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) × 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.
The external dose to the workers of bio-shield of the Kori Unit 1 was evaluated by using the VISIPLAN 4.0 code to secure the radiation safety of its decommissioning work. In particular, radionuclide-specific activity concentration data, derived from prior MCNP simulations, were used to model three-dimensional radiation work scenarios. Each scenario was simulated with shielding and sampling configurations to calculate the external dose rates. Worker operation times were estimated based on the ICRP-recommendation and Nuclear Safety Act of annual average dose limit of 20 mSv. Variations in worker role, position, and task duration resulted in substantial disparities in individual cumulative doses. The maximum dose rate in scenario 1 under the 8-year cooling condition was 2.3 × 10−1 mSv/h at 3 m height, decreasing to 9.3 × 10−2 mSv/h under the 15-year condition (∼60 % reduction). A 20 mm lead shield reduced the dose rate by ∼86 %. The total dose rate in scenario 2 under the 8 year cooling condition was 1.33 × 101 mSv of workers 1 and 2, ∼22 times that of Worker 3. The minimum required shielding thickness was estimated at 14.8 mm. The allowable number of complete sampling operations was 32 and 80 for the 8 and 15 year cooling conditions, respectively.
A transition metal-free binary composite of sulfur nanoparticle (SNPs) and nano silica (SiO2), designated as S-SiO2 was deliberately engineered via a hydrothermal method. SiO2 nanoparticles were synthesized from this residual rice husk using a simple and cost-effective procedure. The observed colour change from orange to pale yellow, along with the appearance of a UV absorption peak at 215 nm after 125 h, confirms the formation of sulfur nano particles. The assembled material was comprehensively characterized by different spectroscopic techniques. The synthesized S-SiO2 nanoconjugate have been examined as a potent photocatalyst for Tetracycline Hydrochloride (TCH) degradation from wastewater in presence of oxone under UV-light exposure. The synergistic combination of the transition metal-free S-SiO2 nanocomposite and oxone significantly boosted the formation of reactive oxidative species like sulfate (SO4-center dot) radicals. Under UV-light, similar to 95.96% of TCH degradation was achieved within 60 min. The intermediates were analyzed through Mass Spectrometric analysis. The catalyst remained stable over acidic, neutral, and basic pH conditions, retaining high activity. Furthermore, the photocatalyst displayed good recyclability upto 5th catalytic cycle with negligible loss in activity.
Landfill leachate is a reservoir of emerging contaminants (ECs), yet persistence is often reported without connecting it to underlying chemical structure. This review addresses this gap by linking molecular determinants of recalcitrance such as type of bond, functional groups, and polymer architecture to the mechanism governing sorption, transport, and transformation under dynamic leachate conditions. The scope integrates chemical principles with environmental determinants of leachate composition across landfill age, providing a perspective on contaminant behaviour. The review also highlights how structural features dictate interactions with complex leachate matrices by focusing on key contaminant classes; microplastics, antibiotic residues and associated antibiotic resistance genes (ARGs), and heavy metals. In particular, microplastics are examined as vectors that sorb and transport metals and ARGs, increasing mobility, persistence and contributing to resistome development in leachate. Advances in analytical techniques including liquid chromatography–mass spectrometry (LC–MS) and gas chromatography–mass spectrometry (GC–MS) techniques, spectroscopic tools such as nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy (FTIR) and aptamer-based biosensors are discussed in the context of resolving speciation and detection challenges. The review further aims to provide insights into sustainable and cost-effective treatment strategies and discuss their effectiveness in relation to contaminant structure and leachate chemistry. Overall, the present review concludes that persistence in landfill leachate can be systematically understood through measurable structural determinants, enabling more targeted monitoring and treatment strategies. Key challenges and future research directions are also addressed to advance management of ECs in leachate system.
Conventional hematoxylin and eosin (H E) staining is destructive and largely limited to two-dimensional sections. We aimed to develop a practical virtual staining method that produces H E-like images from label-free three-dimensional holotomography (HT) while preserving nuclear morphology and requiring only HT at inference. We designed a DAPI-guided conditional diffusion model with a shared encoder and two decoder heads (H E and DAPI). During training, the model receives HT as condition input and predicts diffusion noise for both H E and DAPI targets using mean-squared-error objectives. DAPI is used only during training as nucleus-centric guidance. Data were acquired from the same tissue using a sequential protocol (HT imaging, then DAPI imaging, then H E imaging). Because local nonlinear tissue deformation remains after global affine registration, HT–H E pairs were treated as weakly paired, while HT–DAPI provided stronger local correspondence. Compared with CycleGAN baselines, the proposed model produced more realistic nuclear morphology and better structural consistency. On held-out test tiles, DAPI-guided diffusion achieved lower FID and KID (FID: 9.2158; KID: 0.0091 ± 0.0035 ) than CycleGAN without DAPI (FID: 14.7447; KID: 0.0434 ± 0.0067 ). Training-only DAPI guidance improves virtual H E generation from label-free HT without requiring DAPI during inference. This weakly paired training design reduces dependence on expensive pixel-level registration and supports scalable, nondestructive digital histopathology workflows.
