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Recent advancements in large language models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs' capabilities in zero-shot or few-shot personality inference, few studies have systematically evaluated LLM embeddings within a psychometric validity framework or examined their correlations with linguistic and emotional markers. Additionally, the comparative efficacy of LLM embeddings against traditional feature engineering methods remains underexplored, leaving gaps in understanding their scalability and interpretability for computational personality assessment. This study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC (Linguistic Inquiry and Word Count) and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs RoBERTa). We aim to establish LLM embeddings as a psychometrically valid and efficient alternative for personality assessment. We conducted a multistage analysis using 1 million Reddit posts from the PANDORA Big Five personality dataset. First, we generated text embeddings using 3 LLM architectures (RoBERTa, BERT, and OpenAI) and trained a custom bidirectional long short-term memory model for personality prediction. We compared this approach against zero-shot inference using prompt-based methods. Second, we extracted psycholinguistic features (LIWC categories and National Research Council emotions) and performed feature engineering to evaluate potential performance enhancements. Third, we assessed the psychometric validity of LLM embeddings: reliability validity using Cronbach α and convergent validity analysis by examining correlations between embeddings and established linguistic markers. Finally, we performed traditional feature engineering on static psycholinguistic features to assess performance under different settings. LLM embeddings trained using simple deep learning techniques significantly outperform zero-shot approaches on average by 45% across all personality traits. Although psychometric validation tests indicate moderate reliability, with an average Cronbach α of 0.63, correlation analyses spark a strong association with key linguistic or emotional markers; openness correlates highly with social (r=0.53), conscientiousness with linguistic (r=0.46), extraversion with social (r=0.41), agreeableness with pronoun usage (r=0.40), and neuroticism with politics-related text (r=0.63). Despite adding advanced feature engineering on linguistic features, the performance did not improve, suggesting that LLM embeddings inherently capture key linguistic features. Furthermore, our analyses demonstrated efficacy on larger model size with a computational cost trade-off. Our findings demonstrate that LLM embeddings offer a robust alternative to zero-shot methods in personality trait analysis, capturing key linguistic patterns without requiring extensive feature engineering. The correlation between established psycholinguistic markers and the performance trade-off with computational cost provides a hint for future computational linguistic work targeting LLM for personality assessment. Further research should explore fine-tuning strategies to enhance psychometric validity.

Personality traits are known predictors of substance use (SU), but their expression and association with SU in digital discourse remain largely unexamined. During the COVID-19 pandemic, the online social engagement heightened and led to an amplification in SU rates, thereby creating a unique natural opportunity to investigate these dynamics through large-scale digital discourse data. In our study, we offer insights beyond traditional self-report methods, which are crucial for developing timely and targeted public health interventions. We aim to evaluate whether the associations between the Big Five personality traits and SU discourse shifted during the 2019-2021 period, and to conduct a focused analysis of how these traits predict SU and relate to specific substance types, emotional expression, and demographic factors. We analyzed a corpus of several hundred million public posts from a major social media platform from 2019 to 2021. Using a pipeline of natural language processing and deep learning models, we identified SU-related posts and subsequently extracted scores for the Big Five personality traits, emotions, and user demographics. We used trend analysis to compare annual shifts in trait-SU associations, while detailed 2020 data underwent rigorous modeling using logistic regression, correlation analysis, and topic modeling to elucidate the core relationships. Our analysis revealed that Extraversion (odds ratio [OR] 3.22, 95% CI 2.98-3.49) and, most strikingly, agreeableness (OR 4.04, 95% CI 3.71-4.41) were the strongest positive predictors of being a substance user. In stark contrast to the conventional self-medication hypothesis, neuroticism emerged as a robust protective factor against SU (OR 0.29, 95% CI 0.26-0.31). This counterintuitive finding was supported by a decreased association between neuroticism and SU posts at the pandemic's onset in 2020 (Cohen d=-0.13, 95% CI) and a negative correlation with the expression of negative emotions online. Topic modeling further indicated that SU discourse was frequently embedded in social contexts (social drinking and friendly beverage choices) rather than themes of solitary coping. Our findings challenge traditional models by demonstrating that in large-scale online discourse, SU expression is more powerfully linked to social-affiliative traits than to negative emotionality. The paradoxical protective role of neuroticism suggests that established risk profiles may not apply uniformly to digital environments, particularly during a public health crisis. These insights are vital for refining computational methods for public health surveillance and developing interventions that recognize the potent social drivers of SU in the digital age.

The digital age has fueled a surge in ADHD self-diagnosis as people turn to online platforms for mental health information. However, the relationship between validation-seeking behaviors and self-perception in these online communities and users’ self-perception has received limited scholarly focus. Drawing on self-verification theory and utilizing natural language processing to analyze 452,026 posts from the r/ADHD subreddit, our study uncovers distinct patterns in validation-seeking behaviors. Results show that (a) self-diagnosed individuals with ADHD are more likely to seek social validation and media validation and to report higher levels of negative self-image and internalized stigma than clinically diagnosed individuals, (b) social validation was strongly associated with both positive and negative self-perceptions; and (c) diagnosis status significantly moderated these relationships, such that the effects of social validation on self-image and stigma were consistently weaker for the self-diagnosed group. Theoretically, this study extends self-verification theory by demonstrating that professional verification hierarchically moderates self-verification effectiveness. This implies a practical need for clinicians to acknowledge online validation seeking and for digital communities to affirm user experiences while mitigating stigma.

SnO 2 nanoparticle-modified Ti 3 C 2 MXene (SnO 2 –Ti 3 C 2 ) nanocomposites have been synthesized via hydrothermal method and subsequently used as anode material for lithium-ion batteries (LIBs) with enhanced electrochemical performance. The results of the microstructure analysis indicate that the introduction of SnO 2 nanoparticles enlarged the d-spacing of Ti 3 C 2 layers and increased the Li + storage. Meanwhile, SnO 2 nanoparticles improve the electrochemical performance based on the alloying mechanism. Electrochemical results reveal that SnO 2 –Ti 3 C 2 nanocomposites can greatly improve the reversible capacity compared with pure Ti 3 C 2 T x particles. Remarkably, SnO 2 –Ti 3 C 2 nanocomposites show outstanding initial capacity of 1030.1 mAh g −1 at 100 mA g −1 , and the capacity can remain about 360 mAh g −1 after 200 cycles. The SnO 2 –Ti 3 C 2 nanocomposites demonstrate a stable cycle performance and high reversible capacity for lithium storage.

To evaluate the association between the AL/CR ratio and refractive state and explore the effectiveness of this ratio in the assessment of myopia, especially when combined with uncorrected visual acuity in schoolchildren among whom myopia is common. Cross sectional study. 4686 children from 6 primary schools, aged from 6 to 12 years were selected using the clustered-stratified random sampling method. Uncorrected visual acuity (UCVA), axial length (AL), corneal radius of curvature (CR), and cycloplegic refraction were tested. Refraction was measured as the spherical equivalent (SE). 3922 children were included in the analysis. The mean AL/CR ratio was 2.973±0.002, increased with age, and different in gender. The coefficients of correlations of the SE with the AL/CR ratio, AL, and CR were -0.811, -0.657, and 0.095, respectively. Linear regression showed a 10.72 D shift towards myopia with every 1 unit increase in the AL/CR ratio (P<0.001, r2 = 66.4%). The estimated SE values obtained by substituting the AL/CR ratio and gender back to the regression model that were within a difference of ±0.50 D in ATE/LER (allowable total error and limits for erroneous results) zones compared to the actual measured values was 51%. The area under the ROC curve of the AL/CR ratio, AL, and UCVA for myopia detection were 0.910, 0.822, and 0.889, respectively, and the differences between each pair were statistically significant (P<0.01). At a specificity of 90%, the sensitivities were 72.98%, 50.50%, 71.99%, and 82.96%, respectively, for the AL/CR ratio, AL, UCVA, and the combination of the AL/CR ratio and UCVA. The AL/CR ratio was found to explain the total variance in SE better than AL alone. The effectiveness of the AL/CR ratio was statistically significantly better than UCVA for detecting myopia in children, and combining the two produced increased sensitivity without significantly decreasing specificity.

The economic evaluation of urban land depends critically on two aspects: land use structure and land use efficiency(LUE). Understanding how land use structure and efficiency change in response to urban development is critical. Geographically, coastal regions have higher population densities. However, it is unclear from the current study how changes in land use efficiency and structure relate to distance from the coast. Thus, the land use structure of Jinpu New Area from 2015 to 2020 is evaluated in this paper using the location entropy, Lorenz curve, and Gini coefficient methods. The land use efficiency is assessed using the comprehensive index method of multi-source data fusion, and the coupling analysis is carried out in conjunction with the sea-land gradient to thoroughly examine the space–time variation law of the land use structure and efficiency. The results demonstrate that: (1) The land use structure in Jinpu New Area exhibits distinct gradient differentiation. Within the [0,2] km range, land use types are evenly distributed, with an average Gini coefficient of 0.088. The [14,max] km range shows significant disparities in land use distribution, most notably in railway land (locational entropy > 11) and urban land (Gini coefficient > 0.7); (2) Based on land use intensity and land use efficiency, it is concluded that the coupling efficiency of land use in 2015 and 2020 both showed an obvious land-sea gradient: coastal regions had a higher share of inefficient land use with an average coupling efficiency of 0.170 (CI > 0); inland areas had a larger proportion of overloaded land use with an average coupling efficiency of -1.04 (CI < 0); and the land-sea transition zones demonstrated favorable coupling efficiency, with the coupling index being approximately zero. (3) Land use structure correlates with land use efficiency. Land use efficiency exhibits a significant positive correlation with urban land use(p < 0.05) and a significant negative correlation with railway land use(p < 0.1). In contrast, inland regions are often underdeveloped in terms of land use intensity, and railway land use may serve as a useful indicator of potential for future development.

Single-phase inverters with an output LC filter, can generate low distortion output voltages, which are suitable for uninterruptible power supply (UPS) systems. The UPS system provides emergency power in the case of utility power failure, requiring high reliability and clean power. The sensorless control method is actually a soft-sensing technique, that reduces system cost, measurement-related losses, and, especially important for UPS systems, enhances the system reliability. This paper proposes a load current sensorless finite control set model predictive control (FCS-MPC) scheme for a single-phase UPS inverter. A time varying observer is proposed, which offers the accurate estimation for individual components simultaneously in periodic load current signal, without subsequent complex calculations. Compared with another two typical sensorless methods (the low-pass filter and the Kalman filter), the proposed observer-based FCS-MPC strategy has smaller load current estimation error and lower output voltage distortion, under both linear and nonlinear loads. The theoretical analysis is verified through simulation and experiment. A single-phase inverter rapid control prototype (RCP) is set up with the Speedgoat real-time target machine, to confirm the effectiveness of the system.

Acute myeloid leukemia (AML) is a highly aggressive hematologic malignancy with a poor prognosis for patients. Disulfidptosis response-related long non-coding RNAs (DRLs) have been demonstrated to be closely associated with cancer development. Therefore, this study aims to construct a prognostic DRL signature and investigate the immune microenvironment for AML. RNA-seq and clinical data for AML patients were obtained from The Cancer Genome Atlas (TCGA) database. A total of 344 disulfidptosis-associated lncRNAs were identified, and a prognostic model consisting of seven lncRNAs was constructed and validated. Two risk groups, high-risk and low-risk, were identified. The model demonstrated a robust capacity to predict prognosis, with a worse overall survival for patients in the high-risk group. Additionally, differential expression of the seven lncRNAs were relatively higher in AML samples than in control samples via quantitative polymerase chain reaction(qPCR). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration analysis revealed a substantial infiltration of immune cells and enrichment of immune pathways in the high-risk group. The sensitivity of AML patients to drugs varied according to their risk grade. This study identified a DRL signature, which can effectively predict the prognosis of AML and better understand the mechanism of disulfidptosis in AML. This provides a basis for personalized immunotherapy in AML patients.

The analytical approaches including thin-section petrography, X-ray diffraction, scanning electron microscopy energy dispersive spectrometer, and high-pressure mercury intrusion porosimetry were conducted on the sandstone reservoirs in the Lishu fault depression to investigate the diagenetic alteration and their impacts on pore structure and reservoir quality. The results show that correlations between diagenetic processes and reservoir properties are observed. Six diagenetic facies, namely the strong compaction with moderate cementation facies (SCMCF), weak dissolution with cementation facies (WDCF), moderate cementation with feldspar dissolution facies (MCFDF), moderate cementation with residual intergranular pore facies (MCRIPF), moderate cementation with fracture facies (MCFF), and moderate cementation with mixed dissolution facies (MCMDF), were classified through detailed examination of microscopic petrography. Variations in pore volumes, pore size distribution, and pore fractal dimensions, alongside their influencing factors, were analyzed to elucidate the impacts of diagenetic processes on the reservoir quality of tight sandstones. The diagenetic coefficient and mineral composition are considered critical determinants of pore structure. Specifically, SCMCF and WDCF are regarded as unfavorable diagenetic facies. MCFDF and MCRIPF are categorized as moderate diagenetic facies. Conversely, MCFF and MCMDF are considered favorable diagenetic facies. The SCMCF and WDCF suggest a relatively low heterogeneity, Conversely, the MCFDF, MCRIPF, MCFF, and MCMDF show obvious reservoir heterogeneity and low pore-throat connectivity. The fractal properties of the WDCF, MCFDF, MCRIPF, and MCMDF are obvious, indicating relatively complex pore structures. This article provides insights into the relationships among the diagenetic facies, pore structure, fractal dimension, and reservoir quality of tight sandstones in the Lishu fault depression, in addition, has significance for the reservoir evaluation and exploration of tight sandstone gas the rift Basins.

Treatment of liver metastasis experiences slow progress owing to the severe side effects. In this study, we demonstrate a strategy capable of eliminating metastatic cancer cells in a selective manner. Nucleus-targeting W 18 O 49 nanoparticles (WONPs) are conjugated to mitochondria-selective mesoporous silica nanoparticles (MSNs) containing photosensitizer (Ce6) through a Cathepsin B-cleavable peptide. In hepatocytes, upon the laser irradiation, the generated singlet oxygen species are consumed by WONPs, in turn leading to the loss of their photothermally heating capacity, thereby sparing hepatocyte from thermal damage induced by the laser illumination. By contrast, in cancer cells, the cleaved peptide linker allows WONPs and MSNs to respectively target nucleus and mitochondria, where the therapeutic powers could be unleashed, both photodynamically and photothermally. This ensures the energy production of cancer cells can be abolished. We further assess the underlying molecular mechanism at both gene and protein levels to better understand the therapeutic outcome. Treatment of liver metastasis in cancer patients is associated with severe side effects. Here, the authors develop nucleus and mitochondria targeted nanoparticles, conjugated via a cathepsin B sensitive peptide to selectively target liver metastatic cells.