Search Results (709)

We investigate the statistical entropy of black holes within the framework of the generalized uncertainty principle (GUP) by employing effective metrics that incorporate leading-order and all-order quantum gravitational corrections. We construct three distinct effective metrics induced by the GUP, which are derived from the GUP-corrected temperature, entropy, and all-order GUP corrections, and analyze their impact on black hole entropy using ’t Hooft’s brick wall method. Our results show that, despite the differences in the effective metrics and the corresponding ultraviolet cutoffs, the statistical entropy consistently satisfies the Bekenstein–Hawking area law when expressed in terms of an invariant (coordinate-independent) distance near the horizon. Furthermore, we demonstrate that the GUP naturally regularizes the ultraviolet divergence in the density of states, eliminating the need for artificial cutoffs and yielding finite entropy even when counting quantum states only in the vicinity of the event horizon. These findings highlight the universality and robustness of the area law under GUP modifications and provide new insights into the interplay between quantum gravity effects and black hole thermodynamics. Full article

With the continuous increase in urban population, the subway is the main way to alleviate traffic congestion. However, the construction environment of subway stations is complex, and the safety risks are extremely high. Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. The study found that the combined empowerment–improvement cloud model assessment method is reliable, and the case study verifies that the vulnerability level of the project is “very low risk”, and the investigations of safety hazards and the pressure of surrounding traffic are the key influencing factors, allowing for the proposal of more scientific and effective management strategies for the construction of subway stations. Full article

Superior-type iron formations (IFs) represent a globally significant source of iron ore; yet, their origin remains a subject of ongoing debate. Early models proposed a continental weathering source for the iron, whereas later interpretations—mainly supported by positive europium (Eu) anomalies—favored a hydrothermal source. However, the hydrothermal model largely relies on REE systematics, and whether iron and REEs in Superior-type IFs share the same source remains uncertain. As iron isotopes directly trace the sources and fractionation history of iron, a spatial co-variation between Fe isotopes and Eu anomalies would shed new light on the iron source issue of IFs. In this study, we present new Fe isotope and REE data from the drill core WILDD004 at Wilgena Hill and integrate them with reported data for two additional drill cores: HKDD4 (Hawks Nest) and GWDD1 (Giffen Well). All three cores are stratigraphically equivalent to the Wilgena Hill Jaspilite Formation but span a lateral distance of ~100 km across the Gawler Craton, South Australia. While the Hawks Nest and Giffen Well samples exhibit both positive Eu anomalies and elevated δ⁵⁶Fe values, the Wilgena Hill samples show positive yet smaller Eu/Eu* (1.17–2.41) and negative δ⁵⁶Fe values (−0.60‰ to −1.63‰). The consistent presence of Eu anomalies and the systematic spatial correlation between δ⁵⁶Fe and Eu/Eu* across all three locations provide direct, Fe-based geochemical evidence for a hydrothermal source of iron in this Superior-type IF. Full article

We investigated the thermodynamic topology of quantum-corrected AdS-Reissner-Nordström black holes in Kiselev spacetime using non-extensive entropy formulation derived from Loop Quantum Gravity (LQG). Through systematic analysis, we examined how the Tsallis parameter $\lambda$ influences topological charge classification with respect to various equation of state parameters. Our findings revealed a consistent pattern of topological transitions: for $\lambda =0.1$, the system exhibited a single topological charge $(\omega =-1)$ with total charge $W=-1$, as $\lambda$ increased to 0.8, the system transitioned to a configuration with two topological charges $(\omega =+1,-1)$ and total charge $W=0$. When $\lambda =1$, corresponding to the Bekenstein–Hawking entropy limit, the system displayed a single topological charge $(\omega =+1)$ with $W=+1$, signifying thermodynamic stability. The persistence of this pattern across different fluid compositions—from exotic negative pressure environments to radiation—demonstrates the universal nature of quantum gravitational effects on black hole topology. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

In this work we study the spectral dimensionality of spacetime around a radiating Schwarzschild black hole using a recently introduced formalism of quantum gravity, where the alterations of the gravitational field produced by the radiation are represented on an extended manifold, and describe a non-commutative and nonlinear quantum algebra. The relation between classical and quantum perturbations of spacetime can be measured by the parameter $z\ge 0$. In this work we have found that when $z=(1+\sqrt{3})/2\simeq 1.3660$, a relativistic observer approaching the Schwarzschild horizon perceives a spectral dimension $N\left(z\right)=4\left[\theta \left(z\right)-1\right]\simeq 2.8849$, which is related to quantum gravitational interference effects in the environment of the black hole. Under these conditions, all studied Schwarzschild black holes with masses ranging from the Planck mass to ${10}^{46}$ times the Planck mass present the same stability configuration, which suggests the existence of a universal property of these objects under those particular conditions. The difference from the spectral dimension previously obtained at cosmological scales leads to the conclusion that the spacetime dimensionality is scale-dependent. Another important result presented here is the fundamental alteration of the effective gravitational potential near the horizon due to Hawking radiation. This quantum phenomenon prevents the potential from diverging to negative infinity as the observer approaches the Schwarzschild horizon. Full article

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO₄) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability. Full article

Background/Objectives: To investigate the effect of ellipsoid zone (EZ) integrity and retinal fluid on best-corrected visual acuity (BCVA) in neovascular, age-related macular degeneration. Methods: This was a post hoc treatment-agnostic analysis of the phase 3 HAWK trial. Intraretinal fluid (IRF), subretinal fluid (SRF), and ellipsoid zone (EZ) integrity were quantified over 48 weeks. EZ integrity maintenance was defined as EZ-RPE central subfield thickness (CST) >20 µm; partial EZ attenuation was EZ-RPE CST >0 and ≤20 µm; total EZ attenuation was EZ-RPE CST = 0 µm. Results: During treatment, BCVA in eyes with no fluid (66.5 to 70.2 letters) was greater than in eyes with IRF (59.5 to 62.4 letters) but comparable to BCVA in eyes with SRF (64.9 to 68.8 letters). In eyes with no fluid, BCVA was consistently greater in eyes with EZ integrity maintained (73.4 to 78.4 letters) than in eyes with EZ partial attenuation (65.3 to 66.5 letters) or with EZ total attenuation (55.8 to 59.8 letters). Conclusions: Eyes without fluid with EZ preservation achieved the highest overall BCVA, especially when compared to eyes without fluid and a lack of EZ preservation and to eyes with SRF. Achieving a “dry” status with preservation of EZ integrity is important in optimizing visual outcomes. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

This study conducts a comprehensive bibliometric analysis and predictive modeling of financial risk research from 2015 to 2024, integrating conceptual, knowledge, and collaboration perspectives. Utilizing the PRISMA framework for literature screening, the study identifies publications, research areas, and research institutions. A co-citation network approach reveals the intellectual structure and milestone works, while emergent keyword detection highlights cutting-edge topics such as economic policy uncertainty, climate risk, and green innovation. Furthermore, the study proposes a novel semantic forecasting model, SEF-ACLSTM (Semantic Evolution Forecasting with Aligned Clustered LSTM), to predict the evolution of research themes through 2030. The results identify three major thematic clusters: methodological innovation, traditional risk management, and green finance. The predictive analysis indicates a growing emphasis on methodological and sustainability-oriented topics, suggesting a paradigmatic shift in financial risk research. The findings offer theoretical insights and strategic guidance for future academic inquiry and policy formulation. Full article

Youth violence—the deliberate use of physical force or harm by young people between the ages of 10 and 24 to intimidate or cause harm to others, both online and offline—is a critical public health issue in the United States. Yet, successfully predicting future violent offenders is a complex and challenging task, as the question of why some youths resort to extreme violence while others refrain from it—despite facing similar risk factors—remains widely debated. This article highlights both risk and protective factors of youth violence through a socio-ecological lens to offer a comprehensive understanding of the multifaceted factors driving youth violence in the United States. To understand the interconnectedness between individual factors and the broader environments in which individuals are embedded, we outline the risk and protective factors related to youth violence across five socio-ecological levels: (1) individual, (2) interpersonal, (3) neighborhood, (4) cultural, and (5) life course. Approaching youth violence from a holistic lens offers a greater opportunity to mitigate contributing factors and to address the deleterious impacts of this complex issue. Practice and research implications are discussed. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

New Zealand’s Emissions Trading Scheme (ETS) enables growers to earn payments by accumulating carbon units as their forests increase in carbon stock. For forests of less than 100 hectares, growers use predefined lookup tables (LUTs) to estimate carbon stock changes based on forest age. Using a combination of growth models and productivity surfaces, underpinned by data from 1360 growth plots, the objective of this study was to provide draft updates for the Exotic Hardwoods LUTs. The updated LUTs were based on growth rates of three Eucalyptus species, E. fastigata, E. regnans, and E. nitens, which comprise a major proportion of the Exotic Hardwoods forest type in New Zealand. Carbon tables were first derived for each species. Then, a draft LUT was generated for New Zealand’s North Island, using a weighted average of the species-specific tables based on the relative importance of the species, while the E. nitens table was used for the South Island where this is the predominant Eucalyptus species. Carbon stock predictions at ages 30 and 50 years were 820 and 1340 tonnes CO₂ ha⁻¹ for the North Island, and slightly higher at 958 and 1609 tonnes CO₂ ha⁻¹ for the South Island. Regional variation was significant, with the highest predicted carbon in Southland (1691 tonnes CO₂ ha⁻¹ at age 50) and lowest in Hawke’s Bay/Southern North Island (1292 tonnes CO₂ ha⁻¹). Predictions closely matched the current Exotic Hardwood LUT to age 20 years but exceeded it by up to 45% at age 35. Growth and carbon sequestration rates were similar to other established Eucalyptus species and slightly higher than Acacia species, though further research is recommended. These findings suggest that the three Eucalyptus species studied here could serve as the default species for a revised Exotic Hardwoods LUT and that the current national tables could be regionalised. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

Camouflaged object detection (COD) aims to detect objects that seamlessly integrate with their surrounding environment and are thereby intractable to distinguish from the background. Existing approaches face difficulties in dynamically adapting to scenarios where the foreground closely resembles the background. Additionally, these methods primarily rely on single-domain boundary supervision while overlooking multi-dimensional constraints, leading to indistinct object boundaries. Inspired by the hawk’s visual predation mechanism, namely, global perception and local refinement, we design an innovative two-stage boundary-aware network, namely, SFNet, which relies on supervision in the spatial-frequency domains. In detail, to simulate the global perception mechanism, we design a multi-scale dynamic attention module to capture contextual relationships between camouflaged objects and surroundings and to enhance key feature representation. In the local refinement stage, we introduce a dual-domain boundary supervision mechanism that jointly optimizes boundaries in frequency and spatial domains, along with an adaptive gated boundary guided module to maintain global semantic consistency. Extensive experiments on four camouflaged object detection datasets demonstrate that SFNet surpasses state-of-the-art methods by 4.1%, with lower computational overhead and memory costs. Full article