Logistic Growth Equation Research Articles

Characterization of sexual size dimorphism in mandarin fish and association with igfbp-5a/b regulation.

Mandarin fish (Siniperca chuatsi) is an important cultured fish in East Asia that shows sexual size dimorphism (SSD), with females growing faster than males when reaching marketable size. However, the regulatory mechanism of SSD is not clear. To characterize SSD of mandarin fish and its association with gh/igf1/igfbp-5 expression, gonadal developmental atlas of the females and the males were described, and growth parameters and serum levels of E2 and T, as well as the relative expression levels of gh, igf1, and igfbp-5a/b mRNAs, were determined. The results showed that the logistic growth equation of body mass and total length of female and male were W(♀) = 667.57/(1 + e^(4.19 - 1.24*t)), W(♂) = 582.71/(1 + e^(4.07 - 1.27*t)), L(♀) = 31.47/(1 + e^1.95 - 1.08*t)), L(♂) = 26.20/(1 + e^(2.56 - 1.5*t)). The month of inflection points for body mass for females and males were 3.37 mph and 3.20 mph, respectively, when the body mass were 333.79g and 291.36g. The month of inflection points for total length growth were 1.80 mph and 1.70 mph, respectively, when the total length were 18.52cm and 16.28cm. At 1.5-2.0 mph, SSD was not clearly demonstrated. At 3.0 mph, the body mass of the females was significantly higher than that of the males (P < 0.05), Serum E2, brain gh, and liver igf1 expression of the females was significantly higher than that of the males (P < 0.05); T content of the males was significantly higher than that of the females (P < 0.05). At 4.0months of age, the body mass of the females was highly significantly higher than that of the males (P < 0.01), Serum E2, brain gh, and liver igf1 expression of the females was highly significantly higher than that of the males (P < 0.05); T content of the males was significantly higher than that of the females (P < 0.05). With the continuous development of gonads, muscle and liver igfbp-5a and -5b expression generally tend to increase in females and males, while igfbp-5a showed a gradual increasing trend, and igfbp-5b expression showed a trend of decreasing and then increasing. Male igfbp-5a/b expression was significantly higher than female at the age of 3.0-4.0months (P < 0.05). This work verified that the females had faster growth rate since 3.0 mph compared to the males, which may be related to higher E2 levels in females leading to higher igf1 level, through inhibition of igfbp-5a/b expression.

Effects of turning aeration and the initial carbon/nitrogen ratio on the biodegradation of polylactic acid under controlled conditions

Plastic pollution is primarily caused by the accumulation of petroleum-derived plastics, as they tend to degrade slowly. Sustainable alternatives to these materials are bio-based and biodegradable plastics, such as polylactic acid (PLA). In this study, we assessed how turning aeration and the initial carbon/nitrogen (C/N) ratio impact PLA biodegradation. The study was carried out under controlled composting conditions, over 180 days, with the aim of decreasing the biodegradation time of the PLA. Apple pomace, rice husk, grape pomace compost, and PLA were used as substrates in the composting process. The experiments were conducted using three types of turning aeration: without turning, one turn per week, and two turns per week. Three initial C/N ratios were used: 20, 30, and 40. A stepwise temperature ramp was designed and implemented to simulate industrial composting conditions, which influence microbial activity and thus the rate of decomposition of substrates, including PLA. The data showed behavior; hence, a nonlinear regression model based on the logistic growth equation was used to predict the PLA biodegradation at the end of the composting process. The results showed that two turns per week with an initial C/N ratio of 30 or 40 led to a 90 % biodegradation of the PLA in 130 days. This degradation was verified by Fourier-Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM).

Lactic Acid Production by Lactiplantibacillus plantarum AC 11S-Kinetics and Modeling.

Lactic acid is a versatile chemical with wide application in many industries. It can be produced by the fermentation of different sugars by various lactobacilli and investigations on lactic acid production from different substrates and by different strains are still in progress. The present study aimed to study lactic acid production from lactose by Lactiplantibacillus plantarum AC 11S and to choose a mathematical model describing in the best way the experimental data obtained. The influence of initial substrate concentration was investigated, and optimal pH and temperature were determined. An unstructured mathematical model was developed comprising equations for bacterial growth, substrate consumption, and product formation. The model was solved with different terms for specific growth rates considering substrate and/or product inhibition. The best bacterial growth and lactic acid production were achieved at pH = 6.5 and 30 °C. Production of lactic acid was mainly growth-associated, and at initial substrate concentration over 15 g/L, a considerable product inhibition was observed. The parameters of different models were determined and compared. The modified Gompertz equation gave the best fit when solving only the equation for biomass growth at different initial substrate concentrations. Solving the entire set of differential equations for bacterial growth, substrate consumption, and product formation, the best results were obtained when using a variant of the logistic equation for biomass growth. This variant included a term for product inhibition and described in the best way all experimental data. Solving the model for different biomass concentrations showed that an increase in biomass led to a shorter lag phase and the stationary phase was reached faster. The results obtained, optimum conditions and the kinetic model, are good bases for studying pH-controlled fermentation, as well as a continuous process.

Modelling of mould growth on pine sapwood: the preliminary analysis of input parameters in a logistic mould growth model

This paper deals with a logistic growth equation which was used for calculations of mould development on wood in time. The values of input parameters were determined by mutual comparisons of the logistic growth equation and synthetic mould growth data. Synthetic mould growth curves were calculated by the Finnish mould growth model for pine sapwood. A transition function between dimensionless mould index (the state variable of the Finnish mould growth model) and microscopic mould coverage fraction (the state variable of the logistic mould growth model) was proposed to facilitate numerical comparisons between both models. The insight to input parameters of the logistic mould growth model was then gained by plotting optimal values of parameters as a function of temperature and relative humidity, or moisture content. The setting of input parameters in mould growth models was then briefly discussed. It is concluded that further development of mould growth models should be based on systematic comparison with mould growth experiments. Not only growth under favourable conditions, but also the regrowth process after desiccation period must be correctly addressed in the model.

Applying a logistic growth equation to model flocculation of sediment in the presence of living and dead organic matter

In the present study, we aim to parameterize a flocculation model, based on a logistic growth equation, by conducting laboratory experiments. The flocculation experiments are performed using two types of natural sediments and different flocculating agents: salt (monovalent and divalent), extracellular polymeric substances, and living and dead microalgae Skeletonema costatum. It was found that the median size of flocs (D50) did not exceed the Kolmogorov microscale when salt-induced flocculation was performed (in the absence of organic matter), which is in line with previous studies. Flocs with organic matter reach sizes that are larger than the Kolmogorov microscale, and both their growth and steady-state size are salinity-dependent. In particular, divalent salts are shown to promote flocculation of sediment to organic matter. The logistic growth model can be used to study either the evolution of a class volume concentration as function of time or the change in size of a given class as function of time. The fine particle volume concentration decreases in time, whereas the coarse particle volume concentration increases, during the flocculation process. The mass balance between the two classes as defined by Chassagne and Safar (Modelling flocculation: Towards an integration in large-scale sediment transport models. Marine Geology. 2020 Dec 1;430:106361) is estimated.

Modeling the growth of Pacific white shrimp (Litopenaeus vannamei) using the new Bayesian hierarchical approach based on correcting bias caused by incomplete or limited data

The revenue, costs, and profit of an aquaculture farm are based on the weight of animal protein sold. Thus, there is a relationship between the economic and zootechnical indexes to the growth model of animals in a production system. The growth modeling of cultivated organisms can be used as a production management tool, allowing estimates of anticipated size at harvest, waste outputs, as well as nutrient and feed requirements, helping in decision-making in the face of Aquaculture 4.0. It is important to emphasize that recent research has indicated the possible underestimation of parameters in nonlinear growth models due to the characteristic of incomplete or limited data in aquaculture. Therefore, the objective of this research was to model the growth of Pacific white shrimp (Litopenaeus vannamei) in an industrial-scale shrimp farm in northeastern Brazil. Based on the Bayesian methodology for correcting this bias, six nonlinear hierarchical growth models were evaluated in this research (Morgan-Mercer-Flodin, Michaelis–Menten, Weibull, von Bertalanffy, Gompertz, and logistic growth equation) and fitted to real data from a shrimp farm in northeastern Brazil. The model was validated based on the predictive capacity (accuracy) to forecasting shrimp growth at different hierarchical levels. Finally, for one of the main expected results, a sensitivity analysis was performed to compare different treatments according to the new approach. The Weibull growth equation stands out as the best among all those studied (WAIC = 2661.3, LOO-IC = 2705.2). Although it presented a poor fit for the hierarchical population level, good predictions were realized at the pond level and at the production cycle level. The dataset was split into fit and homologation subsets for a model validation analysis which showed an accuracy of 95.76% and 85.71% at the pond and production cycle levels respectively. The proposed method detected subtle differences between production cycles, which would be imperceptible if analyzed using the zootechnical indices usually practiced in shrimp farms. The new approach can contribute to improving products, processes, and decision-making in aquaculture management.

Toward a logistic model of dynamic mold growth on wood

This paper deals with mathematical modeling of mold growth on wood. The logistic growth equation is used to model mold growth phenomena in changing environmental conditions. Microscopic mold coverage fraction is used in the logistic equation to represent mold development on a material surface over time. The current state-of-the-art dynamic Finnish mold growth model is used as the reference model for development of the logistic mold growth model. The optimal values of mold growth coefficient are estimated by simulations of mold growth under constant environmental conditions and by finding the best agreement with the Finnish mold growth model using mathematical optimization methods. It is shown that the mold growth coefficient can be approximated as a function of relative humidity, temperature, wood species and the quality of the surface. The model parameters are then verified by a comparison with the Finnish mold growth model. In this case, cyclic step changes of constant relative humidity are used. Finally, the logistic mold growth model is compared with published experimental observations of mold growth. The discrepancy of mold growth models and experimental mold growth data, and possible improvements are discussed.

Migration and Proliferation Dichotomy: A Persistent Random Walk of Cancer Cells

A non-Markovian model of tumor cell invasion with finite velocity is proposed to describe the proliferation and migration dichotomy of cancer cells. The model considers transitions with age-dependent switching rates between three states: moving tumor cells in the positive direction, moving tumor cells in the negative direction, and resting tumor cells. The first two states correspond to a migratory phenotype, while the third state represents a proliferative phenotype. Proliferation is modeled using a logistic growth equation. The transport of tumor cells is described by a persistent random walk with general residence time distributions. The nonlinear master equations describing the average densities of cancer cells for each of the three states are derived. The present work also includes the analysis of models involving power law distributed random time, highlighting the dominance of the Mittag–Leffler rest state, resulting in subdiffusive behavior.

The Greenhouse Gas Crisis and the Logistic Growth Curve

The greatest challenge of the coming century will be the consequences of an imbalanced atmosphere. Currently, projections of global heating due to an increasingly imbalanced atmosphere are dire, but they underestimate the near-term heating impacts of the growing concentrations of methane. Industrially mediated carbon capture and storage sometimes gets raised as a promising solution on the CO2 front, but it is presently commercially inviable. Despite these facts, we nonetheless need to act globally to reduce the atmospheric concentrations of greenhouse gases, although our increasingly separate information ecosystems make finding a way to express the reality of the atmospheric imbalance crisis to a wide audience daunting. One approach to presenting the atmospheric imbalances leading to global heating is to strip the discussion down initially to its bare bones with a sharp focus on the variables of the logistic growth equation. Although virtually anything can be politicized, the logistic growth equation’s variables are at least apolitical in their origin. After examining those variables, we can proceed to focus on density-dependent mortality factors (DDMFs) and their relationship to visible climatic changes driven by atmospheric imbalances. Both the Global North and the Global South need to do all that we do to reduce atmospheric greenhouse gas accumulation, reducing DDMFs, while paying careful attention to Indigenous rights and to the need for global gender equity, so that our efforts to control DDMFs do not produce a new expression of colonialism.

Development of a Simple and Robust Kinetic Model for the Production of Succinic Acid from Glucose Depending on Different Operating Conditions

Succinic acid (SA) is one of the main identified biomass-derived chemical building blocks. In this work we approach the study of its production by Actinobacillus succinogenes DSM 22257 from glucose, focusing on the development and application of a simple kinetic model capable of representing the evolution of the process over time for a great diversity of process variables key to the production of this platform bio-based chemical: initial biomass concentration, yeast extract concentration, agitation speed, and carbon dioxide flow rate. All these variables were studied experimentally, determining the values of key fermentation parameters: titer (23.8–39.7 g·L−1), yield (0.59–0.72 gSA·gglu−1), productivity (0.48–0.96 gSA·L−1·h−1), and selectivity (0.61–0.69 gSA·gglu−1). Even with this wide diversity of operational conditions, a non-structured and non-segregated kinetic model was suitable for fitting to experimental data with high accuracy, considering the values of the goodness-of-fit statistical parameters. This model is based on the logistic equation for biomass growth and on potential kinetic equations to describe the evolution of SA and the sum of by-products as production events that are not associated with biomass growth. The application of the kinetic model to diverse operational conditions sheds light on their effect on SA production. It seems that nitrogen stress is a good condition for SA titer and selectivity, there is an optimal inoculum mass for this purpose, and hydrodynamic stress starts at 300 r.p.m. in the experimental set-up employed. Due to its practical importance, and to validate the developed kinetic model, a fed-batch fermentation was also carried out, verifying the goodness of the model proposed via the process simulation (stage or cycle 1) and application to further cycles of the fed-batch operation. The results showed that biomass inactivation started at cycle 3 after a grace period in cycle 2.

Generalized Beta Models and Population Growth: So Many Routes to Chaos

Logistic and Gompertz growth equations are the usual choice to model sustainable growth and immoderate growth causing depletion of resources, respectively. Observing that the logistic distribution is geo-max-stable and the Gompertz function is proportional to the Gumbel max-stable distribution, we investigate other models proportional to either geo-max-stable distributions (log-logistic and backward log-logistic) or to other max-stable distributions (Fréchet or max-Weibull). We show that the former arise when in the hyper-logistic Blumberg equation, connected to the Beta (p,q) function, we use fractional exponents p−1=1∓1/α and q−1=1±1/α, and the latter when in the hyper-Gompertz-Turner equation, the exponents of the logarithmic factor are real and eventually fractional. The use of a BetaBoop function establishes interesting connections to Probability Theory, Riemann–Liouville’s fractional integrals, higher-order monotonicity and convexity and generalized unimodality, and the logistic map paradigm inspires the investigation of the dynamics of the hyper-logistic and hyper-Gompertz maps.

A Simple Bias and Uncertainty Scheme for Tropical Cyclone Intensity Change Forecasts

Abstract To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.

Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.

P105 In vitro interaction of Malassezia and commensal Staphylococcus species

Poster session 1, September 21, 2022, 12:30 PM - 1:30 PM Objective Malassezia is the most abundant fungal skin commensal organism, representing 50%–80% of total fungi present on the skin. It has been associated with many skin disorders such as pityriasis versicolor (PV) and seborrheic dermatitis/dandruff (SD/D). The role of Malassezia in disease manifestation is not discerned. It is important to understand its interaction with bacterial flora such as Staphylococcus epidermidis and S. capitis in vitro. We have studied the interaction of Malassezia and Staphylococcus species isolated from skin flora.Methods Malassezia restricta, M. globosa (n = 5) isolated from patients with SD and M. furfur (n = 5) isolated from PV were sub-cultured on Modified Dixon's agar (MDA). Staphylococcus epidermidis and S. capitis were isolated from patients with SD and sub-cultured on brain heart infusion (BHI) agar. Malassezia species requires media supplemented with lipids (MDA) for its growth. Bacteria and Malassezia were quantified on MDA and BHI agar by Miles and Mishra method to perform interaction between them. For direct interaction, suspensions (100 μl) of M. restricta, M. globose, and M. furfur were prepared in normal saline and added to wells on the plates of lawn cultures containing S. epidermidis and S. capitis (107 CFU/ml). Plates were incubated for 12 h at 35°C and observed for zone of inhibition. To investigate the release of antibacterial compounds into the extracellular environment, M. furfur was inoculated in modified Dixon's broth (MDB) and incubated at 35°C for 5 days. Supernatant was collected at 12 h, 24 h, 48 h, 72 h, 96 h, and 120 h of incubation and evaluated for antibacterial activity by agar-well diffusion assay. Effect of cell-free supernatant of Malassezia on growth of bacteria was also monitored by growth kinetics of S. epidermidis for 24 h in the absence and presence of M. furfur supernatant using Epoch-2 microplate spectrophotometer.ResultsMDA supported the growth of bacteria at different cell densities (107-103 CFU/ml count) and incubation time of S. epidermidis and S. capitis was similar on MDA and BHI. Zone of inhibition (ZOI) was witnessed with M. restricta (20.6 ± 3 mm, 21 ± 3 mm), M. globosa (21 ±1 mm, 22.6 ±2 mm) and M. furfur isolates (16.5 ± 1 mm, 18 ± 2 mm) against S. capitis and S. epidermidis respectively by direct interaction. Inhibition of bacteria by M. furfur was noted from 48-120 h as ZOI (21.7 ±5.1 mm) was observed on bacterial lawn cultured plate. When growth kinetics of S. epidermidis was monitored in presence of M. furfur supernatant, maximum value reached upto 0.26 ± 0.019 only from 0.01 ± 0.001 at OD600 in 9 h including lag phase of 4 h (Fig. 1). However, OD600 value reached upto 0.97 ± 0.005 in 8 h including lag phase of 1.5 h in absence of supernatant. Doubling time calculated from logistic growth equation was 76.6 ± 4.4 and 65.2 ± 2.9 minutes in the presence and absence of supernatant respectively.ConclusionInhibition of bacteria by Malassezia species noted in our study has not been reported earlier. The possible production of antibacterial compounds by Malassezia might be responsible for dysbiosis leading to disease.

Full Delay Logistic Population Model with Sustainable Harvesting

The paper studies the dynamics of a full delay-logistic population model incorporated with a proportionate harvesting function. The study discusses the stability of the model in comparison with the well-known Hutchinson logistic growth equation with harvesting function using the rate of harvesting as a bifurcation parameter to determine sustainable harvesting rate even at a bigger time delay of τ = 3.00 . In all cases, the Hutchinson equation with harvesting was forced to converge to equilibrium using an additional and a different time delay parameter, a deficiency previous researchers have failed to address when the Hutchinson model is used for this purpose. The population fluctuations are catered for with this model making the estimated maximum sustainable growth and harvest reflect realities as this model drastically reduces time-delay associated oscillations compared to the well-known Hutchinson delayed logistic models. The numerical simulations were be done using the MatLab Software.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world. Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production. However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models. Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018. Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models. The model performances were evaluated using seven statistical indicators and a global performance index. The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching. Among the four single factor-driven models, the overall performance of the MlogPTU Model was the best, followed by the MlogAGDD Model. The MlogPPTU Model was better than the MlogAGDD Model in simulating SD and LAI. Among the 10 models, the overall performance of the MlogAGDD–APR Model was the best, followed by the MlogAGDD–ASS Model. Specifically, the MlogAGDD–APR Model performed the best in simulating H and LAI, while the MlogAGDD–ADL and MlogAGDD–ASS models performed the best in simulating SD and DM, respectively. In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Optimization of drip irrigation and fertilization regimes to enhance winter wheat grain yield by improving post-anthesis dry matter accumulation and translocation in northwest China

The excessive irrigation and fertilization are common in wheat (Triticum aestivum L.) production in northwest China, which not only results in resources waste and environmental pollution, but also not necessarily guarantee high wheat yield due to the plant overgrowth. It is thus crucial to explore a suitable irrigation and fertilization strategy that benefits high-yield wheat cultivation by regulating post-anthesis dry matter accumulation and translocation. A consecutive four-season field experiment (2014–2018) was undertaken on drip-fertigated winter wheat with three irrigation levels based on the soil moisture content of moistening layers (W1: normal irrigation, W2: mild deficit irrigation, W3: severe deficit irrigation) and three fertilization (N-P2O5-K2O) rates (F1: 175–117–150 kg ha−1, F2: 125–84–108 kg ha−1, F3: 75–50–65 kg ha−1). The results showed that the logistic growth equation could well describe the accumulation process of aboveground dry matter with R2 ranging 0.989 − 0.999 (P < 0.01). Water stress advanced the thermal time required from sowing to the maximum aboveground dry matter rate, while the maximum aboveground dry matter rate and average accumulated rate of aboveground dry matter increased with the increase of irrigation and fertilization regimes. Dry matter translocation showed a negative relationship with fertilization rate. However, both the post-anthesis dry matter accumulation and its contribution to total dry matter at maturity (CDMA) increased with the increasing fertilization rate. With the increase of water and fertilizer supply, dry matter translocation efficiency decreased, but CDMA showed the opposite trend. The post-anthesis dry matter accumulation was positively related to winter wheat grain yield. When the grain yield reached above 7.5 Mg ha−1 in northwest China, the post-anthesis accumulated dry matter should be more than 4.7 Mg ha−1. It was concluded that the mild deficit irrigation along with N-P2O5-K2O fertilization of 125–84–108 kg ha−1 enhanced the productivity of winter wheat by increasing the post-anthesis dry matter translocation and accumulation.

Research on Forest Carbon Sequestration Based on FSC Model and DeepAR Algorithm

Forestry resources play an irreplaceable role in solving the problem of climate change. Forest managers to find a balance between carbon sinks and forest products, decisions must therefore take into account many aspects of forest value. This paper clarifies that forest carbon sequestration is mainly influenced by three aspects: human, climate, and nonclimatic physical factors, constructs a model for estimating the carbon sequestration rate of forest ecosystems based on forest age and the logistic growth equation, and combines human behaviour and climate influencing factors to make comprehensive corrections to the carbon sequestration rate of forest ecosystems per unit area. A model for calculating the carbon sequestration rate over time in a forest system was developed, which combined with the DeepAR Algorithm to obtain carbon sequestration. Then, this paper then assesses the various values of the forest, dividing the 25 factors affecting forest value assessment into five indices to establish a model of the forest ecosystem value assessment system for application to forest value assessment under different conditions. After determining the index system, we use the EWM‐CVM to integrate the indexes into the value index based on the forest ecosystem to propose a management plan. And the PSO‐BP algorithm is used to determine the transition point for forest management in order to optimise the ecological value of the forest.

A Kinetic model for submerged citric acid production by Aspergillus versicolor using oil palm empty fruit bunch

The fermentation kinetics of citric acid by Aspergillus versicolor was studied in a submerged batch system. The logistic equation for growth, the Luedeking–Piret equation for citric acid production and modified Luedeking–Piret-like equation for glucose consumption was proposed for this study. The model appeared to provide a reasonable description for each parameter during the growth phase. The production of citric acid was growth-associated.

Psi-Caputo Logistic Population Growth Model

This article studies modeling of a population growth by logistic equation when the population carrying capacity K tends to infinity. Results are obtained using fractional calculus theories. A fractional derivative known as psi-Caputo plays a substantial role in the study. We proved existence and uniqueness of the solution to the problem using the psi-Caputo fractional derivative. The Chinese population, whose carrying capacity, K, tends to infinity, is used as evidence to prove that the proposed approach is appropriate and performs better than the usual logistic growth equation for a population with a large carrying capacity. A psi-Caputo logistic model with the kernel function x + 1 performed the best as it minimized the error rate to 3.20% with a fractional order of derivative α = 1.6455.