Composition Model Research Articles

86 Sociodemographic and family characteristics of children with and without neurodevelopmental impairment in a Canadian cohort of extreme preterm children

Abstract Background Despite improved survival of extreme preterm infants (<29 weeks’ gestation), neurodevelopmental impairment (NDI) remains high. Certain sociodemographic characteristics (SDC) and limited family resources are known to adversely affect children health and development, particularly those born preterm. There is a dearth of recent data from large cohorts examining the impact of social determinants of health on the neurodevelopment of preterm children. Objectives (1) to describe SDC and family composition in a population-based cohort of extreme preterm children using the Canadian Neonatal Follow Up Network (CNFUN) database; (2) to develop a logistic regression model examining the association of significant NDI (sNDI any of: CP ≥ GMFCS stage 3, Bayley III < 70 in any domain, deafness requiring aids, or bilateral blindness) with SDC using a unique family descriptor. Design/Methods A retrospective review of a national cohort of extreme preterm infants (230 -286 weeks) born between April 2009 and December 2018. We included children who had neurodevelopmental assessment at 18-24 month corrected age at a CNFUN participating Centre. We compared SDC of infants and caregivers and family descriptors (moderating variables) between children with no NDI and those with any NDI and sNDI. Multivariate logistic regression models were developed in two steps to evaluate the effect of SDC (Model 1) and family composition (Model 2) on the primary outcome of sNDI. GEE was used to account for clustering by multiples and within site. Results Out of 10833 eligible infants, 6219 (57%) were included. Of those, 3412 (55%) had no NDI and 2807 (45%) had any NDI. sNDI accounted for 17% of the cohort and 37% of those with NDI. Comparison of the SDC and family descriptors between the three groups are shown in Table 1. On multivariate analysis using SDC, infant’s gestational age and male sex, and primary caregiver’s ethnicity and level of education were independently associated with sNDI. Immigration and employment status were associated with sNDI when family descriptors were added to the first model (Table 2). Conclusion In this national cohort, half of extreme preterm children developed NDI and 1 in 7 had sNDI. There were differences in sociodemographic and family characteristics between those with and without sNDI. The degree of prematurity and caregiver’s education, employment and immigration status were independently associated with sNDI in those children. Future research is needed to determine what interventions and support to benefit those children at risk.

Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

A partnership composition project between professional composers, a university music education program, and a comprehensive school extended music program

In this Finnish-based study, professional composers ( n = 2), student music teachers ( n = 6), eighth-grade comprehensive school pupils studying in an extended music program ( n = 22), and university and school music educators ( n = 2) were brought together to pilot a partnership model for teaching and learning music composition. The one-year project offered (a) a composition pedagogy course for student music teachers, including lectures by composers and a seminar and teacher training in a comprehensive school, and (b) collaborative composition workshops for eighth-grade pupils under the supervision of student music teachers, professional composers, and a school music teacher. In Part 1, we conducted a survey to investigate pupils’ experiences relating to their creative musical agencies and basic psychological needs. In Part 2, we used a phenomenographic approach to examine educators’ perceptions of student music teachers’ development as composition teachers. In Part 3, we used phenomenography to study the student music teachers’ experiences of their development as composition teachers. The results indicate that (a) the musical agency and basic psychological needs of the eighth-grade pupils were supported; (b) the educators succeeded in planning and implementing the pilot course, taking into account both the social and pedagogical aspects of collaborative composition; and (c) the student music teachers felt that their attitudes toward teaching music composition changed and that their confidence increased during the project. We recommend future modifications to the present educational model and consider its potential implications for music education in the Finnish context.

The emergence of team compassion: Theoretical implications and practical interventions

With the recent experiences involving COVID-19, there is a growing need for organisations to better understand compassion in addressing employees’ suffering and boosting their well-being. Particularly, as teamwork is becoming ubiquitous, organisational scholars have identified positive benefits of compassion at the team level such as improving communication, decreasing interpersonal conflicts and boosting team effectiveness. Using a multilevel theoretical framework in reviewing compassion research, this article advances our understanding of team-level compassion by elucidating the processes through which individual-level compassion gives rise to team-level compassion. First, we delineate composition and compilation models of the emergence of team compassion and review empirical studies with respect to the two models. Second, we explain three social mechanisms in teams – social learning, emotional contagion and reciprocity – that shape the emergence of team compassion. Finally, we discuss interventions that can facilitate the emergence of team compassion and offer practical guidance for managers seeking to foster team compassion. JEL Classification: D23, I31

Thermodynamic modeling of liquid binary alloys of the Al–Er system

The paper presents the results of a study of the thermochemical properties of the Al–Er system. The thermodynamic characteristics were evaluated (△fH0298, S0298, (H0298−H00), Cp(T) and Cp(liq)) for the intermetallic compounds Al3Er, Al2Er, AlEr, Al2Er3, AlEr2. The values of△fH0298 calculated based on the semiempirical Miedema model adapted for the group of Al–REM alloys were taken for calculations and amounted to –47.7, –58.4, –63, –55.2, –46.8 kJ/mol∙at, respectively. The mixing characteristics of liquid alloys of this system were evaluated by Terra software package for modeling the equilibrium states of heterogeneous inorganic systems with an extensive database of properties of individual substances. The model of ideal solutions of interaction products was used as a computational model. Modeling of equilibrium composition and properties of melts was carried out in the temperature range of 1900–2100 K, in an argon atmosphere at a total pressure of 0.1 MPa in the system. Comparison of the obtained results with the simulation results in the approximation of an ideal solution, allowed us to determine the excess integral thermodynamic properties of liquid alloys (Gibbs energy, enthalpy, and entropy). It is shown that in the studied temperature range, with an increase of temperature, there is a natural, though not significant, decrease in the values of these parameters by absolute value. It is established that the formation of liquid alloys of the Al–Er system is accompanied by significant heat release: the value of the integral enthalpy of mixing at a temperature T = 2100 K is –58.9 kJ/ mol∙at. When comparing the thermochemical properties of the Al–Er system with the binary systems Al–Y and Al–Sc studied by the same methods, it is shown that all energy curves pass through the extremum at XSc,Y,Er ≈ 0.5. The strongest interaction of the components is observed in the Al–Y system, (ΔHmix = –58.9 kJ/mol∙at), which is close enough to the maximum modulo value of the enthalpy of mixing in the Al–Er system. The weakest interaction is observed in the Al–Sc system (ΔHmix = –44.8 kJ/mol·at). The results obtained in this work provide a theoretical basis for further experimental study of erbium–containing aluminum alloys.

Two-Stage Global Biomass Pyrolysis Model for Combustion Applications: Predicting Product Composition with a Focus on Kinetics, Energy, and Mass Balances Consistency

This work presents a comprehensive model for lignocellulosic biomass pyrolysis, addressing kinetics, energy balances, and gas product composition with the aim of its application in wood combustion. The model consists of a two-stage global mechanism in which biomass initially reacts into tar, char, and light gases (non-condensable gases), which is followed by tar reacting into light gases and char. Experimental data from the literature are employed for determining Arrhenius kinetic parameters and key energy parameters, like tar and char heating values and the specific enthalpy of primary and secondary reactions. A methodology is introduced to derive correlations, allowing the model’s application to diverse biomass types. This work introduces several novel approaches. Firstly, a pyrolysis model that determines the composition of light gases by solving mass, species, and energy balances is developed, limiting the use of correlations from the literature only for tar and char elemental composition. The mass rate of light gases, tar, and char being produced is also determined. Secondly, kinetic parameters for primary and secondary reactions are determined following a Shafizadeh and Chin scheme but with a modified Arrhenius form dependent on Tn, significantly enhancing the accuracy of product composition prediction. Additionally, correlations for the enthalpies of reactions, both primary and secondary, are determined as a function of pyrolysis temperature. Primary reactions exhibit an overall endothermic behavior, while secondary reactions exhibit an overall exothermic behavior. Finally, the model is validated using cases reported in the literature, and results for light gases composition are presented.

European pollen reanalysis, 1980–2022, for alder, birch, and olive

The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980–2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

Toward Fine Horizontal Resolution Global Simulations of Aerosol Sectional Microphysics: Advances Enabled by GCHP‐TOMAS

AbstractGlobal modeling of aerosol‐particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine‐resolution global models are desirable for representing nonlinear aerosol‐microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol‐microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO‐Moment Aerosol Sectional (TOMAS) microphysics scheme with the High‐Performance configuration of the GEOS‐Chem model of atmospheric composition (GCHP), a coupling termed GCHP‐TOMAS. GCHP's architecture allows massively parallel GCHP‐TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 105 model columns) versus the previous single‐node capability of GEOS‐Chem‐TOMAS (tens of cores, 200 km × 250 km, 1.3 × 104 model columns). GCHP‐TOMAS runtimes have near‐ideal scalability with computing‐core number. Simulated global‐mean number concentrations increase (dominated by free‐tropospheric over‐ocean sub‐10‐nm‐diameter particles) toward finer GCHP‐TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free‐tropospheric total particle number by 18.5%, and over‐ocean sub‐10‐nm‐diameter particles by 39.8% at 4‐km altitude. With a cascade of contributing factors, free‐tropospheric particle‐precursor concentrations increase (32.6% at 4‐km altitude) with resolution, promoting new‐particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality.

Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA

AbstractUnderstanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to (1) assess community resource use among streams fed by different hydrologic sources, (2) explore how variability in resource use relates to feeding strategies, and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice‐fed streams were largely supported by the gold alga Hydrurus. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold‐water species under climate change by maintaining a longer growing season for this high‐quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggest high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.

Chromosome-specific barcode system with centromeric repeat in cultivated soybean and wild progenitor.

Wild soybean Glycine soja is the progenitor of cultivated soybean Glycine max Information on soybean functional centromeres is limited despite extensive genome analysis. These species are an ideal model for studying centromere dynamics for domestication and breeding. We performed a detailed chromatin immunoprecipitation analysis using centromere-specific histone H3 protein to delineate two distinct centromeric DNA sequences with unusual repeating units with monomer sizes of 90-92 bp (CentGm-1) and 413-bp (CentGm-4) shorter and longer than standard nucleosomes. These two unrelated DNA sequences with no sequence similarity are part of functional centromeres in both species. Our results provide a comparison of centromere properties between a cultivated and a wild species under the effect of the same kinetochore protein. Possible sequence homogenization specific to each chromosome could highlight the mechanism for evolutionary conservation of centromeric properties independent of domestication and breeding. Moreover, a unique barcode system to track each chromosome is developed using CentGm-4 units. Our results with a unifying centromere composition model using CentGm-1 and CentGm-4 superfamilies could have far-reaching implications for comparative and evolutionary genome research.

Statistical Computational Model of Fission Products Composition of Irradiated Nuclear Fuel and Their Contribution to Gas-aerosol Emissions of Nuclear Power Plants

Based on the assumption that the radionuclide composition of gas-aerosol emissions from nuclear power plants (NPPs) into the environment is determined by the composition of the fuel in the reactor (but not identical to it), an analysis of the probability of the occurrence of various isotopes in possible emissions is conducted in this paper. It is confirmed that the activity of nuclear fuel, and consequently the emissions, are determined by uranium fission fragments, proving that the decay rate of radionuclides is proportional to the probability of the appearance of radionuclides with certain mass numbers. It is also shown that there is no functional relationship between the mass yield of radionuclides and their atomic mass, as the variability (fluctuations) in the content of radionuclides in VVER-440 reactor fuel and consequently in NPP emissions reaches 20 orders of magnitude, which prevents the application of computational methods for processing the results. This paper proves that, in order to determine the yield of radionuclides from their observable activity, applying a Zipf-Mandelbrot type distribution in “rank - size” coordinates, it is possible to carry out the mathematical modeling based on the appropriate processing of the obtained results.

Experimental measurement, thermodynamic analysis, and mathematical modeling for budesonide solubility in 1-propanol + water mixtures at T = (293.2 to 313.2) K.

Budesonide (BDS) a steroid-based anti-inflammatory drug widely prescribed for various diseases, has a low aqueous solubility. In this study, we investigated cosolvency approach to study the thermodynamic specifications related to the solubility of BDS at the temperature range of 293.2-313.2K in (1-propanol + water) mixtures applying the shaking flask method. The predictive power of different mathematical models for experimental data in the cosolvency systems was evaluated. For this purpose, the linear and nonlinear mathematical equations such as van't Hoff model (as a linear model), Buchowski-Ksiazczak equation (as a non-linear), CNIBS/R-K and MRS models (as a linear model for solvent composition at an isothermal condition), modified Wilson model (as a non-linear model for isothermal condition), the Jouyban-Acree model (as a model that considers temperature and solvent composition), and Jouyban-Acree-van't Hoff model (as a model with no further input data) were studied. Also, the Williams-Amidon excess Gibbs energy model was investigated. In addition, the related apparent thermodynamics of the BDS dissolution process in the desired temperature such as Gibbs free energy, enthalpy, and entropy, were computed by the corresponding equations. Moreover, based on the inverse Kirkwood-Buff integrals, it is demonstrated that BDS is preferentially solvated by water in water-rich mixtures. The accuracy of the fitness was evaluated with mean relative deviations (MRDs%) for back-calculated molar BDS solubility data. The result showed that the maximum solubility of BDS was obtained at 0.7 mass fraction of 1-propanol at all temperatures. Thermodynamic studies demonstrated that BDS dissolution procedures were obtained as endothermic and entropy-driven in almost all cases. The overall MRDs% values for the back-computed BDS solubility in the aqueous mixture of 1-propanol based on van't Hoff model, Buchowski-Ksiazczak equation, CNIBS/R-K model, modified Wilson model, Jouyban-Acree model, Jouyban-Acree-van't Hoff model, MRS model, and Williams-Amidon excess Gibbs energy model were found 1.93%, 1.80%, 11.68%, 33.32%, 12.30%, 9.24%, 10.70%, and 6.57%, respectively.

Corrosion mechanism of press-hardened steel with zinc coating in controlled atmospheric conditions: A laboratory investigation

The formation and evolution of corrosion products on zinc galvanized press-hardened steel during various atmospheric tests were studied. A high protective ability of the coating containing Zn-Fe intermetallics was shown. The composition models of the soluble and stable corrosion products were calculated. A significant effect of barrier corrosion products on corrosion resistance, especially simonkolleite, hydrozincite and dawsonite, was demonstrated. Exposure to constant high relative humidity resulted in the largest mass loss due to the accelerated formation of akaganeite. On the contrary, the presence of sulphates caused formation of additional barrier corrosion products and almost a threefold drop in mass loss.

Anthropometric prediction models of body composition in 3 to 24month old infants: a multicenter international study

Abstract Background Accurate assessment of body composition during infancy is an important marker of early growth. This study aimed to develop anthropometric models to predict body composition in 3–24-month-old infants from diverse socioeconomic settings and ethnic groups. Methods An observational, longitudinal, prospective, multi-country study of infants from 3 to 24 months with body composition assessed at three monthly intervals using deuterium dilution (DD) and anthropometry. Linear mixed modelling was utilized to generate sex-specific fat mass (FM) and fat-free mass (FFM) prediction equations, using length(m), weight-for-length (kg/m), triceps and subscapular skinfolds and South Asian ethnicity as variables. The study sample consisted of 1896 (942 measurements from 310 girls) training data sets, 941 (441 measurements from 154 girls) validation data sets of 3–24 months from Brazil, Pakistan, South Africa and Sri Lanka. The external validation group (test) comprised 349 measurements from 250 (185 from 124 girls) infants 3–6 months of age from South Africa, Australia and India. Results Sex-specific equations for three age categories (3–9 months; 10–18 months; 19–24 months) were developed, validated on same population and externally validated. Root mean squared error (RMSE) was similar between training, validation and test data for assessment of FM and FFM in boys and in girls. RMSPE and mean absolute percentage error (MAPE) were higher in validation compared to test data for predicting FM, however, in the assessment of FFM, both measures were lower in validation data. RMSE for test data from South Africa (M/F−0.46/0.45 kg) showed good agreement with validation data for assessment of FFM compared to Australia (M/F−0.51/0.33 kg) and India(M/F−0.77/0.80 kg). Conclusions Anthropometry-based FFM prediction equations provide acceptable results. Assessments based on equations developed on similar populations are more applicable than those developed from a different population.

Refining Predictive Models for Urolithiasis: Methodological Insights and Clinical Implications.

We have reviewed the article "Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights" by Chmiel et al. with keen interest. The authors have made significant strides in leveraging machine learning to predict urinary stone composition, a crucial factor in the management and treatment of urolithiasis. While the study presents innovative methodologies and insightful findings, there are several areas where the approach and interpretation could be refined to enhance the robustness and applicability of the results.

190 Implementation of an updated beef energetics and body composition model in the Crop Livestock Enterprise Model (CLEM)

Abstract The Crop Livestock Enterprise Model (CLEM) is a whole-farm bioeconomic model designed to simulate and explore ruminant performance across a wide range of production systems and management strategies. The model features a flexible, component-based structure that allows users to simulate multiple types of livestock production within the same system. Animals in the model are represented on an individual basis, allowing users to explore the effects of between-animal variation on a wide range of performance metrics at the animal and farm system levels. Recently, two new features were introduced to improve ability of CLEM to represent ruminant production systems: a user-defined timestep, and a new energetics model (based on the Australian ruminant feeding standards) that adds the ability to model changes in body composition in addition to existing predictions of body weight gain and feed intake. The change in timestep from monthly to daily was required to implement the new energetics model and allows for greater ability to compare CLEM results against field data, but other timesteps (e.g., weekly) can also be used depending on user preference. The Australian ruminant feeding standards were integrated with the existing CLEM modelling framework to create a revised energetics model for CLEM capable of predicting body composition, with additional updates to prediction of relative feed intake and the effect of diet type on feed quality. The energetics model was then evaluated for prediction of feed intake (DMI), final empty body weight (EBW), and fat and protein gain (Table 1) in feedlot cattle. The evaluation dataset consisted of treatment means (n = 29) from studies of Angus or British cross steers whose body composition (fat and protein) had been measured by serial slaughter. The updated model performed well for predicting final EBW, underpredicting by 2.35 kg on average (0.55% of the observed mean). Dry matter intake was underpredicted for 80% of treatments, with mean bias averaging 1.7 kg/d across all treatments and a root mean square prediction error (RMSPE, % of the observed mean) of 25%. Data on fat and protein gain was more variable than for EBW, and the model overpredicted protein gain and underpredicted fat gain in general, with mean bias of -51 g/d (RMSPE 42%) for protein and 270 g/d (RMSPE 58%) for fat. The addition of a daily timestep and a revised energetics model add to utility of CLEM by allowing for more in-depth exploration of the effects of feeding and management over time on intake, body weight gain, and body composition. The current version of CLEM can be downloaded from the APSIM Initiative website; and an upcoming update with additional options for prediction of intake and performance for both feedlot and grazing cattle will be made available.

A knowledge enhanced learning and semantic composition model for multi-claim fact checking

To inhibit the spread of rumorous information and its severe impacts, fact checking aims at retrieving relevant evidence to verify the veracity of a given statement. Fact checking methods typically use knowledge graphs (KGs) as external repositories and develop reasoning mechanism to retrieve evidence for verifying the statement. Existing fact checking methods have focused on verifying the statement of a single claim expressed by a clause. However, as real-world rumorous information is usually complex and a textual statement is often composed of multiple clauses (i.e. represented as multiple claims instead of a single one), multi-claim fact checking is not only necessary but more important for practical applications. Multi-claim statements imply rich contextual information and modeling the interactions of multiple claims can facilitate better verification. In this paper, we propose a knowledge enhanced learning and semantic composition model for multi-claim fact checking. Our model consists of two modules, KG-based learning enhancement and multi-claim semantic composition. To fully utilize the contextual information implied in multiple claims, the KG-based learning enhancement module learns the dynamic context-specific representations via selectively aggregating relevant attributes of entities. To robustly verify multiple claims robustly, the multi-claim semantic composition module learns a unified representation for multiple claims by modeling inter-claim interactions, and then verify them as a whole on the basis of this. We conduct experimental studies to validate our proposed method, and the experimental results on three typically datasets confirmed the efficacy of our model for multi-claim fact checking.

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows.

Empirical Model of Low-Ohmic Nickel-Based Contact Formation on N-Type 4H-SiC Depending on Thermal Budget

In this work, an empirical model of structural and material composition of low-ohmic nickel silicide contact formation on n-type 4H-SiC by laser annealing as well as by RTA is presented. For this purpose, systematic studies with different annealing parameters were performed. The development of the empirical model is based on results from characterization of the nickel silicide by FIB-SEM, TEM, XRD analysis as well as electrical characteristics received from 4-point-measurements.