Electrochemical biosensors have reached a high degree of analytical maturity; however, only a small portion of laboratory demonstrations actually progress to commercial products. In this review, we looked non-analytically at the factors which are in place with respect to this translational gap, specifically looking into supply chain design, scale-up manufacturing strategy, regulatory–quality, and more. Based on a wide range of academic and industrial literature, the paper considers how decisions about what kind of material to use, especially for material that recognizes living things, conductive material made from ink, and the material that is the actual product being made, can make a big difference in whether the product can be reproduced easily, if it will stay stable for a long time, and if it is allowed according to the rules. This review compares the dominant manufacturing paradigms—roll-to-roll printing, and semiconductor-derived microfabrication—and shows how the respective strengths and limitations match the different targets, costs, and risk class. This is more about making manufacturing an upstream optimization problem than treating processes as defects and quality as assurance, rather than making it an upstream optimization problem. And it does this by looking at some other big pathways for regulations in the U.S., EU, and China as well, where we get to see how those differences in classification requirements, what kind of proofs you should have, and different ways about running those quality management systems affect how quickly things can come out after developing them, and what your flexibility with customers is like when those products are already out there in the world. The study looks at some case studies: disposable glucose strips, cartridge-based blood analyzers, and new continuous monitoring systems are used to show how the exact same electrochemical ideas can result in very different commercialization issues based on the clinical context and system integration. Synthesizing those angles creates a review that can give a system level map of matching research design to industrial and regulatory realities, with the goal of making it easier for electrochemical biosensors to go from lab prototypes to ready-for-market diagnostic tools.

This review examines wearable chemical sensors for workplace exposure monitoring, covering sensor types, validation, data management, regulatory challenges, and future directions toward adoption.

With the rapid adoption of generative artificial intelligence (GenAI) chatbots on e-commerce platforms, users’ expectations for anthropomorphic service experiences have risen significantly. Despite the growing presence of GenAI, little is known about how different types of anthropomorphic users’ self-efficacy and the intention to adopt as a decision aid through distinct cognitive pathways. Addressing this research gap, this study draws on the elaboration likelihood model (ELM) to develop a comprehensive framework that integrates central and peripheral cues. Using large-scale survey data from e-commerce users and structural equation modeling, the research empirically examines the mediating role of self-efficacy and the moderating effect of information overload. Results indicate that human-like empathy and perceived warmth (peripheral cues) and perceived competence (central cue) all significantly enhance self-efficacy, which in turn positively influences the intention to adopt as a decision aid. Moreover, information overload intensifies the effect of peripheral cues on self-efficacy but has a limited impact on central cues. These findings advance the theoretical understanding of GenAI–human interaction by clarifying the mechanisms through which anthropomorphic features operate, and provide actionable insights for designing user-centric GenAI recommendation services to optimize user experience and encourage the intention to adopt as a decision aid in e-commerce.

With the rapid proliferation of artificial intelligence (AI) applications, an increasing number of edge devices—such as smartphones, cameras, and embedded controllers—are being tasked with performing AI-based inference. Due to constraints in storage capacity, computational power, and network connectivity, these devices are often categorized as operating in resource-constrained environments. In such scenarios, deploying powerful Transformer-based models like ChatGPT and Vision Transformers is highly impractical because of their large parameter sizes and intensive computational requirements. While lightweight Transformer models, such as MobileViT, offer a promising solution to meet storage and computational limitations, their robustness remains insufficient. This poses a significant security risk for AI applications, particularly in critical edge environments. To address this challenge, our research focuses on enhancing the robustness of lightweight Transformer models under resource-constrained conditions. First, we propose a comprehensive robustness evaluation framework tailored for lightweight Transformer inference. This framework assesses model robustness across three key dimensions: noise robustness, distributional robustness, and adversarial robustness. It further investigates how model size and hardware limitations affect robustness, thereby providing valuable insights for robustness-aware model design. Second, we introduce a novel adversarial robustness enhancement strategy that integrates lightweight modeling techniques. This approach leverages methods such as gradient clipping and layer-wise unfreezing, as well as decision boundary optimization techniques like TRADES and SMART. Together, these strategies effectively address challenges related to training instability and decision boundary smoothness, significantly improving model robustness. Finally, we deploy the robust lightweight Transformer models in real-world resource-constrained environments and empirically validate their inference robustness. The results confirm the effectiveness of our proposed methods in enhancing the robustness and reliability of lightweight Transformers for edge AI applications.

This study aims to examine, through the lens of cognitive heuristics theory, the relationship between generative artificial intelligence (GAI) technological affordance heuristic (TAH) cues, including website vividness, perceived anthropomorphism, personalization, and website design, and user trust and continuous use intention in cross-border e-commerce (CBeC). Additionally, this study investigates the mediating role of trust between GAI TAH cues and continuous use intention while information overload is treated as the moderating factor. A non-probability purposive sampling method was employed to collect 304 valid responses for testing the proposed model. Data were analyzed using partial least squares (PLS) with SmartPLS 4. The results showed that website vividness, personalization, and website design have significant positive impacts on trust, which in turn significantly influences continuous use intention. Trust mediated the relationships between these GAI TAH cues and continuous use intention, while information overload moderated the relationships between the cues and trust. In contrast, perceived anthropomorphism had no significant effect on trust, was not moderated by information overload, and did not indirectly influence continuous use intention through trust. Overall, the findings elucidate how GAI TAH cues affect trust and continuous use intention through heuristic theory, further confirming the role of GAI in CBeC. This study expanded the heuristic theory to creatively integrate with GAI TAH cues to reveal the impact on trust, The findings yielded also illustrate the role of information overload as the moderator of the relationships between GAI TAH cues and trust which in turn influence continuous use intention of CBeC.

Huizhou is located in the Pearl River Delta of Guangdong Province, bordering Shenzhen and Hong Kong. It is one of the rapidly developing cities in the Guangdong-Hong Kong-Macao Greater Bay Area. With the accelerating process of urbanization, the land use change of built-up areas in Huizhou is also increasing. This paper uses Google Earth engine (GEE) cloud computing platform, selects multiple periods Landsat remote sensing data, uses CART data classifier to discriminate urban land, vegetation land and water body, and implements dynamic monitoring of Huizhou urban expansion from 2000 to 2020. The results show that the urban area changes to an increasing trend from 2000 to 2020, with a total increased area of 350.13 km². Based on the extracted urban built-up area of Huizhou in 2000, 2005, 2010, 2015 and 2020, the growth rates of urban area in recent years are: 10.63% in 2000–2005, 3.84% in 2005–2010, 5.82% in 2010–2015 and 8.24%in 2015–2020, respectively. This study will provide a data basis for the analysis of urban future planning in Huizhou.