Response Trajectories Research Articles

Inner race dynamics of a high-speed ball bearing with elastic ring squeeze film damper

Abstract This paper aims at the inner race dynamics of a high-speed ball bearing incorporated with Elastic Ring Squeeze Film Damper (ERSFD). Dynamic model of the inner race with three degrees of freedom (DOFs) is established in an ERSFD integrated ball bearing. Ball contact force and operating contact angle are characterized through an improved quasi-static model where the geometric relationship between the bearing displacement and elastic deformation is developed by considering the outer race movement. Numerical simulations were performed for bearings with or without ERSFD. The model was validated using Campbell diagram, measured displacement response and center trajectory of the inner race at different rotor speeds. The results show that the ERSFD provides adequate stable traction on the ball and limits excessive increase in the inner race contact angle. Such load-carrying condition benefits for decrease in ball orbital revolution skid at high rotor speeds. The ERSFD helps rotor unbalance force dominate bearing motion, leading to a stable periodic motion for the inner race at high rotor speeds. For ball bearings without ERSFD inner race possess stable motion at low and moderate rotor speeds, but is more prone to instability at high rotor speeds.

Natural and Engineered Ocean Inflow Projects to Improve Water Quality Through Increased Exchange

Globally, the health of coastal water bodies continues to be threatened by climate change and mounting anthropogenic pressures related to population increase and associated development. Land use changes have increased the direct runoff of freshwater, nutrients, and other contaminants from watersheds into coastal systems. Exacerbated by increased temperatures, these changes have contributed to a worldwide decline in seagrass coverage and losses of critical habitat and ecosystem functions. For restricted estuaries and lagoons, the influx of nutrients is particularly damaging due to high water residence times and impaired flushing. The result is eutrophication and associated declines in water quality and ecosystem function. To mitigate degraded water quality, engineered ocean–estuary exchanges have been carried out and studied with examples in Australia, New Zealand, India, Denmark, the Netherlands, Portugal, and the United States of America. Based on successes including decreased nutrient concentrations, turbidity, and chlorophyll and increased faunal abundance in some past studies, this option is considered as a management tool for combatting worsening water quality in other estuaries including the Indian River Lagoon, a subtropical, lagoon-type estuary on the central east coast of Florida, USA. Decreased residence times, lower nutrients, higher dissolved oxygen (DO), higher salinity, lower temperature, and lower turbidity all combine for improved ecosystem health. In this review, the successes and failures of past projects intended to increase ocean–estuary exchanges, including biological and geochemical processes that contributed to observed outcomes, are evaluated. The primary indicators of water quality considered in this review include nutrient contents (e.g., nitrogen and phosphorus) and dissolved oxygen levels. Secondary indicators include changes in temperature and salinity pre- and post- engineering as well as turbidity, which can also impact seagrass growth and overall ecosystem health. Each of the sites investigated recorded improvements in water quality, though some were more pronounced and occurred over shorter time scales. Overall, enhanced ocean exchange in restricted, impaired water bodies resulted in system-specific response trajectories, with many experiencing a net positive outcome with respect to water quality and ecosystem health.

Excess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021.

The COVID-19 pandemic overwhelmed health systems, resulting in a surge in excess deaths. This study clustered countries based on excess mortality to understand their response to the pandemic and the influence of various factors on excess mortality within each cluster. This ecological study is part of the COVID-19 MORtality (C-MOR) Consortium. Mortality data were gathered from 21 countries and were previously used to calculate weekly all-cause excess mortality. Thirty exposure variables were considered in five categories as factors potentially associated with excess mortality: population factors, health care resources, socioeconomic factors, air pollution, and COVID-19 policy. Estimation of Latent Class Linear Mixed Model (LCMM) was used to cluster countries based on response trajectory and Generalized Linear Mixture Model (GLMM) for each cluster was run separately. Using LCMM, two clusters were reached. Among 21 countries, Brazil, the USA, Georgia, and Poland were assigned to a separate cluster, with the mean of excess mortality z-score in 2020 and 2021 around 4.4, compared to 1.5 for all other countries assigned to the second cluster. In both clusters the population incidence of COVID-19 had the greatest positive relationship with excess mortality while interactions between the incidence of COVID-19, fully vaccinated people, and stringency index were negatively associated with excess mortality. Moreover, governmental variables (government revenue and government effectiveness) were the most protective against excess mortality. This study highlighted that clustering countries based on excess mortality can provide insights to gain a broader understanding of countries' responses to the pandemic and their effectiveness.

Mental health trajectories across COVID-19 and beyond: South African occupational health surveillance data (2019–2023)

There are inconsistent reports on the impact of the COVID-19 pandemic and its associated social restrictions on mental health, with initial suggestions of an increased prevalence of mental disorders in South Africa. However, the longitudinal trajectories of mental health responses across the COVID-19 pandemic are not clear. The primary aim of this study was to describe the trajectories of self-reported common mental disorders, as determined by repeat administration of clinical screening tools across five consecutive years (from 2019 to 2023). A secondary aim was to consider associations with demographic factors such as gender and age. Point prevalence estimates were obtained from a South African occupational mental health surveillance programme in a stable workplace population. Trajectories of common mental disorders across COVID-19 were determined using the Patient Health Questionnaire-9 and Generalised Anxiety Disorder-7 to identify Major Depressive Disorder and Generalised Anxiety Disorder, respectively. Prevalence estimates for depression and anxiety disorders initially remained stable before increasing towards mid-2021 and returning to pre-COVID-19 estimates by end-2022. Suicidal ideation decreased early into the pandemic but returned to pre-COVID-19 levels by 2021. The study suggests that on a practical level, readily available access to mental health care remains an important mechanism to protect mental health during and after major societal disruptor events. This can further be supported through regular monitoring and through providing high-intensity early intervention for at least 18 to 24 months following such events.

A Geometric Numerical Integration with Simple Cell Mapping for Global Analysis of Nonlinear Dynamical Systems

In order to improve the efficiency in computing the global properties of nonlinear systems, some effective methods have been proposed for the global analysis of these systems. However, there are only few investigations focusing on the numerical algorithms of the system response trajectories. This paper presents a higher-efficiency geometric numerical integration method with simple cell mapping for solving the basins of attraction of nonlinear systems. This numerical algorithm is based on the rule of Lie derivative, using it to calculate the trajectories of the nonlinear systems. Then, the global structure is studied by the numerical algorithm associated with a simple cell mapping method. Compared with the traditional Runge–Kutta methods of orders 4 and 5, it is demonstrated that the proposed Lie derivative iterative algorithm has significant advantages in efficiency.

A Positioning and Tracking Performance–Enhanced Composite Control Algorithm for the Macro–Micro Precision Stage

In the macro–micro composite motion process, the macro stage achieves rapid motion in a large workspace, while the micro stage realizes precise positioning within a small displacement. The large-stroke and high-speed motion of the macro stage is influenced by nonlinear friction, overshooting, and vibrations, making it challenging to achieve rapid stabilization within the travel range of the micro stage, thereby impacting equipment operation efficiency. This paper proposes a composite controller structure designed to solve the fast point-to-point positioning of the macro stage driven by a linear motor and enhance the trajectory tracking performance of the stage. The proposed composite control algorithm (CCA) includes velocity feed-forward, gain-scheduled proportional integral differential (PID) control, and a plug-in repetitive control method. By employing a tracking differentiator, velocity feed-forward, and a gain-scheduled PID controller, the control algorithm can realize rapid stabilization and positioning for the macro stage. Through velocity feed-forward, gain-scheduled PID control, and a plug-in repetitive controller, the control algorithm can reduce the trajectory tracking error of the stage. Simulations and experimental studies of point response and sinusoidal trajectory tracking are carried out on a macro–micro stage to verify the effectiveness of the composite controller for the linear motor. The experimental results demonstrate that the proposed composite controller effectively reduces the macro stage’s settling time and overshoot, and improves the accuracy of sinusoidal trajectory tracking, which lays a foundation for submicron positioning accuracy in high-speed macro–micro motion stages.

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.

Bouncing back from life's perturbations: Formalizing psychological resilience from a complex systems perspective.

Experiencing stressful or traumatic events can lead to a range of responses, from mild disruptions to severe and persistent mental health issues. Understanding the various trajectories of response to adversity is crucial for developing effective interventions and support systems. Researchers have identified four commonly observed response trajectories to adversity, from which the resilient is the most common one. Resilience refers to the maintenance of healthy psychological functioning despite facing adversity. However, it remains an open question how to understand and anticipate resilience, due to its dynamic and multifactorial nature. This article presents a novel formalized framework to conceptualize resilience from a complex systems perspective. We use the network theory of psychopathology, which states that mental disorders are self-sustaining endpoints of direct symptom-symptom interactions organized in a network system. The internal structure of the network determines the most likely trajectory of symptom development. We introduce the resilience quadrant, which organizes the state of symptom networks on two domains: (1) healthy versus dysfunctional and (2) stable versus unstable. The quadrant captures the four commonly observed response trajectories to adversity along those dimensions: resilient trajectories in the face of adversity, as well as persistent symptoms despite treatment interventions. Subsequently, an empirical illustration, by means of a proof-of-principle, shows how simulated observations from four different network architectures lead to the four commonly observed responses to adversity. As such, we present a novel outlook on resilience by combining existing statistical symptom network models with simulation techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy.

To characterise morphological changes in neovascular age-related macular degeneration (nAMD) during anti-angiogenic therapy and explore relationships with best-corrected visual acuity (BCVA) and development of macular atrophy (MA). Post-hoc analysis of the phase III HARBOR trial. SD-OCT scans from 1097 treatment-naïve nAMD eyes were analysed. Volumes of intraretinal cystoid fluid (ICF), subretinal hyperreflective material (SHRM), subretinal fluid (SRF), pigment epithelial detachment (PED) and cyst-free retinal volume (CFRV) were measured by deep-learning model. Volumes were analysed by treatment regimen, macular neovascularisation (MNV) subtypes and topographic location. Associations of volumetric features with BCVA and MA development were quantified at month 12/24. Differences in feature volume changes by treatment regimens and MNV subtypes were observed. Each additional 100 nanolitre unit (AHNU) of residual ICF, SHRM and CFRV at month 1 in the fovea was associated with deficits of 10.3, 7.3 and 12.2 letters at month 12. Baseline AHNUs of ICF, CFRV and PED were associated with increased odds of MA development at month 12 by 10%, 4% and 3%. While that of SRF was associated with a decrease in odds of 5%. Associations at month 24 were similar to those at month 12. Eyes with different MNV subtypes showed distinct trajectories of feature volume response to treatment. Higher baseline volumes of ICF or PED and lower baseline volume of SRF were associated with higher likelihoods of MA development over 24 months. Residual intraretinal fluid, including ICF and CFRV, along with SHRM were predictors of poor visual outcomes.

Predictors of treatment response trajectories to cognitive behavioral therapy for chronic fatigue syndrome: A cohort study

BackgroundThe response to cognitive behavioral therapy (CBT) for chronic fatigue syndrome (CFS) varies greatly between patients, but predictors of treatment success remain to be elucidated. We aimed to identify patient subgroups based on fatigue trajectory during CBT, identify pre-treatment predictors of subgroup membership, and disentangle the direction of predictor – outcome relationships over time. Methods297 individuals with CFS were enrolled in a standardized CBT program consisting of 17 sessions, with session timing variable between participants. Self-reported levels of fatigue, depressive, anxiety, and somatic symptoms, perceived stress, and positive affect were collected pre-treatment, and after 3, 10, and 15 sessions. Latent Class Growth Analysis (LCGA) was used to identify subgroups based on fatigue trajectories and baseline predictors of group membership. Cross-lagged structural equation models were used to disentangle predictor-outcome relationships. ResultsLCGA identified four fatigue trajectory subgroups, which were labelled as “no improvement” (23 %), “weak improvement” (45 %), “moderate improvement” (23 %), and “strong improvement” (9 %) classes. Higher pre-treatment levels of depressive, anxiety, and somatic symptoms, stress, and lower levels of positive affect predicted membership of the “no improvement” subgroup. Reductions in anxiety preceded reductions in fatigue, while the depressive symptoms – fatigue relationship was bidirectional. ConclusionsOn a group level, there were statistically significant reductions in fatigue after 15 sessions of CBT, with important individual differences in treatment response. Higher pre-treatment levels of anxious, depressive, and somatic symptoms and perceived stress are predictors of lack of response, with reductions in anxiety and stress preceding improvements in fatigue.

Shaken to the core: trust trajectories in the aftermaths of adverse cyber events

PurposeAdverse cyber events, like death and taxes, have become inevitable. They are an increasingly common feature of organisational life. Their aftermaths are a critical and under-examined context and dynamic space within which to examine trust. In this paper, we address this deficit.Design/methodology/approachDrawing on pertinent theory and reports of empirical studies, we outline the basis of two alternative subsequent trajectories, drawing out the relationships between trust, vulnerability and emotion, both positive and negative, in the aftermath of an adverse cyber event.FindingsWe combine stage theory and social information processing theories to delineate the dynamics of trust processes and their multilevel trajectories during adverse cyber event aftermaths. We consider two response trajectories to chart the way vulnerability arises at different levels within these social systems to create self-reinforcing trust and distrust spirals. These ripple out to impact multiple levels of the organisation by either amplifying or relieving vulnerability.Research limitations/implicationsThe way adverse cyber events aftermaths are managed has immediate and long-term consequences for organisational stakeholders. Actions impact resilience and the ability to preserve the social fabric of the organisations. Subsequent trajectories can be “negative” or “positive”. The “negative” trajectory is characterised by efforts to identify and punish the employee whose actions facilitated the adverse events, i.e. the “who”. Public scapegoating might follow thereby amplifying perceived vulnerability and reducing trust across the board. By contrast, the “positive” trajectory relieves perceived vulnerability by focusing on, and correcting, situational causatives. Here, the focus is on the “what” and “why” of the event.Practical implicationsWe raise the importance of responding in a constructive way to adverse cyber events.Originality/valueThe aftermaths of cyber attacks in organisations are a critical, neglected context. We explore the interplay between trust and vulnerability and its implications for management “best practice”.

Distinct trajectories of treatment response to mepolizumab toward remission in patients with severe eosinophilic asthma.

Patients with severe eosinophilic asthma, characterised by a high disease burden, benefit from mepolizumab, which improves symptoms and reduces exacerbations, potentially leading to clinical remission in a subgroup. This study aimed to identify treatment response trajectories to mepolizumab for severe eosinophilic asthma and to assess the achievement of clinical remission.Data from the Australian Mepolizumab Registry were used to assess treatment responses at 3, 6, and 12 months. The treatment response trajectories were identified using a group-based trajectory model. The proportions achieving clinical remission at 12 months, which was defined as well-controlled symptoms, no exacerbations, and no oral corticosteroid (OCS) use for asthma management, were compared between trajectories, and baseline predictors of the trajectories were identified using logistic regression analysis.We identified three trajectory groups: group 1, responsive asthma with less OCS use (n=170); group 2, responsive late-onset asthma (n=58); and group 3, obstructed and less responsive asthma (n=70). Groups 1 and 2 demonstrated higher proportions achieving clinical remission at 36.5% and 25.9%, respectively, compared to group 3 with 5.7% (p <0.001). Baseline predictors for assigned groups included lower OCS dose in group 1; greater FEV1% predicted, higher Asthma Quality of Life Questionnaire score, higher OCS dose, and nasal polyps in group 2; with group 3 as the reference.Treatment response to mepolizumab in severe eosinophilic asthma follows 3 trajectories with varying proportions achieving clinical remission and differing baseline characteristics. Treatment response variability may influence the achievement of clinical remission with mepolizumab therapy.

The Trajectory of Antibody Responses One Year Following SARS-CoV-2 Infection among Indigenous Individuals in the Southwest United States

SARS-CoV-2 antibody kinetics based on immunologic history is not fully understood. We analyzed anti-spike and anti-nucleocapsid antibody responses following acute infection in a cohort of Indigenous persons. The models of peak concentrations and decay rates estimated that one year after infection, participants would serorevert for anti-nucleocapsid antibodies and remain seropositive for anti-spike antibodies. The peak anti-spike concentrations were higher for individuals vaccinated prior to infection, but the decay rates were similar across immunologic status groups. Children had significantly lower peak anti-spike concentrations than adults. This study affirms the importance of continued vaccination to maintain high levels of immunity in the face of waning immunity.

Ultrathin Ga2O3 Photodetector with Fast Response and Trajectory Tracking Capability Fabricated by Liquid Metal Oxidation.

Low-dimensional Ga2O3 demonstrates a unique ultraviolet photoresponse and could be used in various electronic and optical systems. However, the low-dimensional Ga2O3 photodetector is faced with the challenges of a complex preparation process and poor device performance. In this work, ultrathin Ga2O3 layers with ∼7 nm thickness are prepared on quartz rods by UV exposure to liquid gallium. Benefiting from low-density oxygen vacancy defects cured by UV exposure, the low-dimensional Ga2O3 photodetector exhibits a high response speed (rise: 64.7 μs; fall: 51.4 μs) and an exceptional linear dynamic range of 120 dB. Furthermore, the photodetector array based on these ultrathin Ga2O3 shows an effective trajectory tracking capability by monitoring UV source motion. This work develops a simple preparation method to construct a low-dimensional UV photodetector array with fast response and useful trajectory tracking capability, exhibiting the significance of ultrathin Ga2O3 in UV optoelectronics.

The multiscale response of global cropland cropping intensity to urban expansion

CONTEXTUrban expansion(UE) and multiple cropping(MC) are key factors in anthropogenic impacts on global environmental change. However, the multi-scale response patterns of UE and MC have not yet been revealed, and how urbanization affects cropland intensification is still not deeply explored. OBJECTIVEThis study examines the spatial and temporal trends in global UE and MC and analyses the multi-scale response patterns of both. METHODSThe GEE (Google Earth Engine) platform was used to count global cropland cropping intensity (CCI) and impervious surface rasters on an image-by-image basis, while GIS was employed for spatial analyses, and the generalized additive model (GAM) was applied to inscribe variable response trajectories. RESULTS AND CONCLUSIONThe global multiple cropping index(MCI) increased significantly (by 4.1 %) over the period 2001–2019, with growth in double- and triple-cropped cropland dominating this change. Double cropping, as a widespread global farming strategy, has led to a shift towards the intensification of agriculture, with countries in the northern hemisphere contributing more. Global UE significantly expands to twice the baseline level at an average annual growth rate of 2. 42 × 104 km2 over the period 2001–2019, with the expansion of world-class urban agglomerations in China, the United States and Europe dominates this trend. The spatial clustering of MC and UE has continued to intensify, with high-intensity cropping strategies progressively clustering towards areas of significant UE, and this tendency has a clear decreasing urban-rural gradient effect. The global CCI grows significantly and non-linearly with UE, but an important inflection point in the growth trajectory has occurred under the influence of threshold effects. Developed countries tend to be more flexible in their cropping intensity strategies as they move forward with urbanization. There is a clear increasing effect of the urban-rural gradient in the degree of non-linearity in the response of urbanization and cropping intensity. SIGNIFICANCEThe results of the study contribute to the understanding of the complex spatial and temporal coupling mechanisms between UE and MC, and provide useful insights for the development of trade-offs between urbanization and maturity strategies.

Trajectory of antibiotic resistome response to antibiotics gradients: A comparative study from pharmaceutical and associated wastewater treatment plants to receiving river

Pharmaceutical wastewater often contains significant levels of antibiotic residues, which continuously induce and promote antibiotic resistance during the sewage treatment process. However, the specific impact of antibiotics on the emergence of antibiotic resistance genes (ARGs), microbiomes, and mobile genetic elements (MGEs), as well as the dose-response relationship remain unclear. Herein, through metagenomic sequencing and analysis, we investigated the fate, transmission, and associated risk of ARGs over a ten-year period of exposure to a gradient of sulfonamide antibiotics at a pharmaceutical wastewater treatment plant (PWWTP), an associated wastewater treatment plant (WWTP), and the receiving river. Through abundance comparison and principal co-ordinates analysis (PCoA), our results revealed distinct ARG, microbiome, and MGE profiles across different antibiotic concentrations. Notably, there was a decreasing trend in the abundance of ARGs and MGEs as the antibiotic concentrations were attenuated (p < 0.05). Further partial least squares path modeling analysis, Procrustes analysis and network analysis indicated that variation in MGEs and microbiomes were the driving forces behind the distribution of ARGs. Based on these findings, we proposed an antibiotic-microbiome-MGE-ARG dissemination paradigm, in which integrons as key drivers were closely associated with prevalent ARGs such as sul1, sul2, and aadA. With a focus on human pathogenic bacteria and the associated health risks of ARGs, we conducted pathogen source analysis and calculated the antibiotic resistome risk index (ARRI). Our findings highlighted potential risks associated with the transition from PWWTP to WWTP, raising concerns regarding risk amplification due to the mixed treatment of antibiotic-laden industrial wastewater and domestic sewage. Overall, the results of our study provide valuable information for optimizing wastewater treatment practices to better manage antibiotic resistance.

Into the storm: the imbalance in the yin-yang immune response as the commonality of cytokine storm syndromes.

There is a continuous cycle of activation and contraction in the immune response against pathogens and other threats to human health in life. This intrinsic yin-yang of the immune response ensures that inflammatory processes can be appropriately controlled once that threat has been resolved, preventing unnecessary tissue and organ damage. Various factors may contribute to a state of perpetual immune activation, leading to a failure to undergo immune contraction and development of cytokine storm syndromes. A literature review was performed to consider how the trajectory of the immune response in certain individuals leads to cytokine storm, hyperinflammation, and multiorgan damage seen in cytokine storm syndromes. The goal of this review is to evaluate how underlying factors contribute to cytokine storm syndromes, as well as the symptomatology, pathology, and long-term implications of these conditions. Although the recognition of cytokine storm syndromes allows for universal treatment with steroids, this therapy shows limitations for symptom resolution and survival. By identifying cytokine storm syndromes as a continuum of disease, this will allow for a thorough evaluation of disease pathogenesis, consideration of targeted therapies, and eventual restoration of the balance in the yin-yang immune response.

Heterogeneity in trajectories of traumatic distress at the start of the COVID-19 pandemic.

The COVID-19 outbreak has led to an increase in posttraumatic stress symptoms (PTSSs; Prout et al., 2020) for some individuals, whereas others appeared to be more resilient. It remains relatively unclear what characterizes these potentially different response trajectories ( Chen & Bonanno, 2020). This study sought to (a) assess individuals' PTSS levels at the start of the pandemic and at two subsequent timepoints 3 and 6 months later, (b) identify different trajectories of PTSSs over time, and (c) describe which individual characteristics influenced the likelihood of each of these different trajectories to occur. A community sample (n = 317) responded to an online survey during the first weeks of the pandemic, 3 and 6 months later. Among those who reported acute levels of PTSSs, latent class growth analyses identified three different resilience trajectories-resilient (low baseline PTSSs and a slight decrease over time), chronic (severe PTSSs at baseline and no change over time), and recovered (severe PTSSs at baseline but a sharp improvement over time). Baseline childhood adversity, depression, anxiety, defensive functioning, and somatization predicted trajectories. Demographics (age, gender, preexisting chronic illness) and COVID-related factors (knowing someone diagnosed with or who died of COVID-19) were unrelated to trajectories. Results suggest that although high PTSS levels decreased over time on average, heterogenous change trajectories can be identified based on baseline psychological characteristics. This implies that mental health, including past and present experiences, as well as adaptational mechanisms may shape individuals' experiences with pandemic-related ongoing stress. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Study of vibration patterns and response transfer relationships in walnut tree trunks

Fruit tree trunk is the support structure of the whole tree, carrying the task of vibration transmission to all levels of branches, fruit tree trunk vibration response to the design and optimization of the vibration harvesting device to provide guiding value. In this paper, the vibration response and harvesting test was carried out for four walnut fruit trees with different morphology and size, and the vibration transmission relationship between the excitation device and the fruit tree as well as inside the fruit tree was analyzed and studied according to the acceleration response curves at each position point under different frequencies. At the same time, the theoretical vibration pattern of the fruit tree and the measured spatial three-dimensional vibration pattern were constructed, and the response transfer law between each point of the fruit tree was further analyzed in terms of the vibration pattern and direction of each position point on the tree. The results show that the excitation response acceleration is basically no attenuation inside the device, but there is some attenuation in the clamping part due to the presence of damping pads. The vibration response in the fruit tree trunk is greater in amplitude farther from the excitation point. The phase relationship between each position point is basically the same in the theoretical vibration mode and the measured vibration mode, but there is damping in the actual fruit tree, which leads to the accumulation of phase hysteresis in the transmission of excitation parameters. The actual response trajectory is a narrow ellipse, and the different position points show different rotational relationships along the ellipse trajectory due to the growth of the fruit tree.

Trajectory modeling and response prediction in transcranial magnetic stimulation for depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. MethodsWe compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. ResultsLCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before. ConclusionsIn showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.