Microalgae are promising photosynthetic platforms for high-value compounds, yet their industrial use is often hindered by a trade-off between robust growth and the metabolic burden of payload production or cell wall disruption. Constitutive engineering for these traits compromises cultivator fitness. Here we report the development of a versatile, thermal-regulated "PLUG-IN" chassis in Nannochloropsis oceanica that enables programmable control of metabolic output and cell integrity. Comparative transcriptomics identify two highly heat-inducible promoters (PNoED and PNoUK), which we use to construct a modular thermal gene-amplification. Heat-activated AtWRI1 expression enhances triacylglycerol and eicosapentaenoic acid accumulation, while temperature-dependent silencing of the cellulose synthase gene CesA1 triggers rapid cell-wall weakening without affecting growth under permissive conditions. Coupling metabolic and cell-wall modules yields strains capable of grind-free lipid recovery and substantially improved intracellular product accessibility. Notably, these engineered thermally controlled programs remain functional in mammalian hosts, demonstrating cross-kingdom compatibility. This work establishes a "plug-in" chassis compatible with mammalian systems that synchronizes growth, production, and cell-wall re-configuration, providing a versatile platform for photosynthetic bioproduction and microalgal synthetic biology.
In vivo gene therapy is rapidly advancing to treat patients with a wide range of genetic disorders. Recombinant adeno-associated virus (rAAV) vectors provide long-term gene expression, low immunogenicity, and adaptable tissue tropism. Production of rAAV in insect cells with the Baculovirus Expression Vector System currently depends on infection with multiple baculoviruses encoding AAV genes Rep, Cap, and the therapeutic gene. In this research article, we present simplified rAAV production using a single baculovirus vector with improved genetic stability, called BAC6Rep, with AAV Rep locked into the baculovirus genome. Robust AAV Rep and Cap expression was maintained upon serial passaging, and high rAAV yields (up to 3.5e11 genome copies/ml) were obtained at high and low multiplicities of infection. Next-generation sequencing revealed a significantly improved genetic stability of BAC6Rep, which can now be used as a plug-in hybrid vector for flexible insertion of different AAV Cap and gene of interest tailored to the therapeutic target.
The transportation sector's reliance on internal combustion engine (ICE) vehicles contributes significantly to energy consumption and environmental degradation. Plug-in hybrid electric vehicles (PHEVs) present a viable alternative by combining electric propulsion with ICE capabilities to enhance fuel efficiency and reduce emissions. This study evaluates the energy consumption and emission characteristics of PHEVs under diverse real-world driving conditions, focusing on charge-depleting (CD) and charge-sustaining (CS) modes. Using chassis dynamometer (5-cycle, WLTC under varying temperatures) and real driving emissions (RDE) tests, the study reveals that CD mode offers superior efficiency in urban driving due to regenerative braking, while CS mode performs better under high-speed or low-temperature conditions. Notably, energy consumption in CS mode was approximately 2.98 times higher than in CD mode during urban RDE tests. Cold start conditions significantly increased emissions and delayed catalyst activation by 100-150 s. Furthermore, maintaining battery state of charge (SOC) at 60 % in CS mode achieved the highest efficiency across urban and motorway scenarios. These findings suggest that adaptive integration of CD and CS modes, optimal SOC management, and mitigation of cold start effects are essential for improving PHEV efficiency and sustainability.
Accurate and efficient weed identification is essential for precision agriculture, yet existing deep learning approaches struggle to balance classification accuracy with computational efficiency required for real-time field deployment. Fine-grained weed classification presents unique challenges due to subtle inter-species morphological differences and significant intra-class variations caused by diverse growth stages and environmental conditions. In this paper, we propose a unified RepVGG model factory with plug-in Squeeze-and-Excitation (SE) attention and Generalized Mean (GeM) pooling for fine-grained agricultural classification. Our approach leverages the structural re-parameterization technique of RepVGG to achieve efficient inference while enhancing discriminative feature learning through channel-wise attention recalibration and adaptive feature aggregation. The SE attention module explicitly models inter-channel dependencies to emphasize informative features, while the learnable GeM pooling enables adaptive interpolation between average and max pooling behaviors for optimal feature aggregation. We further develop a unified model construction interface that supports systematic benchmarking across multiple deep learning backends with consistent experimental protocols. Comprehensive experiments on the CottonWeedID15 dataset demonstrate that our proposed RepVGG-B1 + SE + GeM model achieves state-of-the-art performance with a testing F1-score of 99.5%, outperforming 27 baseline architectures including ResNet101 (99.1%), DenseNet161 (98.9%), and EfficientNet variants. Notably, our model maintains superior inference efficiency at 188.7 ms, which is 8.8% faster than ResNet101 while achieving 0.4% higher accuracy. Ablation studies confirm that SE attention consistently improves accuracy by 0.3% across different RepVGG backbones with minimal parameter overhead (2-3%), while GeM pooling provides complementary gains of 0.1-0.2%. The proposed unified framework offers a flexible and modular solution for deploying high-performance weed identification systems in resource-constrained agricultural environments.
The paper will deal with the problem of optimal charging and discharging coordination of Plug-in Electric Vehicles in a local multi-energy system that will be conducted under uncertain conditions. The current practices are usually based on simplified assumptions and fail to coordinate various energy sources and resources effectively, which restricts their usage in real-life conditions. In order to eliminate these constraints, a hybrid optimization model that involves Rider Optimization and Osprey Optimization integration is suggested. The model developed will take into account both the economic and environmental goals, such as the cost of operation, profitability of the system, and minimization of carbon emission. The lack of certainty in the behavior and generation of the renewable is factored in by using probabilistic modeling, which allows the system to be more realistic. The suggested algorithm constraints global exploration and local exploitation to optimally search the energy scheduling strategies. The outcomes of the simulation display the assumption that the proposed method can deliver the enhanced performance of the operational level as it successfully organizes the activities of distributed energy resources and electric vehicle charging. The model offers computationally effective and flexible framework to be used in practical energy management applications in local multi-energy systems.
Lipid accumulation in microalgae is typically induced by stress but often comes at the expense of growth and energy stability. Here, we introduced the plsXYC module from Cyanobacterium aponinum into Chlamydomonas reinhardtii, aiming to enhance stress-responsive lipid production by a plug-in prokaryotic acyltransferase route. The initial wild-type strain produced lipids at 135.1 mg/g-dry cell, while the engineered CrXYC sustained the highest lipid yield of 385 mg/g-DCW across light and dark cycles. Transcriptomics revealed that light-dark shifts drove 65% of expression variance and elevated ATP levels. During heterotrophic culture under nitrogen starvation, CrXYC preserved ATP up to 1.6-fold higher than the parental background. Carbon repartition was proved using 13C-isotopes, and redox reinforcement showed coordinated upregulation of lipid assembly pathways and repression of starch biosynthesis. This shift coincided with enhanced superoxide scavenging activity, while broad antioxidant capacity remained unchanged. Together, we demonstrate that introducing an orthogonal acyl-acyl carrier protein entry route sustains lipid accumulation under stress while preserving growth and energy balance.
The labor- and experience-intensive creation of 3D assets with physically based rendering (PBR) materials demands an autonomous 3D asset creation pipeline. However, most existing 3D generation methods focus on geometry modeling, either baking textures into simple vertex colors or leaving texture synthesis to post-processing with image diffusion models. To achieve end-to-end PBR-ready 3D asset generation, we present Lightweight Gaussian Asset Adapter (LGAA), a novel framework that unifies the modeling of geometry and PBR materials by exploiting multi-view (MV) diffusion priors from a novel perspective. The LGAA features a modular design with three components. Specifically, the LGAA Wrapper reuses and adapts network layers from MV diffusion models, which encapsulate knowledge acquired from billions of images, enabling better convergence in a data-efficient manner. To incorporate multiple diffusion priors for geometry and PBR synthesis, the LGAA Switcher aligns multiple LGAA Wrapper layers encapsulating different knowledge. Then, a tamed variational autoencoder (VAE), termed LGAA Decoder, is designed to predict 2D Gaussian Splatting (2DGS) with PBR channels. Finally, we introduce a dedicated post-processing procedure to effectively extract high-quality, relightable mesh assets from the resulting 2DGS. Extensive quantitative and qualitative experiments demonstrate the superior performance of LGAA with both text- and image-conditioned MV diffusion models. Additionally, the modular design enables flexible incorporation of multiple diffusion priors, and the knowledge-preserving scheme effectively preseves the 2D priors learned on massive image dataset, which leads to data efficient finetuning to lift the MV diffuison models for 3D generation with merely 69 k multi-view instances.
This study compared two widely used biomechanical models-Plug-in Gait (PiG) and Conventional Gait Model 2.3 (CGM2.3)-during overground walking (WALK) and single-leg squats (SLS) in 24 healthy adults. Data was collected using a 20-camera Vicon system and force plates. Static trials were analyzed with medial knee and ankle markers to align joint axes across models. Kinematic and kinetic outputs were compared using root mean square differences (RMSD) and statistical parametric mapping (SPM) paired t-tests. During WALK, PiG produced greater internal rotation at the knee (RMSD 17.8°, p < 0.001) and hip (RMSD 5.0°, p < 0.001), and smaller sagittal-plane flexion angles (RMSD 2.6° knee, 2.3° hip) compared with CGM2.3. In single-leg squats, these discrepancies increased to 29.1° and 9.0°, respectively, with sagittal-plane differences of 4.4° at the knee and 5.1° at the hip. CGM2.3 yielded higher knee flexion moments (31% in WALK, 104% in SLS), while PiG produced higher frontal-plane knee moments (28% and 89%). The differences were most pronounced at deeper flexion angles. These results demonstrate that biomechanical outcomes differ systematically between models, emphasizing the impact of model selection on joint kinematics and kinetics in human movement analysis.
In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical power of these analyses is a key objective for statisticians. The increasing availability of historical data from registries, prior trials, and health records presents an opportunity to improve trial efficiency. However, many methods for historical borrowing compromise strict type I error rate control. Building on the work by Schuler et al. on prognostic score adjustment for linear models, this paper extends the methodology to the plug-in analysis proposed by Rosenblum and van der Laan using generalized linear models (GLMs) to further enhance the efficiency of RCT analyses without introducing bias. Specifically, we train a prognostic model on historical control data and incorporate the resulting prognostic scores as covariates in the plug-in GLM analysis of the trial data. This approach leverages the predictive power of historical data to improve the precision of marginal treatment effect estimates. We demonstrate that this method achieves local semi-parametric efficiency under the assumption of an additive treatment effect on the link scale. We expand the GLM plug-in method to include Negative Binomial regression. Additionally, we provide a straightforward formula for conservatively estimating the asymptotic variance, facilitating power calculations that reflect these efficiency gains. Our simulation study supports the theory. Even without an additive treatment effect, we observe increased power or reduced standard error. While population shifts from historical to trial data may dilute benefits, they do not introduce bias. We demonstrate reductions in the estimated variance through an analysis of clinical trial data provided by Novo Nordisk A/S.
BACKGROUND: Traditional methods for producing custom-made orthoses are often time-consuming, labor-intensive, and reliant on manual processes, which limit both scalability and the degree of individualization. The development of 3D scanning technologies, computer-aided design (CAD), and additive manufacturing offers a promising alternative enabling patient-specific solutions with greater precision, speed, and efficiency. This study aimed to create an algorithm for automating the design process of personalized knee orthoses based on 3D scanning and intended for 3D printing production. METHODS: A parametric modeling workflow was developed in the Rhino environment using the Grasshopper plug-in to streamline personalized knee orthoses creation. The process began with acquiring high-quality 3D scans using Structure Sensor Mark II scanner mounted on an iPad with 3DsizeMe software. The parametric algorithm was transformed into an autonomous Rhino plug-in using C# language and RhinoCommon API. As part of Post-Market Clinical Follow-up (PMCF), three participants with knee joint disorders used orthoses for one month. Assessment used a 5-point scale (1 = poor, 5 = excellent). Personalized orthoses were manufactured using powder-bed fusion technology with PA11 CF nylon powder reinforced with carbon fibers. RESULTS: Design time was reduced from approximately 8 h to 10,3 ± 1,4 min. In Grasshopper prototype phase, average design time was 26,7 ± 4,5 min. Following the implementation of the Rhino plug-in, the design time was further reduced to approximately 10 min. The tool was shown to meet user requirements and fulfill its intended purpose. All three PMCF participants rated orthoses positively, reporting high comfort, effective stabilization, increased physical activity, and overall satisfaction with functionality and appearance. Participants P1 and P2 noted a large increase in physical activity, with P1 indicating pain reduction that increased mobility. CONCLUSIONS: This study demonstrates that the combined use of Rhino and Grasshopper provides an effective platform for parametric design of personalized knee orthoses based on patient-specific 3D scans. The workflow reduced design time to approximately 10,3 ± 1,4 min, highlighting potential for routine clinical applications. This reduction is economically significant, lowering labor costs and implementation thresholds for personalized orthotic solutions in clinical practice.
Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857.
This study assessed the performance of several entropy estimators for numerical time series and symbolic data on non-trivial one-dimensional dynamical systems whose Kolmogorov-Sinai entropy is known with certified accuracy: recent computer-assisted proof techniques provide rigorous values together with explicit error bounds. We considered four classes of interval maps, including piecewise expanding maps with and without a Markov partition and an intermittent Pomeau-Manneville map, and generated long orbits for each system. We then compared the certified entropy with the output of widely used estimators: Approximate Entropy, Sample Entropy, Permutation Entropy, a symbolic Plug-In estimator of the entropy rate, and the Non-Sequential Recursive Pair Substitution (NSRPS) method (the latter two with Grassberger-type bias correction). Our experiments reveal substantial, dynamics-dependent differences in accuracy and robustness. In particular, Approximate Entropy and the symbolic methods (Plug-In and NSRPS) consistently yielded estimates within the rigorous error bars across all systems, whereas Sample Entropy showed a marked systematic underestimation, and Permutation Entropy exhibited large biases, especially for expanding maps without a Markov partition. The resulting benchmark provides a quantitative testbed for evaluating entropy estimation techniques in deterministic dynamical systems.
Physics-informed machine-learning models increasingly incorporate physical laws and constraints to improve data efficiency and predictive robustness; yet their validation remains dominated by pooled scalar accuracy metrics that are largely insensitive to violations of the underlying governing relationships. Here we introduce a physics-informed validation framework, the Agreement-Entropy Map (AEM), which diagnoses model-data agreement by distinguishing structural incompatibility from conditional stochastic dispersion, rather than by defining a scalar metric or additive error decomposition. Conditioned on a physically motivated linearization of the governing relation and evaluated on matched comparison domains, AEM combines regression geometry with an information-theoretic dispersion measure based on a Gaussian plug-in entropy of residuals, without requiring distributional modeling or inferential assumptions. The framework applies uniformly to experiment-experiment and model-experiment comparisons and is agnostic to model class, architecture, and training procedure. Using thermodynamic systems as a canonical physics-governed testbed, we show that AEM reveals structural bias, variance-driven artefacts, and ensemble effects that remain undetected by conventional scalar validation metrics. By identifying when stochastic interpretation is admissible under a shared physical structure, AEM provides a general and interpretable validation principle for physics-informed machine learning, particularly in regimes involving limited, heterogeneous, or damaged data.
DC microgrids are gaining more attention as their control is simpler, their efficiency is higher and they are more reliable. DC microgrid control aims to regulate load voltage at the nominal value and share load among distributed generation units with a certain ratio. This paper presents a control strategy for radial DC microgrids that achieves these two goals accurately and simultaneously. It doesn't require communication among distributed generators. Only global sensing is required and only one signal is transmitted among DGUs. Tianji Horse Racing Optimization algorithm was used to tune controller parameters. A stability analysis was conducted to evaluate the effect of load change on system stability. This control method has much faster dynamic response compared to previously reported methods. MATLAB Simulink was used to build a model for the microgrid to test the proposed control strategy. Its stability was tested by applying sudden load changes. Also, plug-in and plug-out capability was verified. Constant power load was also used to test the proposed control strategy performance. Communication delay effect was also tested.
The depletion of fossil fuels and the rising demand for electricity are driving the global shift toward renewable energy sources (RES). The primary goal of incorporating RES into the conventional smart grid is to establish a more ecological and eco-friendly energy system. However, due to their lower system inertia, RESs struggle to effectively respond to fluctuations in load demand. This study investigates how a Dual Loop LADRC-FOPIDN-(1 + TD) controller can improve frequency regulation in a multi-area deregulated electricity system while taking RES's intermittent nature into account. By integrating LADRC with a cascade controller, the proposed approach delivers improved transient performance over selected benchmark controllers (LADRC, FOPIDN, FOPIDN-(1 + TD)) under the tested scenarios. Furthermore, an enhanced Quasi Opposition Arithmetic Optimization Algorithm (QOAOA) is employed to optimize controller parameters for improved efficiency. Simulation results highlight its strong adaptability to specific uncertainties modeled in this study, such as RES intermittency, load fluctuations, and delay effects, ensuring grid stability. Moreover, this study addresses the use of electric vehicles (EVs) to regulate frequencies in a hybrid power system under the conditions of real-time load demand variations. Plug-in Electric Vehicles (PEVs) are incorporated in every system region as a measure to reduce undesirable transient components on load frequency and power sharing. PEVs absorb unnecessary electrical energy and give it back to the grid when needed, which offers useful grid support, particularly within RES-dominated networks. The proposed controller is tested on a better IEEE-39 bus system with real-time load variation and variability of RES, through data provided by BSES Rajdhani Power Limited (BRPL), Delhi. Lastly, OPAL-RT hardware is used to run a real-time simulation environment, which targets the integration of real hardware with a virtual test environment, to test the effectiveness and the robustness of the controller. Finally, the MATLAB simulation results are compared with OPAL-RT hardware results.
Changes in body composition (BC) are associated with outcomes in colorectal cancer (CRC), with adverse features such as sarcopenia, myosteatosis, and excess adiposity linked to the systemic inflammatory response (SIR). Whilst ethnic differences in BC have been identified in healthy populations, little is known about how ethnicity influences BC and SIR in patients with CRC. This study is aimed at assessing the impact of ethnicity on preoperative BC and SIR in patients undergoing surgery for CRC. A multivariate analysis was conducted on a prospectively collected database of CRC patients treated between May 2007 and January 2017. Retrospective augmentation of the dataset included CT-derived BC data and inflammatory markers. BC was assessed at the third lumbar vertebra using Slice-O-Matic v5.0 with the ABACS L3 plug-in. Predefined thresholds were applied to classify sarcopenia, myosteatosis, visceral obesity (VO), and sarcopenic obesity (SO). SIR was defined using clinically relevant cut-offs: neutrophil-to-lymphocyte ratio (NLR) > 3, platelet-to-lymphocyte ratio (PLR) > 150, and modified Glasgow Prognostic Score (mGPS) > 0. Ethnicity was recorded using the UK Office for National Statistics 2001 coding and categorised as White (WBB), Black or Black British (BBB), Asian or Asian British (AAB), or Other. WBB served as the reference group for all comparisons. A total of 776 patients were included in the final analysis (56% male; median age 69 years, IQR 60-78). Compared to WBB, BBB patients were significantly less likely to be sarcopenic (OR 0.35, 95% CI 0.194-0.624, p = 0.0001), whilst AAB patients were more likely to be sarcopenic (OR 2.02, 95% CI 1.26-3.24, p = 0.003). BBB patients were also significantly less likely to be myosteatotic (OR 0.39, 95% CI 0.21-0.73, p = 0.003). AAB patients showed a trend toward lower BMI-defined obesity (OR 0.58, 95% CI 0.32-1.04, p = 0.067). No significant ethnic differences were observed for VO or SO. With respect to inflammation, AAB and BBB patients were significantly less likely to exhibit an NLR > 3 (OR 0.34, 95% CI 0.20-0.56, p = 0.0001 and OR 0.35, 95% CI 0.17-0.74, p = 0.006 respectively). AAB patients were also significantly less likely to have a PLR > 150 (OR 0.57, 95% CI 0.36-0.92, p = 0.021). No significant association was found between ethnicity and mGPS. Ethnicity significantly influences body composition and systemic inflammation in patients with CRC. These findings challenge previous models that have not accounted for ethnic variation and highlight the need for ethnicity to be considered in both prognostic modelling and personalised supportive care. Future studies should explore the relationship between BC, inflammation, and oncological outcomes within individual ethnic groups to inform tailored interventions and risk stratification strategies.
Foundation models for embodied artificial intelligence (Embodied AI) increasingly adopt diffusion modules as the action generation core of vision-language-action (VLA) policies, but the diffusion module's iterative denoising imposes prohibitive inference latency for real-time deployment. We address this bottleneck in isolation by rethinking the diffusion action generation module itself. We present Fast Robot Motion Diffusion (FRMD) , a fast robot motion diffusion framework that (i) operates in trajectory-parameter space by predicting movement-primitive coefficients in a low-dimensional manifold, and (ii) collapses multi-step sampling into a single inference step via trajectory-level consistency distillation over the probability-flow ordinary differential equation (ODE). Concretely, FRMD replaces stepwise action generation with a one-pass mapping from noise to full trajectories, followed by a fixed-cost basis expansion; this reduces policy latency from hundreds to tens of milliseconds without modifying upstream vision or language encoders. On standard robotic manipulation task benchmarks, FRMD attains 7 times faster than the vanilla diffusion policy and 10 times faster than the state-of-the-art MPD method, while matching the task success of multi-step diffusion policies. By targeting the diffusion component used throughout VLA systems, FRMD provides a plug-in, latency-optimized motion generator that preserves the advantages of diffusion and makes real-time embodied AI feasible.
BACKGROUND: In Chinese culture, discussions about death are often considered taboo, which may intensify death anxiety among nursing interns when facing end-of-life situations. Death anxiety may influence their ability to provide spiritual care, yet the underlying mechanisms remain unclear. AIM: This study aimed to examine the relationship between meaning in life, death anxiety, and spiritual care competence among nursing interns, and to explore the mediating role of death anxiety. METHODS: This was a cross-sectional study. A cross-sectional survey was conducted among 737 final-year vocational nursing interns from a medical college in China using whole-cohort sampling. Data were analyzed using SPSS 25.0, including t-tests, ANOVA, Pearson correlations, and mediation analysis via Hayes’ process plug-in. RESULTS: Significant associations were observed among meaning in life, death anxiety, and spiritual care competence. Meaning in life, was positively correlated with spiritual care competence (r = 0.520, p < 0.001) and weakly negatively correlated with death anxiety (r = -0.077, p = 0.036). Death anxiety was negatively correlated with spiritual care competence (r = -0.196, p < 0.001). Mediation analysis showed that death anxiety partially mediated the relationship between meaning in life, and spiritual care competence, with an indirect effect of 0.019 (95% CI: 0.001–0.041), accounting for 2.24% of the total effect. CONCLUSION: Death anxiety partially mediated the association between meaning in life and spiritual care competence, with a small but statistically significant indirect effect. These findings suggest that addressing death-related concerns and fostering meaning construction within nursing education may support the development of spiritual care competence, consistent with theoretical perspectives emphasizing the buffering role of existential resources.
Surface electromyography (sEMG) gesture recognition degrades across recording days under domain shift, increasing calibration burden for myoelectric interfaces. Many cross-day adaptation pipelines retrain the deployed recognizer or require labeled target-session data, which can be impractical in assistive-device settings where classifier versions may need to remain locked for traceability and regulatory compliance. We study unsupervised cross-day adaptation under two constraints: the task classifier remains frozen and holdout-day labels are not used when training the adaptor. We propose the Source-Reprojection Module (SRM), a plug-in front end that combines conditional adversarial feature learning with a residual signal-space projector guided by the frozen classifier's gradients, identity regularization, and latent-space distribution matching, using labeled source days and unlabeled adaptation days only. On a multi-day protocol with four healthy participants (at least five calendar-day sessions per participant, split 3:1:1 into source, adaptation, and holdout domains) and three random seeds per participant (12 runs), mean holdout accuracy increases from 70.9% for the frozen classifier alone to 72.8% with SRM (+1.98±0.91 percentage points averaged across subjects). SRM outperforms the frozen baseline in 10 of 12 subject-seed runs. The gain is modest and the cohort is small, so the result supports proof-of-mechanism under the stated protocol rather than population-level clinical generalization.
Autonomous motivation can effectively predict students' academic performance; however, the underlying mechanisms through which this occurs require further exploration. Therefore, the purpose of this study is to explore the mediating roles of self-control and learning habits in this relationship among Chinese college students. A cross-sectional survey design was employed. Data were collected via an online questionnaire platform between November and December 2024. Using a convenience sampling method, a total of 796 university students (Mage = 20.49, SD = 1.32) were recruited. Participants completed a series of questionnaires assessing autonomous motivation, self-control, and learning habits. Academic performance scores were also collected. Data analysis was conducted using SPSS 24.0 and the SPSS PROCESS plug-in developed by Hayes. The findings showed that college students' autonomous motivation directly and positively predicted academic performance (effect = 0.072), accounting for 59.02%. Notably, learning habits partially mediated the relationship between autonomous motivation and academic performance (effect = 0.021). In addition, autonomous motivation indirectly and positively affected academic performance through the chain mediating effect of self-control and learning habits (effect = 0.029). The total indirect effect was significant (effect = 0.050), accounting for 40.98% of the total effect. However, the mediating role of self-control alone was not significant. The results of this study elucidate the internal mechanisms linking autonomous motivation to academic performance and provide actionable insights for college teachers and educational departments to improve students' academic outcomes.