Abstract: This study develops a coarse-grained description of timelike vacuum interfaces in classical general relativity and explores how such interfaces can support an effective dust-like dark contribution without modifying Einstein's equations. The starting point is the thin-shell formalism: a timelike hypersurface \( \Sigma \) separating two vacuum or cosmological-constant regions is endowed with an induced metric \( h_{\mu\nu} \), extrinsic curvature \( K_{\mu\nu} \) and a surface stress tensor \( S_{\mu\nu} \) fixed by the Israel junction conditions. To this purely geometric structure an area-based entropy \( S_\Sigma = k_B A_\Sigma/(4\ell_p^2) \) is assigned to spacelike cross-sections of \( \Sigma \), motivated by the Bekenstein--Hawking area law and the area scaling of entanglement entropy, with the patch number \( N_\Sigma = A_\Sigma/(4\ell_p^2) \) serving as a geometric control parameter for the entropic loading of the interface. After coarse graining over many interface events, the shell stress--energy acquires an entropic contribution \( T^{\mu\nu}_{\mathrm{ent}} \simeq \rho_{\mathrm{ent}} u^\mu u^\nu \) that is well approximated by a pressureless component on large scales. In homogeneous FLRW backgrounds the entropic density obeys \( \dot{\rho}_{\mathrm{ent}} + 3H\rho_{\mathrm{ent}} = 0 \) and thus follows the standard cold-matter scaling \( \rho_{\mathrm{ent}} \propto a^{-3} \), providing an effective dark contribution in the Friedmann equations. In the stationary, weak-field regime the logarithmic temperature potential \( \theta = \ln T_{\mathrm{grav}} \) satisfies a Poisson-type equation \( \nabla^2\theta = -(4\pi G/c^2)(\rho_{\mathrm{vis}}+\rho_{\mathrm{ent}}) \) and yields the gravitational field via \( \mathbf{g} = c^2\nabla\theta \), so that \( \rho_{\mathrm{ent}} \) appears as an apparent halo component in clusters and galaxies. The framework organises familiar dark-matter phenomenology in terms of timelike vacuum interfaces and their entropic state, providing a classical arena for studying coarse-grained gravitational entropy on timelike surfaces and its connections to entanglement- and holography-inspired ideas, while leaving fine-grained microphysical interpretations to future work.

Abstract: In clinical CT imaging, high-density metallic implants often induce severe metal artifacts that obscure critical anatomical structures and degrade image quality, thereby hindering accurate diagnosis. Although deep learning has advanced CT metal artifact reduction (CT-MAR), many methods do not effectively use frequency information, which can limit the recovery of both fine details and overall image structure. To address this limitation, we propose a Hybrid-Frequency-Aware Mixture-of-Experts (HFMoE) network for CT-MAR. The proposed method synergizes the spatial-frequency localization of the wavelet transform with the global spectral representation of the Fourier transform to achieve precise multi-scale modeling of artifact characteristics. Specifically, we design a Hybrid-Frequency Interaction Encoder with three specialized branches, incorporating wavelet-domain, Fourier-domain, and cascaded wavelet–Fourier modulation, to distinctively refine local details, global structures, and complex cross-domain features. Then, they are fused via channel attention to yield a comprehensive representation. Furthermore, a frequency-aware Mixture-of-Experts (MoE) mechanism is introduced to dynamically route features to specific frequency experts based on the degradation severity, thereby adaptively assigning appropriate receptive fields to handle varying metal artifacts. Evaluations on synthetic (DeepLesion) and clinical (SpineWeb, CLINIC-metal) datasets show that HFMoE outperforms existing methods in both quantitative metrics and visual quality. Our method demonstrates the value of explicit frequency-domain adaptation for CT-MAR and could inform the design of other image restoration tasks.

Abstract: Water supply and drainage networks are essential components of urban infrastructure, directly influencing both residents' quality of life and the efficiency of city operations through their safety and stability. Over time, these networks often develop non-structural turbid water conditions, which present challenges for traditional maintenance methods. Leveraging the advantages of spatial visualization, three-dimensional environmental reconstruction technology has emerged as a promising solution to address these issues, while also advancing the use of intelligent maintenance technologies within water supply and drainage systems. This paper focuses on the causes of non-structural turbid water in these networks, and evaluates the optimization, effectiveness, and limitations of turbid water imaging, image feature recognition, and 3D environmental reconstruction technologies. Additionally, it re-views the current technical challenges and outlines potential future research directions, aiming to support the development and application of 3D reconstruction technologies for pipeline networks under non-structural turbid water conditions.

Abstract: Fragrance use is deeply embedded in personal identity, culture, and wellbeing, and many healthcare workers use perfumes and body sprays to feel fresh and confident during long duty hours. In hospitals, however—especially in oncology units, intensive care units, transplant wards, post-operative areas, and respiratory isolation rooms—strong fragrances can provoke patient distress and contribute to avoidable clinical complications, including nausea, headache, bronchospasm, cough, and sensory intolerance in physiologically vulnerable individuals. Several hospitals and health systems have implemented fragrance-free or fragrance-restricted policies, but many existing policies remain binary (“allowed” versus “not allowed”) and rarely provide quantified, clinically reasoned guidance on safe dosing, application sites, or self-assessment methods. This paper proposes a balanced, patient-centred framework that permits respectful fragrance use while prioritising patient safety and infection control. It introduces two practical concepts—hospital-appropriate dosing and micro-dosing zones—translating perfumery fundamentals (concentration categories, projection, sillage, longevity, top/heart/base notes, and fragrance families) into measurable clinical behaviours. The framework includes quantified spray guidance, application-site recommendations relevant to bedside practice, strategies for “taming” heavier perfumes through layering, and detailed self-assessment methods that healthcare workers can use for real-time safety checks. Finally, the paper outlines implementation strategies for hospitals, including staff education, patient-facing communication, and visitor guidance, without advocating blanket bans.

Abstract: The era of green global norms and business competitiveness encourage global companies to increase marketing capability which has an impact on marketing performance. This research examines the gaps in green global norms which are often considered to have consequences for investment in innovation and has an impact on profitability. On the contrary, this pressure encourages companies to remain survive while increasing their competitiveness. This research discusses green dynamic marketing capability as a strategic resource for global companies to survive and continue to improve their competitiveness. Using panel data regression with intervening variables, a study was employed on the variables of organizational learning, green organizational identity, green innovation, green marketing and marketing performance. 40 global companies were used as research samples. Data taken from Refinitiv Eikon Thomson Reuters and Annual Report for the 2019-2023 period. The research results show: (1) the mediating role of green marketing in values transformation of organizational learning and green organizational identity ; (2) green innovation has been shown to have no significant impact on marketing performance. The results clarify the framework of green dynamic marketing capabilities as a process of value transformation of dynamic marketing capabilities ​​to improve marketing performance.

Abstract:

High-resolution land surface temperature (LST) is required for field-scale agriculture, heat-risk services, and land–atmosphere process studies, but existing products show a persistent spatial–temporal trade-off and strong cloud-induced gaps. We develop a hybrid superresolution framework that couples hourly ICON-EU LST with sporadic Landsat 8/9 thermal observations. A U-Net convolutional neural network is trained on 256×256-pixel tiles over central Europe using year-2023 pairs of ICON-EU inputs and five-step Landsat history, and validated on the independent year 2024. The fusion model reconstructs Landsat-scale LST with MAE of 2.55 °C and RMSE of 3.43 °C, improving on bilinear ICON-EU upscaling (MAE 3.24 °C; RMSE 4.40 °C). Qualitative examples show recovery of field and land-cover boundary thermal texture while preserving ICON-EU large-scale temperature level. The framework enables daily 100 m LST estimates independent of current satellite visibility and provides an open pipeline for reproducible NWP–satellite LST fusion.

Abstract: Customer churn prediction is a critical task in the telecommunications industry, where retaining customers directly impacts revenue and operational efficiency. This study proposes a two iteration machine learning pipeline that integrates SHAP (SHapley Additive exPlanations) for explainable feature selection and Optuna-based hyperparameter tuning to enhance model performance and interpretability. In the first iteration, baseline models are trained on the full feature set of the Telco Customer Churn dataset (7043 samples, 25 features after preprocessing). The top-performing models—Gradient Boosting, Random Forest, and AdaBoost—are tuned and evaluated. SHAP is then applied to the best model (Gradient Boosting) to identify the top 20 features. In the second iteration, models are retrained on the reduced feature set, achieving comparable or improved performance: validation AUC of 0.999 (vs. 0.999 for full features) and test AUC of 0.998 (vs. 0.997). Results demonstrate that SHAP driven feature reduction maintains high predictive accuracy (test F1-score: 0.977) while improving interpretability and reducing model complexity. This workflow highlights the value of explainable AI in churn prediction, enabling stakeholders to understand key drivers like "Churn Reason" and "Dependents." What is the research problem? Accurate prediction of customer churn using machine learning models with a focus on explainable features to support business decisions. Why use SHAP? SHAP provides additive feature importance scores, enabling global and local interpretability, feature ranking for dimensionality reduction, and transparency in model predictions. What is the novelty? The iterative pipeline combines baseline training, SHAP-based feature selection, reduced-feature retraining, and hyperparameter retuning, offering a reproducible workflow for explainable churn modeling.

Abstract: To optimize juvenile barramundi (Lates calcarifer) feeding strategies, this study compared cannibalism (CB), formulated feed (FF), and mixed feed (MIX: formulated + biological feed) on growth, physiology, and immune-related gene expression. 36-day-old juveniles (initial body weight) were randomized into 3 groups (1 Lates calcarifer was placed in each tank, with 15 replicates in each group.) for a 20-day trial. Growth performance: MIX group showed significantly higher weight gain rate (862.31 ± 346.66) and specific growth rate (93.2 ± 42.48) than FF and CB groups (P < 0.05); CB group outperformed FF (P< 0.05). Physiology: MIX had the highest alkaline phosphatase (AKP) and pyruvate kinase (PK) activities (P < 0.05), and significantly elevated alanine aminotransferase (ALT) activity, lipid peroxide (LPO) content, but the lowest catalase (CAT) activity (P < 0.05). Gene expression: CB group had the highest lysosomal protease (cts1a) and glycolytic gene (eno3) levels (P < 0.05); FF group showed higher heat-shock protein 90(hsp90) and pro-inflammatory cytokine (IL1β) expression (P < 0.05). FF exhibited the highest SOD activity and IL1β levels (P < 0.05), indicating strong antioxidant. Conclusion: MIX promotes growth but risks liver damage/oxidative stress; CB serves as emergency nutrition but requires management to avoid; FF exhibits significant antioxidant advantages despite poor growth performance. "Mixed feeding + immune enhancers" is recommended for industrial seedling production to balance growth and health.

Abstract: The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising costly-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation slope and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation. In this work, a set of analytical approximations for the frequency-dependent attenuation and Raman gain coefficient is presented, providing an effective balance between computational efficiency and physical fidelity. Through extensive simulations covering C, C+L, and ultra-wideband U-to-E transmission scenarios, the accuracy in reproducing the behavior of the power evolution and NLI profiles of fully numerical SRS models with the proposed approximations is demonstrated.

Abstract: This study proposes a prediction model based on a dynamic spatiotemporal causal graph neural network to address the challenges of complex dynamic dependencies, strong structural correlations, and ambiguous causal relationships in corporate revenue forecasting. The model constructs a time-varying enterprise association graph, where enterprises are represented as nodes and industry or supply chain relationships as edges. A graph convolutional network is used to extract structural dependency features, while a gated recurrent unit captures temporal evolution patterns, achieving joint modeling of structural and temporal features. On this basis, a causal reasoning mechanism is introduced to model and adjust potential influence paths among enterprises. A learnable causal weight matrix is used to describe the strength of economic transmission, suppress spurious correlations, and strengthen key causal paths. The model also employs multi-scale temporal aggregation and attention fusion mechanisms to dynamically integrate multidimensional information, enhancing adaptability to both long-term trends and short-term fluctuations. Experimental results show that the proposed model outperforms mainstream methods in multiple metrics, including MSE, MAE, MAPE, and RMAE, verifying its effectiveness in capturing corporate revenue dynamics, modeling economic causal dependencies, and improving prediction accuracy. This study establishes a unified framework that integrates spatiotemporal dependency modeling with causal structure reasoning, providing new insights and methodological foundations for intelligent forecasting in complex economic systems.

Abstract: Tourist swimming pools are complex aquatic systems where operational failures can favour microbial growth and exposure to opportunistic pathogens. We conducted a four‑year surveillance (2016–2019) in hotel pools across Andalusia (Spain), analysing 2,053 water samples under Spanish regulation (Royal Decree 742/2013) with ISO methods (Pseudalert®/ISO 16266‑2 for Pseudomonas aeruginosa and Colilert‑18/ISO 9308‑2 for Escherichia coli). Overall non‑compliance reached 24.8%, and 2.0% of samples triggered immediate pool closure, most frequently due to P. aeruginosa. The bacterium was detected in 5.1% of samples, with heterogeneous distribution among installations: whirlpools (7.9%) > indoor pools (6.4%) > outdoor pools (4.5%) > cold wells (2.9%). No significant association was observed between use by children and P. aeruginosa detection (p > 0.05). Contamination occurred under both chlorine and bromine disinfection, with comparable prevalence (p = 0.18), and overlapping residual distributions indicated that single‑point disinfectant measurements alone did not predict contamination. Seasonality showed a bimodal pattern with winter (January) and summer (August) peaks, and prevalence markedly increased in 2019 compared with prior years. These findings highlight that P. aeruginosa contamination in tourist pools is driven less by momentary disinfectant levels than by structural and operational determinants (e.g., biofilm‑prone niches and hydraulic performance), underscoring the need for continuous surveillance, hydraulic optimization, routine mechanical cleaning, and robust monitoring across all seasons.

Abstract: Genomic technologies are transforming infectious disease surveillance and control, particularly in resource-limited settings such as Uganda. This review examines ongoing efforts by the Department of National Health Laboratory and Diagnostic Services - Central Public Health Laboratories (NHLDS-CPHL) to integrate genomics into public health strategies. We highlight key advancements, lessons learned, and opportunities, including expanded genomic testing capacity, localized bioinformatics infrastructure, and reinforcement of surveillance systems. Through use-case studies on COVID-19, HIV/AIDS, malaria, Ebola, Mpox, rotavirus, and cholera, we demonstrate the impact of genomics on improving diagnostic accuracy, disease monitoring and outbreak response, identifying drug resistance, and informing targeted public health interventions. Despite these successes, challenges, including but not limited to infrastructure gaps, funding constraints, and ethical considerations, remain, underscoring the need for policy, regulation, and capacity enhancement as well as global collaboration to effectively address these obstacles. Lessons learned from these efforts provide valuable recommendations for optimizing and sustaining genomic programs in low-resource settings. By leveraging genomics, Uganda can further strengthen its ability to detect, monitor, and respond to emerging and re-emerging health threats, ultimately enhancing disease control measures and public health resilience.

Abstract: Farmland quality protection is an important measure to implement the strategy of"storing grain in the land" and a vital part of promoting ecological agriculture development. This study focuses on the main agents of farmland quality protection, farmers, with a sample of 1,013 households from the rice-growing areas of Jiangxi Province, which is one of the major rice-growing provinces in southern China. An ordered probit regression model is used to investigate the influence and mechanism of social capital on farmers' behavior in protecting the quality of farmland. The result shows that:(1)Social capital significantly boosts the farmers' behaviors of farmland quality protection. The promoting effect of bonding social capital is greater than that of linking social capital. These conclusions remain robust after the endogeneity issue has been addressed and robustness tests have been conducted. (2) Ecological cognition plays a mediating role in this relation, while Internet use exerts a significant positive moderating effect. (3) The effect of social capital is greater for full-time farming households than for part-time farming households, and more significant for risk-neutral and risk-appetite farmers than for risk-averse farmers. Accordingly, this study proposes recommendations, including fostering farmers' social capital, improving their ecological cognition, promoting the penetration and use of the Internet, vigorously cultivating new agricultural business entities, and expanding agricultural insurance coverage.

Abstract:

Higgs physics is an active front from both experimental and theoretical aspects. It is a problem how to explain the measured value of Higgs mass, and a simple question like where the quartic coupling potential exactly comes from could not be well answered. This paper described a simple model to calculate the Higgs mass. It seems the Higgs mass may come from the coupling between Hawking energy of the Planck scale Kerr black hole and the thermal energy of cosmological microwave background. And by a logarithm potential, we can naturally get the exact quartic term for the Lagrangian. The Higgs mass we get is proportional to the square root of the temperature of the cosmological thermal background, which may mean it shall be larger at earlier universe.

Abstract:

Background: Tuberculosis (TB) remains a major global health threat, with burdens distributed unevenly across regions. South Africa continues to record some of the world’s highest TB incidence and mortality rates, while Bulgaria—although a low-burden country has shown stagnant or rising mortality among vulnerable groups. Comparing these contrasting settings offers insight into how epidemiological, socio-economic, and health system factors shape TB outcomes. Objective: This study compares TB incidence and mortality trends in South Africa and Bulgaria from 2000 to 2023 and examines how HIV prevalence, migration, poverty, ageing, incarceration, health system performance, and underreporting influence TB dynamics. Methods: A narrative comparative analysis drawing on WHO Global TB Reports, peer-reviewed literature, and demographic and system indicators was conducted across four policy-aligned periods (2000–2009, 2010–2015, 2016–2020, 2021–2023). Results: South Africa experienced a sharp rise in TB incidence in the early 2000s, largely driven by the HIV epidemic and system bottlenecks. Incidence fell substantially after 2010 following ART expansion, GeneXpert implementation, and increased programmatic investment. In Bulgaria, TB incidence steadily declined, yet mortality remains disproportionately high due to underdiagnosis, population ageing, socioeconomic vulnerability, and surveillance gaps. Conclusion: Despite differing epidemiological profiles, both countries show how TB persists at the nexus of social inequity and system performance. Strengthened, equity-focused strategies are needed to improve early diagnosis, treatment outcomes, and progress toward TB elimination.

Abstract:

A novel amide-containing AIE polymer was synthesized via condensation polymerization of pyrazine-2,5-dicarboxylic acid and naphthalene-1,5-diamine. The polymer showed strong fluorescence in aggregates and selective quenching for Fe³⁺, serving as an efficient probe. The chelation-enhanced quenching mechanism was studied. This work offers a simple approach to AIE-active polymeric probes for environmental and biological sensing.

Abstract: A harmonic analysis of Antarctic ice core proxy temperature, CO₂ and CH₄ data is presented spanning 350,000 years. To ensure consistent phase comparison, CO₂ data were converted from AICC2012 to EDC3 chronology using depth as the invariant coordinate. Using a greedy algorithm to select periodic components, the analysis initially obtained 59 periods for temperature but subsequently refined this to 55 periods after removing four components (22,150, 9,000, 8,000, and 4,540 years) that exhibited high correlations in the normalized covariance matrix. This refinement ensures stable, well-conditioned parameter estimates while maintaining excellent fits: R² = 0.952 for temperature and R² = 0.964 for CO₂ (truncated at 1515 CE), R² = 0.873 for CH₄. The algorithm independently recovers the canonical Milankovitch orbital periods (approximately 100,000, 41,000, and 23,000 years) without prior specification, validating both the methodology and the orbital pacing of ice ages (Milankovitch, 1941). Phase analysis reveals a systematic pattern of CO₂ lagging temperature at orbital timescales, with mean lag approximately 1,700 years, consistent with the hypothesis that temperature drives CO₂ through ocean degassing rather than the reverse. Examination of the Last Interglacial (Eemian) reveals a striking asymmetry: CO₂ remained elevated at 275–280 ppm for approximately 13,000 years while temperature declined by 7°C. An R² analysis clearly reveals the Mid-Pleistocene Transition and justifies limiting the input to the fit. The modern CO₂ spike, which departs dramatically from the 350,000-year orbital envelope, is clearly anomalous relative to the harmonic structure of the paleoclimate record.

Abstract: This study proposes and systematically validates a new analytical wake model that incorporates atmospheric stability effects. By introducing a stability-dependent turbulence expansion term with a square of a cosine function and the stability sign parameter, the model dynamically responds to varying atmospheric conditions, overcoming the reliance of tranditional models on neutral atmospheric assumptions. It achieves physically consistent descriptions of turbulence suppression under stable conditions and convective enhancement under unstable conditions. A newly developed far-field decay function effectively coordinates near-wake and far-wake evolution, maintaining computational efficiency while significantly improving prediction accuracy under complex stability conditions. The Present model has been validated against field measurements from the Scaled Wind Farm Technology (SWiFT) facility and the Alsvik wind farm, demonstrating superior performance in predicting wake velocity distributions on both vertical and horizontal planes. It also exhibits strong adaptability under neutral, stable, and unstable atmospheric conditions. This proposed framework provides a reliable tool for wind turbine layout optimization and power output forecasting under realistic atmospheric stability conditions.

Abstract:

The rhesus macaque (Macaca mulatta) is a valuable model for pregnancy research due to its physiological similarity to humans and the ability to conduct studies in a controlled environment. Our previous work used noninvasive imaging methods to assess placental hemodynamics across gestation with correlative tissue analysis post-delivery. Here, we expand access to longitudinal timepoints from ongoing pregnancies by obtaining placental biopsies using ultrasound-guided needle aspiration. This approach aligns with New Approach Methods (NAMs) and supports animal welfare by reducing the number of animals required. We describe a chorionic villus sampling (CVS) simulation model which facilitates training to gain proficiency in technical skills prior to performing the procedure in animals. We report outcomes from three rhesus macaques that underwent CVS three times between gestational days 40 to 106 (term: 165 days). Although biopsy samples are smaller than whole placenta, tissue yields were sufficient for multiple uses. We demonstrate 1) appropriate histology from aspirated samples, 2) good RNA quality and yield, and 3) the ability to isolate trophoblast organoids, a NAMs advancement that reduces the need for first-trimester surgical delivery. No adverse outcomes occurred following serial CVS procedures, supporting the use of this sampling to maximize animal utilization in longitudinal pregnancy studies.

Abstract: The growing global demand for mineral resources is challenging mining operations to maintain productivity while addressing lower-grade ore and increased extraction complexity. Despite the availability of vast datasets across mining stages, much of this information remains underused in decision-making. This study presents an integrated, knowledge-based framework that leverages artificial intelligence (AI) and high-fidelity simulation to model and optimise the full mine to mill process. Using publicly available data from the Barrick Cortez Mine in Nevada, USA, the mining chain from blasting to semi-autogenous grinding (SAG) was modelled using the Integrated Extraction Simulator (IES) from Orica. To mitigate the computational burden of full factorial simulations, three million scenarios were generated to evaluate performance sensitivity. Machine learning models, including linear regression, decision trees, random forests, and XGBoost, were trained and validated. The models achieved an accuracy of more than 90%, underscoring their reliability for predicting process outcomes. SHapley Additive exPlanations (SHAP) were applied to interpret model predictions and quantify feature importance. The findings confirm a strong alignment between simulation and real-world data and highlight key operational parameters that affect downstream process performances. This meta-model approach offers a powerful tool for real-time decision-making, enabling mining operations to improve efficiency, reduce costs, and support sustainable resource management.