Search Results (35,176)

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up. Full article

Coagulation–flocculation is widely recognized as a fundamental step in wastewater treatment, as it promotes the aggregation and removal of suspended particles and organic contaminants following the addition of a coagulant. In this study, a bio-based coagulant was prepared from prickly pear (Opuntia ficus-indica) seed residues obtained after essential oil extraction. The extraction process for bioactive agents was successfully modeled using Central Composite Design (CCD)-based Response Surface Methodology (RSM). Optimal extraction was reached at pH 13, PPSM of 7.5 g, 0.75 M NaCl, and 40 min of stirring, providing maximum yields of 69.63 g proteins, 217.075 g total sugars, and 81.416 g polyphenols. The optimized extract was subsequently used as a bio-coagulant for the treatment of wastewater collected from the Chalghoum El Aid–Oued El Athmania wastewater treatment plant (Mila, Algeria). The effects of three operating parameters, initial turbidity, solution pH, and bio-coagulant dosage, on the coagulation–flocculation performance were investigated using a Box–Behnken design (BBD). Process efficiency was evaluated in terms of turbidity, chemical oxygen demand (COD), and organic matter (OM) removal. The raw wastewater exhibited initial values of 200 NTU for turbidity, 640 mg/L for COD, and 25 for organic matter. Statistical analysis revealed that the developed quadratic models were highly significant (p ≤ 0.05) and showed excellent predictive performance, with coefficients of determination (R² ≥ 0.97). Optimal treatment conditions were identified at pH 7, a bio-coagulant dosage of 1 mL/L, and an initial turbidity of 200 NTU. Under these conditions, removal efficiencies exceeded 98% for turbidity and COD and reached 88.08% for organic matter. Furthermore, Fourier-Transform Infrared (FTIR) Spectroscopy analysis confirmed the presence of functional groups responsible for the coagulation activity of the bio-coagulant. These findings highlight the potential of prickly pear seed residues as an effective, sustainable, and low-cost alternative to conventional chemical coagulants in wastewater treatment. Full article

Sodium hypochlorite (NaOCl) remains the gold standard irrigant in endodontics due to its proteolytic and antimicrobial properties, whereas hydrogen peroxide (HP) is widely used for internal bleaching because of its oxidative capacity. Both agents have been associated with chemical and structural alterations in dentin; however, the impact of their sequential application on the organic–mineral balance has not been fully elucidated. Objective: To evaluate whether the isolated and sequential application of 5.25% NaOCl and 37.5% HP induces chemical alterations in dentin by analyzing changes in the organic matrix and mineral phase using Fourier-transform infrared spectroscopy (FTIR) and Energy-dispersive X-ray spectroscopy (EDX). Methods: Twenty-four independent dentin sections (n = 6 per group) from six human third molars were distributed using a tooth-balanced allocation into four groups: Control, NaOCl (5.25%, 15 min), HP (37.5%, 30 min), and sequential NaOCl+HP. FTIR assessed organic (amide I, II, III, CH₂) and inorganic (phosphate, carbonate) components through baseline-corrected integrated areas, Full Width at Half Maximum (FWHM), and molecular ratios. Surface elemental composition and the calculated Ca/P atomic ratio were determined by EDX. Multiple sub-measurements per specimen were averaged before statistical analysis. Data were analyzed using Kruskal–Wallis and Mann–Whitney U tests with Bonferroni correction (p < 0.05). Results: FTIR revealed treatment-dependent modifications. NaOCl reduced absorbance in organic-associated bands, indicating collagen degradation, whereas HP altered the mineral phase. The NaOCl+HP group exhibited increased numerical values for integrated band areas, with differences detected in carbonate, phosphate, and amide III bands (p < 0.05), reflecting structural disorganization and modified spectral signal rather than tissue preservation. No differences were detected across the calculated infrared ratios (p > 0.05). EDX showed decreased absolute atomic percentages of Ca, P, and O in the NaOCl+HP group (p < 0.05), indicating structural demineralization, while its stoichiometric Ca/P ratio remained at 1.56. Isolated HP shifted the mineral stoichiometry to the highest numerical Ca/P ratio (1.69; range 1.58–1.80). Fluorine decreased across all treated groups (p < 0.001). Conclusions: Sequential NaOCl and HP application triggers distinct chemical alterations compared to individual treatments, inducing severe structural disorganization of the organic network and absolute mineral depletion of Ca and P. This multi-agent sequence alters dentin stoichiometry, which may compromise the biomechanical integrity of the tissue. Full article

The choice of a symmetric encryption algorithm in practice is rarely as straightforward as it may appear from theoretical comparisons alone. In addition to security considerations, real-world selection often depends on execution time, reliability, entropy-related behavior, resource efficiency, and suitability for different types of data. This paper presents an experimental software platform for benchmarking and multi-criteria recommendation of symmetric encryption algorithms. The platform combines automated encryption and decryption tests, metric collection, comparative analysis, and result visualization within a unified evaluation workflow. It also incorporates a multi-criteria model that transforms raw experimental measurements into an overall ranking and supports context-aware recommendation according to the requirements of a given usage scenario. The experimental study includes repeated tests on different input categories in order to examine algorithm behavior under varied operating conditions. The obtained results show that algorithm performance and overall suitability are strongly dependent on the evaluation perspective and the application context, which suggests that no single symmetric method should be regarded as universally optimal. The proposed platform offers a practical basis for comparative cryptographic analysis and may be useful both for research purposes and for informed decision-making in security-oriented software environments. Full article

This study examines the dynamic relationship between financial development and total energy production in emerging market economies (EMEs) using a balanced panel of 20 countries over the period 2000–2020. Unlike much of the existing literature that focuses on energy consumption or specific energy types, this paper conceptualises total energy production as an aggregate supply-capacity indicator that captures infrastructure investment, capital intensity, and long-run energy system expansion. Employing a panel autoregressive distributed lag model with the Pooled Mean Group (ARDL–PMG) estimator, the analysis distinguishes between long-run equilibrium relationships and heterogeneous short-run adjustment dynamics. The results reveal a stable long-run reciprocal relationship between financial development and total energy production, suggesting that deeper financial systems are associated with higher energy production capacity over time, while expansion in energy production is also linked to financial deepening. Short-run dynamics, however, are asymmetric, indicating the presence of adjustment frictions and investment lags in capital-intensive energy sectors. Robustness checks using a two-step System GMM estimator confirm the qualitative consistency of the main findings after accounting for potential endogeneity and simultaneity. Overall, the results highlight the importance of financial system development in supporting aggregate energy supply expansion in EMEs, while underscoring the need to account for transitional constraints and differing adjustment speeds across sectors and countries. The findings offer policy-relevant insights for aligning financial development with energy infrastructure investment during periods of structural transformation. Full article

This paper proposes an intelligent time-series early warning framework based on a production LSTM–Transformer network for petrochemical refining processes. A cascaded encoder–decoder architecture is designed, where the LSTM extracts local temporal patterns and medium-term memory from noisy industrial data, while the Transformer models global dependencies and cross-unit interactions via multi-head self-attention. An adaptive feature fusion layer bridges the representational gap between the two networks. A multi-stage preprocessing pipeline tailored for refining MES data handles missing values, outliers, and mixed operating conditions. Using 120 variables from five units of a fluid catalytic cracking unit, the framework predicts the regenerator bed temperature up to 8 h (48 steps) ahead. Comparative experiments show that the production LSTM–Transformer achieves a mean MAE of 0.088, a mean RMSE of 0.113, and the lowest median MAPE of 19.91% among all models, outperforming standalone LSTM (MAE 0.095, MAPE 20.85%) and Transformer (MAE 0.088, MAPE 20.49%). Robustness analysis confirms stable performance under strong noise (down to 5 dB) and missing rates up to 50%, with a median MAE of 0.1027 across tags. This work provides an effective, end-to-end predictive early warning solution that balances accuracy, production importance coverage, and industrial robustness, offering a generalizable data-driven paradigm for process industries. Full article

This study investigates nitrogen sources and biogeochemical pathways in a highly urbanized shallow aquifer in Shinagawa Ward, Tokyo, using an integrated approach combining hydrochemical analysis, multivariate statistics (PCA and K-means cluster analysis), and stable nitrogen isotopes (δ¹⁵N-NH₄⁺, δ¹⁵N-NO₃⁻, δ¹⁵N-DON, and dual δ¹⁵N–δ¹⁸O-NO₃⁻). K-means clustering (K = 2, silhouette = 0.54) partitioned all 41 samples into a background group (n = 34) and an ion-enriched group (n = 7; wells sbi 1, 2, 3, 4, 5, 13, and 19), with the latter exhibiting hydrochemical signatures consistent with localized sewage leakage. The convergence of hydrochemical, multivariate, and isotopic evidence suggests that soil organic matter may represent the dominant diffuse background source of nitrogen across the study area. DON constitutes the dominant fraction of total dissolved nitrogen (TDN), while the linear correlations between TDN and DON concentrations (r = 0.77, p < 0.001) and between δ¹⁵N-TDN and δ¹⁵N-DON (r = 0.88, p < 0.001) indicate a common primary source. The dominance of DON combined with the theoretical inverse relationship between δ¹⁵N-DON and DON concentration is consistent with active soil DON mineralization, supported by an isotope fractionation factor (ε = −4.4 ± 0.78‰). Dual isotope analysis of NO₃⁻ (δ¹⁵N–N–δ¹⁸O slope = 0.51) points towards denitrification as an ongoing process in the aquifer. Taken together, the isotopic variations among nitrogen species suggest a transformation sequence from soil organic nitrogen → DON → NH₄⁺/NO₃⁻ → N₂, though each step in this sequence is supported to varying degrees of confidence. These findings highlight the value of δ¹⁵N-DON as a tracer for nitrogen source attribution and cycling in urban groundwater systems, and underscore the importance of considering all dissolved nitrogen fractions in contamination assessments. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust, subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism. Together, these components capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring no data leakage across subjects in the training, validation, and test subsets. Learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections provide model interpretability. An ablation study quantifies the contribution of each architectural component. Performance analysis includes evaluation at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT achieved competitive performance among the contrasted models, reaching subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore support evidence that EEG-TACT provides a compact, stable, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings. They also motivate further validation on larger, multi-site, and medication-controlled datasets. Full article

The Tajik Basin is located on the eastern edge of the Central Asian segment of the Tethyan tectonic domain. The basin underwent intense tectonic transformation during the Himalayan period, resulting in complex structural styles, unclear original sedimentary characteristics and oil and gas geological conditions, and a complex process of oil and gas accumulation, which restricts the further evaluation of the basin’s exploration potential. Studying the Tajik Basin in the macro background of the Tethys tectonic domain, the tectonic sedimentary evolution of the Tethys tectonic domain has a significant effect on the basin’s tectonic evolution, sedimentary characteristics, and oil and gas accumulation conditions. The Tajik Basin has gone through four stages of tectonic evolution: the Late Permian to Triassic was the stage of back arc foreland basin; the Jurassic period was the stage of back arc extensional faulting depression; the Cretaceous–Paleogene period was the stage of depression basins; and the Neogene is the stage of the regenerated foreland basins. Through field geological surveys and analysis of outcrop samples, it has been determined that the Tajik Basin has developed three sets of source rocks: the Middle and Lower Jurassic, Cretaceous, and Paleogene. Among them, the organic matter abundance of the Middle and Lower Jurassic is relatively high, most of them are in the mature stage, and they are primarily gas-generating source rocks. The Cretaceous and Paleogene source rocks are mainly oil generating and in a low-mature state. There are four sets of reservoirs developed in the Tajik Basin: Middle-Upper Jurassic carbonate rocks, Lower Cretaceous clastic rocks, Upper Cretaceous carbonate rocks and Paleogene carbonate rocks. Comprehensive research shows that the Tajik Basin mainly develops three types of oil and gas reservoirs: Jurassic carbonate gas reservoirs, distributed in the southwestern Gissar Uplift and Surhan Depression in the western part of the basin; Paleogene carbonate reservoirs, distributed in the southern Vakhsh Depression and the eastern Kuliabu Depression; and multi layer–multi lithology oil and gas reservoirs, distributed in the northern Dushanbe Depression. The primary controlling factor for the three types of oil and gas reservoirs is tectonic movement, which forms traps and simultaneously reshapes the reservoirs, ultimately leading to effective accumulation of oil and gas. The distribution of oil and gas in the Tajik Basin is characterized by “west gas and east oil, west more and east less, west pre-salt and east post-salt, and pre-salt gas and post-salt oil”. Affected by the regional tectonic movements of the Tethys rich oil and gas tectonic domain, the basin has high-quality hydrocarbon source rocks, reservoirs, and cap rock conditions. The pre-salt Jurassic has the potential to form large natural gas reservoirs, while the post-salt Cretaceous and Paleogene still have further potential for exploration. Full article

Background and Objectives: The aim of this study is to evaluate the effects of oridonin on a cisplatin-induced ovarian injury rat model. Materials and Methods: Thirty female rats were divided into three groups. Group 1: control; group 2: cisplatin; group 3: cisplatin plus oridonin group. In groups 2 and 3, the rats were injected with 2.5 mg/kg (twice weekly) cisplatin intraperitoneally (i.p.) for 4 weeks. In Group 3, rats received oridonin (10 mg/kg/day, i.p.). At the end of the study, the ovaries were removed in all groups. Histopathologic analysis and follicle counting were performed. Plasma anti-Müllerian hormone (AMH), malondialdehyde (MDA), and tumor necrosis factor-alpha (TNF-α) levels were measured, while ovarian transforming growth factor-beta 1 (TGF-β1), SMAD family member 3 (SMAD3), and tissue inhibitor of metalloproteinases-1 (TIMP-1) levels were evaluated. Results: Oridonin alleviated cisplatin-induced histopathological changes in the ovarian tissue. The numbers of primordial, primary, secondary, and tertiary follicles were significantly decreased, while ovarian fibrosis was significantly increased in Group 2 compared with Group 1 (p < 0.05). Co-treatment with oridonin statistically significantly increased follicle counts at all developmental stages and markedly reduced ovarian fibrosis in group 2 compared with group 3. Compared with Group 1, AMH decreased, whereas MDA, TNF-α, TGF-β1, SMAD3, and TIMP-1 increased in Group 2 (p < 0.001); these alterations were markedly attenuated in Group 3. Conclusions: These findings suggest that oridonin may exert protective effects against cisplatin-induced ovarian injury. Full article

Landscape observatories have gained increasing relevance as socio-ecological observation systems aimed at monitoring, analysing and communicating landscape transformations and landscape evolution, although the field remains characterised by conceptual fragmentation and the absence of systematised international inventories. This study addresses this gap through the development and analysis of a curated inventory of 113 landscape observatories and related initiatives, constructed from a systematic web-based search and an explicit process of data screening and coding. The research examines the territorial distribution, temporal evolution, declared objectives, methodologies, operational scales and temporal continuity of the identified initiatives through descriptive and interpretative analyses supported by the existing literature, including the role of remote sensing, spatial analysis and repeat photography. The results reveal a strong European predominance, particularly in France, and a marked concentration of initiatives operating at regional scales, temporally associated with the implementation of the European Landscape Convention. The dated subset suggests a marked expansion after 2000, although many initiatives lack explicit chronological information. The analysis also highlights substantial heterogeneity in functional orientations, distinguishing between observatories with continuous photographic monitoring, temporally limited observation systems, landscape study centres, documentary repositories and other initiatives with non-systematic uses of photography. Furthermore, the study identifies a recurrent gap between declared objectives and the explicit articulation of methodologies and temporal monitoring schemes. Overall, the paper proposes a typological synthesis of landscape observatories and related initiatives and discusses their potential role as hybrid socio-ecological monitoring systems for adaptive territorial governance. The results also highlight the need for clearer analytical frameworks and greater methodological transparency to facilitate comparison and strengthen their contribution to territorial knowledge and governance. Full article

The snapshot proper orthogonal decomposition (POD) method relies on synchronously sampled datasets, significantly limiting its utility for analyzing asynchronous measurements in unsteady flow studies. This paper proposes a frequency-domain proper orthogonal decomposition (FDPOD) method tailored for mode extraction and flow field reconstruction from asynchronously sampled data. The FDPOD framework integrates three key components: frequency-domain transformation to decouple phase discrepancies inherent in asynchronous sampling, power spectral density (PSD) analysis combined with segmented ensemble averaging to suppress spectral leakage errors, and eigenvalue decomposition of energy-ranked frequency components to identify dominant coherent structures. Validated through numerical simulations of a subsonic jet and experimental measurements from a low-speed mixed-flow fan, the method demonstrates exceptional performance under asynchronous conditions: cumulative energy errors are reduced to 0.3% across the first 50 modes, while flow field reconstruction achieves 99.5% accuracy. Dominant mode structures exhibit remarkable consistency with those derived from synchronous conditions, with hot-wire measurement errors remaining below 0.03% for both asynchronous and temporally shuffled datasets. These results position FDPOD as a robust and practical tool for analyzing complex unsteady flows where synchronous data acquisition proves impractical, particularly in large-scale or spatially distributed measurement systems. Full article

Cybersecurity insider threats remain a significant challenge for modern organisations due to their potential to cause substantial financial and reputational damage. This paper presents a systematic review of insider-threat research (2019–2026) using the PRISMA methodology and introduces an empirically validated ensemble framework for insider-threat detection. The proposed approach combines User-Based Sequences (UBS), a self-supervised Transformer trained on next-token prediction and time-gap modelling, and an unsupervised anomaly detection ensemble operating on model-derived behavioural features. An answers directory is incorporated to provide grounded truth for insider entities and episodes within the CERT r6.2 dataset, enabling direct validation of detection outcomes. The framework integrates behavioural theory with machine-learning techniques to improve understanding of insider-threat precursors. Evaluation was performed using a seven-stage Isolation Forest ensemble incorporating multimodal behavioural and technical data streams. The approach successfully identified all insider users, achieving 100% recall and an AUROC of 0.93. Comparative analysis against a previously reported model showed comparable AUROC and perfect recall despite differences in evaluation methodology. While precision remained low (0.004) due to the extreme class imbalance in the full CERT r6.2 population (5 insiders among 4000 users), the results highlight the operational challenges of insider-threat detection in realistic enterprise environments. This research contributes a novel, reproducible framework that combines behavioural theory and advanced machine learning to support the detection and analysis of insider threats. Full article

This study focuses on 91 human individuals from the Western Zhou period excavated from the Jucun cemetery in Jiang County, southern Shanxi Province, and examines their dietary structure and its changes within the context of population movements in early China. Stable carbon and nitrogen isotope analysis was employed, combined with archaeological phase divisions, to compare dietary patterns across different periods. The results show that the Jucun population exhibits a diet dominated by C₄ resources, with a mean δ¹³C value of −8.0 ± 0.7‰ and a mean δ¹⁵N value of 8.6 ± 0.9‰, indicating a relatively low level of animal protein intake. Diachronic analysis indicates that δ¹³C values remain generally stable throughout the Western Zhou period, whereas δ¹⁵N values show a decreasing trend. Regional comparison further shows that populations of different origins all fall within the isotopic range characterized by millet-based agriculture in southern Shanxi. Overall, the dietary structure of this population exhibits a convergence toward an agriculture-based pattern centered on millet. This study provides bioarchaeological evidence for subsistence transformation and cultural integration among mobile populations in the Central Plains during the Western Zhou period. Full article

In the context of global climate change and intensifying resource and environmental constraints, improving agricultural eco-efficiency (AEE) has become critical to achieving the green transformation of agriculture. This study develops a comprehensive evaluation index system for AEE that incorporates factor inputs, expected outputs, and undesirable outputs. Using county-level panel data from 2010 to 2022 for the Yangtze River Economic Belt (YEB), it applied the super-efficiency slacks-based measure (SBM) model to quantify AEE. Furthermore, spatial autocorrelation analysis and the spatial Durbin model (SDM) are employed to reveal its spatiotemporal characteristics and influencing factors of AEE. The results indicate that the overall AEE of the YEB exhibited a fluctuating upward trend over the study period, yet significant regional heterogeneity persisted. AEE showed pronounced positive spatial correlations, with regional disparities primarily stemming from hyper-variance intensity, suggesting that high- and low-efficiency counties are spatially interwoven. The SDM results indicate that local temperature, economic development, urbanization, fiscal support for agriculture, and agricultural production structure positively influence local AEE, while rural residents’ income and educational attainment exert negative effects. These factors also demonstrate significant spatial spillover effects, with economic development and ecological conditions in adjacent regions generating positive externalities, while neighboring urbanization and temperature producing negative impacts. This study deepens the understanding of the driving mechanisms underlying AEE from a spatial interdependence perspective, providing a scientific basis for formulating cross-regional collaborative policies aimed at promoting green agricultural development in major river basins. Full article