Search Results (10,265)

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

The flow around circular cylinders is a classic problem in fluid mechanics with significant implications for offshore engineering. While extensive numerical and experimental research has focused on the subcritical and critical Reynolds regimes, the supercritical and postcritical regimes remain challenging and relatively unexplored, primarily due to the complex nature of turbulence and the high computational requirements. In this study, we perform three-dimensional detached eddy simulations using the finite volume method in OpenFOAM v1906, employing Menter’s k-$\omega$ SST turbulence model, to systematically investigate the flow past an infinitely long smooth cylinder from the subcritical through the postcritical regimes. The numerical setup ensures accurate near-wall resolution and reliable representation of unsteady flow features. We present a detailed analysis of vortex shedding patterns, wake evolution, and statistical properties of lift and drag coefficients for selected Reynolds numbers representative of each regime. The simulation results are benchmarked against experimental data from the literature, demonstrating good agreement for Strouhal number and mean drag. Special emphasis is placed on the evolution of wake topology and force coefficients as the flow transitions from laminar to fully turbulent conditions. The findings contribute to the limited numerical literature on flow around circular cylinders across subcritical, critical, supercritical, and postcritical Reynolds number regimes, providing insights that are fundamentally relevant to the broader scope of understanding vortex shedding phenomena. Full article

This study explores the intersection of sociocultural factors, particularly privacy, with energy consumption patterns in residential buildings in Riyadh, Saudi Arabia. While cultural values around privacy have long been recognised as influential in residential design, the impact of these values on energy consumption is underexplored. This research aims to fill this gap by examining how privacy needs, residents’ preferences, and open layouts affect energy efficiency, particularly in terms of natural light and ventilation. A mixed-methods approach was employed, including semi-structured interviews with engineers, data collected from 108 respondents via an online survey, a case study of a residential building in Riyadh, and building performance simulations using IES software. The study also assessed actual energy consumption data and indoor lighting as potential implications of privacy concerns, causing changes in behavioural control of systems (e.g., windows, blinds, lighting, etc.). It focuses on the relationship between privacy needs, energy use, and natural daylight distribution. The IES simulation results for the studied residential building show an annual energy consumption of 24,000 kWh, primarily due to cooling loads and artificial lighting caused by privacy measures applied by the residents. The findings reveal that privacy-driven design choices and occupant behaviours, such as the use of full window shutters, frosted glazing and limited window operation, significantly reduce daylight availability and natural ventilation, leading to increased reliance on artificial lighting and air conditioning. This study highlights the need for human-centric design approaches that address the interplay between sociocultural factors, particularly reinforcing cultural sensitivity, and building performance, offering insights for future sustainable housing developments in Riyadh and similar contexts. Full article

Viral RNA structure plays a critical regulatory role in viral replication, serving as a dual-purpose mechanism for encoding genetic information and controlling biological processes. However, these structural elements also serve as pathogen-associated molecular patterns (PAMPs), which are recognized by pattern recognition receptors (PRRs) of the host innate immune system. This review discusses the complex and poorly understood relationship between viral RNA structure and recognition of RNA by PRRs, specifically focusing on Toll-like receptor 3 (TLR3) and Retinoic acid-inducible gene I (RIG-I). While current interaction models rely upon data generated from use of synthetic ligands such as poly(I:C) or perfectly base-paired double-stranded RNA stems, this review highlights significant gaps in our understanding of how PRRs recognize naturally occurring viral RNAs that fold into highly complex three-dimensional structures. Furthermore, we explore how viral evolution and nucleotide variations, such as those observed in influenza viruses, can drastically alter local and distal RNA structure, potentially impacting immune detection. We conclude that moving beyond synthetic models to understand natural RNA structural dynamics is essential for elucidating the mechanisms of viral immune evasion and pathogenesis. Full article

The systems in nature are rarely isolated and there are different influences that can perturb their states. Dynamic noise in physiological systems can cause fluctuations and changes on different levels, often leading to qualitative transitions. In this study, we explore how to detect and extract the physiological noise, in terms of dynamic noise, from measurements of biological oscillatory systems. Moreover, because the biological systems can often have deterministic time-varying dynamics, we have considered how to detect the dynamic physiological noise while at the same time following the time-variability of the deterministic part. To achieve this, we use dynamical Bayesian inference for modeling stochastic differential equations that describe the phase dynamics of interacting oscillators. We apply this methodological framework on cardio-respiratory signals in which the breathing of the subjects varies in a predefined manner, including free spontaneous, sine, ramped and aperiodic breathing patterns. The statistical results showed significant difference in the physiological noise for the respiration dynamics in relation to different breathing patterns. The effect from the perturbed breathing was not translated through the interactions on the dynamic noise of the cardiac dynamics. The fruitful cardio-respiratory application demonstrated the potential of the methodological framework for applications to other physiological systems more generally. Full article

The flexural behavior of reinforced rubberized concrete beams was assessed, and it was demonstrated that they exhibited a constant performance decline with an increase in rubber content. Numerical simulations are critically important in the study and engineering of concrete elements due to several key reasons, as follows: to allow engineers to anticipate the behavior of concrete components under diverse loads; to help elucidate intricate mechanisms such as crack initiation, propagation, and fracture processes; and to explore new materials, geometries, and reinforcement layouts without the need for extensive prototyping. This paper presents both experimental and numerical investigations on the flexural behavior of conventional and rubberized concrete reinforced beams. The parameters of the research included the percentage replacement of natural aggregates by rubber particles and the change in the longitudinal reinforcement ratio. The results showed an increase in the load-carrying capacity and a decrease in the midspan deflection with an increase in reinforcement ratio. Substituting natural aggregates with rubber particles resulted in a slight decrease in the load-carrying capacity but an increase in the midspan deflections. Numerical simulations using ATENA v5 software predicted the load-carrying capacity, failure mode, and cracking patterns of the reinforced concrete beams. Full article

We study how convolutional neural networks reorganize information during learning in natural image classification tasks by tracking mutual information (MI) between inputs, intermediate representations, and labels. Across VGG-16, ResNet-18, and ResNet-50, we find that label-relevant MI grows reliably with depth while input MI depends strongly on architecture and activation, indicating that “compression’’ is not a universal phenomenon. Within convolutional layers, label information becomes increasingly concentrated in a small subset of channels; inference-time knockouts, shuffles, and perturbations confirm that these high-MI channels are functionally necessary for accuracy. This behavior suggests a view of representation learning driven by selective concentration and decorrelation rather than global information reduction. Finally, we show that a simple dependence-aware regularizer based on the Hilbert–Schmidt Independence Criterion can encourage these same patterns during training, yielding small accuracy gains and consistently faster convergence. Full article

Soil contamination by potentially toxic elements (PTEs) is a growing environmental concern, particularly in agricultural regions where soil quality directly affects crop safety and human health. This study evaluates PTE concentrations and ecological risks in agricultural soils of Hautat Sudair, central Saudi Arabia, using contamination indices, multivariate statistics, and GIS-based spatial modeling supported by RS-derived land use/land cover (LULC) mapping. The results show that the mean concentrations of Ni (35.97 mg/kg) and Mn (1230 mg/kg) exceed international thresholds in several locations, while Pb (8.34 mg/kg), Cr (33.00 mg/kg), Zn (60.09 mg/kg), and As (4.25 mg/kg) remain within permissible limits in most samples. Contamination indices, including the Enrichment Factor (EF), Contamination Factor (CF), and Geo-Accumulation Index (Igeo), highlight hotspot behavior, with isolated sites showing elevated concentrations approaching screening levels (e.g., Pb up to 32.0 mg/kg and Cr up to 52.0 mg/kg), whereas Ni and Mn exhibit the most pronounced local enrichment. The Pollution Load Index (PLI) varies from 0.24 to 0.80, indicating low to moderate contamination levels, while the Risk Index (RI) ranges from 10.43 to 41.38, signifying low ecological risk. Multivariate statistical analyses, including correlation matrices and principal component analysis (PCA), reveal that Ni, Cr, and Mn share a common source, possibly linked to anthropogenic inputs and natural geological background. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirm the adequacy of the dataset for PCA (KMO = 0.797; χ² = 563.845, p < 0.001). Spatial distribution maps generated using GIS and RS highlight contamination hotspots, reinforcing the necessity for periodic monitoring. By integrating indices, multivariate patterns, and spatial context, this study provides a replicable, research-driven framework for interpreting PTE controls in arid agricultural soils. Full article

Coastal pond-to-mangrove restoration has become a prominent Nature-based Solution, yet its short-term ecological effects on waterbird communities remain unclear. We assessed taxonomic, functional, and compositional responses of waterbirds to large-scale restoration in Bamen Bay, Hainan Island, using BACI-style comparisons between restored and unrestored aquaculture ponds in 2021 and 2023. Restored areas exhibited higher taxonomic α diversity and functional richness (p < 0.001), coinciding with rapid habitat diversification following hydrological reconnection. Species richness (p < 0.001), Shannon diversity (p < 0.01), and functional richness (p < 0.01) were consistently higher in restored areas than in aquaculture ponds. In contrast, β diversity patterns diverged between habitats: restored areas remained relatively stable, whereas aquaculture ponds showed greater between-year compositional change (p < 0.05). Guild-specific responses revealed contrasting patterns: herons showed higher diversity in restored habitats (p < 0.05), whereas shorebirds exhibited no significant changes (p > 0.05), consistent with their dependence on open mudflats that were only partially retained. Although no significant declines were detected, functional richness tended to be lower in 2023 (p > 0.05), and ongoing mudflat loss suggests potential long-term risks for mudflat specialists, warranting extended monitoring. Taken together, our findings suggest that effective pond-to-mangrove restoration in Bamen Bay should balance mangrove expansion with the retention of tidal flats and managed shallow-water habitats to support diverse waterbird assemblages. Full article

By analyzing Food and Drug Administration (FDA) enforcement reports from 2004 to 2025, we can determine the incidence of microbial contamination in non-sterile and sterile drugs in the United States of America and, at the same time, compare the trends and patterns over a period of 21 years to determine the distribution and frequency of microbial contaminants. The most common microorganisms detected from 2019 to 2025 were the mold Aspergillus penicilloides, with 17 citations for sterile products, followed by 16 citations for non-sterile products of Burkholderia cepacia complex (BCC) bacteria. Analysis from the last 21 years revealed the dominant microbial contaminants belong to the BCC, reaching a maximum level between 2012 and 2019. Some of the previous microbial contaminants, such as Salmonella and Clostridium, decline in the 2019–2025 period, with no notifications issued. S. aureus and Pseudomonas contamination persisted through the years but at very low levels. Gram-negative bacteria contaminated non-sterile drugs more frequently than Gram-positive. A worrisome trend continued with unacceptable levels of enforcement reports not providing any information on the identity of the microbial contaminant. New species of Bacillus and Acetobacter nitrogenifigens were responsible for a significant increase in non-sterile drug recalls. The main driver for sterile product recalls over a 21-year period is the lack of assurance of sterility (LAS) where major failures in process design, control, and operational execution were not conducive to the control of microbial proliferation and destruction. Enforcement data analysis identified the problematic trends and patterns regarding microbial contamination of drugs, providing important information to optimize process control and provide a framework for optimizing risk mitigation. Although the 21-year landscape demonstrated that some microbial contaminants have been successfully mitigated, others remain resilient. The emergence of new contaminants highlights the evolving nature of microbial risk. The consistent problem with LAS is not only a major regulatory violation but also a potential catalyst for the next major healthcare-associated outbreak. Full article

This study investigates the glycosylation patterns of serum IgG antibodies in relation to COVID-19 infection and vaccination, highlighting the potential of specific glycan profiles as biomarkers for immune responses. Using Spearman correlation analysis, distinct associations among glycan levels and various clinical laboratory parameters were identified, revealing complex, non-linear interactions that influence immune dynamics. Significant differences were observed in sialylated glycan profiles across patient groups, indicating that vaccination and natural infection elicit unique immune mechanisms and suggesting that vaccination induces favorable glycosylation changes. Notably, high-mannose glycans were found to correlate with other glycan types, underscoring their critical role in the immune response and suggesting their potential as biomarkers to differentiate between infection- and vaccination-induced immunity. The findings suggest that understanding these glycosylation dynamics may enhance diagnostic and therapeutic strategies, providing valuable tools for differentiating between immune responses elicited by infection and vaccination. Overall, this study contributes to the understanding of glycosylation’s impact on immune function in the context of COVID-19, emphasizing the importance of specific glycan markers, such as sialylated and high-mannose structures, in clinical applications. Full article

Gold nanoparticles (Au-NP) are frequently used as model nanomaterials to study nanoparticle behavior in plants due to their analytical detectability and negligible natural background in plant tissues. However, the feasibility of visualizing the spatial distribution of foliar-applied Au-NP at low exposure levels using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remains insufficiently explored. In this study, commercially sourced Au-NP were applied to lentil leaves (Lens culinaris var. Beluga) at a low concentration of 0.5 mg·L⁻¹ using a controlled leaf submersion approach. Leaves were sampled at 1 h, 24 h, and 96 h post-exposure and analyzed by LA-ICP-MS imaging to assess time-dependent changes in gold-associated spatial signals, and to compare elemental distribution patterns with non-exposed controls. Untreated control leaves showed no detectable gold at any sampling time point, confirming negligible native Au background. In treated leaves, LA-ICP-MS imaging revealed an initially localized Au hotspot at 1 h, followed by progressive Au redistribution toward the leaf margins and petiole region by 24 h and 96 h. Gold signals persisted over the full 96 h period, indicating stable association of Au-NP with leaf tissue. Comparative elemental mapping of Ca, Mg, K, P, Fe, Zn, and Cu showed no persistent differences in spatial distribution patterns between treated and control leaves as detectable by LA-ICP-MS. This study demonstrates the feasibility of LA-ICP-MS imaging for visualizing the deposition and temporal spatial redistribution of low-dose foliar-applied nanoparticles in intact leaves. The results provide a methodological reference for future hypothesis-driven studies that apply nanoparticles under more controlled conditions, include increased replication, and combine multiple analytical techniques. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and complex, unseen dependencies. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, excel at capturing these non-linear patterns but can be data-hungry and prone to overfitting, especially in noisy financial datasets. This paper proposes a novel hybrid model, HAR-LSTM-GARCH, which synergistically combines the strengths of the HAR model, an LSTM network, and a GARCH model to forecast the realized volatility of crude oil futures. The HAR component captures the persistent, multi-scale volatility dynamics, the LSTM network learns the non-linear residual patterns, and the GARCH component models the time-varying volatility of the residuals themselves. Using high-frequency data on Brent Crude futures, we compute daily Realized Volatility (RV). Our empirical results demonstrate that the proposed HAR-LSTM-GARCH model significantly outperforms the benchmark HAR, GARCH(1,1), and standalone LSTM models in both statistical accuracy and economic significance, offering a robust framework for volatility forecasting in the complex energy sector. Full article

Ataúro Island, located in the inner Banda Arc, provides a natural laboratory to investigate the interplay between magmatic evolution, hydrothermal circulation, and near-surface weathering in an active arc–continent collision setting. This study presents the first systematic island-wide geochemical baseline for Ataúro Island, based on multi-element analyses of stream sediments integrated with updated geological, structural, and hydromorphological information. Compositional Data Analysis (CoDA–CLR–PCA), combined with anomaly mapping and spatial overlays, defines a coherent three-tier geochemical framework comprising: (i) a lithogenic component dominated by Fe–Ti–Mg–Ni–Co–Cr, reflecting the geochemical signature of basaltic to andesitic volcanic rocks; (ii) a hydrothermal component characterized by Ag–As–Sb–S–Au associations spatially linked to structurally controlled zones; and (iii) an oxidative–supergene component marked by Fe–V–Zn redistribution along drainage convergence areas. These domains are defined strictly on geochemical criteria and represent geochemical process domains rather than proven metallogenic provinces. Rare earth element (REE) systematics further constrain the geotectonic setting and indicate that the primary geochemical patterns are largely controlled by lithological and magmatic differentiation processes. Spatial integration of geochemical patterns with fault architecture highlights the importance of NW–SE and NE–SW structural corridors in focusing hydrothermal fluid circulation and associated metal dispersion. The identified Ag–As–Sb–Au associations are interpreted as epithermal-style hydrothermal geochemical enrichment and exploration-relevant geochemical footprints, rather than as evidence of confirmed or economic mineralization. Overall, Ataúro Island emerges as a compact natural analogue of post-arc geochemical system evolution in the eastern Banda Arc, where lithogenic background, hydrothermal fluid–rock interaction, and early supergene processes are superimposed. The integrated geochemical framework presented here provides a robust baseline for future targeted investigations aimed at distinguishing lithogenic from hydrothermal contributions and evaluating the potential significance of the identified geochemical enrichments. Full article