Search Results (3,878)

We report a curious numerical observation: If atomic nuclei are modelled as connect-sums of threefoil knots with alternating chirality, the ropelength of the composite knot—a purely geometric quantity requiring no quantum mechanics—tracks the experimental binding-energy curve from hydrogen to uranium. A two-parameter fit to 50 nuclei gives $R^2=0.9998$ (coefficient of determination; 1 = perfect fit) and $\mathrm{RMS}=6.9\mathrm{MeV}$ (root-mean-square deviation between model and experiment), comparable to the five-parameter Bethe–Weizsäcker formula ($\mathrm{RMS}=8.3\mathrm{MeV}$) at less than half the parameter count. Out-of-sample predictions for ${}^{244}\mathrm{Pu}$ and ${}^{252}\mathrm{Cf}$, not used in the fit, are accurate to $0.4\mathrm{MeV}$ and $8.4\mathrm{MeV}$, respectively. What makes the observation worth reporting is not the fit itself, but the range of nuclear phenomenology that emerges uninstructed from the topology: saturation, surface energy, isospin pairing, odd-even staggering, and geometric analogues of nuclear isomers all appear as consequences of the connect-sum construction, without additional assumptions. We catalogue these correspondences, assess which are structural and which may be coincidental, and identify concrete numerical tests that would distinguish the two possibilities. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

An edge-based and curve-based rule-driven nose detection framework is designed to improve the reliability of face detection. The designed framework combines quadratic curve fitting with a calibrated scoring mechanism that fuses geometric, photometric, and structural information into a unified model. These stages jointly enforce symmetry consistency, reliable tip position, and clear wing boundaries. Candidate face regions are first refined by skin filtering and ellipse validation, from which a mid-lower facial ROI is framed for nasal candidate extraction. We further incorporate eye/mouth hints (EyeMap/MouthMap) to restrict the region of interest (ROI) to the region below the eyes, above the mouth, and between the two eyes. When a mouth is detected, this ROI refinement supersedes the chrominance-red (Cr) channel trimming; otherwise, we fall back to the Cr channel horizontal projection to detect dominant mouth peaks and trim the lower-lip band, thereby suppressing lip interference. A multi-threshold Canny procedure with histogram projection is employed to collect multiple nose rectangles by selecting various vertical and horizontal peaks under three adaptive threshold scales. Within each rectangle, edge contours are quadratically fitted and categorized into U-shape (nasal base), N-shape (nostril rim), and C-shape (nasal wings), enabling rule-based selection of the base, wings, and nostrils. The fused features are then processed by a calibrated geometric–photometric–structural scoring module that uses YCbCr contrasts and red/black penalties to suppress lip and eye confounders. Experiments with diverse faces and lighting conditions show accurate and stable nose localization, with notably reliable wing fitting and nasal base detection, improving the accuracy of face detection. Full article

Existing technologies can achieve relative geometric correction and stabilization of geostationary satellite image sequences through fixed land scene matching or homonymous point adjustment. However, these methods heavily rely on fixed land areas, rendering them completely ineffective in vast ocean regions with only ship targets. Additionally, the trajectories of ship targets after processing still exhibit noticeable jitter, hindering motion information analysis. To address these issues, this paper proposes a joint image adjustment and stabilization method based on multi-target trajectories in marine environments: (1) An optimized target detection algorithm based on a multi-scale heterogeneous convolution module is introduced, which extracts background and target features through convolutions of different scales, enabling accurate detection and tracking of weak small targets in the image sequence frame by frame. (2) Curve fitting is performed on the detected positions of the same ship across multiple frames to simulate its motion trajectory under stabilized conditions. Combined with the prior assumption of uniform motion, an equal-division strategy is adopted to determine the corrected positions of the target in the image sequence. (3) The deviation correction values of multiple targets within the same frame are obtained, and based on the principle of intra-frame deviation consistency, precise image stabilization is achieved under multi-target constraints. Experiments based on Gaofen-4 satellite image sequences demonstrate that this method reduces the average position deviation of ship targets in the original images from 8.5 pixels (425 m) to 3.4 pixels (170 m), a decrease of approximately 59.41%, effectively improving the relative geometric accuracy of the image sequence and significantly eliminating target trajectory jitter. Full article

Background: Chronic low-grade inflammation contributes to early glucose dysregulation, but comparative evidence on complete blood count-derived inflammatory indices in prediabetes remains limited. This study aimed to compare the associations of five complete blood count-derived inflammatory indices with prediabetes and to assess their discriminative performance. Methods: In this retrospective cross-sectional study, 255 adults (128 with prediabetes and 127 with normoglycemia) were identified from 12,540 individuals screened at the internal medicine outpatient clinics of a university hospital. The neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic immune-inflammation index, and systemic inflammation response index were compared between groups. Adjusted logistic regression, hierarchical regression, and receiver operating characteristic curve analyses were performed. Results: All indices except the monocyte-to-lymphocyte ratio were significantly higher in the prediabetes group (all p < 0.001). Among the evaluated indices, the neutrophil-to-lymphocyte ratio showed the strongest association with prediabetes, with the largest standardized odds ratio (2.691; 95% confidence interval, 1.839–3.938) and the highest explanatory power (Nagelkerke R² = 0.326). Its addition to the base model significantly improved model fit (likelihood ratio χ² = 33.62, p < 0.001), and the association remained significant after adjustment for body mass index, C-reactive protein, and lipid parameters. It also yielded the highest area under the curve (0.714). Conclusions: In this cross-sectional analysis, the neutrophil-to-lymphocyte ratio showed the most robust independent association with prediabetes among the evaluated complete blood count-derived inflammatory indices. However, the overall discriminative performance was modest, supporting the use of these indices as adjunctive rather than standalone screening markers. Full article

Background/Objectives: To evaluate the utility of phase angle (PhA) derived from bioelectrical impedance analysis as a screening indicator for pre-frailty in community-dwelling older adults. Methods: This cross-sectional study included 171 participants (36 men and 135 women) in Japan in 2023. PhA at 50 kHz was measured using bioelectrical impedance analysis and evaluated as a potential screening indicator for pre-frailty. Assessments included body composition, physical function tests (maximum walking speed, Timed Up and Go (TUG), grip strength, knee extension strength, and one-leg stance time with eyes open), cognitive function (MoCA-J), and the Motor Fitness Scale (MFS), a questionnaire assessing physical function, along with the Kihon Checklist (KCL). Frailty status was defined using KCL scores (4–7: pre-frailty; ≥8: frailty), and participants were classified into robust and pre-frail/frail groups. Results: PhA was significantly correlated with physical function measures, including grip strength (r = 0.54, p < 0.01), MFS (r = 0.36, p < 0.01), maximum walking speed (r = 0.20, p < 0.05), knee extension strength (r = 0.16, p < 0.05), and TUG (r = −0.17, p < 0.05). In women, logistic regression analysis showed that PhA was independently associated with pre-frailty (age-adjusted odds ratio: 2.38; 95% CI: 1.08–5.23; p < 0.05). ROC analysis yielded an area under the curve of 0.65 (95% CI: 0.56–0.74), indicating modest discriminative ability. Age-adjusted cutoff values of PhA were 4.19° and 4.74°, corresponding to points prioritizing sensitivity and specificity, respectively. Conclusions: PhA is associated with physical function and may serve as a simple, non-invasive indicator for identifying pre-frailty in community settings. However, given its modest discriminative ability, PhA alone may not be sufficient as a standalone screening tool and should be used in combination with other clinical indicators for clinical application. Full article

This study investigates the influence of rock mass discontinuities on blast-induced ground vibration attenuation in a marble quarry in eastern Taiwan. A total of 53 blasts and 106 vibration records were collected and analyzed using image-based rock mass characterization with WipFrag (Version 4) software. Discontinuity conditions were quantified through the joint factor (JF), defined by the median size (D50) and maximum size (D100) from cumulative size distribution curves. The PPV (peak particle velocity) data were fitted using the USBM, Sadovsky, and a modified Simangunsong equation incorporating a discontinuity correction factor. The modified Simangunsong model yielded the highest correlation (R² = 0.8632), followed by the Sadovsky (R² = 0.8067) and USBM (R² = 0.7674) equations, indicating improved in-sample fitting performance when discontinuity effects are included. The results show that explicitly considering discontinuity effects enhances the reliability of PPV estimates for the studied site and that highly fractured rock masses with smaller block sizes result in greater vibration attenuation. The study demonstrates that a practical approach to quantify discontinuities through image analysis and embedding them into empirical PPV attenuation models can be used to refine quarry blasting design for vibration control purposes. Full article

Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted. Full article

Existing research primarily focuses on ordinary straight roads or curves; however, there is a notable lack of recent research on continuous curves. This research employs Computational Fluid Dynamics (CFD) dynamic mesh technology to numerically simulate the external flow field during vehicle overtaking on a continuous curve resembling a sine wave. This study conducts a numerical simulation to analyze the external flow field of vehicles during overtaking on a continuous curve, similar to a sine curve, using CFD. Using different initial velocities, the study analyzes lateral force on the vehicle body during overtaking. It investigates how dynamic changes in the external flow field affect vehicle dynamics by employing tetrahedral meshes, the SST k-ω turbulence model, and UDF programming. To address emergency overtaking scenarios during medical vehicle rescues, a four-factor orthogonal experimental design was employed to identify the safest overtaking condition: overtaking a small vehicle (5 m × 1.8 m) at 22 m per second with 1.5 times the vehicle width and no crosswind. Regression lines were fitted to the data, yielding a nonlinear regression equation that can predict road conditions, thereby providing theoretical support for intelligent driving systems. Full article

Currently, China boasts abundant shale gas resources. However, in the process of flowing production, there remain significant discrepancies in our understanding of the flow patterns of gas and water, and many challenges persist in gas–water measurement. Given the dense pore structure and complex micro-features of shale gas reservoirs, this study proposes a method to estimate the fractal dimension by utilizing shale mercury injection curves based on experimentally determined relative permeability curves, thereby enabling a more accurate fitting of these curves. Experimental results show that the two-phase co-infiltration zone in the shale is narrow overall, with bound water saturation exceeding 50%. The findings indicate that the experimentally measured relative permeability curves closely match those fitted using the fractal dimension approach. Moreover, the lower the permeability, the more the equal-permeability points of the fitted curves shift toward the lower-right quadrant. Overall, the fitting performance is satisfactory, providing additional research directions and insights for determining relative permeability curves of gas and water in shale gas reservoirs. Full article

Traditional simulators predominantly operate with position control at specific frequencies and largely neglect the appropriate imposition of accelerations on the structure. This restricts the application of realistic accelerations during fatigue testing and reduces the fidelity of tests to real road conditions. This study proposes an integrated experimental–analytical framework for motorcycle testing under laboratory conditions. Within the framework, smooth displacement reference signals are generated from noisy field-measured acceleration signals through Fourier-based harmonic curve fitting and analytic integration. Subsequently, a nonlinear adaptive backstepping control algorithm is designed to ensure accurate replication of these references within the 0–25 Hz bandwidth under parametric uncertainties. This approach provides a valuable and repeatable alternative to conventional on-road testing, ensuring that realistic road-induced accelerations are accurately imposed on the motorcycle structure during fatigue testing. Experimental signals were collected from a motorcycle on three different road surfaces, and the performance of the generated reference signals was evaluated in both the time and frequency domains. Experiments conducted on a real-time industrial controller demonstrated that the proposed controller exhibits superior tracking performance across all road profiles, achieving a Root Mean Square Error (RMSE) as low as 1.3 mm, while the Fourier-based reconstruction achieves $R^2$ values approaching 0.97. The controller maintains consistent precision and negligible performance variance despite significant differences in road characteristics, thereby offering a controlled and cost-effective laboratory simulation alternative to conventional on-road durability testing. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

Background/Objectives: Although Drosophila melanogaster is widely used in genetics and nutrition research, developmental kinetics are rarely analyzed using formal mathematical modeling. Most dietary studies present developmental curves without rigorous fitting, limiting quantitative interpretation. This study applies and compares three primary models, as well as develops secondary models, to characterize the effects of high-sugar diets on egg-to-adult (life cycle) development. Methods: Standardized husbandry and an embryo-to-pupa feeding assay were performed across 11 sucrose concentrations. Synchronized embryo collection and high-resolution monitoring were used for this assay. Three primary models—dose–response, Gompertz, and logit-based linearization—were fitted to developmental curves to extract timing (t_mid) and synchrony (s_dvp) parameters. Secondary modeling was used to evaluate how these parameters change with respect to sucrose concentration. Results: Increasing sucrose concentration markedly delayed pupariation and reduced viability at the highest levels. All models showed increasing t_mid and decreasing s_dvp with rising sugar concentration, with the Gompertz model providing the best overall performance. Secondary modeling revealed a consistent bilinear response with a breakpoint at 0.52–0.62 M, separating low-, medium-, and high-sucrose conditions. Reduced sampling frequency decreased model robustness, while twice-daily observations remained sufficient. Conclusions: Mathematical modeling provides a robust, practical framework for quantifying the effects of diet on D. melanogaster development. The Gompertz model provided the best fit and yielded biologically interpretable parameters. The bilinear secondary model effectively captured sucrose-dependent stress responses and quantified plasticity through environment-dependent changes in developmental timing and synchrony. Overall, this work establishes a quantitative practical framework for modeling developmental kinetics under nutritional perturbations, and the approach can be extended with additional secondary environmental factors to improve predictive analyses of nutritional effects. Full article

The effects of thermal and non-thermal pretreatments combined with different drying methods on the drying kinetics, physicochemical properties, and bioactive compounds of the Chilean wild mushroom Morchella conica were investigated. Fresh samples were subjected to hot-air drying (HAD, 60 °C), freeze-drying (FD), and a hybrid process (FD–HAD), applied directly or after pretreatments including thermal pre-drying (55 and 75 °C), ultrasound (US, 10 and 20 min), and high hydrostatic pressure (HHP, 600 MPa). Drying curves were successfully fitted using the Weibull model (R² > 0.987), showing that HAD combined with thermal and ultrasound pretreatments increased drying rates, while FD–HAD reduced total drying time. Freeze-drying better preserved color (ΔE < 2) and minimized shrinkage (<8%), whereas HAD produced darker samples and greater structural deformation. Water activity decreased below 0.30 in most treatments, ensuring microbiological stability. Thermal pretreatments enhanced total phenolic content, while FD preserved antioxidant capacity. Principal component analysis explained 62.2% of the total variance, revealing distinct quality profiles among drying methods. Overall, FD and hybrid FD–HAD combined with moderate pretreatments showed the best balance between drying efficiency and quality preservation, while HHP improved antioxidant properties under specific conditions. These findings highlight the potential of integrating innovative pretreatments with drying technologies to optimize processing of Morchella conica. Full article

As China transitions from rapid urbanization to high-quality development, the competition for population among cities has intensified, characterized by a shift from labor-intensive migration to multi-dimensional lifestyle choices. However, traditional migration models often assume global linearity, failing to capture the complex non-linear thresholds and spatial non-stationarity inherent in migration decisions. This study employs a novel Geographically Weighted Random Forest (GWRF) model to analyze net migration flows across 278 Chinese cities using high-granularity mobile signaling data from the 2020 Spring Festival travel rush. The results reveal that GWRF significantly outperforms traditional OLS, GWR, and global Random Forest models, effectively handling spatial heterogeneity and non-linearity. Wage levels are the dominant global driver, exhibiting a distinct “S-curve” non-linear threshold, while population scale shows a significant U-shaped effect, highlighting the transition from agglomeration economies to congestion costs. Migration drivers exhibit profound spatial heterogeneity: western inland cities are “wage-driven,” the Pearl River Delta is “employment-structure driven,” and the northeastern “Rust Belt” is increasingly sensitive to “innovation investment” (technology expenditure). These findings challenge the “one-size-fits-all” approach to population policy, offering precise, spatially targeted strategies for urban planners to mitigate population shrinkage and enhance urban vitality. Full article