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Laboratory-grade force platforms are the reference standard for quantifying ground reaction forces but remain costly and confined to well-resourced laboratories, limiting field-based use. This study evaluated the accuracy and between-session reliability of a portable force platform (Impera, Spinitalia) under static loading, and its concurrent validity during vertical jumping, against a criterion-standard piezoelectric plate with calibration traceable to national standards (4Jump, Kistler). Accuracy and between-session reliability were assessed against five tared loads (21.6–103.2 kg) applied at five plate locations across two sessions 24 h apart. Concurrent validity was assessed during squat and countermovement jumps performed by ten physically active men, using intraclass correlation coefficients, Bland–Altman analysis and the linear fit method applied to the entire force–time waveform. Both platforms underestimated the lightest load by about 4% and approached zero error at higher loads; the between-device difference was 0.19 kg and did not translate into a difference in proportional accuracy (p = 0.151), with no device × position interaction. Between-session coefficients of variation were below 1% for both devices. Concurrent agreement was good to excellent for all jump variables (ICC 0.867–0.996), and waveform agreement was close to unity (R² ≥ 0.997), with about 2% amplitude underestimation, greater in participants generating the highest impact forces. The Impera platform provides accurate and reliable measurements under static loading, and force–time data that agree closely with the 4Jump during vertical jump testing. Full article

(This article belongs to the Special Issue Wearable and Nearable Sensors for Sleep, Respiration and Activity Monitoring)

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12 pages, 785 KB

Open AccessArticle

Method Comparison of PT, INR, and aPTT Results Obtained Using Two Coagulation Analyzers in Routine Laboratory Practice

by Betül Özbek Kurtul, Bağnu Dündar, Gül İpek Gündoğan, Sevgi Kocyigit Sevinc, Tuğba Elgün and Asiye Gök Yurttaş

Diagnostics 2026, 16(18), 2989; https://doi.org/10.3390/diagnostics16182989 - 15 Sep 2026

Abstract

Background/Objectives: Prothrombin time (PT), international normalized ratio (INR), and activated partial thromboplastin time (aPTT) are routinely used coagulation assays, and analyzer–reagent differences may affect result comparability. This study evaluated analytical comparability between the Sysmex CS-2500 System using Siemens reagents and the Tokra Medical NOVAE II for PT, INR, and aPTT. Methods: Residual routine citrated plasma specimens were measured on both systems. The CS-2500 was designated as the comparator system. Passing–Bablok regression and Bland–Altman analysis were used as the primary method-comparison approaches; Pearson and Spearman correlations and exploratory categorical agreement were secondary analyses. Results: Fifty paired measurements were analyzed for PT and INR and 54 for aPTT. For all three parameters, the 95% confidence interval (CI) for the Passing–Bablok intercept included 0 and the 95% CI for the slope included 1, providing no statistically supported evidence of constant or proportional bias by regression. Mean paired differences (NOVAE II minus CS-2500) were +1.27 s for PT, +0.023 for INR, and +2.32 s for aPTT. The 95% limits of agreement were −0.15 to +2.69 s for PT, −0.112 to +0.159 for INR, and −1.70 to +6.33 s for aPTT. Categorical agreement was influenced by analyzer-specific reference intervals and by the low prevalence of abnormal results. Conclusions: The two analyzer–reagent systems showed positive analytical associations, but the magnitude and dispersion of paired differences varied by assay. Because no clinical equivalence margins were prespecified and markedly pathological or therapeutic-range samples were sparsely represented, these findings support analytical comparison and local verification but do not establish clinical interchangeability. Full article

(This article belongs to the Section Clinical Laboratory Medicine)

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18 pages, 5998 KB

Open AccessArticle

Study of Estrogen Receptor-Mediated PGx-eQTLs Identifies Genetic Determinants of Breast Cancer Endocrine Therapy Response

by Martin Meng, Arnab Ghosh, Huanyao Gao, John August, Shreya Indulkar, Meijie Wang, Xue Wang, Zhuyao Wang, Asadoor Amirkhani Namagerdi, Richard M. Weinshilboum, James N. Ingle and Liewei Wang

Int. J. Mol. Sci. 2026, 27(18), 8217; https://doi.org/10.3390/ijms27188217 - 15 Sep 2026

Abstract

Endocrine therapy remains the cornerstone of treatment for estrogen receptor alpha (ERα)-positive breast cancer, yet the relationship between individual genetic background and molecular response to ERα-targeted therapy remains incompletely understood. Using a well-characterized panel of lymphoblastoid cell lines (LCLs), we performed genome-wide pharmacogenomic expression quantitative trait locus (PGx-eQTL) analysis to identify estradiol (E2)- and tamoxifen (TAM)-induced SNP-gene pairs. PGx-eQTL signals were integrated with previously published breast cancer genome-wide association study datasets to examine their association with clinically relevant breast cancer phenotypes. We identified two ER-mediated PGx-eQTL SNP-gene pairs associated with breast cancer prognosis post-treatment with E2 or TAM. Notable loci included E2-regulated MRPL15 and TAM-regulated SYCP3, which have effects in a genotype-dependent manner, with genotype-dependent survival outcomes, from worse to better relapse-free survival. Similar endocrine-therapy effects on patient survival and breast cancer risk were observed in SIK2, post TAM-treatment, and in LSM4 with E2-treatment. Moreover, qRT-PCR validation in an independent LCL panel confirmed the genotype-specificity of these signals. Overall, we identified ER-mediated PGx-eQTL SNP-gene pairs which represent potential pharmacogenomic tools for identifying patients likely to benefit from ERα-targeted endocrine therapy, offering a foundation for more genotype-informed individualized treatment decisions in breast cancer. Full article

(This article belongs to the Section Molecular Pharmacology)

31 pages, 10296 KB

Open AccessArticle

Farmers’ Livelihood Vulnerability Assessment and Formation Mechanism Considering Natural Disasters: A Case Study of Hehuang Valley

by Weiguo Fan, Jiahui Li, Nan Chen and Chengrou Li

Land 2026, 15(9), 1720; https://doi.org/10.3390/land15091720 - 15 Sep 2026

Abstract

The increasing frequency and severity of natural disasters attributable to climate change have placed growing pressure on farmers’ livelihoods in ecologically fragile areas. Reducing livelihood vulnerability and strengthening resilience are therefore essential for disaster risk reduction and sustainable development. Previous research has predominantly focused on assessing livelihood vulnerability and its influencing factors, leaving gaps in understanding regarding the formation mechanisms. Therefore, this study aimed to develop a framework based on exposure, sensitivity, and adaptability, drawing on survey data from 312 farmers in the Hehuang Valley. This research examined the characteristics of farmers’ livelihood vulnerability and employed structural equation modeling to explore its formation mechanism. Monte Carlo simulation was applied to assess how different scenarios affected the farmers’ livelihood vulnerability index (LVI). The LVI differed markedly across regions, age, education, and income. Pathway analysis identified a dual pattern of association from natural disasters to the LVI: they were positively associated with livelihood vulnerability through certain pathways while showing negative associations through other pathways that may reflect coping responses associated with reductions in the LVI. Within the formation mechanism, natural capital appeared more frequently in the long pathways from natural disasters to the LVI with negative action intensity, whereas housing security appeared more frequently in those with positive action intensity. Scenarios combining improvements in education and household income were associated with clear reductions in the LVI, irrespective of other variables. Accordingly, this study proposes measures to reduce livelihood vulnerability by lowering exposure and sensitivity while improving adaptability, thereby supporting resilience building and risk management in vulnerable areas. Full article

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35 pages, 4976 KB

Open AccessArticle

From Cross-Sectoral Linkage to Sustainable and Coordinated Development: Spatiotemporal Evolution, Structural Barriers, and Transition Pathways of Sports–Culture–Tourism Integration in China

by Zhiwei Zeng, Luping Tang, Tieli Wang, Jiaxin Zhang and Hao Ding

Sustainability 2026, 18(18), 9459; https://doi.org/10.3390/su18189459 - 15 Sep 2026

Abstract

Cross-sectoral integration among sports, culture, and tourism has emerged as a crucial pathway for fostering coordinated and sustainable regional development. Using panel data from 30 provincial-level regions in China from 2013 to 2022, this study applies the entropy weight method, coupling coordination degree (CCD) model, kernel density estimation, spatial autocorrelation analysis, and obstacle degree model to examine the spatiotemporal evolution and structural shortfalls of sports–culture–tourism coordination. The results reveal steady improvement in the cultural subsystem, persistent vulnerabilities in the sports subsystem, and notable volatility in tourism. The national mean CCD rose from 0.342 in 2013 to 0.411 in 2019 but subsequently declined to 0.373 in 2022, demonstrating that structural synchrony can coexist with limited overall coordination. Regional differences remained significant, with no sustained convergence. Global Moran’s I revealed positive but weak spatial dependence. Obstacle diagnosis identified primary structural shortfalls in sports infrastructure and public service provision, cultural value conversion, and tourism market capacity. Overall, the findings suggest that cross-sectoral linkage does not automatically translate into substantive coordination, highlighting the necessity of balancing subsystem capacities and tailoring regional development support. Full article

(This article belongs to the Section Tourism, Culture, and Heritage)

36 pages, 2485 KB

Open AccessArticle

Cloud-Based Distributed Deep Learning for Credit Card Fraud Detection: A Scalability and Data Partitioning Analysis

by Alireza Izaddoost, Bhrigu Celly and Amlan Chatterjee

Appl. Sci. 2026, 16(18), 9157; https://doi.org/10.3390/app16189157 - 15 Sep 2026

Abstract

Recent advances in artificial intelligence have significantly improved credit card fraud detection, with deep learning emerging as an effective approach for learning complex transaction patterns. However, as financial transaction datasets continue to grow, training deep learning models on a single computing node becomes increasingly computationally expensive, motivating the adoption of cloud-based distributed learning. Because distributed deep learning partitions training data across multiple worker nodes, this study evaluates how different data partitioning strategies influence predictive performance, computational efficiency, and scalability. The proposed framework was evaluated under both independently and identically distributed (IID) and non-independent and identically distributed (non-IID) data partitioning strategies, including random, stratified, label skew, quantity skew, temporal skew, and amount skew, using single-node, three-worker, and seven-worker configurations. Experimental results demonstrate a maximum training speedup of 4.181× and a 76.084% reduction in average epoch training time while maintaining consistently high recall across all partitioning strategies; however, the

F_{1}

-score decreased from 0.696 to 0.587 (approximately 16%), due to increased false-positive predictions. The evaluated partitioning strategies exhibited different trade-offs between predictive performance and computational efficiency. These findings demonstrate that the proposed framework provides a scalable solution for cloud-based credit card fraud detection while offering practical insights into the influence of data partitioning strategies on distributed deep learning performance. Full article

(This article belongs to the Special Issue Advances of Edge Computing in Distributed Systems—Second Edition)

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25 pages, 1007 KB

Open AccessArticle

Always Connected, Not Always Well: The Moderating Role of Sleep Quality in the Relationship Between Nomophobia and Mental Health Among University Students in Saudi Arabia

by Ibrahim A. Elshaer, Alaa M. S. Azazz and Chokri Kooli

Eur. J. Investig. Health Psychol. Educ. 2026, 16(9), 140; https://doi.org/10.3390/ejihpe16090140 - 15 Sep 2026

Abstract

As smartphones become progressively embedded in students’ social, academic, and personal lives, concerns have been raised about the emotional consequences of nomophobia (no-mobile-phone phobia). Although previous research has shown an overall correlation between nomophobia and increased mental health symptoms, little is known about whether its main dimensions exerted differential associations with different mental health symptom dimensions or whether sleep quality can influence these relationships. Addressing these gaps, this research tested the associations between four dimensions of nomophobia—Not Being Able to Communicate (NBAC), Losing Connectedness (LC), Not Being Able to Access Information (NBAI), and Giving Up Convenience (GUC)—and three mental health symptoms: depression (Dprtn), anxiety (Enzt), and stress (Strs), while testing the moderating role of sleep quality (SQ) among university students. Drawing upon Conservation of Resources (COR) Theory and Self-Determination Theory (SDT), a quantitative cross-sectional survey was conducted among 980 university students. The proposed research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrated that the psychological consequences of nomophobia are dimension-specific rather than homogeneous. LC emerged as the most consistent predictor, showing significant positive associations with depression, anxiety, and stress. In contrast, NBAC was not significantly associated with any of the three mental health outcomes. NBAI showed mixed effects, with a significant positive association with depression, a significant negative association with anxiety, and no significant association with stress. GUC was significantly and positively associated with depression, anxiety, and stress. Furthermore, sleep quality functioned as a selective moderator, with significant interaction effects for SQ × NBAC on depression and anxiety, SQ × NBAI on depression, and SQ × GUC on depression and stress. No significant moderation effects were observed for the remaining hypothesized interactions. These findings highlight the dimension-specific nature of nomophobia and indicate that the role of sleep quality varies across the specific nomophobia dimension and the mental health outcome considered. Full article

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46 pages, 2918 KB

Open AccessArticle

Global Food Security in a Climate-Oscillating World: Spectral Evidence and Early Warning Implications for Sustainable Food Systems

by Kostiantyn Pavlov, Olena Pavlova, Oksana Liashenko, Tomasz Wołowiec, Maksym Zhytar, Sylwester Bogacki, Eleonora Tankova, Polina Puzyrova and Olena Mykhailovska

Sustainability 2026, 18(18), 9460; https://doi.org/10.3390/su18189460 - 15 Sep 2026

Abstract

In March 2022, the FAO Food Price Index peaked at 159.7 as climate shocks collided with geopolitical disruption, pushing global hunger past 735 million and exposing how deeply climate variability penetrates the economics of agri-food systems. Yet the imprint of ocean–atmosphere oscillations on global food prices—the central economic signal of the agri-food system—has not, to our knowledge, been mapped systematically in the frequency domain. This study delivers, to our knowledge, one of the first multi-oscillation cross-spectral analyses of the climate–food price nexus, matching 7 climate indices across the Pacific, Atlantic, and Indian Ocean basins with 5 disaggregated FAO Food Price Index sub-components over 432 monthly observations (1990–2025), verified through 6 robustness checks, including surrogate data testing. Four findings carry direct policy relevance. ENSO indicators lead global food prices by three to four months with a 100% surrogate test pass rate—one of the cleanest actionable climate–price signals documented to date. The Indian Ocean Dipole leads prices by roughly one to two years (cross-correlation peak at sixteen months, though the peak is broad and not sharply localised within that window), extending the early warning horizon well beyond the ENSO signal. The apparent Atlantic Multidecadal Oscillation–price correlation (r ≈ +0.60) is revealed to be a common-trend artefact. Vegetable oils are the most consistently climate-exposed commodity chain across the seven oscillations; sugar and meat, often assumed less climate-sensitive, in fact show strong coherence with specific oscillations (sugar with the Indian Ocean Dipole and meat with the Southern Oscillation Index), indicating that commodity-level exposure is oscillation-specific rather than uniform and reflects each commodity’s position in the production-to-consumption chain—short-cycle, thinly buffered commodities transmit weather shocks to price quickly, while feed-market intermediation delays and smooths the pass-through for livestock. These results provide the empirical foundation for integrating real-time monitoring of climate oscillations into food system governance—a low-cost policy innovation that aligns economic stability objectives with climate adaptation goals, strengthens the resilience of agri-food value chains, and supports progress towards Sustainable Development Goal 2 (Zero Hunger). Full article

(This article belongs to the Special Issue Balancing Economic Growth and Environmental Performance in Sustainable Agri-Food Systems: The Role of Climate Change and Innovation Policy)

13 pages, 271 KB

Open AccessArticle

Psychosocial Resources and Adolescent Well-Being: Reassessing the Explanatory Role of Vulnerability

by Alberto Horno, Cristian Céspedes-Carreño, Andrés Rubio, Damarys Roy, Sergio Fuentealba-Urra, Fernanda Cancino-Norambuena and Juan Carlos Oyanedel

Adolescents 2026, 6(5), 76; https://doi.org/10.3390/adolescents6050076 - 15 Sep 2026

Abstract

This study examined the relative contribution of structural vulnerability and individual psychological resources in explaining subjective well-being among Chilean adolescents. From an ecological perspective, it assessed whether resilience and perseverance, or grit, mediate the relationship between school vulnerability and subjective well-being. A cross-sectional, non-experimental correlational design was used with a sample of 3774 secondary school students in Chile. Subjective well-being, resilience, and perseverance were measured using self-report instruments, whereas school vulnerability was operationalized through the School Vulnerability Index (SVI). Results indicated that school vulnerability was negatively associated with subjective well-being, although with a small effect size. In contrast, resilience and perseverance showed stronger positive associations, with resilience emerging as the strongest predictor in multivariate models. Hierarchical regression analyses showed that SVI and age explained only a small proportion of variance in well-being, whereas adding resilience and perseverance substantially increased explained variance. Bootstrapped mediation analyses indicated significant but small indirect effects, suggesting partial mediation. Resilience accounted for the largest indirect effect. Overall, findings suggest that adolescent subjective well-being is more strongly explained by psychological resources than structural vulnerability alone. These results challenge deterministic interpretations of school vulnerability and highlight the importance of strengthening socioemotional competencies in educational settings. Full article

15 pages, 291 KB

Open AccessArticle

Preventive and Interceptive Orthodontic Treatment Needs in Early and Late Mixed Dentition: An IPION-Based Assessment in Children

by Nuri Can Tanrısever, Tuğba Bezgin and Tülin Ufuk Toygar Memikoğlu

Children 2026, 13(9), 1252; https://doi.org/10.3390/children13091252 - 15 Sep 2026

Abstract

Background/Objectives: Mixed dentition is critical for identifying developing malocclusions that may benefit from preventive or interceptive orthodontic management. This study aimed to assess IPION-defined preventive/interceptive orthodontic treatment need using the Index of Preventive and Interceptive Orthodontic Needs (IPION), compare early and late mixed dentition, and evaluate associated clinical findings. Methods: This single-center cross-sectional study included 200 children attending their first examination at the university Pediatric Dentistry and/or Orthodontics clinics: 100 aged 6 and 100 aged 9 years. IPION-defined treatment need was assessed using the age-specific IPION-6 and IPION-9 subsystems. Dental, developmental, and occlusal findings were recorded using standardized criteria. Treatment-need categories and clinical findings common to both subsystems were compared between groups, while total IPION scores were summarized separately by age. Relationships of caries and premature primary tooth loss with IPION scores were evaluated. Results: Definite treatment need predominated (58.0% vs. 51.0%), and treatment-need distributions differed between age groups (p = 0.047). Dental caries, molar relationship, overjet, and overbite showed significant differences. In 6-year-olds, carious and prematurely lost tooth counts were positively correlated with IPION scores, with the strongest relationship for premature tooth loss (ρ = 0.609; p < 0.001). Relationships were weaker in 9-year-olds. Conclusions: IPION-defined preventive/interceptive orthodontic treatment need was common within this university-based clinical sample across mixed dentition stages. Differences in treatment-need distribution and clinical findings indicated distinct preventive and interceptive orthodontic profiles across mixed dentition stages. Age-specific IPION assessment may support identification of children who may benefit from further orthodontic evaluation by integrating dental, developmental, and occlusal findings. Full article

(This article belongs to the Section Pediatric Dentistry & Oral Medicine)

12 pages, 1905 KB

Open AccessArticle

Analysis of HA5 Hemagglutinins from Influenza A Viruses for Mass-Charge Patterns to Understand Antigenic Drift Related to Immune Escape

by Wenhao Zhang, Xinran Wu, Rui Zhu, Xinyi Pan, Shancheng Yu, Jie Wang, Jun Steed Huang and Wandong Zhang

Viruses 2026, 18(9), 1025; https://doi.org/10.3390/v18091025 - 15 Sep 2026

Abstract

Influenza A viruses remain a persistent global health threat due to their ability to mutate and evade host immune defences, a process largely driven by changes in hemagglutinin (HA). HA is a glycoprotein present on the surface of influenza virions and plays a critical role in viral entry into host cells by binding to cellular receptors. In this study, 23 HA5 protein sequences from various influenza viral strains collected from wild birds, seagulls, ducks, geese, and chickens, as well as from humans in mainland China during the period from 2023 to 2024, were analyzed for mass-charge patterns by our predictive modelling algorithm to understand the nonlinear trend of viral evolution, particularly regarding their antigenic drift and immune escape potential. Two critical regions in HA5 proteins were identified and analyzed. The first region is a “mutable zone” (the negative gravity centre identified by mass-charge pattern analysis), which frequently accumulates multiple mutations. This leads to antigenic drift and instability, enabling the virus to evade pre-existing herd immunity. Analyzing and monitoring mass-charge changes may help predict the evolutionary trend of the viruses and the emergence of future variants in these regions where the viruses were collected, thereby informing the design and development of vaccines for the viruses analyzed and related strains. The second region is a “stable core” (positive gravity centre), a highly conserved region essential for viral function. Due to its conservation across strains, it may represent a promising target for developing universal vaccines and broad-spectrum antibodies capable of conferring protection against multiple variants simultaneously for the viruses analyzed and related strains. In summary, our analyses, with a predictive modelling algorithm for mass-charge patterns of HA5 protein sequences, provide a novel metric for viral evolution and evaluating the immune evasion potential, and offer a strategic framework for designing vaccines against the viruses analyzed and related strains. Full article

(This article belongs to the Special Issue Influenza Viruses in Wildlife 2026)

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20 pages, 8646 KB

Open AccessArticle

Effects of CO₂ Concentration During Curing on Carbon Sequestration and Strength of Coal-Based Solid Waste Backfill

by Wenchang Feng, Daoxiong Zhang, Meng Li, Binbin Huo, Yuyin Guo, Yazhou Shi, Zhangyu Li and Yunkai Zhang

Appl. Sci. 2026, 16(18), 9155; https://doi.org/10.3390/app16189155 - 15 Sep 2026

Abstract

Coal development produces large amounts of coal-based solid wastes (coal gangue, slag, fly ash) and substantial CO₂ emissions, posing severe ecological burdens on mining areas. This study integrated coal-based solid-waste cemented backfilling with CO₂ mineral sequestration to co-dispose of coal-based solid wastes and CO₂, experimentally investigating the CO₂ sequestration performance and uniaxial compressive strength (UCS) of coal-based solid-waste cemented backfill (CSCB) under mineralization curing with different CO₂ concentrations. TG-DTG, SEM-EDS, and XRD were adopted to investigate the CO₂ mineral sequestration process and the strength reduction of CSCB. The TG-DTG-based calculated CO₂ uptake of CSCB was positively correlated with the CO₂ concentration, rising by 86.9% from 2.52% to 4.71% as the concentration increased from 1.5% to 10%. In contrast, CSCB UCS decreased drastically by 88.1% from 6.05 MPa to 0.72 MPa when concentration increased from 0 to 10%. The combined microcharacterization results suggest that CO₂ reacted with alkaline substances and hydration products (CH, C-(A)-S-H gel, AFt) in CSCB to form carbonates. The UCS reduction is interpreted as arising mainly from a weakened alkaline environment (according to results and speculation), degradation of the C-(A)-S-H gel, and the AFt–CO₂ substitution reaction. Within the investigated conditions (a single mix proportion and a 14-day curing age), this study provides a reference for coal-based solid-waste backfilling coupled with CO₂ mineral sequestration; extrapolation to other mix proportions, longer curing ages, or field-scale applications requires further verification. Full article

(This article belongs to the Section Civil Engineering)

28 pages, 582 KB

Open AccessArticle

The Impact of Digital-Intelligent Integration Level on Innovation Capacity of Chinese Listed Enterprises: An Empirical Analysis Based on Machine Learning

by Yuwei Yan and Xuanhui Yan

Sustainability 2026, 18(18), 9456; https://doi.org/10.3390/su18189456 - 15 Sep 2026

Abstract

Digital-intelligent integration, defined as the integration of data factors and artificial intelligence technologies, serves as a core driver of the high-quality development of enterprises. Its relationship with enterprise innovation capacity and the underlying influencing mechanisms have attracted extensive academic attention. Based on panel data of Chinese A-share listed enterprises from 2000 to 2023, this study employs fixed-effects models, mediation analysis, robustness tests, heterogeneity analysis, and machine learning methods to examine the relationship between digital-intelligent integration and enterprise innovation capacity and explore its underlying mechanisms. The empirical results show that (1) digital-intelligent integration is significantly and positively associated with enterprise innovation capacity, and this core finding remains robust after alternative measures are employed; (2) mediation analysis indicates that digital-intelligent integration is positively associated with R&D intensity, which, in turn, is associated with higher enterprise innovation capacity, providing evidence consistent with the proposed mediating mechanism; (3) heterogeneity analysis indicates that the innovation-promoting association of digital-intelligent integration is significantly stronger among enterprises with lower managerial shareholding ratios, enterprises located in regions with weaker intellectual property protection, and state-owned enterprises. This study provides empirical evidence and practical implications for governments to optimize digital development policies and for enterprises to accelerate the deep integration of digital and intelligent technologies. Full article

(This article belongs to the Special Issue AI-Driven Entrepreneurship and Sustainable Business Innovation)

16 pages, 598 KB

Open AccessArticle

Generative AI-Assisted Academic Writing Experience and University Students’ Academic Self-Efficacy: Parallel Indirect Associations Through Academic Emotions

by Tingzhi Han, Yiwen Yuan, Nitong Zhou and Zijian Fan

J. Intell. 2026, 14(9), 221; https://doi.org/10.3390/jintelligence14090221 - 15 Sep 2026

Abstract

Generative artificial intelligence (GenAI) is increasingly used in academic writing, yet its associations with students’ broader academic emotions and capability beliefs remain unclear. Drawing on control–value theory and social cognitive theory, this cross-sectional study examined positive and negative academic emotions as parallel statistical pathways between GenAI-assisted writing experience and general academic self-efficacy. Participants were 1128 students from universities in eastern China. A covariate-adjusted parallel pathway model was estimated with 5000 bootstrap resamples. GenAI-assisted writing experience was positively associated with self-efficacy (total association B = 0.733, β = 0.670) and broader positive emotions (B = 0.778, β = 0.631), and negatively associated with broader negative emotions (B = −0.457, β = −0.298). The positive-emotion indirect association was 0.471 (standardized β = 0.431), 95% CI [0.409, 0.531], whereas the negative-emotion indirect association was 0.033 (standardized β = 0.030), 95% CI [0.018, 0.052]. The direct association remained positive (B = 0.229, β = 0.209). After positive academic emotions were controlled, the coefficient linking GenAI-writing experience with negative emotions changed from negative to positive, indicating a statistical suppression pattern. A sensitivity model excluding emotional-motivation items reproduced both indirect associations. Given the limited discriminant validity between positive emotions and self-efficacy and the concurrent self-report design, the larger positive indirect association warrants cautious interpretation. The findings indicate that GenAI-assisted writing experience is associated with broader academic emotions and efficacy beliefs while retaining a smaller residual negative-emotion component. Full article

(This article belongs to the Section Studies on Cognitive Processes)

10 pages, 203 KB

Open AccessEditorial

Positive Psychology and Psychology of Religion and Spirituality—Going Beyond the WEIRD Sample

by Adam Anczyk, Halina Grzymała-Moszczyńska and Anna M. Maćkowiak

Religions 2026, 17(9), 1085; https://doi.org/10.3390/rel17091085 - 15 Sep 2026

Abstract

The psychology of religion and spirituality and positive psychology are, of course, two distinct subdisciplines [...] Full article

(This article belongs to the Special Issue Religion, Spirituality, Well-Being and Positive Psychology)

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