Fitting Model Research Articles

Psychometric properties of the Maladjustment Inventory in a Spanish clinical and student sample.

The various systems of diagnosis and classification of mental disorders underline the need to evaluate the interference caused by the different disorders in a person's daily life. The Maladjustment Inventory (MI) evaluates the impairment in the individual's functioning in a brief and self-applied way, through six items. The objective of this research was to explore the psychometric properties of the MI scores through two studies, one with a Spanish clinical sample (Study 1) and another with a Spanish university students' sample (Study 2). The total sample was made up of 928 participants (81.1% women, n = 495 clinical sample). Descriptive analyses, exploration of internal structure and reliability, exploratory and confirmatory factor analyses, relationship with other variables (quality of life, anxiety, depression, neuroticism and extraversion), and percentiles and T-scores were performed. The results showed good psychometric properties of the MI, with a good fit model for one factor solution in both samples, Cronbach's alpha coefficient of 0.84-88, and evidence of validity based on the relationship with other variables. The good psychometric properties of the MI, together with its brevity, make it a recommended instrument for the evaluation of interference in both clinical and research contexts.

An efficient deep learning method for amino acid substitution model selection.

Amino acid substitution models play an important role in studying the evolutionary relationships among species from protein sequences. The amino acid substitution model consists of a large number of parameters; therefore, it is estimated from hundreds or thousands of alignments. Both general models and clade-specific models have been estimated and widely used in phylogenetic analyses. The maximum likelihood method is normally used to select the best fit model for a specific protein alignment under the study. A number of studies have discussed theoretical concerns as well as computational burden of the maximum likelihood methods in model selection. Recently, machine learning methods have been proposed for selecting nucleotide models. In this paper, we propose a method to measure substitution rates among amino acids (called summary statistics) from protein alignments to efficiently train a deep learning network of so-called ModelDetector for detecting amino acid substitution models. The ModelDetector network was trained from 2,246,400 alignments on a computer with 8 cores (without GPU) in about 3.3 hours. Experiments on simulation data showed that the accuracy of the ModelDetector was comparable with that of the maximum likelihood method ModelFinder. It was orders of magnitude faster than the maximum likelihood method in inferring amino acid substitution models and able to analyze genome alignments with millions of sites in minutes. The results indicate that the deep learning network can play as a promising tool for amino acid substitution model selection.

Quantum rotational dynamics of linear C5 at low interstellar temperatures for H2 collision.

The quantum dynamics of carbon chains through H2 and He collisions in the interstellar medium (ISM) is an important step toward accurate modeling of their abundance in non-local thermodynamic equilibrium conditions. The C5(Σg+1) molecule is the longest pure carbon chain detected in the ISM to date. While He collisions are computationally easy to perform, the collision with much more abundant H2 is both complicated and computationally demanding. Using templates for approximating p-H2 collisional rates, such as scaling He rates and using a reduced 4D → 2D potential energy surface (PES), has limited applicability. On the other hand, any such approximation does not exist for o-H2. Therefore, a full rotational dynamics of C5 with both p- and o-H2 is performed considering both molecules as rigid-rotors. The PES is calculated using CCSD(T)-F12a/AVTZ, and a neural network fitting model has been carefully chosen to strictly obey spectroscopic accuracy and augment the PES. The augmented PES is then expanded into radial terms using the bispherical harmonics function, and close coupling calculations have been done to get the cross sections and, subsequently, rate coefficients for various rotational transitions of C5.

X-ray Reflectivity Probing the Structural Evolution of Sunflower Proteins Adsorbed at the Air-Water Interface.

The study delves into the adsorption of sunflower proteins at the air/water interface using specular X-ray reflection. The research involved fitting models of the protein films to the reflectivity data, resulting in detailed images of the X-ray scattering length density profiles perpendicular to the air/water interface. The sunflower protein isolate that is examined consists of multiple components, and the study proposes a transition from a 1-slab model to a 4-slab model to represent the changing layer structure over time. This transition is significant as it reflects the increasing complexity of the protein film as more proteins adsorb at the interface. Initially, sunflower proteins form a monolayer at the air/water boundary, consisting of a protein-rich, hydrophobic portion closest to the interface and a more diffuse, hydrophilic portion extending into the bulk aqueous phase. The structural changes at the interface over time depend on the bulk protein concentration in the solution. For solutions at relatively low concentrations (C ≤ 0.5 g/L), a lower amount of adsorption results in a larger, more extensive interface area for each species and a thinner protein adsorption layer. The overall thickness of a saturated monolayer is approximately 100 Å, which is close to the maximum dimension of sunflower globulins, with the thickness of the corresponding hydrophobic portion being about 20 Å. For solutions at relatively high concentrations (C ≥ 1.0 g/L), even after forming a saturated monolayer, structural evolution continues within the experimental time frame, occurring on both hydrophilic and hydrophobic sides. Additional proteins from the bulk diffuse toward the interface, forming an extra layer in the water phase and causing an increase in the overall thickness. Furthermore, a distinct sublayer develops next to the air phase, indicating a further structuration of the hydrophobic portion.

Prognostic nutritional index and albuminuria in adults aged 20 years and above: a cross-sectional analysis in the United States

Background and objectiveAlbuminuria is an important early marker of kidney damage and progression of chronic kidney disease and is also linked to several chronic systemic diseases. The Prognostic Nutritional Index (PNI) is widely used in the assessment of multiple diseases. However, research dealing with the relationship between PNI and albuminuria remains scarce. This research project aims to examine this association.Methods and materialsThe present study employed data from the National Health and Nutrition Examination Survey (NHANES) between 2017 and 2020, including 7,737 adult participants who met the study criteria. PNI was analyzed as a quartile-categorized variable. Multivariable regression models and smoothing curve fitting were adopted to examine the relationship between PNI and albuminuria. In order to ascertain the stability of the association across different populations, subgroup analyses were performed.ResultsThe study found a statistically significant inverse relationship between higher PNI levels and the prevalence of albuminuria. The fully adjusted model indicates that a one-unit increase in PNI is associated with a 4% reduced odds of albuminuria prevalence [0.96 (0.93, 0.98)]. Quartile analysis showed a stable inverse relationship, with the highest PNI quartile having the significantly lower odds of albuminuria prevalence [0.76 (0.62, 0.94), p for trend = 0.0004]. Smooth curve fitting and two-piecewise linear regression models indicated a nonlinear relationship between PNI and albuminuria, with a turning point at 42. Subgroup analysis confirmed the reliability of the inverse relationship between PNI and albuminuria across all groups.ConclusionThe findings of this study indicated that higher PNI levels are significantly inversely related to the odds prevalence of albuminuria. PNI could serve as an important predictor for the occurrence of albuminuria. Further prospective studies are needed to validate this association.

Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method

Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.

Impact of Stakeholder Collaboration on Project Milestones, Knowledge Sharing, and Conflict Prevention in Construction Companies of Hargeisa, Somaliland

This research delves into the contribution of collaboration among stakeholders towards enhancing the project as a whole by delivering project milestones, sharing knowledge and preventing conflicts. Out of the 210 adherents, data was collected from a sample of 210 who responded through a questionnaire and the results indicated that reasonably high percentages of those surveyed demonstrated a shared sense of understanding and readiness towards working collaboratively within project constraints. The descriptive statistics and the regression analysis that was employed to analyze data has shown that collaboration among stakeholders has got a high positive impact on milestone achievement (β = 0.75, p < 0.001) and knowledge sharing (β = 0.60, p < 0.001). However, on the other hand, while they have an impact, the effects in terms of preventing conflict are not strong, revealing a positive correlation (β = 0.50, p < 0.001) management of conflict, but moderate. These findings support the notion that within the management perspective, strategy focused communication, trust, and participative decision-making processes are key to high success in projects. The Structural Equation Modeling (SEM) results also highlight that the fitted models are of a good fit (CFI=0.98, TLI=0.97, RMSEA=0.04) which support the hypotheses saying there is greatest amount of stakeholder collaboration, there is greatest milestone achievement, there will be more knowledge sharing and conflict will be less. The reliability analysis also supported the hypothesis that constructs were interrelated and associates with one another as all the values of Cronbach’s Alpha were above 0.70 (0.83, 0.78 and 0.81) for Stakeholder Engagement, Project Trust and Conflict respectively.

Application of ARIMA Methods on Unemployment and Inflation Rates in Nigeria

In Nigeria, Unemployment and inflation rate are some of the problems bedeviling the economy. The inability of Job seekers to secure gainful employment tends to create disaffection among these people and cause some of them especially the youth, to resort to social vices. In the motivation to experience low unemployment rate and inflation rate which are economic indicators in Nigeria, deriving appropriate ARIMA model to give insight into future occurrence of these indicators for intervention calls for the application of ARIMA model. This study seek to fit ARIMA model to unemployment rate and inflation rate and forecast future values for both unemployment rate and inflation rate. Utilizing secondary data sourced from the National Bureau of Statistics (NBS) from 1991 to 2020. The findings reveal that, for each of the two series, to obtain the appropriate ARIMA model that fits the data, ten closely suitable ARIMA models were identified. Using the least values of AIC, AICc and BIC as selection criteria, ARIMA (0, 1, 1) was found to be the best fitted model for each of the two series. Subsequent diagnostic checks confirm the adequacy of the fitted models, with residual analyses indicating no significant autocorrelation or heteroscedasticity. The forecasted values for the unemployment rate series and inflation rate series maintain a constant point forecast. This implies that unemployment rate and inflation rate in Nigeria does not reveal a noticeable increase nor decrease but will be constant in Nigeria. Due to the unchanging forecasted value which appears not to be decreasing, there is need for interventions that will lead to the visible reduction of present unemployment rate and inflation rate in Nigeria.

An algorithm for lane detection based on RIME optimization and optimal threshold

In order to address the challenges of low lane line detection rates caused by complex road conditions,we propose a novel algorithm that integrates frost and ice optimisation with optimal thresholding. A pre-processing model based on Retinex theory is used to reduce noise and preserve grey scale detail. The optimal OTSU threshold is determined for segmentation, which is enhanced by tent mapping. To further enhance the precision of the detection process, the binarized image is transformed into a bird’s-eye view, and the lane line pixel features are identified through the use of an adaptive sliding window. Ultimately, the RANSAC algorithm is utilized in conjunction with a parabolic model for lane line fitting. The experimental results demonstrate that, in comparison to similar image segmentation algorithms, the proposed method exhibits a notable advantage in terms of threshold calculation error and computational efficiency. Moreover, in comparison to analogous line detection algorithms, the detection accuracy rate reaches 93.87%, effectively reducing the impact of interference factors and demonstrating remarkable robustness that surpasses the traditional Hough Transform, which has an accuracy of 43.2%, and sliding window and Hough transform, with an accuracy of 89.16%. The code of our research work is publicly available at: https://github.com/zx2000430/rime.

Statistical mechanics of crystal nuclei of hard spheres.

In the study of crystal nucleation via computer simulations, hard spheres are arguably the most extensively explored model system. Nonetheless, even in this simple model system, the complex thermodynamics of crystal nuclei can sometimes give rise to counterintuitive results, such as the recent observation that the pressure inside a critical nucleus is lower than that of the surrounding fluid, seemingly clashing with the strictly positive Young-Laplace pressure we would expect in liquid droplets. Here, we re-derive many of the founding equations associated with crystal nucleation and use the hard-sphere model to demonstrate how they give rise to this negative pressure difference. We exploit the fact that, in the canonical ensemble, a nucleus can be in a (meta)stable equilibrium with the fluid and measure the surface stress for both flat and curved interfaces. Additionally, we explain the effect of defects on the chemical potential inside the crystal nucleus. Finally, we present a simple, fitted thermodynamic model to capture the properties of the nucleus, including the work required to form critical nuclei.

Deformation-fractional change in resistance model of SMA fiber reinforced mortar beams

Superelastic shape memory alloys (SMA) are metallic alloys that can enable components with self-recovery of cracks and deformation when combined with cement-based materials. Meanwhile, the monitoring method based on fractional change in resistance (FCR) can achieve self-monitoring of components throughout the entire loading process. In this study, the relationship between crack recovery characteristics and FCR of SMA fiber reinforced mortar beams subjected to flexural cyclic loading was analyzed. The beams with SMA fiber volume fractions of 0.1 %, 0.3 %, 0.5 %, and 1.0 % were tested under three-point bending loads. During the loading process, the FCR of the specimens were monitored by the four-electrode method. Finally, the deformation-FCR model for the whole process of the specimens was established to realize the real-time monitoring of the loading state of mortar beams. The results show that SMA fibers can enhance the flexural strength of mortar beams by a maximum of about 64 %, reduce residual deformation, and improve deformation self-recovery ability. The mid-span deflection recovery rate and crack size recovery rate generally increase with higher fiber volume fraction, which can maintain a better deformation recovery effect at the early stage of cracking and gradually decrease with the expansion of cracks. There is a close relationship between the FCR and the degree of crack development that the FCR increases with crack expansion and the magnitude of FCR decreases with increasing fiber content. The established first-order logarithmic fitting model can well describe the relationship between the FCR and cracking, which can provide reference for crack monitoring.

Association between cardiometabolic index (CMI) and periodontitis in US adults: analysis of NHANES data (2009–2014)

BackgroundThe cardiometabolic index (CMI) is a new predictor of obesity-related diseases, but its link to periodontitis is under-researched. This study aims to examine the potential association between the CMI and periodontitis.MethodsWe conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database (2009–2014) to examine the potential association between CMI and periodontitis. The study utilized a weighted multivariate logistic model to assess the association between TG, HDL-C, TG/HDL-C, obesity indices (BMI, WC, WHtR, CMI), and periodontitis, employing the area under the receiver operating characteristic curves (ROC) to estimate areas under the curve (AUC). Furthermore, generalized smooth curve fitting was conducted to examine the relationship between CMI and periodontitis. Finally, the study incorporated subgroup analysis and interaction tests to examine consistency across different populations.ResultsTG/HDL-C, WHtR, and CMI were positively associated with periodontitis in the fully adjusted classification model. It was observed that for each unit increase in CMI, there was a corresponding 17.8% increase in the prevalence of periodontitis [1.178 (1.049, 1.322) 0.00562] and 18.7% increase in the prevalence of moderate/severe [1.187 (1.057, 1.334) 0.00376] in the fully adjusted model. When CMI was used as a categorical variable, the adjusted OR for periodontitis prevalence increased significantly with increasing CMI after adjusting for all potential covariates (T3 vs. T1: OR, 1.28 [1.06, 1.55], p < 0.05). The ROC analysis indicated that a larger area under the curve was found in the CMI [0.554 (0.538–0.570)] than in the WC [0.544 (0.528–0.560)] and WHtR [0.544 (0.528–0.561)]. Nonetheless, the discrepancy observed did not reach statistical significance (all p > 0.05). Further generalized smooth curve fitting and threshold effect models indicated a positive linear correlation between CMI and periodontitis. Moreover, there is no interactive association between TG/HDL-C, WHtR, CMI, and periodontitis.ConclusionsThis cross-sectional study revealed a positive relationship between CMI and periodontitis. Further prospective studies are warranted to validate our findings.

Persepsi Penggunaan Kosmetik Halal Di Kalangan Muslim Generasi Z

This study focuses on the perception of halal cosmetics use for Muslim generation z. This study uses a descriptive analysis research method with a quantitative approach. This study uses primary data with data collection carried out by distributing questionnaires to respondents who meet the criteria, namely individuals who use halal cosmetics in the last 3 months and individuals who are included in generation Z. The questionnaire was distributed via Google Forms. The sample used in data processing was 180 respondents who were analyzed using the Structural Equation Modeling (SEM) analysis unit with the AMOS program and also tested the goodness of fit model to test its feasibility. The conclusion obtained from this study is that there is a positive influence between attitude and purchase intention, there is a positive influence between perceived behavioral control and purchase intention, and there is a positive influence between religiosity and purchase intention. However, other results revealed that there was no positive influence between subjective norms and purchase intention.

Augmented pre-training-based carbon emission accounting method using electricity data under small-sample condition

IntroductionAccurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.MethodsTherefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.ResultsThis approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.DiscussionCase studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

Utilization of Stochastic P-bifurcation for Simultaneous Energy Harvesting and Vibration Suppression: An Experimental Investigation

Abstract This research focuses on the prediction and experimental verification of P-bifurcation as well as the effectiveness in reducing vibrations and harvesting energy with the use of an inertially nonlinear energy harvesting device attached to a single-degree-of-freedom structure subjected to Gaussian broadband base excitation, modeled as white noise. Four experimental scenarios were tested, including three with different resistive loads and one with an open circuit. Frequency domain optimization involved an optimization routine that was designed to minimize the mean squared error in the pendulum velocity's frequency content below two cycles per second while constraining the RMS velocity discrepancy between the simulations and actual experiments to be below 3%. This facilitated accurate predictions of power, vibration suppression, and P-bifurcation. Using the fitted model, an analytically derived P-bifurcation boundary in the noise intensity versus electrical damping plane was presented and experimentally verified. Additionally, power spectral densities for electric power and relative suspended mass velocity were determined for the IPVA system and compared with a top-performing linear system. Results indicated that the monomodal system was the least effective in energy harvesting, while the bimodal and rotational systems significantly enhanced mean and resonant peak power by up to four and two times, respectively. Near the resonant frequency, the peak relative velocity power spectral density decreased by a factor of four, and the mean square relative velocity improved by a factor of two.

Developing a Scale Measuring Comprehensive Sex Education Attitudes in K-12 Schools (CSEA-K12)

ABSTRACT Comprehensive sex education (CSE) is linked to several favorable health outcomes yet is consistently underfunded and under-implemented. This is likely due to the perceived controversy of CSE, but research indicates that CSE in the K-12 system is widely supported in most areas of the U.S. Legislators, school board members, and funding agencies need a more thorough and accurate understanding of their communities’ attitudes toward CSE. An attitudinal scale for the general adult population could help generate research that would give these stakeholders the confidence they need to make decisions about CSE in their local areas. The current study created this needed scale using a structured 6-step process that integrated several methods establishing validity and reliability. Scale structure was tested using both exploratory and confirmatory factor analyses, in addition to invariance testing and validity checks. The scale performed similarly across several groups and met predictions for known-groups and predictive validity, while showing some evidence for convergent validity as well. However, although findings indicate that the one-factor model of the CSEA-K12 was the best fitting model, the fit was weaker than desired. Suggestions for future work further addressing internal consistency of the scale are noted.

MODELING TOURIST ARRIVALS IN THE ISLAND GARDEN CITY OF SAMAL: IMPLICATIONS TO BUSINESS OPERATIONS OF TOURISM-RELATED INDUSTRY

&lt;p&gt;This study aimed to model the tourist arrivals in the Island Garden City of Samal (IGaCoS) by utilizing the autoregressive integrated moving average (ARIMA) method. Specifically, this paper sought to determine whether there was a statistically appropriate forecasting model using the ARIMA approach, based on which it would be possible to reasonably forecast the number of tourist arrivals in IGaCoS. Quantitative secondary data were analyzed by employing the Box-Jenkins ARIMA. The time series plot was characterized by an apparent increasing trend combined with intermittent sinusoidal oscillations. The time series revealed a steady, gradual increase up until 2020. Next, a sharp decline was observed midway through 2020, indicative of the ill effects of the COVID-19 pandemic. A gradual increase was noted once more, starting in 2021, with some fluctuations. The Augmented Dickey-Fuller (ADF) test revealed that the time series was non-stationary at level— but was made stationary after differenced twice. The ARIMA (1,2,1) model was found to be statistically significant. The fitted model initially showed some notable deviations from the original data, but, with time, converged closely to the actual values, proving it is a reliable way to represent underlying patterns. The ARIMA (1,2,1) model, selected for having the lowest AIC and BIC values and a MAPE within the acceptable range for reasonable forecasting, was used to predict tourist arrivals in IGaCoS over the next six months. The forecasted values suggested a mix of fluctuations and stability. There was a noticeable upward trajectory at the beginning, which was followed by a slight decline at the end that yet remained stable. The forecasted results from the ARIMA (1,2,1) model may provide businesses with a roadmap for comprehensive planning in terms of operational and strategic concerns. This study helped address Sustainable Development Goal (SDG) No. 8, Decent Work and Economic Growth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JEL:&lt;/strong&gt; L83, R11, C53&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0776/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Prediction Model for Fracture Toughness of Waterborne Polyurethane Modified Concrete at Different Temperatures

In certain extreme environments, the bearing capacity of concrete structures diminishes significantly as temperatures soar, simultaneously exposing them to a heightened risk of brittle cracking. The paper aims to elucidate the fracture toughness of waterborne polyurethane modified concrete (WPMC) at different temperatures. Furthermore, a predictive model for the fracture toughness of WPMC, which incorporates both temperature and the waterborne polyurethane (WP) content, is proposed. The flexural strength and fracture toughness of WPMC were tested separately at 20℃, 40℃, 60℃, and 80℃. Utilizing digital image correlation (DIC) technology, the bottom longitudinal strain of WPMC under flexural loading was analyzed. The impact of temperature and WP content on the energy absorption capacity and deformation behavior of WPMC exposed to extreme environment was also investigated. By introducing the microstructural parameters C and Cw to characterize the elastic and plastic deformations of WPMC before and after cracking, a fitting model between the microstructural parameters and temperature, WP content was established. This model enables the prediction of the fracture toughness KIC of WPMC at different temperatures by measuring Fmax.

The Upper Atmosphere of Uranus from Stellar Occultations. II. Revised Temperatures in the Upper Stratosphere and Lower Thermosphere

Measurements made by Voyager 2 during its flyby of Uranus in 1986 found warm stratospheric and hot thermospheric temperatures that cannot be explained by solar energy alone. This contributes to what is known as the “giant planet energy crisis”: there is a fundamental lack of understanding of the energy balance of giant planets in the solar system. Uranus, in particular, has the coldest stratosphere temperatures, the hottest thermospheric temperatures, and the weakest internal heat flux of all four giant planets. Moreover, the Voyager 2 temperature measurements are at odds with many contemporaneous Earth-based stellar occultations. In Paper I, we introduced two updated techniques for reanalyzing these 26 occultations—forward modeling and inversion—and demonstrated them on the 1977 Uranus occultation. This work (Paper II) presents the results of applying these techniques to 26 total Uranus stellar occultations, observed between 1977 and 1996. We find that the lower thermosphere of Uranus is much cooler than the Voyager 2 profiles but slightly warmer than the originally published occultations. We find that the stratosphere of Uranus is nearly isothermal and the stratosphere–thermosphere boundary is much higher in altitude than previously believed. Furthermore, we present a family of new, one-dimensional atmospheric models for Uranus fit to our results. The models suggest that there is a significant source of heat transport or dissipation away from the stratosphere, which we discuss as resulting from gravity-wave activity. We offer our models as new representative profiles and discuss how they improve the understanding of the energy balance of Uranus.

The detrimental effect of rainforest conversion to rubber plantations on soil dissolved organic carbon and C: N stoichiometry, mediated by altered soil biogeochemistry

Rainforest conversion into rubber (Hevea brasiliensis) plantations (RP) alters global carbon cycling and contributes to climate change. However, the impact of this widespread tropical land use change on various elements of the carbon cycle is poorly understood. Here, we aimed to investigate the impact of rainforest conversion into RP on soil-dissolved organic carbon (DOC), one of the most mobile organic matter (OM) in the terrestrial ecosystem that causes the transformation and migration of C. We also explored the underlying edaphic factors regulating soil DOC changes. Our study sites were rubber monoculture, mixed-rubber plantations (H. brasiliensis, Ficus langkokensis, and Actinodaphne henryi), and a reference rainforest. We found that soil DOC concentration was 150-200% higher in RP than in rainforests, with an unchanged pattern across the seasons (dry and rainy) and plantation type. These results were concomitant with degradation in main soil properties, markedly including lower pH, electrical conductivity, SOC, available nitrogen, available phosphorus, total nitrogen (TN), and total phosphorus (TP), following the RP establishment and explicitly having a significant negative correlation with DOC. Our fitted structure equation model (SEM) highlights that RP caused accelerated DOC production and a higher DOC/DN ratio by decreasing SOC (38.5%) and nutrients (TN and TP). Further, the SEM revealed a significant negative correlation between microbial biomass C (MBC) and N (MBN) and the DOC/DN ratio, implying limited microbial degradation of DOC under RP. This is further supported by our findings of 81.1% lower MBC per unit DOC and 37.1% lower MBN per unit DN under RP compared to rainforests, indicating poor transformation of DOC to microbial biomass under RP. Collectively, our findings suggest that RP with high nutrient demands and altered soil properties lead to increased leaching of DOC due to its limited utilization by microbes. These findings underscore the importance of robust and sustainable soil management (such as optimizing plant density and legume intercropping) in RP to improve soil health and minimize DOC leaching and its potential environmental consequences.