Search Results (2,104)

Background/Objectives: Uterine fibroids are one of the most prevalent forms of benign tumors in women and may substantially impair quality of life due to heavy menstrual bleeding, pelvic pain, and pressure-related symptoms. Transvaginal radiofrequency ablation (TV-RFA) has emerged as a promising minimally invasive, uterus-sparing treatment approach. However, there exists a paucity of data regarding the early vascular response evaluated through quantitative Doppler parameters. This study aimed to assess the short-term clinical outcomes and ultrasound effectiveness of TV-RFA in treating symptomatic uterine fibroids, with particular emphasis on early vascular and morphological response. Methods: This retrospective study included 38 women who presented with symptomatic uterine fibroids and underwent TV-RFA between July 2024 and December 2025. Inclusion criteria were as follows: (1) presence of up to three intramural fibroids (FIGO types 3–6) and (2) maximum diameter of fibroids: ≤6 cm. Patients were assessed at baseline and at 1- and 3-month follow-up visits. Ultrasound evaluation included the measurement of fibroid dimensions and volume as well as quantitative Doppler parameters (Pixels Power, Ratio, and CM² Power Index). Clinical outcomes were assessed based on the intensity and duration of menstrual bleeding. Statistical analysis was performed using nonparametric tests with significance set at p < 0.05. Results: Significant reductions in fibroid dimensions and volume were observed at both follow-up time points, with the greatest effect at 3 months (p < 0.001). Doppler analysis demonstrated a marked decrease in vascularization parameters, particularly CM² Power Index and Pixels Power (p < 0.001), suggesting an early vascular response to treatment. Clinically, the proportion of patients experiencing heavy menstrual bleeding considerably reduced, accompanied by a significant shortening of bleeding duration (p < 0.001). No major complications requiring surgical intervention were reported. Conclusions: TV-RFA was associated with significant short-term reductions in fibroid vascularization, fibroid volume, and bleeding-related symptoms in this cohort of women with symptomatic uterine fibroids. Quantitative Doppler parameters may serve as valuable early markers of treatment response; however, further studies with larger cohorts and a longer follow-up duration are warranted. Full article

Used engine oil (UEO) is a hazardous waste stream that poses significant environmental risks when improperly managed. However, its high heating value makes it a promising candidate for energy recovery. In Kuwait, rising vehicle ownership has led to increasing quantities of UEO, while the power sector remains heavily dependent on conventional fossil fuels. Although extensive research has examined UEO treatment methods and combustion characteristics, limited attention has been given to its integration into utility-scale power-generation systems. This study presents a national-scale techno-economic and environmental assessment of using UEO as a supplementary fuel for electricity generation in Kuwait. East Doha Power Station was selected as a representative case study to evaluate fuel-substitution potential and the practicality of integrating UEO into existing power-generation infrastructure. Historical vehicle-registration data were used to estimate UEO generation, and future availability was projected through 2035 based on vehicle-growth trends. The corresponding thermal energy potential, equivalent electricity generation, fuel-displacement capacity, economic benefits, and environmental impacts were subsequently evaluated. The results indicate that annual UEO generation is projected to increase from approximately 181,800 tonnes/year in 2024 to 303,300 tonnes/year in 2035. This quantity corresponds to about 12,126 TJ/year of recoverable thermal energy and an equivalent electricity-generation potential of approximately 1.1 TWh/year (4000 TJ/year), assuming a power-plant efficiency of 33%. The recovered UEO could displace approximately 311,000 tonnes/year of heavy oil or 287,000 tonnes/year of crude oil, with estimated net annual fuel-cost savings of approximately 28–30 million KD. Based on literature-reported emission factors, UEO utilization could reduce combustion-related CO₂ emissions by up to 19.0% and NO_x emissions by up to 45.5% compared with heavy oil. Sensitivity analysis further confirmed the robustness of the findings under a range of recovery and operating conditions. To the best of the authors’ knowledge, this study represents the first comprehensive national-scale assessment of the potential use of UEO for utility-scale power generation in Kuwait. The findings indicate that UEO has the potential to serve as a strategic secondary energy resource that supports waste reduction, fuel conservation, economic savings, and circular-economy objectives. However, practical implementation will require appropriate collection and treatment infrastructure together with further technical validation, pilot-scale demonstration, and regulatory evaluation. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

Yellow mealworm (Tenebrio molitor) larvae can convert low-value organic residues into insect biomass, but their performance depends on substrate composition and feeding strategy. We evaluated vegetable wastes, okara–wheat–bran diets and kitchen waste–wheat–bran mixtures under a dynamic feeding regime. Ingredient and proximate compositions were determined, and larval growth, fresh-weight-based waste reduction (WR), bioconversion rate (BCR) and feed conversion efficiency (FCE), pupal output, nutritional composition and heavy metal contents were assessed. Among vegetable wastes, potato showed the highest numerical WR (95.18 ± 0.73%) and relatively high BCR and FCE, whereas pumpkin produced the most pupae (109.00 ± 8.62 per replicate). Cabbage showed reduced biomass conversion and pupal output. In the okara trial, 40% okara showed the highest numerical WR (75.88 ± 0.39%), 10% okara maximized FCE (15.83 ± 0.38%) and 20% okara produced the greatest pupal output. Principal component analysis (PCA) indicated treatment-specific conversion and developmental patterns, but was interpreted as an exploratory association analysis rather than a causal model. Kitchen waste mixtures increased the relative fat proportion and reduced the relative protein proportion of larvae; the 3:2 mixture increased crude fat to 37.90 ± 0.22% while retaining 57.39 ± 0.40% crude protein. Higher kitchen waste inclusion was associated with greater larval arsenic (As) content, reaching 0.803 ± 0.001 mg/kg in the 5:2 treatment. Substrate composition should therefore be optimized together with conversion efficiency, product quality and safety screening. Full article

Heavy metal contamination of soils represents a persistent environmental challenge, for which in situ immobilization has emerged as a cost-effective and technically viable alternative to conventional invasive remediation technologies. This review comparatively evaluates three distinct categories of soil amendments—biochar, zeolite and attapulgite—within a unified analytical framework integrating extractable fractions (TCLP, DTPA, and CaCl₂) and geochemical fractionation approaches (BCR and Tessier). The novelty of this study lies in the systematic assessment of the dominant immobilization mechanisms associated with each amendment in relation to soil properties and the chemical speciation of the target metal, as well as in distinguishing between an apparent reduction in metal extractability and a genuine shift toward more stable geochemical fractions. The findings identify ion exchange as the primary immobilization mechanism in zeolites (NaA zeolite, 1–5% w/w, 96% reduction in TCLP-extractable Pb and 91% reduction in TCLP-extractable Cd), the synergistic action of adsorption, complexation, and precipitation in biochar systems (manure-derived biochar, 0–5% w/w, 97.4% reduction in the exchangeable Pb fraction according to the Tessier scheme), and the critical role of surface modification in attapulgite-based amendments (C-ATP, 4% w/w, 95.1% and 74.3% reductions in TCLP-extractable Pb and Cd, respectively). Because these efficiencies were obtained using different extraction protocols, they are not directly comparable. At the same time, cases of adverse responses were identified, including increased As extractability following the application of phosphate-modified biochar and the redistribution of Pb and Cd after amendment with natural zeolite in industrially contaminated soil. These observations highlight that amendment performance is not an intrinsic property of the material itself, but rather the outcome of specific geochemical interactions occurring within the soil system. Increased soil pH emerged as the principal common factor promoting metal stabilization across all amendment categories, whereas substantial variability in amendment dosage, incubation period, and analytical methodology limited direct quantitative comparisons among studies. Consequently, the selection of an appropriate soil amendment should be based on the integrated evaluation of soil physicochemical properties, contaminant speciation, and the intended scale of application, supported by long-term monitoring under field conditions. Full article

Steam-assisted gravity drainage (SAGD) remains one of the most energy-intensive technologies for heavy-oil recovery because production response is controlled not only by injected heat but also by spatial heat delivery, wellbore losses, viscosity reduction and steam chamber geometry. This paper develops a physics-guided digital twin for SAGD with distributed thermocable assistance and a bounded residual machine learning correction layer. The framework combines a heat-delivery model, temperature-dependent oil mobility, scenario analysis and decision-oriented visualization within a reproducible computational experiment. A reference operating envelope was formulated for heavy-oil reservoirs, including depth, horizontal well length, permeability, porosity, oil viscosity, steam temperature, injection rate, thermocable power and cable coverage. The analysis includes sensitivity testing, cumulative-production/SOR dynamics and Pareto-type operating-window mapping. In the reference computational scenario, which is treated as an illustrative screening case rather than as field-history validation, the thermocable-assisted hybrid configuration changed the model-calculated eight-year cumulative oil from 452.5 × 10³ m³ to 615.2 × 10³ m³ and the mean SOR from 3.17 to 2.72 t/t relative to the conventional SAGD physics-core case. The largest sensitivities were associated with steam rate, steam temperature, initial viscosity and permeability. Within the declared operating envelope, the results support the use of the framework as a pre-field screening tool and indicate that thermocable assistance should be interpreted primarily as spatial heat distribution control rather than as a field-validated production-improvement guarantee. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems. Full article

Semiconductor–biological hybrid systems (SBHS) have emerged as a disruptive technology for solar-driven chemical manufacturing, effectively bypassing the thermodynamic bottlenecks of natural photosynthesis. However, the aggressive pursuit of record-breaking solar-to-chemical conversion efficiencies has inadvertently fostered an efficiency trap. A profound interdisciplinary schism exists wherein the acute environmental toxicity and long-term interfacial instability of these hybrid architectures are frequently overlooked. This review provides a critical appraisal of the oft-ignored environmental risks inherent in current SBHS designs. We systematically dissect the heavy metal leaching toxicity of first-generation inorganic photosensitizers and unveil the complex, bidirectional degradation mechanisms at the abiotic–biotic interface. Specifically, we highlight the dual threats of photogenerated reactive oxygen species inducing cellular oxidative stress and active, microbially induced material dismantling via reductive dissolution driven by extracellular electron transfer. To navigate beyond this purely performance-driven paradigm, we propose a multidimensional, standardized evaluation matrix that systematically balances catalytic efficiency with biological safety and life-cycle sustainability. Ultimately, this review offers a comprehensive roadmap to transition biohybrid platforms from fragile laboratory concepts into robust, scalable, and ecologically benign negative-emission technologies. Full article

Grocery shopping remains a heavily car-dependent activity in urban areas, even for short-distance trips within residential neighbourhoods. A primary barrier to shifting toward active travel (walking or cycling) is the physical burden of carrying heavy or bulky goods. This study investigates whether a retailer-managed home delivery service could encourage consumers who currently rely on motorised modes for grocery shopping to shift towards active travel while preserving the in-store shopping experience. The analysis focuses on urban Italian consumers who currently use motorised modes for grocery shopping. Using a Stated Preference (SP) experiment and a Mixed Logit (MMNL) model (n = 88), we analyse the conditions under which such a service may encourage the adoption of active travel modes and support proximity-based shopping patterns. Given the exploratory nature of the study and the small, non-representative sample, the findings should be interpreted as preliminary evidence for urban motorised grocery shoppers rather than as representative of the Italian population. The results indicate a substantial willingness among respondents to adopt the proposed service configuration. Delivery time, service cost, and the availability of delivery time-window selection emerge as critical factors influencing consumers’ choices. Acceptance of the service is also influenced by perceptions of walking and cycling infrastructure quality, trust in the integrity of delivered groceries, preferences for local products, and concerns regarding the working conditions of delivery personnel. Additionally, the model reveals significant heterogeneity in preferences regarding delivery by drone/autonomous vehicle and a 100% reduction in greenhouse gas emissions relative to conventional motorised transport. Younger respondents exhibit a more favourable attitude towards automated delivery technologies, while differences in the valuation of environmental benefits emerge between male and female respondents. The findings suggest that retailer-managed home delivery may represent a promising mechanism for encouraging active travel among current motorised grocery shoppers, while maintaining consumers’ relationship with neighbourhood retail services. These results provide retailers and urban policymakers with valuable insights, suggesting that appropriately designed delivery services may support more sustainable and proximity-oriented shopping behaviours. Such services could potentially contribute to maintaining the accessibility and vitality of neighbourhood retail activities, particularly in ageing urban contexts. Full article

Heavy-duty transit vehicles (N1–N3) (heavy vehicles) can generate disproportionate environmental and economic impacts in urban transport corridors despite representing a relatively small share of total traffic volume. This study develops an integrated probabilistic framework for assessing the relationships between traffic-flow variability, CO₂e emissions, particulate-matter-derived climate impacts, and external pollution costs associated with transit transport. The methodology combines traffic-flow modeling, emission estimation, PM-to-CO₂e transformation, probabilistic analysis, Monte Carlo simulation, sensitivity analysis, and scenario-based intervention assessment. Separate analyses were conducted for M1 passenger vehicles and heavy vehicles to evaluate differences in emission behavior, uncertainty, and economic impacts. The results indicate substantial structural differences between light-duty and heavy-vehicle regimes. Passenger-car traffic exhibited relatively stable emission distributions, whereas heavy vehicles demonstrated significantly greater variability, uncertainty, and emission intensity. Sensitivity analysis identified heavy-vehicle flow as the dominant factor influencing overall system emissions and pollution costs. Scenario analysis indicated that restrictions targeting heavy-vehicle traffic have the potential to generate considerably larger environmental benefits than generalized traffic-reduction measures. Probabilistic assessment further revealed that heavy vehicles contribute disproportionately to high-emission risk regimes and uncertainty propagation within the system. The proposed framework provides an integrated approach for evaluating climate impacts, uncertainty and economic externalities of transit transport. The results highlight the importance of heavy-vehicle management in reducing emissions and pollution costs while supporting risk-informed transport policy development. Full article

Steam-assisted gravity drainage (SAGD) remains one of the most effective thermal enhanced-oil-recovery technologies for heavy-oil reservoirs; however, its energy performance is strongly constrained by wellbore heat losses, delayed steam-chamber development, and an increase in the steam–oil ratio (SOR) under deep or thermally unfavorable conditions. This study develops a physics-based computational digital-twin framework for thermocable-assisted SAGD and evaluates the influence of steam temperature, oil viscosity, permeability, reservoir depth, thermocable linear power, and heating time on oil production and SOR. The model couples wellbore heat transfer, temperature-dependent viscosity reduction, steam-chamber geometry, heat-loss compensation by an electrical thermocable, and production response. The results show that increasing steam temperature from 220 to 300 °C raises the oil rate by approximately 13–15% and reduces SOR from about 2.47 to 2.30. Increasing oil viscosity from 300 to 1500 mPa·s decreases the oil rate by more than 25% and increases SOR above 3.0. Thermocable integration increases the oil rate by approximately 8–12% in the base scenario and reduces SOR by 5–10% compared with conventional SAGD. The highest relative benefit is obtained in deeper reservoirs, where additional heat input compensates wellbore heat losses and stabilizes the temperature profile. These findings indicate that thermocable-assisted SAGD can improve energy efficiency and extend the practical operating window of thermal recovery in heavy-oil reservoirs. Full article

Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport. Full article

The development of electric bus superstructures requires an integrated engineering approach combining structural design, numerical simulation, experimental validation and durability assessment. This need is particularly important for electric buses, where heavy roof-mounted battery systems and auxiliary components influence structural load paths, fatigue durability and rollover crashworthiness. This paper presents a measurement-supported workflow for the development of a virtual electric bus superstructure model, including finite element analysis, multibody dynamics simulations, operational load assessment, fatigue-oriented evaluation and rollover crashworthiness analysis. The finite element model is used to assess static load cases, modal properties and structural response under selected design conditions. A multibody vehicle model with nonlinear suspension characteristics is applied to simulate representative operating scenarios and to support the definition of dynamic load cases. Operational measurement data from previous work are used as a basis for realistic load characterization. Experimental torsional stiffness and modal tests are used to validate the numerical model. The main contribution of the study is the integration of these numerical, experimental and operational-data-based activities into a consistent early-stage verification process. The proposed workflow supports early identification of critical structural regions, assessment of design modifications and reduction in prototype-based design iterations. Full article

To investigate the rutting resistance of Large Stone Asphalt Mixture (nominal maximum aggregate size of 53 mm, abbreviated as LSAM-50), this study evaluated the effects of temperature, load, and their interaction on the rutting performance of LSAM-50 through large-thickness rutting tests. It analyzed the characteristics of rutting deformation under varying thermal and loading conditions, established a permanent deformation-temperature-load dependency model, and explored the correlations between permanent deformation and high-temperature evaluation indicators. The findings indicate that the temperature-load interaction fundamentally alters the load-transfer mechanism between the viscoelastic matrix and coarse aggregates within LSAM-50, thereby activating the interlocking effect of its thick structural skeleton. The dynamic stability undergoes a pronounced reduction as temperature or load increases, peaking at a degradation rate of 40–57% within the 40–50 °C interval. Furthermore, the rutting deformation of the LSAM-50 mixture demonstrates significant temperature and load dependency; as the number of loading cycles increases, the deformation exhibits an initial rapid escalation before reaching a plateau. During temperature elevation and load escalation, the rutting deformation increases in a step-wise manner. Notably, the preliminary application of low temperatures and light loads imparts a substantial “training” effect on the material’s rutting resistance. Once the mixture is wheel-tracked to densification under high temperatures or heavy loads, negligible new deformation is generated during the subsequent cooling or unloading phases. Specifically, upon the initial unloading from 1.1 MPa to 0.9 MPa, the incremental deformation is merely 0.04 mm; upon further unloading to 0.7 MPa, the additional deformation approaches 0 mm. The established permanent deformation-temperature-load dependency model for LSAM-50 yields a high predictive correlation of 96%. Moreover, the permanent deformation exhibits robust linear relationships with 1-h rutting depth (R² = 0.95), compressive strength (R² = 0.91), and shear strength (R² = 0.97). These indicators can thus facilitate the rapid and precise estimation of permanent pavement deformation. Full article