Fitting Model Research Articles

Study of adsorption isotherms and kinetics models for lead ions removal from simulated wastewater using three-dimensional, printed water-filtration system with synthesized α-Fe2O3

In this study, lead ions were removed from simulated wastewater by batch adsorptive filtration. A three-dimensional printed water filtration was designed using a blender and successfully synthesized from a PLA spool and a 3d printer machine. The filter surface was treated with NaOH solution to hydrolyze the polyester group in PLA. After that, α-Fe2O3 was prepared by the coprecipitation method and coated on the filter through doping and drying. α-Fe2O3 was characterized by X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDX), and Brunauer-Emmet-teller (BET). The results successfully indicate the synthesis of α-Fe2O3 consisting of 67.3% Fe and 32.7% O2. α-Fe2O3 appeared to have a surface area of 95 m²/g with distinct morphology and functional group. The effect of initial Pb concentration (100-800) ppm and the effect of contact time (5-120) min on the removal process were studied. Lab data was collected, and the adsorption was investigated kinetically. The 3d printed filter system coated by iron oxide showed significant and promised results for wastewater treatment and lead ions removal using this technique. Also, the results showed that the adsorption of Pb ions on the α-Fe2O3 surface fitted with the Langmuir model, with a correlation coefficient (R²) of 98.65%. The kinetic model’s investigations revealed that the pseudo-second-order model was the most fitted model for the process with R² of 99.997%. The highest adsorption saturation capacity was 165.2 mg/g adsorbent.

On the function of TRAP substrate-binding proteins: Conformational variation of the sialic acid binding protein SiaP

Tripartite ATP-independent periplasmic (TRAP) transporters are analogous to ABC transporters in that they use a substrate-binding protein to scavenge metabolites (e.g., N-acetylneuraminate) and deliver them to the membrane components for import. TRAP substrate-binding proteins are thought to bind the substrate using a two-state (open and closed) induced-fit mechanism. We solved the structure of the TRAP N-acetylneuraminate substrate-binding protein from Aggregatibacter actinomycetemcomitans (AaSiaP) in both the open ligand-free and closed liganded conformations. Surprisingly, we also observed an intermediate conformation, where AaSiaP is mostly closed and is bound to a non-cognate ligand, acetate, which hints at how N-acetylneuraminate binding stabilises a fully closed state. AaSiaP preferentially binds N-acetylneuraminate (KD = 0.4 μM) compared to N-glycolylneuraminate (KD = 4.4 μM), which is explained by the closed-N-acetylneuraminate bound structure. Small-angle X-ray scattering data alongside molecular dynamics simulations suggest the AaSiaP adopts a more open state in solution than in crystal. However, the open unliganded conformation can also sample closed conformations. Molecular dynamics simulations also demonstrate the importance of water molecules for stabilising the closed conformation. Although our data is consistent with an induced fit model of binding, we suggest that the open unliganded conformation may sample multiple states capable of binding substrate. The mechanism by which the ligand is released for import remains to be determined.

Mapping Risk–Return Linkages and Volatility Spillover in BRICS Stock Markets through the Lens of Linear and Non-Linear GARCH Models

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making.

Acoustic Detection of Pipeline Blockages in Gas Extraction Systems: A Novel Approach

Gas extraction is crucial for coal mine safety, yet pipeline blockages by solid slag and water severely hinder efficiency and pose risks. Traditional detection methods are limited by rapid signal attenuation and noise interference. In this study, an acoustic detection technology is introduced for pipeline blockages, utilizing sensors at potential blockage points to collect sound wave data. Experiments with a scaled pipeline model reveal that slag blockages produce characteristic peaks in the 1200 Hz–2000 Hz range, while water blockages show peaks in the 1 kHz–2 kHz and 3.5 kHz–4.5 kHz bands. The longitudinal blockage intensity and extraction pressure significantly affect the sound pressure levels. A reliable fitting model predicts the blockage intensity based on acoustic signals, achieving high accuracy. This novel method enhances blockage identification, offering a non-invasive, cost-effective solution that improves coal mine safety and efficiency.

Frequency-of-seeing curves (psychometric functions) for perimetric stimuli in age-related macular degeneration.

Frequency-of-seeing (FoS) curves (psychometric functions) for perimetric stimuli have been widely used in computer simulations of new visual field test procedures. FoS curves for age-related macular degeneration (AMD) are not available in the literature and are needed for the development of improved microperimetry test procedures, which are of particular interest for use as clinical trial endpoints. Data were refitted from a previous study to generate FoS curves for 20 participants with AMD, each tested at nine locations within the central 10°. Stimulus parameters, background luminance and dB scale were matched to the MAIA-2 microperimeter, and stimuli were presented in a method of constant stimuli to build up FoS curves over multiple runs. FoS curves were fitted with a modified cumulative Gaussian function. The relationship between sensitivity and slope of fitted FoS curves was modelled by robust linear regression, producing models both with and without an eccentricity parameter. FoS curves were satisfactorily fitted to data from 174 visual field locations in 20 participants (age 65-83 years, 11 female). Each curve was made up of a median of 243 (range 177-297) stimulus presentations over a median of 12 (range 9-32) levels. Median sensitivity was 25.5 dB (range 3.8-31.4 dB). The median slope (SD of fitted function) was 1.6 dB (range 0.5-8.5 dB). As in previous studies of other conditions, the slope of fitted FoS curves increased as sensitivity decreased (p < 0.001). FoS are provided for participants with AMD, as well as models of the relationship between sensitivity and slope. These fitted models and data may be useful for computer simulation studies of microperimetry procedures. Full details of the fitted curves are provided as supporting information.

Typological classification of attitudes toward premarital sexual behavior among Chinese undergraduates

BackgroundPremarital sexual behavior (PSB) is a controversial topic in China. However, in recent times, changes in attitudes have led to PSB being more common among college students. This study investigated the attitudes of Chinese undergraduates toward PSB to identify whether a typological classification exists among them.MethodsA total of 278 undergraduate students from two public universities in Mainland China completed a 17-item survey that included five dimensions( emotions, responsiveness, sexual health, sexual freedom, and condemnation) on attitudes toward PSB. Data were collected online from April 2023 to July 2023.ResultsThe PSB questionnaire demonstrated good reliability and construct validity in this study, with a Cronbach’s α of 0.759 and a KMO value of 0.769. Meanwhile, a series of models were estimated specifying one through five latent classes and three typologies on the attitude were identified: “Affective,”“Avoidant,” and “Open.” After the best fitting model was determined, multiple analysis of variance testing of different factors such as gender, year of study and where one came from were found to have significant effects on attitude complexity (p < .01).ConclusionThis research will contribute to the dissemination of information on PSB in China, which will be helpful in understanding relevant issues associated with PSB.

Plunge-diving into dynamic body acceleration and energy expenditure in the Peruvian booby.

Daily energy expenditure (DEE) is the result of decisions on how to allocate time among activities (resting, commuting, and foraging) and the energy costs of those activities. Dynamic body acceleration (DBA), which measures acceleration associated with movement, can be used to estimate DEE. Previous studies of DBA-DEE correlations in birds occurred on species foraging below their thermoneutral zone, potentially decoupling the DBA-DEE relationship. We used doubly-labelled water (DLW) to validate the use of DBA on plunge-diving seabirds, Peruvian boobies (Sula variegata), foraging in waters above their thermoneutral zone (>19 °C). Mass-specific DEEDLW in boobies was 1.12 kJ/d/g, and higher in males than in females. DBA alone provided the best fitting model to estimate mass-specific DEEDLW compared to models partitioned per activity and time-budget models. Nonetheless, the model parametrizing activity at and away of their onshore breeding colony was the most parsimonious model (r=0.6). This r value, although high, is lower than all other avian studies, implying that temperature is not the main cause of DBA-DEE decoupling in birds. Time at the colony (∼80% of the day) was the largest contributor to DEE as it was the most time-consuming activity and involved nest defense. However, foraging was the most power-consuming activity (4.6 times higher activity-specific metabolic rate than resting at the colony), and commuting-flight was higher than in other gliding seabirds. In short, DBA alone can act as a proxy for DEE, opening avenues to measure the conservation energetics of this seabird in the rapidly-changing Peruvian Humboldt Current System.

A new hybrid approach to the impact of renewable energy consumption on economic growth: sectoral differences in European Union countries

The current energy crisis has shown all states that energy from renewable sources can be a determining factor in the states’ sustainable development. Several papers have studied the relationship between renewable energy consumption and economic development, finding various situations, but there is no consensus. Thus, this study aims to first investigate the causal relationship between economic growth and total and sectoral renewable energy consumption (European Union and each Member State, for 2004–2020) by testing various linear and non-linear regressions to choose the fit model. Second, the investigation extends to analysing the impact of renewable energy consumption by sector on economic development. A hybrid approach is used, namely structural equation modelling and artificial neural networks. The study findings indicate the effect and the meaning (directly or inversely) exerted by the three sectoral components on economic growth, with different intensities from one country to another. There is a significant influence on the consumption of renewable energy in the heating and cooling sectors and transport on gross domestic product at the European Union level and for most member states. Based on the obtained results, a series of theoretical, practical, and political implications are provided.

Mediating effect of ambiguity tolerance in automated writing evaluation research model

In light of the advancement of generative artificial intelligence (GenAI) technology-empowered automated writing evaluation (AWE) system represents a revolutionary paradigm at the forefront of mobile assisted English learning (MAEL). Numerous empirical studies have been carried out to test the efficacy of automated writing corrective feedback in writing assistance. However, it has seldom been investigated from the cognitive stance of AWE technology enhanced embodied learning. Embodied cognitive linguistics intensifies that learning experience is enhanced in the process of conceptualizing the empirical world. Therefore, the current study explores technology characteristics and individual characteristics incorporating the psychological construct of ambiguity tolerance. Emanating from psychology, ambiguity tolerance describes people’s preponderance to manage uncertainties and unpredictable challenges in the learning process. This study utilized the partial least square structural equation modeling (PLS-SEM) method to analyze 679 valid questionnaire responses via SPSS 29 and SmartPLS 4. The results elucidated that the AWE research model incorporating the technology acceptance model (TAM model), task technology fit model (TTF model), and individual characteristics could predict the user’s adoption of AWE software in the completion of writing tasks. Moreover, ambiguity tolerance functions as an effective mediator in the GenAI technology-empowered automated writing evaluation research model. This study provides technical implications for AWE developers to design AWE software suitable for individual characteristics. Future research could combine qualitative research methods with multivariant statistical approaches to meticulously investigate the interaction of literacy cultivation and emotional intelligence in the utilization of AWE technology for academic purposes in the GenAI era.

Characterization method of conductive film state of micro-cathode arc thrusters

Micro-Cathode Arc Thruster (μCAT) is an advanced electric propulsion system developed in the past decades to meet the maneuvering requirements of space microsatellites. However, the constraint on the its application is the short lifespan of the thruster. This is attributed to the conductive film, a key component of thruster discharge. In this study, we propose a measurement method for μCAT conductive film, and enables the evaluation of the evolution process of film thickness. The method is based on the four-probe resistivity testing theory and size effect fitting model, and establishes a method for characterizing the thickness and resistivity of μCAT conductive films. A comparison was made between the corrected resistivity test results of 1, 2, 5, and 10 μm film and the fitting results of the titanium film, revealing an error ofapproximately 10%. Additionally, a total of 2500 ignition experiments were conducted to verify the film thickness and resistance measurement of this method. This research serves as a valuable basis for the lifetime investigation of μCAT. Consequently, the thickness of the film progressively decreases with each discharge cycle, and the resistivity exhibits a corresponding increase due to the size effect. This method could be used as an effective method for exploring the μCAT lifespan and performance.

Enhancing environmental sustainability in Asian textile supply chains: insights from agile practices and mediating variables

The current energy crisis has shown all states that energy from renewable sources can be a determining factor in the states’ sustainable development. Several papers have studied the relationship between renewable energy consumption and economic development, finding various situations, but there is no consensus. Thus, this study aims to first investigate the causal relationship between economic growth and total and sectoral renewable energy consumption (European Union and each Member State, for 2004–2020) by testing various linear and non-linear regressions to choose the fit model. Second, the investigation extends to analysing the impact of renewable energy consumption by sector on economic development. A hybrid approach is used, namely structural equation modelling and artificial neural networks. The study findings indicate the effect and the meaning (directly or inversely) exerted by the three sectoral components on economic growth, with different intensities from one country to another. There is a significant influence on the consumption of renewable energy in the heating and cooling sectors and transport on gross domestic product at the European Union level and for most member states. Based on the obtained results, a series of theoretical, practical, and political implications are provided.

Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras

The aim of this study was to estimate field-grown tomato yield (weight) and quantity of tomatoes using a self-developed robot and digital single lens reflex (DSLR) camera pictures. The authors suggest a new approach to predicting tomato yield that is based on images taken in the field, 3D scanning, and shape. Field pictures were used for tomato segmentation to determine the ripeness of the crop. A convolution neural network (CNN) model using TensorFlow library was devised for the segmentation of tomato berries along with a small robot, which had a 59.3 % F1 score. To enhance the accurate tomato crop model and to estimate the yield later, point cloud imaging was applied using a Ciclops 3D scanner. The best fitting sphere model was generated using the 3D model. The most optimal model was the 3D model, which gave the best representation and provided the weight of the tomatoes with a relative error of 21.90 % and a standard deviation of 17.9665 %. The results indicate a consistent object-based classification of the tomato crop above the plant/row level with an accuracy of 55.33 %, which is better than in-row sampling (images taken by the robot). By comparing the measured and estimated yield, the average difference for DSLR camera images was more favorable at 3.42 kg.

Percentage of variance accounted for in structural equation models: The rediscovery of the goodness of fit index.

This article delves into the often-overlooked metric of percentage of variance accounted for in structural equation models (SEM). The goodness of fit index (GFI) provides the percentage of variance of the sum of squared covariances explained by the model. Despite being introduced over four decades ago, the GFI has been overshadowed in favor of fit indices that prioritize distinctions between close and nonclose fitting models. Similar to R² in regression, the GFI should not be used to this aim but rather to quantify the model's utility. The central aim of this study is to reintroduce the GFI, introducing a novel approach to computing the GFI using mean and mean-and-variance corrected test statistics, specifically designed for nonnormal data. We use an extensive simulation study to evaluate the precision of inferences on the GFI, including point estimates and confidence intervals. The findings demonstrate that the GFI can be very accurately estimated, even with nonnormal data, and that confidence intervals exhibit reasonable accuracy across diverse conditions, including large models and nonnormal data scenarios. The article provides methods and code for estimating the GFI in any SEM, urging researchers to reconsider the reporting of the percentage of variance accounted for as an essential tool for model assessment and selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hyperbaric oxygen for moderate-to-severe traumatic brain injury: outcomes 5-8 years after injury.

The use of hyperbaric oxygen (HBO 2 ) in the field of traumatic brain injury (TBI) is becoming more widespread and increasing yearly, however there are few prognostic reports on long-term functional efficacy. The aim of this study was to assess the functional prognosis of patients with moderate-to-severe TBI 5-8 years following HBO 2 treatments and to explore the optimal HBO 2 regimen associated with prognosis, using a retrospective study. Clinical data were retrospectively collected as a baseline for patients with moderate-to-severe TBI treated with HBO 2 during inpatient rehabilitation from January 2014 to December 2017. The primary outcome measure was the Disability Rating Scale (DRS) and the secondary outcome measure was the Glasgow Outcome Scale. A total of 133 patients enrolled, with 9 (6.8%) dying, 41 (30.8%) remaining moderately disabled or worse (DRS scores 4-29), 83 (62.4%) remaining partially/mildly disabled or no disability (DRS scores 0-3). Logistic regression analysis revealed that age at injury (odds ratio (OR), 0.96; 95% confidence interval (CI), 0.92-0.99), length of intensive care unit stay (OR, 0.94; 95% CI, 0.88-0.99), and HBO 2 sessions (OR, 0.97; 95% CI, 0.95-0.99) were variables that independently influenced long-term prognosis. Cubic fitting models revealed that 14 and 21.6 sessions of HBO 2 could be effective for moderate and severe TBI, respectively. This study highlighted that HBO 2 in moderate-to-severe TBI may contribute to minimize death and reduce overall disability in the long-term. However, clinicians should be cautious of the potential risk of adverse long-term prognosis from excessive HBO 2 exposure when tailoring individualized HBO 2 regimens for patients with moderate-to-severe TBI. The study was registered on ClinicalTrials.gov (NCT05387018) on March 31, 2022.

Cross-cultural adaptation and validation of the Arabic version of the knee and hip health-related quality of life (Mini-OAKHQOL) questionnaire in male Saudi patients with osteoarthritis: a methodological observational design.

Osteoarthritis (OA) is common in Saudi Arabia, has a significant impact on quality of life (QoL), and lacks a specific questionnaire to measure QoL. The primary objective of this study was to translate and cross-culturally adapt the Mini Osteoarthritis Knee and Hip Quality of Life (Mini-OAKHQOL) questionnaire into Arabic and to determine its psychometric properties among OA knee and/or hip patients in Saudi Arabia. A methodological observational design was conducted and followed standard guidelines for cross-cultural adaptation of Mini-OAKHQOL into Saudi Arabic. Two hundred and eight primary OA knee and/or hip male participants aged between 45 and 80 years with a mean age of 58.65±13.8 years and a BMI of 29.5±1.2 kg/m2 were included and performed the stages of translation to target Arabic language (forward T1 and T2), synthesized an Arabic draft (T12), then back-translated to English (BT1 and BT2), followed by expert committee review to rectify the deficiencies leading to a prefinal stage involving a pilot test on native Arabic speakers, thereby finalized a final Arabic version. The Arabic Mini-OAKHQOL, Arabic Short Form 12 (SF12), and visual analog scale (VAS) were administered to analyze internal consistency (IC), test-retest reliability at baseline as well as one week later (up to the 10th day). The construct validity was tested using Spearman's rank correlation, and factor analysis was done to establish a five-factor fit model. Homogeneity was determined using principal component analysis (PCA). Floor and ceiling effects calculated in percentages. The Arabic Mini-OAKHQOL showed an excellent Cronbach's alpha of the overall scale (α=0.931) for its internal consistency and an excellent intraclass correlation coefficient (ICC) of 0.947 for its retest reliability, with a high response rate of 93.75%. The construct validity of this scale was good with Ar-SF12 and VAS pain. A five-factor model fit was considered acceptable, and factor loading for each item found within the permissible limits confirmed the factorial validity. None of the items, dimensions, or overall scale showed either a floor or ceiling effect. The adapted and tested Arabic Mini-OAKHQOL is a reliable and valid questionnaire to measure the impact of knee and/or hip OA on quality of life in the Saudi Arabian male OA population to reduce the respondent's burden for use in clinical and prospective studies.

Spatialization and Analysis of China’s GDP Based on NPP/VIIRS Data from 2013 to 2023

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.

Facile synthesis of zeolitic‐imidazole framework‐67 (ZIF‐67) for the adsorption of indigo carmine dye

AbstractThis study aims to synthesize ZIF‐67 for the adsorption of indigo carmine (I.C.) dye. ZIF‐67 materials were synthesized under different conditions and the synthesis condition of ZIF‐67 giving the best adsorption efficiency was obtained. In batch adsorption studies, pH, adsorbent amount, contact time, initial dye concentration, and temperature effects were investigated. Adsorption data were applied to Langmuir, Freundlich, and Temkin isotherm models. The best correlation value was obtained in Langmuir isotherm (R2 = 0.999). The adsorption of I.C. dye with ZIF‐67 occurred in a monolayer with a homogeneous surface according to Langmuir isotherm. The highest adsorption capacity value of ZIF‐67 metal–organic framework for I.C. dye was found as 370 mg/g. Kinetic model studies were also performed and pseudo first‐order, pseudo second‐order, and intraparticle diffusion models were used. Pseudo second‐order was the best fitting kinetic model to the data (R2 = 0.997). Adsorption mechanisms of ZIF‐67 for I.C. dye include electrostatic interaction, π–π interaction, and hydrogen bonding. Thermodynamic studies showed that I.C. removal by ZIF‐67 is an exothermic and spontaneous process. ZIF‐67 was characterized by X‐ray diffraction (XRD), Fourier transform infrared spectroscopy (FT‐IR), field emission scanning electron microscope (FE‐SEM), and N2 adsorption–desorption. The surface area of synthesized ZIF‐67 was found 1493 m2 g−1. The salt concentration effect on I.C. adsorption by ZIF‐67 was also investigated, showing an increase in adsorption capacities with increasing salt concentration. The regeneration study was carried out and after 5 cycles, the reduction in efficiency was 21%.

Assessing Self-Compassion in Older Adults: Factorial Structure of the Self-Compassion Scale and Invariance Across Sex at Birth

Abstract Objectives Self-compassion is a valuable resource for positive ageing and should be measured in a reliable and valid manner. However, findings regarding the factor structure of the Self-Compassion Scale (SCS) have been inconsistent and have been particularly lacking in older adults. Hence, the present study intended to investigate the factor structure, internal consistency, and measurement invariance across sex at birth of the SCS in adults ≥ 65 years old. Method The present study used a sample of 418 community-dwelling, Portuguese older adults aged 65–94 years (M = 74.45, SD = 6.94; 59.3% female). Confirmatory factor analyses were conducted to test six different measurement models, and reliability and multigroup analyses were performed for the best fitting model. Results Although all models initially showed poor adjustment, the correlated 6-factor model had the comparatively better fit. After modifications, this model achieved an acceptable fit (RMSEA = 0.060 and SRMR = 0.062). Internal consistency of this model was adequate (Cronbach's alpha values ranging from 0.67 to 0.86) and strong invariance across sex at birth was demonstrated (i.e., configural, metric, and scalar models showed non-significant Δχ2). Conclusions Current findings suggest that assessing the self-compassion components is advised in older adults, as this 6-component model was reliable and provided an acceptable fit for both male and female older adults. This 6-component model is a parsimonious, theoretically sound, and statistically valid option to assess self-compassion in this population. It is, however, not an ideal solution, and an acceptable fit was only achieved after modifications in the initial 6-component model. Preregistration This study is not preregistered.

On the calibration and improvement of human mobility models in intercity transportation system

As one of the research highlights of physics-level modelling, human mobility has generated numerous universal laws over the past decades. However, the majority of research has concentrated on the intracity networks, leaving the intercity mobility systems with insufficient attention despite its increasingly crucial role in the development of urban agglomerations. Related research gaps further extend to the limited understanding of spatiotemporal heterogeneity in intercity human mobility. To bridge these gaps, our study systematically validated and improved the modelling framework of intercity mobility flows utilizing real-world data sources. Specifically, building upon the nationwide Location-based Services (LBS) datasets in China, the applicability of classic human mobility models, including gravity model and intervening opportunities-class models, was extensively explored in the intercity domain by developing fitting models that incorporated multi-class urban attributes. Then, we contributed to proposing improved models that consider the diverse attraction effect of the origin and potential destinations. Moreover, our research scope was expanded to incorporate spatiotemporal heterogeneity through model comparisons among various city sets during both regular period and holiday. The findings suggested that our improved models effectively enhance the modelling accuracy while strengthening the explanatory power. They especially demonstrate a balanced performance even when handling datasets with spatiotemporal heterogeneity. Consequently, this study provides valuable insights into understanding intercity human mobility from the intrinsic mechanism of opportunity attraction. Our models hold practical significance in accurately modelling intercity mobility flows utilizing observable urban attributes and spatial layouts, further providing effective tools for preemptive traffic management.

Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R2 of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.