Trajectories Of Change Research Articles

Safety and accuracy of the computer interpretation of normal ECGs at triage.

ECGs performed at ED triage are mandatorily assessed by an emergency physician contributing to task interruptions, decreased quality of care and increased error risk. Recent literature suggests that a triage ECG interpreted as normal by the ECG machine software correlates with benign interpretation from attending cardiologists.Ambiguity persists regarding the safety of the normal computerized ECG interpretation and whether real-timephysician review is needed. In this prospective cohort study, we evaluated the GE Marquette 12SL ECG software's clinical accuracy for normal triage ECGs. We compared machine interpretation, ED physicians' real-time interpretation and management as well as cardiologists systematic but delayed review of the ECGs to establish diagnostic accuracy and safety of a normal software interpretation. Additional data were collected from the emergency department patient tracking system and charts to determine patient outcomes when machine normal interpretation differed from ED physicians and cardiologists' analysis. Between November 16th 2020 and November 19th 2021, we collected 1220 machine-normal ECGs performed at Charles-Le Moyne Hospital ED triage. 1051 ECGs were eligible for inclusion. ED physicians agreed with the machine for 1028 normal diagnoses (97.8% IC (0.95): 96.7-98.5) and cardiologists agreed with the machine for 1037 ECGs (98.7% IC (0.95) 97.8-99.2). Relevant changes in ED management were recorded in three patients (0.29% IC (0.95): 0.1-0.8) whose ECGs were read as normal by the software: two emergent referrals to the catheterization lab and one emergent transfer to the resuscitation bay with next day coronarography. A normal ECG interpretation from the GE Marquette 12SL ECG software at ED triage has a very high accuracy and a very low probability of clinically relevant change in patient outcome and ED trajectory.

Level 4 seizure monitoring unit admissions are associated with reduced long-term health care costs.

This study was undertaken to determine whether admission to dedicated seizure monitoring units (SMUs) result in reduced health care use (HCU). This was a retrospective open cohort study covering the years 2010-2018 of patients residing in Alberta, Canada, who were referred to the Calgary Comprehensive Epilepsy Program and admitted to a level 4 SMU. Patients were required to have ≥3 years pre- and postadmission follow-up. The outcome was the change in trajectory of composite HCU (primary care, specialist outpatient visits, emergency department visits, and hospitalizations) for the 3 years prior to and 3 years following SMU admission using the point of admission as the "index date." Secondary outcomes were HCU limited to specific settings. We excluded the first 30 days following the point of admission to mitigate the confounding admission would have on the postadmission HCU trajectory. We used adjusted restricted maximum likelihood linear and nonlinear effects models to determine trajectories expressed as Canadian dollars. A total of 315 of 600 (53%) patients met eligibility criteria. Mean age was 40 years (SD = 17.4), 176 (56%) were female, 220 (70%) had focal epilepsy, and 60 (19%) had functional seizures or physiologic seizure mimics without epilepsy as adjudicated by the attending physician at the point of discharge. Mean per person health care costs increased by CAD$341.28 (95% confidence interval [CI] = -25.17 to 707.74) for each successive 6-month interval prior to SMU admission (p = .07). Following admission, mean per person costs decreased by CAD$802.34 (95% CI = 699.62-905.06, p < .001) for each successive 6-month interval up to 3 years postdischarge. Similar trends were noted for primary and specialist care, emergency department, admitted care, and when nonlinear models were applied. Admission to an SMU is associated with significant and enduring declines in HCU. Each 6-months following discharge overall HCU declined by a mean of CAD$802.34 and acute inpatient, emergency department, and outpatient physician interactions declined by 25%, 26%, and 18% respectively. Comprehensive epilepsy care not only reduces morbidity and mortality but also reduces cost.

New Astronomical Observatory Design for the Detection and Tracking of Satellite Objects: The Satellite Robotic Observatory (SRO)

Robotic Astronomical Observatories (RAOs) have provided very good results in different research projects in astrophysics/astronomy. Their applications in the detection, tracking, and identification of near-Earth objects have contributed to the identification of potentially dangerous objects for our security, such as near-Earth Objects (NEOs), near-Earth Asteroids (NEAs), meteors, and comets, whose trajectory changes can cause an impact on our planet. If advances in astrometry techniques (measuring the position and trajectory of Earth-orbiting objects) and photometry (variation in light curves) are considered together with the new sensors that work in the optical and near-infrared spectral ranges, a new observatory system that allows for the detection of nearby satellite objects in different spectral ranges and with better-defined optics can be developed. The present paper describes the design of a new observatory applied to the surveillance and tracking of satellites and other debris objects, the Satellite Robotic Observatory (SRO). Starting from general constraints from astronomy observatories, the design process has been determined, considering the main objectives, the necessary sensors, and several technical improvements that have contributed to a final configuration proposed for the SRO. The result is the design of a portable observatory model that can host at least two sensors to track and monitor satellite objects simultaneously.

Abstract 4127413: High-normal Blood Pressure (130-139/85-89mmHg) is Associated with an Increased Risk of Mortality

Background: The relationship between high-normal blood pressure (BP) and mortality lacks high-quality evidence based on large population cohort. The aim of this study was to comprehensively investigate the association of high-normal BP and its trajectory with all-cause and cause-specific mortality. Methods: In a community-based population cohort from the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) project, 3,598,940 participants aged 35-75 years with baseline BP were included. High-normal BP was defined as a systolic blood pressure (SBP) of 130-139 mmHg and/or a diastolic blood pressure (DBP) of 85-89 mmHg at baseline. Four trajectories of BP change were identified in 105,894 participants included in the study, who had more than 2 follow-up BP measurements, using latent mixture modeling. Among them, high-normal-increasing was defined as an increase in SBP from 130-139 at baseline to over 140 at the last follow-up. Findings: Compared with the optimal BP group (SBP &lt; 120 mmHg and DBP &lt; 80 mmHg, [18.1%]), participants with high-normal BP (18.7%) had a 4% increase in all-cause mortality risk (hazard ratio [HR] 1.04, 95% confidence interval [CI]: 1.01-1.07) and a 28% increase in CVD mortality risk (HR 1.28, 95% CI: 1.21-1.34), with the greatest increase in mortality risk observed from hemorrhagic stroke (HR 1.75, 95% CI: 1.55-1.98). Among BP trajectories, compared with participants with optimal-stable BP (25.3%), those with high-normal-increasing BP (3.5%) had a 25% increase in all-cause mortality risk (HR 1.25, 95% CI: 1.00-1.54) and a 67% increase in CVD mortality risk (HR 1.66, 95% CI: 1.21-2.30), with the greatest increase in mortality risk also observed from hemorrhagic stroke (HR 3.15, 95% CI: 1.38-7.20). In our study cohort, approximately 0.7% and 1.3% of all-cause mortality was attributable to the high-normal BP at baseline and high-normal-increasing BP trajectory, respectively. Interpretation: Individuals presenting high-normal BP at baseline or high-normal-increasing BP trajectory afterward exhibited a significantly elevated mortality risk, which is most pronounced for hemorrhagic stroke. Early management of BP in patients with high-normal BP is crucial for the prevention and treatment of hypertension.

Abstract 4137820: Extreme heat and cardiovascular disease in a changing climate: An urgent need for risk management and health research

Background: Extreme heat can trigger or exacerbate cardiovascular disease (CVD) through ischemia or thrombosis, and its deleterious impacts are inclined to worsen as increased heat exposure and heightened vulnerability from climate change interacted with natural and social environment. Research Questions: Previous studies have primarily focused on quantifying the increased risk of CVD due to heat exposure, while lacking a comprehensive understanding of the association, underlying mechanisms, and vulnerability in CVD patients. Consequently, there is a scarcity of clinical and public health interventions aimed at preventing the convergence of extreme heat exposure and the risk of CVD within the context of climate change. Methods: We conducted a systematic search in PubMed, Embase, and Web of Science from inception to December, 2023, and we retrieved articles which assessed the effects of extreme heat or climate change on CVD. Results: In this review, we outline the existing evidence on the epidemiology and pathophysiological mechanisms that elucidate how extreme heat affects the risk of CVD. Extreme heat is associated with increased risks for CVD morbidity and mortality, where older persons, low socioeconomic status, pre-existing medical conditions, and unfavorable living environments confer greater susceptibility to heat. The adverse effects of extreme heat on cardiovascular system involved a wide range of complex mechanisms between different organs and regulatory pathways (Figure 1). Additionally, we identify the factors that contribute to the vulnerability of CVD patients to heat exposure. The discrepancy in heat-related risk can be attributed to individual susceptibility, sociocultural factors, population temperature adaptations, and environmental factors. Future climate change is projected to exacerbate the burden of extreme heat, particularly due to the interaction of air pollution, population aging, and urbanization (Figure 2). Conclusions: Addressing extreme heat should be within the framework of the prevention of CVD. Health professionals and scientists are responsible for mitigating the cardiovascular effects mediated by extreme heat, with an emphasis on engaging in risk management tactics and health research to implement preventive strategies. As CVD is a climate sensitive disease, evidence-based interventions during heat extremes are urgently needed to cope with the cardiovascular risks associated with the inevitable trajectory of climate change.

Exploring the Multidimensional Influences on Sleep and Active Heart Rate Dynamics: A Comprehensive Study

This study offers an in-depth examination of how various social, personal, physiological, environmental, and behavioral factors are associated with both sleep and active heart rate dynamics among college students. Utilizing data collected from 487 participants over a span of 637 days via wearable technology, this research aims to unravel the intricate relationships that influence heart rate variations. Through the application of latent growth-curve modeling, we meticulously analyzed the trajectory of heart rate changes and their associations with a broad spectrum of influencing factors. This methodological approach allowed for a nuanced understanding of the dynamic interplay between heart rate and its determinants over time. The analysis revealed a consistent increase in both sleep and active heart rates across the study period, accompanied by stable standard deviations. Peer influence significantly impacted sleep and active heart rates, especially at rest. Gender and race/ethnicity were associated with heart rate dynamics, as were conscientiousness and depression levels. Environmental factors, including days of the week, academic periods, and weather conditions, exhibited significant effects. Behavioral factors, such as physical activity and daily class attendance, played a substantial role in heart rate patterns. Our findings underscore the complex interplay of factors influencing heart rate dynamics in young adults. Tailored interventions should consider these multifaceted influences to promote optimal cardiovascular well-being.

The polarisation of Italian metropolitan areas, 2000–2018: structural change, technology and growth

Abstract Large cities are a key driver of technological innovation and economic growth. This paper investigates the developments of Italian metropolitan areas, building on insights from economic geography and innovation studies. The key questions to be investigated are the following: (i) Which trajectories of population and economic change can be identified for Italian metropolitan areas? Are we facing a process of economic and technological polarisation that may worsen the country’s imbalances? (ii) What is the role played in such developments by technological and structural change, and in particular by digital technologies and the rise of finance? The empirical analysis investigates the patterns of technological and economic indicators for the period 2000–2018 for 14 Italian metropolitan areas, proxied by their provinces, providing evidence of growing polarisation between Milan, where most positive developments are concentrated, and the other metropolitan zones. Rome has been losing ground in most fields; Venice and Genoa are characterised by industrial decline. Few mid-sized cities show some economic dynamism—including Bologna and Cagliari—while most southern and insular Italian cities increase their gap relative to the performances of leading metropolitan areas.

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Examining interrelations among trajectories of mindful awareness, acceptance, and values-consistent actions in acceptance-based behavioral therapy for generalized anxiety disorder

ABSTRACT The goal of this study was to understand how mindful awareness, acceptance, and values-consistent action change across acceptance-based behavioral therapy (ABBT) for generalized anxiety disorder (GAD) and determine their effect on symptoms. We examined weekly data from 31 individuals who received ABBT as part of a randomized control trial for individuals with GAD (Hayes-Skelton, Roemer, & Orsillo, 2013). Participants answered questions weekly about three components of ABBT, including the percentages of time they spent over their past week: 1) aware of the present moment, 2) accepting of their internal experiences, and 3) engaging in values. GAD symptoms were examined using two self-report measures (DASS-21 Stress subscale and PSWQ) and two clinician-rated interview measures (CSR and SIGH-A) at pre- and post-treatment. 1 Mindful awareness, acceptance, and values-consistent action increased linearly across ABBT. All three change trajectories were positively correlated (Z’s 2.99 to 8.74, p’s < .001). Mindful awareness, acceptance, and values-consistent action across treatment predicted decreases in GAD symptoms above and beyond baseline for most outcome measures (Z’s −1.95 to −3.03, p’s < .05), with the exception that mindfulness did not predict DASS-stress (Z = −1.39, p = .17). These findings are consistent with the structure and model of ABBT.

Mentalising severe self‐harm: A longitudinal single case study of an add‐on AMBIT program

AbstractBackgroundMentalization‐based treatment (MBT) has been proposed as a promising approach when treating patients suffering from borderline personality disorder (BPD) and severe self‐harm. Though study findings on MBT for patients who self‐harm are convincing, a meta‐analysis showed mixed results, calling for further research and the development of treatment protocols (Gross et al., 2024).AimThe aim of this study was to describe an add‐on AMBIT intervention for adult patients suffering from severe self‐harm. To demonstrate the add‐on AMBIT’s potential, a descriptive longitudinal study design was facilitated and illustrated with a single case of a woman in her mid‐twenties suffering from BPD and severe self‐harm.Materials and MethodsMultiple data sources were applied to show the longitudinal development of the patient one year before entering treatment, during treatment, and one year after treatment including calculation of number of hospitalized bed days, coercion episodes, and suicide attempts. Additionally, the Deliberate Self‐Harm Inventory, the Affect Integration Inventory, the Inventory of Interpersonal Problems, the Hopkins Symptom Checklist, and the WHO’s Quality of Life questionnaire were measured every third month during a 2‐year treatment period to examine trajectories of change across time.ResultsResults revealed significant improvements on all measured parameters including a reduction in the patient’s number of bed days (from 166 to 0), hospitalizations (from 4 to 0), coercion episodes (from 22 to 0), and suicide attempts (from 2 to 0). Furthermore, measures during the treatment period showed reduced self‐harm, decreased symptomatology, increased affect integration, better interpersonal functionality, and improved everyday functioning. Measures 1 year after treatment suggested lasting effects.DiscussionThe potential of the add‐on AMBIT intervention is discussed, including limitations and future research recommendations.

Numerical simulation of influence of jet flow parameters on cone-cylinder-flare configuration

Abstract Reaction control system technology is used to realize attitude control or trajectory change. The hot jet effect should be considered to get more accurate aerodynamic characteristics as the flight vehicle develops. The simulation for hot jet flow needs more calculation resources, so it is important to research the suitable similarity parameters of jet flow. Then, we can use cold jet flow to simulate hot jet flow. In this paper, the influence of jet flow parameters is numerically studied on cone-cylinder-flare configuration. The numerical simulation method for hot jet flow is established, containing finite volume method, multi-component three-dimensional Reynolds Navier-Stokes equation with source terms, multi-block patched grid technique, and fully implicit processing method. The numerical method is verified by using experimental reference results, and the grid independence is assured. The numerical results reveal that the cold jet flowfield under the same mass flow ratio is more similar to the hot jet flowfield than the cold jet flowfield under the total temperature of 293 K. The maximum amplification factor difference between cold and hot jet flow is reduced from 14.1% to 1.2%.

Adapting to Individual Differences: An Experimental Study of Language Evolution in Heterogeneous Populations.

Variations in language abilities, use, and production style are ubiquitous within any given population. While research on language evolution has traditionally overlooked the potential importance of such individual differences, these can have an important impact on the trajectory of language evolution and ongoing change. To address this gap, we use a group communication game for studying this mechanism in the lab, in which micro-societies of interacting participants develop and use artificial languages to successfully communicate with each other. Importantly, one participant in the group is assigned a keyboard with a limited inventory of letters (simulating a speech impairment that individuals may encounter in real life), forcing them to communicate differently than the rest. We test how languages evolve in such heterogeneous groups and whether they adapt to accommodate the unique characteristics of individuals with language idiosyncrasies. Our results suggest that language evolves differently in groups where some individuals have distinct language abilities, eliciting more innovative elements at the cost of reduced communicative success and convergence. Furthermore, we observed strong partner-specific accommodation to the minority individual, which carried over to the group level. Importantly, the degree of group-wide adaptation was not uniform and depended on participants' attachment to established language forms. Our findings provide compelling evidence that individual differences can permeate and accumulate within a linguistic community, ultimately driving changes in languages over time. They also underscore the importance of integrating individual differences into future research on language evolution.

Pathways to queer thriving in an LGBTQ+ intergenerational community.

LGBTQ+ people and communities continue to survive and thrive within the context of complex and unrelenting personal, structural, and collective trauma. Psychological research has examined this adaptive capacity through frameworks of resilience and posttraumatic growth. Through multidisciplinary engagement, we have identified limitations of these frameworks when they are applied to LGBTQ+ communities. In the first half of this article, we reconceptualize resilience and posttraumatic growth as queer thriving and offer the Möbius strip as a metaphor to challenge and expand normative ideas around direction, trajectory, timeline, and outcomes of positive change through adversity. In the second half of this article, we explore pathways to queer thriving within an LGBTQ+ intergenerational community project-an ethnographic experiment-that we have cofacilitated since 2019. We view generational divisions in LGBTQ+ communities as both a reflection and a form of trauma. In our ethnographic experiment, LGBTQ+ younger and older adults have the rare opportunity to heal this division by coming together for storytelling, dialogue, and artmaking around themes and issues important to their lives. In this article, we present three ethnographic vignettes that powerfully illustrate the potential for queer thriving through intergenerational social connection. We conclude by emphasizing the importance of mixed-disciplinary, community-engaged, and descriptive approaches to examining resilience and posttraumatic growth within marginalized communities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Outcomes After Palliative Radiotherapy in Patients with Symptomatic Locoregionally Advanced Breast Cancer.

Symptomatic locoregionally advanced breast cancer (SLABC) can cause troublesome pain or wound complications that negatively impact quality of life. Although palliative radiotherapy (RT) can minimize tumor-related symptoms, how best to tailor RT to achieve the most meaningful and durable response is not well defined. This is a single-institution, multi-site retrospective review of patients with SLABC treated between 2016 and 2023 with palliative RT to symptomatic disease in the breast, chest wall, and/or regional lymph node basins. Overall survival (OS), locoregional control (LC), clinical and radiographic treatment response, overall pain scores, and treatment-related toxicities were analyzed. 164 patients with a median age of 57 years were analyzed with a median follow-up time of 4.97 months. 86% had distant metastases. The most common presenting symptom was pain (87%), followed by ulcerating or fungating lesion (76%) and discharge (45%). The median cumulative biologically effective dose to the gross tumor volume (BEDGTV) was 69 Gy. The 1-year OS and LC rates were 37% and 63%, respectively. Eighty-one percent experienced improvement in symptoms within 3 months after RT, the odds of which increased per Gy BEDGTV (OR 1.029, p=0.003). Acute toxicities were associated with number of fractions and BEDGTV (both p<.001), but not with concurrent systemic therapy nor reirradiation (both p>.05). Trends in pain scores showed a significant change in pain trajectory that was sustained during the first year after RT. OS and LC were not different among patients who received 1 vs 2-10 vs >10 fractions, and between ≤70 vs >70 Gy BEDGTV. In this large series of patients with SLABC, palliative RT was effective at relieving locoregional symptoms with acceptable toxicity, with the likelihood of symptom improvement associated with radiation dose. Survival of these patients remains poor, highlighting the importance of palliative care strategies that minimize overall symptom burden while maximizing quality of life.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Multi-Omic characterization of the effects of Ocrelizumab in patients with relapsing-remitting multiple sclerosis

The study examined changes in the plasma proteome, metabolome, and lipidome of N = 14 patients with relapsing-remitting multiple sclerosis (RRMS) initiating treatment with ocrelizumab, assayed at baseline, 6 months, and 12 months. Analyses of >4000 circulating biomarkers identified depletion of B-cell associated proteins as the early effect observed following ocrelizumab (OCR) initiation, accompanied by the reduction in plasma abundance of cytokines and cytotoxic proteins, markers of neuronaxonal damage, and biologically active lipids including ceramides and lysophospholipids, at 6 months. B-cell depletion was accompanied by decreases in B-cell receptor and cytokine signaling but a pronounced increase in circulating plasma B-cell activating factor (BAFF). This was followed by an upregulation of a number of signaling and metabolic pathways at 12 months. Patients with higher baseline brain MRI lesion load demonstrated both higher levels of cytotoxic and structural proteins in plasma at baseline and more pronounced biomarker change trajectories over time. Digital cytometry identified a putative increase in myeloid cells and a pro-inflammatory subset of T-cells. Therapeutic effects of ocrelizumab extend beyond CD20-mediated B-cell lysis and implicate metabolic reprogramming, juxtaposing the early normalization of immune activation, cytokine signaling and metabolite and lipid turnover in periphery with changes in the dynamics of immune cell activation or composition. We identify BAFF increase following CD20 depletion as a tentative compensatory mechanism that contributes to the reconstitution of targeted B-cells, necessitating further research.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Application of 3D Motion Graphic Design Technology of Object Simulation Human Body in Sports Teaching

In the current era, the rapid development of computer technology has brought unprecedented opportunities for innovation in physical education teaching. The introduction of computer technology not only improves the efficiency of physical education teaching but also provides more accurate and scientific means for sports training. In response to this trend, this paper proposes an innovative human body model design method and explores in detail the key implementation techniques of computer simulation of 3D motion maps, aiming to further optimize and improve the quality of physical education teaching. The design of human body models plays a crucial role in physical education teaching and training, as it can help students and teachers better understand the mechanisms and principles of human movement. By introducing computer technology, this paper can construct a more precise, dynamic, and realistic human body model that conforms to the actual motion situation. This model can simulate various movement states of the human body, providing students with an intuitive and vivid learning experience. In the classroom, teachers can use projection or virtual reality devices to display 3D motion models, allowing students to intuitively see the details and changes of motion movements, deepening their understanding of the essentials of the movements. Teachers can demonstrate different movements in real time by operating models, and explain the technical points and precautions of each movement. Students can observe the motion trajectory and posture changes of the model from multiple perspectives to improve learning efficiency. The system generates personalized feedback reports based on students’ movement data, pointing out the problems and improvement directions that students have during the exercise process. Teachers can provide more accurate guidance to students based on these feedbacks, helping them correct incorrect movements and improve their skill levels.

Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy

Objectives Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy. Methods Data from 95 patients who underwent 16-session therapy were analyzed using machine learning. Weekly progress was monitored with the Outcome Questionnaire (OQ45) and Target Complaints (TC). A machine learning model identified change trajectories for responders and non-responders, with a random forest algorithm and elastic net modeling predicting trajectory group membership using pre-treatment data. Results A weak positive relationship was found between the trajectories of the two outcome variables. The results of the different analysis methods were compared and discussed. Important predictors of OQ45 trajectories, based on random forest modeling, included initial symptom severity, difficulties in emotion regulation, coldness, avoidant attachment, conscientiousness, interpersonal problems, non-acceptance of negative emotion, neuroticism, emotional clarity, impulsivity, and emotion awareness (72.8% accuracy). Initial problem severity, self-scarifying extraversion, and non-assertiveness were the most dominant predictors for TC trajectories (62.8% accuracy). Conclusions These findings offer data-driven insights for selecting short-term dynamic therapy. Predicting response for the OQ45, a nomothetic measure, does not extend to the TC, an idiographic measure, and vice versa, highlighting the importance of multidimensional outcome evaluations for personalized treatment.

Trajectory of change in body mass index in Parkinson's disease

IntroductionGastrointestinal (GI) symptoms are some of the most common non-motor symptoms in Parkinson's. Weight is a nutritional metric and can be affected by dysfunction of the gastrointestinal (GI) tract. This study aims to explore the change in trajectory of body mass index (BMI) in individuals with Parkinson's over the course of the disease including the prodromal and post-diagnostic periods. MethodsThis was a retrospective longitudinal study of data from participants from the PRIME Parkinson UK cross-sectional study. Participants were included if they had had one or more weights and height recorded in the primary care electronic health record. Results287 patients were initially included but only 234 could be included in the analysis of BMI trajectory. Using a piecewise linear mixed model, we determined that there was a ‘change point’ in BMI trajectory. This occurred on average 3.73 years after diagnosis, when the mean BMI was 26.4 kg/m2. Prior to this change point, the estimated mean rate of change in BMI was −0.09 kg/m2 (95 % credible interval −0.20,0.00 kg/m2) per year. However, after the change point, we observed a more accelerated decline in BMI, with an estimated mean rate of change of −0.34 kg/m2 (95 % credible interval −0.70,-0.07 kg/m2) per year. ConclusionThere was a modest weight loss trajectory in the pre-diagnostic period consistent with clinically stable weight. However, after several years, post-diagnosis BMI loss became more marked. In clinical practice interventions could be targeted at this time point to optimize and maintain nutritional intake.