Search Results (8,932)

Change point models are widely used in medical and epidemiological studies to capture the threshold effects of continuous covariates on health outcomes. These threshold effects represent critical points at which the relationship between biomarkers or risk factors and disease risk shifts, often reflecting underlying biological mechanisms or clinically relevant intervention points. While most existing methods focus on right-censored data, interval censoring is common in large-scale clinical trials and follow-up studies, where the exact event times are not observed but are known to fall within time intervals. In this paper, we propose a semiparametric transformation model with an unknown change point for interval-censored data. The model allows flexible transformation functions, including the proportional hazards and proportional odds models, and it accommodates both main effects and their interactions with the threshold variable. Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. We establish the asymptotic properties of the proposed estimators and evaluate their finite-sample performance through extensive simulations, showing good accuracy and coverage properties. The method is further illustrated through an application to the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Internal fins are commonly utilized as a passive technique to enhance natural convection, but their efficiency depends on complex interplay between fin design, material properties, and convective strength. This study presents an extensive numerical analysis of buoyancy-driven flow in square cavities containing a single horizontal fin on the hot wall. Over 9000 simulations were conducted, methodically varying the Rayleigh number (Ra = 10 to 10⁵), Prandtl number (Pr = 0.1 to 10), and fin characteristics, such as length, vertical position, thickness, and the thermal conductivity ratio (up to 1000), to assess their overall impact on thermal efficiency. Thermal enhancements compared to scenarios without fins are quantified using local and average Nusselt numbers, as well as a Nusselt number ratio (NNR). The results reveal that, contrary to conventional beliefs, long fins positioned centrally can actually decrease heat transfer by up to 11.8% at high Ra and Pr due to the disruption of thermal plumes and diminished circulation. Conversely, shorter fins located near the cavity’s top and bottom wall edges can enhance the Nusselt numbers for the hot wall by up to 8.4%, thereby positively affecting the development of thermal boundary layers. A U-shaped Nusselt number distribution related to fin placement appears at Ra ≥ 10³, where edge-aligned fins consistently outperform those positioned mid-height. The benefits of high-conductivity fins become increasingly nonlinear at larger Ra, with advantages limited to designs that minimally disrupt core convective patterns. These findings challenge established notions regarding passive thermal enhancement and provide a predictive thermogeometric framework for designing enclosures. The results can be directly applied to passive cooling systems in electronics, battery packs, solar thermal collectors, and energy-efficient buildings, where optimizing heat transfer is vital without employing active control methods. Full article

The genus Sonchus (Asteraceae) comprises 98 species, including 17 predominantly herbaceous taxa native to the Mediterranean region. These plants have long been utilized as traditional wild food sources due to their high nutritional value, as they are rich in vitamins A, C, and K, essential minerals, and bioactive compounds with antioxidant and anti-inflammatory properties. This review aims to provide a comprehensive synthesis of the taxonomy, geographic distribution, phytochemical composition, traditional uses, historical significance, and pharmacological properties of Sonchus species. A systematic literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar, focusing on studies from 1980 to 2024. Inclusion and exclusion criteria were applied, and methodological quality was assessed using standardized tools. A bibliometric analysis of 440 publications (from 1856 to 2025) reveals evolving research trends, with S. oleraceus, S. arvensis, and S. asper being the most extensively studied species. The review provides detailed taxonomic insights into 17 species and 14 subspecies, emphasizing their ecological adaptations and biogeographical patterns. Additionally, it highlights the cultural and medicinal relevance of Sonchus since antiquity while underscoring the threats posed by environmental degradation and changing dietary habits. Sonchus oleraceus and S. tenerrimus dominate the culinary applications of the genus, likely due to favorable taste, wide accessibility, and longstanding cultural importance. The comprehensive nutritional profile of Sonchus species positions these plants as valuable contributors to dietary diversity and food security. Finally, the study identifies current knowledge gaps and proposes future research directions to support the conservation and sustainable utilization of Sonchus species. Full article

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications. Full article

Wound care in military and combat environments poses distinct challenges that set it apart from conventional medical practice in civilian settings. The nature of injuries sustained on the battlefield—often complex, contaminated, and involving extensive tissue damage—combined with limited access to immediate medical intervention, significantly increases the risk of infection, delayed healing, and adverse outcomes. Traditional wound dressings frequently prove inadequate under such extreme conditions, as they have not been designed to address the specific physiological and logistical constraints present during armed conflicts. This review provides a comprehensive overview of recent progress in the development of advanced wound dressings tailored for use in military scenarios. Special attention has been given to multifunctional dressings that go beyond basic wound coverage by incorporating biologically active macromolecules such as collagen, chitosan, thrombin, alginate, therapeutic peptides, and growth factors. These compounds contribute to properties including moisture balance control, exudate absorption, microbial entrapment, and protection against secondary infection. This review highlights the critical role of advanced wound dressings in improving medical outcomes for injured military personnel. The potential of these technologies to reduce complications, enhance healing rates, and ultimately save lives underscores their growing importance in modern battlefield medicine. Full article

We introduce I-fp convergence (ideal convergence in fuzzy paranormed spaces) and develop its core theory, including stability results and an equivalence to $I^{*}$-fp convergence under the AP Property. Building on this foundation, we design an adaptive base-stock policy for a single-echelon inventory system in which weekly demand is expressed as triangular fuzzy numbers while holiday or promotion weeks are treated as ideal-small anomalies. The policy is updated by a simple learning rule that can be implemented in any spreadsheet, requires no optimisation software, and remains insensitive to tuning choices. Extensive simulation confirms that the method simultaneously lowers cost, reduces average inventory and raises service level relative to a crisp benchmark, all while filtering sparse demand spikes in a principled way. These findings position I-fp convergence as a lightweight yet rigorous tool for blending linguistic uncertainty with anomaly-aware decision making in supply-chain analytics. Full article

Hydrogels, especially Polyvinyl alcohols, have received extensive attention as alternative materials for articular cartilage. Aiming at the problems such as low strength and poor toughness of polyvinyl alcohol hydrogels in practical applications, an enhancement and modification strategy is proposed. Sodium alginate fibers were introduced into polyvinyl alcohol hydrogel network through physical blending and freezing/thawing methods. The prepared composite hydrogels exhibited a three-dimensional porous network structure similar to that of human articular cartilage. The mechanical and tribological properties of hydrogels have been significantly improved, due to the multiple hydrogen bonding interaction between sodium alginate fibers and polyvinyl alcohol. Most importantly, under a load of 2 N, the friction coefficient of the PVA/0.4SA hydrogel can remain stable at 0.02 when lubricated in PBS buffer for 1 h. This work provides a novel design strategy for the development of high-performance polyvinyl alcohol hydrogels. Full article

Ricinus communis (Euphorbiaceae), commonly known as the castor oil plant, is prized for its versatile applications in medicine, industry, and agriculture. It features large, deeply lobed leaves with vibrant colours, robust stems with anthocyanin pigments, and extensive root systems for nutrient absorption. Its terminal panicle-like inflorescences bear monoecious flowers, and its seeds are enclosed in prickly capsules. Throughout its various parts, R. communis harbours a diverse array of bioactive compounds. Leaves contain tannins, which exhibit astringent and antimicrobial properties, and alkaloids like ricinine, known for anti-inflammatory properties, as well as flavonoids like rutin, offering antioxidant and antibacterial properties. Roots contain ellagitannins, lupeol, and indole-3-acetic acid, known for anti-inflammatory and liver-protective effects. Seeds are renowned for ricin, ricinine, and phenolic compounds crucial for industrial applications such as biodegradable polymers. Pharmacologically, it demonstrates antioxidant effects from flavonoids and tannins, confirmed through minimum inhibitory concentration (MIC) assays for antibacterial activity. It shows potential in managing diabetes via insulin signalling pathways and exhibits anti-inflammatory properties by activating nuclear factor erythroid 2-related factor 2 (Nrf2). Additionally, it has anti-fertility effects and potential anticancer activity against cancer stem cells. This review aims to summarize Ricinus communis’s botanical properties, therapeutic uses, chemical composition, pharmacological effects, and industrial applications. Integrating the current knowledge offers insights into future research directions, emphasizing the plant’s diverse roles in agriculture, medicine, and industry. Full article

Carbon black (CB)-filled rubber materials are extensively used in civil engineering seismic isolation. However, CB-filled rubber materials often experience mechanical property degradation because of exposure to environmental factors. To better understand the influences of thermo-oxidative, ultraviolet and ozone aging on mechanical property degradation, uniaxial tension and dynamic mechanical analysis (DMA) tests were carried out. In the uniaxial tension tests, the stress strength and elongation decreased with an increase in aging time. In the DMA tests, the effective temperature ranges decreased by 3.4–14%. And the neo-Hookean model was applied to simulate the hyperelasticity of CB-filled rubber materials. The relationship between the elastic modulus (a constant of the neo-Hookean model) and aging time was established, which provided a qualitative relationship between crosslink density and aging time. In addition, the dispersion of the CB aggregate was investigated using an atomic force microscope (AFM). The results indicated that the mechanical property degradation might be closely related to the aggregate diameter. This paper establishes a bridge between the microstructure and mechanical properties of CB-filled rubber materials, which can improve the understanding of the mechanical property degradation mechanisms of rubber materials and the fabrication of rubber components. Full article

Natural polymer materials are increasingly utilized in drilling fluid additives. Starch has come to be applied extensively due to its low cost and favorable fluid loss reduction properties. However, its poor temperature resistance and high viscosity limit its application in high-temperature wells. This study innovatively introduces for the first time diatomite as an inorganic material in the modification process of starch-based fluid loss additives. Through synergistic modification with acrylamide and acrylic acid, we successfully resolved the longstanding challenge of balancing temperature resistance with viscosity control in existing modification methods. The newly developed fluid loss additive demonstrates remarkable performance: It remains effective at 160 °C when used independently. When added to a 4% sodium bentonite base mud, it achieves an 80% fluid loss reduction rate—significantly higher than the 18.95% observed in conventional starch-based products. The resultant filter cake exhibits thin and compact characteristics. Moreover, this additive shows superior contamination resistance, tolerating 30% NaCl and 0.6% calcium contamination, outperforming other starch-based treatments. With starch content exceeding 75%, the product not only demonstrates enhanced performance but also achieves significant cost reduction compared to conventional starch products (typically containing < 50% starch content). Full article

Metasurfaces have drawn extensive research attention for their unique optical properties and vast application potential. Among the various resonant modes induced in metasurfaces, BIC and electric anapole modes stand out as particularly interesting due to their distinctive physical characteristics. In this work, we designed and investigated novel dimeric dumbbell-shaped metasurfaces incorporating two independently tunable asymmetric parameters. This structural innovation enables the simultaneous excitation of both electric anapole and QBIC modes under normally incident MIR illumination. More importantly, by adjusting these two asymmetric parameters, one can independently tune the resonance peaks of the two modes, thereby overcoming the performance limits of conventional single-peak modulation. This metasurface design demonstrates outstanding performance for dielectric environment-sensing applications. We conducted a comprehensive investigation of the sensing sensitivity for dumbbell-shaped metasurfaces of various geometries. Our simulation results show that the circular-shaped configuration achieved high sensitivity, reaching 20,930 GHz/RIU. This work offers a novel design paradigm for multi-mode control and functionalization of metasurface structures. Full article

The eastern North Pacific Ocean coastline (from the Salish Sea to the western Aleutian Islands) is highly glaciated with relic sediment deposits scattered throughout a highly contoured and variable bathymetry. Oceanographic conditions feature strong currents and tidal exchange. Sand wave fields are prominent features within these glaciated shorelines and provide critical habitat to sand lance (Ammodytes spp.). Despite an awareness of the importance of these benthic habitats, attributes related to their structure and characteristics remain undocumented. We explored the micro-bathymetric morphology of a subtidal sand wave field known to be a consistent habitat for sand lance. We calculated geomorphic attributes of the bedform habitat, analyzed sediment composition, and measured oceanographic properties of the associated water column. This feature has a streamlined teardrop form, tapered in the direction of the predominant tidal current. Consistent flow paths along the long axis contribute to well-defined and maintained bedform morphology and margin. Distinct patterns in amplitude and period of sand waves were documented. Strong tidal exchange has resulted in well-sorted medium-to-coarse-grained sediments with coarser sediments, including gravel and cobble, within wave troughs. Extensive mixing related to tidal currents results in a highly oxygenated water column, even to depths of 80 m. Our analysis provides unique insights into the physical characteristics that define high-quality habitat for these fish. Further work is needed to identify, enumerate, and map the presence and relative quality of these benthic habitats and to characterize the oceanographic properties that maintain these benthic habitats over time. Full article

High-energy solid propellants are multiphase engineering materials, whose mechanical behavior is predominantly governed by the characteristics of embedded crystalline particles. While microstructural influences have been extensively examined, quantitative correlations between microstructure and macroscopic mechanical properties remain underexplored. This work develops a cohesive finite element method (CFEM) framework to quantify the thermomechanical response of high-energy solid propellants at the microstructural scale. The analysis focuses on impact loading at strain rates ranging from 10³ to 10⁴ s⁻¹, accounting for large deformation, thermomechanical coupling, and microcrack-induced failure. Damage evolution under impact conditions was evaluated using a combined neural network-based inverse identification method and a three-dimensional cohesive finite element model to determine temperature-dependent bilinear-polynomial cohesive parameters. Results demonstrate a strong dependence of the propellant’s mechanical behavior on both strain rate and temperature. Validation against experimental data confirms that the proposed temperature-sensitive CFEM accurately predicts both damage progression and macroscopic mechanical responses. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article