Nonlinear Exponential Regression Research Articles

Assessing the Performance of NDVI as A Proxy for Land Surface Temperature Using Linear and Nonlinear Models in Different Intensity of Anthropogenic Activities: A Case Study of Yogyakarta City, Indonesia

Abstract Numerous studies have tested the relationship between vegetation density (using normalized difference vegetation index/NDVI as a proxy for vegetation density) and land surface temperature (LST) using a linear regression model. Few studies have explicitly compared linear and nonlinear regression models in describing the relationship between NDVI and LST in different intensities of anthropogenic activities. Hence, this study aims to investigate multitemporal variations of LST and to compare the performance of NDVI as a proxy for LST over various urban activities in Yogyakarta City, Indonesia. NDVI and LST were extracted from Landsat images for 2019, 2020, 2021, and 2022. The years of 2019, 2020, 2021, and 2022 represent different anthropogenic activities i.e. pre Covid-19, emergency response to Covid-19, transition period of Covid-19, and new normal conditions, respectively. The mean LST of 2019, 2020, 2021, and 2022 were 29.64 °C, 24.52 °C, 28.93 °C, and 29.47 °C, respectively, in which the lowest temperature was during emergency response to Covid-19. During emergency response to Covid-19 in 2020, the highest R2 was non-linear logarithmic regression by 0.31, while the highest R2 for 2019, 2021, and 2022 was R2 of nonlinear exponential regression by 0.36, 0.45, and 0.56, respectively. However, linear regression models were still relatively good at describing the relationship between NDVI and LST during the whole period by having R2 of 0.30 to 0.55.

Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review

Machine learning (ML) algorithms may play a role in predicting the adverse health impacts of climate-sensitive extreme weather events because accurate prediction of such effects can guide proactive clinical and policy decisions. To systematically review the literature that describe ML algorithms that predict health outcomes from climate-sensitive extreme weather events. A comprehensive literature search was performed in the following databases from inception–October 2022: Ovid MEDLINE, Ovid EMBASE, The Cochrane Library, Web of Science, bioRxiv, medRxiv, Institute of Electrical and Electronic Engineers, Google Scholar, and Engineering Village. The retrieved studies were then screened for eligibility against predefined inclusion/exclusion criteria. The studies were then qualitatively synthesized based on the type of extreme weather event. Gaps in the literature were identified based on this synthesis. Of the 6096 records screened, seven studies met the inclusion criteria. Six of the studies predicted health outcomes from heat waves, and one for flooding. Health outcomes described included 1) all-cause non-age standardized mortality rates, 2) heat-related conditions and 3) post-traumatic stress disorder. Prediction models were developed using six validated ML techniques including non-linear exponential regression, logistic regression, spatiotemporal Integrated Laplace Approximation (INLA), random forest and decision tree methods (DT), and support vector machines (SVM). Use of ML algorithms to assess adverse health impacts from climate-sensitive extreme weather events is possible. However, to fully utilize these ML techniques, better quality data suitable for use is desirable. Development of data standards for climate change and health may help ensure model robustness and comparison across space and time. Future research should also consider health equity implications.

Application of the Falling Number Method in the Evaluation of the α-Amylase Activity of Malt Flour

The Falling Number (FN) test is not suitable for the determination of α-amylase activity in malt flour because the amylolytic activity is too high to be determined by the FN instrument. The aim of this study was to modify the FN method to allow quantification of high α-amylase activity in malt flour. The modifications were performed in two ways: using different amounts of malt flour (0.05, 0.1, 0.2, and 0.3 g) or by using malt extracts (0.5, 1, 2, 3, 4, 5, and 6 mL). In both cases, 6 g of standard wheat starch was used as substrate. The results of the modified methods were compared with the α-amylase activity determined by the standard Ceralpha method. Linear and nonlinear exponential regression was used to calculate the predicted amylase activity, and Bland-Altman analysis was used to measure the agreement between standard and modified methods. The modifications of the FN method using 0.1 g of malt flour showed that the modified method was able to accurately measure high levels of α-amylase activity in malt flour, and the results obtained by the modified method were in agreement with the results obtained by the standard Ceralpha method.

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.

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.

Modelling and forecasting reservoir sedimentation of irrigation dams in the Guinea Savannah Ecological Zone of Ghana

Abstract Effective management of reservoir sedimentation requires models that can predict sedimentation of the reservoirs. In this study, linear regression, non-linear exponential regression and artificial neural network models have been developed for the forecasting of annual storage capacity loss of reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of Ghana. Annual rainfall, inflows, trap efficiency and reservoir age were input parameters for the models whilst the output parameter was the annual sediment volume in the reservoirs. Twenty (20) years of reservoirs data with 70% data used for model training and 30% used for validation. The ANN model, the feed-forward, back-propagation algorithm Multi-Layer Perceptron model structure which best captured the pattern in the annual sediment volumes retained in the reservoirs ranged from 4-6-1 at Karni to 4-12-1 at Tono. The linear and nonlinear exponential regression models revealed that annual sediment volume retention increased with all four (4) input parameters whilst the rate of sedimentation in the reservoirs is a decreasing function of time. All the three (3) models developed were noted to be efficient and suitable for forecasting annual sedimentation of the studied reservoirs with accuracies above 76%. Forecasted sedimentation up to year 2038 (2019–2038) using the developed models revealed the total storage capacities of the reservoirs to be lost ranged from 13.83 to 50.07%, with 50% of the small and medium reservoirs filled with sediment deposits if no sedimentation control measures are taken to curb the phenomenon.

Prediction of Daily COVID-19 Active Cases in Bangladesh

Recording daily active cases statistics benefits instantly when studying the ongoing COVID-19 dashboard for a country. It would help to illuminate the probable strategy of action for policy makers, as well as planners of a country. The goal of the paper is to forecast daily active COVID-19 cases for Bangladesh using various forecasting models, namely the regression model, the non-linear exponential regression model and some time series models based on the daily positive cases, daily deceased cases, daily recovered cases and some environmentally important variables. Our findings suggest that time series forecasting models have better ability to predict daily active cases as compared to other selected forecasting models.

A detailed comparison of oxygen uptake kinetics at a range of exercise intensities

It is believed that exercise performed in the heavy intensity exercise (above Gas Exchange Threshold (GXT)) domain will reach a steady state (albeit delayed). However reported modelled time constants for the slow component indicate the VO² response would not be complete within the duration of the exercise performed. This raises important questions regarding the concept of heavy intensity exercise and the suitability of current exponential models to describe the slow component of VO². .The purpose of this study was; to comprehensively describe the relationship between exercise intensity and the slow component of VO², and to investigate whether a steady-state in VO² was achieved during constant work-rates above the gas exchange threshold (GXT). Eight recreationally active male participants volunteered for this study (age: 24±8 y; Stature: 1.78±0.09 m; mass: 76.5±10.1 kg; VO²peak: 3.89±0.72 L.min-1). The participants were required to visit the laboratory on nine occasions for testing. The first visit involved determination of GXT and VO²peak with a progressive ramp exercise test. The following tests involved multiple laboratory visits, with the participants performing a square wave transition from rest to one of eight exercise intensities; -20%Δ (minus 20% of the difference in VO² between that at GXT and VO2peak), -10%Δ, GXT, 10%Δ, 20%Δ, 30%Δ, 40%Δ and 50%Δ. The VO² response was modelled using both mono and bi exponential non-linear regression techniques. Difference in the SEE for the mono and bi exponential models were analysed using a paired samples t-test, and the slope of VO² vs Time (for the final minute of exercise) was analysed using a one-sample t-test. A slow component of VO² was found for all exercise intensities. The SEE’s were significantly lower in the bi vs. mono exponential model across all exercise intensities (p<0.05). The slope was not different from 0 (p<0.05) for the final minute of any exercise intensity, indicating that a steady-state was achieved. The modelled slow component time constants are typical of literature reported values, but would indicate that VO² would not be achieved during the duration of the exercise. These findings demonstrate that VO² was in steady-state for all exercise intensities for the final minute of exercise. These findings also demonstrate that using a bi exponential model, a slow component can be modelled even below GXT and that the time constant of the slow component would be too long to result a steady-state.

Quantification of the mandibular defect healing by micro-CT morphometric analysis in rats

PurposeThe goal of this study was the evaluation of the bone tissue structural characteristics over the time course of mandibular defect healing using micro-CT technique, as well as determination of the inter-relationships between different micro-CT parameters used for assessment of the bone regeneration process and the patterns of their dynamic changes. Materials and methodsThe body and ramus of the mandible was exposed in 24 Wistar rats. A 2-mm full thickness bony defect was created. Animals were randomized into four groups, which were ended 3, 6, 12 and 24 weeks after operation. The mandible was excised and underwent micro-CT analysis. For statistical evaluation, the Mann–Whitney U test, polynomial or exponential regression and Spearman analysis were applied. ResultsThe absolute volume of the bone regenerate increased from 1.69 ± 0.53 mm3 (3 weeks) to 3.36 mm3 ± 0.56 (6 months), as well as percentage of bone volume, increased significantly from 12.5 ± 2.3% at the 3-week term to 26.4 ± 8.7% at the 3-month term or 23.1 ± 8.7% at the 6-month term. Structural (trabecular) thickness gradually increased from 0.13 ± 0.007 mm at the 3-week term to 0.3 ± 0.11 mm at the 6-month term. The structural model index was 0.79 ± 0.46 in the early phase after trauma and then decreased to negative values. ConclusionThe bone regeneration process was characterized by a significant increase (p < 0.05) in bone volume, percentage of bone volume, structural thickness and bone mineral density, and a decrease in bone surface-to-volume ratio and volume of pore space from the 3-week term to the 6-month term. These changes can be mathematically described by nonlinear exponential regression models.

Assessing the bioavailability of the Novel Met-Met product (AQUAVI® Met-Met) compared to dl-methionine (dl-Met) in white shrimp (Litopenaeus vannamei)

This trial was conducted to compare Novel Met-Met product (AQUAVI® Met-Met) and dl-methionine (dl-Met) regarding their availability to juvenile white shrimp. Eleven experimental diets were formulated (Basal, dl-Met 0.06, dl-Met 0.12, dl-Met 0.18, dl-Met 0.24, dl-Met 0.3, AQUAVI 0.06, AQUAVI 0.12, AQUAVI 0.18, AQUAVI 0.24, AQUAVI 0.3), the basal diet was formulated without any additional methionine addition; the other ten diets (dl-Met 0.06, dl-Met 0.12, dl-Met 0.18, dl-Met 0.24, dl-Met 0.3; AQUAVI 0.06, AQUAVI 0.12, AQUAVI 0.18, AQUAVI 0.24, AQUAVI 0.3) were formulated with five graded levels of methionine with two different methionine sources of dl-Met and AQUAVI® Met-Met, respectively. Each diet was fed at a restricted rate to four groups of 30 shrimp for 63days. The highest value of survival was observed with shrimp fed AQUAVI 0.24 diet, which was significantly higher than that of shrimp fed the basal, dl-Met 0.06 and dl-Met 0.18 (P<0.05) diets, but without statistical difference with shrimp fed other diets (P>0.05). The highest values of growth performance (WG and SGR) were observed with shrimp fed AQUAVI 0.12 diet, which were significantly higher than those of shrimp fed the basal, dl-Met 0.06 and dl-Met 0.18 (P<0.05) diets, but without statistical difference with shrimp fed other diets (P>0.05). A non-linear exponential regression model indicated that AQUAVI® Met-Met was found to be more bioavailable than dl-Met with calculated relative bioavailability values of 285.8%, 276.4% and 300.4% based on weight gain, specific growth rate and feed efficiency, respectively. These findings lead to the conclusion that differences in bioavailability do exist between sources of synthetic methionine, and AQUAVI® Met-Met is more available than DL-Met for L. vannamei.

The value of serum chromogranin A as a predictor of tumor burden, therapeutic response, and nomogram-based survival in well-moderate nonfunctional pancreatic neuroendocrine tumors with liver metastases.

To investigate the usefulness of serum chromogranin A (CgA) for the prediction of tumor burden, therapeutic response, and nomogram-based survival in well-moderate nonfunctional pancreatic neuroendocrine tumors (NF-PNETs) with liver metastases (LMs). This prospective study included 51 NF-PNETs of LMs patients, 134 other neuroendocrine tumors, and 125 controls. Serum CgA levels were determined by enzyme-linked immunosorbent assay at baseline and after treatment. LMs tumor burden was computed simultaneously from computed tomography/MRI scan with thin slices using a semiquantitative three-dimensional reconstruction approach. Predictive CgA for therapeutic response was assessed using the response evaluation criteria in solid tumors criteria. A nomogram to predict the prognostic value of CgA with variables selected in the multivariate Cox proportional hazards model was constructed; the accuracy of the nomogram was quantified by the (concordance index) C-index and a calibration plot. Considering NF-PNETs, CgA correlated with the tumor grade and differentiation (P<0.05). There was a nonlinear exponential regression between LMs tumor burden and CgA levels (P<0.001). The alteration in CgA correlated with therapeutic response (P<0.001). Increased CgA presented significantly lower progression-free survival than the stable/decreased CgA subgroup (P<0.001). For overall survival, a baseline CgA increase greater than 2.5 upper limit of normal level was predictive of a poor prognosis (P<0.001). Baseline CgA level, LMs tumor burden, and Ki-67 were selected as independent factors for the nomogram to predict overall survival; the nomogram showed fitting calibration with a C-index of 0.87 (95% confidence interval, 0.82-0.92). Serum CgA could be used to reflect tumor burden, evaluate the therapeutic response, and predict the survival outcomes for NF-PNETs with LMs. An effective nomogram including CgA was proposed for prediction.

Using partial volumes to estimate available browse biomass in Southern African semi-arid savannas

Aims With the increase of game farming in woody areas, as well as the pressing need to assess the spatial distribution of carbon, the quantification of available browse biomass has become an important subject to refine. We aimed to (1) calculate tree canopy volume (CVol) for varying tree canopy shapes and for strata within those shapes to cater for different herbivore browse heights, (2) develop a relation to estimate available browse of deciduous (micro- and mesophyllous) and evergreen (macrophyllous) semi-arid savanna tree species using our calculated CVols, and (3) demonstrate the application of the use of partial volumes and the resultant regression models in the determination of available browse biomass for browsing game species. Location Four key savanna tree species were sampled in the open wooded grassland areas of three game reserves in northeastern KwaZulu-Natal, South Africa: Bonamanzi Game Park, Mun-Ya-Wana Private Game Reserve and Pongola Game Reserve. Methods Tree CVol determination involved calculation of partial volumes, using the integration of a series of circles, allowing a wider range of tree shapes to be included. Non-linear regression was used to investigate the relation between available browse biomass of deciduous microphyllous Acacia nilotica, evergreen macrophyllous Euclea divinorum and Gymnosporia senegalensis and deciduous mesophyllous Spirostachys africana and their respective calculated partial CVols. Results Exponential non-linear regression of the natural logarithm of CVol data provided an accurate and precise description of the tree CVol–available browse biomass relation. Significant relations were obtained over a 2-yr period (2003–2004) for species–site models and general species models. The incorporation of tree foliage densities in available browse biomass estimation further improved model accuracy and precision. Conclusion The general models provided an objective means of determining the browsing capacity of a tree population. In addition, estimation of carbon sequestration could be related to the model output, particularly when changes over time are recorded. Regular measurement of available browse comprises a useful monitoring tool to evaluate impact of animals on trees and shrubs.

Application of Adaptive Neuro-fuzzy Inference System for road accident prediction

Since the last two decades, several modeling approaches have been developed in road safety literature to establish the relationship between traffic accidents and road characteristics. However, to the best of the authors’ knowledge, no extensive research work has been published on application of Adaptive Neuro-fuzzy Inference System (ANFIS) on road accident modelling. Therefore, the present paper aims to develop an ANFIS technique for modelling traffic accidents as a function of road and roadside characteristics. To achieve the objective, accident data and road characteristics were collected over a two-year period along the Qazvin-Loshan intercity roadway in Iran. The candidate set of explanatory variables included the Mean Horizontal Curvature (MHC), Shoulder Width (SW), Road Width (RW), Land Use (LU), Access Points (AP), Longitudinal Grade (LG), and Horizontal Curve Density (HCD). The results showed that RW, SW, LU, and AP significantly affected accident frequencies. Using statistical performance indices, the ANFIS model was compared with the Poisson, negative binomial, and non-linear exponential regression models. Based on the comparative results, the proposed model had higher prediction performance than the other three traditional models which has been widely used in the literature. To conclude, the proposed model could be used as a robust approach to handle uncertainty and complexity existed in accident data. In general, ANFIS model can be an effective tool for transportation agencies since intervention decisions and plans aiming at improving road safety depend on the prediction capabilities of a system.

Learning effect of dark adaptation among normal subjects

To examine and quantify any change in the dark adaptation (DA) function of normal subjects due to learning effect on test-retest. Sixteen normal subjects (12 women, four men) whose ages ranged between 24 and 52years (mean 34.6 ± 6.7years) were studied. The interval period between test and retest ranged between 0.92 and 2.37months (mean 1.38 ± 0.40months). DA was measured with a Goldmann-Weekers (GW) dark adaptometer, and subjects were pre-adapted using a light intensity of 2,700cd/m(2) Ganzfeld background for 5 minutes. Exponential non-linear regression analysis was used to determine seven parameters of DA function. These were time of cone-rod break, cone and rod final thresholds, and magnitude of change and time constant of the cone and rod limbs. The mean cone-rod break time with 95% confidence intervals (CI) was 0.098 (CI: -0.527, 0.330) minutes faster on retest (p = 0.630)). Fourteen of the 16 subjects demonstrated an increase or 'worsening' of their final cone and rod thresholds on the second visit. The mean final threshold differences on retest for the cone limb was 0.105 (CI: 0.032, 0.179) log cd/m(2) (p = 0.008) and 0.093 (CI: -0.039, 0.225,) log cd/m(2) (p = 0.155) for the rod limb. The magnitude of change for the cone limb was 0.016 (CI: -0.122, 0.155) log cd/m(2) (p = 0.805) and -0.196 (CI: -0.435, 0.827) log cd/m2 (p = 0.518) for the rod limb, while the time constant on retest for the cone limb was -0.021 (CI: -0.128, 0.169) minutes, (p = 0.770) and 0.276 (CI: -0.424, 0.976) minutes (p = 0.410) for the rod limb. None of the DA parameters that were examined demonstrated a learning effect of clinical significance between test and retest. None of the changes in mean from test to retest for the seven parameters were found to be statistically significant, and the changes were clinically negligible. Therefore, any change among patients that may occur in dark adaptation between a visit interval may be considered real, and not due to the effect of learning.

A Modified Gauss-Newton Iterative Method for Nonlinear Models with Right-Censored Data

This paper presents Modified Gauss-Newton iteration algorithm for the nonlinear regression models for Failure Time Data set. The convergence of the iteration is proved carefully. Simulation illustrated that our method is available. Our results may be regarded as an extension of Wei (1998) for exponential nonlinear regression models without failure time data.

Activation of Colicin M by the FkpA Prolyl Cis-Trans Isomerase/Chaperone

Colicin M (Cma) is specifically imported into the periplasm of Escherichia coli and kills the cells. Killing depends on the periplasmic peptidyl prolyl cis-trans isomerase/chaperone FkpA. To identify the Cma prolyl bonds targeted by FkpA, we replaced the 15 proline residues individually with alanine. Seven mutant proteins were fully active; Cma(P129A), Cma(P176A), and Cma(P260A) displayed 1%, and Cma(P107A) displayed 10% of the wild-type activity. Cma(P107A), Cma(P129A), and Cma(P260A), but not Cma(P176A), killed cells after entering the periplasm via osmotic shock, indicating that the former mutants were translocation-deficient; Cma(P129A) did not bind to the FhuA outer membrane receptor. The crystal structures of Cma and Cma(P176A) were identical, excluding inactivation of the activity domain located far from Pro-176. In a new peptidyl prolyl cis-trans isomerase assay, FkpA isomerized the Cma prolyl bond in peptide Phe-Pro-176 at a high rate, but Lys-Pro-107 and Leu-Pro-260 isomerized at only <10% of that rate. The four mutant proteins secreted into the periplasm via a fused signal sequence were toxic but much less than wild-type Cma. Wild-type and mutant Cma proteins secreted or translocated across the outer membrane by energy-coupled import or unspecific osmotic shock were only active in the presence of FkpA. We propose that Cma unfolds during transfer across the outer or cytoplasmic membrane and refolds to the active form in the periplasm assisted by FkpA. Weak refolding of Cma(P176A) would explain its low activity in all assays. Of the four proline residues identified as being important for Cma activity, Phe-Pro-176 is most likely targeted by FkpA.

A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater

A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate ( R V), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (±3)% and an average volumetric TCOD removal rate of 6.87 (±3.93) kg TCOD removed/m 3-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98.

Size really does matter—role of gastrojejunostomy in postoperative weight loss

Background Although the published data have clearly related the size of the gastrojejunostomy anastomosis to the subsequent likelihood of a stricture, a correlation between the anastomosis size and postoperative weight loss has not previously been described. Methods A retrospective comparison was made of 124 anastomoses accomplished with the 21-mm circular stapler followed by 100 anastomoses created with the 45-mm linear stapler technique at 6 community hospitals in Southern California. Age, gender, and preoperative weights were not significantly different between the 2 groups. The precise size of the anastomosis created using the linear stapler technique could not be determined, but it was calculated to be slightly larger than a 25-mm circular stapled anastomosis. Both weight loss trends were fit with a 1-phase exponential nonlinear regression analysis. The resulting curves were compared using an F test. A 1-tailed t test was also used to compare the weight loss at 12 months. Results An F test comparison of the exponential weight loss curves generated by the 2 anastomosis groups showed a significantly different trend in weight loss ( P <.001). A 1-tailed t test comparison of the 2 groups at 12 months revealed significantly different results ( p <.0025). Conclusion The results of this study show that the size of the anastomosis has a clear correlation with postoperative weight loss. A smaller opening results in significantly more weight loss.

CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Assessing The Best Track And Field Performances From East To West

Assessing the limits of human athletic performance has been a very attractive topic not only to scientists but also to common people. Human being has been running faster and faster and jumping higher and higher through the world from east to west. Especially in last two decades, the best track and field performances in the Far East countries such as China and Korea have been improving dramatically. One is always wondering whether this trend would continue and when is the end. Is sky the limit for improving the track and field performances? There is little research in the literature to assess the trend of the track and field performances of the east and west countries. A suitable procedure is real demand for the assessment of the limits of human athletic performances. PURPOSE (1) to find an appropriate procedure to assess the limits of human track and field performances, (2) to examine the trend of the best track and field performances in Far East (China and Korea) and to compare the Far East best performances with the world best performances, and to investigate whether sky is really the limit. METHODS Both the male and female best annual performance data of the four selected events over the last three decades (800 meters, 1500 meters, Marathon, and high jump) were used. The annual best performances were recorded based on Chinese, Korean, and world athletes' competitions in national and international levels. Different nonlinear regression models (polynomial and exponential models) and the autoregressive integrated moving average model (ARIMA) in time-series analysis were used to fit the male and female data separately. In the nonlinear model, the best performance is dependent variable and the chronicle year is the independent variable. The best fitting models were identified to explain whether the selected track and field events at Chinese, Korean and international levels have reached an asymptotic level (a stationary time series). RESULTS For both male and female performances, the exponential model was the best fitting model in all the selected events. An asymptotic level has been achieved in all the selected events at both the east country (China and Korea) and international competitions. An ARIMA(0.0.0) model was identified for the last two decades data. CONCLUSION (1) Using the exponential nonlinear regression model and ARIMA time series model is a valid procedure to assess the track and field best performance data. (2) The waiting time to establish new records would be considerably longer in future, and it may show a random phenomenon.