Artificial Intelligence (AI) has reached memory studies in earnest. This partly reflects the hype around recent developments in generative AI (genAI), machine learning, and large language models (LLMs). But how can memory studies scholars handle this hype? Focusing on genAI applications, in particular so-called ‘chatbots’ (transformer-based instruction-tuned text generators), this commentary highlights five areas of critique that can help memory scholars to critically interrogate AI’s implications for their field. These are: (1) historical critiques that complicate AI’s common historical narrative and historicize genAI; (2) technical critiques that highlight how genAI applications are designed and function; (3) praxis critiques that centre on how people use genAI; (4) geopolitical critiques that recognize how international power dynamics shape the uneven global distribution of genAI and its consequences; and (5) environmental critiques that foreground genAI’s ecological impact. For each area, we highlight debates and themes that we argue should be central to the ongoing study of genAI and memory. We do this from an interdisciplinary perspective that combines our knowledge of digital sociology, media studies, literary and cultural studies, cognitive psychology, and communication and computer science. We conclude with a methodological provocation and by reflecting on our own role in the hype we are seeking to dispel.

Perfluorinated compounds (PFCs) are synthetic chemicals commonly used in various industries for their water-, grease-, and stain-repellent properties. These compounds are highly persistent in the environment and can be absorbed by farm animals, subsequently contaminating animal-derived products. This contamination poses a significant health risk to humans who consume these products. Previous studies have identified cow's milk as one of the primary animal products contaminated with PFCs. However, it remains unclear which specific PFCs increase in concentrations over time. In this study, we analysed data on the concentrations of 24 PFCs in cow's milk sourced from a milk processing plant in Taiyuan, Shanxi Province, China, over a three-year period, as provided by the National Agriculture Science Data Centre. Our analysis revealed that perfluoropentanoic acid (PFPeA) and perfluorobutanoic acid (PFBA) were the dominant PFCs that tended to accumulate in cow's milk over time. Consequently, consumers and milk producers should monitor the levels of PFPeA and PFBA in cow's milk to mitigate potential health risks associated with these pollutants.

The aim of this study is to explore how large language models (LLMs) integrated with structured versus unstructured concept generation techniques (CGTs) influence designers’ creative thinking processes and outputs. Using human–human collaboration (HHC) as a baseline, a 2 × 2 mixed factorial design was adopted to investigate the effects of collaborator type (between-subjects: LLM-based agents vs. experienced designers) and CGT type (within-subjects: brainstorming vs. TRIZ). Two LLM-based agents, IntelliStorm and EvoluTRIZ, were developed for the study, with 32 participants randomly assigned to either the HHC or human–agent collaboration (HAC) groups. Brain activity was measured using functional near-infrared spectroscopy, while outputs were assessed through expert evaluations. Results showed that designers exhibited lower cognitive load, better cognitive resource coordination, and enhanced fluency and flexibility in thinking in HAC than in HHC. Moreover, distinct patterns were revealed in different CGTs: brainstorming activated the right dorsolateral prefrontal cortex (PFC) as the core connectivity region, enhancing ideational fluency, whereas TRIZ activated the left dorsolateral PFC, facilitating refined thinking. Although HAC demonstrated stronger overall performance, HHC retained unique advantages in originality. This research offers novel neuroscientific insights and provides evidence-based guidance for developing more effective LLM-based design agents.

To fully understand resilience and to inform resilience-promoting interventions, it is important to explore how resilience develops and the factors that influence it. Using a multidimensional approach that considers both well-being resilience (higher than expected wellbeing after adversity) and depression resilience (lower than expected depression after adversity), this study examined resilience trajectories among Chinese 0adolescents and the associations of gratitude and perceived stress with resilience trajectories. Data from a four-wave longitudinal study were analyzed from 563 Chinese adolescents (mean age at Time 1 = 12.83 years, 51.87% boys). Parallel-process latent class growth modeling identified four distinct trajectories of resilience development: flourishing resilience (increasing resilience; 21.67%), increasing wellbeing resilience but decreasing depression resilience (28.24%), declining resilience (29.48%), and increasing depression resilience but decreasing wellbeing resilience (20.61%). Gratitude was associated with greater odds of being in the flourishing resilience group. Furthermore, perceived stress was associated with lower odds of being in the flourishing resilience group and higher odds of being in the declining resilience group. The findings suggest that resilience is a dynamic and multidimensional construct with highly heterogeneous developmental trajectories. Gratitude and perceived stress may be effective targets for interventions to enhance adolescent resilience.

High-order harmonic generation (HHG) in noble gases driven by femtosecond lasers is currently a feasible solution to obtain ultrafast pulses in the extreme ultraviolet (EUV) wavelength range. Implementation of high-flux EUV sources requires driving HHG using an ultrafast laser source in the visible wavelength range with MHz repetition rate. In this paper, we employ a multi-pass cell followed by chirped mirrors to compress 1-MHz, 200-W, 300-fs pulses at 1.03 μm to a duration of 35 fs. The resulting 186-W compressed pulses are focused onto 0.5-mm thick beta barium borate crystal to drive second-harmonic generation and produce positively chirped pulses at 520 nm. These green pulses are de-chirped to 26 fs in duration with an average power of 64 W, which, to the best of our knowledge, represents the highest average power of green pulses with a duration below 100 fs.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

The incorporation of trace metals into land snail shells may record the ambient environmental conditions, yet this potential remains largely unexplored. In this study, we analyzed modern snail shells (Cathaica sp.) collected from 16 sites across the Chinese Loess Plateau to investigate their trace metal compositions. Our results show that both the Sr/Ca and Ba/Ca ratios exhibit minimal intra-shell variability and small inter-shell variability at individual sites. A significant positive correlation is observed between the shell Sr/Ca and Ba/Ca ratios across the plateau, with higher values being recorded in the northwestern sites where less monsoonal rainfall is received. We propose that shell Sr/Ca and Ba/Ca ratios, which record the composition of soil solution, may be controlled by the Rayleigh distillation in response to prior calcite precipitation. Higher rainfall amounts may lead to a lower degree of Rayleigh distillation and thus lower shell Sr/Ca and Ba/Ca ratios. This is supported by the distinct negative correlation between summer precipitation and shell Sr/Ca and Ba/Ca ratios, enabling us to reconstruct summer precipitation amounts using the Sr/Ca and Ba/Ca ratios of Cathaica sp. shells. The potential application of these novel proxies may also be promising for other terrestrial mollusks living in the loess deposits globally.

Measurements in high-speed flows are difficult to acquire. To maximise their utility, it is important to quantify the preceding events that can influence a sensor signal. Flow perturbations that are invisible to a sensor may prevent the detection of key physics. Conversely, perturbations that originate away from a sensor may impact its signal at the measurement time. The collection of the latter perturbations defines the domain of dependence (DOD) of the sensor, which can be evaluated efficiently using adjoint-variational methods. For Mach 4.5 transitional flat-plate boundary layers, we consider the DOD of an instantaneous and localised wall-pressure observation, akin to that by a piezoelectric probe. At progressively earlier times prior to the measurement, the DOD retreats upstream from the probe, and the sensitivity to flow perturbations expands spatially and is amplified. The expansion corresponds to a wider region where initial disturbances can influence the measurement, and the amplification is because these perturbations grow during their forward evolution before reaching the probe. The sensitivity has a wavepacket structure concentrated near the boundary-layer edge, and a portion that radiates into the free stream. The DOD is further interpreted as the optimal initial perturbation with unit energy that maximises the norm of the measurement, establishing a link to transient-growth analysis. We test this formulation for a laminar condition and contrast the sensor dependence on different components of the state vector. When the boundary layer is transitional, we adopt the general formulation to assess the impact of sensor placement within the transition and turbulent zones on the DOD, and we characterise the flow disturbances that most effectively influence the measurement in each regime.

While the innovation behaviors of family firms (FFs) have attracted burgeoning scholarly attention, few studies have investigated how intergenerational succession, one of the most critical aspects of family dynamics changes among FFs, affects innovation behaviors. Based on the socioemotional wealth perspective (SEW), we have introduced a concept of innovation decoupling that refers to the tendency of prioritizing the symbolic disclosures over substantive changes of innovation and proposed that FFs that have experienced intergenerational succession would exhibit a greater extent of innovation decoupling. By tracking a sample of Chinese publicly listed FFs from 2012 to 2021 while applying the machine learning approach, we have confirmed the proposition and further unveiled that such inclination becomes weaker when the focal FF is influenced by the family affective endowment and the successor with ascribed bureaucratic connections. Overall, this study brings new nuances to the knowledge of the innovation behaviors of FFs by highlighting the inter-firm heterogeneities and impacts of family dynamics.

The ubiquity of social media platforms allows individuals to easily share and curate their personal lives with friends, family, and the world. The selective nature of sharing one’s personal life may reinforce the memories and details of the shared experiences while simultaneously inducing the forgetting of related, unshared memories/experiences. This is a well-established psychological phenomenon known as retrieval-induced forgetting (RIF, Anderson et al.). To examine this phenomenon in the context of social media, two experiments were conducted using an adapted version of the RIF paradigm in which participants either shared experimenter-contrived (Study 1) or personal photographs (Study 2) on social media platforms. Study 1 revealed that participants had more accurate recall of the details surrounding the shared photographs as well as enhanced recognition of the shared photographs. Study 2 revealed that participants had more consistent recall of event details captured in the shared photographs than details captured or uncaptured in the unshared photographs. These results suggest that selectively sharing photographs on social media may specifically enhance the recollection of event details associated with the shared photographs. The novel and ecologically embedded methods provide fodder for future research to better understand the important role of social media in shaping how individuals remember their personal experiences.

This work focuses on the intensity variation mechanisms in the mean inner and outer shear layers of a premixed swirling flame. In order to close the gap between the Lagrangian vorticity transport and the Eulerian shear layer intensity ($\gamma$), we propose a combined Reynolds-vorticity transport approach to obtain the streamwise variation of $\gamma$ as the integrals of vorticity generation terms, including tilting, baroclinic torque, diffusion and dilatation. However, different from the classical vorticity (transport) equation, the vortex stretching vanishes, and the original dilatation is replaced by a shear-layer dilatation in the new model. It enables the quantitative evaluation of how the different vorticity transport terms affect the shear layer intensity; in particular, we have identified vortex tilting and baroclinic torque as the main cause of the inner shear layer enhancement in the swirling flame’s near field. Although this model is initially developed to study the flame-attached shear layers, the broader significance lies in its applicability to general axisymmetric shear flows.