Search Results (10,428)

Uncertainty in optimization models often causes awkward properties in their deterministic equivalent formulations (DEFs), even for simple linear models. Chance-constrained programming is a reasonable tool for handling optimization problems with random parameters in objective functions and constraints, but it assumes that the distribution of these random parameters is known, and its DEF is often associated with the complicated computation of multiple integrals, hence impeding its extensive applications. In this paper, for optimization models with chance constraints, the historical data of random model parameters are first exploited to construct an adaptive approximate density function by incorporating piecewise linear interpolation into the well-known histogram method, so as to remove the assumption of a known distribution. Then, in view of this estimation, a novel confidence set only involving finitely many variables is constructed to depict all the potential distributions for the random parameters, and a computable reformulation of data-driven distributionally robust chance constraints is proposed. By virtue of such a confidence set, it is proven that the deterministic equivalent constraints are reformulated as several ordinary constraints in line with the principles of the distributionally robust optimization approach, without the need to solve complicated semi-definite programming problems, compute multiple integrals, or solve additional auxiliary optimization problems, as done in existing works. The proposed method is further validated by the solution of the stochastic multiperiod capacitated lot-sizing problem, and the numerical results demonstrate that: (1) The proposed method can significantly reduce the computational time needed to find a robust optimal production strategy compared with similar ones in the literature; (2) The optimal production strategy provided by our method can maintain moderate conservatism, i.e., it has the ability to achieve a better trade-off between cost-effectiveness and robustness than existing methods. Full article

Background: Quantitative flow ratio (QFR) is a novel technology to assess the functional significance of coronary stenoses based on standard coronary angiography, which can be alternatives to invasive fractional flow reserve (FFR) assessment. However, the evidence is limited to single-center studies and small sample sizes. This study systematically determined the diagnostic performance of QFR to diagnose functionally significant stenosis with FFR as the reference standard. Methods: A systematic review and meta-analysis of studies assessing the diagnostic performance of angiography-derived QFR systems were performed. All relevant studies from six literature databases were searched and screened according to the inclusion and exclusion criteria. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR), along with their 95% confidence intervals (CIs), were calculated using DerSimonian–Laird methodology. The summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Meta-regression analysis was performed to identify a potential source of heterogeneity. Results: Fifty-seven studies comprising 13,215 patients and 16,125 vessels were included in the final analysis. At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798–0.851) and 0.919 (95% CI: 0.902–0.933). Pooled LR+ and LR− were 10.198 (95% CI: 8.469–12.281) and 0.189 (95% CI: 0.163–0.219), with a pooled DOR of 53.968 (95% CI: 42.888–67.910). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91–0.96). The summary AUCs were 0.90 (95% CI: 0.87–0.92) for fixed-flow QFR (fQFR), 0.95 (95% CI: 0.92–0.96) for contrast-flow QFR (cQFR), 0.97 (95% CI: 0.95–0.98) for Murray law-based QFR (μQFR), and 0.91 (95% CI: 0.89–0.94) for non-specified QFR. The adjusted pooled DORs were as follows: 126.25 for μQFR, 45.49 for cQFR, 26.12 for adenosine-flow QFR (aQFR), 25.88 for fQFR, and 36.54 for non-specified QFR. Conclusions: The accuracy of angiography-derived QFR was strong to assess the functional significance of coronary stenoses with FFR as a reference. μQFR demonstrated the highest diagnostic performance among the five evaluated modes. Full article

Monitoring the size of wildlife populations is crucial for the effective implementation of conservation management strategies, and a variety of methods have been developed for this purpose. One such approach is the double-observer method, which has recently gained prominence in monitoring programs for the southern greater glider (Petauroides volans), an iconic nocturnal arboreal marsupial native to Australia. While this method has been successfully applied at lower population densities, its reliability and applicability at higher-density sites has not been evaluated. This case study represents the first instance of an investigation and discussion on the application of the double-observer method in greater glider monitoring at higher-density sites. We found that in higher-density areas, the proximity of individuals makes it more difficult to reliably distinguish unique (individual) animals between observers, and the increased number of observations per transect extends the time required for data recording. Transects with more animal observations showed significantly longer delays between observers (z = 5.062, p < 0.001). Additionally, discrepancies in the number of animal observations between observers significantly altered the intended 15–20 min interval (z = 2.71, p = 0.007). Deviations from the standard 15–20 min interval between observers were common, occurring at 44 of the 66 sites, where actual time-lags ranged from 0 to 64 min. Consequently, longer intervals increased the potential for animal movement, while shorter intervals risked observer independence. These factors, combined with our experience applying the double-observer method across sites with markedly different greater glider densities, suggest that the critical non-movement assumption may be violated more frequently than previously recognised. We discuss the limitations of applying the double-observer method to survey high-density populations and recommend prioritising research on greater glider movement patterns and alternative survey techniques to improve the accuracy and reliability of monitoring programs at higher-density sites. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

Within the Rajidae family, the blonde ray (Raja brachyura) is considered one of the less resilient species to fishing pressure and other anthropogenic pressures, primarily due to its late maturity and large maximum size, which can exceed 120 cm total length. This is the first study to provide comprehensive insights into the reproductive biology of Raja brachyura in the continental waters of Portugal, with insights into its timing, maturity, and fecundity. It was determined that egg-laying occurs from February to November, with a peak observed between April and September. Males were reproductively active throughout the year, with highest proportions of active males observed between January and May. The length at first maturity was estimated at 95.2 cm for females and 90.0 cm for males, corresponding to 85% of the maximum observed length in each sex. The potential fecundity was estimated at 115 follicles per female per year, and evidence suggests that the species has a determinate fecundity. The findings reinforce the appropriateness of current management measures in Portuguese continental waters, namely seasonal closure when overlapping with the peak of the reproductive season (May and June), and provide valuable scientific support for future conservation and management measures. Full article

A key challenge in automated marigold harvesting lies in the accurate identification of picking points under complex environmental conditions, such as dense shading and intense illumination. To tackle this problem, this research proposes a lightweight instance segmentation model combined with a harvest position estimation method. Based on the YOLOv11n-seg segmentation framework, we develop a lightweight PDS-YOLO model through two key improvements: (1) structural pruning of the base model to reduce its parameter count, (2) incorporation of a Channel-wise Distillation (CWD)-based feature distillation method to compensate for the accuracy loss caused by pruning. The resulting lightweight segmentation model achieves a size of only 1.3 MB (22.8% of the base model) and a computational cost of 5 GFLOPs (49.02% of the base model). At the same time, it maintains high segmentation performance, with a precision of 93.6% and a mean average precision (mAP) of 96.7% for marigold segmentation. Furthermore, the proposed model demonstrates enhanced robustness under challenging scenarios including strong lighting, cloudy weather, and occlusion, improving the recall rate by 1.1% over the base model. Based on the segmentation results, a method for estimating marigold harvest positions using 3D point clouds is proposed. Fitting and deflection angle experiments confirm that the fitting errors are constrained within 3–12 mm, which lies within an acceptable range for automated harvesting. These results validate the capability of the proposed approach to accurately locate marigold harvest positions under top-down viewing conditions. The lightweight segmentation network and harvest position estimation method presented in this work offer effective technical support for selective harvesting of marigolds. Full article

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation. Full article

Optimizing immunosuppressant dosing presents significant challenges in kidney transplantation due to narrow therapeutic ranges and considerable inter-patient pharmacokinetic differences. Emerging strategies for precision dosing, encompassing Bayesian population pharmacokinetic models, pharmacogenomic integration, and artificial intelligence algorithms, aim to enhance drug monitoring by moving beyond traditional trough-based approaches. This review critically assesses available evidence for predictive dosing models targeting immunosuppressants, including calcineurin inhibitors, antimetabolites, and mTOR inhibitors in kidney transplant patients. Available observational and simulation studies demonstrate substantial methodological diversity, with Bayesian PopPK-guided strategies showing 15–35% better target exposure achievement compared to trough-based monitoring. The absence of pooled estimates precludes a precise summary effect size, and evidence from randomized controlled trials remains limited. Machine learning models, particularly for tacrolimus, frequently reduced prediction error relative to traditional regression approaches, but substantial heterogeneity in study design, outcome definitions, and external validation limits quantitative synthesis. Hybrid Bayesian–AI frameworks and explainable AI tools show conceptual promise but are largely supported by proof-of-concept studies rather than reproducible clinical implementations. Overall, Bayesian pharmacokinetic modelling represents the most mature and clinically interpretable approach for precision dosing in transplantation, whereas AI-driven and hybrid systems remain investigational. Key gaps include the need for standardized reporting, rigorous risk-of-bias assessment, prospective validation, and clearer regulatory and implementation pathways to support safe and equitable clinical adoption. Full article

We used chlorophyll fluorescence techniques to investigate seasonal variations in photosystem II (PSII) quantum yield in five-year-old saplings of the sclerophyllous Peumus boldus Molina (evergreen) and Colliguaja odorifera Molina (semideciduous) planted in a semiarid site with a Mediterranean-type climate. Chlorophyll fluorescence rise kinetics (OJIP) were monitored monthly for one year (September 2024 to September 2025). With this information, we estimated the relative deviation of the performance index (PI_ABS) of each species from the average PI_ABS in each season (denoted as ∆PI_ABS). P. boldus was associated with destruction of PSII reaction centers and incapacity for electron transport, i.e., higher values of parameters ABS/RC (effective antenna size of an active reaction center) and F₀ (minimal fluorescence), whereas C. odorifera was associated with higher photosynthetic performance i.e., higher values of PI_ABS, PI_TOT (total performance index), F_V/F₀ (ratio between variable and minimal fluorescence), and F_V/F_M (maximum quantum yield of primary PSII photochemistry). P_IABS exhibited a 52 and 38% reduction (i.e., −∆PI_ABS) during spring and winter in P. boldus, but an increase (i.e., +∆PI_ABS) of 52 and 37% in the same seasons for C. odorifera. P. boldus was considerably more depressed during the winter–spring season than the summer months. This suggests that PSII function in P. boldus is more sensitive to low temperatures in winter and spring than the lack of water and high temperatures during summer. Full article

This study presents an improved Nishenko–Buland (NB) model to address systematic biases in estimating the coefficient of variation for earthquake recurrence intervals based on a normalizing function ${\displaystyle \frac{\mathrm{T}}{{\mathrm{T}}_{\mathrm{a}\mathrm{v}\mathrm{e}}}}$. Through Monte Carlo simulations, we demonstrate that traditional NB methods significantly underestimate the coefficient of variation when applied to limited paleoseismic datasets, with deviations reaching between 30 and 40% for small sample sizes. We developed a linear transformation and iterative optimization approach that corrects these statistical biases by standardizing recurrence interval data from different sample sizes to conform to a common standardized distribution. Application to 26 fault segments across 15 major active faults in the Hetao graben system yields a corrected coefficient of variation of α = 0.381, representing a 24% increase over the traditional method (α₀ = 0.307). This correction demonstrates that conventional approaches systematically underestimate earthquake recurrence variability, potentially compromising seismic hazard assessments. The improved model successfully eliminates sampling bias through iterative convergence, providing more reliable parameters for probability distributions in renewal-based earthquake forecasting. Full article

Background/Objectives: Detecting copy number variations (CNVs) from next-generation sequencing (NGS) is challenging, particularly in targeted sequencing panels, especially for cell-free DNA (cfDNA), where the signal is weak and noise is high. Methods: We present BayesCNV, a Bayesian hierarchical model for gene-level copy ratio estimation from targeted amplicon read depths compared to a CNV-neutral reference sample. The model provides posterior uncertainty for each gene and supports interpretable calling based on effect size and posterior confidence. The model also provides a principled quality-control strategy based on the marginal log likelihood of each sample, with low values indicating low confidence in the calls. BayesCNV uses thermodynamic integration, a technique to reliably estimate this quantity. We benchmark our method against two publicly available CNV callers using Seracare^® reference samples with known CNVs on the OncoReveal^® Core Lbx panel. Results: Our method achieves a sensitivity of 0.87 and specificity of 0.996, dramatically outperforming two competitor methods, IonCopy and DeviCNV. In a separate FFPE dataset using the OncoReveal^® Essential Lbx panel, we show that the marginal log likelihood cleanly separates, degraded from high-quality samples, even when conventional sequencing QC metrics do not. Conclusions: BayesCNV provides accurate and interpretable gene-level CNV estimates and uncertainty quantification, along with an evidence-based quality control metric that improves robustness in targeted cfDNA workflows. Full article

Laboratory maintenance of mosquitoes is important for studying vector biology and transmission of diseases, and for testing vector control tools. Standard operating procedures require feeding larvae with commercial fish meal. However, for many insectaries in sub-Saharan Africa, the commonly used feeds are imported and accompanied by procurement challenges. Changing the larval feed abruptly without allowing the larvae to adapt to new brands of feed also leads to a decrease in mosquito colonies in the laboratory. We investigated locally acquired beans, maize, and dried herrings as alternate feeds for mosquito larvae reared under laboratory conditions. Four replicates for each treatment were prepared, each containing 100 first instar larvae of Anopheles gambiae Tiassalé mosquitoes. The larvae were introduced into 500 mL of dechlorinated tap water and maintained under standard environmental insectary conditions. The larvae were provided with 40 mg of the designated powdered feed—beans, maize, and herring fish—in single and combined treatments. Tetra^® goldfish meal was included as a control. The larval mortality, developmental time, and number of pupae were recorded to evaluate the effectiveness of the feeds. Adult mosquitoes were weighed and measured to assess fitness, and females from each treatment were blood-fed and allowed to lay eggs to evaluate fertility. Larval survival differed significantly across diets (Kruskal–Wallis, p = 0.01), with maize-fed larvae showing the highest mortality (41.3%) and those with herring-based diets the lowest. Pupation and adult emergence were poorest in the maize and maize–bean groups, while the maize–herring combination achieved the highest emergence (92.6%, p = 0.03). Although overall differences were detected among the groups, conservative pairwise tests did not pinpoint specific group contrasts, but effect size estimates suggested biologically meaningful patterns. Generally, adult body weight and wing length did not differ by treatment except in maize-fed males (β = 0.371 mm, p = 0.022). Herring fish-based diets consistently supported larval survival, timely development, and robust fecundity, whereas maize-based diets were nutritionally inadequate. These findings highlight herring fish-based diets as a sustainable and cost-effective alternative to commercial feeds for maintaining Anopheles mosquito colonies, with potential to strengthen vector research capacity in resource-limited laboratories. Full article

The effects of pelleting a total mixed ration (TMR) for dairy sheep were investigated in an experiment involving 24 lactating Assaf ewes, which were assigned to two groups and fed the same TMR ad libitum, offered either in pelleted (PTMR group, n = 12) or in unpelleted form (CTMR group, n = 12). The experiment lasted 28 days, during which feed intake, eating behavior (including meal frequency and size, meal duration, eating rate, between-meal interval), and milk yield were recorded daily. Body weight (BW) was recorded on days 1 and 28 and milk samples were collected on days 1, 8, 15, 22 and 28 for milk composition analysis. Blood acid-base status was determined at the beginning and at the end of the trial. Ewes fed the CTMR diet exhibited (p < 0.05) a higher meal frequency and longer meal duration, along with a smaller meal size and slower eating rate. However, feed intake in this group was less than that in ewes fed PTMR only during the final two weeks of the experimental period. Total eating time was also longer (p < 0.001) in the CTMR group, whereas the average time between meals was shorter (p < 0.002). No differences (p > 0.05) were observed between dietary treatments in blood acid-base status, milk yield or milk composition. However, a diet x day interaction (p < 0.05) was detected for milk yield, as during the last 2 weeks of the experimental period the ewes fed the PTMR yielded more milk than those fed the CTMR. Feed conversion ratio did not differ between groups (p > 0.05), but body weight loss was greater in ewes fed the CTMR diet (−3.00 vs. −0.58 kg; p < 0.05). A trend toward improved feed efficiency was observed in the PTMR group when calculated based on milk yield corrected for that theoretically derived from the mobilization of body reserves (1.98 vs. 1.41 g DMI/kg milk; p = 0.077), with estimated contributions from body reserves of 485 g/day in the CTMR group and 70 g/day in the PTMR group. In conclusion, the use of pelleted total mixed rations in high-yielding dairy ewes enhances feed intake, feed efficiency, milk yield, and energy balance without adversely affecting milk composition or animal health in the short term. Full article

Accurate processing of low-probability-of-intercept (LPI) radar signals poses a critical challenge in electronic warfare support (ES). These signals are often transmitted at very low signal-to-noise ratios (SNRs), making reliable analysis difficult. Noise interference can lead to misinterpretation, potentially resulting in strategic errors and jeopardizing the safety of friendly forces. Accordingly, effective noise suppression techniques that preserve the original waveform shape are crucial for reliable analysis and accurate parameter estimation. In this study, we propose the recognize-then-denoise network (RTDNet), which effectively removes noise while minimizing signal distortion. The proposed approach first employs a modulation recognition network to infer the modulation scheme and then feeds the inferred label to an attention-based denoiser to guide feature extraction. By leveraging prior information, the attention mechanism preserves key features and reconstructs challenging patterns such as polytime and polyphase codes. Simulation results indicate that RTDNet more effectively removes noise while maintaining the waveform shape and salient signal structures compared with existing techniques. Furthermore, RTDNet improves modulation classification accuracy and parameter estimation performance. Finally, its compact model size and fast inference meet the performance and efficiency requirements of ES missions. Full article

This study investigates whether national innovation systems contribute to sustainable well-being in emerging EU Member States by examining the long-run relationship between innovation performance and a multidimensional Quality of Life Index (QoLI). Using a balanced panel covering 2013–2024 for ten countries, the analysis integrates the Global Innovation Index, economic development dynamics, and demographic pressure to assess whether innovation-led progress translates into broad societal benefits. Panel cointegration tests confirm a stable long-run equilibrium among variables, while FMOLS estimation reveals three key results: (i) While the bivariate Pearson correlation indicates a positive association between innovation capacity and quality of life, the multivariate FMOLS estimation reveals a statistically significant negative long-run effect of innovation performance on QoLI, once economic development and demographic pressures are jointly controlled for. (ii) Economic development contributes positively to sustainable well-being, reinforcing the role of income-driven improvements in living conditions, and (iii) population size exerts a strong negative effect, reflecting demographic stress and unequal access to essential services. The findings indicate an innovation–well-being gap in which technological progress advances faster than the institutional and social mechanisms needed to ensure equitable diffusion. These results underscore the need to reorient innovation strategies toward inclusive growth, social accessibility, and environmental resilience so that innovation systems can effectively support sustainable well-being in emerging European economies. Full article