Search Results (709)

Digital retail is undergoing a paradigm shift driven by the deep integration of artificial intelligence (AI) and augmented reality (AR). Although prior studies have examined the independent effects of AI-based personalized recommendation (cognitive path) and AR-enabled immersion (experiential path), how their integration systematically shapes user behavior through internal psychological mechanisms remains an important unresolved theoretical gap. To address this gap, this study develops an integrated model grounded in the stimulus–organism–response (S-O-R) framework and trust transfer theory. Specifically, the model examines how personalized recommendation, as a dynamic external stimulus, influences users’ cognitive state (perceived usefulness) and experiential state (immersion); how the overall trust of users in the integrated platform can be used as a key boundary condition to adjust the transformation efficiency from the above stimulus to the internal state; and how the above cognitive and experiential states can ultimately drive the continued usage intention through the mediation of positive emotional response. Based on survey data from 400 Chinese consumers with AR shopping experience on Taobao, analyzed using structural equation modeling (SEM), the results indicate that (1) personalized recommendation positively affects both immersion and perceived usefulness; (2) platform trust significantly and positively moderates the effects of personalized recommendation on both immersion and perceived usefulness; (3) both cognitive and experiential states stimulate positive emotions, which in turn enhance continued usage intention, with perceived usefulness exerting a stronger effect; (4) a key theoretical finding is that there is a significant positive correlation between perceived usefulness and immersion, revealing the coupling of psychological paths in an integrated environment; however, immersion does not moderate the effect of personalized recommendation on emotional responses, suggesting that the current integration mode emphasizes the formation of a stable psychological structure rather than real-time interaction. This study makes three contributions to the existing literature. First, it extends the application of S–O–R theory in a complex technological environment by analyzing the “organism” as a parallel and related cognitive-experience dual path and confirming its coupling relationship. Second, it elucidates the enabling role of trust as a moderating mechanism rather than a direct antecedent, thereby enriching micro-level evidence for trust transfer theory in the context of technology integration. Finally, by contrasting path coupling with process regulation, this study provides a more detailed distinction for understanding the theoretical connotations and boundaries of AI–AR technology integration, which may mainly be a kind of structural integration. Full article

The growing demand for miniature mechanical components has increased the importance of ultra-precision micro machine tools and real-time monitoring. This study examines acoustic emission (AE) sensing for the intelligent control of an ultra-precision micro lathe. AE signals were measured while brass and aluminum alloys were turned with cermet and diamond tools at different spindle speeds and cutting depths. Finite element simulations were performed to clarify the AE generation mechanisms. The AE waveform amplitude changed stepwise corresponding to tool–workpiece contact, elastoplastic deformation, and chip formation, enabling precise contact detection at the 0.1 μm level. The AE amplitude increased with increasing spindle speed and increasing depth of cut except during abnormal conditions (e.g., workpiece adhesion). Frequency analysis revealed a dominant peak near 0.2 MHz during normal cutting, as well as high-frequency (>1 MHz) components linked to built-up edge formation. Simulations confirmed that these AE features reflect variations in the strain rate in the shear zone and on the rake face. They also confirmed that cutting force spectra under high friction reproduce the experimentally observed high-frequency peaks. These findings demonstrate the feasibility of using AE sensing to identify the cutting state and support the development of self-optimizing micro machine tools. Full article

A wide range of non-microbial contaminants—such as heavy metals, pesticide residues, antibiotics, as well emerging foodborne contaminants like micro- and nanoplastics and persistent organic pollutants—can enter the human body through daily diet and exert subtle yet chronic effects that are increasingly recognized to be gut microbiota-dependent. However, the relationships among multi-contaminant exposure profiles, dynamic microbial community structures, microbial metabolites, and diverse clinical or subclinical phenotypes are highly non-linear and multidimensional, posing major challenges to traditional analytical approaches. Artificial intelligence (AI) is emerging as a powerful tool to untangle the complex interactions between foodborne non-microbial contaminants, the gut microbiota, and host health. This review synthesizes current knowledge on how key classes of non-microbial food contaminants modulate gut microbial composition and function, and how these alterations, in turn, influence intestinal barrier integrity, immune homeostasis, metabolic regulation, and systemic disease risk. We then highlight recent advances in the application of AI techniques, including machine learning (ML), deep learning (DL), and network-based methods, to integrate multi-omics and exposure data, identify microbiota and metabolite signatures of specific contaminants, and infer potential causal pathways within “contaminant–microbiota–host” axes. Finally, we discuss current limitations, such as data heterogeneity, small-sample bias, and interpretability gaps, and propose future directions for building standardized datasets, explainable AI frameworks, and human-relevant experimental validation pipelines. Overall, AI-enabled analysis offers a promising avenue to refine food safety risk assessment, support precision nutrition strategies, and develop microbiota-targeted interventions against non-microbial food contaminants. Full article

Educational chatbots are increasingly deployed to scaffold student learning, yet educators lack scalable ways to assess the cognitive depth of these dialogues in situ. Bloom’s taxonomy provides a principled lens for characterizing reasoning, but manual tagging of conversational turns is costly and difficult to scale for learning analytics. We present a reproducible high-confidence pseudo-labeling pipeline for multi-label Bloom classification of Socratic student–chatbot exchanges. The dataset comprises 6716 utterances collected from conversations between a Socratic chatbot and 34 undergraduate statistics students at Nanyang Technological University. From three chronologically selected workbooks with expert Bloom annotations, we trained and compared two labeling tracks: (i) a calibrated classical approach using SentenceTransformer (all-MiniLM-L6-v2) embeddings with one-vs-rest Logistic Regression, Linear SVM, XGBoost, and MLP, followed by per-class precision–recall threshold tuning; and (ii) a lightweight LLM track using GPT-4o-mini after supervised fine-tuning. Class-specific thresholds tuned on 5-fold cross-validation were then applied in a single pass to assign high-confidence pseudo-labels to the remaining unlabeled exchanges, avoiding feedback-loop confirmation bias. Fine-tuned GPT-4o-mini achieved the highest prevalence-weighted performance (micro-F1 $=0.814$), whereas calibrated classical models yielded stronger balance across Bloom levels (best macro-F1 $=0.630$ with Linear SVM; best classical micro-F1 $=0.759$ with Logistic Regression). Both model families reflect the corpus skew toward lower-order cognition, with LLMs excelling on common patterns and linear models better preserving rarer higher-order labels, while results should be interpreted as a proof-of-concept given limited gold labeling, the approach substantially reduces annotation burden and provides a practical pathway for Bloom-aware learning analytics and future real-time adaptive chatbot support. Full article

Existing research has focused chiefly on the impact of artificial intelligence (AI) on economic growth. This study developed an AI dictionary using machine learning methods. Based on data from 3646 Shanghai- and Shenzhen-listed A-share companies from 2011 to 2022 and a panel mediation effect model, the relationships between AI application, human capital structure adjustment, and corporate ESG performance were examined. Theoretical research suggests that when corporates adopt AI, demand for high-skilled labor will increase while some low-skilled positions will be replaced. This leads to optimization of the human capital structure, which in turn improves corporate ESG performance. The results of the mechanism examination show that enhancing corporate ESG performance through AI use is achieved by modifying the human capital structure. Analysis of heterogeneity finds that for non-state-owned, large-sized, and non-technology-intensive corporates, the impact of AI applications on corporate ESG performance is more pronounced. This research further deepens the understanding of AI’s role in the corporate governance process at the micro-corporate level and offers suggestions to promote the development of AI technology. Full article

Abusive supervision remains a pervasive and damaging phenomenon in organizations, prompting a critical need to understand preventive mechanisms. We adopt a leader-centric, actor-focused perspective to investigate how a positive organizational context can inhibit leaders’ destructive behaviors. Drawing on Affective Events Theory (AET), we propose that leaders’ awareness of their organization’s philanthropic activities serves as a positive, morally salient event that generates feelings of moral pride. This pride, in turn, is theorized to reduce the likelihood of abusive supervision. Furthermore, we posit that this process is contingent on leaders’ moral reputation maintenance concerns, such that the negative relationship between moral pride and abusive supervision is stronger for leaders who are highly concerned with being perceived as moral. We tested this model using a three-wave survey study involving 434 leaders. The results support our hypotheses, indicating that perceived philanthropy awareness is positively associated with moral pride, which, in turn, predicts lower abusive supervision. This indirect effect is significantly stronger for leaders with high moral reputation maintenance concerns. Our findings contribute to the literature by identifying a novel, positive, and self-regulatory pathway for preventing abusive supervision and showing that applying AET to understand how macro-level organizational good deeds can translate into improved micro-level leader conduct. Full article

Contemporary food systems have reached a turning point, as they are required to simultaneously ensure food security and minimize the pressure they exert on the environment, aiming to balance human needs and the rhythm of nature. The low efficiency of current models of food production and distribution systems have revealed the need for a major transition toward circular solutions based on resource circulation, local adaptation, and the responsible use of urban spaces. This study explored the integration of circular economy principles with urban agriculture as a new framework for developing resilient, low-emission, and human-centered cities. In addition, a multiscale (micro, midi, and maxi) approach, combined with SWOT, Weighted SWOT, and TOWS analyses, was applied to identify key factors, barriers, and possible directions for implementation and development strategies. The results showed that the greatest potential of these systems lies in the synergy between water and energy recovery and resource efficiency, while energy intensity and regulatory frameworks have remained major challenges. The proposed strategic approach highlights the need to link food production to renewable energy sources, implement simplified evaluation standards (TEA/LCA-lite), and strengthen social acceptance through education and transparency. Circular urban agriculture emerged as a new type of infrastructure, both technological and social, that may become a pillar of sustainable and resilient cities in the future, supporting the achievement of SDGs 11, 12, and 13. Full article

Engaged spiritualities face a central challenge: how to transform moments of transcendence into enduring forms of shared life under modern conditions of pluralism, critique, and expressive individualism. This article asks what enables certain forms of spiritual life to last while others fade. It offers an emergentist, systems-theoretical account of how sacred life endures by viewing religion as a self-organizing symbolic system in which meaning and communal practice continually reinforce one another. In plain terms, it examines how myth, ritual, and transformative experience interact to turn inspiration into a lasting sacred world. The study identifies this interaction as the metaperformative loop, a feedback process linking a named yet inexhaustible mystery, inherited ritual authority, and formative submission. The loop functions as the minimal ecological unit through which sacred systems engage and rebuild the symbolic environments that sustain them. At the micro scale, a comparative vignette of the Grateful Dead’s Deadhead community and its cultic offshoot, the Spinners, shows how episodic ecstasy can crystallize into a durable sacred world. At the meso scale, the paper examines contemporary “spiritual-but-not-religious” life as a test case in symbolic ecology and outlines four adaptive strategies (enclosure, membrane, micro-habitats, and drift) that explain why some spiritualities reproduce themselves across generations while others dissipate. Full article

The pervasive environmental contamination by micro- and nanoplastics (MNPs) presents a formidable analytical challenge, necessitating the development of rapid and sensitive detection methods. While conventional techniques often suffer from limitations in sensitivity and throughput, fluorescent probe-based technology has emerged as a powerful alternative. This review charts the evolution of these probes, from initial stains relying on hydrophobic adsorption to advanced molecular designs engineered for specific chemical recognition. We critically examine key operational mechanisms, including the solvatochromic response of Nile Red, polarity-discriminatory probes enabling a “microplastic rainbow,” and targeted systems achieving turn-on fluorescence via restriction of intramolecular rotation. Furthermore, we highlight cutting-edge signal enhancement strategies, such as plasmon- and metal-enhanced fluorescence, which amplify detection to the femtogram level. Special emphasis is placed on the distinct challenges posed by nanoplastics, including their propensity for aggregation in aqueous matrices that exacerbates false positives and their superior ability to breach biological barriers, and how AIE luminogens and PEF/MEF strategies mitigate these issues through enhanced signal-to-noise ratios and subcellular resolution, differing from their application to microplastics. Critically, we address the imperative for low-toxicity probe designs, emphasizing biocompatibility and biodegradability criteria to facilitate safe, long-term in vivo tracking and widespread ecological surveillance. The integration of these sophisticated probes with smart, “activate-on-target” systems is paving the way for next-generation MNP analysis, offering critical insights for environmental monitoring and toxicological assessment. Full article

Nowadays, growing evidence indicates that plant-derived nanovesicles cross biological barriers between species, including humans, and deliver therapeutic molecules that influence gene expression, affecting various processes such as inflammation, oxidative stress, and cancer. For these reasons, plant-derived nanovesicles are gaining attention as a valuable substitute for mammalian exosomes as they offer benefits such as reduced immunogenicity, enhanced bioavailability, and the inclusion of beneficial plant metabolites. However, the development of affordable plant-derived nanovesicle-based therapies requires a robust characterization of their molecular structure and cargo, which in turn depends on obtaining sufficient quantities of homogeneous nanovesicle populations. In this study, we used an advanced purification platform combining ultrafiltration and anion exchange chromatography to isolate highly pure plant-derived nanovesicles from a new source, Beta vulgaris L. These particles were characterized in terms of size, charge, and morphology, and their molecular content was analyzed by omic technologies, including proteomics, lipidomics, and miRNomics. Their ability to promote wound healing and reduce inflammation was demonstrated in vitro using human cells. Furthermore, bioinformatic analysis linking the microRNA profile with potential human target genes provides insights into the biochemical pathways that underlie the bioactivity of nanovesicles. Full article

The ubiquitous environmental pollution with micro- and nano-sized plastic particles (MNPs) is a current and significant problem today. At the same time, lung cancer is responsible for the largest number of cancer-related deaths worldwide. Many research groups have investigated the relationship between lung cancer development and exposure to MNPs in recent years. Studies have demonstrated that these particles could enter the respiratory system in a variety of ways—both directly through inhaled air and through the bloodstream, and through internalization in the intestines and other digestive organs. Data regarding the possibility of their aggregation in the respiratory system, thyroid gland, and brain are also concerning, as the harmful effects of MNPs have been proven to depend on their concentration and exposure time. The primary response of cells to plastic particles is an increase in oxidative stress. This is generated both by the cell itself (especially macrophages) and induced by damage caused by mechanical damage to cellular organelles by MNPs. The consequences of MNP exposure can include metabolic disturbances, DNA damage, and mutations, ultimately inducing neoplastic transformation in healthy cells. This can lead to changes in tissue architecture and increase their susceptibility to other pathogens, such as pathogenic microorganisms or heavy metals. These, in turn, can be internalized along with MNPs, forming a corona surrounding them. Full article

Cancer remains a persistent global health challenge, continuously driving the search for novel and effective therapeutic strategies. In the case of breast cancer, treatment decisions are primarily guided by factors such as the disease stage, histological grade, molecular receptor status, and the presence of genetic mutations. Understanding these parameters is crucial for tailoring interventions and improving clinical outcomes. To enhance prognostic and diagnostic accuracy, attention has increasingly turned to identifying molecular targets that play key roles in breast cancer development. Currently, standard treatments include surgery, chemotherapy, and radiotherapy. However, these approaches are often associated with significant side effects and a diminished quality of life. As a result, many breast cancer patients are turning to complementary therapies—including phytotherapy, nutritional interventions, and dietary supplements—to support conventional treatment, alleviate adverse effects, and improve overall well-being. Within the vast realm of medicinal flora, anticancer plants represent a compelling area of study, serving as a rich reservoir of bioactive compounds. These compounds have demonstrated significant promise in the ongoing battle against cancer. Often highlighted in traditional medicinal practices, these plants harbor a wide array of phytochemicals, such as alkaloids, flavonoids, polyphenols, and terpenoids. These phytochemicals manifest diverse biological activities, notably exhibiting pronounced anticancer properties. The exploration of these natural compounds has opened new avenues for developing innovative and targeted therapeutic strategies in cancer treatment. They achieve definitive chemotherapeutic and chemopreventive roles by integrating with specific molecular signals. Their multiple biological functions include antimutagenic, antiproliferative, antimetastatic, anti-angiogenesis, anti-inflammatory, antioxidant, and immunomodulatory properties, which collectively enable them to control cancer progression and intervene at various stages of cancer cell development. Moreover, these compounds are involved in regulating the cell cycle and microRNA, ultimately leading to cancer cell death by promoting apoptosis and autophagy, often mediated through ROS signaling. Thus, based on a large theoretical revision, we conclude that high-quality evidence is necessary in order to advise these products concerning their efficacy and safety. Also, clinical evidence should be supported by a comprehensive individual diagnosis and adequate research protocols in order to evaluate whether the benefits of these plant-produced interventions can outweigh their harms. Full article

Composite materials are widely utilized for their excellent properties; however, the mismatch in phase response during processing often induces surface and subsurface damage. While reducing the cutting depth is a common strategy to improve quality, it shifts the material removal mechanism from shear to ploughing–extrusion, which can, in fact, degrade the final surface integrity. Energy field assistance is a promising approach to suppress this issue, yet its underlying mechanism remains insufficiently understood. This study investigates high-silicon aluminum alloy by combining turning experiments with molecular dynamics simulations to elucidate the origin and evolution of damage under different energy fields, establishing a correlation between microscopic processes and observable defects. In conventional turning, damage propagation is driven by particle accumulation and dislocation interlocking. Ultrasonic vibration softens the material and confines plastic deformation to the near-surface region, although excessively high transient peaks can lead to process instability. Laser remelting turning disperses stress within the remelted layer, significantly inhibiting defect expansion, but its effectiveness is highly sensitive to variations in cutting depth. The hybrid approach, laser remelting ultrasonic vibration turning, leverages the dispersion buffering effect of the remelted layer and the localized plastic deformation from ultrasonication to reduce peak loads, control deformation depth, and suppress defects, while simultaneously mitigating the depth sensitivity of damage and maintaining removal efficiency. This work clarifies the mechanism by which a composite energy field controls damage in the micro-cutting of high-silicon aluminum alloy, providing practical guidance for the high-quality machining of composite materials. Full article

With the surging demand for dynamic, real-time, and rapid qualitative analysis of chemical components, chip-scale mass spectrometers have attracted widespread attention. Ion traps have become the preferred mass analyzer for chip-scale mass spectrometers due to their excellent analytical performance. However, the miniaturization of ion traps inevitably leads to a reduction in ion storage capacity, which in turn affects their sensitivity and dynamic range. In this study, a Miniature Four-Channel Linear Ion Trap Array (M-FLITA) with hyperbolic electrodes and a 1 mm field radius was established and optimized. Concurrently, unidirectional ion ejection was accomplished by the application of asymmetric RF voltages on M-FLITA. The results demonstrate that, in the stretched structure, the mass resolution is improved to 732, while the unidirectional ion ejection efficiency is maintained at 96%. M-FLITA demonstrates advantages in terms of high ion storage capacity and mass resolution under high ion flux conditions, providing an ideal solution for high-performance micro mass analyzers in chip-scale mass spectrometers. Full article

This study proposes a scenario-based conceptual model for transforming underground parking structures into sustainable interior green spaces, directly addressing two core research dimensions: energy efficiency and user experience. The originality of the research lies in repositioning subterranean spaces—often overlooked in urban planning—as climate-responsive, multi-functional public environments. Using a site-specific case in downtown Rize, Türkiye, three design scenarios—passive green walls, active modular systems, and experimental micro-farming—were comparatively analyzed. These scenarios were assessed through AI-assisted simulations and climate-based performance evaluations in terms of environmental benefits, thermal regulation, carbon reduction, and experiential quality. Underground space leads to green design interventions, which in turn generate environmental, energy, and social benefits. The results demonstrate that passive systems provide cost-effective improvements, active modular systems achieve balanced performance, and experimental micro-farming yields the highest ecological and social benefits. The study uniquely contributes to urban sustainable design by integrating climate-adaptive strategies, biophilic design principles, and AI-supported visualization into the transformation of underground structures. This research not only advances academic discourse but also provides policy-relevant insights for local governments, developers, and communities in the context of urban renewal. Full article