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A study of the effects of job insecurity on organizational citizenship behavior based on the chained mediating effects of emotional exhaustion and organizational identification
Objectives This study aimed to explore the influence mechanism of job insecurity on organizational citizenship behavior (OCB). Specifically, it sought to examine the chained mediating role of emotional exhaustion and organizational identification in this relationship. Methods A longitudinal time-lagged survey was conducted on 330 employees at two time points. The data were collected using established scales for job insecurity, emotional exhaustion, organizational identification, and OCB. Structural equation modeling (SEM) was used to test the hypothesized chained mediation model. Results The results confirmed that emotional exhaustion and organizational identification are crucial mediators. Three significant indirect pathways were identified: (1) a simple mediation path through emotional exhaustion; (2) a simple mediation path through organizational identification; and (3) a chained mediation path where job insecurity increased emotional exhaustion, which in turn decreased organizational identification, ultimately leading to lower OCB. Notably, emotional exhaustion emerged as the most dominant mediating mechanism. Conclusions This study reveals a complex mechanism through which job insecurity impacts OCB, highlighting a sequential process from emotional strain to cognitive detachment. These findings offer important theoretical insights for job stress models and provide practical guidance for organizations to mitigate the negative effects of job insecurity by addressing both employee well-being and their sense of belonging.
Effects of aging and risk-taking behaviors on fatal injuries among old motorcyclists in Taiwan: Evidence from 2011 to 2022
Background and objectives Fatality rates of motor vehicle crashes among the old population have risen, primarily in association with age-related declines in health and functional abilities. Comparatively little research has been conducted to examine the impacts of risk-taking behaviors (such as unhelmeted, unlicensed, and drunk riding) on fatalities among old motorcyclists. Materials and methods This study employed the Taiwan National Traffic Crash Dataset from 2011 to 2022 to investigate fatal injuries among old motorcyclists. To identify risk factors associated with injury fatality, chi-square tests were employed. Subsequently, stepwise logistic regression models incorporating multiple variables were constructed to examine these factors. To explore potential interactions between aging and specific behavioral variables, joint effect analysis was performed, followed by a subgroup analysis specific to very old motorcyclists (75 years or above). Results The analyzed dataset comprised 338,231 crashes involving old motorcyclists, of which 2,703 were fatal and 335,528 were nonfatal. A linear relationship was observed between the risk of fatal injuries and increasing age. The adverse effect of aging on fatalities was exacerbated among unhelmeted (AOR = 4.46; CI = 3.79–5.26), unlicensed (AOR = 1.76; CI = 1.55–1.99), and drunk riders (AOR = 5.35; CI = 4.06–7.06). Our subgroup analysis revealed similar detrimental effects of these three behaviors among very old motorcycles when they were unhelmeted (AOR = 3.67; CI = 3.09–4.36), unlicensed (AOR = 1.31; CI = 1.13–1.50) and intoxicated (AOR = 3.06; CI = 2.31–4.06). Further stratification by geographic location demonstrated that certain risk-taking behaviors, such as unhelmeted and drunk riding, were consistently associated with higher fatality rates. Notably, the magnitude of the effects of drunk riding in rural settings on fatal injuries was higher. Conclusions The study established a linear relationship between age and the risk of fatal injuries among old motorcyclists. The three risk-taking behaviors apparently are risk factors for fatal injuries, and evident when motorcyclists were very old. This underscores the importance of tailored interventions targeting individuals aged 75 years or above who engage in unlicensed riding, which remains an important area for continued research.
Multi-objective optimizing spring placement and stiffness in slider-crank mechanisms for enhanced dynamic parameters
The slider-crank mechanism (SCM) is fundamental to various mechanical systems. However, optimizing its dynamic performance remains a pressing challenge due to excessive torque, joint reactions, and energy consumption. This study introduces two key innovations to address these challenges: (1) the integration of springs into SCM to optimize dynamic performance and (2) a novel hybrid optimization approach combining the Conjugate Direction with Orthogonal Shift (CDOS) method and Parameter Space Investigation (PSI). The mathematical model evaluates the effects of spring placement and stiffness on critical performance parameters such as energy efficiency, torque demands, and joint forces. The hybrid CDOS-PSI approach systematically identifies optimal design configurations to balance these performance objectives. The methodology’s efficacy is validated through a case study on a wood splitter, a commonly used agricultural and industrial machine. Experimental tests were carried out to measure splitting forces for different wood types, enabling accurate model calibration. Results demonstrate that the spring-integrated SCM reduces dynamic loads significantly compared to conventional designs. Comparative numerical analysis confirms the proposed model’s accuracy, with less than 5% deviations. This research offers innovative contributions to SCM design by combining spring-based dynamic enhancement with a novel hybrid optimization framework for improved efficiency and durability in practical applications.
A structural equation modeling approach to examine determinants of nutritional status in Palestinian children 6–59 months
Background Undernutrition remains a persistent public health concern among young children in Palestine, shaped by a range of socioeconomic and dietary factors. This study applies a Structural Equation Modeling (SEM) approach to explore both direct and indirect determinants of child growth among children aged 6–59 months in the West Bank. Methods Data were drawn from a 2022 cross-sectional survey involving 300 children selected from 1,400 households. Child growth was assessed using anthropometric indicators (HAZ, WAZ, MUAC) and dietary adequacy via the Mean Nutrient Inadequacy Ratio (MNIR). Latent constructs were developed for socioeconomic status, food insecurity, dietary intake (macro- and micronutrients), feeding practices, neonatal health, and food availability. SEM was used to examine interrelationships and the pathways influencing child nutritional status. Results Descriptive analysis showed substantial variation in undernutrition and nutrient intake across regions, family size, and parental education. SEM results showed that macronutrient intake had the strongest direct effect on child growth, followed by socioeconomic status and neonatal health. Infant feeding practices and micronutrient intake also contributed positively, though their effects were primarily indirect. Food availability acted as a significant mediator, linking structural access issues to poor dietary diversity and feeding behavior. The model demonstrated good fit across multiple indices. Conclusion Child nutrition in Palestine is shaped by interrelated determinants of dietary intake, caregiver practices, food access, and early-life health conditions. Effective interventions must target both nutritional quality and the broader social and environmental context to improve growth outcomes. SEM offers a powerful framework to understand these pathways and guide evidence-based policy design.
ACG-SFE: Adaptive cluster-guided simple, fast, and efficient feature selection for high-dimensional microarray data in binary classification
Advances in data collection have resulted in an exponential growth of high-dimensional microarray datasets for binary classification in bioinformatics and medical diagnostics. These datasets generally possess many features but relatively few samples, resulting in challenges associated with the “curse of dimensionality”, such as feature redundancy and an elevated risk of overfitting. While traditional feature selection approaches, such as filter-based and wrapper-based approaches, can help to reduce dimensionality, they often struggle to capture feature interactions while adequately preserving model generalization. Therefore, this paper introduces the Adaptive Cluster-Guided Simple, Fast, and Efficient (ACG-SFE) feature selection, a hybrid approach designed to address the challenges of high-dimensional microarray data in binary classification. ACG-SFE enhances the Simple, Fast, and Efficient (SFE) evolutionary feature selection model by integrating hierarchical clustering to dynamically group correlated features based on the optimal number of clusters determined by the Silhouette index, Davies-Bouldin score, and the feature-to-observation ratio while adaptively selecting representative features within clusters using mutual information and adjusting the selection threshold through a progress factor. This hybrid filter-wrapper approach improves feature interactions, effectively minimizing redundancy and overfitting while enhancing classification performance. The proposed model is assessed against four state-of-the-art evolutionary feature selection models on 11 high-dimensional microarray datasets. Experimental results indicate that ACG-SFE effectively selects a small yet pertinent feature subset, minimizing redundancy while attaining enhanced classification accuracy and F-measure. Furthermore, its reduced RMSE between train and test accuracy substantiates its capability to reduce overfitting, outperforming comparative models. These findings establish ACG-SFE as an effective feature selection model for handling high-dimensional microarray data in binary classification, enhancing classification accuracy while selecting minimal relevant features to reduce unnecessary complexity and the risk of overfitting.
Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification
For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a “mother machine”. These time-lapses are then used to train deep artificial neural networks (Convolutional Neural Networks and Vision Transformers) to identify the species. We have previously demonstrated this approach on four different species, which is now extended to seven common pathogens causing human infections: Pseudomonas aeruginosa , Escherichia coli , Klebsiella pneumoniae , Acinetobacter baumannii , Enterococcus faecalis , Proteus mirabilis , and Staphylococcus aureus . Furthermore, we expand upon our previous work by evaluating real-time performance as additional frames are captured during testing, and investigating the role of training set size, data quality, and data augmentation as well as the contribution of texture and morphology to performance. The experiments suggest that spatiotemporal features can be learned from video data of bacterial cell divisions, with both texture and morphology contributing to classifier decision. The method could be used simultaneously with phenotypic antibiotic susceptibility testing (AST) in the microfluidic chip. The best models attained an average precision of 93.5% and a recall of 94.7% (0.997 AUC) on a trap basis in a separate, unseen experiment with mixed species after around one hour. However, in a real-world scenario, one can assume many traps will contain the actual species causing the infection. Still, several challenges remain, such as isolating bacteria directly from blood and validating the method on diverse clinical isolates. This proof of principle study brings us closer to real-time diagnostics that could transform the initial treatment of acute infections.
Exploration of the decontamination of common nonmetallic materials by Ce(IV)/HNO3
With the rapid development of the nuclear medicine business worldwide, the removal of iodine-131 from specific contaminated environments to protect public health has important application prospects. In this study, the surface decontamination mechanism of Ce(IV)/HNO3 as a decontaminant for iodine-131-contaminated nonmetallic materials was investigated by using an orthogonal experimental method and scanning electron microscopy (SEM). During the preparation experiments with the contaminated materials, both quartz glass and ceramics reached peak activity concentration levels at 4 h of adsorption (contamination) by using immersion; the decontamination factor (DF) was selected as the test index for the decontamination experiments. The influence order of temperature, Ce(IV) concentration, HNO3 concentration and decontamination time on the decontamination factor (DF) was investigated with an orthogonal test and extreme difference analysis. The optimal combination of factors under the set experimental conditions was obtained after a comprehensive analysis. The optimal combination for quartz glass was a decontamination time of 2.0 h>temperature of 60°C > Ce(IV) concentration of 0.02 mol/L > HNO3 concentration of 1.5 mol/L; the optimal combination for the ceramic sheet was a Ce (IV) concentration of 0.02 mol/L>temperature of 80°C >decontamination time of 1 h > HNO3 concentration of 2.0 mol/L. Additionally, from the SEM analysis, the material surface decontamination process removed the surface iodine-131 and the highly accumulated organic substances; overall, a better decontamination effect was achieved.
The association between limiting longstanding illness and serious psychological distress in adolescents: A secondary analysis of the UK Millennium Cohort Study
Background Previous studies have shown associations between specific limiting longstanding illnesses and mental health difficulties using cross-sectional studies in the UK. This study explored the association between having any limiting longstanding illness and serious psychological distress or of currently receiving treatment for depression or serious anxiety at age 17 years. Methods A secondary analysis of the UK Millennium Cohort Study was conducted. Outcome measures were dichotomised responses from the self-administered Kessler-6 questionnaire for nonspecific psychological distress, and self-reported current treatment for depression or serious anxiety. Limiting longstanding illness data and covariates were taken from questionnaires with parents and adolescents, from birth up to age 17 years. Data were analysed using multiple binary logistic regression, first using complete-case analysis, and then using multiple imputation using chained equations. Results Adolescents with a history of limiting longstanding illness were at an increased odds of both serious psychological distress (odds ratio = 1.53, 95%CI = 1.27–1.86) and self-reporting currently receiving treatment for depression or serious anxiety at age 17 years (odds ratio = 3.02, 95%CI 2.24–4.07). Conclusion Children and young people with a limiting longstanding illness are at increased risk of having serious psychological distress, depression, and serious anxiety. Practitioners should be aware of this and routine screening for psychological distress, plus additional preventative support, may be beneficial.
Recreational screen time and adolescents’ school adjustment based on latent profile analysis: The mediating role of perceived physical health
This study surveyed 12529 adolescents and employed latent profile analysis to explore the types of adolescents’ school adjustment. Multiple linear regression and the Bootstrap method were used to investigate the predictive role of recreational screen time in adolescents’ school adjustment and the mediating mechanism of perceived physical health. The results revealed three types of school adjustment in adolescents: “Ideal Type”, “Growth Type”, and “Ambivalent Type”. Recreational screen time was found to significantly and negatively predict both adolescents’ school adjustment and their classification into adjustment types. Furthermore, recreational screen time indirectly predicted adolescents’ school adjustment through the mediating role of perceived physical health. These findings suggest the importance of appropriately controlling recreational screen time, especially for adolescents in the ideal type category, and further exploring the physical and mental development patterns of adolescents in the ambivalent type, to provide a theoretical basis for improving school adjustment in adolescents.
Improvement of mask R-CNN and deep learning for defect detection and segmentation in electronic products
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products. Y-MaskNet combines the high efficiency of YOLOv5 in target detection with the fine segmentation capability of Mask R-CNN and optimizes the overall performance of the model through a multi-task learning framework. Experimental results show that Y-MaskNet achieves a significant improvement in detection and segmentation tasks, with mAP@[0.5:0.95] reaching 0.72 (up from 0.62 for YOLOv5 and 0.65 for Mask R-CNN) on the PCB Defect Dataset, and IoU improving by 7% compared to existing methods. These improvements are particularly notable in small object detection and fine-grained defect segmentation, making Y-MaskNet an efficient and accurate solution for defect detection in electronic products, offering strong technical support for future industrial intelligent quality control.
The surprising ways bridges resist collapse
Sedimentary and reservoir characterization of wandering braided river: a case of the Xiashihezi Formation 1 member in Shilijiakhan west zone, Hangjinqi, Northern Ordos Basin
The Ordos Basin’s Hangjinqi Shiligahan west zone Xiashihezi Formation 1 Member gas reservoir exhibits significant exploration and development potential. However, its sedimentation and reservoir characteristics are poorly understood. To address this, geological, seismic, macroscopic, and microscopic methods are combined. The available data includes core observation, thin sections, physical property analysis, mercury injection curve interpretation, seismic attribute predictions, and so on. The results show that the target stratum lithology and sedimentary structure are complex and diverse. They are dominated by gravelly coarse sandstone facies with strong hydrodynamic conditions, intercalated with thin mudstone and siltstone. The vertical meter-scale cycle sedimentation characteristics are distinct, representing a typical shallow braided river deposition. Through the mutual calibration and verification of lithology, logging, and seismic facies, the braided channel and its internal microfacies boundaries were accurately delineated. The reservoir primarily consists of gravel-bearing coarse sandstone, featuring intergranular and intragranular dissolved pores as the primary types of reservoir space. The pore types are predominantly mesopores and small pores, with pore-throat combinations favoring mesopores and medium throats, as well as medium to small pores and fine throats. The reservoirs average porosity and permibility are 7.6% and 0.53mD, respectively. This defines a typical reservoir with low to ultra-low porosity and low to ultra-low permeability. The sedimentary microfacies and their associated hydrodynamic conditions are crucial for the development of the reservoir in the Xiashihezi Formation 1 Member. This leads to variations in reservoir properties and pore structures. Thus, the reservoirs are predominantly located in the channel bar microfacies, clearly demonstrating characteristics of facies-controlled reservoir development.
My blue is your blue: different people’s brains process colours in the same way
Unveiling the mechanism of Pogostemon cablin (Blanco) Benth in treating heat illnesses via network pharmacology and molecular simulation
Background Heat illness is a dangerous condition marked by a widespread inflammatory response. Although Pogostemon cablin (Blanco) Benth and its derivatives are clinically used, their mechanisms remain unclear. Methods 11 heat illness patients and 14 healthy volunteers from Southwest Medical University Affiliated Hospital were enrolled. Bulk RNA sequencing of peripheral blood samples identified disease-relevant modules via weighted gene co-expression network analysis (WGCNA). Active ingredients and targets of P. cablin were retrieved from TCMSP. GO/KEGG, protein-protein interaction (PPI), and ROC analyses were performed. Core genes were localized through single-cell RNA sequencing, with compound-target interactions validated by molecular docking. Results Enrichment analysis revealed nine cross-targets in TNF/NF-κB pathways. Core targets (NFKBIA, PARP1) showed high diagnostic sensitivity/specificity. Single-cell data indicated predominant expression in monocytes and CD1C-CD141 dendritic cells. Molecular docking demonstrated strong affinity of quercetin/quercimeritrin for NFKBIA/PARP1/LACTB, with molecular dynamics confirming structural stability of complexes (RMSD < 2Å after 100 ns). Conclusion This pioneering study integrates network pharmacology and molecular simulations to elucidate P. cablin’s therapeutic targets for heat illness, providing a foundation for advanced therapies.
Research misconduct: how the scientific community is fighting back
Risk factors for Overweight/Obesity among people living with HIV on antiretroviral therapy: An ambidirectional cohort study at a tertiary health facility in Zambia
Background Overweight and obesity are major concerns among people living with HIV (PLWH), particularly those on integrase inhibitors, as they elevate the risk of cardiovascular diseases. However, longitudinal data on the burden and risk factors for overweight/obesity in sub-Saharan Africa (SSA) remain limited. This study aimed to estimate the incidence and identify factors associated with overweight and obesity among PLWH who switched to a dolutegravir (DTG)-based ART regimen at Livingstone University Teaching Hospital. Methods We enrolled 249 adults aged ≥18 years living with HIV on ART [non-nucleoside/nucleotide reverse transcriptase inhibitor (NNRTI) n = 174, protease inhibitor (PI) n = 21, and DTG n = 54] with a baseline body mass index (BMI) < 25 kg/m² between April 2019 and May 2020 and conducted a single follow-up assessment between December 2022 and June 2023. Participants were followed for a median of 43 months (interquartile range [IQR]: 42, 44). At follow-up, all participants were on a DTG-based regimen for a median time of 23 months (IQR: 19, 40). Demographic, clinical, and laboratory data were collected using a structured questionnaire. The primary outcome was overweight/obesity, defined as BMI ≥ 25 kg/ m2. Poisson regression with robust standard errors was used to determine risk factors for being overweight and obesity. Results The median age was 44 years (interquartile range (IQR) 36, 51) at baseline, with the majority being female (59.4%, n = 148). Over a total follow-up of 871.5 person-years, 44 incident cases of overweight/obesity occurred, yielding a cumulative incidence of 17.7% (44/249) and an incidence rate of 5.05 per 100 person-years. Factors positively associated with the risk of being overweight/obesity included being married (adjusted incidence rate ratio [aIRR] 2.34; 95% CI 1.24, 4.40), lower baseline CD4 count (aIRR 4.13; 95% CI 1.41, 13.38) and higher waist circumference (WC) values (aIRR 1.07; 95% CI 1.03, 1.11). While older age was associated with a lower risk of overweight/obesity (aIRR 0.97; 95% CI 0.94, 0.99). Conclusion The burden of overweight/obesity was high, and it was significantly driven by demographic, anthropometric, and immunological factors among our study participants. The findings suggest the importance of implementing targeted screening and management strategies for overweight and obesity, particularly among married individuals with higher WC values. Studies investigating the underlying mechanisms of excessive weight gain among PLWH on an integrase inhibitor-based regimen in resource-limited settings are warranted.
Electrochemical grooving of tube inner walls with emphasis on feed strategy and multi-pass effects on material removal and groove geometry
Electrochemical (EC) grooving minimises tool wear and residual stress when machining hard-to-cut tube materials. This study examines how the number of passes and tool feed direction affect material removal rate (MRR) and removed area (RA) in Stellite 21 tubes. Two feed strategies were tested: Unidirectional Electrolyte Flow (UEF), where the tool moves entirely opposite to the electrolyte flow; and Hybrid Electrolyte Flow (HEF), where the tool first moves against and then with the flow direction. Results showed that the highest MRR values (26.67 mg/s for HEF, 24.8 mg/s for UEF) were observed with two passes, but dropped significantly at four and six passes due to extended machining time. RA increased along the tool path under UEF, reaching up to 327% at the tool exit. Flow simulations revealed that low velocity and conductivity at the tool entry caused under-machining, whereas turbulence at the exit enhanced material removal. These findings offer valuable guidance for optimising EC grooving processes in aerospace and biomedical applications.
Estimating the associations between women’s maltreatment in childhood and inflammatory biomarker levels prior to and during pregnancy
Background Maternal childhood maltreatment has been associated with higher risk of adverse neurodevelopment in offspring. Chronic systemic inflammation has been associated with childhood maltreatment and has been identified as a gestational risk factor for adverse neurodevelopment in offspring. Thus, inflammation may be a mechanism by which maternal exposure to maltreatment affects offspring neurodevelopment. To estimate associations between women’s childhood maltreatment and four inflammatory biomarkers, C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha receptor 2 (TNF-R2), and interferon gamma (IFN-γ), among women prior to or during pregnancy. Methods A sub-study of the Nurses’ Health Study 3, a prospective cohort, included 329 women, 204 who were contemplating pregnancy and 124 who were pregnant. Approximately 90% of participants were non-Hispanic White and over 60% had a Master’s degree or higher. Maltreatment was assessed using the validated 28-item Childhood Trauma Questionnaire. Associations were calculated using generalized estimating equations. Results Fifty participants (15.2%) were not exposed abuse (n = 28 contemplating pregnancy; n = 22 pregnant) and 81 participants (24.6%) were exposed to moderate or severe abuse (n = 55 contemplating pregnancy; n = 26 pregnant). Childhood maltreatment was not associated with the four inflammatory biomarkers either among participants contemplating pregnancy or among pregnant participants. Conclusions No statistically significant associations were identified between childhood maltreatment and selected inflammatory biomarkers in this sample of well-educated, primarily non-Hispanic White women. These findings should be interpreted with caution given the limited statistical power and measurement variability. Further investigation of these associations in more vulnerable populations might enhance our understanding of biological mechanisms linking maternal childhood abuse to adverse neurodevelopment in offspring.
The effect of mobile phone addiction on sleep procrastination in adolescents: The longitudinal mediating role of physical activity behavior
Purpose This study explores the impact of adolescent mobile phone addiction on sleep delay and analyzes the longitudinal mediating role of physical activity behavior. It provides a reference for cultivating good habits and healthy behaviors among adolescents. Methods The study was based on the theory of planned behavior and used the Mobile Phone Addiction Scale (MPA), the Bedtime Procrastination Scale (BPS), and the Physical Activity Rating Scale to conduct a longitudinal follow-up survey of 376 healthy high school students in S Province. The survey was conducted three times at intervals of three months, and the data were processed using SPSS and AMOS. Results (1) There is a positive correlation between mobile phone addiction and sleep delay (p < 0.01), and a negative correlation between mobile phone addiction, sleep delay, and physical activity (p < 0.01). (2) T1 mobile phone addiction significantly and negatively predicts T2 physical exercise behavior (β = −0.21, p < 0.001), T1 physical exercise behavior significantly and negatively predicts T2 sleep delay (β = −0.24, p < 0.001), and T1 mobile phone addiction significantly and positively predicts T2 sleep delay (β = 0.25, p < 0.001); T2 mobile phone addiction significantly and negatively predicts T 3 physical activity behavior (β = −0.27, p < 0.001), T2 physical activity behavior significantly negatively predicts T3 sleep delay (β = −0.37, p < 0.001), and T2 mobile phone addiction significantly positively predicts T3 sleep delay (β = 0.24, p < 0.001). (3) The direct effect of mobile phone addiction on physical exercise behavior is 0.08, with a confidence interval of [0.034, 0.153], indicating that the direct effect is established. The longitudinal mediating effect of sleep delay in mobile phone addiction and physical exercise behavior is 0.051, with a confidence interval of [0.02, 0.098], indicating that the longitudinal mediating effect is established. Conclusion (1) There is a two-way correlation between adolescent mobile phone addiction, physical activity behavior, and sleep delay. (2) Adolescent mobile phone addiction can directly affect sleep delay across time and can also indirectly affect sleep delay across time through physical activity behavior.