using a mixed-methods approach that included K-means clustering, multinomial logit regression, and Structural Equation Modeling (SEM). Four important insights are shown by our findings. 1. The paradox of density and efficiency: The notion that high-density development inevitably ensures lively public space is called into question by the lack of a significant linear correlation between the Floor Area Ratio (FAR) and spatial performance (r = 0.32, p > 0.05), despite a core–periphery gradient in development intensity. 2. Structural Supply Demand Mismatch: Although overall spatial performance is strong (β = 0.81 ± 0.07), there is a notable shortfall in cultural and artistic venues, where young adults’ demand (0.27) is 145% greater than supply (0.11). 3. Polycentric Networking vs. Transport Polarization: While spatial structures show a networked polycentric pattern (mean α = 6.40), transportation synergy is affected by core–periphery polarization, which results in “vitality islands” in the periphery. 4. Dual-Path Driving Mechanisms: According to SEM results, cultural spaces have a considerable indirect impact (39.7% mediation) by boosting brand uniqueness and “cultural capital,” while composite plaza spaces have a strong direct effect on commercial performance (γ = 0.682). Based on these findings, we suggest distinct optimization strategies: aging projects need climate-responsive design interventions; growing areas should create family-oriented consumption ecosystems; and core districts should give priority to cultural “IP” integration. For the planning and revitalization of commercial land use in high-density global environments, this study offers a solid analytical framework and practical insights. Full article

Inadequate charging infrastructure is considered a major challenge in the widespread adoption of electric vehicles (EVs), especially the absence of the optimal number of chargers in multi-unit residential buildings (MURBs) where several EVs need to share the same chargers. Therefore, this study proposes an optimization approach to determine the optimal number of chargers in MURBs, considering continuous and flexible charging options. First, the daily travel behavior of drivers is estimated using National Household Travel Survey (NHTS) data. Then, based on the technical parameters of EVs, the daily energy consumption of EVs is estimated. Subsequently, a mathematical problem with a unified objective function and scenario-specific constraints is developed. Finally, an index is proposed to quantify the unserved energy in EVs. Simulation results demonstrate the effectiveness of the flexible method in reducing the required number of chargers while ensuring satisfactory service. This research contributes to sustainable energy management, aligning with the United Nations’ Sustainable Development Goals (SDGs) for 2030. Full article