Background Sustainable biomass-to-multifunctional fluorescent carbon dots (CDs) with biocompatibility, photostability, and security-related optical properties are attractive for bioimaging and anti-counterfeiting technologies. However, developing such materials from renewable biomass through a simple and sustainable process remains challenging. Methods Fluorescent CDs were prepared from Bergera koenigii (curry) leaves through a simple hydrothermal carbonization approach, utilizing the naturally occurring phytoconstituents in the leaves as carbon and heteroatom precursors. The resulting curry leaf-derived carbon dots (CCDs) were systematically characterized using UV–Vis spectroscopy, photoluminescence spectroscopy, ATR-FTIR, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). Their stability and cytocompatibility were subsequently assessed, along with their potential for cellular imaging and anti-counterfeiting applications. Significant findings The resulting CCDs exhibited excitation-dependent cyan-blue fluorescence, high photostability, and prolonged storage stability. Spectroscopic analyses revealed abundant oxygen- and nitrogen-containing surface functionalities, while TEM showed quasi-spherical nanoparticles with an average diameter of approximately 5 nm and a graphitic lattice spacing of 0.21 nm. The CCDs displayed low cytotoxicity toward HCT-116 human colon cancer cells and enabled effective fluorescence-based cellular imaging. In addition, their strong fluorescence under ultraviolet excitation allowed their application as invisible fluorescent ink for anti-counterfeiting. These findings demonstrate that curry leaves can serve as a renewable precursor for producing multifunctional fluorescent CCDs with potential in biomedical imaging and information-security applications.
Customer-facing AI agents are becoming frontline service interfaces, yet firms still face a consequential design question: should these agents look human-like or robot-like? This research examines how AI agent appearance shapes consumers' intentionality attributions in AI-mediated service encounters and how these inferences translate into satisfaction and future AI use intention. Drawing on attribution theory and anthropomorphism research, we propose that human-like and robot-like appearances do not have uniformly positive or negative effects; rather, their effects depend on consumers' evaluations of service outcomes and their familiarity with AI. A 2 (AI agent appearance: human-like vs. robot-like) & times; 2 (service outcome: success vs. failure) between-subjects experiment was conducted with 350 U.S. participants in an airline customer-service chatbot context. Results reveal a reversal across outcome evaluations. When consumers experienced service success, robot-like AI elicited higher perceived intentionality than human-like AI, particularly among those with lower AI familiarity. When consumers experienced service failure, human-like AI elicited higher perceived intentionality than robot-like AI, although this effect weakened and reversed among consumers with higher AI familiarity. Perceived intentionality, in turn, increased satisfaction and future AI use intention, supporting a moderated mediation process. These findings reframe AI appearance as an attributional cue rather than a simple humanization strategy and demonstrate that consumers interpret the same AI appearance differently depending on how they evaluate the service encounter. Managerially, the results suggest that firms should not assume that increasing human likeness will universally improve customer responses. Instead, marketers should consider how different customer segments, particularly those varying in AI familiarity, are likely to interpret and respond to AI appearance cues across diverse service experiences.
Accurate assessment of structural resilience under time-varying deterioration and external loading requires explicit consideration of cumulative failure behavior and disruption-dependent system response. To address this need, this study makes two main contributions. First, a new time-dependent system-reliability-based resilience formulation is proposed, in which reliability and redundancy are defined using first-passage events. Unlike instantaneous indices, the proposed first-passage indices preserve the cumulative threshold-crossing behavior of component disruption and subsequent system collapse over a service-life horizon. Second, a damage-aware neural-operator acceleration framework is developed to enable efficient evaluation of the proposed indices. A boundary-emphasized loss is introduced to improve surrogate accuracy near limit-states, where small prediction errors can affect the identification of first-passage events. In addition, a Gaussian-mixture-model-based augmentation strategy is used to improve sample support for selected underrepresented disruption scenarios. The proposed approach is demonstrated using a Daniels load-sharing system and a truss bridge subjected to time-varying loading. The results demonstrate that the proposed formulation and acceleration strategy provide an efficient framework for time-dependent resilience assessment of deteriorating structural systems.
Recycled waste paper strip (WPS) materials contain two distinct pore systems: inter-strip macropores formed by loose packing and intra-paper micropores inherited from the cellulose fiber network. Although macropores are usually regarded as the main sound-propagation pathway in loosely packed strip-like absorbers, the acoustic contribution of intrinsic paper micro-porosity remains unclear. This study develops a micro-porosity-aware Double-Porosity Johnson-Champoux-Allard-Lafarge (DP-JCAL) inverse modeling framework to determine whether a separate micro-pore branch is required for sound absorption prediction. WPS samples with different strip geometries and two thicknesses were prepared and tested under normal incidence using an impedance tube. Total porosity, pore-size distribution, true density, sample geometry, and Gurley-derived air permeance were used as physical constraints in the inverse model. Compared with macro-only JCAL inversion based only on the inter-strip pore network, the DP-JCAL model reproduced the measured absorption spectra with higher accuracy for both 50 mm and 100 mm samples. Micro-branch suppression further confirmed that the intra-paper micro-pore pathway is necessary for accurate spectral reconstruction, acting mainly as a highly resistive background loss contribution within the measured frequency range. A thickness-transfer prediction test showed that parameters identified from 50 mm samples could predict the corresponding 100 mm absorption spectra without refitting, supporting the physical consistency of the fitted transport parameters. These results show that intrapaper micro-porosity is an acoustically active loss pathway required for physically interpretable inverse modeling of recycled paper strip absorbers.
Abstract Pigging is widely employed to remove deposits from water distribution pipelines, yet technical guidelines for determining the optimal number of passes remain insufficiently established. This study investigates the mechanistic reason why repeated pigging provides superior cleaning performance compared with a single pass by integrating visualization experiments and computational fluid dynamics (CFD) analysis. A serrated scraper pig, consisting of two rows of circumferential serrated rubber pads with a Shore hardness of 60D, was tested in a laboratory-scale recirculating system using a 100 mm diameter transparent acrylic test pipe and artificial sediments designed to simulate strongly adhered pipeline deposits. Direct visualization showed that the second pigging pass achieved a sediment removal efficiency of 37.55%, which was approximately five times higher than that of the first pass (7.46%) and substantially greater than that of conventional flushing (0.55%). Transient CFD simulations (pimpleFoam solver with the γ – Re θ transition model coupled to the shear stress transport (SST) k – ω turbulence model) clarified the physical basis for this enhancement. During the first pass, sediment accumulation within the serrated pad gaps suppresses bypass flow development. After these gaps are partially cleared, accelerated annular flow develops more effectively during the subsequent pass, increasing the local wall shear stress and skin-friction coefficient in the noncontact regions. This combined action of mechanical scraping and hydrodynamic shear explains why the major cleaning effect appears primarily after the first pass. The main practical implication is that at least two pigging passes are recommended for the effective removal of strongly adhered deposits. These findings provide a mechanistic basis for optimizing pigging protocols in water distribution pipeline maintenance.
