Incremental Analysis Research Articles

Seismic capacity evaluation of reinforced concrete moment-resisting frames with steel damper columns using incremental critical pseudo-multi impulse analysis

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) moment-resisting frames (MRFs) and those often used for multistory housing. In a previous study, the authors proposed an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (VΔE1*)–peak equivalent displacement (D1*max) relationship is essential for improved prediction. In this article, the seismic capacity curve (VΔE1*–D1*max relationship) of RC MRFs with and without SDCs is evaluated using incremental critical pseudo-multi impulse analysis (ICPMIA). In the ICPMIA, which is based on a study by Takewaki and coauthors, the structure is subjected to various intensities of critical pseudo-multi impulsive lateral force. An ICPMIA of planer four 8- and 16-story RC MRFs with and without SDCs is performed to obtain their structural behaviors under various intensities of pulsive input. Then, the seismic capacity curve obtained from the ICMPIA results are compared with the predicted results based on the simplified equations. The main findings of this article are as follows. (i) The seismic capacity curve of RC MRFs without SDCs strongly depends on the number of impulsive lateral forces (Np). As Np increases, the seismic capacity decreases. The predicted seismic capacity curve severely underestimates that obtained from the ICMPIA in the case of large Np. This trend is notable when the level of pinching behavior in the RC members is severe. (ii) In the case of an RC MRF with SDCs, however, the influence of Np on its seismic capacity curve is small. The predicted seismic capacity curve agrees very well with that obtained from the ICMPIA. The influence of the level of pinching behavior in RC members on the seismic capacity curve is small.

Reliability assessment for pipelines corroded by longitudinally aligned defects

Internal corrosion poses a significant threat to offshore pipeline services. Toward offshore pipeline integrity management, this paper aims to use Finite Element Method (FEM) to assess the reliability of corroded pipelines. The FEM analysis was conducted using ABAQUS software, facilitated through Python scripts, to optimize the modeling process on a large scale. Additionally, the input database was generated through a random sampling method using the Latin Hypercube Sampling (LHS) method. The reliability of 32” oil and gas offshore internally corroded pipelines has been evaluated by fragility curve assessment along with Incremental Pressure Analysis (IPA) which is a new method inspired by Incremental Dynamic Analysis (IDA). Multiple corrosion defects pose a higher risk to offshore pipelines during service, with potentially more severe consequences than a single defect alone. Therefore, the interaction of corrosion defects has been considered in this study. Following the reliability assessment the most appropriate Probability Density Function (PDF) for the Maximum Von Mises Stress (MVMS) and failure probability at various Internal Pressure (IP) and longitudinal spacing (Sl) levels has been evaluated.

Non-linear seismic behavior of historic Adana Great Clock Tower

In this study, the non-linear seismic behavior of the historic Adana Great Clock Tower was investigated. The dynamic characteristics (natural frequencies and mode shapes) of the Clock Tower were determined by operational vibration tests. The finite element model of the Clock Tower was manually updated in the light of the experimentally obtained dynamic characteristics. For the updated finite element model, pushover analyses and non-linear time history analyses were performed. Incremental pushover analyses were performed in the x and y directions proportional to the first lateral displacement modes of the Clock Tower in the x and y directions. The time history analyses took into account the Adana-Ceyhan (1998) earthquake acceleration records. Each of the two horizontal components of the earthquake acceleration records was separately scaled according to the design spectrum defined in TBEC-2018 for earthquake levels with a 10 % and 50 % probability of exceedance in 50 years. These scaled earthquake acceleration records were applied to the structure. The two scaled horizontal components for the same earthquake level were rotated 90° and reapplied to the structure. As the response quantities, displacement, base shear force and plastic strains were evaluated.

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Cost-Effectiveness of Dolutegravir Compared With Efavirenz for Prevention of Perinatal Transmission in Women Presenting With HIV in Late Pregnancy in Uganda

ObjectivesDolutegravir (DTG) has proved to be more efficacious, tolerable, and safer than efavirenz (EFV) among mothers living with HIV and their infants in Uganda. This study assessed the cost-effectiveness of the DTG-based antiretroviral therapy (ART) compared with the standard of care for preventing perinatal transmissions among pregnant women initiating ART in late pregnancy in Uganda. MethodsWe used data from a randomized open-label trial (DolPHIN-2) and a 2-part cost-effectiveness model composed of a short-term decision tree to estimate the perinatal transmission rate and costs and an individual-based 3-state Markov model (HIV, advanced HIV, dead) to estimate the long-term costs and health outcomes from the Ugandan payer perspective using a lifetime horizon and a 1-year Markov cycle. The main outcomes were the mean annual costs in US dollars ($), disability-adjusted life-years (DALYs), and incremental cost-effectiveness ratio. Both the deterministic and probabilistic sensitivity analyses were conducted to assess the effect of parameter uncertainties on the ultimate results and the model’s robustness. ResultsCompared with the EFV-based ART, the DTG-based ART was associated with fewer mean annual costs ($43.58 vs $68.44) and DALYs (0.33 vs 0.56), leading to cost savings of $110 per DALY averted. In the incremental analysis, the DTG-based ART dominated the EFV-based ART; that is, it was less costly and more effective. These results were robust to deterministic and probabilistic sensitivity analyses. ConclusionThe DTG-based ART is a highly cost-effective strategy compared with the EFV-based ART among women initiating treatment in the third trimester of pregnancy in a low-income setting.

Design for early-age structural performance of 3D printed concrete structures: A parametric numerical modeling approach

This paper presents a design framework based on structural performance of 3D concrete printing (3DCP) that considers three strength criteria for early-age failure modes: plastic collapse, elastic buckling, and flexural collapse. A simulator of structural behavior during printing was developed to take any shell or linear model, slice it into layers, perform incremental structural analysis layer by layer, and assign the respective material properties by adjusting the age of each layer when a new one is added. Then, finite element analysis for each printing stage indicates whether collapse might occur based on rheological and mechanical material properties over time. Printing experiments validated the criteria based on collapse layer estimation for a solid cylinder (plastic collapse), a thin-wall structure (elastic buckling), and an overhang structure (flexural collapse). Plastic collapse was most accurately predicted by a Drucker-Prager yield criterion for an unrestrained base and by a Modified Lade and Mohr-Coulomb criterion for a partially restrained base, while elastic buckling was predicted by linear elastic analysis. Additionally, this paper introduces flexural collapse as a distinct failure mode in early age 3DCP, proposing design criteria for overhang structures based on printing experiments. Experimental lessons learned were used to establish design constraints to aid the design of 3DCP structures, which were tested in the design of a hollow column and a cantilever structure. This study advances computational design for 3DPC structures with a new design methodology that combines constrained design and parametric numerical modeling.

Use of Fin Rays and Fin Spines in Nonlethal Age Estimation of Florida Bass

Abstract Nonlethal age-estimation methods for Florida Bass Micropterus salmoides using fin rays and spines would enhance trophy-bass research, management, and conservation. Nonlethal removal of fin structures would also expand opportunities for collecting age-related information, specifically during small-population projects as well as tournaments and citizen-science programs, for which sacrificing fish is not an option. We assessed the bias of age estimates derived from Florida Bass anal-, pelvic-, dorsal-, and pectoral-fin rays and anal-, pelvic-, and dorsal-fin spines by comparing them to age estimates derived from otoliths (i.e., sagittae), which are the standard for ageing Florida Bass. We evaluated the precision of age estimates by calculating the absolute difference between replicate reads of each ageing structure for each reader. Linear-mixed effects model selection indicated that precision was most affected by age, structure, and the age*structure interaction, in which the effect of age varied by structure. Age estimates from otoliths were the most precise whereas pelvic spines were the least precise among all structures examined. Bias was similar across all fin structures, but the effect of age on bias varied by reader. Marginal increment analysis indicated that annulus formation was completed from November to March in Florida Bass dorsal spines, 5 months earlier than in Florida Bass otoliths. Our results suggest that nonlethally removed fin structures, especially dorsal spines, have potential for use in estimating age, growth rates, mortality, and age structure of bass populations, which could be particularly beneficial where bass can reach unusual trophy sizes.

Multimodal machine learning approach for emotion recognition using physiological signals

This study explores a novel approach to emotion recognition through machine learning, addressing the limitations of previous methods. While deep learning has shown promise in this field, it often requires significant computational resources and time. In response, we propose a multimodal approach utilizing Feature-level Fusion (FLF) and Decision-level Fusion (DLF) to enhance performance while reducing complexity. The study focuses on integrating electroencephalogram (EEG), electromyography (EMG), and electrooculogram (EOG) signals. Signal preprocessing involves extracting statistical features, power spectral density (PSD), and incremental entropy analysis. Recursive Feature Elimination (RFE) is employed as a feature selector, facilitating the fusion of different signal features. Three fusion strategies are explored: EEG with EOG, EEG with EMG, and a combination of EEG with EOG and EMG. For classification, the Bagging Classifier and K-Nearest Neighbors Algorithm are chosen. Results demonstrate promising accuracy rates, with 95.7% for arousal, 96.41% for valence in subject-dependent classification, 93.68% for arousal, and 93.23% for valence in subject-independent classification. This approach presents a viable alternative to deep learning methods, offering improved performance with reduced computational burden.

Ecology of large ungulates in the northeastern Iberian Peninsula during the Upper Palaeolithic through stable isotopes and tooth wear analysis

The northeastern Iberian Peninsula acted as a refuge zone during the Late Pleistocene where the persistence of terrestrial ecosystems could provide hunter-gatherers with large prey, mainly horse (Equus ferus) and red deer (Cervus elaphus). Isotopic (δ13C, δ18O, δ15N) and dental wear (mesowear and microwear) analyses have been applied on the remains of both species from the archaeological sites of Arbreda and Bora Gran at Serinyà (Girona), where evidence of human occupation from the Mousterian to the Magdalenian has been attested. The incremental enamel analysis on horse teeth revealed seasonal variation in carbonate δ18O but no detectable change in carbonate δ13C values, reflecting a rather stable diet and habitat over the year. Nevertheless, higher inter-individual than intra-individual contrast in carbonate δ13C indicate different environmental conditions from one individual to another for each stratigraphic unit. In red deer teeth, seasonal signals in enamel carbonate δ13C and δ18O demonstrated mirrored trends. Further, red deer show higher δ13C values than those of horses, both in enamel carbonate and bone collagen, as well as higher variability in mesowear and microwear scores, reflecting a mixed-feeding habit. Despite a strong grazing signal in mesowear and microwear, the lower δ13C values for horses suggest a higher dependence on relatively more humid habitats than red deer, which likely foraged in dryer environments with xeric plants during winter. These differences in ecological partitioning are particularly well illustrated in distinct collagen δ13C and δ15N values during the harsh climatic conditions of the Final Gravettian coeval to the GS-3 or Last Glacial Maximum. The capacity of red deer to adapt to fluctuating environmental conditions contrasts with the niche persistence of horse allowed by the availability of mosaic habitat.

Mechanical and Thermal Analysis of a Rocker Arm Shaft Support Based on Finite Element Method

Abstract The Finite Element Method (FEM) is a powerful numerical technique for solving complex physical problems involving differential equations and partial derivatives. This study presents the mechanical and thermal analysis of a rocker arm shaft support using FEM. Employing the ADINA (Automatic Dynamic Incremental Nonlinear Analysis) software, the behavior of the shaft support has been modelled, leveraging ADINA’s extensive material libraries, diverse loading conditions, and customizable behavioral properties. The analysis focuses on determining deformations, stresses, and displacements, particularly in the median plane section at the point of maximum stress. Both treated and untreated material conditions are evaluated to assess stress transmission in the transition zone between surface-hardened and base materials. A 2D analysis is conducted, given the geometry of the rocker arm spindle support, to ensure fidelity to real structural behavior while optimizing computational efficiency. The validated model provides insights into design constraints for new materials, supporting industrial applications.

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

#2628 A haemodialysis patient-centred model of care with a multidisciplinary programme: what is its clinical and health efficiency impact?

Abstract Background and Aims Chronic kidney disease (CKD) constitutes a public health concern due to its notable morbidity & mortality and associated costs. The Patient Care Coordination program (PCC) provides comprehensive and personalized care to haemodialysis (HD) patients. This study evaluated the cost-effectiveness of PCC compared to standard of care (SoC). Method The study included patients in 9 HD clinics (5 PCC, 4 SoC) in Catalonia, Spain, in 2020-2021. A comparative effectiveness evaluation was conducted to compare change in Individual Patient Performance Score (IPPS, score 0-100, evaluates parameters such as vascular access, adequacy of HD, anaemia, mineral bone disease, arterial hypertension, nutrition/hydration status) and number of hospitalisations of PCC patients vs SoC during the first year in the program. Instrumental variables (IV) regression analysis was performed to address potential confounding; number of interactions with program specialists was a valid IV to estimate degree of PCC use. An incremental cost-effectiveness analysis (iCEA) through microsimulation was performed using the effectiveness data from the previous step, population data from the renal patient registry of Catalonia, and the cost of hospitalisations obtained from the Public Health System. Results 127 patients in PCC and 363 patients in SoC were included. The degree of PCC use had a positive impact on IPPS variation during the first year of the program (p = 0.012). In the iCEA the PCC program resulted in a mean gain of 3.4 additional IPPS points, a reduction of 0.2 hospitalisations per patient, and reduced mean costs of €1,265 per patient in the first year compared to SoC, resulting to be a dominant alternative (Fig. 1). Conclusion The holistic and interdisciplinary approach to the patient has further highlighted the relevance of programme use and not only belonging to the programme in achieving clinical goals. The PCC program represents thus a dominant alternative, with greater effectiveness and total cost savings that could sum up to €5.5 million per year in the region due to reduced hospitalizations. This approach may be an alternative to address the challenges of CKD and optimise the allocation of healthcare resources.

Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)

Abstract. In a cycling system where data assimilation (DA) and model simulation are executed consecutively, the model forecasts initialized from the analysis (or data assimilation) can be systematically affected by dynamic imbalances generated during the analysis process. The high-frequency noise arising from the imbalances in the initial conditions can impose constraints on computational stability and efficiency during subsequent model simulations and can potentially become the low-frequency waves of physical significance. To mitigate these initial imbalances, the incremental analysis update (IAU) has long been utilized in the cycling context. This study introduces our recent implementation of the IAU in the Model for Prediction Across Scales – Atmospheric (MPAS-A) coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system called MPAS-Workflow. During the integration of the compressible nonhydrostatic equations in MPAS-A, analysis increments are distributed over a predefined time window (e.g., 6 h) as fractional forcing at each time step. In a real case study with the assimilation of all conventional and satellite radiance observations every 6 h for 1 month, starting from mid-April 2018, model forecasts with the IAU show that the initial noise illustrated by surface pressure tendency becomes well constrained throughout the forecast lead times, enhancing the system reliability. The month-long cycling with the assimilation of real observations demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system. Along with the comparison between the forecasts with and without the IAU, several aspects regarding the implementation in MPAS–JEDI are discussed. Corresponding updates have been incorporated into the MPAS-A model (originally based on version 7.1), which is now publicly available in MPAS–JEDI and MPAS-Workflow version 2.0.0.

Diagnostics for Imbalance on the Convective Scale

Abstract The analyses produced by a data assimilation system may be unbalanced, that is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flowon this scale is strongly divergent and non-hydrostatic. In this studywe compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1. Latent Heat Nudging (LHN), 2. Local Ensemble Transform Kalman Filter (LETKF), and 3. LETKF in combination with incremental analysis updates (IAU). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and theWTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure, but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance andWTG departures are similar, and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.

Computational study of the influence of microstructure on fracture behavior near crack-tip field in nacre-like materials

The small-scale failure process around a plane strain mode I crack in nacre-like materials is analyzed. The displacement increments of boundary nodes are specified by the analytical solutions based on the orthotropic linear elasticity to conduct an incremental finite element analysis. We set up a structural model composed of staggered arranged brittle aragonite tablets and cohesive zones within a process window surrounding the crack tip to investigate the toughness as well as the deformation mechanism in the fracture process zone. The results obtained by the parametric studies of different geometries and mechanical properties in the microstructure of the materials show that the aspect of failure in the near-tip fields can be categorized into four distinct fracture mechanisms. These mechanisms significantly affect the fracture toughness with differences of hundreds of times. We show a failure-mechanism map in which these fracture mechanisms can be divided into regions of geometry and mechanical properties in microstructure. Findings obtained in this study about the toughening mechanisms give us the useful knowledge to design novel materials with specific microstructures to control fracture mechanisms.

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Development and Psychometric Evaluation of a Novel Measure of Nicotine E-cigarette Withdrawal for use with Adolescents and Young Adults.

Nicotine withdrawal is a well-established construct that prompts continued nicotine product use and contributes to failed cessation efforts. Despite ongoing public health concerns about nicotine e-cigarette use in adolescents and young adults (AYAs), no psychometrically-sound measure of nicotine e-cigarette withdrawal exists for this population. A mixed methods approach comprising a literature review to identify existing nicotine withdrawal items; subject matter expert feedback on existing items and novel item generation; cognitive interviews assessing the measure's instructions, items, and response options; and a large quantitative validation survey (N = 997) was employed to develop the novel retrospective measure of nicotine e-cigarette withdrawal. A 25-item solution comprising four subscales - Negative Affect, Negative Physical Symptoms, Craving, and Appetite/Food - and an Overall Withdrawal score was supported. Internal reliability was excellent (mean alpha = 0.91), and scalar measurement invariance was reached for all subgroups tested (e.g., sex, age, exclusive e-cigarette use versus dual tobacco product use, daily versus non-daily nicotine vaping). Overall Withdrawal and its subscales evidenced concurrent validity with time to first vape in the morning, e-cigarette dependence, and previous vaping cessation attempts, although not each subscale was associated with each outcome. Importantly, cross-sectional incremental validity analyses indicated that retrospective withdrawal accounted for variance in each outcome above and beyond e-cigarette dependence. The novel retrospective AYA Nicotine E-cigarette Withdrawal Scale (AYA NEWS) evidenced strong psychometric properties for use in this population. Future research can determine whether the AYA NEWS can be used to assess acute e-cigarette nicotine withdrawal. While e-cigarette use remains the most prevalent form of nicotine product use among adolescents and young adults, there are limited options for psychometrically-sound measures of e-cigarette-related constructs in this population. Withdrawal from nicotine is associated with failed cessation attempts and continued use of nicotine-containing products, making it a critical construct within tobacco-related research. This study outlines the development and preliminary psychometric evaluation of a novel, retrospective measure of nicotine e-cigarette withdrawal. Study findings support using the Adolescent and Young Adult Nicotine E-cigarette Withdrawal Scale (AYA NEWS) to assess retrospective withdrawal from nicotine e-cigarettes in young people.

Assessing the potential impact of assimilating total surface current velocities in the Met Office’s global ocean forecasting system

Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office’s global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-square-errors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system.

Efficient word segmentation is preserved in older adult readers: Evidence from eye movements during Chinese reading.

College-aged readers use efficient strategies to segment and recognize words in naturally unspaced Chinese text. Whether this capability changes across the adult lifespan is unknown, although segmenting words in unspaced text may be challenging for older readers due to visual and cognitive declines in older age, including poorer parafoveal processing of upcoming characters. Accordingly, we conducted two eye movement experiments to test for age differences in word segmentation, each with 48 young (18-30 years) and 36 older (65+ years) native Chinese readers. Following Zhou and Li (2021), we focused on the processing of "incremental" three-character words, like (meaning "kindergartens"), which contain an embedded two-character word (e.g., , meaning "children"). In Experiment 1, either the three-character word or its embedded word was presented as the target word in sentence contexts where the three-character word always was plausible, and the embedded word was either plausible or implausible. Both age groups produced similar plausibility effects, suggesting age constancy in accessing the embedded word early during ambiguity processing before ultimately assigning an incremental word analysis. Experiment 2 provided further evidence that both younger and older readers access the embedded word early during ambiguity processing, but rapidly select the appropriate (incremental) word. Crucially, the findings suggest that word segmentation strategies do not differ with age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychometric Properties of the Metacognitions About Online Gaming Scale in the Chinese Population and Its Relationship With Internet Gaming Disorder: Cross-Sectional Study.

Metacognitions about online gaming have been shown to be correlated with Internet Gaming Disorder (IGD). Knowledge of metacognitions about online gaming can help to understand IGD. The Metacognitions about Online Gaming Scale (MOGS) is a reliable and valid tool to measure specific metacognitions about online gaming in both adults and adolescents, which is lacking in China. This study was conducted to assess the psychometric properties of the Chinese version of the MOGS (C-MOGS) and its relationship with IGD in the Chinese population. A total of 772 Chinese individuals (age: mean 21.70, SD 8.81 years; age range: 13-57 years; 458/772, 59.3% male) completed a web-based questionnaire survey, including the C-MOGS and a battery of validated scales measuring IGD, gaming motives, depression, and anxiety. Through exploratory and confirmatory factor analyses, the 3-factor structure was confirmed to have adequate model fit and internal consistency reliability (Cronbach α≥.799, Guttman split-half coefficients≥0.754). Concurrent validity of the C-MOGS was supported by its correlations with IGD (P<.001), gaming motives (P<.001), depression (P<.001), and anxiety (P<.001). Furthermore, the incremental validity analysis showed that the C-MOGS predicted 13% of the variance in IGD while controlling for gender, age, weekly gaming hours, gaming motives, depression, and anxiety. This study provides evidence that the psychometric properties of the C-MOGS are appropriate and emphasizes its positive association with IGD. The C-MOGS is a reliable and valid instrument for mental health workers to assess metacognitions about online gaming in the Chinese population.