Search Results (3,097)

Acetylsalicylic acid (ASA) is frequently detected in wastewater and sewage sludge, which may represent potential pathways of entry into agricultural environments. However, its effects on cultivated plants at trace concentrations remain unexplored. This study evaluated the effects of ASA (1–1000 ng·L⁻¹) on germination and root growth of Daucus carota, Solanum lycopersicum, and Cucumis sativus, as well as cytotoxic and genotoxic effects in the root meristems of Allium cepa bulbs and biochemical responses associated with redox homeostasis in the roots of all four species. No significant adverse effects were observed at the lowest concentrations tested; however, exposure to 100 and 1000 ng·L⁻¹ reduced root growth in all species, with a relative growth index below 0.8. In A. cepa, these concentrations induced mitodepressive effects, with mitotic indices below 70% relative to controls, and increased the frequency of chromosomal abnormalities, with total CAI values of 9.6% and 11.9%, respectively. At 100 ng·L⁻¹, C-metaphases and chromosomal disorganization during prophase were observed, characterizing an aneugenic effect, whereas at 1000 ng·L⁻¹, these abnormalities were accompanied by metaphases with sticky chromosomes, indicating a clastogenic effect. ASA also altered redox homeostasis in the roots of all four species, as indicated by concentration-dependent modulation of antioxidant enzymes, increased lipid peroxidation, and the absence of a compensatory non-enzymatic antioxidant response. These findings indicate that trace concentrations of ASA can impair early plant development and induce cytotoxic, genotoxic, and biochemical alterations in the roots of the species studied under the experimental conditions used. These findings emphasize the importance of assessing ASA effects under soil-based and field-relevant conditions to better understand its environmental implications. Full article

A quantum-classical correspondence for non-Hermitian symmetry breaking has recently been established using coordinate-space path integrals, providing a semiclassical understanding of spectral transitions at the level of individual eigenstates. Here we develop its dual formulation in momentum space by constructing the corresponding trace formula and quantization condition. We show that the real or complex nature of individual eigenvalues is determined by the symmetry properties of the associated semiclassical orbits, as in the coordinate-space path-integral approach. Moreover, we demonstrate that the topology of semiclassical orbits determines the natural formulation of the quantization condition: the coordinate- and momentum-space formulations are equivalent for contractible periodic orbits in phase space, whereas for noncontractible orbits, the quantization condition along the winding direction remains valid, but its dual form must be corrected by a boundary term. In particular, real-space-winding and Brillouin-zone-winding orbits naturally select coordinate- and momentum-space quantization, respectively. As a nontrivial application, we investigate the boundary-induced spectral transition of Bloch oscillations in a finite non-Hermitian lattice, where Bloch-oscillation orbits winding across the Brillouin zone preserve the relevant symmetry and yield real energy levels, whereas boundary-reflected orbits form symmetry-related pairs and give rise to complex-conjugate eigenvalues. Our work completes the quantum-classical correspondence framework for non-Hermitian symmetry breaking, extending its applicability to a broader class of non-Hermitian problems. Full article

The increasing occurrence of emerging organic contaminants (EOCs) in environmental matrices has intensified the demand for sensitive, selective, and sustainable analytical methods. Sample preparation plays a pivotal role, particularly for trace-level determination in complex matrices. Biochar is a promising sorbent for sample preparation because of its porous structure, tunable surface chemistry, low cost, and potential to be produced from renewable biomass waste. This review examines the use of biochar-based sorbents to extract EOCs from environmental matrices since 2020, with special emphasis on the relationships among precursor biomass, production conditions, surface chemistry, sorption mechanisms, and extraction performance. Agricultural waste was the predominant biomass class, while solid-phase extraction (SPE), magnetic solid-phase extraction (MSPE), and solid-phase microextraction (SPME) were the most frequently reported approaches. Biochar-based extraction has been applied to a wide range of EOCs, with pesticides being the most investigated analyte class. The authors discuss how pyrolysis conditions, activation, surface chemistry, and other parameters affect analyte retention and desorption. The authors compare different extraction approaches in terms of extraction efficiency, precision, and other analytical parameters. Finally, the authors discuss current limitations and research opportunities to guide the development of biochar-based sorbents as sustainable materials for sample preparation and environmental monitoring. Full article

This study examines 22@Barcelona (22@BCN), a widely cited case in the development of innovation districts. Initially conceived as a means of transforming Poblenou from a post-industrial area into a knowledge-economy hub, 22@BCN also formed part of Barcelona’s broader trajectory of entrepreneurial urban transformation. Its slowdown after 2008, Spain’s property-market bust, and the subsequent rise in residential and office rents brought into view a tension between innovation-led redevelopment and real-estate financialization. The analysis follows an explanatory single-case design and draws on documentary sources and secondary statistical data, primarily from official databases, to examine 22@BCN across multiple scales from 2000 to 2025. It traces the relationships among dependence on real-estate finance, housing pressures, changing regulatory arrangements, and uneven outcomes within 22@BCN. The findings suggest that these contradictions cannot be explained simply as a result of local planning failure or incomplete implementation. Rather, they need to be understood in relation to the interaction between innovation policy, real-estate finance, and multi-scalar governance. In doing so, the study contributes to critical research on innovation districts by highlighting the wider political–economic conditions through which districts are produced, financed, and governed. Full article

This study provides a review of recent advances in percussion-based bolt looseness monitoring. Beginning with the engineering mechanism of the tap-induced vibro-acoustic response in bolted joints, this study traces the technical evolution of the field across three progressive levels: basic signal preprocessing, discriminative feature extraction, and intelligent diagnosis modeling. Subsequently, a multi-dimensional comparative analysis of typical monitoring methods is conducted, and targeted combined detection schemes tailored to different engineering scenarios are synthesized. Furthermore, the main constraints impeding engineering application are elucidated, and prospective research directions oriented to complex engineering conditions are outlined. This review is intended to provide a systematic technical roadmap and theoretical framework for the further development and engineering deployment of intelligent percussion-based bolt looseness monitoring technology. Full article

Nitrate contamination threatens surface water and groundwater quality in karst regions, posing risks to drinking water safety and aquatic ecosystems. Strong surface water–groundwater connectivity in karst recharge areas can accelerate contaminant transport through fractures and conduits. In this study, 166 samples, comprising 100 groundwater samples and 66 surface-water samples, were collected under wet-season, normal-flow, and dry-season conditions from a typical karst recharge area in Fengshan Township, Dafang County, Guizhou Province, China. Hydrochemical analyses, dual nitrate isotope analysis, and isotope-based mixing models were integrated to evaluate potential nitrate source contributions and examine the hydrochemical factors associated with nitrate variability. Groundwater was dominated by Ca–HCO₃ and mixed hydrochemical facies and exhibited relatively stable ionic compositions, whereas surface water showed more diverse facies and greater variability in total dissolved solids, SO₄²⁻, Na⁺, K⁺, and Cl⁻, reflecting a stronger response to external inputs and short-term hydrological processes. NO₃⁻ concentrations ranged from 0.02 to 16.24 mg/L in groundwater and from 0.00 to 41.20 mg/L in surface water, with mean concentrations of 3.07 and 4.38 mg/L, respectively. Mixing-model estimates identified manure and sewage (47%) and soil nitrogen (30%) as the leading potential contributors to groundwater nitrate, whereas manure and sewage had the largest estimated contribution to surface-water nitrate (68%). Given the overlap among the isotopic signatures of potential sources, these percentages represent probable source combinations rather than exact apportionments. The absence of consistent covariation between NO₃⁻ and Cl⁻ indicated that nitrate transport was not controlled solely by conservative mixing but was jointly regulated by source-input intensity, rapid surface-runoff responses, conduit transport, subsurface mixing, dilution, and water–rock interactions. Statistical modeling further showed that groundwater NO₃⁻ variability was associated with the major-ion composition, whereas surface-water NO₃⁻ variability was partly explained by a multiple regression model incorporating SO₄²⁻ and Cl⁻. Together, these findings support a conceptual source-to-transport framework involving external inputs, rapid surface-water responses, karst conduit transport, subsurface mixing, and water–rock interaction. This study provides insight into contrasting potential nitrate sources and transport processes in connected karst surface water-groundwater systems and supports pollution-source tracing, recharge-area management, and drinking-water source protection. Full article

Background/Objectives: Detecting panoramic curves is a critical step in dental imaging, as it serves as the foundation for generating high-quality panoramic radiographs from Cone Beam Computed Tomography (CBCT) scans. These curves trace the dental arch, ensuring that key anatomical structures, such as teeth, alveolar ridges, and jaws, are accurately represented in a single 2D image. This study aimed to evaluate the performance of the PAN_CURVE system across diverse clinical scenarios. Methods: The system was evaluated in a preliminary external validation performed within a single imaging platform on a dataset of 50 CBCT scans acquired with the same CBCT device and including diverse dental conditions, such as edentulous zones and metal elements like implants and orthodontic devices. Performance was assessed in terms of root mean square error (RMSE), mean absolute error (MAE), and processing time. Results: The system achieved an average RMSE of 1.55 ± 2.91 mm and a MAE of 1.28 ± 2.43 mm. Its processing time averaged 4.70 ± 4.33 s per scan, demonstrating efficiency while meeting usability requirements. Conclusions: These preliminary findings support the potential suitability of the PAN_CURVE system for integration into clinical visualization and planning software for dental and maxillofacial applications; confirmation on larger, multi-device datasets is required before generalized conclusions can be drawn. Full article

Deep reinforcement learning (DRL) has become an important approach for manufacturing scheduling because it supports sequential decision-making under complex and changing production conditions. However, existing reviews primarily organize the literature by scheduling problem or learning method, providing less explicit support for tracing how manufacturing context, Markov Decision Process (MDP) formulation, scheduler architecture, and evaluation choices interact across heterogeneous studies. This study presents an evidence-based systematic literature review of DRL for manufacturing scheduling using a structured methodology for corpus construction, configuration-level coding, evidence traceability, study-quality assessment, and cross-study synthesis. The validated corpus comprises 52 primary studies and 54 independently coded DRL configurations. The evidence is synthesized across manufacturing scheduling characteristics, MDP design, DRL scheduler design, hybrid optimization, and empirical evaluation. The results show that scheduler design is context-dependent and architecturally diverse: manufacturing requirements are associated with differences in state, action, and reward formulation, while DRL schedulers combine different learning algorithms, representation architectures, control structures, and complementary optimization mechanisms. The evidence does not establish universal superiority for individual representations, algorithms, or hybrid architectures because reported outcomes remain strongly conditioned by problem formulation and experimental design. Evaluation evidence further highlights limited generalization, uneven statistical and component-level validation, and a continuing gap between benchmark or simulation studies and live industrial deployment. By linking study-, configuration-, and evidence-level information, this review provides a traceable basis for interpreting methodological relationships, identifying research gaps, and guiding the development and evaluation of DRL-based manufacturing scheduling systems. Full article

Magmatic oxygen fugacity (fO₂) and dissolved H₂O content are fundamental physicochemical parameters governing redox conditions, volatile behavior, mineral stability, and multivalent-element partitioning during magma evolution. Analysis of magmatic fO₂ and H₂O helps constrain magma differentiation, volatile evolution, and magmatic–hydrothermal processes. ZirconMelt-H₂O–fO₂ was developed to calculate these parameters from zircon trace-element data paired with user-provided melt compositions or user-designated representative whole-rock proxies, using LREE-I-assisted data review, Ti-in-zircon thermometry, Ce–U–Ti oxybarometry, lattice-strain fitting for melt Ce⁴⁺/Ce³⁺ estimation, NBO/T calculation, and H₂O inversion. The graphical interface is available in English and Simplified Chinese and supports batch import and sample-identifier matching of CSV and Excel datasets, user confirmation of calculation settings, export of results to Excel, CSV, and JSON, and generation of standardized plots in SVG, PDF, and PNG formats. Full article

Electromagnetic scattering from plunging breaking waves generates strong sea-surface radar returns that degrade radar-based maritime surveillance and target detection performance. This study develops a deep learning framework for automatic classification of simulated plunging-breaker scattering conditions using high-range-resolution profile (HRRP) data. The electromagnetic scattering data are generated using a physics-based Capillary Wave Modification Facet Scattering Model (CWMFSM) combined with ray-tracing techniques. Eight simulated plunging-breaker scattering conditions are constructed by combining two wind speeds, 7 m/s and 10 m/s, with four temporal conditions, Δt1, Δt10, Δt14, and Δt16. A total of 8000 HRRP samples are generated, with 100 normalized range-cell features extracted from each sample. Two deep learning classifiers, an artificial neural network (ANN) and a one-dimensional residual convolutional neural network (1D ResNet CNN), are comparatively evaluated. The ANN achieves an overall classification accuracy of 96%, compared with 91% for the 1D ResNet CNN under the simulated dataset and adopted model configurations. Robustness analysis under controlled additive white Gaussian noise (AWGN) conditions further shows that classification performance decreases as the signal-to-noise ratio is reduced, while noise-augmented training improves the robustness of both classifiers. Overall, the results demonstrate the feasibility of HRRP-based deep learning for distinguishing simulated plunging-breaker scattering conditions from sea-surface radar returns, providing a basis for further investigation of sea-clutter characterization and maritime radar applications. Full article

Online social networks increasingly serve as platforms for collective action, yet identifying minimal winning coalitions remains challenging due to heterogeneous user features, noisy behavioral traces, and the strategic ambiguity of swing members. Traditional neighborhood rough set models effectively handle numerical data but typically weight either attributes or objects in isolation, neglecting the joint influence of feature importance and user reliability. Moreover, boundary-region users, often critical for tipping coalition outcomes, are frequently discarded or misclassified by hard-thresholding mechanisms. To bridge these gaps, we propose a Dual-Weighted Neighborhood Rough Set framework, called DWNRS, that combines attribute-weighted neighborhood construction with object-reliability-weighted rough-membership estimation. In this formulation, attribute weights determine the geometry of distance-based neighborhoods, while object weights determine how reliably neighboring users contribute to membership estimation and coalition strength. We formalize dual-weighted rough membership, define lower, boundary, outside, and candidate approximation regions, and introduce a dependency-guided reduction algorithm that extracts coalitions that are winning and inclusion-wise minimal under the induced DWNRS strength function, whenever a winning coalition exists within the allowed candidate pool. Theoretically, we prove that DWNRS generalizes classical neighborhood rough sets and Pawlak rough sets, preserves the monotonicity required by simple games, and provides precise conditions under which boundary recruitment and inclusion-wise minimality hold. We further establish a boundary-change accounting identity that characterizes when and how the boundary region contracts, showing that contraction is a data-dependent empirical effect rather than a universal guarantee. Empirically, we validate the framework on a controlled synthetic benchmark and a semi-real US Congress Twitter interaction network under a fair common-strength evaluation protocol. Against the original non-hybrid baselines, DWNRS is the only method that consistently returns coalitions that are both winning and inclusion-wise minimal. A new GWNRS-style hybrid experiment shows that object-weighted geometric denoising can also produce winning and inclusion-wise minimal coalitions; on the synthetic benchmark, the hybrid obtains a slightly higher mean F1 than DWNRS under the fixed-quota protocol, while on the Congress benchmark both methods bypass boundary recruitment because the core alone is sufficient. These results clarify DWNRS’s distinct role without claiming universal classification superiority over the hybrid: DWNRS preserves a regulated boundary region and the strategic option value of swing-user recruitment in regimes where the core alone may be insufficient. Among the original non-hybrid baselines, DWNRS leads all classification metrics on the Congress topology and produces coalitions 23–35% smaller than the original winning baselines. Full article

Underground subway stations are highly vulnerable to flooding due to their below-grade configuration, multiple interconnected levels, and reliance on a limited number of vertical access points, yet the extent to which flood progression affects evacuation performance remains insufficiently quantified. This study evaluates the impact of flooding on evacuation performance in an actual four-level transfer station in Seoul, South Korea, by coupling a hydraulic inundation model with a node–link-based evacuation simulation. Water depth and flow velocity obtained from an EPA SWMM-based inundation simulation were converted into walking-speed reduction factors and applied to link weights in a Dijkstra’s algorithm-based evacuation routing model, together with crowd-congestion-based speed reduction. Evacuation performance was compared across three scenarios: a non-flooded baseline and evacuation initiated under 300 s and 600 s flood conditions. The maximum evacuation time increased from 453 s under the non-flooded condition to 523 s and 596 s under the 300 s and 600 s flood conditions, respectively, corresponding to increases of approximately 15.5% and 31.6%. Tracing the route of the last evacuated group showed that this delay arose not from a change in route length—the governing route measured approximately 78 m in all three cases—but from a congestion-driven shift in which platform side produced the binding delay, as evacuee flow was redistributed away from the flooded exit toward the unaffected side. These findings indicate that the onset of flooding measurably increases evacuation time by altering the spatial distribution of congestion rather than the route itself, and that evacuation guidance based on a fixed, pre-computed route cannot fully offset this time-dependent penalty, underscoring the need for real-time, flood-adaptive evacuation routing in underground station environments. Full article

We propose a two-stage joint estimation method combining a multi-physics-constrained physics-informed neural network (PINN) with an adaptive extended Kalman filter (AEKF) for lithium-ion battery state-of-charge (SOC) estimation. Three constraints—an RC polarization dynamics ODE residual, discharge voltage–SOC monotonicity, and terminal-voltage physical bounds—are embedded into the PINN loss function; the converged network then serves as the nonlinear observation model within the AEKF. On LG 18650HG2 cells across six temperatures (−20 to 40 °C) under a strict cross-condition setup (training on LA92/UDDS; testing on US06 and two mixed profiles), the method maintains a temperature-averaged SOC mean absolute error (MAE) of 2.33% (1.54–4.48%, three random seeds), whereas the MAE for the equivalent circuit model with the AEKF (ECM-AEKF) degrades to 9.46% and 11.66% at −20 °C and −10 °C, and remains 1.5–4.6× worse with the per-temperature re-identified parameters; an end-to-end long short-term memory (LSTM) baseline averages 1.90% but degrades to a per-condition maxima of 22.6% at low temperatures. Multi-seed ablations locate the constraints’ value in providing low-temperature stability and physical consistency, with the RC-ODE constraint contributing most. The study further reveals that goodness of terminal-voltage fit does not imply SOC accuracy; the controlled comparisons trace this to the model structure rather than parameter settings, exposing the risk of voltage-fitting-based evaluation over wide temperature ranges. Full article

Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in dynamic environments with frequent blockages. In this paper, we propose a fast and robust beamforming strategy enabled by a digital twin (DT) framework. Specifically, we develop a Conditional Generative Adversarial Network (cGAN)-based DT module that serves as a high-fidelity virtual surrogate for site-specific ray-tracing. By processing environmental 3D geometry and dynamic obstacle data, the cGAN predicts real-time Beam-Power Maps (BPM) with minimal computational latency. Building upon these predictions, we introduce a Graph Neural Network (GNN)-based resource allocation agent that models the network as a spatial interference graph to perform coordination and power control. Numerical results demonstrate that our proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase. Furthermore, the framework achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep learning and heuristic benchmarks. Full article

Rapid and accurate quantification of residual guar gum in bauxite slurry flocculation is a critical bottleneck for closed-loop control of flocculant dosage. In this study, we systematically compared the anti-interference performance of the two UV-Vis spectrophotometer approaches, the single-wavelength absorbance method (A₂₆₃) and the peak-trough difference method (ΔA), in complex slurry matrices. Our results revealed that the two methods respond differently to pH and salt concentration variations, leading to the proposal of a dual-mode synergistic detection strategy. The A₂₆₃ method provides quantification under the specific conditions tested within the ranges of pH 3.0–8.0 and CaCl₂ concentration below 4.5 mmol/L. Beyond these boundaries, deviations from the Beer-Lambert law occurred, attributable to conformational transitions or salting-out aggregation of the polymer chains. In contrast, the ΔA method partially mitigated background drift through differential calculation and exhibited a more stable signal trend than A₂₆₃ across the tested ranges (pH 2.0–11.0, CaCl₂ 0–18 mmol/L), suggesting its potential utility as a semi-quantitative indicator in challenging matrices. However, its quantitative precision was constrained by small absolute signal values and systematic dependence on pH and salt conditions. Based on these findings, we propose a synergistic strategy-preferring the A₂₆₃ method under routine conditions while recommending the ΔA method for high-salinity or wide-pH scenarios- and accordingly define the preliminary applicability boundaries based on signal response observations at a single concentration. Flocculation–sedimentation tests confirmed that the method successfully determined the optimum dosage (5.0 g/kg dry slurry), at which the residual concentration in the supernatant correlated negatively with the sedimentation rate (R² > 0.95). The supernatant matrix after sedimentation (pH ≈ 7.1, low ionic strength) fell exactly within the safe window. This work provides a methodological reference for spectrophotometric quantification of trace organics in turbid, saline, and pH-variable slurry systems, and lays an analytical foundation for intelligent dosage control in bauxite slurry dewatering. Full article