搜索结果：Entropy (Basel, Switzerland)

Rain gauge networks are the core infrastructure for hydrological and water resource monitoring, flood control and disaster mitigation early warning, and water resource planning and regulation. The rationality of their layout directly determines the accuracy, representativeness, and economy of regional precipitation data acquisition. Considering that information entropy can accurately characterize the spatial distribution law and information complexity of rainfall, and spatiotemporal deep learning models have strong capabilities in fitting spatiotemporal features, this paper couples mutual information entropy with a spatiotemporal deep learning model and proposes a novel optimal layout method for rain gauge networks. Daily observed rainfall data from 50 ground-based rain gauges in the upper reaches of the Tuojiang River during 2015-2024, as well as the PERSIANN-CCS remote sensing precipitation product for the same period, were used in the study. A CNN-LSTM spatiotemporal deep learning model integrating spatial features and temporal dependence was constructed, coupled with the mutual information entropy index, and the GA-PSO hybrid optimization algorithm was applied for solution. The superiority of the proposed method was verified by comparison with the calculation results of the traditional mutual information entropy-based greedy optimization algorithm. The results show that the hybrid optimization algorithm driven by the spatiotemporal deep learning model coupled with mutual information entropy is significantly superior to the comparison algorithm in terms of the rationality of the station network structure, the ability to characterize spatial rainfall distribution, the control of average relative error, and the improvement of total information entropy. After optimization, the number of rain gauges in the upper reaches of the Tuojiang River can be reduced from 50 to 25. While greatly reducing the number of stations, the optimized network can still relatively accurately reflect the spatiotemporal characteristics of rainfall in the basin, which can provide a theoretical basis and technical support for the optimal layout of basin rain gauge networks and water resource management.

Entropy generation analysis provides a thermodynamic framework for quantifying irreversibility in thermal systems. However, most existing second-law studies rely on simplified boundary conditions and do not consider fully coupled conjugate heat transfer involving fluid convection, wall conduction, and external heat exchange. Consequently, thermodynamic assessments under realistic conditions remain limited. This study presents an entropy generation-based assessment of turbulent conjugate heat transfer in circular pipes by considering the combined effects of wall thickness ratio (0.02-0.08), wall thermal conductivity (0.2-400 W/m·K), and external convection (5-100 W/m2·K). A three-dimensional steady RANS-based conjugate heat transfer model is employed, and entropy generation is evaluated to quantify irreversibility within fluid and solid domains. The results indicate that wall-related thermal resistances significantly affect thermodynamic performance. Variations in wall conductivity lead to approximately 15-20% changes in total irreversibility, while increasing external convection from 5 to 20 W/m2·K results in up to 25-30% variation. Increasing wall thickness enhances conductive entropy generation, whereas higher Reynolds numbers increase overall irreversibility. These findings demonstrate that the Biot number is a key parameter governing irreversibility distribution. The results provide energy-efficient design insights for optimizing thermally coupled engineering systems under realistic operating conditions.

Underwater images often exhibit low contrast and loss of detail due to light scattering and absorption, which poses significant challenges for visual analysis in aquatic environments. Polarization imaging addresses these issues by exploiting the polarization states of light, effectively reducing backscatter and enhancing image contrast. In this paper, we propose a polarization image fusion method guided by information entropy and a hierarchical-adaptive fusion strategy. Local information entropy is first employed to perform multiscale denoising on Degree of Linear Polarization (DOLP) images, enabling adaptive detail reconstruction while distinguishing texture from noise. Subsequently, a hierarchical fusion framework is applied: low-frequency components are enhanced through detail injection, while high-frequency components are fused using a structure-guided mechanism that leverages low-frequency gradient information to generate soft masks for phase-aligned detail integration and edge sharpening. Experiments conducted on self-collected underwater images, two public underwater datasets, and three general-scene datasets demonstrate that the proposed method improves objective metrics, including information entropy, average gradient, and edge strength. Subjective evaluations further confirm its effectiveness in preserving details and adapting to diverse scenes. Furthermore, rigorous ablation studies and runtime analyses demonstrate that the optimized framework achieves a highly favorable balance between robust, artifact-free detail enhancement and computational efficiency. The proposed approach provides a practical solution for underwater image enhancement, with potential applications in target detection and infrastructure inspection.

High-emissivity coatings on flexible fibrous fabrics are promising thermal-insulation materials for thermal protection systems in hypersonic vehicles. However, the interfacial bonding strength between the high-emissivity coating and the flexible fibrous substrate remains a critical challenge. A high-emissivity double-layer TiO2 interlayer modified by metal ions (Mg2+, Co2+, Ni2+, and Zn2+) with high-entropy (M2+@TiO2-HE) coating was developed on an aluminosilicate fiber fabric. The interlayer of the M2+@TiO2-HE coating not only enhances bonding strength through mechanical interlocking but also improves thermal insulation performance by acting as an infrared reflective layer. The bonding strength between the M2+@TiO2-HE coating and the ASFF substrate showed an 183% enhancement compared to that of the single-layer high-emissivity high-entropy coating. Under one-sided heating at 1400 °C, the backside temperature of the coated ASFF stabilizes at 330 °C, which is approximately 50 °C lower than that of the sample with the single-layer high-emissivity high-entropy coating. In the wavelength range of 0.3-2.0 μm, the average reflectivity of the M2+@TiO2 coating was 0.70, and in the wavelength range of 1-14 μm, the average emissivity of the M2+@TiO2-HE coating was 0.89. The M2+@TiO2-HE coating with high emissivity and reflectivity enhances both the bonding strength and thermal insulation performance of ASFF, showing its potential for applications in aerospace thermal protection systems.

As one of the most promising new materials in the field of materials science, high-entropy alloys (HEAs) have attracted widespread attention due to the unique structure, exceptional properties and engineering performance, and complex composition. The CoCrFeNiZr0.5 eutectic high-entropy alloys (EHEAs) exhibits excellent high-temperature thermal stability, ductility, creep resistance, and corrosion resistance, demonstrating great potential for applications in marine equipment. This paper explores the engineering feasibility of electrical discharge machining (EDM) of CoCrFeNiZr0.5 EHEAs and investigates the EDM of micro-holes using a hollow copper electrode on a CNC EDM drilling machine under various machining parameters, including different gap voltage, pulse-on time, pulse-off time, and pulse amplifier settings. The effects of these parameters on the inlet diameter, outlet diameter, and recast layer of the micro holes are analyzed. The optimal micro-hole machining parameters are determined by comprehensively considering machining efficiency and electrode wear: gap voltage of 33 V, pulse-on time of 3 μs, pulse-off time of 1 μs, and pulse amplifier output of 3 A. Adopting the parameters to process a button ingot sample with a depth of 5 mm, it was found that the machining speed is 7.79 mm/min and the electrode wear is 1 cm. This research renders the foundation for further development and engineering application of CoCrFeNiZr0.5 EHEAs in the context of high-value material design and manufacturing.

High-entropy alloys, due to their excellent mechanical properties and service stability, hold broad application prospects under extreme working conditions. However, their high strength and complex multi-component characteristics also pose significant processing challenges. This study investigates the nanoscale material removal mechanisms of single-crystal and polycrystalline FeCrNiCoCu high-entropy alloys (HEAs) under abrasive scratching using molecular dynamics simulations. In single-crystal HEAs, dislocations preferentially nucleate along <110> directions, with significant lattice self-healing and elastic recovery. Crystallographic orientation strongly affects dislocation density, phase transformation, and residual plastic deformation, with the (100) plane exhibiting the most favorable machining performance. For polycrystalline HEAs, subsurface deformation is dominated by dislocation migration, grain boundary rupture, and dislocation entanglement, leading to higher dislocation density, larger residual plastic deformation, and increased phase transformation compared with single crystals. Elemental composition significantly modulates these behaviors: higher Cu and Cr contents suppress dislocation motion and reduce subsurface defects, improving surface quality, whereas higher Fe content slightly increases plastic deformation but mitigates phase transformation and amorphization. Grain size effects are also pronounced, with smaller grains showing higher dislocation density and residual deformation. These findings provide atomic-scale insights into the combined effects of crystallography, grain size, and elemental composition on the machining response of FeCrNiCoCu HEAs, offering guidance for precision machining and alloy design.

Explainable Artificial Intelligence (XAI) has become a critical requirement in medical image analysis, where transparency and interpretability are essential for clinical trust and decision support. Melanoma is recognized as one of the most deadly types of skin cancer, with its occurrence exhibiting an increasing pattern in recent times. However, detecting this cancer in its initial stages greatly increases patients' chances of long-term survival. Various computer-based techniques have recently been proposed to diagnose skin lesions at their early stages. Even though the machine learning community has achieved a certain degree of success, there is still an unresolved research challenge regarding high error margins and the limited interpretability of automated systems. This study focuses on addressing both segmentation and classification tasks, with particular emphasis on two key concepts: (1) improving image quality to maximize distinguishability between foreground and background regions, thereby enhancing visual interpretability and segmentation accuracy and (2) eliminating redundant and cluttered feature information to generate the most discriminative and compact feature representations. The input images are initially processed using a novel metaheuristic contrast-stretching method to estimate image-specific key parameters, thereby enhancing lesion boundary clarity in a clinically interpretable manner. Following this, the improved images are fed into selected pre-trained deep models, including DenseNet-201, Inception-ResNet v2, and NASNet-Mobile. The extracted features from all pre-trained models are fused to produce resultant vectors, which are then refined using a bio-inspired feature selection method, termed entropy-controlled whale optimization, to retain only the most informative attributes. The selected discriminative feature set is subsequently classified using multiple classifiers. The results indicate that the proposed framework achieves superior performance compared to existing methods in terms of accuracy, sensitivity, specificity, and F1-score. Additionally, it facilitates a more explainable, transparent, and structured diagnostic pipeline appropriate for medical applications.

A superconducting high-entropy alloy (HEA) Nb0.67(TiZrHf)0.33 powder was successfully synthesized via mechanical alloying for the first time. X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, magnetic measurements, and specific heat were used to investigate its structural and physical properties. The alloy was crystallized in a single-phase body-centered cubic structure with a small amount of non-magnetic impurities coming from ball milling. Specific heat data confirms the presence of bulk superconductivity in the as-synthesized state, with the broadness of the thermodynamic anomaly reflecting the significant chemical disorder and distribution of critical temperatures typical of HEAs. Tc is in the range 6-7.5 K, and the upper critical field &#x3bc;0Hc2 is in the range 6.4-7.6 T. These results demonstrate that mechanical synthesis is a viable route for producing superconducting HEA powders, which are promising candidates for consolidation via sintering and provide a robust platform for investigating superconductivity in highly disordered systems.

This study proposes a novel synergistic design strategy to enhance the oxidation resistance of FeNiCuAl-based high-entropy alloys by integrating multi-element alloying (Cr-Co-Mn), trace Y modification, and laser-cladding-induced nanocrystallization. While the Base Alloy exhibited a mass gain of approximately 15 mg/cm2 after oxidation at 900 &#xb0;C for 120 h, the addition of Cr2.5Co2.5Mn2.5 promoted the formation of a multilayered oxide scale (outer MnCr2O4/inner Al2O3), reducing the parabolic oxidation rate constant to 1.7 &#xd7; 10-5 mg2&#xb7;cm-4&#xb7;s-1. The originality of this work lies in the coupling of compositional and microstructural engineering; further addition of 0.5 at.% Y decreased this constant to 1.7 &#xd7; 10-6 mg2&#xb7;cm-4&#xb7;s-1-a three-order-of-magnitude reduction relative to the Base Alloy, while increasing the apparent oxidation activation energy to ~350 kJ/mol. After 100 thermal cycles at 1000 &#xb0;C, the designed alloy showed a mass change of only 0.05 &#xb1; 0.02 mg/cm2, with its critical load and interfacial fracture energy reaching 78 N and 14.8 J/m2, respectively. Furthermore, the alloy retained a hardness of 310 HV, an elastic modulus of 135 GPa, and a tensile strength of 240 MPa at elevated temperature. These results demonstrate that the synergistic integration of chemical and structural optimization provides a new paradigm for designing low-cost, high-performance FeNiCuAl-based protective coatings.

With the strategic shift toward reducing reliance on critical raw materials, Cobalt-free eutectic high-entropy alloys (EHEAs) have emerged as a pivotal frontier for high-performance structural applications. This review systematically elucidates the synergistic relationship between Co-free alloy design and the non-equilibrium solidification mechanisms of Selective Laser Melting (SLM). The ultra-high cooling rates (105-108 K/s) inherent in SLM are shown to refine eutectic lamellae to the sub-micron scale (typically <300 nm), effectively suppressing the macro-segregation common in conventional casting. We evaluate the design principles of Al-Cr-Fe-Ni and related systems, noting that SLM-processed Co-free EHEAs frequently achieve yield strengths exceeding 1000 MPa and ultimate tensile strengths (UTSs) surpassing 1300 MPa, while maintaining tensile elongations above 10%-a significant improvement over the coarse-grained structures produced by traditional methods. Furthermore, the study identifies critical processing windows, such as laser energy densities (60-120 J/mm3), required to mitigate micro-cracking and achieve near-full density (>99.5%). By synthesizing recent experimental breakthroughs and AI-driven modeling, this review provides a quantitative roadmap for the precision manufacturing of cost-effective, high-performance EHEAs, bridging the gap between theoretical alloy design and industrial additive manufacturing.

Ultra-high-temperature ceramics (UHTCs), including transition-metal carbides, nitrides, and diborides, have emerged as a class of promising structural materials for applications in extreme aerospace and energy environments. Their strong covalent-metallic bonding endows them with exceptionally high melting points, elastic moduli, and thermal stability. Nevertheless, intrinsic brittleness, limited oxidation resistance, and poor sinterability remain key challenges for the engineering application of conventional UHTCs. Recently, novel material design strategies such as multiphase composites, microstructural engineering, and compositional complexity have emerged. Among these, high-entropy UHTCs (HE-UHTCs) have attracted significant attention due to their configurational entropy, lattice distortion, and sluggish diffusion effects, which collectively enhance oxidation resistance, thermal stability, sinterability, and mechanical performance. This review summarizes the crystal chemistry, mechanical behavior, oxidation, and ablation properties of conventional UHTCs and HE-UHTCs. The four core effects of HE-UHTCs-configurational entropy, lattice distortion, sluggish diffusion, and cocktail effects-are discussed in relation to their mechanical properties and oxidation resistance. The roles of computational materials science, including density functional theory (DFT), molecular dynamics (MD), and machine learning, in composition screening and property prediction are critically reviewed. Finally, key challenges and future directions for the rational design and engineering application of UHTCs are discussed.

Background/Objectives: First responders frequently encounter high-stress environments that challenge physiological resilience and autonomic regulation. Heart rate variability (HRV) complexity is a critical marker of adaptive capacity and stress regulation. This study assessed the impact of a wearable-based mindfulness intervention on HRV complexity among first responders using a smartwatch. Methods: A total of 87 first responders participated in a one-month wearable-based intervention. Participants wore Garmin Vivosmart 5 devices to continuously collect PPG data (photoplethysmogram), focusing on beat-to-beat intervals (BBIs). The intervention involved daily Ecological Momentary Assessments (EMAs) and individual randomization to either a mindfulness message, a prompt to access an audio exercise, or no-treatment/control; interventions were delivered via the MYAPT.MIND mobile application. HRV metrics, including Sample Entropy, Multiscale Entropy (MSE), Recurrence Rate (RR), and Determinism (Det), were analyzed pre- and post-intervention/control using paired-samples t-tests. Results: Significant improvements were observed in HRV complexity metrics post-intervention. Sample Entropy increased (M = 1.42, SD = 0.11) compared to pre-intervention (M = 1.39, SD = 0.10; p = 0.007). MSE also showed significant gains (p = 0.038), particularly at lower scales, indicating enhanced short-term autonomic flexibility. Reductions were noted in RR (p = 0.025) and Det (p = 0.018), suggesting improved cardiovascular adaptability and reduced physiological rigidity. Other traditional time-domain metrics, such as Mean HR, SDNN, and RMSSD, did not exhibit significant changes. Conclusions: The wearable-based intervention significantly enhanced HRV complexity, reflecting improved autonomic regulation and adaptive capacity in first responders. These findings support the integration of digital mindfulness strategies for stress management in high-risk occupations. Future research should explore the longitudinal effects and mechanisms mediating these autonomic adaptations.

Phosphogypsum (PG) is an industrial solid waste whose use in cementitious materials is limited by strength reduction at high dosages. This study evaluated a clinker-free multi-solid-waste binder containing 40 wt.% PG for cemented paste backfill using steel slag powder (SSP) and granulated blast-furnace slag (GBFS) as co-binders, with phosphate mine tailings and slime as aggregates. Uniaxial compressive strength (UCS), X-ray diffraction, scanning electron microscopy, and nuclear magnetic resonance were combined with image-based pore-structure sensitivity analysis to examine the relationships among hydration products, pore evolution, and strength development. The results showed that AFt and C-S-H-like gels were associated with pore refinement and strength gain. All mixtures reached UCS values above 0.5 MPa at 7 days and 1.0 MPa at 28 days. The A2 mixture achieved the highest 7-day UCS of 0.8 MPa, whereas A1 showed the highest 28-day UCS of 1.6 MPa. Porosity, pore probability entropy, and fractal dimension were negatively correlated with UCS, with pore probability entropy showing the highest sensitivity to 7-day strength. These findings support the use of high-PG clinker-free binders for targeted phosphate-mine backfill.

This paper studies a dynamic price adjustment system in platform markets, where sellers continuously revise prices, and examines its implications for market stability. We develop a platform-led discrete-time Stackelberg game model to describe the evolution of sellers' prices and price adjustment speeds under bounded rationality. Unlike previous studies that treat adjustment speed as exogenous, we model it as an endogenous state variable shaped by profit incentives, behavioral inertia, and price fluctuations. We derive the interior symmetric equilibrium and show that profit-driven acceleration increases sellers' adjustment speed. When this speed exceeds the stability threshold, the system may leave the stable region, causing bifurcations and complex dynamics. We then introduce a platform-imposed upper bound on adjustment speeds and demonstrate that appropriate regulation can restore stability while balancing market responsiveness and efficiency. Numerical simulations illustrate that moderate acceleration improves profitability, whereas excessive acceleration can lead to low-profit regimes. Entropy-based metrics are used to quantify system complexity, and an entropy-triggered feedback-control mechanism is proposed to mitigate excessive volatility while maintaining flexibility. Overall, the study highlights the importance of governing adjustment dynamics rather than solely focusing on price levels.

With the growing security requirements of sensor nodes in Internet of Things (IoT) systems, conventional silicon-circuit-based physical unclonable functions (PUFs) still face limitations in circuit overhead, design complexity, and system integration. To address these challenges, this paper proposes a lightweight gas sensor PUF (GS-PUF) design based on a Ni-doped SnO2 nanoscale gas sensor array. The proposed method exploits both the unavoidable process randomness introduced during sensor fabrication and the device-to-device electrical response variations induced by gas-material interactions as entropy sources, thereby enabling high-quality PUF response generation. At the device level, Ni-SnO2 nanomaterials are prepared by electrostatic spray deposition (ESD), and an indirectly heated gas sensor array is constructed to enhance the sensitivity and stability of the sensing response. At the algorithmic level, a random resistance balancing algorithm based on multi-sensor combinational comparison is proposed. By randomly comparing the summed resistances of multiple sensor clusters, a 128-bit multi-bit PUF response is generated, while the uniformity and independence of the output bits are effectively improved. Experimental results demonstrate that the proposed GS-PUF exhibits excellent randomness, uniqueness, and reliability: the information entropy of the PUF responses is greater than 0.99, approaching the ideal value; the probabilities of output bits "1" and "0" are 0.4988 and 0.5012, respectively, indicating a well-balanced distribution; the inter-device uniqueness reaches 49.8%, close to the ideal value of 50%; all items in the NIST randomness test suite are passed, with all p-values exceeding 0.01 and the minimum p-value being 0.0368, confirming a high level of statistical randomness confidence. In addition, long-term measurements under fixed laboratory conditions show that the PUF response reliability remains above 96%. Compared with other sensor-based PUFs, the proposed method provides a lightweight sensing-security integration approach for IoT sensor nodes by reusing intrinsic gas-sensor response variations and avoiding an additional dedicated silicon PUF circuit.

Background. Facial expression recognition depends on how visual information is sampled across the face over time. Static area-of-interest (AOI) measures describe where observers look but provide limited information about the sequential organization of gaze. This study examined how gaze is organized during facial expression recognition and whether this organization remains comparable across two conditions differing in the temporal order of contextual and facial stimuli. Methods. Eye-tracking data were collected from 27 participants performing a facial expression recognition task. Fixations on faces were mapped onto three AOIs: Upper Facial Zone (UFZ), Central Facial Zone (CFZ), and Lower Facial Zone (LFZ). Gaze organization was examined using first- and second-order Markov models, entropy estimates, spatial repositioning measures, and a gaze stability index. Results. Gaze transitions showed a structured, non-random organization centered on the CFZ. In the first-order Markov model, transitions from both the UFZ and LFZ were directed primarily toward the CFZ, and within-zone transitions were also most likely in the CFZ. Entropy was lower for the CFZ than for the upper and lower regions, indicating lower transition uncertainty in the central region. The second-order model showed an influence of recent fixation history while preserving the predominance of the CFZ. Spatial repositioning varied across facial zones in both conditions. However, mixed-effects analyses showed no effect of condition on gaze stability. Conclusions. Facial expression recognition was associated with a pattern of exploration in which the central facial region emerged as the most likely fixation destination, with limited evidence of condition-related differences in gaze organization.

With the continuous advancement of thrust-to-weight ratios in modern aero-engines, turbine inlet temperatures have reached levels that far exceed the thermal endurance limits of conventional superalloys and emerging ceramic matrix composites (CMCs). Consequently, thermal barrier coatings (TBCs) and environmental barrier coatings (EBCs) have become indispensable multifunctional systems for hot-section component protection. This review systematically delineates the evolutionary trajectory of TBC/EBC systems, transitioning from traditional yttria-stabilized zirconia (YSZ) and simple silicates to advanced multi-rare-earth-doped oxides, A2B2O7 pyrochlore structures, and high-entropy ceramic systems. A critical comparative assessment is provided regarding their phase stability, thermal-physical properties, and durability challenges above 1200 &#xb0;C. Furthermore, this paper provides an in-depth analysis of high-temperature degradation mechanisms, focusing on the thermochemical and thermomechanical interactions under calcium-magnesium-alumino-silicate (CMAS) attack, water-oxygen corrosion, and molten salt infiltration. By synthesizing current research gaps, we highlight the trade-offs between low thermal conductivity, high toughness, and environmental resistance. Finally, a strategic roadmap for next-generation coatings is proposed, emphasizing the integration of high-entropy material design, multi-scale structural optimization, and AI-driven life prediction models to meet the stringent reliability requirements of future propulsion systems.

Background: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) differs from HPV-negative OPSCC in its molecular, biological, and clinical characteristics. We reviewed the literature on radiological differences between HPV-positive and HPV-negative OPSCC across ultrasound, CT, MRI, and PET-CT as this appears to be a critical gap in the literature. Methods: We performed a narrative review of studies reporting imaging findings in OPSCC by HPV status. Two reviewers independently searched PubMed, Ovid MEDLINE, Ovid EMBASE, and the Cochrane Library, from inception to February 2026, supplemented by searching major radiology journals and the grey literature. Eligible English-language studies included patients with OPSCC of known HPV status, assessed at least one imaging modality, and reported imaging findings stratified by HPV status. After title, abstract, and full-text screening, 66 studies were included. Results: HPV(+) OPSCC was more commonly associated with well-defined primary tumours and a higher prevalence of nodal metastases, particularly cystic nodal metastases and extranodal extension. These findings were broadly concordant with reported radiomic signatures. MRI studies suggested lower apparent diffusion coefficient values, while PET-CT studies suggested higher entropy and smaller primary lesions in HPV-positive disease. Conclusions: Selected imaging features may help distinguish HPV(+) from HPV(-) OPSCC, but current evidence remains insufficient for reliable standalone clinical application. Prospective multicentre validation and integration of multimodal imaging, radiomics, and radiogenomics are needed to improve non-invasive HPV stratification and support future precision diagnostics.

The goal of laser polishing (LP) is to improve the surface quality of functional parts, components, and assemblies. LP is a complex nonlinear thermophysical process, in which laser radiation induces localized melting of a material with an initially rough surface topography. During LP, the thermodynamic state evolves dynamically due to transient melt flow, heat transfer, and rapid solidification within the laser-material interaction zone. A smooth surface is formed through the interplay between surface tension-driven flow, which promotes energy minimization, and nonequilibrium effects associated with melting and solidification. From the perspective of self-organization, LP can be interpreted as an open system driven by energy input, where complex material redistribution leads to the evolution of surface topography. In this work, the self-organization of molten material is analyzed using chaos-based descriptors, including the Lyapunov exponent, phase portrait, approximate entropy, and the Hurst exponent, calculated from measured surface topographies before and after laser polishing. The results show that LP acts as a spatial low-pass filter, reducing high-frequency surface components associated with micromilling marks, and exhibits a directional bias in material redistribution relative to the laser scanning direction. Among the evaluated descriptors, the Lyapunov and Hurst exponents demonstrate consistent behaviors, indicating their suitability as robust indicators of surface state in post-process analysis. For the investigated conditions (Inconel 718), a laser fluence of 158.3 mJ/cm2 provided the best-achieved surface quality, corresponding to an improvement in surface roughness (Ra) of approximately 70% and the lowest Lyapunov exponent of 1.966 and highest Hurst exponent of 0.859. This study demonstrates that chaos-based analysis of surface topography provides a phenomenological framework for assessing process stability and surface evolution, offering a basis for thermophysics-informed development of LP in applications such as mold and die manufacturing.