Fitted Model Parameters Research Articles

The mediating role of nurses' spiritual well-being between moral resilience and compassion fatigue: A multicenter structural equation model study.

To investigate the mediating role of spiritual well-being in the association between compassion fatigue and moral resilience among nurses. Nurses are inevitably placed in situations of compassion fatigue. Their moral resilience and spiritual well-being may play a crucial role in mitigating the impacts of compassion fatigue. Nonetheless, spiritual well-being, which mediates the influence between compassion fatigue and moral resilience, remains scarce among nurses. Cross-sectional and correlational design. Nurses (n=465) from four government-owned tertiary hospitals in Saudi Arabia were recruited and completed three self-report scales from July to December 2023. Descriptive statistics (e.g., mean, standard deviation, frequency, and proportions) and inferential statistics (Spearman rho and structural equation modeling) were used for data analysis. The emerging model afforded acceptable model fit parameters. Moral resilience had a negative effect on compassion fatigue (β=-0.05, p=0.003) and a positive influence on spiritual well-being (β=0.51, p=0.003). Spiritual well-being negatively influenced compassion fatigue (β=-0.90, p=0.003). Moral resilience had a moderate, negative, indirect effect on compassion fatigue through the mediation of spiritual well-being (β=-0.47, p=0.002). Our study offered a model that validated the mediating role of spiritual well-being in the association between moral resilience and compassion fatigue. Moral resilience directly and indirectly influences spiritual well-being and compassion fatigue, respectively. Healthcare institutions that employ nurses must continually assess compassion fatigue levels and provide necessary interventions. Nurses, nurse managers, and healthcare institutions may leverage moral resilience to improve nurses' spiritual well-being while averting the negative effects of compassion fatigue. Healthcare institutions may incorporate spiritual care into their mainstream support interventions to enhance their compassion, reduce fatigue, and enhance their mental well-being.

Nursing students' personality traits, sleep quality, social media addiction, and academic performance: A multi-site structural equation model analysis

BackgroundThe associations between personality traits, sleep quality, social media addiction, and academic performance have been well-reported across the literature among university students. However, the interaction among these variables remains understudied among nursing students of underdeveloped countries like the Philippines. PurposeThis study investigated the interrelationships of personality traits, sleep quality, social media addiction, and academic performance among Filipino nursing students. MethodsA correlational, cross-sectional design and convenience sampling was used to recruit nursing students (n = 823) from five nursing colleges in the Philippines. Three standardized instruments (10-item Personality Inventory, 19-item Pittsburgh Sleep Quality Index, and 6-item Bergen Social Media Addiction Scale) were used to collect data from June to August 2023. Covariance-based structural equation modeling (CB-SEM) was employed to analyze the data. STROBE checklist guided in reporting study results. ResultsThe emerging model demonstrated satisfactory model fit parameters. Nursing students reported high levels of extraversion (x̅=6.13, SD = 2.08) with low scores in emotional stability (x̅ =6.76, SD = 2.30) and openness to experiences (x̅ =8.80, SD = 2.09). The majority had poor sleep quality (x̅=8.35, SD = 3.12) and were at low risk for social media addiction (x̅=17.73, SD = 5.61). Conscientiousness (β = −0.13, p = 0.031) and emotional stability (β = −0.35, p = 0.002) inversely influenced sleep quality. Agreeableness (OR = 1.13, p = 0.007) and openness to experiences (OR = 1.11, p = 0.050) had a positive effect, while conscientiousness (OR = 0.84, p = 0.006) had a negative effect on high-risk social media addiction. Conscientiousness (β = 0.12, p = 0.010) significantly and positively influenced academic performance. Conscientiousness and emotional stability were able to measure 15.00 % of sleep quality's total variance. ConclusionPolicymakers, nursing college administrators, and nurse educators could use these findings to evaluate students' personality traits and create programs that will promote better sleep quality and prevent social media addiction. Hence, enhancing academic performance.

A novel data-driven framework of elastoplastic constitutive model based on geometric physical information

The advantages of data science have inspired the development of data-driven approaches for solving constitutive modeling problems, which have become a new research focus in engineering mechanics. These approaches help fully utilize the information inherent in the data, bypassing the traditional modeling processes.In order to advance the development of Constitutive Model Based on Data-Driven (CMBDD), we introduced a novel framework called the Geometric Physical Information-enhanced Data-Driven ElastoPlastic constitutive model (IDD-EP) under hysteretic loading paths. IDD-EP adopts an ”Encoder-Decoder” framework, with the information transmission between the encoder and decoder facilitated by the ”Geometric Physical Information” proposed in this paper. Specifically, IDD-EP-I, serving as the encoder, extracts Geometric Physical Information from experimental constitutive images, which is then transmitted to the modular data-driven decoder IDD-EP-II, designed based on physical mechanisms, to compute material responses under arbitrary paths. IDD-EP aims to establish a constitutive model relying solely on a single small sample without using deep learning techniques and avoids the challenge of model parameter fitting in classical models through a non-mathematical model design.In addition to discussing the general framework of IDD-EP, this paper specifically demonstrates a specialized version of the IDD-EP framework based on uniaxial buckling-restrained braces (BRBs), which are commonly used in structural vibration control, in order to showcase a specific implementation example of the IDD-EP model. The IDD-EP method in this paper accurately predicts the mechanical response of the BRB using only one constitutive experimental image, without the need to pre-select a base constitutive model or fit model parameters. This innovative approach to IDD-EP opens a new avenue for constitutive modeling of elastoplastic materials and may offer solutions to a wider range of history-dependent constitutive modeling challenges in the future.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Reliability analysis of the vehicle door system EDCU based on Weibull distribution

The paper established a two-parameter Weibull distribution model to analyze the failure law of key components of metro vehicles and employed the least squares method to fit the parameters of the Weibull function. The paper provided a detailed introduction of the model's parameter fitting method based on actual historical failure data. Taking the EDCU (Electrical Door Control Unit) as an example, the failure rate, cumulative failure rate, and reliability of EDCU for 3 different vehicle models were calculated, and the calculation results were verified to conform to the Weibull distribution through Q-Q diagrams. The results show that although the designed service life of EDCU of vehicle door system is 15 years, the reliability of the EDCU decreases significantly around 10 years. The EDCU reliability of the three vehicle models declines by 22%, 4.2%, 10.6% and the failure rates are 2.7%, 0.57%, 1.13%. Based on the reliability of EDCU mentioned above, while ensuring the reliability of components, the maintenance methods of components can be optimized to reduce maintenance costs.

Acceptability and psychometric properties of four scales assessing the impact of Type 2 diabetes on quality of life-Results of 'YourSAY: Quality of Life'.

To assess and compare the psychometric properties and acceptability of four diabetes-specific quality of life (QoL) scales among adults with Type 2 diabetes (T2D). Adults (≥18 years) with T2D living in the United Kingdom (n = 1465) or Australia (n = 248) completed a cross-sectional, online survey including the following: ADDQoL, DCP, DIDP and Diabetes QoL-Q (presented in randomised order), followed by rating scales to assess clarity, relevance, ease of completion, length and comprehensiveness of each scale. Demographic, clinical and psychosocial characteristics were collected. Acceptability (scale completeness and user ratings), response patterns, structure (exploratory and confirmatory factor analyses) and validity (convergent, confirmatory, divergent and known-groups) were examined. Data were analysed by country to assess cross-country reproducibility. High completion rates (≥89%) and positive user ratings were observed across scales indicating broad acceptability. The DIDP was the strongest performing scale: highest completion rate (97%), user ratings (≥84% positive) and most satisfactory psychometric properties (highest variance explained, consistent factor loadings >0.5 on all items and most permissible model fit parameters). Scale-level floor effects may suggest domain omissions for the brief DIDP. The current study provides novel insights into the acceptability, validity and reliability of diabetes-specific QoL measures for adults with T2D. Consistent with the published Type 1 diabetes cohort findings, the DIDP is recommended as a brief, acceptable and psychometrically sound measure. However, selection needs to be considered in the context of the specific research or clinical aims and further evidence (e.g. responsiveness) may be required before it can be recommended for use in trials or prospective studies.

MATHEMATICAL MODEL OF LASSA FEVER TRANSMISSION DYNAMICS IN PREVALENT COMMUNITIES IN NIGERIA: THE CASE STUDY OF ONDO STATE

With the current waive of global health problems and resurgence of many disease around the world. Cholera, Yellow fever,SARS-CoV-2, Monkey pox and Lassa fever resurgence in some West African countries, with Ondo State recording highest number of Lassa fever case in Nigeria. Prompting Nigeria Centre for Disease Control (NCDC), Ondo State Primary Health (OSPH) expert and researchers begin ways to reduce transmission dynamics of Lassa Fever Disease (LFD). In this research, we developed and investigated using System of Ordinary Differential Equation (ODE) mathematical model of Lassa fever disease transmission dynamics, verifying positivity of system of equation as well as feasible region of the model. However, the Disease Free Equilibrium (DFE) of the model is computed and analysed with basic reproduction number $R_0$ of the model, showing the global stability of the DFE. Furthermore, we determined using model-fitting parameters the condition to attain stability. Finally, numerical simulations shows reduction in transmission with effective pest control measure.

Estimating pulmonary arterial remodeling via an animal-specific computational model of pulmonary artery stenosis.

Pulmonary artery stenosis (PAS) often presents in children with congenital heart disease, altering blood flow and pressure during critical periods of growth and development. Variability in stenosis onset, duration, and severity result in variable growth and remodeling of the pulmonary vasculature. Computational fluid dynamics (CFD) models enable investigation into the hemodynamic impact and altered mechanics associated with PAS. In this study, a one-dimensional (1D) fluid dynamics model was used to simulate hemodynamics throughout the pulmonary arteries of individual animals. The geometry of the large pulmonary arteries was prescribed by animal-specific imaging, whereas the distal vasculature was simulated by a three-element Windkessel model at each terminal vessel outlet. Remodeling of the pulmonary vasculature, which cannot be measured in vivo, was estimated via model-fitted parameters. The large artery stiffness was significantly higher on the left side of the vasculature in the left pulmonary artery (LPA) stenosis group, but neither side differed from the sham group. The sham group exhibited a balanced distribution of total distal vascular resistance, whereas the left side was generally larger in the LPA stenosis group, with no significant differences between groups. In contrast, the peripheral compliance on the right side of the LPA stenosis group was significantly greater than the corresponding side of the sham group. Further analysis indicated the underperfused distal vasculature likely moderately decreased in radius with little change in stiffness given the increase in thickness observed with histology. Ultimately, our model enables greater understanding of pulmonary arterial adaptation due to LPA stenosis and has potential for use as a tool to noninvasively estimate remodeling of the pulmonary vasculature.

Biodegradation of glucose and paracetamol and process optimisation with open mixed microbial cultures in lab-scale sequencing batch reactors

In the context of biological wastewater treatment, this study investigated xenobiotics (man-made chemicals) biodegradation and process optimisation to maximise sustainability. Lab-scale sequencing batch reactors (SBRs) fed with paracetamol (model xenobiotic) and glucose (model biogenic substrate) each as only carbon source (feed concentration 1 g L−1) were run in a range of HRTs (hydraulic retention time, range 0.5–2 d) and SRTs (solids retention time, range 1–28 d). For both substrates, the removal of COD (chemical oxygen demand) increased as the SRT increased, reaching 97 % for glucose and 86 % for paracetamol. At the lowest HRT, the high solid losses with the effluent caused a reduction in the SRT with worse substrate removal. The fraction of the removed COD which was converted into biomass increased for lower SRT and overall the best performance for sustainability (compromise between substrate removal, energy consumption and plant footprint) was observed with HRT 1 d and SRT 11–12 d. The experimental data were modelled using two different approaches: linearisation of a continuous-flow model and non-linear parameter fitting with a periodic steady-state SBR model. The results indicated faster growth rate and slightly higher growth yield for glucose than for paracetamol.

Inspiring nurses’ sustainability mindset: Exploring the Mediating Role of Organizational Culture on the relationship between Pro-social Leader behaviors and nurses’ sustainability consciousness

BackgroundSince nurses are at the frontline of healthcare delivery, their actions and understanding of the environment have a big impact on how long healthcare systems can last. It is essential to comprehend the elements that impact nurses’ sustainability consciousness to encourage ecologically conscious actions in the healthcare industry.AimThis study aimed to explore the relationship between pro-social leader behaviors and nurses’ sustainability consciousness and testify to the mediating role of organizational culture in this relationship.DesignA cross-sectional descriptive correlational design by STROBE criteria was used.Methods and toolsAn approach to a judgmental non-probability sampling technique was employed to obtain data from 350 nurses in an Egyptian hospital. Three measurement surveys were employed: Organizational Culture Survey, Prosociality Scale, and, Sustainability Consciousness Questionnaire (SCQ-S). Relationships were shown using structural equation modeling and descriptive and inferential statistics.Results53.4% of nurses have high perceptions of organizational culture, and the majority of nurses (85.7%) have high perceptions of prosocial leader behaviors. Furthermore, 60.9% of nurses have high perceptions of sustainability consciousness. Additionally, Prosocial leader behaviors positively correlated with organizational culture (r = 0.129) and nurses’ sustainability consciousness (r = 0.274). The indirect effect of prosocial leader behaviors on nurses’ sustainability consciousness through organizational culture is calculated by multiplying the coefficients of both direct effects (0.129 * 0.159 = 0.811). This means that for each unit increase in prosocial leader behaviors, we would expect a 0.811 unit increase in nurses’ sustainability consciousness through the mediating effect of organizational culture. The model appears to match the data well based on the model fit parameters (CFI = 1.000, IFI = 1.000, RMSEA = 0.114).ConclusionsThe study highlights the impact of pro-social leader behaviors on nurses’ sustainability consciousness through the organizational culture as a mediating factor. Nursing Implications: Findings from this research can promote environmental stewardship and sustainable practices in the healthcare sector by illuminating the elements that can encourage and support a sustainability-oriented mindset among nurses. To promote a more sustainable future for the nursing profession, the findings can guide activities in nursing education, corporate culture transformation, and leadership development.

Computer modeling of a new type galactic cosmic rays simulator

A new type of a galactic cosmic rays (GCR) simulator, provided at the JINR Laboratory of Radiation Biology, is potentially capable of generating a complex radiation field with inclusions of a variety of ions with a wide energy range and with required abundance at the charged particle accelerators. This complex multicomponent radiation field simulates radiation environment inside a spacecraft during an interplanetary flight, for example, to Mars. The article provides an analytical description of the GCR simulator as well as a description of a specially developed software that enables selection of necessary parameters of a simulator model for creating relevant mixed radiation conditions. The software implements processing of data obtained with Monte Carlo-based FLUKA and PHITS programs, fitting and optimization of model parameters as well as data visualization tools.

Simplified Physics-Based Battery Model for Stationary Energy-Storage Applications

To perform safe battery operation and avoid unnecessary degradation, battery models are needed. How complex, or close to reality the model should be, depends on the purpose of the model and the computational power available. The possibility for accurate parameterization before model implementation is another crucial aspect for the validity. Physics-based models have the advantage to solve for internal battery states, which includes important information to avoid accelerated degradation and to evaluate state of health. But the parameterization effort can be extensive and the computational cost too high to solve for in real time. Identifying the most essential processes and if possible, simplify the model is therefore motivated.Parameter sensitivity is a powerful tool to investigate how much certain parameters and the related processes effect the output signal, i.e. battery voltage. Under normal operating conditions the cells are rarely completely discharged, and as a result the diffusion processes show low sensitivity during operation [1]. In this work we therefore explore the utilization of a physics-based model solving for the electrolyte dynamics, rather than the solid phase diffusion commonly applied in the so-called single particle model. By neglecting the diffusion processes, the second dimension in the pseudo-two-dimensional (P2D) model can be ignored and the computational complexity simplified. The parameterization strategy and validation of the model is further discussed.We evaluate our model by comparing simulations with experimental data from batteries subjected to stationary energy storage applications. The cells are commercial 18650-type NMC/Graphite cells with 2.6 Ah capacity. Different service cycles have different needs in terms of current and voltage, see examples in Figure 1. Capturing these behaviours with a physics-based model might pose different challenges and will further affect the degradation of the cells [2]. A wide range of models exist in literature to capture degradation mechanisms during battery operation [3]. To keep the computational and parameterization effort as low as possible, our methodology is not to include more physics, but instead update the parameters to the processes already included in the model [4]. We explore this strategy comparing the behaviour from different types of services, as well as batteries where the application changes, to see the model response and analyse the battery state of health, see Figure 2. Electrochemical techniques such as differential voltage analysis and electrochemical impedance spectroscopy is included to support our conclusions.Our results highlight the value of application considerations when designing battery models, rather than only focusing on physical extreme points. We present an alternative physics-based model with the ability to capture crucial degradation phenomena and validate it against operational data. We believe our findings can be of value for smarter battery operating strategies and a greater understanding of application dependant lithium-ion battery degradation.Figure 1. Some of the application duty cycles part of the experimental study. a) Current profile peak shaving b) Current profile frequency regulation c) Voltage response peak shaving d) Voltage response frequency regulation.Figure 2. Degradation trends from the experimental study. Purple cell initially performing PS following application change to FR_high cycling a) Capacity evolution of the cycled cells b) tortuosity parameter evolution obtained from model parameter fitting c) Measured impedance response during cell reference performance tests d) Calculated charge transfer resistance from measured impedance data.

A new dimension in the variability of AGB stars: Convection patterns size changes with pulsation

Context. Stellar convection plays an important role in atmospheric dynamics, wind formation, and the mass-loss processes in asymptotic giant branch stars. However, a direct characterization of convective surface structures in terms of size, contrast, and lifespan is quite challenging, as spatially resolving these features requires the highest angular resolution. Aims. We aim to characterize the size of convective structures on the surface of the O-rich AGB star R Car to test different theoretical predictions based on mixing-length theory from solar models. Methods. We used infrared low-spectral resolution (R ∼ 35) interferometric data in the H-band (∼1.76 μm) obtained by the instrument PIONIER at the Very Large Telescope Interferometer (VLTI) to image the star’s surface at two epochs separated by approximately six years. Using a power spectrum analysis, we estimated the horizontal size of the structures on the surface of R Car. The sizes of the stellar disk at different phases of a pulsation cycle were obtained using parametric model fitting in the Fourier domain. Results. Our analysis supports that the sizes of the structures in R Car are correlated with variations in the pressure scale height in the atmosphere of the target, as predicted by theoretical models based on solar convective processes. We observed that these structures grow in size when the star expands within a pulsation cycle. While the information is still scarce, this observational finding highlights the role of convection in the dynamics of those objects. New interferometric imaging campaigns with the renewed capabilities of the VLTI are envisioned to expand our analysis to a larger sample of objects.

Blind and robust estimation of adaptive optics point spread function and diffuse halo with sharp-edged objects

Context. Initially designed to detect and characterise exoplanets, extreme adaptive optics (AO) systems open a new window onto the Solar System by resolving its small bodies. Nonetheless, their study remains limited by the accuracy of the knowledge of the AO-corrected point spread function (AO-PSF) that degrades their image and produces a bright halo, potentially hiding faint moons in their close vicinity. Aims. To overcome the random nature of AO-PSFs, I aim to develop a method that blindly recovers the PSF and its faint structured extensions directly into the data of interest, without any prior on the instrument or the object’s shape. The objectives are both to deconvolve the object and to properly estimate and remove its surrounding halo to highlight potential faint companions. Methods. My method first estimated the PSF core via a parametric model fit, under the assumption of a sharp-edged flat object. Then, the resolved object and the PSF extensions were alternatively deconvolved with a robust method, insensitive to model outliers, such as cosmic rays or unresolved moons. Finally, the complex halo produced by the AO system was modelled and removed from the data. Results. The method is validated on realistic simulations with an on-sky AO-PSF from the SPHERE/ZIMPOL instrument. On real data, the proposed blind deconvolution algorithm strongly improves the image sharpness and retrieves details on the surface of asteroids. In addition, their moons are visible in all tested epochs despite important variability in turbulence conditions. Conclusions. My method shows the feasibility of retrieving the complex features of AO-PSFs directly from the data of interest. It paves the way towards more precise studies of asteroid surfaces and the discovery and characterisation of Solar System moons in archival data or with future instruments on extremely large telescopes with ever more complex AO-PSFs.

Irradiance separation model parameter estimation from historical cloud cover statistical properties

Irradiance separation models allow the decomposition of Global Horizontal Irradiance into Diffuse Horizontal and Direct Normal Irradiances. These models need fitting to the irradiance characteristics of the location of interest using locally measured ground data. For locations that only measure Global Horizontal Irradiance, current state of the art establishes the use of parameters obtained for another location that measures the three components, with similar climate characteristics. Nevertheless, this results in a lack of localized character for estimates and requires fitting model parameters for all possible climates, which can be infeasible given data availability. This work presents a novel approach based on the hypothesis that the separation model's parameters are a function of the statistical properties of satellite-derived cloud cover estimates. The proposed methodology was evaluated in 23 sites covering all main Köppen-Geiger climatic types and different cloud coverage properties using the Boland-Ridley-Lauret diffuse fraction model. The model performs similarly as locally adjusting the model, with Root Mean Square Errors of 0.087–0.15 diffuse fraction units versus 0.077–0.127 for locally optimized parameters, and offers adequate performance across climates and cloud characteristics. These results encourage future research by generalizing parameter estimation for other diffuse fraction models. The main applications for this research are the estimation of irradiance components where no local data is available for model fitting and the enhancement or complementarity of satellite estimates of surface irradiance. Furthermore, it allows the estimation of missing irradiance components due to equipment failure in locations with insufficient data for a representative, locally adapted model.

Cost share-induced technological change: An analytical classical-evolutionary model

This paper builds on prior work by the author on cost share-induced technological change. The theoretical model views selection of candidate innovations as a capital budgeting exercise. In this paper it treats the case in which firms target an incremental rate of profit, which introduces a nonzero threshold into a “selection frontier”. This presents analytical challenges, which are resolved in this paper by assuming that the probability distribution of potential increases in productivity among the set of fit innovations is normal. That permits an explicit derivation of a micro-level model of cost share-induced technological change that can be taken as a candidate functional form for an aggregate model. The model is calibrated against historical data for India, China, and the United States, three large continental economies at different levels of per capita GDP. The model is able to fit the data with reasonable fidelity, and the fitted model parameters can be given a reasonable interpretation. The paper further shows that combining cost share-induced technological change with price-setting behavior produces theoretically interesting results.

Cell-Electrode Models for Impedance Analysis of Epithelial and Endothelial Monolayers Cultured on Microelectrodes.

Electric cell-substrate impedance sensing has been used to measure transepithelial and transendothelial impedances of cultured cell layers and extract cell parameters such as junctional resistance, cell-substrate separation, and membrane capacitance. Previously, a three-path cell-electrode model comprising two transcellular pathways and one paracellular pathway was developed for the impedance analysis of MDCK cells. By ignoring the resistances of the lateral intercellular spaces, we develop a simplified three-path model for the impedance analysis of epithelial cells and solve the model equations in a closed form. The calculated impedance values obtained from this simplified cell-electrode model at frequencies ranging from 31.25 Hz to 100 kHz agree well with the experimental data obtained from MDCK and OVCA429 cells. We also describe how the change in each model-fitting parameter influences the electrical impedance spectra of MDCK cell layers. By assuming that the junctional resistance is much smaller than the specific impedance through the lateral cell membrane, the simplified three-path model reduces to a two-path model, which can be used for the impedance analysis of endothelial cells and other disk-shaped cells with low junctional resistances. The measured impedance spectra of HUVEC and HaCaT cell monolayers nearly coincide with the impedance data calculated from the two-path model.

Automated data-driven method for creating digital building models from dense point clouds and images through semantic segmentation and parametric model fitting

This paper proposes an automated method for creating semantic digital building models using dense point clouds and images. The method employs a hybrid bottom-up, top-down approach, integrating artificial intelligence capabilities in scene understanding with domain engineering knowledge to overcome challenges in indoor 3D reconstruction. The pre-trained PointTransformer semantic segmentation model extracts thirteen building objects, where the wall and ceiling segments are utilized in a 3D space parsing algorithm. The parameterized floor plan map is then generated using a data-driven approach, enabling the creation of an extruded volumetric digital model. Additionally, the YOLOV8 object detection network recognizes doors and windows in images derived from projected points of the wall instances. The validation results for six building datasets with different layouts showcase the effectiveness of the proposed model reconstruction algorithm, with a mean error of about 7 cm between the parameters of elements in digital reference models and reconstructed models. This highlights AI’s potential in automating the creation of digital models for the real world.

Migration Rules and Mechanisms of Nano-Biochar in Soil Columns under Various Transport Conditions.

Compared to traditional biochar (BC), nano-biochar (NBC) boasts superior physicochemical properties, promising extensive applications in agriculture, ecological environments, and beyond. Due to its strong adsorption and migration properties, NBC may carry nutrients or pollutants to deeper soil layers or even groundwater, causing serious environmental risks. Nevertheless, the migration rules and mechanisms of NBC in soil are still unclear. Therefore, this study employed soil column migration experiments to systematically explore the migration rules and mechanisms of NBC under various flow rates, initial soil water contents, soil depths, and soil textures. The results showed that regulated by smaller particle size differences and greater surface charges, NBC exhibited a stronger migration ability compared with traditional BC. As the soil texture transitioned from fine to coarse, the migration capability of NBC significantly improved, driven by both pore structure and interaction forces as described by the DLVO theory. The migration ability of NBC was also greatly boosted as the soil transitioned from saturated to unsaturated conditions, primarily because of preferential flow. When the flow rate increased from 70% KS to 100% KS and 130% KS, the migration ability of NBC also increased accordingly, as changes in injection flow rates altered the velocity distribution of pore water. NBC in 25 cm soil columns was more prone to shallow retention compared with 10 cm soil columns, resulting in weaker overall migration ability. In addition, through fitting of the two-site kinetic model and related parameters, the penetration curves of NBC under various variable conditions were effectively characterized. These findings could offer valuable insights for NBC's future efficient, rational, and sustainable utilization, facilitating the evaluation and mitigation of its potential environmental risks.

Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees.