Scale Problems Research Articles

Eating Problems in Young Adults: The Role of Unbalanced Family Cohesion and Difficulties in Emotion Regulation

Strong emotional bonds with the family often result in many positive consequences for human functioning, including emotional regulation and eating patterns. The study aimed to determine the relationship between unbalanced family cohesion, emotion regulation, and eating problems in young adults. A correlation research design was used with a non-probability purposive sampling technique to recruit a sample consisting of 300 university students, 50% men and 50% women, with an age range from 18 to 25 years ( M = 21.60, SD = 1.95) from the four universities of Lahore. Demographic performa, Family Cohesion Scale (FCS), Difficulties in Emotion Regulation Scale (DERS), and Eating Problem Scale (EPS) were used to explore the study variables. Data was analyzed using SPSS 21. Pearson product-moment correlation indicated a significant positive relationship between unbalanced family cohesion, difficulties in emotion regulation, and eating problems. It also revealed that a positive relationship was found among unbalanced family cohesion, difficulties in emotion regulation, and eating problems in young adults. Mediation analysis shows that emotion regulation fully mediates the relationship between unbalanced family cohesion and eating problems in young adults. The findings will help students and family counselors identify risk factors of eating problems and its impact on their transition into marriage life. They will contribute to a deeper understanding of unbalanced family dynamics, difficulties in emotion regulation, and eating problems.

학교폭력 가해자에 대한 경찰다이버전 정책 기반 다중체계치료 사례연구

Objectives This purpose of the study was conducted to explore the effectiveness of Multisystemic Therapy for school violence perpetrators as part of the police diversion policy. Methods The subject of the study was a 14-year-old male student who was reported to have school violence, which is extortion and intimidation, and was released on the condition of participating MST. In accordance with the multisystemic therapy manual, five key goals were derived by analyzing risk factors for adolescents' problematic behaviors through evaluation of various systems surrounding him. An intervention strategies were established to achieve the core goals(i.e., stop smoking and alcohol, controlled use of SNS, making pro-social friends, stay away from delinquents, promoting positive parenting skills of parents). The essential parts of MST as like the therapy team, the visiting service, and weekly supervision were implemented. In order to measure pre-post changes, Children Behavior Checklist 6-18, the Parenting Stress Index, the Parenting Attitude Test, and the MMPI-A were conducted. The package of scales was implemented at 3 points as pre-time, post-time, and 8 months follow-up. Results First of all, the level of the externalization problem scale of CBCL 6-18, which was at the clinical range at the pre-test, recovered to the level of the normal range in the post-test, and these results were maintained even at the 8 months follow-up. Regarding MMPI-A, the score was lowered by nearly 2 standard deviations in the scale of Pd compared to pre-test. Then, parenting stress decreased and positive parenting attitudes of parents increased. In addition, all the five core goals were achieved. Conclusions The results of this study showed that multisystemic therapy for school violence perpetrator can help decrease adolescents’ problematic behaviors including school violence.

Derivative dynamic time warping algorithm with introduced correction for varying load fault diagnosis of nuclear power system steam turbine units

The Derivative Dynamic Time Warping (DDTW) algorithm is improved to address the multi-parameters time series classification problem faced by nuclear power system steam turbine units varying load fault diagnosis. Firstly, the entire load changing process is treated as a single sample rather than multiple time-step-samples. This ensures the complete information on the load changing processes, while avoiding interference from normal data fluctuations during faults. Secondly, Time Series Position Coefficient and Time Series Length Coefficient are proposed to correct the DDTW algorithm from two perspectives: the sequences lengths and the data positions in the sequences. This solves the singularities and timeline scaling problems, thereby preventing interference introduced by data sequences' lengths differences and ''similar data appearing at different times'' problem. The nuclear power system steam turbine unit simulation model was built to obtain load changing processes data under normal and faults statuses. In the varying load fault diagnosis test based on these data, the improved DDTW algorithm achieved an accuracy of 1.38%–12.06% higher than other methods, reaching 87.50%. Finally, The Deep Convolutional Generative Adversarial Networks (DCGAN) model was used to generate data to supplement the limited samples of complete load changing processes, and the accuracy of the novel method increased to 95.51% with the increase of data used to support the comparison.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Obsessive-compulsive symptoms relating to psychosocial functioning for people with schizophrenia, schizoaffective disorder, or bipolar disorder.

To assess the psychosocial functioning concerning obsessive-compulsive symptoms (OCS) and/or obsessive-compulsive disorder (OCD) comorbidity in people with schizophrenia, schizoaffective disorder, or bipolar disorder diagnosed in a large case register database in Southeast London. Data were retrieved from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) register using Clinical Record Interactive Search (CRIS) system, a platform allowing research on full but de-identified electronic health records for secondary and tertiary mental healthcare services. Information of schizophrenia, schizoaffective disorder, bipolar disorder diagnosis and OCS/OCD status was ascertained from structural or free-text fields through natural language processing (NLP) algorithms based on artificial intelligence techniques during the observation window of January 2007 to December 2016. Associations between comorbid OCS/OCD and recorded Health of the Nation Outcome Scales (HoNOS) for problems with activities of daily living (ADLs), living conditions, occupational and recreational activities, and relationships were estimated by logistic regression with socio-demographic confounders controlled. Of 15,412 subjects diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, 2,358 (15.3%) experienced OCS without OCD, and 2,586 (16.8%) had OCD recorded. The presence of OCS/OCD was associated with more problems with relationships (adj.OR = 1.34, 95% CI: 1.25-1.44), ADLs (adj.OR = 1.31, 95%CI: 1.22-1.41), and living conditions (adj.OR = 1.31, 95% CI: 1.22-1.41). Sensitivity analysis revealed similar outcomes. Comorbid OCS/OCD was associated with poorer psychosocial functioning in people with schizophrenia, schizoaffective disorder, or bipolar disorder. This finding highlights the importance of identification and treatment of comorbid OCS among this vulnerable patient group.

Association of Cannabis Use Reduction With Improved Functional Outcomes: An Exploratory Aggregated Analysis From Seven Cannabis Use Disorder Treatment Trials to Extract Data-Driven Cannabis Reduction Metrics.

This exploratory analysis sought to determine whether decreases in cannabis use are associated with improvements in cannabis-related problems and functional outcomes, and if so, what percentage decrease is associated with improvement. Data were aggregated from seven cannabis use disorder treatment trials conducted in the United States (N=920; ages 13 years and older; mean age, 25 years; 30% female, 7% Black, 11% Hispanic/Latinx). Outcome measures included the patient-reported Marijuana Problems Scale (MPS), Health-Related Quality of Life scale (HRQOL), and Pittsburgh Sleep Quality Index and the clinician-rated Clinical Global Impressions (CGI) severity and improvement scales (CGI-S and CGI-I). Generalized estimating equations tested the association between changes in 4-week cannabis use and improvements in functional outcomes. Classification and regression tree (CART) models were developed to determine what reductions in cannabis use could be used as classifiers of improvement. Decreases in the amount and frequency of cannabis use were significantly associated with improvements in MPS severity and total scores as well as improvements on the CGI-I and in sleep quality, but not improvements on the HRQOL. CART models performed best for CGI-I scores (72%-75% correct classification), while other outcome measures did not perform as well (40%-62% correct classification). CART models showed improvements on the CGI at 74% reduction in use amounts and 47% reduction in use days. Reductions in cannabis use (∼50% reduction in use days and ∼75% reduction in use amounts) were associated with clinician-assessed improvement, which suggests that cannabis use reduction may yield benefit among individuals with cannabis use disorder. These exploratory results extract a data-driven metric to inform future studies, clinicians, patients, and policy recommendations.

Evaluating User Acceptance of Mobile Payment Systems in Uganda

Purpose: This research evaluates user acceptance of mobile payment systems. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: Mobile payment providers should prioritize user-centric design principles, focusing on simplicity, usability, and intuitive interfaces. Policymakers should develop regulatory frameworks that establish clear standards for mobile payment systems, focusing on data protection, privacy, and security. Keywords: User Acceptance, Mobile Payment Systems

Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem

This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism. A mathematical model is first established to describe this problem, which considers a series of complex constraints and takes the profit ratio of completed tasks as the optimization objective. Then, a neural network framework with an encoder–decoder structure is adopted to generate high-quality solutions, and a local attention mechanism is designed to improve the generation of solutions. In addition, an adaptive learning rate strategy is proposed to guide the actor–critic training algorithm to dynamically adjust the learning rate in the training process to enhance the training effectiveness of the proposed network. Finally, extensive experiments verify that the proposed algorithm outperforms the comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.

Mediterranean-DASH Diet Intervention for Neurodegenerative Delay Diet and Psychological Problems in Maintenance Hemodialysis Patients by the Structural Equation Modeling Analysis of Malnutrition and Inflammation Markers Approach

ObjectiveAlthough it is known that diet quality affects psychological problems in hemodialysis patients, there is need to explain the role of modifiable risk factors in this relationship. Therefore, the aim of this study was a) to investigate the relationship between diet quality and modifiable risk factors to depression and anxiety in end-stage renal disease (ESRD) patients receiving maintenance hemodialysis; b) to explore the mediating roles of modifiable factors in the relationship with diet quality to depression and anxiety. MethodsThis cross-sectional study included 216 patients with ESRD receiving maintenance hemodialysis treatment. We assessed participants' dietary records for Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) quality, Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition and Hospital Anxiety and Depression (HAD)-scale for psychological problems. Furthermore, biochemical findings and anthropometric measurements were performed to evaluate nutritional markers, metabolic risk factors and inflammation. We applied hierarchical regression analysis to estimate modifiable risk factors for depression and anxiety and Structural-Equation-Modeling analysis to determine the mediating role of modifiable risk factors between diet quality and psychological problems. ResultsDepression symptoms were observed in 59.2% (n=128) of the participants, whilst the rate was 35.1% (n=76) for anxiety. Depression and anxiety were found to be negatively correlated with MIND levels after covariate adjusting model, and the rates of explanation were found to be 16.2% and 12.2%, respectively. C-reactive protein (CRP), albumin levels and the presence of malnutrition were shown to be significant predictors of depression (ΔF=14.761 and ΔR2:0.071 for covariate-adjusted model). Albumin levels, CRP, hemodialysis duration, and malnutrition were found to be independent predictors of anxiety (ΔF=16.174 and ΔR2:0.077 for covariate-adjusted model). It was found that CRP and malnutrition partially mediated the association of MIND score with depression, and CRP mediated the association with anxiety. ConclusionIt was concluded that adherence to the MIND diet is associated with a better nutritional profile and reduced inflammation, which in turn may be linked to fewer psychological problems.These further studies are needed to validate and expand upon our findings.

Thin-film evaporation in microchannels: A combined analytical and computational fluid dynamics approach to assessing meniscus curvature impact

Computational fluid dynamics (CFD) is employed to investigate the effects of variation in bulk meniscus curvature on evaporative heat and mass transfer in microchannels. Evaporation in microchannels presents a classic multiscale problem, encompassing both microscopic and macroscopic length scales in the thin-film and bulk meniscus regions, respectively. Therefore, the liquid-vapor interface or meniscus shape is first determined through thin-film evaporation analysis using numerical approaches found in the literature. However, a new approach for establishing boundary conditions is also proposed, allowing for the representation of varying curvatures within the bulk meniscus region. Then, the obtained meniscus thickness profiles are exported to a two-dimensional CFD framework. The primary advantage of this developed CFD framework lies in its capability to simultaneously analyze evaporative heat and mass transfer from both microscopic and macroscopic regions, a feature that enhances research endeavors concerning micro and miniature heat pipes. The developed model is applied to various rectangular microchannel sizes, ranging from 10 to 100 μm in width, with wall superheats varying from 0.1 to 3.0 K. The aim is to quantify the effects of variations in bulk meniscus curvature on the evaporative characteristics of the extended meniscus (comprising both thin-film and bulk meniscus regions), using the concept of thermal resistance. Consequently, based on the CFD simulation results, multiple regression analysis is employed to formulate the total thermal resistance in terms of independent parameters, namely the microchannel width, wall superheat, and the radius of curvature (Rc). It is observed that the radius of curvature has a marginal impact on the total thermal resistance, allowing its influence to be expressed by a power-law function of (Rc/Rm)0.102, where Rm represents the minimum radius of curvature.

A Robust Mean-Field Actor-Critic Reinforcement Learning Against Adversarial Perturbations on Agent States.

Multiagent deep reinforcement learning (DRL) makes optimal decisions dependent on system states observed by agents, but any uncertainty on the observations may mislead agents to take wrong actions. The mean-field actor-critic (MFAC) reinforcement learning is well-known in the multiagent field since it can effectively handle a scalability problem. However, it is sensitive to state perturbations that can significantly degrade the team rewards. This work proposes a Robust MFAC (RoMFAC) reinforcement learning that has two innovations: 1) a new objective function of training actors, composed of a policy gradient function that is related to the expected cumulative discount reward on sampled clean states and an action loss function that represents the difference between actions taken on clean and adversarial states and 2) a repetitive regularization of the action loss, ensuring the trained actors to obtain excellent performance. Furthermore, this work proposes a game model named a state-adversarial stochastic game (SASG). Despite the Nash equilibrium of SASG may not exist, adversarial perturbations to states in the RoMFAC are proven to be defensible based on SASG. Experimental results show that RoMFAC is robust against adversarial perturbations while maintaining its competitive performance in environments without perturbations.

Attention Deficit Hyperactivity Disorder among Children with Speech Difficulties

Abstract: Speech sound disorders and attention deficit / hyperactivity disorder are developmental disorders that can significantly impact academic success. The comorbidity of these disorders may substantially add to the difficulties experienced by the child and require a combined treatment approach. Objectives: (1) to assess sociodemographic characteristics of children’s with speech difficulties (2) to assess attention deficit hyperactivity disorder and speech difficulties (3) to identify the relationship between attention deficit hyperactivity disorder and speech difficulties. Methodology: A descriptive cross-sectional study design was carried out in Baghdad Teaching Hospital. A non- probability (purposive) study sample of (300) child, who attended Hearing and Speech Center for the period from 12/April/2023 to 25/January/2024. The Questionnaire which is composed of: Socio-demographic characteristics, scales of behavioral problem, attention deficit hyperactivity disorder. Results: The study results reveal that the children experiencing severe attention deficit hyperactivity disorder and that significant relationship have been reported among speech sound disorder with attention deficit hyperactivity disorder at p-values= 0.046, also indicates that there was significant relationship among childhood apraxia and attention deficit hyperactivity disorder at p-values= 0.007. Conclusion: The children experience severe of attention deficit hyperactivity disorder, the significant relationship has been reported among speech sound disorder with attention deficit hyperactivity disorder, also indicates that there was significant relationship among childhood apraxia and attention deficit hyperactivity disorder.Recommendations: An intervention program to be planned accordingly and areas of deficit will be targeted in therapy. Liaison with parents and school teachers is indicated in order to achieve the child’s maximum potential.

Modeling and performance estimation for L-shaped OWC wave energy converters with a theoretical correction for spring-like air compressibility

The investigation of Oscillating Water Columns (OWC) has gained significant attention in recent years thanks to their resilient structural design, allowing them to withstand harsh environmental conditions. This study focuses on the L-shaped OWC (L-OWC) due to its excellent energy capture efficiency. It should be noted that air compressibility plays a significant role in the plenum chamber of the OWC, especially at full scale. The present paper proposes a scaling-rematched approach, facilitating the evaluation of hydrodynamic coefficients and the performance estimation with a theoretical correction for the unmatched scaling problem of spring-like air compressibility. The methodology is applied successfully to a model-scale L-OWC design, employing three-dimensional, incompressible-flow simulations combined with an impeller model. The present study reveals the hydrodynamic characteristics (advantages) of the L-OWC: small fluid damping coefficient and large added mass, and hence the possibility that the spring-like air compressibility can be used to raise the efficiency of power capturing. Furthermore, there exists an interval where the air compressibility has a positive effect on the performance, not only having a significantly longer span than that of the conventional OWC but also much more directly matching the period range of high energy potential found in the wave climate of Northeastern Taiwan waters.

Scaling-Up for the Counter-Rotating Twin Screw Extrusion of Polymers.

A novel and original computer scaling-up system for the counter-rotating twin screw extrusion of polymers was developed. In the system, each scaling parameter (scaling criterion) may be considered as an objective function to be minimized for the single parameters or the functional relationships along the length of the screw. Scaling was based on the process simulation, which was performed using the comprehensive (or global) counter-rotating twin screw extrusion program called TSEM (Twin Screw Extrusion Model). The extrusion process was scaled by applying GASESTWIN (Genetic Algorithms Screw Extrusion Scaling) software developed for this purpose using genetic algorithms. Scaling up the extrusion process was carried out to enhance extrusion process throughput according to the scaling criteria specified by the single quantities of polymer melt temperature at the die exit and relative melting length, and by distributions along the screw length of the extrusion parameters of the polymer melt temperature and polymer plasticating. The global objective function had the lowest value for the selected extrusion parameters, which means the minimal differences between the values of the scaled-up processes and extrusion throughput was significantly increased. The solution to the problem of scaling the counter-rotating process presented in this paper complements the existing solutions for optimizing and scaling basic variants of the extrusion process, i.e., flood-fed and starve (metered)-fed single-screw extrusion, as well as co-rotating and counter-rotating twin-screw extrusion.

Lignin Nanoparticles: Transforming Environmental Remediation.

In the face of escalating environmental challenges driven by human activities, the quest for innovative solutions to counter pollution, contamination, and ecological degradation has gained paramount importance. Traditional approaches to environmental remediation often fall short in addressing the complexity and scale of modern-day environmental problems. As industries transition towards sustainable paradigms, the exploration of novel materials and technologies becomes crucial. Lignin nanoparticles have emerged as a promising avenue of exploration in this context. Once considered a mere byproduct, lignin's unique properties and versatile functional groups have propelled it to the forefront of environmental remediation research. This review paper delves into the resurgence of lignin from an environmental perspective, examining its pivotal role in carbon cycling and its potential to address various environmental challenges. The paper extensively discusses the synthesis, properties, and applications of lignin nanoparticles in diverse fields such as water purification and soil remediation. Moreover, it highlights the challenges associated with nanoparticle deployment, ranging from Eco toxicological assessments to scalability issues. Multidisciplinary collaboration and integration of research findings with real-world applications are emphasized as critical factors for unlocking the transformative potential of lignin nanoparticles. Ultimately, this review underscores lignin nanoparticles as beacons of hope in the pursuit of cleaner, healthier, and more harmonious coexistence between humanity and nature through innovative environmental remediation strategies.

Model simplification to simulate groundwater recharge from a perched gravel-bed river

Gravel-bed rivers are an important source of groundwater recharge in some regions of the world. Their interactions with groundwater are complex and highly variable in space and time, with considerable water storage in the riverbed sediments. In losing river sections, where most of the groundwater recharge occurs, the river can be separated from the regional groundwater system by an unsaturated zone (i.e., perched). The complexity of groundwater–surface water interactions in these environments calls for the use of 3D fully integrated hydrological models to represent them, but their computational intensity limits their practicality for parameter inference, uncertainty quantification and regional scale problems. On the other hand, the simple groundwater–surface water exchange functions currently implemented in regional scale groundwater models are not suited to represent complex gravel-bed river systems such as braided rivers. There is therefore a need for developing groundwater–surface water exchange functions tailored to gravel-bed rivers that can be used in regional scale models.To address this issue, we developed a model simplification framework that combines a 3D integrated surface and subsurface hydrological model, a 2D cross-sectional river-aquifer model and a 1D conductance-based analytical model. We aim at broadly simplifying the 3D model while ensuring the appropriate simulation of groundwater recharge. We demonstrate our modelling approach on the Selwyn River (New Zealand) using piezometric data and groundwater recharge estimates derived from field observations and satellite imagery.Our results indicates that groundwater recharge from this river can be simulated using a simple 1D analytical model, which can easily be implemented in regional groundwater models (e.g., MODFLOW models). However, to represent properly the time variability of groundwater recharge, it is essential to use the groundwater level in the shallow aquifer associated with the river as input to the regional groundwater model. Our approach is generally transferable to other gravel-bed rivers but requires some observations of river losses for proper calibration.

Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a significant challenge. Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitations. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are identified using Koopman-reweighted time-lagged independent component analysis (KTICA), which are subsequently leveraged by on-the-fly probability enhanced sampling (OPES) to efficiently explore the FES. The effectiveness of our pipeline is demonstrated and further compared with the common Markov State Model (MSM) approach on two model systems with increasing complexity: alanine dipeptide (Ala2) and deca-alanine (Ala10), underscoring its applicability across diverse biomolecular simulations.

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise.

Bi-stochastic normalization provides an alternative normalization of graph Laplacians in graph-based data analysis and can be computed efficiently by Sinkhorn-Knopp (SK) iterations. This paper proves the convergence of bi-stochastically normalized graph Laplacian to manifold (weighted-)Laplacian with rates, when [Formula: see text] data points are i.i.d. sampled from a general [Formula: see text]-dimensional manifold embedded in a possibly high-dimensional space. Under certain joint limit of [Formula: see text] and kernel bandwidth [Formula: see text], the point-wise convergence rate of the graph Laplacian operator (under 2-norm) is proved to be [Formula: see text] at finite large [Formula: see text] up to log factors, achieved at the scaling of [Formula: see text]. When the manifold data are corrupted by outlier noise, we theoretically prove the graph Laplacian point-wise consistency which matches the rate for clean manifold data plus an additional term proportional to the boundedness of the inner-products of the noise vectors among themselves and with data vectors. Motivated by our analysis, which suggests that not exact bi-stochastic normalization but an approximate one will achieve the same consistency rate, we propose an approximate and constrained matrix scaling problem that can be solved by SK iterations with early termination. Numerical experiments support our theoretical results and show the robustness of bi-stochastically normalized graph Laplacian to high-dimensional outlier noise.

The developmental and dynamic relationship between psychopathology and psychotic-like experiences trajectories in children and adolescents

BackgroundThe previous cross-sectional and prospective studies have reported that psychopathology was associated with the occurrence of psychotic-like experiences (PLEs). However, few of these studies have examined this longitudinal association considering the different developmental trajectories of PLEs, as well as the growth or changes of psychopathology over time. MethodsFour waves PLEs and psychopathology assessments from Adolescent Brain Cognitive Development (ABCD) study were used. The latent class growth modeling (LCGM) and latent growth curve modeling (LGCM) was used to assess latent PLEs class (group) and time-varying psychopathology, respectively. Lastly, the multinomial logistic regression model was used to examined the dynamic and developmental relationship between intercept/slope in psychopathology and different PLEs trajectories. ResultsThree PLEs trajectory classes were confirmed: low decreasing PLEs (84.7 %), persistent PLEs (7.01 %) and high decreasing PLEs trajectories (8.29 %). We also found that the intercept of anxious/depressed problems and total problems scales and the slope of social problems were associated with the persistent PLEs trajectory compared with the low decreasing PLEs trajectories, indicating both the early onset and the growth of psychopathology over time are needed to be clinical attention. LimitationsThe CBCL as the sole outcome measure for psychopathology and a widely acknowledged definition for PLEs is lacking. We lacked the mechanisms underlying the current results. ConclusionThese longitudinal and dynamic results suggest that future intervention studies aimed at preventing the transition from persistent PLEs to psychotic disorders can focus on both the early onset and the growth of psychopathology over time.

Scalable intimacy: Rethinking intimate sociality and its problem of scale in digital China

With the development of the Internet in China, people have increasingly turned to digital communication and networking tools for personal connections and social relations. From messaging and dating apps to microblogging and live-streaming platforms, mediated forms of sociality constitute a significant part of people’s intimate lives. Scholarly works on modern and contemporary China have shown how practices and politics of intimacy are formulated and contested against the backdrop of the tensions between the residual culture of Confucianism, the socialist ideological regime, and China’s participation in global capitalism. On the contrary, research on global network societies has foregrounded the role of information and media technologies in mediating intimacy with conceptual frameworks such as programmed sociality and networked intimacy. Bridging these two areas of research, this essay attempts to understand how intimate sociality is configured within China’s burgeoning digital economy and culture. It first provides a review of previous works on forms of intimacy and sociality mediated by network technologies ranging from mobile phones to online dating. Using the popularity of live streaming in current-day China as a case study, this essay then introduces the problem of “scale” to consider how intimacy is mediated at scale with Internet-based media technologies and discuss how scalability shapes intimate lives in Chinese society as a shared rationale behind modernization, network technologies, and new forms of economies. This essay proposes the concept of scalable intimacy to move beyond the platform-centered approach and develop a more contextualized framework for understanding intimate sociality in digital China.