Abstract Rapid nurse decision-making is needed to detect patient deterioration and prevent mortality. Current approaches to support nurses’ decisions involve diagnostic data processing and providing a decision with little explanation. Our team aimed to demonstrate the utility of attention architecture to model sequential nurse–patient care actions. Experienced nurses and students completed patient care simulations. Nurse actions were systematically coded and analyzed using our model, consisting of an attention encoder to sequentially process and predict nurse behavior. Performance of our model was compared to recurrent neural networks and long-short term memory models based on accuracy, precision, recall, and F1 score. Behavioral data from 24 nurses (11 experienced nurses and 13 nursing students) were collected during patient care simulations. Nineteen unique types of actions were distilled down to 8 common actions. There were 33 episodes captured (i.e., 33 unique sequences of patient care actions), including a total of 1024 actions (i.e., an average of 31 ± 11 actions). Results showed that the attention model outperformed the other models on all metrics except for precision. Our team demonstrated that machine learning can model sequential nurse actions. These results could be leveraged to provide real-time guidance to support novice nurses’ decision-making in the simulated environment.

This study evaluated the probiotic, technological, and safety properties of 124 lactic acid bacteria isolated from traditional Beyaz cheese, and the in situ survival of selected strains in fermented milk. Eighteen isolates showing over 80% tolerance in simulated gastric juice (pH 3.0) were subjected to further characterization. On the basis of 16S rRNA gene sequencing, most isolates belong to Lacticaseibacillus paracasei and Lactiplantibacillus plantarum, while Lactobacillus helveticus, Lentilactobacillus kefiri, and Limosilactobacillus fermentum were also identified. L. plantarum EH140 showed the greatest resistance to the simulated gastric environment (pH 2.0), whereas L. paracasei EH131 exhibited the highest bile salt tolerance. L. plantarum EH106 demonstrated strong auto-aggregation, and L. fermentum EH132 displayed notable hydrophobicity. Nine isolates exhibited bile salt hydrolase activity, but none showed γ-hemolysis, gelatinase, or DNase activity. All the isolates were susceptible to ampicillin, erythromycin, clindamycin, and chloramphenicol. Organic acid analysis revealed lactic acid as the major metabolite, followed by acetic acid. Virulence gene screening identified the efaAfs gene only in L. paracasei strains, and no biogenic amine genes were detected. The selected isolates maintained viability above 6 log CFU/mL in milk during storage. Overall, L. plantarum EH106, EH109, EH140, and EH141 were identified as the most promising candidates because of their safety and superior probiotic potential.

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection.

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC > 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC > 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation.

Against the backdrop of a "cold reality, hot virtual reality" situationthe deep development of the digital entertainment industry and the shifting social and family dynamicsdomestic otome games have transformed from mere emotional consumer goods into an important arena for contemporary women to construct their identities and hedge risks. This study takes domestic otome game players as its research subjects, adopting a post-feminist perspective to explore how players reclaim their individual subjectivity within an industrialized romantic narrative through a set of "intellectual" strategies. The study employs questionnaire surveys and in-depth interviews. Through thematic analysis of 135 valid questionnaires and interview texts with 15 in-depth players, it was found that players are not the "daydreamers" who cannot distinguish between reality and fiction, as traditionally assumed in academia, but rather highly proactive actors. The results show that players use intellectual strategies to gain psychological empowerment within a virtual ideal self, and through the domain established by community connections, they transform emotional premiums into real-world survival skills through fan labor. The conclusion indicates that the behavioral practices of otome game players are essentially strategic risk avoidance and cognitive training implemented in virtual space under the guise of consumerism, completing a subjectivity reshaping from passive audience to active practitioner. This research fills a gap in existing research regarding the logic of player agency in reality.

Using Think-Aloud methodology and protocol analysis in mammographic practice research.

Evaluating Performance With the Use of a Novel All-in-One Acute Central Venous Catheter Insertion System.

Stitching wisdom: Midwifery students' and educators' experiences with perineal repair trainers.

Surface-state-regulated corrosion resistance and differential pre-immersion film evolution of B30 Cu-Ni alloy in simulated marine environments

Friction stir processing–induced microstructure refinement enhances corrosion resistance of ZE52 magnesium alloy in simulated physiological environment

Molecular adaptation analysis of Hemerocallis fulva roots to seawater-logging stress in a simulated natural environment

Corrosion behavior of pure copper as a material for high-level radioactive waste in simulated groundwater environments

Multiphysics-coupled corrosion mechanisms of H62 brass pipes in simulated marine environment: Synergistic effects of stress and Cl⁻ under static load conditions

A pH-sensitive smart hydrogel based on oxidized hyaluronic acid with synergistic antibacterial properties.

Biodegradation of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) in simulated sublittoral marine environment, as affected by reinforcing and antimicrobial agents

Investigation of anti-corrosion and wave absorption performance evolution of carbonyl iron powder electromagnetic wave-absorbing coating in simulated marine environment

Development of immersive radiotherapy clinical learning experience prototype (IRCLEP).

Purpose The purpose of this study is to investigate the susceptibility of L80, and other steels with 1% Cr, 3% Cr and 9% Cr, to stress corrosion cracking (SCC), pitting, crevice and stress concentration effect at the existing conditions of an oil reservoir. Design/methodology/approach The 4-points bent beam specimens were employed in the experimental works. The experimental works were performed at 82°C in a simulated environment that contains CO2 and Cl−. The effect of stress concentration on corrosion rate was confirmed by the novel circumferential notched tensile (CNT) specimen approach. Findings The results indicate that these steel grades are not susceptible to SCC in the given condition, and are promising materials for the application. The L80, 1% Cr and 3% Cr steels were attacked by pitting and crevice corrosion, while the 9% Cr steel was insignificantly affected, presumably due to its higher Cr, Mo and Ni contents. The local corrosion concentrated on sites that have high stress concentration. The CNT specimens confirmed the effect of stress concentration on promoting the local corrosion. Originality/value The study provides an essential insight into the susceptibility of downhole tubular to SCC for L80, 1% Cr, 3% Cr and 9% Cr steels in simulated reservoirs environment that contains CO2 and Cl−. A novel fracture mechanics approach using the CNT specimen method was introduced in investigating the effect of stress intensity on corrosion rate.

This study examines the convergence between classical Islamic philosophy and contemporary digital theory, emphasizing how foundational Islamic concepts can respond to social fragmentation in post-digital Muslim societies. As digital technologies increasingly mediate religious learning, identity formation, and communal interaction, Muslim communities face growing challenges, including the weakening of social bonds and the decentralization of religious authority. Online platforms encourage individualized interpretations of faith and pluralistic expressions of religiosity, which, while expanding participation, often contribute to fragmented identities and diminished communal cohesion.Drawing upon core classical concepts such as ummah (community), akhlaq (ethics), and asabiyya (social solidarity), this research argues that Islamic intellectual heritage offers a normative framework capable of addressing these disruptions. These concepts underscore collective responsibility, moral discipline, and the cultivation of shared values as the foundation of a stable and just society. Using a qualitative approach grounded in textual analysis of classical Islamic scholarship and contemporary digital sociology, the study recontextualizes these principles within present-day digital environments.The findings suggest that adapting traditional values to online engagement can strengthen ethical conduct, mutual accountability, and constructive dialogue in virtual spaces. The notion of a “cyber ummah” further illustrates how transnational digital networks may foster solidarity across geographic and ideological boundaries. Ultimately, this study affirms the continuing relevance of classical Islamic philosophy in guiding the development of an inclusive, morally grounded, and socially cohesive digital society.

Dylan Mulvaney, a transgender woman, has amassed a significant following on social media, with 2 million followers on Instagram, 9.4 million on Tik Tok, and hundreds of thousands of views on her You Tube videos. In part, her popularity can and should be understood as a reinforcement of the rigid gender binaries of femininity and masculinity, and a reification of “transitioning” into this hegemonic beauty standard of and for woman. Indeed, it is through her use of social media that she is able to both construct and reinforce what is acceptable as a (transgendered) woman’s body. And this vast social media following has translated into sociopolitical capital. Mulvaney has become a de facto spokeswoman for transwomen, sitting down with President Biden and snagging a sponsorship with Bud Light. However, it was this sponsorship, which included Bud Light’s creation of a personalized can of beer for Mulvaney, and its reaction across social media, that created a transphobic countermovement by the political right. Using the traditional social movement techniques of repertoires of contention and framing, this countermovement attempted to defend the existing power structures, or more precisely, the heteropatriarchal concepts of gender and sex, and their own positions of privilege. The boycott of Bud Light, as its primary form of collective action, re-framed both the product itself and masculinity as one defined by race (white), nationality (American), and sex-as-gender (hegemonic masculinity). Moreover, this countermovement primarily occurred in the virtual space, with social media offering a platform to circulate widely this counter-ideology, normalizing and valorizing such efforts.