Search Results (318)

This study examines the relationship between the Potts model in statistical mechanics and mathematical knots. This is done by transforming the Potts model into knot polynomials. The knot polynomial in the Kauffman square brackets is used. Temperley–Lieb algebra is used to obtain the dichromatic polynomial of a graph. A special family of knots called Zengi knots (links) is considered, consisting of four different models. We reveal the partition functions of these knots (links) by using a strain factor corresponding to the particles in the Potts model. One of the deep connections between physics and mathematics is the existence of the relationship between the Potts model and similar models developed for some algebras and knot and link invariants. This is clearly stated here by the given applications. Full article

Polycyclic aromatic hydrocarbons (PAHs) represent a major class of ubiquitous environmental pollutants, posing significant risks to ecosystems and human health due to their persistence, toxicity, and potential for bioaccumulation. This review provides a comprehensive synthesis of current scientific knowledge on PAHs, integrating insights from chemical kinetics, environmental fate, and toxicological mechanisms. The fundamental structural chemistry of PAHs and its direct influence on their physicochemical properties and environmental properties are discussed. The major anthropogenic and natural sources of PAHs are detailed, alongside the chemical kinetics behind their formation during incomplete combustion and their transformation in environmental media. Unlike previous reviews that address PAH sources, remediation, or health effects as separate topics, this review uniquely traces the mechanistic continuum from molecular formation kinetics through physicochemical partitioning and environmental transport to toxicological endpoints, providing a causally linked framework for understanding how structural properties ultimately determine biological outcomes. A central focus is placed on the environmental fate and transport of PAHs across atmospheric, aquatic, and terrestrial compartments, highlighting processes such as gas–particle partitioning, sediment accumulation, and long-range transport. The review further elucidates the complex toxicological pathways of PAHs, including metabolic activation to reactive intermediates, DNA adduct formation, oxidative stress, and their roles in carcinogenesis and other systemic health effects. The analysis reveals strong scientific consensus on the carcinogenic mechanism of parent PAHs via CYP450-mediated metabolic activation to diol-epoxide intermediates while identifying critical areas of uncertainty: the current regulatory framework based on 16 priority PAHs underestimates total carcinogenic risk by a factor of 2–5, mixture toxicology remains poorly characterized, and dose–response relationships for non-cancer endpoints (cardiovascular, neurodevelopmental, immunotoxic) lack the quantitative data needed for robust risk assessment. Finally, human exposure pathways and health risk characterization approaches are discussed, highlighting the need for cumulative, mixture-based assessment frameworks. Full article

Offshore wind energy offers abundant resources and significant potential for large-scale development. Efficient design of collection systems is critical to the economic viability of offshore wind farms (OWFs). This study proposes an upper–lower level topology optimization framework based on the Artificial Lemming Algorithm (ALA) to address the complexity arising from large numbers of wind turbines (WTs). At the upper level, wind turbines can be partitioned into different numbers of regions according to practical engineering requirements using the Radial Fuzzy C-Means (RFCM) clustering algorithm. At the lower level, the ALA is applied to optimize the collection system topology within each region, aiming to minimize total construction cost while satisfying operational constraints. A case study involving a 75-WT offshore wind farm is conducted. Comparative simulations against various heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) show that the proposed method achieves faster convergence, lower total costs and greater robustness. Specifically, the ALA reduces the best cost by 9.9% and improves average runtime by 28.5%, indicating its advantages in best-cost search and computational efficiency in the tested case. In addition, based on 10 independent runs, the ALA achieves the lowest median cost of $6684\times {10}^4$ CNY, with an interquartile range of 6593–$6813\times {10}^4$ CNY and a cost range of 6362–$7087\times {10}^4$ CNY. Overall, the proposed framework provides a practical optimization approach for obtaining low-cost feasible collection-system layouts in the studied offshore wind farm case. Full article

Accurate and reliable localization is a fundamental requirement for autonomous mobile robots operating in structured indoor environments. Adaptive Monte Carlo Localization (AMCL), widely used due to its probabilistic flexibility, suffers from performance degradation in challenging situations such as low-motion, sensor degradation, symmetry ambiguity, and abrupt position changes (kidnapped robot). This study proposes the Confidence-Gated Virtual Scan Motion AMCL (CG-VSM-AMCL) approach, which extends the standard AMCL structure with a selective and confidence-based posterior enhancement mechanism to overcome these limitations. The proposed method integrates beam partitioning, cluster-based dominance analysis, observability-aware gating, and recovery-driven adaptive particle injection components within a holistic architecture. The method was evaluated on a structured department map under seven representative scenarios: cold-start, low-motion, kidnapped robot recovery, odometry bias, scan dropout, world–model mismatch, and symmetry ambiguity. Experimental results demonstrate that the proposed approach systematically reduces localization error, false-lock rate, and convergence time compared to basic AMCL variants, and improves stability under challenging conditions. The significant improvements achieved, particularly in low-motion and symmetry-containing environments, reveal that selectively activated correction strategies can substantially increase localization robustness without altering the fundamental probabilistic structure of AMCL. Full article

The flooded rice rhizosphere is a continuous reactive interface composed of sediment, porewater, root-surface oxic microdomains, and iron plaque, where redox processes and Fe cycling regulate Cd/As speciation, bioavailability, and plant accumulation. Engineered nanomaterials (ENMs) have shown potential for reducing Cd/As uptake in rice, but the coupled roles of microbial community responses, iron-plaque gating, and cross-interface elemental migration remain insufficiently integrated. This review synthesizes the current evidence on ENM transformation and partitioning at flooded rhizosphere microinterfaces, focusing on front-end speciation changes, root-surface retention, microbial functional regulation, and plant sequestration or transport. Correlative evidence suggests that rhizosphere microorganisms are associated with altered redox conditions, Fe cycling, As methylation potential, and metabolite secretion, which may influence Cd/As partitioning and cross-interface migration. However, direct causal validation of the complete ENM transformation–microbial response–Fe cycling–Cd/As flux–grain accumulation sequence within a single integrated system remains lacking. We further discuss how elevated CO₂, micro-/nanoplastics, Fe/DOM dynamics, and water management regimes may modify this framework, and we identify Sb as a theoretical boundary case because direct ENM–rice evidence remains limited. Finally, we highlight the need to integrate spatial tracing and imaging methods, including persistent luminescence tracing, LA-ICP-MS, NanoSIMS, and µ-XRF/µ-XANES, with metaomics to connect particle localization, microbial function, and contaminant fate. Full article

The construction cost of an offshore wind farm collection system accounts for 15–30% of the total investment, and its efficient design is crucial to the economy; however, traditional methods in large-scale scenarios suffer from slow convergence and local optimization problems. In this study, we propose an upper and lower topology optimization framework based on the sled dog optimizer (SDO). The upper layer adopts polar coordinate partitioning combined with dynamic minimum spanning tree (DMST) to realize wind farm partitioning, and deals with the current-carrying capacity constraints and cable no-crossing requirements in a synchronized manner. The lower layer applies the SDO algorithm to optimize the topology structure within the partitioning range. The performance of genetic algorithm (GA), immunity algorithm (IA), particle swarm optimization (PSO), and SDO approaches is compared by the dynamic minimum spanning tree method through the case of an offshore wind farm with 62 wind turbines (WTs). The results show that the SDO-DMST framework significantly outperforms the comparison algorithms in terms of computational efficiency and cost optimization, and the proposed method can stably obtain high-quality cable topology solutions, which proves its superiority in unit group partitioning and cable routing co-optimization. In this paper, the SDO is introduced to collection system optimization for the first time, providing an efficient and robust design solution for large-scale offshore wind farms. Full article

Particle swarm optimization (PSO) is a simple and efficient metaheuristic algorithm that has been widely applied to solving various practical problems. However, PSO has some inherent limitations, such as a tendency to get trapped in local optima and an imbalance between global exploration and local exploitation. To overcome these challenges, this paper proposes a novel algorithm called the multi-swarm particle swarm optimization algorithm with multi-learning strategy (MPLPSO). First, the entire swarm is randomly partitioned into multiple sub-swarms, each comprising three distinct types of particles, which enables the algorithm to explore multiple potential solutions simultaneously. Next, a pool elite learning strategy combined with a convergence learning mechanism is employed to effectively reduce the risk of premature convergence. Furthermore, an elimination-replacement mechanism is integrated with a hierarchical competition strategy to further enhance the solution accuracy. Extensive experiments conducted on the CEC 2017 and CEC 2022 benchmark test suites demonstrate that the proposed MPLPSO significantly outperforms the classical PSO and several state-of-the-art PSO variants. Additionally, MPLPSO is also applied to the traveling salesman problem, and the experimental results further validate the superior performance and robustness of the proposal. Full article

Concrete contributes significantly to global CO₂ emissions due to high energy demand for cement production. This research integrates multiple advanced ensemble ML-based prediction models by combining experimental evaluation, explainable framework, and life cycle sustainability analysis for SCM (supplementary cementitious materials)-incorporated concrete mixtures. A comprehensive experimental program was conducted to evaluate the compressive and tensile strength of concrete revealing that the hybrid mix of GF4 with a 40% replacement level of cement with fly ash (FA) and ground granulated blast furnace slag (GGBFS) exhibited optimum synergistic performance due to balanced hydration kinetics and improved microstructure characteristics. For computational model development, a k-fold cross validation technique was deployed to evaluate robustness across multiple data partitions and to control overfitting in models. Model performance was assessed through multiple metrics including R², RMSE, and MAE with particular emphasis on the gap between training and testing performance. The best performing model was optimized using Particle Swarm Optimization (PSO) and Bayesian Optimization (BO) techniques providing an additional safeguard against overfitting. Shapley Additive Explanation (SHAP) interpretation revealed w/b ratio and curing age as key parameters for compressive strength, while fine aggregate content and curing age influenced tensile strength. For compressive strength, XGBoost model performed well with an R² value of 0.879 which was increased to 0.918 with the PSO optimization technique. For tensile strength, the Gradient Boosting model was selected with an R² value of 0.840 which was optimized to 0.879 after the PSO optimization technique. Moreover, life cycle assessment was performed to evaluate the environmental impacts in terms of embodied carbon and energy associated with concrete mixes. The hybrid GF4 mix demonstrated a 36% reduction in embodied carbon compared to the control mix, indicating strong potential for low carbon concrete applications. This integrated research contributes to the advancement of green construction practices and supports global efforts to reduce atmospheric impacts through the circular use of industrial byproducts. Full article

This study applies the Constructal Design method to the geometric optimization of a branched symmetric concentration divider for calibrating ultrasound devices used to monitor tumor response with dynamic contrast. Accurate calibration ensures image quality and diagnostic reliability. The geometry consists of a three-dimensional, tree-shaped flow network with two inlets and three outlets, where inlet 1 carries water containing contrast particles, while inlet 2 carries only water. Laminar flow simulations are performed using Computational Fluid Dynamics (CFD) with Ansys Fluent, assuming no-slip wall conditions and zero-pressure outlets. The analysis investigates the effects of the inlet velocity ratio, the diameter ratio, and the vertical positions of the central outlet and inlet tubes, while keeping the total volume and inlet diameter constant. Additionally, velocity, pressure, particle distributions, flow partition ratio, and hydraulic resistance are evaluated. Results show nearly linear concentration responses among the outlets (100%, 50%, and 0%) when the device approaches geometric symmetry with equal inlet velocities, demonstrating efficient control of flow splitting. Although the diameter ratio imposes a trade-off with hydraulic resistance, geometric symmetry combined with Constructal Design promotes improved flow uniformity and enhanced performance, with potential applications in microfluidic mixers that require precise intermediate concentrations. Full article

Phosphogypsum (PG) stacks are traditionally assessed as sources of legacy inorganic contaminants, but the behavior of emerging contaminants in these chemically complex systems remains poorly understood. This opinion article proposes that PFAS, if present in PG stacks, may not be adequately described by partitioning concepts derived from dilute groundwater or ordinary soil porewater systems. Instead, the low-pH, high-ionic-strength, and calcium–sulfate-rich conditions of PG leachate may promote hydrochemistry-mediated repartitioning of PFAS. Under such conditions, PFAS may exhibit reduced apparent aqueous stability, enhanced association with PG particles or colloids, retention on particle surfaces, and enrichment at air–water interfaces, forming potential hidden reservoirs with the potential for delayed release and episodic remobilization. Consequently, dissolved concentrations alone may underestimate total PFAS storage and long-term groundwater risk in and around PG stack systems. Overall, this study highlights the need to shift from conventional dilute-system assumptions toward a hydrochemically mediated multiphase framework for PFAS occurrence assessment, monitoring design, and risk evaluation in phosphogypsum environments and other chemically complex industrial waste systems. Full article

The management of fine bauxite tailings, rich in clay minerals, represents an environmental and operational challenge for the aluminum industry. This study (Part 1) presents a pilot-scale investigation into the dewatering of these ultrafine tailings using a decanter centrifuge, 0.62 m in diameter, as an alternative to conventional wet storage. Tests were conducted at three bowl speeds, 1600 rpm, 1700 rpm, and 1800 rpm, corresponding to G-forces of 888, 1003, and 1124 G. The feed slurry behaved as a non-Newtonian, yield-pseudoplastic fluid, as confirmed by rheology tests. A comprehensive mass balance and performance analysis were conducted. The results demonstrated a monotonic improvement in key performance metrics with increasing bowl speed. Accordingly, increasing the G-force from 888 G to 1124 G improved the final cake solid content from 66.3% to 71.5% (by weight), together with an increase in the average solid recovery from 40.0% to 56.2%. Partition curve analysis revealed the primary limitation: while recovery of particles coarser than 20 µm was very high (>98%), recovery of particles finer than 20 µm remained low, ranging from 22.0% to 35.1%. Partition curve analysis using the Whiten model identified a mechanical cut size (d_50c) ranging from 9.72 µm to 12.0 µm. Hydraulic bypass increased from 8.35% to 14.9% with increasing bowl speed, indicating a significant non-size-selective component of separation. Rheological analysis further showed that the apparent viscosity at 100 s⁻¹ decreased from 0.332 to 0.111 Pa·s across the tested conditions, confirming enhanced slurry mobility and its contribution to increased ultrafine bypass. While overall solid recovery reached 56.2% at 1124 G, the mechanical capture of the ultrafine fraction (<5 µm) remains the primary bottleneck for industrial viability. It is concluded that while the decanter centrifuge is mechanically viable for producing a high-solid cake, the limited recovery of fines would create an unsustainable circulating load in an industrial plant. These results demonstrate that G-force alone, within the tested range, is insufficient to manage these tailings and provide the basis for the mathematical modeling required to design the process, as described in Part 2 of this investigation. Full article

The present study provides a comprehensive investigation into the hydrodynamic performance of grooved rubber journal bearings (GRJBs) employed as shaft supports in various rotating systems, with particular emphasis on marine applications. These bearings are lubricated with non-Newtonian fluids such as modern oil containing additives and viscoelastic water-based lubricant, which—owing to its complex composition including hydrocarbon chains, metal oxides, and impurity particles and contaminants such as salts, organic substances, microalgae, biopolymers, and microorganisms—deviates from the ideal Newtonian fluid model and demonstrates non-Newtonian rheological behavior. By examining various theories used in the analysis of non-Newtonian fluid behavior, the power-law model, which has a high degree of generality, has been employed in the present study. Also, to improve modeling accuracy, the elastic deformation of the rubber bush in this study is characterized using the Winkler foundation approach and analyzed via the finite element method (FEM). This advanced mechanical formulation, integrated with non-Newtonian lubrication modeling of lubricant using the power-law fluid model, and the parametric assessment of groove number and dimensions on steady-state bearing performance parameters, constitutes the core of this research. The investigation focuses on groove configurations of 4, 6, 8, and 10 channels. The findings indicate that increasing the groove count partitions the convergent pressure film zone into discrete segments, thereby reducing the maximum hydrodynamic pressure while intensifying the overall energy dissipation within the bearing. Additionally, the influences of rheological properties of the fluid—namely the power-law index (n) and the consistency index (m)—on key performance characteristics are thoroughly examined. An increase in both parameters enhances the effective viscosity and load carrying capacity; however, the exponential amplification due to the power-law index exhibits a more pronounced effect on load capacity and peak pressure compared to the consistency index. Full article

Salt-bearing foreland fold–thrust belts represent a critical tectonic system for ultra-deep hydrocarbon exploration. In the Kalasu structural belt of the Kuqa Depression—characterized by the “four extremes” of ultra-high temperature, pressure, salinity, and stress—conventional single-detachment models fail to adequately resolve the complex subsalt structures. To address this challenge, this study integrates high-resolution 3D seismic data, field outcrop observations, well logs, balanced cross-sections, and particle image velocimetry (PIV)-monitored physical modeling to propose a ramp–flat multi-detachment model. Our results demonstrate that deformation is governed by four regional detachment horizons: gypsum-salt layers, thick mudstones, coal-bearing strata, and the basement, which vertically partition the basin into six tectonic units: supra-salt, salt, subsalt, supra-coal, coal, and sub-coal basement. The structural architecture is controlled by five key factors: (1) paleo-uplift geometry, (2) distance from the South Tianshan orogenic front, (3) orientation of basin-bounding faults, (4) regional stress regime (pure compression versus transpression), and (5) rheological contrasts among detachment layers. The kinematic evolution follows a progressive sequence: basement-involved thrusting → multi-level ramp–flat detachment folding → cover detachment. Three primary trap levels are identified—subsalt, supra-coal, and sub-coal—hosting six distinct trap styles: pop-up anticlines, imbricate faulted anticlines, structural triangle zones, fault-bend fold anticlines, supra-coal anticlines, and inter-coal/sub-coal anticlines. Notably, under transpressional stress, oblique paleo-uplifts control the formation of enigmatic “fish-scale” arcuate trap belts composed of fault-bend fold anticlines. Full article

Accurate prediction of the Remaining Useful Life (RUL) of special gas cylinders is critical for industrial safety management. However, the nonlinear, strongly coupled degradation behaviors of these cylinders, combined with non-stationary and high-noise monitoring data, limit the performance of single deep learning models. Traditional hyperparameter tuning and signal processing methods often fail to meet the required prediction accuracy. To address these challenges, this study proposes a hybrid SSA–PSO–ResNet–LSTM–Attention framework for RUL prediction of special gas cylinders. The framework first applies Singular Spectrum Analysis (SSA) to decompose and reconstruct the 12-dimensional multi-source sensor signals, effectively suppressing noise while extracting core degradation trends. Subsequently, a ResNet–LSTM–Attention collaborative model is constructed, where ResNet ensures stable spatial feature propagation, LSTM captures long- and short-term temporal dependencies, and a multi-head attention mechanism emphasizes critical time steps associated with abrupt degradation. Furthermore, a Particle Swarm Optimization (PSO) algorithm is employed to globally optimize key hyperparameters, including the number of convolutional kernels, LSTM hidden units, and learning rate, mitigating the subjectivity of manual tuning. Experimental validation is conducted on 1000 real monitoring samples from 100 composite material gas cylinders, with a cylinder ID-based 7:1:2 train–validation–test split and stratified sampling covering four operating conditions. PSO optimizes hyperparameters using the validation set RMSE as the fitness function, and the test set is exclusively used for final performance evaluation. All results are reported as the mean ± standard deviation from grouped 5-fold cross-validation on the cylinder-wise partition. The proposed model achieves a test RMSE of 71.55, MAE of 50.63, and R² of 0.9584, representing a 34.2% and 30.2% reduction in RMSE and MAE, respectively, compared with the second-best CNN-LSTM model, and significantly outperforming SVR, MLP, and other benchmark models. Ablation studies confirm the positive synergistic effect of each component, with the removal of either the attention mechanism or the ResNet module causing substantial performance degradation. By employing physically calibrated RUL labels and a balanced multi-condition dataset, the proposed framework achieves high predictive accuracy and good potential for industrial application, providing an effective solution for RUL prediction of special gas cylinders and similar high-pressure vessels, with potential applications in intelligent maintenance of complex industrial equipment. Full article

Coastal systems are vital to human societies, delivering numerous ecosystem services. However, human activities introduce contaminants, especially trace metals (TM) that contribute to their degradation. These environments are inherently dynamic and complex, characterized by rapidly occurring biogeochemical processes. As a consequence, high-frequency sampling is required to evaluate short-term TM dynamics. The hourly temporal variations in nine TM (V, Mn, Ni, Cu, Zn, Cd, Pb, Co and U) concentrations and size-partitioning (<0.02, <0.2 µm, raw sample and in the suspended particulate matter) were investigated during a 27 h diurnal cycle within the Arcachon Bay (SW France). The results demonstrated that: (i) the TM were mainly represented in the potentially bioavailable fraction (<0.02 µm), except for Pb which remained predominantly associated with the particles, (ii) the temporal variability for U and V was only due to the mixing of water bodies contrarily to the 7 other TM, (iii) there was no clear influence of daytime conditions on TM concentration and/or size-partitioning, and (iv) a superimposition of multiple processes controlling TM speciation. Finally, the calculated risk quotients for species demonstrated an ecological risk for the marine biota for Co and Cu. These findings highlight the importance of high-frequency sampling combined with size-fractionation approaches to better resolve TM speciation dynamics, thereby helping to address the persistent knowledge gap in the distribution and biogeochemical cycling of TM between particulate, colloidal and truly dissolved phases in aquatic systems. Full article