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Numerous studies have highlighted the role of X-ray computed tomography (X-ray CT) in understanding root architecture. Nevertheless, setting definitive scanning parameters for diverse soils in varied container sizes remains challenging. This study investigates the influence of X-ray CT system voltage on the penetration capability in diverse soils and container sizes, focusing on two key parameters: (1) gray values, which indicate X-ray attenuation and contribute to image contrast, and (2) signal-to-noise ratio, a measure of image clarity. Five soil samples were collected from various depths within a soil profile to encompass bulk density values ranging from 1.34 to 1.84 g·cm-3 to conduct the experiment. Containers with dimensions of 6×6×6 cm³, 8×8×6 cm³, 10×10×6 cm³,12×12×6cm³,14×14×6cm³, and 16×16×6cm³ were used. Voltage levels spanning 75 to 225 kV, in 25-kV increments, were applied to each sample. The observed gray values of the X-ray images were fitted using a logistic model of three parameters. Results showed that increasing voltage leads to enhanced penetration up to a plateau point, irrespective of soil density or container size. This plateau could potentially yield higher quality scans, given that lower voltages result in subdued gray values and reduced image contrast. Notably, it was observed that soil properties, including mineral composition, directly affect image gray values. This study established optimal voltage settings for specific soil types at fixed densities, offering valuable insights for researchers investigating soil-root interactions. Although the current findings are based on five soils, a more extensive sampling encompassing diverse soil textures and densities is necessary for a comprehensive understanding of X-ray penetration behavior across various soil types. [ABSTRACT FROM AUTHOR]

This study investigates the behavior of the approximate solutions of the generalized logistic equation $ y' = y\left(1-y^{\alpha}\right) $, where $ \alpha>0 $. Based on the concept of Ulam stability, a concrete estimate of the amplitude of the difference between the approximate solution and the exact solution is obtained using the comparison principle and some sharp inequalities. The obtained results can be applied to the nonautonomous Richards model $ C' = r(t)C\left(1-\frac{C^{\alpha}}{K}\right) $. [ABSTRACT FROM AUTHOR]

In this paper, a single-species logistic model with both fear effect-type feedback control and additive Allee effect is developed and investigated using the new coronavirus as a feedback control variable. When the system introduces additive Allee effect and fear effect-type feedback control, more complicated dynamical behavior is obtained. The system can undergo transcritical bifurcation, saddle-node bifurcation, degenerate Hopf bifurcation and Bogdanov–Takens bifurcation. By numerical simulations, the system exhibits homoclinic bifurcation and saddle-node bifurcation of limit cycles as parameters are altered. Remarkably, it is the first time that two limit cycles have been discovered in a single-species logistic model with the Allee effect. Further, stronger Allee effect or stronger fear effect can lead to the extinction of the species population. [ABSTRACT FROM AUTHOR]

This study investigates the diffusion and network structure of hydrogen technology from 1980 to 2022. Hydrogen fuel cell technology has the shortest diffusion cycle and high global acceptance. Production technology shows significant global demand, particularly within regional patent families. In contrast, hydrogen storage and fuel cell technologies have more concentrated diffusion, indicating higher adaptation thresholds. Japanese firms are crucial in spreading storage and production technologies, while U.S. enhances diffusion through licensing and transfer. Hydrogen refrigeration technology exhibits a more dispersed bidirectional network. Hydrogen production and fuel cell technologies demonstrate notable activity and network scale in the global market. This research provides valuable insights into the global development of hydrogen technology. [Display omitted] • The evolution of hydrogen technology diffusion was evaluated from 1980 to 2022 using patent data. • Hydrogen fuel cell technology shows the shortest diffusion cycle, hydrogen storage and fuel cell technologies have concentrated diffusion in core countries. • Hydrogen production technology and hydrogen refrigeration technology exhibits broader diffusion. [ABSTRACT FROM AUTHOR]

Objective: We aimed to establish a practical diagnostic index for Lewy body diseases (LBD), such as Parkinson's disease and dementia, with Lewy bodies in outpatient settings and criteria for exempting patients from late imaging. Methods: We acquired early and late 123I-metaiodobenzylguanidine (MIBG) images from 108 consecutive patients with suspected LBD and standardized heart-to-mediastinum (H/M) ratios for collimator conditions. Exclusions included young-onset Parkinson's disease (age < 50 years) and genetic transthyretin-type amyloidosis. We developed logistic models incorporating H/M ratios with or without age (n = 92). The sympathetic MIBG index for LBD (SMILe index), categorized LBD likelihood from 0 (lowest) to 1 (highest). Diagnostic accuracy was assessed as the area under the receiver operating characteristic (ROC) curve (AUC). The characteristics of the new index were compared with H/M ratios. The need for late imaging was explored using the SMILe index. Results: Early or late SMILe indexes using a single H/M ratio variable discriminated LBD from non-LBD. The AUC values for early and late SMILe indexes were 0.880 and 0.894 (p < 0.0001 for both), identical to those for early and late H/M ratios. The sensitivity and the specificity of early SMILe indexes with a 0.5 threshold were 76% and 90%, achieving accuracy of accuracy 86%. Similarly, the late SMILe index demonstrated a sensitivity of 76% and specificity of 87%, with an accuracy of 84%. Early SMILe indexes < 0.3 or > 0.7 (representing 84% patients) indicated a diagnosis without a late MIBG study. Conclusion: The 123I-MIBG-derived SMILe indexes provide likelihood of LBD, and those with a 50% threshold demonstrated optimal diagnostic accuracy for LBD. The index values of either < 0.3 or > 0.7 accurately selected patients who do not need late imaging. [ABSTRACT FROM AUTHOR]

Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of "semi‐competing risks" in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call "clinically immediate" and "non‐immediate" terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions. [ABSTRACT FROM AUTHOR]

Objective: Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios. Methods: We collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness. Results: The logistic model's performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes. Conclusions: The study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy. [ABSTRACT FROM AUTHOR]

Since 2019, a new strain of coronavirus has challenged global health systems. Due its fragile healthcare systems, Africa was predicted to be the most affected continent. However, past experiences of African countries with epidemics and other factors, including actions taken by governments, have contributed to reducing the spread of SARS-CoV-2. This study aims to assess the marginal impact of non-pharmaceutical interventions in fifteen African countries during the pre-vaccination period. To describe the transmission dynamics and control of SARS-CoV-2 spread, an extended time-dependent SEIR model was used. The transmission rate of each infectious stage was obtained using a logistic model with NPI intensity as a covariate. The results revealed that the effects of NPIs varied between countries. Overall, restrictive measures related to assembly had, in most countries, the largest reducing effects on the pre-symptomatic and mild transmission, while the transmission by severe individuals is influenced by privacy measures (more than |$10\%$|⁠). Countries should develop efficient alternatives to assembly restrictions to preserve the economic sector. This involves e.g. training in digital tools and strengthening digital infrastructures. [ABSTRACT FROM AUTHOR]

In survival analysis, cure models have been developed to account for the presence of cured subjects that will never experience the event of interest. Mixture cure models with a parametric model for the incidence and a semiparametric model for the survival of the susceptibles are particularly common in practice. Because of the latent cure status, maximum likelihood estimation is performed via the iterative EM algorithm. Here, we focus on the cure probabilities and propose a two‐step procedure to improve upon the maximum likelihood estimator when the sample size is not large. The new method is based on presmoothing by first constructing a nonparametric estimator and then projecting it on the desired parametric class. We investigate the theoretical properties of the resulting estimator and show through an extensive simulation study for the logistic‐Cox model that it outperforms the existing method. Practical use of the method is illustrated through two melanoma datasets. [ABSTRACT FROM AUTHOR]

The nabla fractional-order uncertain difference equation with Caputo-type was analyzed in this article. To begin, the existence and uniqueness theorem of solutions for nabla Caputo uncertain difference equations with almost surely bounded uncertain variables was presented. Furthermore, the uncertainty distributions of the solutions for the proposed equations were obtained by establishing a connection between the solutions of equations and their α-paths based on new comparison theorems. Finally, an application of the uncertain difference equations in a logistic population model involving Allee effect was provided and examples were performed to demonstrate the validity of the theoretical results presented. [ABSTRACT FROM AUTHOR]

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Abstract Objective Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios. Methods We collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness. Results The logistic model’s performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes. Conclusions The study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy.

Abstract Despite the amplified vector-control measures, sporadic-epidemic outbreaks of dengue incidence occurred in Delhi, intermittently. This study aimed to identify the major individual, household, and community level predictors of dengue cases in the hot-spots of Delhi. Primary survey data was collected from the selected sample of 347 households, at the South- West district of Delhi. This survey has interviewed the head of the household to collect the information of dengue incidences within last one year and household information related to socio-economic, demographic, environmental factors, such as housing pattern, density, water storage containers, drainage and garbage collection site and method, mosquito protection measures and awareness. Among 347 households, 54 households had reported dengue cases, and 69 individuals had reported dengue cases in last one year. Garbage and water collection site and methods, drainage and household type, household monthly income, indoor bamboo plants, construction site (within 500 m), presence of tertiary care hospital, were the significant predictors of dengue incidences in Delhi. In conclusion, strategic control measures and intense social interventions such as household and community awareness, promotion of healthy practices should be promoted to control the dengue incidences.

ObjectiveTo describe different non-alcoholic fatty liver disease （NAFLD） outcomes among community inhabitants， and further to explore the correlation between bio-indicator level variance and the outcomes.MethodsPhysical indicators （height， weight， waist circumstances， hip circumstances， blood pressure， etc）， biochemical indicators ［fasting plasma glucose， HbA1c， serum triglycerides（TG）， serum total cholesterol（TC）， low-density lipoprotein cholesterol（LDL-C）， high-density lipoprotein cholesterol（HDL-C）， liver related transaminase， etc］ and clinical imaging （B-scan ultrasonography） were collected during the follow-up from the Songjiang Natural Population Sub-cohort. The identification of NAFLD was supported by the definition criteria from Guidelines for the diagnosis and treatment of non⁃alcoholic fatty liver disease. Paired t-test and multifactorial logistic regression model were used to compare the difference between the indicator level of the subjects from different outcome subgroups and to further analyze the correlation between these indicator variance and different NAFLD outcomes.ResultsDuring a median follow-up time of 2.94 years， 12 076 subjects were involved. The cumulative NAFLD incidence and remission rate were 21.57% and 31.15%， respectively. The proportion of subjects who still had NAFLD was 27.96%. Among subjects with newly-developed NAFLD， indicators including blood pressure， BMI， fasting plasma glucose， and plasma lipid level increased， while in the remission subgroup， blood pressure， BMI（WHR）， waist-hip ratio（WHR）， and TG level were significantly decreased. Increased level of systolic pressure， WHR， BMI， HbA1c， and LDL-C might be the risk factors to the occurrence of NAFLD. While decreased level of WHR， BMI， TC and LDL-C level and elevated HDL-C level were likely to be the influencing factors of NAFLD remission process.ConclusionThe NAFLD morbidity in the community inhabitants is relatively high. BMI， WHR， fasting plasma sugar and plama lipid level variance may act as the influencing factors towards different NAFLD outcomes.

Accurately estimating the number of infections that actually occur in the earliest phases of an outbreak and predicting the number of new cases per day in various countries is crucial for real-time monitoring of COVID-19 transmission. Numerous studies have used mathematical models to predict the progression of infection rates in several countries following the appearance of epidemiological outbreaks. In this study, we analyze the data reported and then study several logistical-type phenomenological models and their application in practice for forecasting infection evolution. When several epidemic waves follow one another, it is important to stress that a traditional logistic model cannot necessarily be fully adapted to the data made available. New models are being introduced to simultaneously take account of human behavior, measures taken by the government, and epidemiological conditions. This research used a generalized logistic model based on parameters that vary over time to describe trends in COVID-19-infected cases in countries that have undergone several waves. In two-wave scenarios, the parameters of the model evolve dynamically over time following a logistic function, where the first and second waves are characterized by two extreme values for the early period and the late one, respectively.

Abstract Background The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model. Methods We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis. Results Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model. Conclusions The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Background: This study analyzed the risk factors associated with positive surgical margins (PSM) and five-year survival after prostate cancer resection to construct a positive margin prediction model. Methods: We retrospectively analyzed the clinical data of 148 patients treated with prostatectomy. The patients were divided into PSM group and Negative surgical margins (NSM) group. Several parameters were compared between the groups. All patients were followed up for 60 months. The risk factors for PSM and five-year survival were evaluated by univariate analysis, followed by multifactorial dichotomous logistic regression analysis. Finally, ROC curves were plotted for the risk factors to establish a predictive model for PSM after prostate cancer resection. Results: (1) Serum PSA, percentage of positive puncture stitches, clinical stage, surgical approach, Gleason score on puncture biopsy, and perineural invasion were significantly associated with the risk of PSM (P < 0.05). Serum PSA, perineural invasion, Gleason score on puncture biopsy, and percentage of positive puncture stitches were independent risk factors for PSM. (2) Total prostate-specific antigen (tPSA) by puncture, nutritional status, lymph node metastasis, bone metastasis, and seminal vesicle invasion may be risk factors for five-year survival. Lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. (3) After plotting the ROC curve, the area under the curve (AUC) [AUC: 0.776, 95%, confidence interval (CI): 0.725 to 0.854] was found to be a valid predictor of PSM; the AUC [AUC: 0.664, 95%, confidence interval (CI): 0.576 to 0.753] was also a valid predictor of five-year survival (P < 0.05). (4) The scoring system had a standard error of 0.02 and a cut-off value of 6. It predicted PSM after prostate cancer resection with moderate efficacy. Conclusions: Serum PSA, perineural invasion, puncture biopsy Gleason score, and percentage of positive puncture stitches were independent risk factors for positive surgical margins (PSM). Also, lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. Overall, the prediction efficacy of this scoring system concerning the risk of PSM after prostate cancer resection was moderate. [ABSTRACT FROM AUTHOR]

Oases play a crucial role in arid regions within the human–environmental system, holding significant ecological and biological importance. The Oasis Cold Island Effect (OCIE) represents a distinct climatic feature of oases and serves as a vital metric for assessing oasis ecosystems. Previous studies have overlooked the spatial extent of the Oasis Cold Island Effect (OCIE), specifically the boundary delineating areas influenced and unaffected by oases. This boundary is defined as the Oasis Cold Island Footprint (OCI FP). Utilizing Logistic modeling and MODIS data products, OCI FPs were calculated for the Ejina Oasis from 2000 to 2019. The assessment results underscore the accuracy and feasibility of the methodology, indicating its potential applicability to other oases. Spatial and temporal distributions of OCI FPs and the intensity of the Oasis Cold Island Effect Intensity (OCIEI) in the Ejina Oasis were analyzed, yielding the following findings: (1) OCI FP area and complexity were smallest in summer and largest in autumn. (2) Over the period 2000–2019, OCI FPs exhibited a pattern of increase, decrease, and subsequent increase. (3) OCIEI peaks in summer and reaches its lowest point in winter. Lastly, the study addresses current limitations and outlines future research objectives. [ABSTRACT FROM AUTHOR]

Brazil is the world's largest producer and consumer of passion fruit; however, productivity is restricted by phytosanitary factors, mainly viruses. Passion fruit woodiness disease (PWD) is the major viral disease of the crop, caused in Brazil by the cowpea aphid-borne mosaic virus (CABMV), and non-persistently transmitted by aphids. In this study, a protocol was developed and validated using real-time RT-PCR with a specific TaqMan probe combined with a direct sample preparation method for the detection of CABMV. The new protocol is 100 times more sensitive than the PTA-ELISA in detecting CABMV in passion fruit. The nucleotide sequence of the CABMV isolates from Santa Catarina (SC), had high identity (above 99%) in the capsid protein (CP) genome region, with isolates from São Paulo (SP). CABMV isolates from SC formed a monophyletic cluster with 99% bootstrap support containing CABMV isolates and strains from SP and Sergipe (SE). The logistic model showed a better fit to the virus incidence data compared to the Gompertz and monomolecular models and was able to predict that half of the orchard would be infected in about 110 days after transplantation (DAT). The TaqMan assay will be used for the detection of CABMV in propagation plant material and insect vectors, contributing to avoiding the dissemination of PWD. [ABSTRACT FROM AUTHOR]

Despite the amplified vector-control measures, sporadic-epidemic outbreaks of dengue incidence occurred in Delhi, intermittently. This study aimed to identify the major individual, household, and community level predictors of dengue cases in the hot-spots of Delhi. Primary survey data was collected from the selected sample of 347 households, at the South- West district of Delhi. This survey has interviewed the head of the household to collect the information of dengue incidences within last one year and household information related to socio-economic, demographic, environmental factors, such as housing pattern, density, water storage containers, drainage and garbage collection site and method, mosquito protection measures and awareness. Among 347 households, 54 households had reported dengue cases, and 69 individuals had reported dengue cases in last one year. Garbage and water collection site and methods, drainage and household type, household monthly income, indoor bamboo plants, construction site (within 500 m), presence of tertiary care hospital, were the significant predictors of dengue incidences in Delhi. In conclusion, strategic control measures and intense social interventions such as household and community awareness, promotion of healthy practices should be promoted to control the dengue incidences. [ABSTRACT FROM AUTHOR]

Background: The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. Methods: This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. Results: From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI = 28 kg/m²) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. Conclusion: We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m²), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI. [ABSTRACT FROM AUTHOR]

Coal spontaneous combustion is the main disaster affecting the safety production of mines, and efficient early warning is the key to controlling coal spontaneous combustion disasters. In order to construct an early warning system for spontaneous combustion disasters of coal with different degrees of metamorphism, the programmed-temperature control experiment was conducted to obtain the change curve of the volume fraction of the index gases of the low, medium and high degrees of metamorphosis, and then the Logistic function is used to fit the volume fraction and temperature of the index gases, and the mathematical model of the index gas and temperature of the index gases with different degrees of metamorphism is obtained, and the inflection point temperature is determined, and finally the spontaneous combustion hazard level of the coal is divided in combination with the initial temperature of the index gases. The results show that the changes of coal sample index gases and characteristic temperatures with different degrees of metamorphism are different; the initial appearance temperature of CO is about 30°C, while the initial appearance temperature of C2H4 and C2H2 is positively correlated with the degree of metamorphism, and the initial appearance temperature is 117°C (low), 123°C (medium) and 137°C (high) for C2H4, 291°C (low), 297°C (medium) and 297°C (high) for C2H2; For the same marker gas, the inflection point temperature increases with the degree of metamorphism, indicating that the higher the degree of metamorphosis, the less likely it is to oxidize at the beginning; According to the variation characteristics of coal spontaneous combustion, based on the initial emergence temperature of CO and C2H4 and CO inflection point temperature, coal spontaneous combustion is divided into five states: safe, low risk, medium risk, high risk and catastrophic state, and the energy level transition map between each state indicates that the energy required to transition from safe state to low risk and high risk state is the largest. Once the high-risk state is crossed, it does not require higher energy to maintain the continuous development of spontaneous combustion of coal and evolve into a catastrophic state. The classification of coal spontaneous combustion hazard levels with different degrees of metamorphism can provide prior data and form effective support for intelligent early warning of coal spontaneous combustion. [ABSTRACT FROM AUTHOR]

Because of its capability to convert organic wastes into renewable energy and into some components useful for agriculture, the anaerobic digestion technology can reduce greenhouse gas emissions in the atmosphere and the pollution. Thus, anaerobic digestion can contribute to achieving some of sustainable development goals. Consequently, many theoretical and empirical approaches are proposed for estimating, predicting and optimizing the methane produced by anaerobic digestion. In this context, the logistic function is a mathematical model that can be used to approximate empirical data of the temporal methane production in anaerobic digestion. In a previous paper, under some appropriate approximations, we have derived from AM2 model a single analytical expression in a form of a logistic function for describing the evolution of methane production in batch bioreactors. In the present paper, by comparing the three standard parameters associated with the classical empirical logistic function with that of the derived one from AM2 model; some relationships between them have been established. These relations are exploited for estimating some coefficients and parameters of AM2 model with respect to empiric logistic function parameters and vice-versa. Moreover, this possibility enables more qualitative insight about the evolution of the methane production and the influence of AM2 parameters and coefficients as well as their interaction over its processes. [ABSTRACT FROM AUTHOR]

Accurately estimating the number of infections that actually occur in the earliest phases of an outbreak and predicting the number of new cases per day in various countries is crucial for real-time monitoring of COVID-19 transmission. Numerous studies have used mathematical models to predict the progression of infection rates in several countries following the appearance of epidemiological outbreaks. In this study, we analyze the data reported and then study several logistical-type phenomenological models and their application in practice for forecasting infection evolution. When several epidemic waves follow one another, it is important to stress that a traditional logistic model cannot necessarily be fully adapted to the data made available. New models are being introduced to simultaneously take account of human behavior, measures taken by the government, and epidemiological conditions. This research used a generalized logistic model based on parameters that vary over time to describe trends in COVID-19-infected cases in countries that have undergone several waves. In two-wave scenarios, the parameters of the model evolve dynamically over time following a logistic function, where the first and second waves are characterized by two extreme values for the early period and the late one, respectively. [ABSTRACT FROM AUTHOR]

Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation. [ABSTRACT FROM AUTHOR]

Polyethylene Terephthalate (PET), renowned for its exceptional physical and chemical properties, finds widespread use in our daily lives. However, conventional PET drying methods are time consuming and energy intensive. Leveraging microwave heating effects, we investigated drying characteristics concerning both microwave parameters and PET permittivity. The PET permittivity variation during heating is related to the microwave reflection at the incident port. Our innovative approach involves frequency and power tuning based on reflection. This method not only significantly improved heating uniformity and reduced temperature covariance (COVT) but also led to a more uniformly distributed temperature profile and a drastic reduction in energy consumption. Integrating precise and rapid frequency tuning, we compared our method's efficiency with traditional approaches, revealing an impressive time savings of 2 h and an energy consumption limited to approximately less than 3 kWh/kg. Notably, our laboratory system's logistic models of the Moisture Ratio (MR) related to rotary rates, masses, and powers whose averages of R-square are 0.9972, 0.9982, and 0.9977, respectively, which provide intriguing insights for industrial production forecasts during variable frequency PET drying. [ABSTRACT FROM AUTHOR]

This article is concerned with the optimal design problem of efficient statistical inference for comparing regression mean curves in two generalized linear models (GLMs) estimated from two samples of independent measurements. The main objective is to find the locally μ p -optimal designs for given values of the model parameters that minimize an L p -norm of the asymptotic variance of the difference between the two estimated regression curves. Two equivalence theorems are given to verify the locally μ p -optimality of the designs in the set of all approximate designs for the comparison of regression curves in two GLMs. Several numerical examples are presented to illustrate the superiorities of the locally μ p -optimal designs ( p = 1 , ∞ ) by comparing them with equidistant designs and individual D-optimal designs for the comparison of regression curves in different scenarios. [ABSTRACT FROM AUTHOR]

This paper aims to identify factors influencing household decisions on whether to consume edible termites and the level of termite consumption in the Vhembe district of Limpopo province in South Africa. The study used secondary data sourced from the Gauteng Department of Agriculture and Rural Development and the University of Witwatersrand that was collected from 104 households in the Vhembe District in 2016. A descriptive statistical analysis was used to profile consumers’ socio-economic characteristics. In addition, a logistic model and Ordinary Least Squares were used to determine the factors influencing a household’s decision to consume edible termites and the level of consumption respectively. The results revealed that demographic factors (the age and gender of the household head) and religious beliefs influenced both consumption and the level of consumption. In addition, the type of termite preferred positively influenced the level of consumption, while knowledge influenced the decision to consume termites. The recommendations based on these results are that efforts should be made to promote the consumption of edible termites among the younger population, particularly males. Education programs could be designed to increase knowledge about termites and their nutritional benefits, which may increase consumption. Additionally, efforts should be made to understand and address the negative influence of religion on the consumption of edible termites. [ABSTRACT FROM AUTHOR]

The problem of constructing locally D-optimal designs for two-variable logistic model with no interaction has been studied in many literature. In Kabera, Haines, and Ndlovu (2015), the model is restricted to have positive slopes and negative intercept for the assumptions that the probability of response increases with doses for both drugs and that the probability of response is less than 0.5 at zero dose level of both drugs. The design space mainly discussed is the set [ 0 , ∞) × [ 0 , ∞) , while the finite rectangular design space is presented only in scenarios where the results for the unlimited design space are still appropriate. In this paper, we intend to loose these restrictions and discuss the rectangular design spaces for the model where the D-optimal designs can not be obtained. The result can be extended to the models where drugs have negative or opposite effects, or the models with positive intercept, by using translation and reflection in the first quadrant. [ABSTRACT FROM AUTHOR]

Background: The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model. Methods: We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis. Results: Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model. Conclusions: The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making. [ABSTRACT FROM AUTHOR]

'New Red Fire' is one of most cultivated cultivars of leafy lettuce in Europe. Considering lettuce is a salt-sensitive crop, assessment of its responses to salt stress and applying efficient techniques to alleviate stress effects are highly important. Here, the influence of seed priming with different seaweed (Ascophyllum nodosum (Sc) and Sargassum angustifolium (Sa)) and microalgal (Chlorella vulgaris (Cv) and Arthrospira platensis (Sp)) extracts on germination indices of leafy lettuce under salt stress conditions (irrigation with saline water with EC levels of 1.37, 2.5, 4, 5.5 and 7 dS m-1) was investigated (with a modeling approach). Under control level of salinity, highest germination percentage was recorded in seeds primed with microalgae extracts. Based on Logistic model, the highest (a) parameter (predicted germination percentage) was for seeds primed with Cv (75.09). Also, the highest (x50) parameter (salt tolerance threshold) was recorded in Cv-primed seeds (15.76 dS m-1). According to liner regression model, (b) parameter (reduction rate of germination speed per unit increase in salinity level) of Cv-primed seeds was lower than that of Sp-, Sc- and Sa-primed seeds. In conclusion, it is recommended to apply C. vulgaris extract as seed priming solution to alleviate negative effects of salt stress on germination indices and seedling growth parameters of leafy lettuce. It is also recommended to quantify the germination responses of leafy lettuce to the other biotic and abiotic stresses for future studies. [ABSTRACT FROM AUTHOR]

One of the major challenges in population economics is accurately predicting population size. Incorrect predictions can lead to ineffective population control policies. Traditional differential models assume a smooth change in population, but this assumption is invalid when measuring population on a small-time scale. To address this change, we developed two-scale fractal population dynamics that can accurately predict population size with minimal experimental data. The Taylor series method is used to reveal the population's dynamical properties, and the Padé technology is adopted to accelerate the convergence rate. [ABSTRACT FROM AUTHOR]

Aim: To identify predictive variables and construct a predictive model along with a decision algorithm to identify nephrourological malformations (NUM) in children with febrile urinary tract infections (fUTI), enhancing the efficiency of imaging diagnostics. Methods: We performed a retrospective study of patients aged <16 years with fUTI at the Emergency Department with subsequent microbiological confirmation between 2014 and 2020. The follow-up period was at least 2 years. Patients were categorised into two groups: 'NUM' with previously known nephrourological anomalies or those diagnosed during the follow-up and 'Non-NUM' group. Results: Out of 836 eligible patients, 26.8% had underlying NUMs. The study identified six key risk factors: recurrent UTIs, non- Escherichia coli infection, moderate acute kidney injury, procalcitonin levels >2 μg/L, age <3 months at the first UTI and fUTIs beyond 24 months. These risk factors were used to develop a predictive model with an 80.7% accuracy rate and elaborate a NUM-score classifying patients into low, moderate and high- isk groups, with a 10%, 35% and 93% prevalence of NUM. We propose an algorithm for approaching imaging tests following a fUTI. Conclusion: Our predictive score may help physicians decide about imaging tests. However, prospective validation of the model will be necessary before its application in daily clinical practice. [ABSTRACT FROM AUTHOR]

This paper investigates the growth of mobile telecommunications across the 23 circles (or states) in India for the period 2005–2021. From this study, we note that diffusion of mobile phones in Indian circles (or states) was influenced by the telecommunication policy of the Indian government. We noted that the growth rates of mobile telephony in all the 23 states followed a logistic model. From this, we inferred that some of the Indian states, with low GDP per capita income started rolling out mobile telephony operations later than states with high GDP per capita income, but could catch with the later. It also follows that states with more rural regions had more adoption of mobile telephony. From this study, we can inform policy makers and telecom providers that electrical power consumption, Human Development Index (HDI), Herfindahl Index (HHI) and proportion of rural to urban ratio do not form an important parameter for mobile growth. [ABSTRACT FROM AUTHOR]

Social participation (SP) is often argued to have a favorable impact on health outcomes. This study examines whether and to what extent social participation can reduce and postpone the need for long-term care (LTC) among middle-aged and older adults in Japan. We used longitudinal data of 17,454 individuals born from 1946 to 1955, obtained from a 17-wave nationwide panel survey conducted from 2005 to 2021 in Japan. We examined (1) whether SP at baseline (2005) was associated with a lower risk of having LTC needs in the last wave (2021) using logistic regression models and (2) whether SP at baseline postponed the onset of these needs using Cox proportional hazards models, both controlling for baseline covariates. The results of different SP activities were compared. Participating in one or more SP activities in 2005 was associated with lower needs for LTC in 2021, with an odds ratio of 0.69 (95% confidence interval [CI], 0.49–0.96), and postponed their onset, with a hazard ratio of 0.71 (95% CI, 0.65–0.78). However, these favorable features of SP were observed only for leisure activities, and no satisfaction with SP activities had a limited association with LTC needs. The results suggest that SP can be associated with a lower risk of LTC needs and postpone their onset; however, these favorable features are limited to leisure activities, and satisfaction with SP activities is needed to enhance them. [ABSTRACT FROM AUTHOR]

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Currently, human-caused greenhouse gas emissions are one of the main causes of global warming. Burning fossil fuels (such as coal, oil, and gas) have become a climate change due to the uptake of heattrapping gases. A lot of CO2 is produced from this, which helps in the creation of greenhouse gases. On the other hand, global electricity demand has been rising for decades, such to rising populations, increasing industrialization, and higher incomes. The power sector is the biggest source of carbon dioxide emissions because of fossil fuel, the main source of energy used for power generation all over the world that's why climate change as well as increased global warming. Therefore, most countries have set targets for the use of renewable energy (RE) to reduce their electricity and need for energy and carbon emissions. In this study, RE is used to keep the environment sustainable, where the system of ODEs has been formed using different types of parameters to analyze the mathematical structure of four variables associated with RE. Positivity test, stability analysis, and bifurcation analysis are examined to prove the truth for the sustainability of the environment. The model plays a special role in increasing electricity production and reducing greenhouse gases in the environment. This study emphasizes the significance of employing RE in the power sector for environmental sustainability. [ABSTRACT FROM AUTHOR]

Numerous studies have highlighted the role of X-ray computed tomography (X-ray CT) in understanding root architecture. Nevertheless, setting definitive scanning parameters for diverse soils in varied container sizes remains challenging. This study investigates the influence of X-ray CT system voltage on the penetration capability in diverse soils and container sizes, focusing on two key parameters: (1) gray values, which indicate X-ray attenuation and contribute to image contrast, and (2) signal-to-noise ratio, a measure of image clarity. Five soil samples were collected from various depths within a soil profile to encompass bulk density values ranging from 1.34 to 1.84 g·cm−3 to conduct the experiment. Containers with dimensions of 6 × 6 × 6 cm³, 8 × 8 × 6 cm³, 10 × 10 × 6 cm³, 12 × 12 × 6 cm³, 14 × 14 × 6 cm³, and 16 × 16 × 6 cm³ were used. Voltage levels spanning 75 to 225 kV, in 25-kV increments, were applied to each sample. The observed gray values of the X-ray images were fitted using a logistic model of three parameters. Results showed that increasing voltage leads to enhanced penetration up to a plateau point, irrespective of soil density or container size. This plateau could potentially yield higher quality scans, given that lower voltages result in subdued gray values and reduced image contrast. Notably, it was observed that soil properties, including mineral composition, directly affect image gray values. This study established optimal voltage settings for specific soil types at fixed densities, offering valuable insights for researchers investigating soil–root interactions. Although the current findings are based on five soils, a more extensive sampling encompassing diverse soil textures and densities is necessary for a comprehensive understanding of X-ray penetration behavior across various soil types.

Purpose: We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-β1 pathway with RP. Methods: We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFβ pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. Results: Severe RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). Conclusion: We successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

A more precise and rigorous assessment of the impact of environmental, social, and governance (ESG) performance in business necessitates evaluating various firm characteristics. This study, focused on the ESG impact on enterprise credit risk, employed logistic models that incorporated the ESG rating index alongside other financial-related factors, including organizational structure, risk, and performance. The data were selected from all related listing companies in the Shanghai and Shenzhen stock exchanges. The results affirmed that (1) the risk of default decreased with improved ESG performance; (2) the return on assets, asset turnover ratio, leverage ratio, and operating income growth rate were the main financial factors affecting the default probability of enterprises; and (3) including ESG variables in the prediction model significantly improved the prediction accuracy of the model. The potential policy implications are presented in three perspectives. Businesses should prioritize developing good governance, fulfilling social obligations, and protecting the environment. Second, investors should integrate ESG ratings when making investment strategies. Third, the regulatory authorities are recommended to rapidly harmonize the ESG rating criteria and gradually develop the enterprise ESG information disclosure framework.

Regulating planting density and nitrogen (N) fertilization could delay chlorophyll (Chl) degradation and leaf senescence in maize cultivars. This study measured changes in ear leaf green area (GLAear), Chl content, the activities of Chl a-degrading enzymes after silking, and the post-silking dry matter accumulation and grain yield under multiple planting densities and N fertilization rates. The dynamic change of GLAear after silking fitted to the logistic model, and the GLAear duration and the GLAear at 42 d after silking were affected mainly by the duration of the initial senescence period (T1) which was a key factor of the leaf senescence. The average chlorophyllase (CLH) activity was 8.3 times higher than pheophytinase activity and contributed most to the Chl content, indicating that CLH is a key enzyme for degrading Chl a in maize. Increasing density increased the CLH activity and decreased the Chl content, T1, GLAear, and GLAear duration. Under high density, appropriate N application reduced CLH activity, increased Chl content, prolonged T1, alleviated high-density-induced leaf senescence, and increased post-silking dry matter accumulation and grain yield.

Birth defects (BD) are an important cause of neonatal mortality and can be associated with premature birth. The study aimed to develop a prognostic model for congenital malformations in mothers with a history of preterm delivery, using logistic regression analysis. The study included 665 mothers of children with BD, of which 432 (65%) had a history of preterm delivery (main group), and 233 (35%) had term delivery (control group). Variables examined included pregnancy history, genetic factors, and biochemical markers. Statistical analysis found significant associations between BD and preterm delivery, intrauterine malformations, miscarriages, MTHFR polymorphism, and HLA antigens. The logistic model showed good predictive performance. The area under the ROC curve was 0.769 for pregnancy history, 0.699 for miscarriages, and 0.630 for intrauterine malformations, indicating moderate predictive ability. A statistical relationship was found between BD risk and pregnancy history, intrauterine malformations, miscarriages, and genetic factors. The resulting logistic model may help predict BD risk in mothers with a preterm delivery history.

A nonautonomous logistic population model with a feature of an Allee threshold has been investigated in a periodically fluctuating environment. A slow periodicity of the harvesting effort was considered and may arise in response to relatively slow fluctuations of the environment. This assumption permits obtaining the analytical approximate solutions of such model using the perturbation approach based on the slow variation. Thus, the analytical expressions of the population evolution in the situation of subcritical and the supercritical harvesting were obtained and discussed in the framework of the Allee effect. Since the exact solution was not available due to the nonlinearity of the system, the numerical computation was considered to validate our analytical approximation. The comparison between the two methods showed a remarkable agreement as the time progressed, while such agreement fell off when the time was close to the initial density. Moreover, in the absence of the periodicity of the harvesting term, the expressions of the population evolution reduced to the exact solutions but in implicit forms. The finding results were appropriate for a wide range of parameter values, which lead to avoiding extensive recalculations while displaying the population behavior.