Logistic Function Research Articles

Modelling the impact of host galaxy dust on type Ia supernova distance measurements

ABSTRACT Type Ia Supernovae (SNe Ia) are a critical tool in measuring the accelerating expansion of the universe. Recent efforts to improve these standard candles have focused on incorporating the effects of dust on distance measurements with SNe Ia. In this paper, we use the state-of-the-art Dark Energy Survey 5 year sample to evaluate two different families of dust models: empirical extinction models derived from SNe Ia data and physical attenuation models from the spectra of galaxies. In this work, we use realistic simulations of SNe Ia to forward-model different models of dust and compare summary statistics in order to test different assumptions and impacts on SNe Ia data. Among the SNe Ia-derived models, we find that a logistic function of the total-to-selective extinction $R_V$ best recreates the correlations between supernova distance measurements and host galaxy properties, though an additional 0.02 mag of grey scatter is needed to fully explain the scatter in SNIa brightness in all cases. These empirically derived extinction distributions are highly incompatible with the physical attenuation models from galactic spectral measurements. From these results, we conclude that SNe Ia must either preferentially select extreme ends of galactic dust distributions, or that the characterization of dust along the SNe Ia line-of-sight is incompatible with that of galactic dust distributions.

Inactivation kinetics of dairy phages using photocatalytic paint under diverse environmental conditions and radiation sources

This article investigates the efficacy of a photocatalytic paint for bacteriophages inactivation under various levels of incident radiation flux, relative humidity and radiation sources. Two phages that infect lactic acid bacteria were selected: J-1, infective of Lacticaseibacillus casei ATCC 27139, and M13-G1b, infective of Streptococcus thermophilus G1b. Phage suspensions were deposited on coated and uncoated plates and exposed to radiation typically used for indoor illumination (λ = 360–720 nm). A first-order inactivation kinetics was proposed with respect to the Plaque Forming Units per mL (PFU/mL) that correlates the inactivation constant with the operating conditions. The kinetic constant of this novel model combines a potential function for the incident radiation flux and a logistic function for the relative humidity. The results showed that as these variables increased, phage inactivation turned higher for both uncoated and coated plates. Photonic and quantum efficiencies were also calculated for both phages, when treated with photocatalytic paint under UV-A radiation. The photonic efficiencies were 8.03 × 1012 PFU/Einstein for J-1 and 4.05 × 1012 PFU/Einstein for M13-G1b, while the quantum efficiencies were 1.19 × 1013 PFU/Einstein for J-1 and 6.03 × 1012 PFU/Einstein for M13-G1b. Good agreement was obtained between predictions of the proposed kinetic model and experimental data, with a Root Mean Square Error of 4.43 % for J-1 and 2.98 % for M13-G1b. The developed model would be useful to predict phage inactivation under diverse combinations of operating conditions levels in environments of dairy plants, different from those tested in this work.

The Logistic Function in Glass Transition Models of Amorphous Polymers: A Theoretical Framework for Isobaric Cooling Processes

AbstractStudying the macroscopic‐phenomenological behavior of amorphous polymers at glass transition is often subject to limitations because the ordinary differential equations that describe the material behavior require numerical solution. To avoid these limitations, ad‐hoc‐formulated models of the glass transition have been proposed. However, their scope of application is expected to be limited due to insufficient theoretical foundation. This work establishes a theoretical framework for models that use the logistic function to approximate the macroscopic‐phenomenological behavior of amorphous polymers at glass transition. For this purpose, an exactly‐solvable Riccati equation is derived within thermodynamics with internal state variables. A closed‐form expression in terms of mathematical functions for the temperature derivative of a single internal state variable is the result. This closed‐form expression contains the logistic function thus featuring a continuous transition region centered around a pressure and cooling‐rate dependent transition temperature. Based on comparison of existing models with the exact solution derived from the Riccati equation, generalized models that approximate the thermal expansion coefficient and heat capacity at glass transition are proposed. This work thus demonstrates the validity of the logistic function in glass transition models of amorphous polymers and provides suggestions as to how existing models can be extended in their applicability.

Analysis of characteristics of rice tillering dynamics influenced by sowing dates based on DTM

To quantitatively analyze the regularity and characteristics of tillering dynamics of rice at different sowing dates. In this paper, the whole process of rice tillering was decomposed into two aspects: tiller occurrence and tiller extinction, and two Logistic functions were used to describe them respectively, so as to establish a dynamic tillering model in rice based on double Logistic function (Dynamic Tillering Model, DTM). Then, according to DTM, dynamic tillering indexes (DTIs) were defined and their calculation formulas were derived. Finally, the characteristics and laws of rice tillering dynamic response to sowing dates were analyzed using DTM and DTIs with the observed tillering data of three rice varieties, (Hui-Liangyou 898 (HLY898), Y-Liangyou 900 (YLY900) and Y-Liangyou 911 (YLY911)), in six sowing dates (March 15, March 20, March 25, March 30, April 5, April 10). The results show that: 1. The model fits well. The normalized RMSE (RMSEn) of the DTM fitted to the observed tillering data of different rice varieties sown at different times were all less than 10 %, and their mean values were less than 5 %. 2.The variation degree of DTIs under the influence of sowing dates had certain consistency among the three rice varieties. the inherent rate of tillering (Rit), the maximum tillering rate (Rmt), the maximum tillers extinction rate (Rme) and the duration of tillering (Dt) varied greatly, while the total number of grow tillers (Ng), the peak time of tillering (Tpt), the peak time of tillers extinction (Tpe) and the end time of tillering (Tet) had smaller variation. 3. The eight DTIs, the inherent rate of tillering (Rit), the duration of tillering (Dt), the maximum tillering rate (Rmt), the number of retained tillers (Nr), the peak time of tillering (Tpt), the end time of tillering (Tet), the start time of tillers extinction (Tse) and duration of tillers extinction (De), had a consistent linear response to the sowing dates among the three rice varieties. 4. Under different sowing dates, the dynamic characteristics of tillering of YLY900 and YLY911 were relatively close, while HLY898 had great differences from YLY900 and YLY911. In this paper, the evolution process of the number of tillers of rice was accurately described by the DTM, and the regularity and characteristics of tillering dynamics of rice were deeply revealed using the DTIs, with agronomic experiment of three varieties with six sowing dates. Therefore, it has important theoretical value to deeply understand the law of the tillering dynamic of rice affected by sowing dates and has important practical significance for guiding accurate planting and fine management of rice production from the perspective of grasping tillering dynamics.

Dynamics of Interaction of One Prey and Two Competing Predators with Population Heterogeneity

A mathematical model of two predators competing for a single prey system incorporating population heterogeneity has been proposed and analyzed by extending the system of coupled logistic equations for three species system. The local stability analysis of all the equilibria has been carried out and also globally stable in Varies 2-D planes. Through the analytical results, it is observed that the interior equilibrium of one prey and two competing predators in prey dependent case is always unstable. At the end, discussed the dynamical behavior of a three species interaction with the help of some numerical examples by using MATLAB to support the analytical presentations.

Antecedents for Consumer’s Switching Behavioural Intention Retail Banks in Zimbabwe: Integrating the Theory of Planned Behaviour

The purpose of this research is to examine the antecedents of the behavioural intention of consumers to switch a retail bank for another in Zimbabwe. The study used positivism research philosophy where quantitative data was collected from a purposively chosen sample of 150 participants. Data was analyzed using logistic regression equation with two dependent variables. Our study confirms the probability of switching retail banks in Zimbabwe was driven by factors such as; brand image, changing demographics, brand loyalty, brand image, ease of use, and the perceived usefulness. The recommends that retail banks should invest more in building brand image to retain loyal customers. A combination of technological innovations and transaction cost reductions could help to increase brand loyalty, brand image, and the attraction of new customers while retaining the old ones. The study contributes to the literature on bank switching behaviour by pioneering the use of binary choice models.

Bifurcation analysis and exploration of noise-induced transitions of a food chain model with Allee effect

Seeking shelter from threats is a widespread instinct across species, specially employed by prey to avoid direct confrontations with predators. The present investigation centers on a three-species food chain model wherein the basal prey, characterized by logistic growth, seeks refuge to evade the intermediate predator, while the intermediate predator, in turn, seeks refuge to avoid encounters with the top predator. Additionally, our model assumes that the presence of the top predator induces a mate-finding Allee effect among the intermediate predator. We investigate how varying levels of refuge, along with other critical parameters such as the reproduction rate of the basal prey and the natural mortality rate of the top predator, influence system’s dynamics within the biparametric planes. Our model displays multistability and undergoes transcritical, saddle–node, Bogdanov–Takens and cusp bifurcations across different parameters. Moreover, the external environmental noise can induce interesting dynamics in the predator–prey system, resulting in noise-induced frequent transitions between distinct interior attractors or from interior to axial attractors. This phenomenon is particularly notable in scenarios where the deterministic model exhibits tristability. In summary, our findings offer potential new avenues for developing control strategies within the realm of community ecology in constant as well as fluctuating environments.

The Early Prediction of Kimchi Cabbage Heights Using Drone Imagery and the Long Short-Term Memory (LSTM) Model

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R2 = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization.

Q-Deformed and delta-parametrized A-generalized logistic function induced Banach space valued multivariate multi layer neural network approximations

Abstract. Here we research the multivariate quantitative approximation of Banach space valued continuous multivariate functions on a box or RN, N ∈ N, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We investigate also the case of approximation by iterated multilayer neural network operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a q-deformed and λ-parametrized A-generalized logistic function, which is a sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network are with one or multi hidden layers. Mathematics Subject Classification (2010): 41A17, 41A25, 41A30, 41A36. Keywords: Multi-layer approximation, q-deformed and λ-parametrized A- generalized logistic function, multivariate neural network approximation, quasi-interpolation operator, Kantorovich type operator, quadrature type operator, multivariate modulus of continuity, iterated approximation.

Equality Between and the Hearing-Impaired Based on the Relationship with Peers with Hearing Disabilities

Objective: This study examines the prediction of high equality scores between hearing people and those with hearing disabilities, according to the type of relationship existing between them. Theoretical Framework: Inequality regarding hearing-impaired individuals is a challenge that must be addressed by society to ensure that this population enjoys a full quality of life. Therefore, policies should be adopted to ensure inclusion and to address the harmful perspectives that continue to exist. Method: The study sample consisted of 823 adolescents aged 13 to 16, enrolled in Compulsory Secondary Education. For data collection, the Scale of Attitudes towards People with Hearing Disabilities and the Scale to Evaluate the Relationship with Peers with Hearing Disabilities were administered. Logistic regression and ROC Curves were used to analyze the influence of social relationships on equality between hearing and hearing-impaired individuals. Results and Discussion: The Odds Ratio predicts that the probability of earing a high score on equality between the hearing and the hearing-impaired will increase with each point of increase of the analyzed variables. Research Implications: The results suggest the need to promote positive attitudes among hearing and hearing-impaired students to encourage social relationships between them and to promote equality. These implications could influence depending on the social and educational context in which the student is immersed. Originality/Value: This study contributes to the literature by showing how the relationships, interactions and contact between students with and without disabilities provide an egalitarian and quality environment.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments

BackgroundIschemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features.MethodsTo enhance our understanding of the mechanistic underpinnings of disulfidptosis in IS, we initially retrieved the expression profile of peripheral blood from human IS patients from the GEO database. We then utilized a suite of machine learning algorithms, including LASSO, random forest, and SVM-RFE, to identify and validate pivotal genes. Furthermore, we developed a predictive nomogram model, integrating multifactorial logistic regression analysis and calibration curves, to evaluate the risk of IS. For the analysis of single-cell sequencing data, we employed a range of analytical tools, such as "Monocle" and "CellChat," to assess the status of immune cell infiltration and to characterize intercellular communication networks. Additionally, we utilized an oxygen–glucose deprivation (OGD) model to investigate the effects of SLC7A11 overexpression on microglial polarization.ResultsThis study successfully identified key genes associated with disulfidptosis and developed a reliable nomogram model using machine learning algorithms to predict the risk of ischemic stroke. Examination of single-cell sequencing data showed a robust correlation between disulfidptosis levels and the infiltration of immune cells. Furthermore, "CellChat" analysis elucidated the intricate characteristics of intercellular communication networks. Notably, the TNF signaling pathway was found to be intimately linked with the disulfidptosis signature in ischemic stroke. In an intriguing finding, the OGD model demonstrated that SLC7A11 expression suppresses M1 polarization while promoting M2 polarization in microglia.ConclusionThe significance of our findings lies in their potential to shed light on the pathogenesis of ischemic stroke, particularly by underscoring the pivotal role of disulfidptosis-related genes (DRGs). These insights could pave the way for novel therapeutic strategies targeting DRGs to mitigate the impact of ischemic stroke.

Assessing the accuracy of the revised Cardiac Risk Index compared to the American Society of Anaesthesiologists physical status classification in predicting Pulmonary and Cardiac complications among non-cardiothoracic surgery patients at Muhimbili National Hospital: a prospective cohort study

BackgroundThe Revised Cardiac Risk Index (RCRI) and the American Society of Anaesthesiologists (ASA-PS) classification system are two commonly used tools for preoperative risk assessment. This study aimed to assess the accuracy of RCRI compared to the ASA-PS classification system in preoperative risk assessment for pulmonary and cardiac problems among non-cardiothoracic surgery patients admitted at Muhimbili National Hospital (MNH).MethodsThis was a prospective cohort study design conducted from August 2022 to April 2023 among 184 patients of 18 years and above admitted at MNH for elective non-cardiothoracic surgery. Data Analysis was conducted using STATA software version 16. Means and standard deviations were used to summarize continuous data. Frequencies and percentages were used to summarize categorical data. The logistic regression and ROC curve analysis were used to determine the correlation between variables.ResultsThe majority of patients (43.3%) had an RCRI score of 1 point, and 39.9% were classified as ASA class 1. Patients in ASA classes 3 and 4 had higher odds of developing cardiac and pulmonary complications (AUC = 0.75 and 0.77, respectively). Patients with an RCRI score of 2 or ≥ 3 points were also more likely to experience cardiac and pulmonary complications (AUC = 0.73 and 0.72, respectively). There was no significant difference in the predictive ability of the two tools. Both RCRI and ASA-PS classification systems were equally effective in predicting these complications.ConclusionBoth the RCRI and the ASA-PS classification system demonstrated good predictive ability for cardiac and pulmonary complications among patients undergoing non-cardiothoracic surgery.

Optimal acquisition time estimation method for CSEM with high-order pseudo-random signal

High-order pseudo-random signal is gradually being applied in controlled-source electromagnetic (CSEM) exploration. In contrast to the conventional single-frequency sweep mode, the high-order pseudo-random signal enables simultaneous transmission of multiple frequencies. However, estimating a fixed acquisition time based on observed noise levels often results in poor adaptability for high-order pseudo-random signal, which only require reception of one set of waveform. In this study, we presented an estimation method for acquisition time for CSEM with high-order pseudo-random signal using an improved logistic function. The improved logistic function was proposed to introduce a time-decay factor into the governing equation for the first time. By considering the transformation rule of noise statistical characteristics with time, the specific parameters of the function have been determined to better describe the dynamic evolution process of the signal quality. The effective frequencies were extracted at various acquisition times based on the noise evaluation number, and the resulting quantity of effective frequencies was used as the fitting target. Guidance for the fieldwork was determined based on the average time of the saturation period, in accordance with the properties of the function. The reliability of the improved logistic function was validated through a transmission current data simulation. The proposed method was demonstrated through the measured data from both strong and weak interference areas.

Comparison and development of the descriptive model with polynomial structures to fit multi-component dynamic breakthrough curves with roll-up, stepwise, and saddle-shaped structure

Dynamic breakthrough curves with roll-up, stepwise, and saddle-shaped structures have been commonly observed in the adsorption/separation process, but could be rarely described by the descriptive model, which limits further analysis and explanation of the breakthrough data. In this work, two new descriptive models, including a general polynomial logistic equation (GPLE) and a general polynomial Gompertz equation (GPGE) are developed to correlate these roll-up, stepwise, and saddle-shaped breakthrough curves. Their correlation coefficients (R2) between the descriptive model and breakthrough data are higher than 0.95, indicating the good applicability of the two descriptive models. Taking some difficult-to-fit literature data as an example, by adjusting these characteristic parameters, six types of breakthrough curves can be fitted by the GPLE or GPGE model. Representatively, satisfying N=2.0, a1≠a2 or b1≠b2, CN<0 or CN<1.0 (∑1NCN=1.0), various roll-up or stepwise breakthrough curves could be fitted accurately. If N=3.0, the GPGE model could explain the multiple roll-up, the multiple stepwise, and the saddle-shaped breakthrough curves. As a result, two new descriptive models can be easily used by workers in various fields to analyze and calculate breakthrough data. Although some reasonable physical explanations need to be explored in the future, they will provide a modeling basis for studying multi-component dynamic adsorption behavior.

Pubertal timing in adolescence and adulthood: Relations among contemporaneous and retrospective measures.

Pubertal development has short- and long-term effects on psychological adjustment. Many studies of long-term effects rely on retrospective measurement of pubertal timing, but such measures often reflect different aspects of puberty than those assessed in adolescence, raising questions about the utility and interpretation of retrospective reports. The present study leveraged longitudinal data collected in adolescence and established adulthood to determine: (1) the correspondence between pubertal timing indexed from logistic growth curves of self-reported physical development assessed contemporaneously across adolescence and pubertal timing indexed relative to peers assessed retrospectively in adulthood; (2) the associations between the two pubertal timing measures and psychological adjustment; and (3) potential recall biases. Participants were 748 individuals (50.1% female; 91.6% White) from two longitudinal studies who reported on their pubertal development annually from Grades 3-9, psychological adjustment (age at sexual initiation, substance use, depression) in late adolescence, and retrospective pubertal timing in established adulthood (Mage = 32.76; SD = 4.43). Results indicate moderate-to-high convergence between retrospective and contemporaneous indices. Most participants, especially women, had the same pubertal timing classification (i.e., early, on time, or late), but early-maturing adolescents often recalled on-time development as adults. Retrospective and contemporaneous indices were associated with psychological adjustment in similar ways, with some attenuation in the retrospective measure, especially for men. There was little evidence of recall bias due to age at retrospective assessment or time since puberty. Findings generally support the use of retrospective pubertal timing measures, with the recognition that some relations with adjustment may be weakened. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Continuous solutions for a two-dimensional cross-diffusion problem involving doubly degenerate diffusion and logistic proliferation

Emerging from the modeling of intricate structures formed by bacterial motion in nutrient-deficient environments, the doubly degenerate nutrient taxis system [Formula: see text] is considered in smoothly bounded domains [Formula: see text]. In contrast to frameworks devoid of logistic growth where only small-data global solutions have so far been found in the literature, quadratic degradation in the logistic growth is shown to markedly amplify the overall dissipative effects to the extent that, for arbitrary smooth initial data, an associated no-flux type initial-boundary problem possesses global continuous weak solutions. Central to our findings is the identification of an advantageous dissipative structure subtly embedded in the system, the exploitation of which leads to the establishment of a positive lower bound for nutrients levels within any finite time interval, thereby ruling out the appearance of cross-degeneracies within such timeframes.

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Assessing the phenological state of evergreen conifers using hyperspectral imaging time series

Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: Thuja occidentalis, Platycladus orientalis, Pinus sylvestris and P. nigra subsp. pallasiana. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R2 exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.

Optimized ensemble-based flood hazard mapping in low altitude subtropical riverine terrane

Among various natural hazards, those induced by hydrological and meteorological factors have emerged as the most frequent and damaging. India is at the second from the top, after Bangladesh, of flood hazard-affected countries of the world, causing immense damage to life and property every year. The Middle Ganga Plain in the country is one of the most severely affected regions due to the convergence of Himalayan rivers in the Ganga (Ganges) plain. This study focuses on optimizing novel ensemble models to enhance the mapping of flood hazard susceptibility in specific topoclimatic setting, characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment of the tectonically active Middle Ganga Plain. This region forms part of the Ganga Foreland Basin prone to frequent floods. We employ a comprehensive flood inventory and twelve selected flood conditioning factors and is the first study that has developed these ensemble models in low relief riverine floodplain environmental setting with high resolution geomorphology as one of the conditioning factors. Four artificial intelligence ensemble models were employed in this work: Logistic Regression-Evidential Belief Function (LR-EBF), Logistic Regression-Frequency Ratio (LR-FR), Multi-Layer Perceptron-Evidential Belief Function (MLP-EBF), and Multi-Layer Perceptron-Frequency Ratio (MLP-FR). Results showed that LR-based ensembles outperformed MLP-based ensembles in the studied topoclimatic setting, with LR-EBF model achieving 87.2% and 84.7% success and prediction rates respectively. Our results reveal consistent performance differences between LR-based ensembles with FR and EBF models across different environmental settings. MLP-based ensembles, particularly with Frequency Ratio, displayed more significant performance variations. The findings underscore the importance of considering specific topoclimatic and altitudinal range environments when applying flood hazard susceptibility models. Our study also provides valuable insights for planning and policymaking in flood management practices in similar topoclimatic settings worldwide.