Search Results (505)

Generic and consistent formulations for measurement of the backscattering cross section $\left({\sigma }_{bs}\right)$ and the volume backscattering coefficient $\left(s_v\right)$ using broadband pulse compression and narrowband echo integration are derived, for small- and finite-amplitude sound propagation. The theory applies to backscattering operation of echosounders and sonars in general, with focus on fisheries acoustics. Formally consistent mathematical relationships for broadband and narrowband operation of such instruments are established that ensure consistency with the underlying power budget equations on average-power form, bridging a gap in prior literature. The formulations give full flexibility in choice of transmit signals and reference signals for pulse compression. Generic and general criteria for quantitative consistency between broadband and narrowband operation are derived, establishing new knowledge and analysis tools. These criteria become identical for small- and finite-amplitude sound propagation. In addition to general criteria, two special cases are considered, relevant for actual operation scenarios. The criteria serve to test and evaluate the extent to which the methods used in broadband pulse compression and narrowband echo integration operating modes are correct and consistent, and to identify and reduce experienced discrepancies between such methods. These are topics of major concern for quantitative acoustic stock assessment, underlying national and international fisheries quota regulations. Full article

Background: Lower limb lymphedema (LLL) is a frequent complication after gynecological cancer treatment, with a significant impact on quality of life. Despite the common use of compression therapy in managing established lymphedema, its role in prevention remains insufficiently explored. Methods: In this prospective randomized study, 64 women treated for gynecological malignancies were assigned to either a compression group (CG) using medium-pressure stockings (23–32 mmHg) or a no-compression group (NCG). All participants received standard education and physical activity guidance. Limb volume, symptom burden, and quality of life were assessed over 12 months. Results: The incidence of LLL was significantly lower in the CG (3.4%) compared to the NCG (38%, p = 0.003). Compression use resulted in significant reductions in limb volume and symptom severity, as well as improved physical functioning. Compliance with compression therapy was high, and patients reported good comfort and usability. Conclusions: Medium-pressure compression stockings combined with education and physical activity are effective and well-tolerated in preventing LLL following gynecological cancer treatment. Full article

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. Full article

The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based on a site classification system at the lowest level—i.e., forest stands, at the forest owner level—as a tool for differentiated management. ESs were assessed within the Czech Republic and are expressed in units in accordance with the very sophisticated Forest Site Classification System. (1) Biomass production: The vertical differentiation of ecological conditions given by vegetation tiers, which reflect the influence of altitude, exposure, and climate, provides a basic overview of biomass production; the highest value is in the fourth vegetation tier, i.e., the Fageta abietis community. Forest stands are able to reach a stock of up to 900–1200 m³·ha⁻¹. The lowest production is found in the eighth vegetation tier, i.e., the Piceeta community, with a wood volume of 150–280 m³·ha⁻¹. (2) Soil conservation function: Geological bedrock, soil characteristics, and the geomorphological shape of the terrain determine which habitats serve a soil conservation function according to forest type sets. (3) The hydricity of the site, depending on the soil type, determines the hydric-water protection function of forest stands. Currently, protective forests occupy 53,629 ha in the Czech Republic; however, two subcategories of protective forests—exceptionally unfavorable locations and natural alpine spruce communities below the forest line—potentially account for 87,578 ha and 15,277 ha, respectively. Forests with an increased soil protection function—a subcategory of special-purpose forests—occupy 133,699 ha. The potential area of soil protection forests could be up to 188,997 ha. Water resource protection zones of the first degree—another subcategory of special-purpose forests—occupy 8092 ha, and there is potentially 289,973 ha of forests serving a water protection function (specifically, a water management function) in the Czech Republic. A separate subcategory of water protection with a bank protection function accounts for 80,529 ha. A completely new approach is presented for practical use by forest owners: based on the characteristics of the habitat, they can obtain information about the fulfillment of the habitat’s ecosystem services and, thus, have basic information for the determination of forest categories and the principles of differentiated management. Full article

In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. This study proposes a cost-effective strategy using open-access tools and data to characterize and estimate wood volume in these plantations. Two stratification approaches—classical and cluster-based—were compared to a modeling method based on Principal Component Analysis (PCA). Data came from open-access national LiDAR point clouds, acquired using manned aerial vehicles under the Spanish National Aerial Orthophoto Plan (PNOA). Moreover, two volume estimation methods were applied: one from the Xunta de Galicia (XdG) and another from Spain’s central administration (4IFN). A Generalized Linear Model (GLM) was also fitted using PCA-derived variables with logarithmic transformation. The results show that although overall volume estimates are similar across methods, cluster-based stratification yielded significantly lower absolute errors per hectare (XdG: 28.04 m³/ha vs. 44.07 m³/ha; 4IFN: 25.64 m³/ha vs. 38.22 m³/ha), improving accuracy by 7% over classical stratification. Moreover, it does not require precise field parcel locations, unlike PCA modeling. Both official volume estimation methods tended to overestimate stock by about 10% compared to PCA. These results confirm that clustering offers a practical, low-cost alternative that improves estimation accuracy by up to 18 m³/ha in fragmented forest landscapes. Full article

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

Background: Furosemide is a loop diuretic used extensively to treat adult and pediatric patients. In some hospitals, furosemide oral liquids are not available in stock, thus necessitating the extemporaneous preparation of the drug. This study evaluates the stability of on-the-spot formulations of furosemide oral suspensions from crushed tablets evaluated in various vehicles: Dextrose 50%, Dextrose 70%, Ora-Sweet, and Ora-Plus over 60 days. This examination was prompted by the frequent shortage of certain excipients in the hospital, leading to the need to switch to Dextrose 50% or Dextrose 70% when Ora-Sweet and Ora-Plus are out of stock. Methods: The extemporaneous furosemide oral suspensions were prepared following the same compounding method used in the pharmacy. The suspensions were maintained at 4 °C in the refrigerator and assessed immediately and later, on days 7, 14, 30, and 60. The assessed parameters included visual appearance, redispersion time, sedimentation volume, and pH levels for stability analysis. We also examined the drug content, dissolution of the suspension, and microbiological stability. Results: Initial examinations indicated that Dextrose 50% and Ora-Plus maintained pH levels and stable appearances, while significant changes, mainly in appearance and redispersion time, indicated the instability of Dextrose 70%. Ora-Sweet showed fluctuations but stabilized by day 30. Dissolution studies demonstrated that Ora-Plus had dissolution characteristics superior to the other formulations, while Dextrose 50% showed declining dissolution percentages over time. Overall, the Ora-Plus vehicle showed superior stability (60 days), followed by Ora-Sweet (30 days), while Dextrose 70% and Dextrose 50% showed shorter stability durations of 14 and 7 days, respectively. The microbiological test results showed no microbial growth. Conclusions: This study demonstrates that the vehicle used in extemporaneous furosemide suspensions critically affects their stability and performance. Ora-Plus emerged as the most suitable vehicle, maintaining physical, chemical, and microbiological stability over 60 days, with consistent pH, redispersion, and dissolution behavior. Ora-Sweet showed intermediate stability (30 days), while Dextrose 50% and 70% exhibited early instability—7 and 14 days, respectively—marked by sedimentation, poor redispersibility, and declining drug release. These findings underscore the importance of vehicle selection and regular stability monitoring in compounded formulations to ensure therapeutic reliability and patient safety. Full article

Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. Full article

Cruise construction involves a lengthy logistical cycle, complex processes, and large volumes of diverse materials, inevitably generating reverse flows. To mitigate risks such as stock congestion, production disruption, and occupational hazards, this study proposes a novel reverse logistics network optimization model that integrates cost, efficiency, and Health, Safety, Environment (HSE) risk factors. Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. A three-phase Levy mutation discrete crow search algorithm (DCSA) was developed, introducing the Levy flight strategy to replace random search and enhance the discretization and solution diversity. The comparative analysis shows that DCSA performs as well as NSGA-II, while outperforming DGWO, demonstrating both stability and efficiency. Comparative analysis with a cost-only scenario revealed that although short-term economic gains may be achieved under cost minimization, such approaches often overlook risks with potential long-term impacts. This highlights the necessity of integrating safety concerns into reverse logistics planning, and confirms the model’s robustness and practical value, thus supporting decision makers in aligning reverse logistics planning in shipyards with sustainability and operational efficiency goals. Full article

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

Autonomous inflow control devices (AICDs) offer a promising means of delaying early water breakthrough in heavy oil horizontal wells; yet, current design practices remain largely empirical. A three-dimensional, field-calibrated computational fluid dynamics (CFD) model was developed to establish a mechanistic basis that solves the transient Navier–Stokes equations for turbulent two-phase flow via a volume-of-fluid formulation. Pressure-controlled inflow boundaries were tuned to build up data from four Colombian heavy oil producers, enabling a quantitative comparison with production logs. Model predictions deviate by no more than ±14% for oil rate and ±10% for water rate over a 500-day horizon, providing confidence in subsequent scenario analysis. Replacing a slotted liner completion with optimally sized AICDs lowers cumulative water-cut by up to 93%, reduces annular friction losses by 18%, and cuts estimated life cycle CO₂ emissions per stock-tank barrel by 82%. Sensitivity analysis identifies nozzle diameter as the dominant design variable, with a nonlinear interaction between local drawdown pressure and the oil–water viscosity ratio. These findings demonstrate that CFD-guided AICD design can materially extend wells’ economic life while delivering substantial environmental benefits. The validated workflow establishes a low-risk, physics-based path for tailoring AICDs to reservoir conditions before field deployment. Full article

Forests grow under dynamic conditions influenced by vegetation structure and environmental factors. However, empirical models to enhance growing stock volume GSV) estimation are commonly established based on structural information from airborne laser scanning (ALS) data, raising important questions regarding the models’ performance across time (temporal transferability). This study presents the integration of ALS and microclimate and site-specific data to assess the temporal transferability of GSV models at the plot level in a mixed forest located in Milicz, Poland, between 2007 (t₁) and 2015 (t₂). We compared random forest (RF), multiple linear regression (MLR), and generalized additive models (GAMs) across three modelling scenarios, ALS + site type + climate (sa), ALS only (sb), and ALS + site type (sc), and also performed internal and external validation to assess temporal transferability. Among the three modelling approaches, GAMs outperformed the MLR and RF models in internal validation, improving the R² by 6%–8% and reducing the rRMSE by 6%–12%. We found that climate was significant in GSV prediction when integrated with ALS and site conditions, with a permutation test (p ≤ 0.023) based on the rRMSE confirming climate significance. The direct contribution of climate to model performance was marginal on a broad scale. However, its influence on GSV and temporal transferability seem stronger in homogenous sites. In general, RF was the most stable in both the forward (t₁→t₂) and backward (t₂→t₁) directions in the sa scenario unlike the GAM, which was more stable in the backward direction. This study provides a framework for assessing the reliability of GSV models and addresses a critical gap in forest monitoring. Full article

Accurate stock price prediction requires the integration of heterogeneous data streams, yet conventional techniques struggle to simultaneously leverage fine-grained micro-stock features and broader macroeconomic indicators. To address this gap, we propose MambaLLM, a novel framework that fuses macro-index and micro-stock inputs through the synergistic use of state-space models (SSMs) and large language models (LLMs). Our two-branch architecture comprises (i) Micro-Stock Encoder, a Mamba-based temporal encoder for processing granular stock-level data (prices, volumes, and technical indicators), and (ii) Macro-Index Analyzer, an LLM module—employing DeepSeek R1 7B distillation—capable of interpreting market-level index trends (e.g., S&P 500) to produce textual summaries. These summaries are then distilled into compact embeddings via FinBERT. By merging these multi-scale representations through a concatenation mechanism and subsequently refining them with multi-layer perceptrons (MLPs), MambaLLM dynamically captures both asset-specific price behavior and systemic market fluctuations. Extensive experiments on six major U.S. stocks (AAPL, AMZN, MSFT, TSLA, GOOGL, and META) reveal that MambaLLM delivers up to a 28.50% reduction in RMSE compared with suboptimal models, surpassing traditional recurrent neural networks and MAMBA-based baselines under volatile market conditions. This marked performance gain highlights the framework’s unique ability to merge structured financial time series with semantically rich macroeconomic narratives. Altogether, our findings underscore the scalability and adaptability of MambaLLM, offering a powerful, next-generation tool for financial forecasting and risk management. Full article