Exponential Regression Model Research Articles

Cumulative burden of late, major surgical intervention in survivors of childhood cancer: a report from the Childhood Cancer Survivor Study (CCSS) cohort.

Multimodal cancer therapy places childhood cancer survivors at increased risk for chronic health conditions, subsequent malignancies, and premature mortality as they age. We aimed to estimate the cumulative burden of late (>5 years from cancer diagnosis), major surgical interventions among childhood cancer survivors, compared with their siblings, and to examine associations between specific childhood cancer treatments and the burden of late surgical interventions. We analysed data from the Childhood Cancer Survivor Study (CCSS), a retrospective cohort study with longitudinal prospective follow-up of 5-year survivors of childhood cancer (diagnosed before age 21 years) treated at 31 institutions in the USA, with a comparison group of nearest-age siblings of survivors selected by simple random sampling. The primary outcome was any self-reported late, major surgical intervention (defined as any anaesthesia-requiring operation) occurring 5 years or more after the primary cancer diagnosis. The cumulative burden was assessed with mean cumulative counts (MCC) of late, major surgical interventions. Piecewise exponential regression models with calculation of adjusted rate ratios (RRs) evaluated associations between treatment exposures and late, major surgical interventions. Between Jan 1, 1970, and Dec 31, 1999, 25 656 survivors were diagnosed (13 721 male, 11 935 female; median follow-up 21·8 years [IQR 16·5-28·4]; median age at diagnosis 6·1 years [3·0-12·4]); 5045 nearest-age siblings were also included as a comparison group. Survivors underwent 28 202 late, major surgical interventions and siblings underwent 4110 late, major surgical interventions. The 35-year MCC of a late, major surgical intervention was 206·7 per 100 survivors (95% CI 202·7-210·8) and 128·9 per 100 siblings (123·0-134·7). The likelihood of a late, major surgical intervention was higher in survivors versus siblings (adjusted RR 1·8, 95% CI 1·7-1·9) and in female versus male survivors (1·4; 1·4-1·5). Survivors diagnosed in the 1990s (adjusted RR 1·4, 95% CI 1·3-1·5) had an increased likelihood of late surgery compared with those diagnosed in the 1970s. Survivors received late interventions more frequently than siblings in most anatomical regions or organ systems, including CNS (adjusted RR 16·9, 95% CI 9·4-30·4), endocrine (6·7, 5·2-8·7), cardiovascular (6·6, 5·2-8·3), respiratory (5·3, 3·4-8·2), spine (2·4, 1·8-3·2), breast (2·1, 1·7-2·6), renal or urinary (2·0, 1·5-2·6), musculoskeletal (1·5, 1·4-1·7), gastrointestinal (1·4, 1·3-1·6), and head and neck (1·2, 1·1-1·4) interventions. Survivors of Hodgkin lymphoma (35-year MCC 333·3 [95% CI 320·1-346·6] per 100 survivors), Ewing sarcoma (322·9 [294·5-351·3] per 100 survivors), and osteosarcoma (269·6 [250·1-289·2] per 100 survivors) had the highest cumulative burdens of late, major surgical interventions. Locoregional surgery or radiotherapy cancer treatment were associated with undergoing late surgical intervention in the same body region or organ system. Childhood cancer survivors have a significant burden of late, major surgical interventions, a late effect that has previously been poorly quantified. Survivors would benefit from regular health-care evaluations aiming to anticipate impending surgical issues and to intervene early in the disease course when feasible. US National Institutes of Health, US National Cancer Institute, American Lebanese Syrian Associated Charities, and St Jude Children's Research Hospital.

Predictive model for available soil phosphorus in Enamor Ener Woreda, southern Ethiopia

AbstractBackgroundPredicting soil phosphorus (P) needs and P fertilizer requirements are important for plant nutrition and reducing environmental risk. Thus, the precise estimation of available soil P (ASP) is required for the better management of environmental resources and improving agricultural productivity. Yearly, direct measurements of most nutrients at large scales are time‐consuming and expensive within the economic point of view.AimTherefore, to tackle such limitations indirect measurement of them in an accurate estimating manner is a possibility.MethodsThus, during this study, an exponential regression model for predicting ASP was suggested, and it was estimated as a function of soil organic carbon (OC). OC and ASP were used to investigate and analyze the soil to determine empirical relationships between them. Determination of the mentioned nutrient within the laboratory is somewhat difficult and time‐consuming. Therefore, an exponential regression model for predicting ASP from soil OC was suggested, and it was estimated as a function of soil OC. The ASP predicted from the ASP‐OC model was compared to the ASP determined by laboratory test using the paired samples t‐test and the Bland–Altman approach using SPSS.ResultsThe ASP predicted by the ASP‐OC model was not significantly different from the ASP determined by laboratory test since (p > 0.05).ConclusionsThe findings of the study indicated that the ASP‐OC model provides a simple, economic, and brief methodology to estimate ASP, and so to predict ASP‐supported soil OC using the exponential regression model (Y = aebX), the suggested model based on the findings for ASP is ASP = 9.786 e0.486 OC with R2 = 0.901 often suggested.

Purslane (Portulaca oleracea L.) salt tolerance assessment

ABSTRACT The present study was aimed to evaluate the effect of irrigation water salinity on some agronomic and physiological attributes of purslane (Portulaca oleracea L.) and its tolerance to salinity stress. The treatments included seven levels of electrical conductivity of irrigation water (0.6 (control), 3, 6, 9, 12, 15, and 18 dSm−1) arranged in the form of a completely randomized design with three replications. The results showed that salinity stress successively decreased agronomic attributes such as root, shoot, and stem dry weight, plant height and leaf area. Physiological attributes such as relative water content as well as ion leakage differently responded to salinity stress. Relative water content response to salinity followed a quadratic regression model with the maximum of 81% at electrical conductivity of 6.87 dSm−1 and the minimum of 65% at highest salinity level of 18 dSm−1. Following an exponential regression model, ion leakage remained relatively the same till irrigation water salinity of 15 dSm−1 and reached to the maximum of 78% at highest irrigation water salinity of 18 dSm−1. In addition, shoot potassium content of purslane under non saline conditions equaled to 11% that was around 10 times more than the typical of potassium sufficient concentration for adequate growth. Moreover, with increasing salinity stress, shoot potassium content decreased to 7% at highest salinity level of 18 dSm−1 of irrigation water. However, with increasing irrigation water salinity, sodium shoot content of purslane was increased from 1.9% at the lowest salinity stress to 6.7% at the highest salinity stress. Based on the linear and nonlinear models, 10% of purslane biomass reduction occurred at soil electrical conductivity of around 10 dSm−1. In addition, 25 and 50% yield reduction observed at 16 and 25 dSm−1 of electrical conductivity of soil saturated paste. Therefore, purslane can be considered as a very salt tolerant plant, halophyte, and can successfully grow in soils with electrical conductivities not suitable for most crops.

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

AbstractThe temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

Application of a Multispectral UAS to Assess the Cover and Biomass of the Invasive Dune Species Carpobrotus edulis

Remote sensing can support dune ecosystem conservation. Unoccupied Aircraft Systems (UAS) equipped with multispectral cameras can provide information for identifying different vegetation species, including Carpobrotus edulis—one of the most prominent alien species in Portuguese dune ecosystems. This work investigates the use of multispectral UAS for C. edulis identification and biomass estimation. A UAS with a five-band multispectral camera was used to capture images from the sand dunes of the Cávado River spit. Simultaneously, field samples of C. edulis were collected for laboratorial quantification of biomass through Dry Weight (DW). Five supervised classification algorithms were tested to estimate the total area of C. edulis, with the Random Forest algorithm achieving the best results (C. edulis Producer Accuracy (PA) = 0.91, C. edulis User Accuracy (UA) = 0.80, kappa = 0.87, Overall Accuracy (OA) = 0.89). Sixteen vegetation indices (VIs) were assessed to estimate the Above-Ground Biomass (AGB) of C. edulis, using three regression models to correlate the sample areas VI and DW. An exponential regression model of the Renormalized Difference Vegetation Index (RDVI) presented the best fit for C. edulis DW (R2 = 0.86; p-value < 0.05; normalised root mean square error (NRMSE) = 0.09). This result was later used to estimate the total AGB in the area, which can be used for monitoring and management plans—namely, removal campaigns.

Battery cycle life assessment for a lift+cruise electric vertical takeoff and landing transporter drone

Advanced Air Mobility (AAM) is a new future concept of air transportation to move people and cargo effectively and safely between places that are under or not served by different transportation modes. Transporter drone is one of the potential vehicles envisaged to fulfill this future AAM needs. One of the key challenges and crucial components in enabling successful transporter drone flight is the batteries used as they have limited usage cycle lifespan that affects the system airworthiness significantly. Hence to ensure continuous airworthiness of the platform, it is vital to know or predict the battery State of Health (SOH) and cycle life accurately. This paper describes a systematic approach on battery cycle life assessment through experimental battery usage cycle test for a lift+cruise transporter drone using realistic operating flight profile. Analysis on the battery cycle test data collected provides useful battery health and usage cycle lifespan information for system preventive and predictive maintenance in maintaining its airworthiness. Battery SOH estimation was done using linear predictive models with the collected battery cycle data while battery cycle life model was formulated using a double exponential parametric regression model. The battery cycle data collected is made publicly available for the community.

Incidence of death and its predictors among neonates admitted with sepsis in referral hospitals, northwest Ethiopia, a prospective cohort study.

Each year, approximately 2.7 million neonates die in their first month of life worldwide, and the majority of these deaths occur in low-income countries. According to the Global Burden of Disease estimation, 1.3 million annual incident cases of neonatal sepsis were reported worldwide, resulting in 203,000 sepsis-attributable deaths. Little is known about the time to death of neonates and predictors after admission with a diagnosis of sepsis. This study aimed to assess the incidence and predictors of death among neonates admitted to the neonatal intensive care unit with a diagnosis of sepsis in referral hospitals in Northwest Ethiopia. A multicenter prospective follow-up study was conducted from November 11 to December 7, 2021. A stratified random sampling technique was employed to select 412 neonates. Neonates admitted with sepsis were followed until they develop event for a maximum of 28 days of age. A face-to-face interview was conducted with the mother of the neonate using a pretested and structured questionnaire, and neonatal charts were reviewed to collect baseline factors. Data were entered into Epi-data version 4.6 and exported to STATA version 14 for analysis. A bivariable and multivariable exponential Cox regression model was fitted to identify predictors of death. The adjusted hazard ratio (AHR) with 95% CI was calculated, and statistical significance was declared at a P-value of 0.05 in the multivariable analysis. A total of 75 (18.47%) neonates died during the study period, with a 95% CI of 14.82-22.60. The incidence rate of death was 28 (95% CI, 22, 35) per 1,000 person-days of observation, with a total follow-up time of 2,677 person-days of observation. Birth weight (<2,500 g) (AHR = 2.12, 95% CI: 1.01, 4.43), prematurity (AHR = 2.06, 95% CI: 1.02, 4.15), duration of labor >24 h (AHR = 3.89, 95% CI: 1.38, 11.01), breast feeding (AHR = 0.43, 95% CI: 0.23, 0.80), having respiratory distress syndrome (AHR = 1.77, 95% CI: 1.02, 306), oxygen saturation less than 90% (AHR = 2.23, 95% CI: 1.02, 306) were significant predictors of death among neonates admitted with sepsis. The incidence of neonatal mortality in this study was high. Early detection and appropriate management of patients' presentations like respiratory distress syndrome and low oxygen saturation are necessary to reduce neonatal sepsis-related mortality. Special attention should be given to low birth weight and premature neonates and mothers should be encouraged to breastfeed their newborns after delivery.

Geo-spatial modelling of carbon stock assessment of date palm at different age stages: An integrated approach of fieldwork, remote sensing and GIS

The aim of this study is to develop a geospatial model for the assessment and mapping of carbon stock (CS) of date palms (DP) in Abu Dhabi, at three age stages: mature, medium and young. The approach relied on the use of correlated remote sensing predictors tested and derived from Landsat-8 imagery over the study period to estimate CS. Multiple predictors including single bands, vegetation indices (VIs) and their combination were used in linear and non-linear regression models to identify the best performing predictors and regression models for DP. Mature DP, older than 10 years, attained the most significant correlation when using a second-order polynomial model with the tasseled cap for wetness as a predictor, yielding R² of 0.7643. For non-mature DP, less than 10 years, the exponential regression model using renormalized difference vegetation index (RDVI) as a predictor provided the most significant correlation for AGB estimation with R2 of 0.4987. The study demonstrated that moderate-resolution Landsat-8 OLI imagery has the potential to estimate the CS in DP of drylands at different age stages. This is the first attempt to estimate CS in DP using geospatial technologies with minimal fieldwork hence, expanding our approach to other remote and less-studied drylands.

Prediction of COVID-19 Pandemic Trend by Machine Learning

Since 2020, COVID-19 has had a huge impact on people's lives. Including but not limited to economic, educational, medical, and other aspects. During this period, all sectors of society and the government have intervened reasonably, so it is necessary to analyze the data on COVID-19 so far and make scientific predictions. This article starts with the analysis of raw data on COVID-19 from the World Health Organization (WHO). Then four machine learning methods, including the time series model, exponential smoothing model, XGBRegressor method, and polynomial regression model, are leveraged for trend prediction of the original data. The data, with the time ranging from January 2020 to May 2021, is taken as the training object, and then the epidemic in Jul 2021 is used for testing. The number of cases is predicted and compared with real data. It is concluded that the WHO has indeed carried out effective intervention in areas seriously affected by the epidemic and that the time series model predicts the minimum loss value.

Lime-based mortar reinforced with randomly oriented polyvinyl-alcohol (PVA) fibers for strengthening historical masonry structures

The seismic vulnerability of unreinforced masonries underlines the importance of developing new methods and materials for the retrofitting of historical structures. Nowadays, two are the most widely diffused strengthening systems for masonry structures, i.e. fiber reinforced polymers (FRP) and fabric reinforced cementitious matrix (FRCM) composites. Despite some degree of effectiveness, FRPs show poor chemical compatibility with masonry supports, that makes them unsuitable for intervention on historical heritage structures. FRCMs frequently consists of a cement-based mortar and, because of this, the cementitious nature of the inorganic matrix can not ensure the chemical compatibility with the historical masonry substrate; also, being FRCMs composites made out of fiber fabrics embedded in an inorganic matrix, the mono- or bi-directionality characterizing the fiber fabric makes this type of reinforcement ineffective out of the fiber axial direction. This paper aims to overcome the listed drawbacks presenting a systematic study on a newly developed lime-based mortar, reinforced with randomly oriented polyvinyl alcohol (PVA) fibers: the lime-based nature of the mortar meets the requirement of chemical compatibility with the historical substrate, that is fundamental for the restoration of heritage buildings, and the random orientation of the PVA fibers makes them globally effective, being the stress state induced by seismic action directionally unknown. Specimens measuring 160 mm × 40 mm x 40 mm characterized by six different fiber contents and two different fiber lengths, namely 6 mm and 12 mm, in addition with plain mortar samples, are tested in three-point-bending configuration to compute both flexural strength and fracture energy. Then, the two broken pieces resulting from the flexural tests, each one measuring 80 mm × 40 mm x 40 mm, are tested in splitting configuration and in compression, and the resulting tensile strength and compressive strength are computed. An analytical formulation is finally proposed by fitting the experimental data with linear, parabolic and exponential regression models. The results related to averaged values of mechanical properties show that, for both short and long fiber lengths, the flexural strength and the fracture energy linearly increase with the fiber content and that the tensile and the compressive strengths parabolically increase with the fiber content. Overall, the newly proposed mortar stands as a valid system for the strengthening of masonry structures, being especially suitable for intervention on cultural heritage buildings.

Modeling Covid-19 Cases and Deaths in Benelux Countries (Belgium, the Netherlands, and Luxembourg) With Different Regression Methods

In this study, the number of Covid-19 cases and deaths in Benelux countries were examined with linear, quadratic, cubic, logarithmic, and exponential regression models. The number of Covid-19 cases and deaths, within a 50-day period between March 1, 2022, and April 19, 2022, in Belgium, the Netherlands, and Luxembourg were examined. Models with the highest coefficient of determination (R2 ) and corrected coefficient of determination with significant parameter estimates were preferred in the selection of the most appropriate regression model. (𝑅̅2 ) The cubic regression model for the number of cases and the quadratic, cubic, and linear regression models for the number of deaths were found to be appropriate respectively in Belgium, the Netherlands, and Luxembourg. The R2 values of these models are 0.997, 0.9993 and 0.9982; 𝑅̅2 respectively and the values are 0.996, 0.9987 ve 0.9979 respectively. Regression models for the number of deaths were obtained as quadratic, cubic, and linear regression models in these countries, respectively. The R2 values of these models were 0.996, 0.9982 and 0.9848 respectively; 𝑅̅2 and the values were 0.995, 0.9977 and 0.9845 respectively. As a result, it was observed that regression models were an appropriate method to reveal the number of cases and deaths.

Evaluation of regression models' accuracy for estimating soil heavy metals content from topographic rectified satellite imagery (Case study: Irankouh from Iran)

Although the traditional approach is vital in environmental studies, satellite images have recently become one of the main tools for estimating soil pollution due to their advantages. Among sources of soil pollution, heavy metals (HMs) play a crucial role; in this regard, evaluating the soil contamination anomaly is indispensable due to an enormous number of HMs mines and agricultural land irrigation by industrial sewage in developing countries. Therefore, the main goals of this study are: to evaluate Landsat-8 image capability for estimating HMs content (Cr, Cd, Co, and AS) of the soil, performance evaluation of linear and nonlinear regression models, and topographic influence on the estimation accuracy of heavy metals. To do so, we applied multivariate-linear, partial least square, exponential regression, and neural network models to achieve a relation between the HMs content and soil spectral reflectance. Regarding the R2 value of models, the neural network demonstrated a more acceptable performance than other models. The results show that topographic correction improves the accuracy of HMs estimation; for example, the Minnaert model obtained the highest score (equal to 30.02), which is sufficient to support the preceding sentence. Furthermore, the neural network and Minnaert (with a score of 17.56) would produce the best results for estimating HMs content.

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as Then, the developed model is used to create a spatiotemporal data set of values at the county level in the United States. Second, it investigates relationships between and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios.

Inversion models of aboveground grassland biomass in Xinjiang based on multisource data.

Grassland biomass monitoring is essential for assessing grassland health and carbon cycling. However, monitoring grassland biomass in drylands based on satellite remote sensing is challenging.Statistical regression models and machine learning have been used for the construction of grassland biomass models, but the predictive power for different grassland types is unclear. Additionally, the selection of the most appropriate variables to construct a biomass inversion model for different grassland types must be explored. Therefore,1201 ground-truthed data points collected from 2014-2021,including 15 Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices,geographic location and topographic data,and meteorological factors and vegetation biophysical indicators were screened for key variables using principal component analysis (PCA). The accuracy of multiple linear regression models, exponential regression models, power function models, support vector machine (SVM) models, random forest (RF) models, and neural network models was evaluated for the inversion of three types of grassland biomass. The results were as follows: (1) The biomass inversion accuracy of single vegetation indices was low, and the optimal vegetation indices were the soil-adjusted vegetation index (SAVI) (R2 = 0.255), normalized difference vegetation index (NDVI) (R2 = 0.372) and optimized soil-adjusted vegetation index (OSAVI) (R2 = 0.285). (2)Grassland above-ground biomass (AGB) was affected by various factors such as geographic location,topography, and meteorological factors, and the inverse models using a single environmental variable had large errors. (3) The main variables used to model biomass in the three types of grasslands were different. SAVI, aspect, slope, and precipitation (Prec.) were selected for desert grasslands; NDVI,shortwave infrared 2 (SWI2), longitude, mean temperature, and annual precipitation were selected for steppe;and OSAVI, phytochrome ratio (PPR), longitude, precipitation, and temperature were selected for meadows. (4) The non-parametric meadow biomass model was superior to the statistical regression model. (5) The RF model was the best model for the inversion of grassland biomass in Xinjiang, and this model had the highest accuracy for grassland biomass inversion (R2 = 0.656, root mean square error (RMSE) = 815.6 kg/ha),followed by meadow (R2 = 0.610, RMSE = 547.9 kg/ha) and desert grassland (R2 = 0.441, RMSE = 353.6 kg/ha).

Interpreting the effects of shale rock properties on seismic anisotropy by statistical and machine learning methods

The elastic anisotropy of unconventional shale reservoirs plays a crucial role in their economic hydrocarbon production. The strong anisotropy of organic-rich shale rocks, characterized by clay platelets, elongated pores, and organic material, presents challenges for traditional reservoir characterization methods, which are designed for isotropic or weakly anisotropic conventional sandstone reservoirs. To effectively describe the geomechanical behaviors of organic-rich shales and estimate changes in stress caused by hydraulic fracturing, it is necessary to develop practical methods for predicting anisotropy parameters from conventional rock property logs. This study proposes a statistical approach for predicting independent elastic stiffness coefficients for vertically transverse isotropic (VTI) shale from three shale rock properties: clay volume fraction, kerogen volume fraction, and total porosity. The results of this study show that the stiffness coefficients, C11 and C33, are related to P-wave anisotropy and porosity has a greater impact on P-wave anisotropy than clay and kerogen volume fractions. In contrast, changes in clay volume have a more sensitive effect on C44 and C66, providing S-wave anisotropy information that can help detect lithology changes. Furthermore, kerogen variation has a greater influence on both C12 and C13 than the effects of porosity and clay variations. From these relationships, we develop exponential nonlinear regression (ENLR) models for predicting independent elastic stiffness coefficients from three shale rock properties. These models are validated with the Midland Basin log data, and we improve the accuracy of the C13 estimation model using the deep feedforward neural network (DFNN) method. The results show that the statistical approach can help understand the effect of shale rock components on seismic anisotropy variations and better predict the variations in lithology, fluid, and organic contents in shales from anisotropic seismic data. Furthermore, the methodology can provide robust estimates of the stress changes from hydraulic fracturing, supporting the efficient and economically viable production of shale hydrocarbons.

May board committees reduce the probability of financial distress? A survival analysis on Italian listed companies

The aim of this research was to study the effect of the composition and functioning of board committees on firms' financial distress. Exponential, Weibull and Cox regression models were used to conduct a survival analysis on a sample of 273 Italian listed companies for the period 2004–2017, which indicated that the presence of non-executive members on remuneration and audit committees, and remuneration committees meeting more frequently may enhance firms' stability. In contrast, a high frequency of nomination committee meetings seems to be positively related to the probability of financial distress.Although we only partially controlled for endogeneity issues, our findings contribute to the literature on financial distress-prediction models by deepening the importance of the composition and functioning of board committees (beyond other corporate governance variables and financial ratios). We can also provide firms with practical suggestions to promote financial stability.

Integrating exponential regression model optimizations for wheat area, productivity and population through statistical and machine learning approaches

Integrating exponential regression model optimizations for wheat area, productivity and population through statistical and machine learning approaches

Droplet Penetration Model Based on Canopy Porosity for Spraying Applications

Analysing the penetration and droplet deposition characteristics in the canopy of fruit trees is critical for optimising the operational parameters of air-assisted spraying equipment, achieving precise application of chemicals, and improving the effectiveness of fruit tree pest and disease control. We used a mobile LIDAR system to detect the tree canopy characteristics and optical porosity and conduct wind tunnel experiments to investigate the interaction between the tree canopy, the airflow field, and the droplet penetration ratio in the canopy of fruit trees. The results show that the relative wind velocity decreases rapidly during canopy penetration, and that the minimum value occurs at the back of the canopy. The smaller the optical porosity, the greater the reduction in wind velocity is. The quadratic exponential regression model had the highest coefficient of determination (R2) (0.9672) and the lowest root mean square error (RMSE) (5.56%). This paper provides information on optimising the spraying parameters, improving the pesticide utilisation rate, and selecting the optimum spraying conditions and application parameters.

Aberrant neurovascular coupling in Leber's hereditary optic neuropathy: Evidence from a multi-model MRI analysis.

The study aimed to investigate the neurovascular coupling abnormalities in Leber's hereditary optic neuropathy (LHON) and their associations with clinical manifestations. Twenty qualified acute Leber's hereditary optic neuropathy (A-LHON, disease duration ≤ 1 year), 29 chronic Leber's hereditary optic neuropathy (C-LHON, disease duration > 1 year), as well as 37 healthy controls (HCs) were recruited. The neurovascular coupling strength was quantified as the ratio between regional homogeneity (ReHo), which represents intrinsic neuronal activity and relative cerebral blood flow (CBF), representing microcirculatory blood supply. A one-way analysis of variance was used to compare intergroup differences in ReHo/CBF ratio with gender and age as co-variables. Pearson's Correlation was used to clarify the association between ReHo, CBF, and neurovascular coupling strength. Furthermore, we applied linear and exponential non-linear regression models to explore the associations among ReHo/CBF, disease duration, and neuro-ophthalmological metrics. Compared with HCs, A_LHON, and C_LHON patients demonstrated a higher ReHo/CBF ratio than the HCs in the bilateral primary visual cortex (B_CAL), which was accompanied by reduced CBF while preserved ReHo. Besides, only C_LHON had a higher ReHo/CBF ratio and reduced CBF in the left middle temporal gyrus (L_MTG) and left sensorimotor cortex (L_SMC) than the HCs, which was accompanied by increased ReHo in L_MTG (p < 1.85e-3, Bonferroni correction). A-LHON and C-LHON showed a negative Pearson correlation between ReHo/CBF ratio and CBF in B_CAL, L_SMC, and L_MTG. Only C_LHON showed a weak positive correlation between ReHo/CBF ratio and ReHo in L_SMC and L_MTG (p < 0.05, uncorrected). Finally, disease duration was positively correlated with ReHo/CBF ratio of L_SMC (Exponential: Radj2 = 0.23, p = 8.66e-4, Bonferroni correction). No statistical correlation was found between ReHo/CBF ratio and neuro-ophthalmological metrics (p > 0.05, Bonferroni correction). Brain neurovascular "dyscoupling" within and outside the visual system might be an important neurological mechanism of LHON.

Reduced norovirus epidemic follows increased sales of hand hygiene products in Japan, 2020–2021

During the recent emergence of COVID-19, an increased practice of hand hygiene coincided with the reduced incidence of the norovirus epidemic in Japan, which is similar to experience with the pandemic flu in 2009. We investigated the relationship between the sales of hand hygiene products, including liquid hand soap and alcohol-based hand sanitizer, and the trend of norovirus epidemic. We used national gastroenteritis surveillance data across Japan in 2020 and 2021 and compared the base statistics of incidence of these two years with the average of the previous 10 years (2010-2019). We calculated the correlations (Spearman's Rho) between monthly sales of hand hygiene products and monthly norovirus cases and fitted them to a regression model. In 2020, there was no epidemic, and the incidence peak was the lowest in recent norovirus epidemics. In 2021, the incidence peak was delayed for five weeks to the usual epidemic seasons. Correlation coefficients between monthly sales of liquid hand soap and skin antiseptics and norovirus incidence showed a significantly negative correlation (Spearman's Rho = -0.88 and p = 0.002 for liquid hand soap; Spearman's Rho = -0.81 and p = 0.007 for skin antiseptics). Exponential regression models were fitted between the sales of each hand hygiene product and norovirus cases, respectively. The results suggest hand hygiene using these products is a potentially useful prevention method against norovirus epidemics. Effective ways of hand hygiene for increasing the prevention of norovirus should therefore be studied.