Rn Model Research Articles

Estimation models of photovoltaic module operating temperature under various climatic conditions

Accurate control of the operating temperature is essential for achieving optimal efficiency in the use of solar panels. The Normal Operating Cell Temperature (NOCT) is a widely used method for estimating module temperature, but it is not always accurate for all conditions. In this paper, the authors propose four different models for estimating module temperature using various techniques including Neural Networks, a linear method proposed by Ross, and a Fitting method. The objective is to find more accurate methods than the NOCT model. The results show that all four models provide good agreement between measured and calculated module temperatures, with R² > 0.91 and RMSE < 3.74 for clear days, and considering the resources of time, cost, and expertise necessary for simulating or evaluating the proposed model, the two models based on RN neural networks offers a more cost-effective solution, by utilizing straightforward mathematical skills, the RN model demonstrates applicability in any environment, making it a preferable choice over the NOCT model for predicting polycrystalline photovoltaic module temperature under different climatic conditions. The proposed models can be used to estimate PV module temperature with good precision under different climatic conditions.

3D finite element analysis of stress distribution on the shape of resected root-end or with/without bone graft of a maxillary premolar during endodontic microsurgery

Background/purposeApical root resection pattern affects the stress distribution behavior in the apical region of the resected tooth. The purpose of the study was to compare the biomechanical responses of resected teeth between endodontic microsurgery (horizontal resection) and targeted endodontic microsurgery (round resection). Materials and methodsFive different models were developed. The basic model without resection (NR) was regarded as the control model, and the others involved: horizontal resection without bone grafting (HN), horizontal resection with bone grafting (HG), round resection without bone grafting (RN), and round resection with bone grafting (RG) models. A static load of 100 N was applied to the buccal and palatal cusps of all the teeth in a 30° oblique direction. The maximum von-Mises stress and tooth displacement values were analyzed and compared. ResultsBoth the HN and RN models exhibited lower stress distribution values on bone compared with the NR (control) model. Regarding maximum stress distribution at the root apex, the stress value of the RN model was slightly higher compared to the HN model, whereas the RG model displayed a slightly lower stress value in comparison with the HG model. For maximum tooth displacement value, there were no significant differences between the HN and RN models, as well as the HG and RG models. ConclusionThe round resection pattern had comparable stress distribution behaviors at the root apex and tooth displacement values with the horizontal resection pattern. Targeted endodontic microsurgery might provide better biomechanical response of the resected tooth after root-end resection.

N-mixture model-based estimate of relative abundance of sloth bear (Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India.

Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.

Advertising and pricing of online direct selling considering network externalities

PurposeConsidering the network externalities of online selling, this paper builds three different online direct selling models: manufacturer direct selling (MN model), network platform direct selling (NN model) and retailer direct selling (RN model). The optimal advertising and pricing decision and corporate profits under each selling model are investigated.Design/methodology/approachCombining the characteristics of online direct selling, this paper formulates direct selling models that are dominated by different companies as Stackelberg game models. Numerical analyses are carried out, along with the comparison of the equilibrium solutions for each model.FindingsThe authors' research shows that increasing network externalities is conducive to the development of enterprises. The network platform's profit is the lowest in the RN model and the highest in the NN one. The comparison of manufacturers' profits between the MN model and the NN model primarily depends on consumers' sensitivities for sales price and advertising promotion level. The manufacturer does not benefit from the RN model due to the lowest efficiency.Originality/valueCoupled with three different online direct selling models and detailed analyses of the optimal solutions, this study has enriched the theoretical foundation of online direct selling. Moreover, this study extends the research of network externalities to the field of e-commerce, revealing the network externalities' influence on the decision-making of the e-supply chain.

Improving inferences and predictions of species environmental responses with occupancy data

Abstract Occupancy models represent a useful tool to estimate species distribution throughout the landscape. Among them, MacKenzie et al.’s model (2002, MC), is frequently used to infer species environmental responses. However, the assumption that detection probability is homogeneous or fully explained by covariates may limit its performance. Species should be more easily observed at sites with a higher number of individuals. We simulated data following occupancy model (RN) that accounts for abundance‐driven heterogeneous detection and two variants with overdispersion in the detection probability and local abundances. Then, we compared the performance of the MC model against that of RN. In addition to model misspecifications, insufficient information in data (i.e. infrequent detections) can limit our ability to detect existing effects with affordable sampling designs. To deal with this source of error, we extended RN approach to a community‐level joint species model (RN‐JSM), where species responses and detectability depended on their traits and phylogeny. Then, we tested RN‐JSM performance in simulated and out‐of‐sample field data. High abundance‐driven heterogeneity in detection (i.e. common and secretive species) limited the ability of the MC model to quantify covariate effects; especially, when the number of visits was low. Both models (MC and RN), often failed to detect existing effects when data were overdispersed. Moreover, the RN model consistently lacked sufficient power when analysing data from uncommon species (even when simulations and model specifications perfectly matched). This problem was solved by our RN‐JSM, which yielded more precise and accurate estimates of species environmental responses. Increased accuracy in rare species held when the RN‐JSM was tested with real and out‐of‐sample datasets. In the light of our results, we propose: (i) for common and secretive species analyse occupancy data with the RN model and prioritize revisiting sites; (ii) for species that may have overdispersed detectability or local abundances (e.g. with correlated behaviours or occurring in clusters), apply RN extensions that account for this extra‐variation (e.g. Poisson‐beta or zero‐inflated models). Finally, (iii) for uncommon species (mean abundances <1), whenever possible, gather data at the community level and apply joint species modelling techniques.

A deep learning framework for prostate localization in cone beam CT-guided radiotherapy.

To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup. A two-step task-based residual network (T2 RN) is proposed to automatically identify inherent landmarks in prostate PTV. The input to the T2 RN is the pretreatment CBCT images of the patient, and the output is the deep learning-identified landmarks in the PTV. To ensure robust PTV localization, the T2 RN model is trained by using over thousand sets of CT images with labeled landmarks, each of the CTs corresponds to a different scenario of patient position and/or anatomy distribution generated by synthetically changing the planning CT (pCT) image. The changes, including translation, rotation, and deformation, represent vast possible clinical situations of anatomy variations during a course of radiation therapy (RT). The trained patient-specific T2 RN model is tested by using 240 CBCTs from six patients. The testing CBCTs consists of 120 original CBCTs and 120 synthetic CBCTs. The synthetic CBCTs are generated by applying rotation/translation transformations to each of the original CBCT. The systematic/random setup errors between the model prediction and the reference are found to be <0.25/2.46mm and 0.14/1.41° in translation and rotation dimensions, respectively. Pearson's correlation coefficient between model prediction and the reference is higher than 0.94 in translation and rotation dimensions. The Bland-Altman plots show good agreement between the two techniques. A novel T2 RN deep learning technique is established to localize the prostate PTV for RT patient setup. Our results show that highly accurate marker-less prostate setup is achievable by leveraging the state-of-the-art deep learning strategy.

Assessment of three models for estimating daily net radiation in southern Africa

Accurate quantification of net radiation flux (Rn) is of paramount importance for the estimation of reference evapotranspiration (ET0) rate, which is used to estimate crop water use. A widely recommended Penman-Monteith procedure outlined in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper No. 56 for estimation of Rn (FAO56- Rn) is often used to estimate Rn. However, the FAO56- Rn model is data-intensive and also requires site-specific calibrations of coefficients to attain a high level of accuracy of Rn estimates. These coefficients are often used without site-specific calibrations as a result of' the lack of large and representative radiative flux measurements in data-scarce regions such as southern Africa and Rn estimates are therefore considered questionable. Assessment of different models in-situ measurements of Rn is critical to identify an alternative approach that could be used for accurate estimation of Rn with minimal data input and without any site-specific calibrations in this region. In this study, two new Rn models, which differ only in the procedures used to compute atmospheric emissivity were proposed. The first model requires measurements of solar irradiance (Rs) maximum and minimum air temperatures (Tair min and Tair max) while the second model requires additional measurements of relative humidity (RHmin and RHmax) for estimation of the actual vapour pressure (eair). Two new Rn models and a widely recommended FAO56- Rn model were evaluated using daily Rn measurements acquired from five sites which represent different climatic and land cover conditions of southern Africa. The results showed that the first model performed better than all the evaluated models at four sites, with regression coefficient (r2) values greater than 0.90 and index of agreement ( d) values greater than 0.97. These findings suggest that the first model presented here is the most promising and suitable to estimate Rn with minimum input data in southern Africa without any site-specific calibrations. The findings of this study can be used to inform the decision on selecting a model to be used for reliable estimates of Rn for improved estimation of crop water requirement in climatic conditions similar to this region.

Abstraction of meso-scale network architecture in granular ensembles using ‘big data analytics’ tools

Network partitioning, an unsupervised method of learning helps in discovering the ‘communities’ that reside in a network and share common properties of interest. We have partitioned a network generated from an ensemble of granular materials by using a highly accurate and resolution limit free community detection model proposed by Ronhovde and Nussinov (RN model). We associate the best communities with naturally identifiable structures in the ensemble to find out the ‘best’ resolution for partitioning the network. The Shannon entropy distribution of individual communities at different resolutions indicates existence of hierarchical structure in the network which is non-obvious and otherwise non-observable. Effect of disorder (irregular regions present in the granular ensemble) on the community evolution in the invariant network and its stability is analysed.

Evaluation of a moderate resolution imaging spectroradiometer triangle-based algorithm for evapotranspiration estimates in subalpine regions

The current study evaluates the application of a moderate resolution imaging spectroradiometer (MODIS) triangle-based method to estimate evapotranspiration (ET) in subalpine environments. Topographic corrections and improved soil moisture representation are applied to a previously developed net radiation (Rn) model and triangle algorithm to develop an 8-day average ET product based solely on satellite products. We evaluate modeled Rn and MODIS ET (MOD-ET) against ground-based values at four sites in the Sierra Nevada of northern California and also present a comparison between two monthly distributed ET datasets [operational simplified surface energy balance (SSEBop) and MODIS MOD16]. Modeled daily Rn results indicate a systematic underestimation (between −83 and −110 W/m2 bias). Consequently, Rn is bias-corrected before calculating MOD-ET. MOD-ET validation shows correlations between 0.15 and 0.45 with errors between 73 and 126 W/m2. MOD-ET and SSEBop ET report correlations of 0.36 and 0.20, respectively, on average, compared to ground-based monthly ET. MOD16 underestimates monthly totals, with bias values on the range of −14 to −144 W/m2. Semiarid conditions and scale differences between the MODIS pixel and station contribute to errors with respect to observation. Overall, MOD-ET provides reasonable ET estimates and may better capture temporal dynamics in environments undergoing chronic disturbance.

A new empirical procedure for estimating intra-annual heat storage changes in lakes and reservoirs: Review and analysis of 22 lakes

Evaporation is an important component of the water and energy balance of lakes and reservoirs. The energy balance combination method for estimating evaporation requires the heat storage changes (Qt) to be known. The lack of data on water temperature profiles and water depth fluctuations hinders routine computations of Qt. Following successful estimation of the soil heat flux density of land surfaces (G) and heat storage change of urban areas and wetlands from net all wave radiation (Rn), we investigated in this paper whether a similar generic Qt(Rn) empirical relationship can be developed for lakes and reservoirs. A comprehensive literature survey was conducted and experimental datasets for 22 lakes with different characteristics were collected. A linear Qt(Rn) model with a hysteresis function to describe seasonal warming and cooling effects was developed (Qt=a∗Rn+b+c∗dRn/dt) that fits the 22 independently gathered datasets satisfactory (R2 of 0.83 and RMSE of 22Wm−2) for bi-weekly and monthly time scales. Predictive models for the coefficients a, b and c were also developed, using Rn and water surface temperature measurements that can be retrieved from routine earth observation measurements. The average R2 between measured and modeled Qt was 0.84 and the RMSE was 37Wm−2 if predictive models were used for the assessment of lake specific Qt(Rn) functions. Two independent satellite-derived products were explored: the ARC-Lake (ATSR Reprocessing for Climate) product for water surface temperature using the ATSR (Along Track Scanning Radiometer) series data, and the CM SAF (Satellite Application Facility on Climate Monitoring) product for solar radiation Rs based on the AVHRR (Advanced Very High Resolution Radiometer) data. The proposed procedure using purely satellite-derived data as inputs resulted in comparably good Qt estimates as those using in-situ measurements. The new Qt hysteresis model can thus be applied together with satellite measurements for supporting the computation of evaporation from open water bodies based on energy balance equations.

Gasification kinetic analysis of the three pseudocomponents of biomass-cellulose, semicellulose and lignin

The gasification kinetic analysis of the three pseudocomponents (hemicellulose, cellulose and lignin) of biomass decomposition in the agent of CO2 were investigated. The Multi-peaks method was used to fit the Gaussian distribution model of DTG curves. The Friedman method was used to estimate the effective Eα, and the master plot method was used for the determination of the kinetic model. The results showed that there were two reaction stages for semi-cellulose and lignin. The DTG curves of semicellulose and lignin cannot be fitted by Gaussian distribution model. The Eα were ranged from 80 to 220kJmol(-1) for the three pseudocomponents. The Fn model could describe the kinetic process of stage I of semi-cellulose decomposition. Both cellulose and stage II of semicellulose decomposition could be described by An model and the two reaction stages of the lignin were fitted the Rn model very well.

Models of steel mass loss by atmospheric corrosion in Colombia using

In order to classify the corrosivity of the different Colombian atmospheres, as part of an extensive research project [1], plates of carbon steel were placed in 21 stations spread along the country electrical infrastructure (transmission lines and substations). There were measured among others at these stations, the time of wetness and deposition of sulfates and chlorides for 12 months, in addition steel plates were taken bimonthly to the laboratory in order to measure the mass loss suffered by these during the time of exposure. The classification of the 21 stations was done in 4 groups, considering the time of moisture, content of chlorides and sulfates, height above sea level and the plates exposure time; these are considered linearly independent variables according to the implemented technique of decomposition unique values (DPS). The criterion used for classification was the similarity of the variables using the Euclidean rule considered in the Kohonen unsupervised neural network. Additionally, models were implemented for the steel mass loss for each one of the groups using feed forward neural networks (RN), defining the above variables as inputs and the mass loss as the output. Besides, the comparison between the RN model for the group 1, with other models using genetic algorithms (GA) and the Simplex method is presented.

The Integration of a Specialized Eating Disorders Nurse on a General Inpatient Pediatric Unit

Eating disorders (EDs) affect approximately 5% of Canadian adolescents. Patients experiencing acute medical complications of their illness are often treated on a general pediatric ward with mixed populations. Twenty-one health care providers shared their experiences caring for youth with EDs and provided feedback regarding the integration of a specialized ED nurse (ED-RN). Findings suggest that an RN and ED-RN model produces consistent care for ED patients and satisfying therapeutic relationships for nurses. ED-RNs caring for youth with EDs need support from their peers, whereas other nurses need training regarding this population. Suggestions for the integration of specialized RN roles are discussed.

Evaluating the Impact of Daily Net Radiation Models on Grass and Alfalfa-Reference Evapotranspiration Using the Penman-Monteith Equation in a Subhumid and Semiarid Climate

Net radiation Rn is the main driving force of evapotranspiration ET and is a key input variable to the Penman-type combination and energy balance equations. However, Rn is not commonly measured. This paper analyzes the impact of 19 net radiation models that differ in model structure and intricacy on estimated grass and alfalfa-reference ET ETo and ETr, respectively and investi- gates how climate, season and cloud cover influence the impact of the Rn models on ETo and ETr. Datasets from two locations Clay Center, Nebraska, subhumid; and Davis, California, a Mediterranean-type semiarid climate were used. Rn values computed from the 19 models were used in the standardized ASCE-EWRI Penman-Monteith equation to estimate ETo and ETr on a daily time step. The influence of seasons on the estimation of Rn and on estimated ETo and ETr was investigated in winter November-March and summer May-September months. To analyze the influence of clouds on the impact of Rn models, relative shortwave radiation Rrs was used as a means to express the cloudiness of the days as: 0 Rrs0.35 for completely cloudy days; 0.35 Rrs0.70 for partially cloudy days; and 0.70 Rrs1.0 for clear sky days. The performances of Rn models showed variations at the same location and between the locations for the same model based on methods used to calculate various model parameters. The most significant impact of Rn on estimated ETo and ETr was related to the methods used to calculate atmospheric emissivity rather than methods used to calculate clear sky solar radiation Rso or cloud adjustment factor f. Rn models that used average air temperature to compute and an estimated f resulted in good performances at both locations. Empirical models that assumed f=1.0 showed poor to average performances at both locations. While model performances varied based on methods used to calculate Rso, f, and , there were significant seasonal variations in performances of models that calculated as a function of actual vapor pressure of the air ea. The seasonal variations in performances of these models were greater under subhumid climate at Clay Center than in semiarid climate at Davis, Calif. The models that calculated as a function of ea performed better under completely cloudy days than on other days, more so at Clay Center. Methods used to calculate have a significant impact on the Rn model performance, especially in unstable climatic conditions such as at Clay Center where there are frequent and rapid changes in climatic variables in a given day and throughout the year. The results of this study can be used as a reference tool to provide practical information on which method to select based on the data availability for reliable estimates of daily Rn relative to the ASCE-EWRI Rn method in subhumid and semiarid climates similar to Clay Center, Neb. and Davis, Calif.

An evaluation of the net radiation sub-model in the ASCE standardized reference evapotranspiration equation: Implications for evapotranspiration prediction

Net radiation (Rn) is a key component of the surface energy balance, but it is expensive and difficult to measure accurately. For these reasons, Rn is often predicted in evapotranspiration (ET) calculations with a model requiring measurements of incoming shortwave radiation, air temperature, and vapor pressure. We compared Rn predictions from the Rn sub-model used in the American Society of Civil Engineers (ASCE) standardized reference ET equation to mean Rn measurements from five 4-component reference net radiometers. The radiometers were part of a recent comparison study of multiple net radiometer models conducted over irrigated and clipped turfgrass in northern Utah (Blonquist et al., 2009). In the Rn model, net shortwave radiation is determined by direct measurement of solar radiation and an assumed value of albedo for the surface (0.23 for fully vegetated surfaces), and net longwave radiation is calculated with a Brunt (1932, 1952) approach for predicting net surface emissivity, calculated from near surface vapor pressure. Additionally, the ratio of measured incoming shortwave radiation to predicted clear-sky shortwave radiation is used as a surrogate variable for cloud cover in the net longwave radiation calculation. Relative to the reference Rn measurements (average of five 4-component net radiometers), modeled Rn was high during the day by an average of 8.6% and high in magnitude (more negative) at night by an average of 13.4% over hourly time intervals. Daily total Rn calculated by summing the hourly model predictions was always higher than the reference measurements, by an average of 8.1%, whereas daily total Rn calculated from the model over daily time intervals was closer to the reference measurements, 2% high on average. The model Rn error during the day was partly caused by the assumption in the model that surface albedo is a constant value of 0.23. Measurements showed albedo ranged from approximately 0.21 at solar noon to 0.30 near the beginning and end of the day, with a mean value of 0.23. However, most of the model Rn error was due to the prediction of net longwave radiation, where the empirical equation in the model typically yielded values that were too low in magnitude (less negative), by approximately 20% on average, but the error was dependent on time of day. The Rn error at night was largely caused by the inability to measure the surrogate for cloud cover at night, which relies on measurement of solar radiation from a previous time period of sufficient solar zenith angle. All five of the net radiometer models tested in the comparison study matched the mean of the reference net radiometers better than the ASCE model. When modeled hourly Rn was used to calculate ET over hourly time intervals, or when hourly ET values were summed to yield daily ET, ET was typically high, by 6% on average, relative to ET calculated from measured reference Rn. When modeled Rn was calculated over daily time intervals and used to calculate ET over daily time intervals, ET was more accurate, 1% high on average, relative to ET calculated over daily time intervals from measured reference Rn. While new models for Rn are being developed, the sub-model in the ASCE standardized reference ET equation has been in use for the past two decades in thousands of ET stations. As newer models are developed we hope to use this data set to evaluate them

Phylogenetic distances for neighbour dependent substitution processes

We consider models of nucleotidic substitution processes where the rate of substitution at a given site depends on the state of the neighbours of the site. We first estimate the time elapsed between an ancestral sequence at stationarity and a present sequence. Second, assuming that two sequences are issued from a common ancestral sequence at stationarity, we estimate the time since divergence. In the simplest non-trivial case of a Jukes–Cantor model with CpG influence, we provide and justify mathematically consistent estimators in these two settings. We also provide asymptotic confidence intervals, valid for nucleotidic sequences of finite length, and we compute explicit formulas for the estimators and for their confidence intervals. In the general case of an RN model with YpR influence, we extend these results under a proviso, namely that the equation defining the estimator has a unique solution.

Effect of CaCO3 Filler Component on Solid State Decomposition Kinetic of PP/LDPE/CaCO3 Composites

In this study, the effect of addition Calcium carbonate (CaCO3) filler component on solid state thermal decomposition procedures of Polypropylene-Low Density Polyethylene (PP-LDPE; 90/10 wt%) blends involving different amounts (5, 10, 20 wt%) Calcium carbonate (CaCO3) was investigated using thermogravimetry in dynamic nitrogen atmosphere at different heating rates. An integral composite procedure involving the integral iso-conversional methods such as the Tang (TM), the Kissinger-Akahira-Sunose method (KAS), the Flynn-Wall-Ozawa (FWO), an integral method such as Coats-Redfern (CR) and master plots method were employed to determine the kinetic model and kinetic parameters of the decomposition processes under non-isothermal conditions. The Iso-conversional methods indicated that the thermal decomposition reaction should conform to single reaction model. The results of the integral composite procedures of TG data at various heating rates suggested that thermal processes of PP-LDPE-CaCO3 composites involving different amounts of CaCO3 filler component (5, 10, 20 wt%) followed a single step with approximate activation energies of 226.7, 248.9, and 252.0 kJ.mol− 1 according to the FWO method, respectively and those of 231.3, 240.1 and 243.0 kJ mol− 1 at 5°C min− 1 according to the Coats-Redfern method, the reaction mechanisms of all the composites was described from the master plots methods and are Pn model for composite C-1, Rn model for composites C-2 and C-3, respectively. It was found that the thermal stability, activation energy and thermal decomposition process changed by the increasing CaCO3 filler weight in composite structure.

Further notes on the analysis of mammal inventory data collected with camera traps

Further notes on the analysis of mammal inventory data collected with camera traps

Kinetic study on the thermal dehydration of CaCO3 · H2O by the master plots method

AbstractThermal dehydration process of calcium oxalate monohydrate, CaC2O4 · H2O, was reinvestigated from a viewpoint of reaction kinetics. On the basis of data of thermogravimetry, kinetic analysis was performed under nonisothermal conditions using an integral composite procedure, which includes a integral isoconversional method and an integral master plots method. The results of the integral isoconversional method of TGA data at various heating rates suggested that dehydration of CaC2O4 · H2O followed a single step with an activation energy of 99.02 ± 2.61 kJ/mol, and from the master plots method, the reaction was described by an Rn model. Finally, it was estimated that the pre‐exponential factor A = (2.82 ± 1.81) ×109 s−1, and the kinetic exponent n = 2.3 ± 0.2. The results of nonisothermal kinetic analysis of the thermal dehydration reactions of CaC2O4 · H2O suggested that the integral composite procedure be very successful in evaluating kinetic parameters and describing kinetic model of solid‐state reactions. © 2006 American Institute of Chemical Engineers AIChE J, 2006

The topology of boundaries

High-level representations used in reasoning distinguish a special set of boundary locations, at which function values can change abruptly and across which adjacent regions may not be connected. Standard models of space and time, based on segmenting Rn, do not allow these possibilities because they have the wrong topological structure at boundaries. This mismatch has made it difficult to develop formal mathematical models for high-level reasoning algorithms. This paper shows how to modify an Rn model so as to have an appropriate topological structure. It then illustrates how the new models support standard reasoning algorithms, provide simple models for previously difficult situations, and suggest interesting new analyses based on change or non-change in scene topology.