Static Factor Research Articles

Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome

BackgroundThe mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored.MethodsBased on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test.ResultsA total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48–5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19–4.08) (P = 0.012) for group 2, 2.70 (1.40–5.23) (P = 0.003) for group 3, 2.16 (1.04–4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020).ConclusionsFor RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

Estimation of Newmark displacement of homogeneous soil slopes from static factor of safety and ground motion parameters

Estimation of Newmark displacement of homogeneous soil slopes from static factor of safety and ground motion parameters

Procedural Approach to the Public Interest in Migration Control when Applying Article 8 of the ECHR

This research explores the European Court of Human Rights’ (ECtHR or the Court) application of Article 8 of the European Convention of Human Rights (ECHR) when engaging the public interest in migration control. The study research explains the current case law of the Court and examines when the public interest in migration control can be applied as a legitimate aim. The research is questioning whether the public interest in controlling migration can be used as a legitimate aim when an interference of the right to family life has been established and whether the public interest in migration control should be seen as a static factor. The research claims that the Court’s unclear way of distinguishing between positive and negative obligations and its lack of assessing the public interests when balancing the personal interests against the public interests in controlling migration makes the case law inconsistent and unclear. In order to make the case law more consistent the research suggests that the Court should use a procedural approach like in cases where the State’s interest in public safety is engaged.

Stress Simulation on Four-Bar Link-Type Transplanting Device of Semiautomatic Vegetable Transplanter

The aim of this study is to analyze the stress exerted on a four-bar link-type transplanting device using two distinct methods: stress measurement performed during a field test and stress simulation. A field test is conducted to measure stress using a strain gauge positioned at 15 specific points on the transplanting device. Subsequently, the measured strain data are converted into calculated stress data. In another method, stress is simulated using specialized multibody dynamic simulation software. The simulation results are compared with the stress measured during field tests to verify the simulation model. Based on the results, the maximum stress derived from the simulation correlates with the measured results, although notable discrepancies are shown, particularly at strain gauge positions 11 and 13. The maximum stress derived from the simulation is used to calculate the static safety factor of the transplanting device. The peak stress derived from the simulation aligns with the measured results, although significant discrepancies are observed at positions corresponding to strain gauges 4 and 10. The maximum stress (150.82 MPa) is observed on the link of the transplanting device, and the static safety factor determined via the simulation is 1.39.

Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.

STUDY OF THE LEVEL OF FLOOD VULNERABILITY BASED ON THE BIOPHYSICAL ASPECTS OF THE LAND IN THE KRUENG JREUE SUB-DASIN ACEH BESAR

The increasing intensity of land conversion in the Krueng Jreue Aceh Besar Sub-watershed due to changes in land use causes changes in the biophysical characteristics of the land. Changes in the biophysical characteristics of land cause an increase in the level of flood vulnerability. This research uses a descriptive method (survey). The research results show: Variables determining the level of flood vulnerability are based on biophysical aspects of the land, namely: dynamic factors (rainfall, land use), and static factors (soil infiltration, slope slope).Flood Vulnerability Level Classes (TKB) in the Krueng Jreue Sub-watershed consist of: Very Vulnerable, settlements and rice fields (42≤TKB≤50); Vulnerable, moor (34≤TKB≤41); Moderately Vulnerable/Moderate open land, bushes, grasslands, secondary forests and primary forests (26≤TKB≤33); mean 32.38 (moderately vulnerable class).Residential land and rice fields in the cultivation area, covering an area of ​​624.76 ha or 2.70% of the total area of ​​the Krueng Jreue Sub-watershed (23,218.06 ha), have the highest level of vulnerability to flood disasters. Flood disasters occur in November-December, when rainfall is high.

A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.

Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.

THE INFLUENCE OF STATIC AND DYNAMIC FACTORS ON LONGITUDINAL PATTERNS OF CAREGIVER NETWORK

Abstract Care networks are defined as the collection of individuals who provide support for older adults with health challenges. Although many older adults receive care from multiple caregivers, with varying intensities of caregiving, little is known about the structure of their care networks and whether or to what extent it changes over time. This study aims to examine the stability of care networks and the predictive effects of static and dynamic factors on patterns of caregiving networks. Using data from the National Health Aging Trends (NHAT) and National Study of Caregiving (NSOC), we examine the static and dynamic intrapersonal structures of care arrangements with regard to systematic continuity across 7 years. We utilize both Latent Profile Analysis and Latent Transition Analysis to identify different types of caregiving arrangements and patterns of transitions over time. Moreover, we use logistic regressions to examine how individual and time-varying factors affect those arrangements and transitions. The results show three groups of caregiving arrangements: 1) intense care with one caregiver, 2) moderate-level care with multiple caregivers, and 3) low-level care with multiple caregivers. Among the groups, moderate-level care with multiple caregivers is the most stable group. Moreover, static factors, such as gender and race, and dynamic factors, such as psychological resilience of care recipients, significantly impacts the probability of transitioning from low-level care to intense care over time. The findings highlight the utility of Latent Transition Analysis in caregiving networks, suggesting that patterns of caregiving outcomes should be understood as process as well as a condition.

Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

The geoscientific community primarily focuses on predicting where landslides are likely to occur through data-driven susceptibility models. Recently, few researchers have turned to statistical estimation of landslide planimetric area within a given terrain unit and exploration of the spatiotemporal distribution of landslide occurrence. However, these data-driven approaches cannot fulfill the commonly accepted definition of landslide hazard and cannot predict the location, time/frequency, and magnitude of landslide occurrence simultaneously. This study proposes a unified and data-driven framework for landslide hazard spatiotemporal modeling, enabling dynamic and probabilistic estimation of landslide occurrence within a given slope unit at a specific time period and for a specific landslide magnitude. This framework not only involves static and dynamic factors in modelling, but also considers spatial and temporal interactions to explore the spatiotemporal variation effects of landslide hazard. We test this framework on the main island of Taiwan with a multi-temporal landslide inventory from 2004 to 2018. Specifically, this framework assumes that the occurrence and size of landslides spatiotemporally follows a binomial and a Log-Gaussian distribution, respectively, and then uses generalized additive models to achieve the estimation of landslide hazard probability. Finally, the performance is validated by a spatiotemporal leave-one-out cross-validation scheme. We believe that this framework will lay the foundation for the community to estimate landslide hazard in a unified and probabilistic data-driven prototype. We envision it could lead to studies of dynamic hazard responses to climate change.

The diffusion path of distributed photovoltaic power generation technology driven by individual behavior

The widespread adoption of distributed photovoltaic (PV) power generation technologies among electricity consumers is a crucial factor in enabling the power system's low-carbon transition. While extensive research has explored consumers' willingness to adopt this technology, prior studies have primarily focused on static psychological factors. This study, however, takes a heterogeneous behavioral perspective by examining the dynamic effects of individual behavioral interactions on technology diffusion. Utilizing behavior-driven simulation technology, we developed a diffusion model pertaining to photovoltaic power generation technology. The model enhances the explanatory capacity and predictive accuracy of subjects' behavioral choices by quantifying the effects of adoption scale and external economic interventions. Our simulation results indicate that residential environment constraints, resulting from individual behavioral differences, can influence the diffusion potential of technology. Furthermore, non-mandatory promotion methods are more effective in enhancing user adoption willingness. Interestingly, we found that free installation interventions tend to reduce the diffusion effect in later stages and should not be implemented in isolation. These insights can contribute to enhancing the diffusion of distributed PV power generation technology and furthering the development of low-carbon electricity.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.

Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators. EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs. To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning. The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2). n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram). EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.

МЕТОД ПРОЕКТУВАЛЬНОГО РОЗРАХУНКУ НА МІЦНІСТЬ ПОПЕРЕЧНОГО СТИКУ ПАНЕЛЕЙ ВІД'ЄМНОЇ ЧАСТИНИ КРИЛА ТА ЦЕНТРОПЛАНА ТРАНСПОРТНОГО ЛІТАКА

Due to the limitation in the dimensions of the structural elements of the modeled wing contour and the possibility of increasing their cross-sections in the area of the transverse joint, only on the inside, there is a need to find an effective solution for the design, mass of the joint, as well as its statical strength at the modeling stage. The article presents the method and its application using the example of a preliminary analysis of the design calculation of the design of the flange connection of the center section with the outer wing section (OWS) of a transport aircraft. The method is based on the calculation of stresses caused by the discrete transmission of forces between elements. In the calculation method, a simplified statically indeterminate joint model is obtained according to the geometric characteristics and force transmission characteristics of each node section in the flange joint structure. During the calculations, the following are determined: diagrams of the bending moment and axial forces obtained using the force method, the distribution of force loads in each section of the model for subsequent analysis of the static safety factor. The resulting calculation results are compared with the airworthiness requirements to determine whether they meet the design requirements. For components with greater static strength, or those that do not meet the requirements, it is recommended to further modify the design parameters to ensure an effective design of the center section joint with the outer wing section and subsequent re-calculation. The calculation method used has practical value as a preliminary engineering analysis.The geometric characteristics of the junction of the center section with the negative part of the wing were modeled using the CAD/CAMCAE CATIA system.

Stress Simulation on Cam-Type Transplanting Device of Semiautomatic Vegetable Transplanter

Stress measurements play a crucial role in safety analyses of transplanting devices. Strain gauges for stress measurements during field tests can be expensive and time-consuming. The aim of this study was to investigate the stress on the transplanting device of a cam-type semiautomatic vegetable transplanter using a simulation method. A three-dimensional simulation model was established, considering the dimensions and material properties of the transplanting device. The stress distribution and maximum stress values were obtained through simulations. The maximum stress values at 15 points within the transplanting device determined via the simulation were compared with the experimental stress data to verify the stress simulation model. The results show that the maximum stress obtained from the simulation correlated with that of the measured results, although differences were observed at different locations, particularly at strain gauge positions 11 and 13. Based on the simulation results, the maximum stress occurs at the upper link of the cam-type transplanting device, reaching a magnitude of 201.21 MPa, and the static safety factor is 1.04.

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.

The Impact of the Local Green Economy of Ghana: A General Equilibrium Analysis

The Ghanaian forestry sector has experienced a paradigm shift toward resilient and sustainable forest management practices through climate change mitigation and adaptation policies. Ghana has acknowledged the urgent need to address these issues via comprehensive policy measures as a nation that is vulnerable to the adverse effects of climate change. To assess economy-wide impacts, this paper employs a computable general equilibrium with a static specific factor model that can be readily simulated with various vectors of price changes. The model uses a competitive production structure that assumes constant returns, full employment, competitive pricing, and perfect labor mobility across sectors. Factor shares, industry shares, and substitution matrices from 2022 Labor Force Report’s data were constructed for four skilled groups (managers, professionals, service, and production) across industry, service, the rest of agriculture, and forestry sectors. Predictions of price increases from climate change mitigation policies compliance were simulated for the long run and short run. Forestry, agricultural, and industrial sectors will enjoy higher prices under a green economy, but these would not translate into higher wages. It is rather capital owners in agriculture and forestry that would benefit. Wage adjustments to the green economy are smaller than what might be expected in a partial equilibrium model.

Limited prosocial emotions (LPE) specifier in conduct disorder and offending behavior: findings from a 10-year prospective longitudinal study of juveniles in residential care

BackgroundSince the introduction of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5, a limited prosocial emotion (LPE) specifier has been added to the conduct disorder (CD) diagnosis in addition to the age of onset specifier. It was suggested that this would identify a subgroup with severe antisocial and/or aggressive behavior with serious current and future (mental health) impairment. Research in recent years has shown that this is indeed a subgroup with severe antisocial behavior; however, mental health problems do not appear to differ from those of youth with CD without LPE. Most research to date has been cross-sectional. However, longitudinal research is urgently needed to better understand the predictive value of the LPE specifier. The aim of the current longitudinal study is to examine future offending behavior of youth with CD with compared to youth without the LPE specifier. In addition, the predictive value of the categorical LPE specifier and the dimensional LPE score will be examined beyond factors that are strongly associated with future offending (i.e., gender, age, and prior offending).MethodsAdolescents and young adults (12–25) with CD (assessed with the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version [K-SADS-PL]) with (N = 61) and without (N = 75) the LPE specifier (assessed with the Callous-Unemotional [CU] dimension of the Youth Psychopathic traits Inventory [YPI]) (in line with Jambroes et al., 2016) were compared on sociodemographic characteristics, mental health problems and offending behavior. Future (general and violent) offending was based on official conviction data.ResultsOur results showed that youth with CD with and without the LPE specifier did not differ in self-reported and informant-reported mental health problems. However, youth with CD with the LPE specifier showed more offending behavior and personality pathology at baseline. In addition, the categorical LPE specifier was associated with future general offending, but not with future violent offending. The dimensional LPE score was associated with both future general and violent offending. However, after adjustment for gender, age, and prior delinquency, these associations disappeared, with the exception of the association between the dimensional LPE score and violent offending, which remained significant even after controlling for gender, age, and prior violent offending.DiscussionIn conclusion, there seems to be evidence of a relationship between limited prosocial emotions and future offending behavior in youth with CD. This relationship, however, should not be overestimated, as there are other (static) factors (e.g. gender and prior offending behavior) that also have a strong influence on future (violent) offending behavior. Still, from a clinical point of view, a dynamic factor like prosocial emotional skills is a good focus for reducing the risk of future offending behavior.

Modeling the lagged impacts of hourly weather and speed variation factors on the segment crash risk of rural interstate freeways: Applying a space–time-stratified case-crossover design

In the realm of traditional roadway crash studies, cross-sectional modeling methods have been commonly employed to investigate the intricate relationship between the crash risk of roadway segments and variables including roadway geometrics, weather conditions, and speed distribution. However, these methodologies assume that the explanatory variables and target variable are only associated within the same time period. Although this assumption is well-founded for static factors like roadway geometrics, it proves inadequate when dealing with highly time-varying variables related to weather conditions and speed variation. Recent investigations have unveiled that these time-varying variables may exhibit lagged impacts on segment crash risk, necessitating the adoption of more comprehensive time-series modeling methods. This study employs two interpretable statistical methods, namely the distributed lag model (DLM) and the distributed lag nonlinear model (DLNM), to elucidate meaningful and interpretable patterns of the lagged impacts of weather and speed variation factors on segment crash risk. Empirical evidence based on crash data collected from rural interstate freeways in the state of Texas demonstrates coherent and interpretable lagged impact patterns of these variables. This study's results serve as strong support for the existence of lagged impacts on roadway segment-level crash risk, emphasizing the need for considering time-series effects in future crash modeling research. Furthermore, these findings could offer practical implications for the design of real-time crash warning systems and the effective implementation of variable speed limits to enhance road safety.

Molecular design, gel properties, and anti-settling ability with natural rosin-based sulfonamide compounds regulated by conjugation

The design of compounds with excellent controllable weak forces is of great significance to solve current industrial problems. Herein, three natural rosin-based sulfonamide compounds (i.e., dehydroabietyl-based benzene sulfonamide (DBS-1); dehydroabietyl-based benzyl sulfonamide (DBS-2), and dehydrofiranic acid-based p-toluene sulfonamide (DBS-3)) were designed and synthesized by regulating the conjugation of sulfamide, carbonamide, and benzene. Organogels with rod-like, spherical, and layered morphologies were formed by π–π stacking and one-dimensional hydrogen bonding in the absence of an external driving force. Rheological studies emphasized the mechanical strength of these organogels, and gel three-dimensional networks at the nano- and micron-scales can effectively capture six volatile organic solvents. Notably, the presence of conjugation can effectively regulate the dynamic and static equilibrium of organogels to construct stable supermolecular assemblies. DBS-1, which contained abundant conjugated structures and weak intermolecular forces, exhibited amazing anti-settling ability in synthetic oil-based drilling fluids, with a static sedimentation factor of 0.512. Thus, industrial problems such as barite settlement can be solved by using DBS-1 as an additive agent. This report provides inspiration for the effective design of natural anti-settling agents.