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Optimizing the mechanism for modulating THz phases by reversibly switching layers of liquid crystals between two in-plane and one out-of-plane states

We previously proposed electrode structures to reversibly switch liquid crystals (LCs), which respond reasonably rapidly, between three orthogonal orientational states. Here, to leverage the excellent inherent tunability of LCs for use in terahertz (THz) phase modulators, we reframe the working principles of these electrode structures for driving a thick LC layer, and we investigate how low-voltage operation affects the switching behavior. Phase changes of 100° are demonstrated, but in principle, the phase range can be broadened by increasing the retardation of the LC medium. According to the operating principles, a pair of electrodes with identical layouts must precisely mirror each other on the inner surfaces of the top and bottom substrates separated by a gap; however, misaligning the two substrates scarcely affects the switching characteristics, providing wiggle room for sufficiently accurate alignment. Statically, even a low voltage enables switching one state to another while reaching the maximum phase shift. By contrast, the dynamic responses to low voltages degrade and are extremely slow. Furthermore, although high-voltage operation provides reasonable response times between the three states, it is incompatible with continuous tunability. This problem prompted us to consider a stack of thin, rapid-response LC layers and to simultaneously or independently switch each layer to create a continuous or discrete range of possible phase shifts, respectively. In other words, replacing a two-dimensional pixel array, such as a display, with a one-dimensional stack of LC layers would enable tunability compatible with reasonably fast responses, suggesting a technical advance toward realizing LC-based THz modulators.

Reply to Auspurg: On the limits of “justified” model spaces

A vibration analysis of an acoustic mass sensor structure with a c-axis tilted ZnO thin film based on silicon

The effect of mindfulness-based stress reduction on presenteeism among ICU nurses: A cluster randomized controlled trial

Background Due to high-pressure environments, heavy workloads, and working in “three-shift” schedules, Intensive Care Unit (ICU) nurses experience high-level presenteeism. This may compromise nursing quality and patient safety and damage nurses’ physical and mental health. Therefore, there’s an urgent need for effective interventions to promote the healthy development of nursing human resources and maintain nursing team stability. Aim To evaluate the effect of an 8-week Mindfulness-Based Stress Reduction (MBSR) training on presenteeism among ICU nurses. Methods ICU nurses with high levels of presenteeism were invited to participate in the study. The ICU wards were randomly assigned to either the intervention group or the control group. The intervention group (40 nurses) received an 8-week MBSR program delivered by a certified mindfulness therapist. In comparison, the control group (40 nurses) received standard psychological counseling, including emotional control, psychological regulation, and sleep management. Both groups were assessed using the Stanford Presenteeism Scale-6 (SPS-6) and the Five Facet Mindfulness Questionnaire (FFMQ) before and after the intervention, and 12 weeks after the intervention. Methods This study employed a cluster randomized controlled trial with a two-arm design. ICU nurses with high presenteeism were invited and randomly assigned to groups by floor. The intervention group (40 nurses) underwent an 8-week MBSR program delivered by a certified mindfulness therapist, while the control group (40 nurses) received standard psychological counseling, including emotion regulation, psychological adjustment, and sleep management. Both groups were assessed using the Stanford Presenteeism Scale-6 (SPS-6) and the Five Facet Mindfulness Questionnaire (FFMQ) at baseline, post-intervention, and 12 weeks post-intervention. Results Linear mixed model analysis showed significant group, time, and group-time interaction effects on SPS-6 scores ( P  < 0.05). The experimental group had significantly lower SPS-6 scores at 8 and 12 weeks post-intervention than the control group and their pre-intervention scores ( P  < 0.05). For FFMQ scores, significant group and time effects ( P  < 0.05) but no significant group-time interaction ( P  > 0.05) were found. The experimental group’s FFMQ scores were significantly higher at 8 and 12 weeks post – intervention than the control group and their pre-intervention scores ( P  < 0.05). Conclusion The Mindfulness-Based Stress Reduction intervention was associated with increased mindfulness levels over time, and it significantly reduced presenteeism, with sustained effects observed over time. Clinical implications for nursing management MBSR, as a psychological intervention method, has the advantages of improving nurses’ mental health and work efficiency, reducing presenteeism, and ensuring patient safety. Nursing managers can integrate MBSR into hospital policies by organizing regular MBSR sessions on mental health days or during team-building activities. This not only enhances nurses’ psychological resilience but also promotes a positive work environment, contributing to a safer and more efficient healthcare setting. Patient or Public Contribution Participants were involved solely in the data collection process. No participant contributions were required for the study’s design, outcome measurement or implementation.

Machine learning potential-accelerated multiscale dynamical simulations of nanodiamond structural reconstruction

Atomistic understanding of structural transformations in nanodiamonds (NDs) is vital for manipulating their physicochemical properties, yet remains limited due to the inherent trade-off between simulation accuracy and scale. Here, we develop a machine learning potential (MLP) with density functional theory accuracy and implement it within the deep potential molecular dynamics framework to enable large-scale simulations of NDs comprising 103–104 atoms over nanosecond timescales. Our simulations reveal that the transformation dynamics are governed by morphology, surface facets, particle size, and temperature. We identify a multistage transformation pathway, sequentially characterized by outward-in graphitization, inward-out atomic migration, and a subsequent self-healing process, driven by surface energy minimization and internal stress relaxation. These results provide atomistic insight into the evolution of NDs and demonstrate the power of MLP-based approaches for modeling complex, multiscale structural transformations in nanocarbon materials.

Important design rules discovered for supramolecular multivalent ligands interacting with dynamic receptors

Spatial distribution and business environment of specialized and sophisticated Little Giant enterprises in Zhejiang Province

The influence of occupational stress and job satisfaction on burnout among healthcare workers in the UAE: A cross-sectional study

Background Healthcare Workers (HCWs) frequently face high levels of occupational stress, job dissatisfaction, and burnout due to the demanding nature of their work. In the United Arab Emirates (UAE), these challenges are further intensified by the rapid growth of the healthcare sector and increasing workloads, making it particularly critical to study these variables. Therefore, this study aimed to investigate the influence of occupational stress and job satisfaction on burnout and to identify the key predictors of burnout among HCWs in the UAE. Methods A cross-sectional study was conducted among 498 HCWs from hospitals and primary healthcare centers under Emirates Health Services/ Ministry of Health and Prevention. Data was collected using the Work Stress Questionnaire (WSQ), Maslach Burnout Inventory (MBI), and Minnesota Satisfaction Questionnaire (MSQ). Results The mean level of occupational stress was moderate, with a mean of 34.68 (SD = 10.15). The most affected subscales were “work-to-leisure time interference” and “influence at work.” The level of job satisfaction was also moderate, with a mean of 3.13 (SD = 0.75), and the highest satisfaction was related to extrinsic factors. The levels of burnout were notably high for emotional exhaustion and depersonalization, whereas “personal accomplishment” was less affected. Stepwise multiple regression analysis revealed significant predictors of emotional exhaustion (R 2  = 0.530), including individual demands, work-to-leisure conflict, job satisfaction, income, and marital status. Depersonalization was predicted by indistinct organization, income, and employment type (R 2  = 0.254). The least affected personal accomplishment subscale was predicted by occupational stress, age, education, nationality, and working hours, accounting for 6.9% of the variance (R 2  = 0.069). Conclusion The present study has highlighted the urgent need for targeted interventions to reduce occupational stress and improve job satisfaction to combat burnout among HCWs in the UAE. Organizational strategies should focus on workload management, promoting a healthy work-life balance, and clearly defining roles. These findings offer a foundation for informed policy actions to safeguard HCW well-being and elevate healthcare quality.

Response to “Comment on ‘On the Fresnel factor correction of sum-frequency generation spectra of interfacial water’” [J. Chem. Phys. 163, 044701 (2025)]

Do color similarity judgments vary with age, and do they reveal anything about qualia?

Magnetic reduced graphene oxide aerogel decorated silver nanoparticles towards efficient recognition of doxorubicin in patients’ blood plasma

Classification of videogames for amblyopia treatment in perceptive and cognitive domains

Video games are increasingly used in vision science and clinical interventions, particularly in the treatment of amblyopia. Among them, action video games have shown promise in enhancing visual functions such as attention, spatial resolution, and contrast sensitivity. However, the classification of games in current studies typically relies on broad commercial genre labels, which lack functional specificity and fail to capture the perceptual, cognitive, and motor demands relevant to therapeutic use. This imprecision can lead to suboptimal game selection and limit comparability across studies. To address this gap, we developed a data-driven framework to classify commercial video games based on functional load profiles. Twelve experts evaluated seven games across nine dimensions derived from prior literature on action video games, including Perceptual Load , motor demands, Working Memory , and attentional control. We applied Multidimensional Scaling and K-means clustering to group games based on similarity ratings, and validated the structure using Principal Component Analysis. Three distinct clusters emerged: (1) Action video games with high motor and Perceptual Load (e.g., Call of Duty , Unreal Tournament ); (2) puzzle and arcade games with moderate visuomotor and cognitive engagement (e.g., Tetris , Pac-Man ); and (3) low-demand simulation games ( The Sims ). Notably, Tetris reflected moderate visuomotor but higher cognitive demands, confirming its hybrid profile. This multidimensional classification provides a reliable and objective tool to guide therapeutic video game selection and development, offering a valuable alternative to the subjective genre-based selection of video games in both research and clinical applications.

The glass transition and the dynamics of water within pectin and metal–organic framework nanochannels

The glass transition temperature (Tg) of water confined in nanoscale environments critically influences its dynamics and structure, thereby impacting the design of sustainable materials. Determining Tg in different confinement matrices remains challenging owing to variations in pore chemistry and geometry. Here, we investigated water confined within calcium-cross-linked pectin (PE–Ca) and metal–organic framework (Fe–BTC) nanochannels using differential scanning calorimetry, broadband dielectric spectroscopy, and FT-IR spectroscopy. We found that confined water exhibited a Tg between 170 and 200 K, with PE–Ca showing a higher Tg (193 K) than Fe–BTC (170 K), correlating with differences in the hydrogen bonding networks. Water in pectin forms a network similar to that of bulk water, whereas Fe–BTC confinement induces distorted structures with strong interfacial hydrogen bonds. These findings suggest that the Tg of bulk water is higher than that previously reported (∼136 K) and highlight how confinement chemistry governs water dynamics, informing the development of eco-friendly materials and advancing our understanding of supercooled water.

Oxygen isotopic evidence that Gale crater, Mars, was home to an Early Hesperian water reservoir that underwent significant evaporation

Simultaneous measurements of HDO, H 2 18 O, and H 2 16 O in water evolved during pyrolysis of powdered rock samples acquired by the Curiosity rover within Gale crater’s clay-bearing units indicate extreme and variable heavy-isotope enrichments averaging ~4.5 times the D/H ratio and ~1.03 times the 18 O/ 16 O ratio of terrestrial seawater. These enrichments are recorded in water desorbed from mineral surfaces and evolved from poorly crystalline phases, hydrated salts, jarosite, and clays. All evolved waters are deuterium-enriched relative to common terrestrial waters, reflecting hydrogen loss to space. Because oxygen in structurally bound hydroxyl groups is least likely to exchange with other sources over geologic timescales, we focus on oxygen in water evolved during dehydroxylation of smectite clays. Several samples have 18 O/ 16 O ratios commensurate with precipitation from, or near-complete equilibration with, water moderately 18 O-enriched relative to terrestrial meteoric waters—consistent with other evidence that Mars’s hydrosphere is basically like Earth’s in terms of oxygen isotopes. Unlike hydrogen, oxygen atmospheric escape did not lead to extreme 18 O enrichments on Mars. Locally, however, most Gale smectites’ 18 O/ 16 O values require a pronounced 18 O-enrichment of their parental waters. On Earth, the most extreme 18 O enrichments in surface waters are found in closed basins having undergone significant evaporative loss into a low-humidity atmosphere, and the 18 O/ 16 O of authigenic clay minerals formed in these environs reflect those enrichments. A similar process acting on the hydrologic reservoir local to Gale at the time of clay formation and early diagenesis is a plausible explanation for the distinctive oxygen isotopic compositions of these clays.

Evaluation of the efficacy of combination therapy in the treatment of fungal otitis externa

Participatory process to design community-driven solutions for reducing antibiotic use in chicken production in Vietnam

International organizations emphasize the urgent need to reduce antibiotic use to combat antimicrobial resistance, including in livestock farming. Technical, regulatory, and awareness-raising strategies exist, but they often fail due to a misalignment with farmers’ realities. We hypothesize that actively engaging communities in the design of solutions will more effectively reduce antibiotic usage. We have therefore adapted and applied the ImpresS ex ante approach (impact of research in the South), to co-design solutions with stakeholders from the chicken and veterinary value chain at a local level in Vietnam. Eighteen participants (chicken farmers, drug sellers’ representatives, public and private veterinarians, a chicken retailer, and academic staff), working at the communal, district, or provincial level, were involved in three half-day workshops organized in Thai Nguyen province in April 2022. Through this participatory process, participants collectively envisioned a 10-year future with reduced antibiotic use in chicken farms. They identified barriers including the lack of outlets for organic meat products, lack of knowledge and awareness of biosecurity and organic farming, low compliance of small-scale farms with biosecurity, and lack of science and technology related to alternative products. Participants decided to address “knowledge gaps” barrier. They have designed two strategies to improve the training of farmers and drug sellers, so that it is closer to the chicken value chain realities and reaches a greater audience. In this study, we identify systemic barriers to reducing antibiotic use, while recommending practical solutions. We also advocate the need to include locally-developed solutions in the national action plan on antimicrobial resistance in Vietnam and to involve policy-makers in participatory processes to design effective strategies.

Machine learning prediction and experimental exploration of liquid-state density and viscosity for rare earth alloys

The liquid-state density and viscosity of multicomponent alloys are essential thermophysical properties for computational materials science. Nevertheless, such physicochemical parameters in the high-temperature liquid state are difficult to measure due to their strong chemical activity. In this work, based on our measured thermophysical properties datasets, including Fe–Nd–B, Fe–Dy–B, and Fe–Tb–B based rare-earth alloys, the random forest, support vector machine, and deep neural network models were established. It was found that support vector machine models displayed the highest prediction accuracies of 0.973 and 0.986 in the density and viscosity test sets. The temperature dependence of density and viscosity for liquid Fe76Nd5Tb3B16 and Fe78Nd10Dy3Tb3B6 alloys, with maximum undercoolings of 198 and 218 K (0.14 TL), was measured through electrostatic and electromagnetic levitation techniques, respectively. The experimental data showed satisfactory determination coefficients of 0.817 and 0.921 with the calculated values by support vector machine models, indicating the high accuracy of machine learning models in predicting liquid-state properties for rare-earth alloys.

Invariant HVC size in female canaries singing under testosterone: Unlocking function through neural differentiation, not growth

Testosterone administration to nonsinging adult female canaries induces song, making this a model for behavioral plasticity and its underlying neural mechanisms in vertebrates. The song control nucleus HVC is traditionally believed to undergo a substantial size change when transitioning from a nonfunctional to a functional (song-producing) state. Using 2-photon in vivo imaging, we tracked the spatial distribution and anatomical properties of HVC neurons over several weeks of testosterone-induced song development. Surprisingly, despite ultrastructural changes of HVC neurons, testosterone did neither alter neuronal spacing nor HVC size. Instead, spatial transcriptomics revealed that testosterone modulates gene networks throughout HVC, aligning transcriptomic profiles between its peripheral and central HVC regions in singing birds, thereby mimicking the histological appearance of an enlarging HVC. Our results demonstrate that changes in HVC size in adults reflect phenotypic changes in neurons within a stable framework. Importantly, the nonfunctional state is not associated with a reduced brain area volume, preserving HVC’s capacity for functional differentiation throughout life.

Quantum adaptive clonal genetic algorithm for low-energy clustering in agricultural WSNs

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband

The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the z-score, the logit (logistic regression model), the kernel-based virtual machine (KVM), and neural network approaches. Nevertheless, the outcomes from these methods have often fallen short of expectations. Three major challenges in previous works are feature selection, imbalanced classification, and hyperparameter optimization. This paper presents a method for credit risk prediction for listed companies that uses an off-policy proximal policy optimization (PPO) algorithm for feature selection and imbalanced classification. The off-policy PPO, a reinforcement learning (RL) approach, enhances sample efficiency by more effectively utilizing past experiences during policy updates. This approach improves feature selection and the management of imbalanced classification by optimizing data use, thereby enhancing model training outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) approach to refine the hyperparameters of the method. BOHB merges Bayesian optimization and Hyperband, significantly speeding up the optimization process. We assess our model using the China Stock Market and Accounting Research (CSMAR), MorningStar, KMV default, Give Me Some Credit (GMSC), and the University of California, Irvine Credit Card Default (UCICCD) datasets. Our experimental findings demonstrate the excellence of the model over existing state-of-the-art models, achieving F-measures of 90.763%, 86.358%, 87.047%, 90.576%, and 89.485% on these datasets. These findings validate the efficiency of the method in economic settings, signifying a major progression in systems for predicting credit risk and enhancing investigative approaches.