Growth Prediction Models Research Articles

Temperature versus Relative Humidity: Which Is More Important for Indoor Mold Prevention?

Temperature is known as one of the abiotic factors that can affect mold growth. Many mold growth prediction models consider temperature as one of the parameters that can significantly impact mold growth indoors, and hence temperature has been targeted by different indoor mold prevention strategies on different premises. For example, European guidelines for libraries suggest a temperature of 19 °C to preserve books. However, running low temperature air-conditioning (AC) costs substantially more energy, and thus a higher temperature (e.g., 25.5 °C) has been regularly proposed as the recommended indoor temperature for general indoor environments in Hong Kong. It is, therefore, needed to understand whether or not the reduction of indoor temperature would lead to better effectiveness of mold prevention. Using Cladosporium cladosporioides (C. cladosporioides) as the model, its germinating spores were challenged in C. cladosporioides to wet-dry cycles with different combinations of relative humidity (RH, 40%, 60% and 80%) and temperature (19 °C and 28 °C) levels. The survival, lipid peroxidation and catalase (CAT) activity of the treated spores were monitored and compared. C. cladosporioides spores showed similar levels of viability, lipid peroxidation and CAT activity when they were exposed to 19 °C and 28 °C at the same RH, but substantially lower survival and higher oxidative stress were observed under the wet-dry cycles with 40% RH dry periods compared with 60% and 80% RH at both temperatures, suggesting that indoor temperature does not tend to affect the resistance of C. cladosporioides to wet-dry cycles as significantly as the RH level of the dry period. Collectively, this study suggests a more important role for moisture over temperature in indoor mold prevention. The outcome of this study may facilitate the sustainable management of indoor mold problems in buildings.

Equivalent crack growth model for correlation and prediction of fatigue crack growth under different stress ratios

An equivalent crack growth model that accounts for the effect of stress ratio is proposed for the correlation and prediction of crack growth under different stress ratios. A previously proposed equivalent driving force ΔKeq that yields the same growth rate (da/dN)R as combined applied stress intensity factor range ΔKR and stress ratio, R is modified, and its corresponding equivalent growth rate (da/dN)eq is derived. This yields an equivalent crack growth model of (da/dN)eq versus ΔKeq capable of collapsing data of different R-ratios unto a common curve. The model was validated with excellent agreement between the model and published data.

Crack path investigations in a pearlitic rail steel after pre-deformation under cyclic Mode-II loading

The crack path under cyclic Mode-II loading in undeformed and pre-deformed pearlitic rail steel R260 is investigated. To produce large-scale specimens with pre-deformed microstructures two methods are applied. Firstly, cylinder bars are processed using an axial-torsion machine resulting in a moderate deformation. Secondly, a new high pressure torsion (HPT) setup is developed to achieve a more strongly deformed microstructure. In the fatigue tests, a distinctive change in the crack path is observed. In the undeformed and moderate deformed specimens, the cracks bifurcate and turn into a Mode-I dominated mixed-mode. In the HPT material, the cracks grow in Mode-II without mixed-mode propagation. For rolling contact fatigue cracks in rails these experimental results indicate a strong connection between the propagation mode and the anisotropy of the highly deformed pearlitic microstructure. This microstructure is characteristic near the wheel-rail contact surface. In future, these findings are important to improve numerical-based crack growth prediction models.

A circulation prediction model for ramp and vortex generator in supersonic flow: A numerical study

The circulation growth of supersonic streamwise vortex (SSV) formed by a ramp is complicated and essential to estimate scramjet mixing behavior, while a predictive model for circulation growth is still lacking. Through well-validated RANS simulations, it is observed that the circulation growth of streamwise vortex for a ramp is similar to one for vortex generator, which determines three important geometric parameters for SSV growth, i.e., ramp angle, swept angle, and length of the leading edge. The results of different geometries with these three parameters further show that the SSV circulation growth is proportional to the downwash speed influenced by oblique shock intensity. By building the relation between downwash speed and geometric parameters, an SSV circulation prediction model is proposed and validated by the circulation curves of all cases under a wide range of free stream Mach numbers. The novel circulation model has the potential for the preliminary mixing enhancement design for a scramjet ramp injector.

Growth of the harmful benthic cyanobacterium Microseira wollei is driven by legacy sedimentary phosphorous

Models for cyanobacterial harmful algae blooms (cHABs) in fresh waters are usually predicated on the relationship between cyanobacterial ecology and dissolved nutrients, particularly phosphorous. Here we show legacy sediment-associated phosphorous as the primary driver of a benthic cHAB, not phosphorous in the water column. Biogeographical surveys by teams of citizen science volunteers working with the University of South Carolina identified over 200 distinct mats of Microseira wollei in Lake Wateree, SC based on toxin characterization. In sum these were estimated to affect approximately 175 km of the lake's shoreline. This growth occurred under water quality conditions that were near or below the regulatory total maximum daily load for phosphorous and nitrogen. A series of established predictive models for cyanobacterial biomass growth were applied retroactively to match the measured growth with measured water quality parameters. The only component of the system that successfully predicted microbial biomass was sedimentary phosphorous. Concentrations of the Lyngbya wollei toxins (LWTs) 1, 4, 5, and 6 were determined at multiple sites over an 18-month period and a toxin inventory for the lake was calculated. Toxin profiles between sites differed at the 95% level of confidence, establishing each site as a unique mat. An empirical model of toxin production potential based on sedimentary phosphorous was developed.

Leveraging Structural Health Monitoring Data Through Avatars to Extend the Service Life of Mass Timber Buildings

Mass timber construction systems, incorporating engineered wood products as structural elements, are gaining acceptance as a sustainable alternative to multi-story concrete or steel-frame structures. The relative novelty of these systems brings uncertainties on whether these buildings perform long-term as expected. Consequently, several structural health monitoring (SHM) projects have recently emerged to document their behavior. A wide and systematic use of this data by the mass timber industry is currently hindered by limitations of SHM programs. These limitations include scalability, difficulty of data integration, diverse strategies for data collection, scarcity of relevant data, complexity of data analysis, and limited usability of predictive tools. This perspective paper envisions the use of avatars as a Web-based layer on top of sensing devices to support SHM data and protocol interoperability, analysis, and reasoning capability and to improve life cycle management of mass timber buildings. The proposed approach supports robustness, high level and large-scale interoperability and data processing by leveraging the Web protocol stack, overcoming many limitations of conventional centralized SHM systems. The design of avatars is applied in an exemplary scenario of hygrothermal data reconstruction, and use of this data to compare different mold growth prediction models. The proposed approach demonstrates the ability of avatars to efficiently filter and enrich data from heterogeneous sensors, thus overcoming problems due to data gaps or insufficient spatial distribution of sensors. In addition, the designed avatars can provide prediction or reasoning capability about the building, thus acting as a digital twin solution to support building lifecycle management.

Using weed emergence and phenology models to determine critical control windows for winter-grown carinata (Brassica carinata)

AbstractAdoption of the new biofuel crop carinata (Brassica carinata A. Braun) in the southeastern United States will largely hinge on sound agronomic recommendations that can be economically incorporated into and are compatible with existing rotations. Timing of weed control is crucial for yield protection and long-term weed seedbank management, but predictive weed emergence models have not been as widely studied in winter crops for this purpose. In this work, we use observed and predicted emergence of a winter annual weed community to create recommendations for timing weed control according to weed and crop phenology progression. Observed emergence timings for four winter annual weed species in North Carolina were used to validate previously published models developed for winter annual weeds in Florida by accounting for temperature and daylength differences, and this approach explained more than 70% of the variability in observed emergence. Emergence of stinking chamomile (Anthemis cotula L.) and cutleaf evening primrose (Oenothera laciniata Hill.) followed biphasic patterns comparable to wild radish (Raphanus raphanistrum L.), which were predicted with previously published models accounting for 82% and 84% of the variation, respectively. Using the predictive models for weed emergence and carinata growth, critical control windows (CCW) were estimated for Clayton, NC, and Jay, FL, according to different planting dates. The results demonstrated how early planting coincided with the emergence of three competitive winter weeds, but early control could also remove a large proportion of the predicted emergence of these species. The framework for how planting timing will affect winter weed emergence and crop growth will be an instructive decision-making tool to help prepare farmers to manage weeds in carinata, but it could also be useful for weed management planning for other winter crops.

Evaluating Decision Support Tools for Precision Nitrogen Management on Creeping Bentgrass Putting Greens.

Nitrogen (N) is the most limiting nutrient for turfgrass growth. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the lone turfgrass growth prediction model only takes into account temperature, limiting its accuracy. This study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass (Agrostis stolonifera) putting green management by estimating short-term clipping yield. This method was compared against three alternative N application strategies including (1) PACE Turf growth potential (GP) model, (2) an experience-based method for applying N fertilizer (experience-based method), and (3) the experience-based method guided by a vegetative index, normalized difference red edge (NDRE)-based method. The ML-RF model was built based on a set of variables including 7-day weather, evapotranspiration (ET), traffic intensity, soil moisture content, N fertilization rate, NDRE, and root zone type. The field experiment was conducted on two sand-based research greens in 2020 and 2021. The cumulative applied N fertilizer was 281 kg ha−1 for the PACE Turf GP model, 190 kg ha−1 for the experience-based method, 140 kg ha−1 for the ML-RF model, and around 75 kg ha−1 NDRE-based method. ML-RF model and NDRE-based method were able to provide customized N fertilization recommendations on different root zones. The methods resulted in different mean turfgrass qualities and NDRE. From highest to lowest, they were PACE Turf GP model, experience-based, ML-RF model, and NDRE-based method, and the first three methods produced turfgrass quality over 7 (on a scale from 1 to 9) and NDRE value over 0.30. N fertilization guided by the ML-RF model resulted in a moderate amount of fertilizer applied and acceptable turfgrass performance characteristics. This application strategy is based on the N cycle and has the potential to assist turfgrass managers in making N fertilization decisions for creeping bentgrass putting greens.

Long-term effectiveness of growth hormone therapy in children born small for gestational age: An analysis of LG growth study data.

PurposeGrowth hormone (GH) treatment has been used to improve growth in short children who were born small for gestational age (SGA). The aim of this study was to investigate the long-term efficacy of GH treatment in these children.MethodsData from a multicenter observational clinical trial (ClinicalTrials.gov NCT01604395, LG growth study) were analyzed for growth outcome and prediction model in response to GH treatment. One hundred fifty-two children born SGA were included.ResultsThe mean age of patients born SGA was 7.13 ± 2.59 years. Height standard deviation score (SDS) in patients born SGA increased from -2.55 ± 0.49 before starting treatment to -1.13 ± 0.76 after 3 years of GH treatment. Of the 152 patients with SGA, 48 who remained prepubertal during treatment used model development. The equation describing the predicted height velocity during 1st year of GH treatment is as follows: the predictive height velocity (cm) = 10.95 + [1.12 x Height SDS at initial treatment (score)] + [0.03 x GH dose (ug/kg/day)] + [0.30 x TH SDS at initial treatment (score)] + [0.05 x age (year)] + [0.15 x Weight SDS at initial treatment (score)] ± 1.51 cm.ConclusionsGH treatment improved growth outcome in short children born SGA. We also developed a prediction model that is potentially useful in determining the optimal growth outcome for each child born SGA.Trial registrationClinicalTrials.gov Identifier: NCT01604395.

Using double ellipsoid heat source model for prediction of HAZ grain growth in GTAW of stainless steel 304

The grain growth in HAZ of austenitic stainless steels during welding is an important metallurgical change that affects the mechanical properties. In this study, the finite element simulation of heat transfer in GTAW of stainless steel 304 is investigated using double ellipsoid heat source model. The simulation results are compared with the results obtained from the analytical Rosenthal model and also experimental data. The simulated peak temperatures are close to the experimental results at the measurement points (the maximum relative difference is 10%), while the Rosenthal model has a maximum relative difference of 34%. It is also observed that the simulated weld pool size is also better compatible with the experimental results than the Rosenthal model. Additionally, the calculated grain size based on the simulation results is also more consistent with the experimental data than the Rosenthal model, particularly near the fusion boundary.

Individual-level distance-independent-based growth and yield prediction models for long-term Japanese cedar (Cryptomeria japonica)

Yield prediction has been determined to be vital in sustainable forest management. Recently, research trends have shifted from stand-level to individual-level yield prediction. In this study, we examined the effectiveness of yield prediction models based on a distance-independent approach for Japanese cedar (Cryptomeria japonica (L.f.) D.Don) trees in western Japan. We further examined the accuracy of the models with reference to existing data collected over the long-term. First, we constructed distance-independent height, diameter growth, and survival models. Then, we simulated for approximately 50 years individual tree height, diameter at breast height (DBH), and volume growth using the test data. We then compared the predicted and observed values and calculated root-mean-square error (RMSE) and bias to evaluate the model accuracy. The models were noted to perform well when predicting mean height, DBH, and volume for Japanese cedar trees; in fact, they adequately predicted the diameter distribution. Our results suggest that distance-independent models could adequately predict long-term mean values and diameter distribution. However, RMSE and bias indicated that error propagation occurred over longer time spans. Thus, it is effective to conduct actual measurements at some point in the forest development phase and use the measurements as initial values for short- or medium-term predictions.

Research on Mechanical Properties and Influencing Factors of Cement-Graded Crushed Stone Using Vertical Vibration Compaction.

To study the mechanical properties of cement-graded crushed stone for use in the transition sections of intercity railways, the growth laws governing unconfined compressive strength, splitting strength and resilience modulus of cement-graded crushed stone and their influencing factors were studied by the vertical vibration compaction method (VVCM). The strength growth equations of cement-graded crushed stone are proposed, and strength prediction equations are established. The research shows the unconfined compressive strength, splitting strength and resilience modulus of cement-graded crushed stone with a strong interlocked skeleton density type (VGM-30) are significantly enhanced to 20, 20 and 17% higher, respectively, than those of standard cement-graded crushed stone. The growth law of mechanical properties of cement-graded crushed stone is similar, with the fastest growth occurring before 14 days, and the rate decreasing after 28 days. The strength growth tended to be stable after 90 days, increasing with the increase in curing time, compaction coefficient and cement dosage. The correlation coefficients (R2) of the strength growth prediction models were found to be 0.99, 0.97, and 0.99, respectively. These values can be used to accurately predict the strength growth curve. This paper verifies the superiority of VGM-30 gradation through laboratory tests, providing a reference for gradation selection in the construction of intercity railway transition sections.

Getting a Foot in the Door: Trade Credit Strategies and the Pursuit of Entrepreneurial Internationalisation

Trade credit strategies – comprised of the approaches used by internationalising firms to set the payment terms and schedules for their customers – are one of the few levers available to new small firms to overcome the liability of outsidership. They are vital to entrepreneurial firms seeking to achieve international market growth in the context of scarce resources and limited knowledge of foreign markets and partners. Though under-explored and under-theorised to-date, trade credits may unlock material insights for predictive models of entrepreneurial growth. As such, our study develops and tests the notion that trade credits constitute an important mechanism for entrepreneurial firms to initiate goodwill and generate valuable referrals. However, our findings also suggest that while riskier trade credit strategies help first-time exporters combat exclusion from networks key to a firm’s ability to internationalise, the attendant risks often have a detrimental impact on long-term profitability and survival, thus creating a decision-making conundrum for growth-aspirants. Drawing upon a sample of 88,000 export transactions by 1040 small lumber firms from 52 countries, we advance theory and practice by showing how trade credit strategies shape both growth and performance.

Research on fatigue crack growth behavior: Experimental and prediction method of titanium alloy

A new type of titanium alloy for the pressure shell of a manned deep-sea submersible is studied. The fatigue performance and the fracture toughness of the titanium alloy are obtained under R = 0.03, 0.3, and 0.5. The curves of fatigue crack growth rate under three load ratios are predicted based on the existing fatigue crack growth rate prediction model. The results show that the fatigue crack growth threshold decreases with the increase of the load ratio, and the fatigue crack growth rate increases with the increase of the load ratio. The fatigue crack growth prediction model can accurately predict the fatigue crack growth rate of titanium alloy under different load ratios.

Predictive model for growth of Clostridium botulinum from spores at temperatures applicable to cooling of cooked ground pork

Cooling deviations and temperature abuse are two main reasons leading to the risk of Clostridium botulinum outgrowth in cooked pork. The aim of this research was to create a model that could be used to estimate C. botulinum growth from spores in cooked pork at temperatures similar to those used to chill cooked pork in processing facilities and food establishments. A cocktail of proteolytic C. botulinum types A and B consisting of five strains per type were used to inoculate pork to a final spore concentration of approximately 2 log CFU/g and cooked to 71 °C to heat shock the spores and kill vegetative microbes. The growth of C. botulinum was established at constant storage temperatures from 10 to 46 °C. C. botulinum growth was also studied under dynamic temperature conditions with cooling set to start at 54.4 °C and end at 4.4 °C or 7.2 °C in monophasic or biphasic cooling profiles, respectively. Growth parameters were estimated using the Baranyi model as a primary model and growth rates were fitted using the modified Ratkowsky secondary model with respect to temperature. The R2 values ranged from 0.7653 to 0.9995 indicating that the Baranyi primary model was well suited to the growth data. The modified Ratkowsky secondary model's R2 was 0.9653 and its root mean square error (RMSE) was 0.0687. All 11 prediction error values computed were within the limit of acceptable prediction zone (−1.0 to 0.5) suggesting a good fit of the model. The predictive model can provide information for the safety of cooked pork exposed to longer chilling times or for customized process schedule development as cooling of larger diameter products presents a processing challenge in the meat process operations.

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI.

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area

Due to the increased coastal population growth and urbanization along with rising sea levels, more people and properties will be under increased flood risk globally. Urban growth prediction models are now used to simulate potential future urban growth scenarios but impacts of future flood risks due to sea level rise (SLR) on future development have not been studied well. Owing to its higher predictive accuracy, this study employs the Multi-layer perceptron (MLP) based Artificial Neural Network-Markov Chain (ANN-Markov) model to simulate three future urban growth scenarios (business as usual (BAU), planned growth (PG), and sustainable growth (SG)) in Miami Metropolitan Area (Miami MSA), and three SLR scenarios (1 ft, 2 ft, and 3 ft) were spatialized with the current high-risk flooding (HF) zone to delineate future flood risk areas. Then the flood risks of future urban development in each growth scenario were assessed at both regional (MSA) and local (County) scales. Results show that current land use plan (PG) slightly decreases flood risks at the regional scale but not always at the local scale compared to the without growth regulation scenario (BAU). Nevertheless, flood risks in the PG scenario are significantly higher compared to the without growth in the HF zone scenario (SG). Urban growth scenario predictions can help prepare for and understand the SLR impacts.

A probiotic based product using multi-strain Bacillus species and predictive models for shrimp growth following probiotic intervention

Probiotic bacteria in aquaculture found to have significant effect on health of shrimp and growth by changing the microbial composition of water. The primary objective of the study is to develop a probiotic based product to improve the yield of shrimp by using multi-strain Bacillus species. Towards this, various Bacillus strains, namely, B. subtilis, Bacillus clausii, B. cereus, B. Megaterium, B. pumilus and Bacillus polymyxa were collected and confirmed the strains through biochemical tests in subculture. Further, it is found that all the six strains exhibited good antifungal, antibacterial, antagonistic and enzyme activities. Multi-strain probiotic product as feed for the growth of shrimp was prepared by using the solid state fermentation technique and quality check was done to make sure no contamination. After the product development, the efficacy of the product has been tested by conducting field studies. The parameters such as (i) temperature (ii) pH (iii) dissolved oxygen (iv) total ammonia nitrogen and (v) shrimp weight & (vi) length were investigated for about 120 days and measurements were taken at every 5-day interval. The average initial weight and length of shrimp is 4.2 ± 0.2 g and 0.8 ± 0.1 cm respectively. The average values obtained for 120 days include (i) Temperature (°C): Control = 28 ± 0.3; Treated = 28 ± 0.15 (ii) pH: Control = 8 ± 0.19; Treated = 8 ± 0.12 (iii) Dissolved oxygen (ppm): Control = 5 ± 0.23, Treated = 5 ± 0.21 (iv) Total ammonia nitrogen (ppm): Control = 2 ± 0.2; Treated = 0.8 ± 0.01 (v) Shrimp weight (gm): Control = 16 ± 0.21; Treated = 22 ± 0.14 (vi) Length (cm): Control = 6 ± 0.14; Treated = 8 ± 0.26. The average survival rate was found to be 88.67 ± 1.15% in probiotics ponds and 70.00 ± 3.0% in controlled ponds. From the overall study, it is observed that the weight and length of shrimp has been increased significantly up to sixty days compared to control environment. Two-way ANOVA has been performed to understand the interdependent relation between the input and output variables. Advanced statistical models were developed by using the concepts of Artificial neural network, Multivariate adaptive regression splines and Relevance vector machine to predict the shrimp weight and length. The predicted shrimp weight and shrimp length is found to be in close agreement with those of the corresponding experimental observations. The models are found to be robust, reliable and efficient.

Development of predictive growth models of Aeromonas hydrophila on raw tuna Thunnus orientalis as a function of storage temperatures

This study developed predictive growth models for Aeromonas hydrophila on raw tuna as a function of storage temperatures (2–15 °C). At these storage temperatures, the primary models fit well (R2; 0.97–0.98) with the Baranyi model to obtain lag time (LT) and specific growth rate (SGR). As the temperature increased, A. hydrophila growth increased. However, at 2 and 5 °C, no growth of A. hydrophila was observed over 7 days. The LT values were 4.99, 3.41, and 3.21 h, and SGR values were 0.02, 0.06, and 0.18 log CFU/h at 8, 11, and 15 °C, respectively. The secondary models were determined by nonlinear regression analysis; LT = 15.288–1.864 × T+0.069 × T2, SGR = 0.149–0.037 × T+0.003 × T2 (T; storage temperature). The suitability of the secondary models for LT and SGR was verified using mean square error (MSE; <0.01 internal validation, <0.02 external validation), bias factor (Bf; 0.980–1.056 internal validation, 0.841–0.995 external validation), and accuracy factor (Af; 1.296–1.305 internal validation, 1.127–1.231 external validation). These predictive models may be used in the prediction of A. hydrophila growth on raw tuna at various cold temperatures. Ultimately, the developed models could be beneficial for maintaining safe levels A. hydrophila during the processing and distribution of raw fish.

Construction of an Endothelial Cell Alignment Model for Prediction of Abdominal Aortic Aneurysm Growth

Construction of an Endothelial Cell Alignment Model for Prediction of Abdominal Aortic Aneurysm Growth