Logistic Function Model Research Articles

Growth-associated and Non-growth-associated Bioethanol Production Kinetics from Nanoadsorbent-Detoxified Pretreated Hydrolysate

Lignocellulosic-based (LCB) bioethanol production is challenged by the presence of inhibitory compounds in pretreated LCB hydrolysates limiting productivity. The negative impact of these inhibitory compounds on LCB bioethanol production kinetics remain understudied. Hence, this study modelled the kinetics of bioethanol fermentation using nanoadsorbent-detoxified potato peel waste (PPW) hydrolysate. Four different fermentation processes under both separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) conditions, including A (SHF with non-detoxified hydrolysate), B (SSF with non-detoxified hydrolysate), C (SHF with detoxified hydrolysate), and D (SSF with detoxified hydrolysate) were evaluated for bioethanol productivity. Higher productivity of 1.23 and 1.16-fold increments were recorded for fermentation processes C and D. Thereafter, the experimental data for cell growth, bioethanol production and substrate utilisation were well-fitted by the logistic function, modified Gompertz, and Luedeking-Piret models respectively. Moreover, the obtained root-mean-square error (RMSE) and mean square error (MSE) were low, while the accuracy factor (AF), bias factor (BF), slope and regression coefficient (R2) were close to 1. The bioethanol production processes were largely growth-associated (α) as α values (g ethanol/g substrate) were higher than β values (g ethanol/g substrate/h). The models were effectively implemented, demonstrating their usefulness to elucidate bioethanol productivity kinetics for improved process design and the development of large-scale bioethanol production.Graphical

Fatigue Life of Pre-Cut Seam Asphalt Mixture Composite Beams: A Combined Study of Fatigue Damage Evolution and Reflective Cracking Extension

This study investigated the impact of reflective cracking on the fatigue performance of asphalt pavements after milling and resurfacing under various conditions. Fatigue life was assessed through four-point flexural fatigue tests, while the crack extension pattern of composite beams was analyzed by digital image correlation (DIC) at both macroscopic and microscopic scales. Evaluation parameters such as stress ratios, immersion time, porosity, and types of viscous oils were assessed. A fatigue life prediction model of composite beams was established, accounting for the combined influence of these factors. To enhance the accuracy of determining composite beam failure, the critical fatigue damage was calculated by defining the damage variable in terms of the dynamic modulus. A nonlinear fatigue damage model was proposed, incorporating this critical damage under the combined influence of various factors. Additionally, a modified logistic function model was developed to describe the relationship between crack extension and failure life under different stress ratios, porosities, and viscous layer oil conditions. It was found that the modulus decay curves and the crack extension curves intersected at different stress levels as the life ratio increased. At the intersection, the modulus ratios were consistently around 0.55, marking the transition of the specimen from a stable to an unstable state. Beyond this point, the crack rapidly propagated, leading to a sharp reduction in the modulus until the specimen ultimately failed. Our results provide a basis for timing and conservation decisions.

PET/CT-Based Absorbed Dose Maps in 90Y Selective Internal Radiation Therapy Correlate with Spatial Changes in Liver Function Derived from Dynamic MRI.

Functional liver parenchyma can be damaged from treatment of liver malignancies with 90Y selective internal radiation therapy (SIRT). Evaluating functional parenchymal changes and developing an absorbed dose (AD)-toxicity model can assist the clinical management of patients receiving SIRT. We aimed to determine whether there is a correlation between 90Y PET AD voxel maps and spatial changes in the nontumoral liver (NTL) function derived from dynamic gadoxetic acid-enhanced MRI before and after SIRT. Methods: Dynamic gadoxetic acid-enhanced MRI scans were acquired before and after treatment for 11 patients undergoing 90Y SIRT. Gadoxetic acid uptake rate (k1) maps that directly quantify spatial liver parenchymal function were generated from MRI data. Voxel-based AD maps, derived from the 90Y PET/CT scans, were binned according to AD. Pre- and post-SIRT k1 maps were coregistered to the AD map. Absolute and percentage k1 loss in each bin was calculated as a measure of loss of liver function, and Spearman correlation coefficients between k1 loss and AD were evaluated for each patient. Average k1 loss over the patients was fit to a 3-parameter logistic function based on AD. Patients were further stratified into subgroups based on lesion type, baseline albumin-bilirubin scores and alanine transaminase levels, dose-volume effect, and number of SIRT treatments. Results: Significant positive correlations (ρ = 0.53-0.99, P < 0.001) between both absolute and percentage k1 loss and AD were observed in most patients (8/11). The average k1 loss over 9 patients also exhibited a significant strong correlation with AD (ρ ≥ 0.92, P < 0.001). The average percentage k1 loss of patients across AD bins was 28%, with a logistic function model demonstrating about a 25% k1 loss at about 100 Gy. Analysis between patient subgroups demonstrated that k1 loss was greater among patients with hepatocellular carcinoma, higher alanine transaminase levels, larger fractional volumes of NTL receiving an AD of 70 Gy or more, and sequential SIRT treatments. Conclusion: Novel application of multimodality imaging demonstrated a correlation between 90Y SIRT AD and spatial functional liver parenchymal degradation, indicating that a higher AD is associated with a larger loss of local hepatocyte function. With the developed response models, PET-derived AD maps can potentially be used prospectively to identify localized damage in liver and to enhance treatment strategies.

Prediction of anaerobic degradation kinetics based on substrate composition of lignocellulosic biomass

This study presents a comprehensive biochemical predictive approach for assessing biogas production kinetics across ten lignocellulosic substrates in batch operation. The methodology employs a range of kinetic and regression models, all grounded in the substrates' chemical composition. Among the kinetic models, the cone model demonstrated superior performance, achieving an average error of 1.67 % in describing biogas production from all substrates. The quadratic Monod type model followed closely, with an error of 1.96 %. Among the regression models, on the other hand, the logistic function model exhibited enhanced predictive capabilities, yielding an average error of 6.02 %, while the Chen and Hashimoto one showed a higher error of 60.54 %. The findings underscore the potential of precise biogas production forecasting and tracking the daily rates of gas generation, rather than solely relying on cumulative gas yields at the end of the process.

Co-production and enhancement of biogas and biohydrogen by optimizing NaOH-urera pretreatment conditions for kitchen waste

Kitchen waste (KW) constitutes a major portion of municipal solid waste, posing an imminent environmental challenge. Given its high water and organic content, KW is suitable for Anaerobic Digestion (AD) prior to its disposal, as it reduces Chemical Oxygen Demand (COD) and simultaneously generates biogas. The unique contribution of this study is the investigation of NaOH-urea pretreatment as a method to enhance AD of KW and sludge, ultimately leading to more efficient and environmentally friendly waste disposal. NaOH-urea pretreatment is selected for its ability to improve hydrolysis, increase organic matter solubilization, and enhance nutrient balance, ultimately boosting biogas yield during AD. Response surface methodology (RSM) was used for optimizing pretreatment conditions. AD experiments were performed batch-wise. For optimization, pretreatment time, temperature, and NaOH-urea concentration were selected as independent variables. RSM results indicated that optimum NaOH-urea concentration, time, and temperature conditions were 30 g/L, 30 min, and −5 °C, respectively. At optimum conditions, cumulative biogas was 316 ml, whereas biohydrogen % (v/v) was 36.21 %. Furthermore, the Modified Gompertz Model (MGM) and Logistic Function Model (LFM) were used to observe the efficacy of NaOH-urea pre-treatment of KW., The reaction kinetics were also discussed. Akaike's Information Criterion was employed to determine which model best fits the obtained experimental data. It was found that MGM is better than LFM for this study. Through experimental investigations and numerical modeling, this research provides insights into optimizing biogas and biohydrogen production from KW.

LECOM (Lead Extraction COMplexity): a new scoring system for prediction of a difficult procedure

Abstract Background Transvenous lead extraction (TLE) can become far more complex when unanticipated difficulties arise. Aim The aim of this study was to develop a simple scoring system that allows prediction of difficulty and complexity of this important procedure. Methods Based on a retrospective analysis of 3741 extraction procedures (extended fluoroscopy time, need for second-line instruments, and advanced techniques and instruments), a 5-point Complex Indicator of Difficulty of T(LE) Procedure (CID-TLEP) scale was developed. Two or more points on the CID-TLEP scale indicated a higher level of procedure complexity. Results Multivariate regression analysis showed that patient age below 51 years at first CIED implantation, number of abandoned leads, number of previous CIED-related procedures, passive fixation leads to be extracted, multiple leads to be extracted, and a ratio of dwell time of oldest extracted lead to patient age during TLE of &amp;gt;0.13 are significant predictors of higher levels of lead extraction complexity. The ROC analysis demonstrated that the total number of points (the sum of the odds ratios of the above variables) of &amp;gt;9.697 indicated a 21.83% higher probability of complex TLE (sensitivity 74.08%, specificity 74.46%). Finally, the logistic function model was employed, and a simple equation for Lead Extraction COMplexity was constructed to predict the probability of a difficult procedure. The risk of complex extraction (in percentage) was calculated as [1/(1 + 55.34·0.754X)]·100 (P &amp;lt; 0.001). Conclusion (1) The LECOM score can effectively predict the risk of a difficult transvenous lead extraction procedure, and (2) Predicting the probability of a more complex procedure may help clinicians in planning lead removal and improving patient management. (3) Simple on-line calculator should be available to predict a difficulty of planned TLE procedure and to allow to send more demanding patient to more experienced high-volume centre.

Parametric Evaluation of Biogas Production from Sawdust and Treated Human Faeces: Optimal Custom Design Approach

As energy demand is increasing astronomically, fossil-based fuels became scarce and more expensive, and carbon dioxide emission levels became of greater concern. Thus, the need to convert the co-digestion of sawdust and treated human faeces to produce biogas, a carbon-neutral gas to replace fossil fuels. From the outcome, the logistic function model performance was satisfactory in the simulation of the experimental process. The codigestion of sawdust and human faeces at 2.5 kg each produced the highest biogas output of 5.43 L using Optimal (Custom) Design of Mixture approach but the human faeces alone gave a better biogas quality. Additionally, the high coefficient of determination (R2) ranging between 0.9959 - 0.9879 and standard deviation of 0.023 - 0.321 for temperature - biogas yield respectively, fall within an ideal range. Thus, the model proved to be a useful tool in predicting anaerobic digestion and biogas production processes. Furthermore, the FTIR analysis of sawdust and human faeces indicates their potential to produce biogas if subjected to anaerobic environment. In addition, the GCMS analyzed the % of methane to be 58.40% which is within accepted range. The biogas was tested with a locally fabricated gas burner for flammability and it ignited with a blue flame. Thus, this experiment can be translated to an industrial scale biogas production for cooking and electricity to boost local economy and alleviate poverty.

Optimization of biomethane production from lignocellulosic biomass by a developed microbial consortium

Biomethane production from lignocellulosic biomass via anaerobic digestion (AD) is a promising avenue for bioenergy. However, maximizing biomethane yield using developed microbial consortium (DMC) remains a complex challenge. This study aims to elucidate the comparative efficacy of a purposefully DMC under controlled conditions for AD of kallar grass (KG), rice husk (RH) and wheat residue (WR). The DMC encompasses Bacteroidetes, Firmicutes, Proteobacteria, Euryarchaeota, and Chloroflexi, with methanogens representing 14.6% of the community. Thorough physico-chemical characteristics and ultimate analyses provided comprehensive insights into compositions and bio-methane potentials prediction of biomasses. The digester revealed higher biomethane potentials (BMPs) with short lag phase and retention time and showed methane content of 61–64.6% during the first week of AD. The amount of volatile fatty acids (VFA) did not exceed the threshold levels, which facilitated the smooth operation of AD. The BMP was found highest in KG (289.7 mL/g VS), followed by RH (283.3 mL/g VS), and WR (269.7 mL/g VS) which is significantly higher than previously reported studies. The modified Gompertz model showed the best fit, followed by logistic and transference function models. The observed results signpost the tremendous potential of waste biomass particularly KG, RH and WR to produce biomethane by DMC. This study provides crucial insights for optimizing BMP emphasizing the promising prospects of KG, RH, and WR for sustainable bioenergy applications.

Experimental simulation and kinetic modeling of bioenergy potential of Eichornia crassipes biomass from the Volta River basin of Ghana under mesophilic conditions

The study presents the results of the laboratory experiment performed on the anaerobic digestion of water hyacinth biomass harvested from the Volta River basin of Ghana and Fruit waste sludge as the inoculum. Batch mode of experiment was performed under mesophilic conditions (29 ± 3 °C) with a hydraulic retention time of 61 days. The experimental setup was made up of three (3) fermenter bottles of 5.0 L capacity namely F1, F2 and F3. F1 served as the control whiles F2 and F3 served as the test fermenter bottles. Each of the fermenter bottles was fitted with BlueSens methane sensor and BlueVcount flowmeter for the measurement of methane composition and biogas volumes, respectively. The Theoretical Biomethane Potential (BMP) of the water hyacinth biomass was estimated to be 422.23 ml/gVS. The experimental BMP was determined to be 402.62 ml CH4/gVS and 356.03 ml CH4/gVS for F2 and F3, respectively. Consequently, a biodegradability value of 95.36 and 84.32 % was obtained from F2 and F3, respectively. Moreover, the F2 fermenter bottle recorded a cumulative net methane and biogas volumes of 5576.3 ml and 12,014 ml, respectively. Likewise, a total net cumulative methane and biogas volumes of 4931.0 ml and 11,384 ml were produced from the F3 fermenter bottle. Using the First order kinetic model, an average value of hydrolysis constant for water hyacinth biomass was determined as 0.051 day−1. Furthermore, the modified Gompertz model, the logistic function model and Transference function models were used to fit the experimental cumulative biogas and methane production data. The outcome of the study shows that, the production of biogas from water hyacinth biomass harvested the Volta River basin of Ghana could offer sustainable control solutions to its invasion on water bodies whiles providing a cheap and reliable means of biofuel to the riparian communities.

Eye growth pattern of myopic children wearing spectacle lenses with aspherical lenslets compared with non-myopic children.

To evaluate eye growth of children wearing spectacle lenses with highly aspherical lenslets (HAL), slightly aspherical lenslets (SAL) and single-vision lenses (SVL) compared to eye growth patterns in non-myopes in Wenzhou, China. The randomised trial had 170 myopic children (aged 8-13 years) randomly assigned to the HAL, SAL or SVL group. Normal eye growth was examined using 700 non-myopic schoolchildren (aged 7-9 years) in the Wenzhou Medical University-Essilor Progression and Onset of Myopia (WEPrOM) cohort study using logistic function models. Slow, normal and fast eye growth was defined as range of values <25th, 25th-75th and >75th percentiles, respectively. The predicted upper limits of slow eye growth (25th percentile) among non-myopes aged 7-10 years and 11-13 years were 0.20-0.13 and 0.08-0.01 mm (after 2-year period; 0.37-0.33 and 0.29-0.14 mm), respectively, while the upper limits of normal eye growth (75th percentile) were 0.32-0.31 and 0.28-0.10 mm (after 2-year period; 0.58-0.55 and 0.50-0.24 mm), respectively. The 2-year trial had 157 children, 96 of whom wore their lenses full time (everyday ≥12 h/day). The mean 2-year axial length change for HAL, SAL and SVL was 0.34, 0.51 and 0.69 mm (0.28, 0.46 and 0.69 mm in full-time wear), respectively. Slow eye growth was found in 35%, 17% and 2% (44%, 29% and 3% in full-time wear); normal eye growth in 35%, 26% and 12% (44%, 32% and 9% in full-time wear) and fast eye growth in 30%, 57% and 86% (12%, 39% and 88% in full-time wear), respectively (p < 0.001). The eye growth pattern in approximately 90% wearing HAL full time (compared with about 10% wearing SVL full time) was similar or slower than that of non-myopic children both after 1- and 2-year periods.

Intermittent mixing facilitates energy recovery and low carbon emissions from high-solids anaerobic co-digestion of food waste and sewage sludge

Mixing inside high-solids anaerobic co-digestion (HS-AcoD) is essential for process feasibility and economic sustainability. This study developed a proper energy assessment for a high solids anaerobic digestion-combined heat and power (AD-CHP) system to clarify the impact of mixing on methane production, energy recovery and carbon emission reduction during HS-AcoD of food waste (FW) and sewage sludge (SS). Results indicated that intermittent mixing enhanced methane production and shortened the lag phase compared with unmixing and continuous mixing. The modified Gompertz model yielded better fitting than the logistic and transfer function models via kinetic analysis. In the case of a scaled-up AD-CHP system, intermittent mixing with 15 min/h boosted energy output (2.14 × 103 kWh/tonne VS) at 21 d. Compared with continuous mixing, 15 min/h intermittent mixing at 13 d improved the energy recovery ratio from 31% to 45% and carbon emissions reduction from 0.25 t CO2/t VS to 0.35 t CO2/t VS. For a high availability of FW and SS in China, the AD-CHP system with intermittent mixing would have higher net energy output (128.4 × 109 kWh) and carbon emission reduction (15.3 million tonnes) by the full utilization of these biomasses. These results are expected to provide theoretical support for the high solids AD-CHP system in determining the optimal mixing strategy with maximum energy production for FW and SS disposal.

Geochemical mapping by stream sediments of the NW portion of Quadrilátero Ferrífero, Brazil: Application of the exploratory data analysis (EDA) and a proposal for generation of new gold targets in Pitangui gold district

Quadrilátero Ferrífero (QF) is a worldwide known metallogenetic province and has in its NW portion the Pitangui greenstone belt, one of the new frontiers for gold exploration in that region, presenting important gold deposits, such as Turmalina and São Sebastião. Among several actions of the geological and mineral resources projects carried out by Geological Survey of Brazil (SGB/CPRM) in the QF region, a high-density geochemical survey (1 sample/4 km2) by stream sediments was accomplished in its NW portion to better understand the behavior and distribution of chemical elements along the main geological units, especially those related to gold mineralization (Au, As, Sb, Bi, etc.). The exploratory data analysis (EDA), regarding uni-, bi-, and multivariate analyses, point to an important chemical control of the stream sediments by the geological units and help to describe some lithological features and to understand the behavior and distribution of hydrothermal processes and gold mineralization. This procedure defined the main geochemical associations, including the association As, Sb, Cu and Bi linked to Ni, Cr and Co, interpreted as gold mineralization hosted in the metavolcanosedimentary lithotypes. The staged factor analysis (SFA) considering compositional data (CoDa), logistic function, and fractal model approaches provided the generation of the geochemical mineralization probability index (GMPI), which produce a GMPI map for gold mineralization. The calculations did not take into account the gold concentrations but only the pathfinder elements and the model validation by the prediction-area plot, confirm the high performance of this approach. The gold mineralization map, in addiction to satisfactorily predicting the location of most known gold occurrences, was able to indicate new potential targets for prospecting new gold deposits.

Modelling Plant Growth Based on Gompertz, Logistic Curve, Extreme Gradient Boosting and Light Gradient Boosting Models Using High Dimensional Image Derived Maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) Phenomic Data

Modelling of plant growth is vital for hypotheses testing and carrying out virtual plant growth and development experiments, which may otherwise take a long time under field conditions. Modelling of plant growth has been aggravated by new phenotyping platforms that generate high dimensional data non-destructively over the entire growth time of a plant using a set of camera system. Such platforms generate high-throughput phenomic data, which is complex and constitute many features collected at multiple growth points for the same plant. However, the classical models are limited in that they can only model a single feature at a time. The objective of this study was to apply dynamic plant growth models that could be used to dissect complex relationships between plant growth and development using several modelling strategies. These included sigmoid, light GBM and XGBoost models. The image derived phenomic data was obtained from the Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben, Germany. The models were fitted using R statistical software and compared based on RMSE, R-squared, AIC and BIC performance metrics. The results showed that the XGBoost (RMSE = 2.1641) and Light GBM (RMSE = 2.7776) performed better than the Gompertz (RMSE = 3.8378) and the logistic function (RMSE = 3.8378) models in modelling maize plant growth. The XGBoost model (RMSE = 2.1641) showed better performance than Light GBM model (RMSE = 2.7776) in modelling maize plant growth. The Gompertz model using plant volume had AIC and BIC values for 139738.3 and 139763.4, respectively. The Gompertz model for plant side area had AIC and BIC values for 98436.15 and 98461.31, respectively. The logistic function model for plant volume had AIC and BIC values for 139749.2 and 139774.4, respectively. The logistic function model for plant side area had AIC and BIC values for 98415.95 and 98441.11, respectively. The Gompertz model and logistic function models showed almost the same performance in modelling maize plant growth. The non-parametric models, the XGBoost and light GBM, were found to perform better than the classical models (Gompertz and logistic functions) in modelling maize plant growth. Therefore, the study recommends the use of XGBoost as a generic model to fit high dimensional and complex phenotypic data in modelling plant growth and prediction of plant biomass yield at different growth points.

Facial emotion perception abilities are related to grey matter volume in the culmen of cerebellum anterior lobe in drug-naïve patients with first-episode schizophrenia.

Impaired capability for understanding and interpreting the expressions on other people's faces manifests itself as a core feature of schizophrenia, contributing to social dysfunction. With the purpose of better understanding of the neurobiological basis of facial emotion perception deficits in schizophrenia, we investigated facial emotion perception abilities and regional structural brain abnormalities in drug-naïve patients with first-episode schizophrenia, and then examined the correlation between them. Fifty-two drug-naive patients with first-episode schizophrenia and 29 group-matched healthy controls were examined for facial emotion perception abilities assessed with the Facial Emotion Categorization and performed magnetic resonance imaging. The Facial Emotion Categorization data were inserted into a logistic function model so as to calculate shift point and slope as outcome measurements. Voxel-based morphometry was applied to investigate regional grey matter volume (GMV) alterations. The relationship between facial emotion perception and GMV was explored in patients using voxel-wise correlation analysis within brain regions that showed a significant GMV alterations in patients compared with controls. The schizophrenic patients performed differently on Facial Emotion Categorization tasks from the controls and presented a higher shift point and a steeper slope. Relative to the controls, patients showed GMV reductions in the superior temporal gyrus, middle occipital gyrus, parahippocampa gyrus, posterior cingulate, the culmen of cerebellum anterior lobe, cerebellar tonsil, and the declive of cerebellum posterior lobe. Importantly, abnormal performance on Facial Emotion Categorization was found correlated with GMV alterations in the culmen of cerebellum anterior lobe in schizophrenia. This study suggests that reduced GMV in the culmen of cerebellum anterior lobe occurs in first-episode schizophrenia, constituting a potential neuropathological basis for the impaired facial emotion perception in schizophrenia.

The red blood cell damage after long-term exposure to shear stresses.

Artificial cardiovascular devices, such as vascular stents, artificial valves, and artificial hearts, can rebuild human cardiovascular functionalities via rebuilding the blood flow passing through these devices. To evaluate the red blood cells (RBCs) damage induced by a non-physiological blood flow in these devices, many hemolysis models have been proposed, of which the most popular one is a power function model. However, it was found that the newly obtained experimental data often did not match the existing power function model. In addition, the experimental period was usually short and the summarized power function model cannot reflect the RBCs damage after long-term exposure to shear stress. To address this issue, in this study a shear device was established on a torque rheometer; the changes of plasma free hemoglobin (FHB) of sheep blood under the shear stress from 10 to 70Pa and exposure time from 5 to 30min were recorded and compared. The results showed that as the shear stress and exposure time increased, FHB also increased, but the increase rate gradually decreased. As a result, after undergoing high shear stress or a long period of exposure time, FHB eventually became stable. Obviously, the existing power function model cannot describe this FHB change. In the current study, we used a sigmoidal logistic function model to describe the FHB increment upon the increase of shear stress and long exposure time. The results showed that the proposed model can provide better predictions of hemolysis, particularly in these cases under long exposure time.

Bioconversion of wheat straw to energy via anaerobic co-digestion with cattle manure in batch-mode bioreactors (Experimental investigation and kinetic modeling)

Rising concerns about environmental issues including pollution control, waste management and energy demands have drawn the world’s attention to the merits of renewable energy sources. In this regard, the anaerobic digestion of organic wastes for the purpose of bioenergy production is of particular importance. This study investigated the potential for biogas production from the co-digestion of a lignocellulosic biomass (wheat straw) with cattle manure after physical and chemical pretreatment at two Total Solids (TS) levels of 3% and 6% and three Inoculum-to-Substrate Ratios (ISRs) of 1, 2 and 4 (based on volatile solids). The materials were loaded into single-stage 1-liter digesters in batch mode under mesophilic conditions (35 °C) for 125 days. The best anaerobic digestion performance was observed in the treatment with 3%TS and ISR1, where the biogas and methane production yields were 0.510 and 0.307 m3/kg-VSadded, respectively. At both TS levels, biogas production increased with the increase in the Volatile Solids (VS) content of wheat straw and the decrease in ISR. This can be due to easier access of microorganisms to the organic matter and better microorganism-substrate balance, which ultimately result in improved digestion performance. The kinetic modeling results showed that the modified Gompertz model and the logistic function model had the highest level of consistency with the experimental results for TS3% and TS6%, respectively. The first-order kinetic model failed to provide a good estimate of biogas production from the co-digestion of wheat straw with cattle manure, irrespective of the TS level.

The Relationship Between Knowledge, Attitude, Practice, and Fall Prevention for Childhood in Shanghai, China.

BackgroundEarly childhood fall is a pressing global public health problem and one of the leading causes of child injury. China has a high proportion of children and a high burden of illness from falls. Therefore, educational interventions to prevent childhood fall would be beneficial.MethodsWe used the outcome of knowledge, attitude and practice questionnaire, which was conducted by Pudong New District of Shanghai Municipal Government, to summarize demographic and baseline characteristics grouped by intervention or not, and analyzed descriptive statistics of continuous and categorical variables. A logistic stepwise function model was established to study the influence of different covariables on the degree of injury, and AIC/BIC/AICC was used to select the optimal model. Finally, we carried out single-factor analysis and established a multifactor model by the stepwise function method.ResultsAttitude and actual behavior scores had significant differences. The intervention and control groups had 20.79 ± 3.20 and 20.39 ± 2.89 attitude scores, respectively. Compared to the control group (5.97 ± 1.32), the intervention group had higher actual behavior scores (5.75 ± 1.50). In the univariate analysis results, fathers' education level, mothers' education level, actual behavior and what cares for children had a significant influence on whether children got injured. In multivariate analysis, attitude had a positive influence on whether injured [odds ratio: 1.13 (1.05–1.21), P < 0.001].ConclusionEducational intervention for children and their guardians can effectively reduce the risk of childhood falls, and changes in behavior and attitude are the result of educational influence. Education of childhood fall prevention can be used as a public health intervention to improve children's health.

Enzyme pretreatment of yard waste to improve anaerobic biodegradability: Modeling the interactive effects of enzyme dose, treatment temperature and treatment duration on delignification

Laccase pretreatment of yard waste (YW) was conducted for improving its anaerobic biodegradability. The optimum pretreatment condition based on response surface methodology for maximum delignification was: solid loading 19.7% (w/v) of total solid, treatment duration 9 h, and pretreatment temperature 44.5 °C. The mechanism of biomass delignification was further investigated by the change in physical and chemical properties. Specifically the increase in water retention value (2.05 times), specific porosity (1.72 times), and crystallinity index (1.25 times) in pretreated YW indicates the capability of laccase pretreatment to remove lignin selectively. The methane yield after 30 days of anaerobic digestion for untreated YW was 294 ± 12 mL/g volatile solid (VS) and for laccase pretreated yard waste at the optimum condition was 405 ± 15 mL/gVS. The Modified Gompertz model showed the best fit for untreated yard waste however for pretreated YW logistic function model was the best. The energy balance analysis of the studied method confirmed a positive energy gain of 1.6 kJ/g VS indicates further validation in field scale studies.

Obtaining a light intensity regulation target value based on the tomato dry weight model

The subsequent plant growth is determined by tomato seedling quality which is affected by light intensity. The present study proposes a method for obtaining the regulatory target value by using the U-chord length curvature method based on the dry weight model-fitted curve characteristics to obtain the optimum light intensity at the seedling stage. Tomato seedlings were selected as the experimental material, and six photosynthetic photon flux densities (PPFDs) were set under completely artificial side lighting. Depending on the scattering trend, the tomato shoot dry weights (SDWs) were fitted by the polynomial, logarithmic, and half logistic function models after optimization of parameters by using the differential evolution algorithm (DE-HLFM). According to the curve variation characteristics, the discrete curvature values corresponding to the models calculated using the U-chord length curvature method were 335, 108, and 241.3 µmol·m−2·s−1, respectively. Physiological tomato growth indicators exhibited that L4 had the smallest root–shoot ratio and the highest level of seedling index and SDW significance. Moreover, L4 had the highest SDW per PPFD improvement efficiency. Thus, tomato seedlings under L4 exhibited the best quality and highest efficiency among those under the six PPFDs. DE-HLFM optimization was closest to the optimal PPFD L4 (243.17 ± 4.37 µmol·m−2·s−1), indicating that DE-HLFM is the most suitable model with a coefficient of determination (R2) of 0.99897 and root-mean-square error of 0.00554. Thus, the PPFD corresponding to the maximum curvature point of the appropriate model exhibited physiological and production regulation significance. In conclusion, based on the curve characteristics, the curve feature point obtained using the U-chord curvature method can be used as the control target value for the precise control of the artificial controllable light environment.

Photo fermentative biohydrogen production potential using microalgae-activated sludge co-digestion in a sequential flow batch reactor (SFBR).

Biohydrogen (bioH2) is a sustainable energy source that can produce carbon-free energy upon combustion. BioH2 can be generated from microalgae by photolytic and anaerobic digestion (AD) pathways. The AD pathway faces many challenges when scaling up using different bioreactors, particularly the continuous stirred tank reactor (CSTR) and sequential flow batch reactor (SFBR). Therefore, the performance characteristics of SFBR were analysed in this study using Chlorella vulgaris and domestic wastewater activated sludge (WWAS) co-culture. An organic loading rate (OLR) of 4.7 g COD L-1 day-1 was fed to the SFBR with a hydraulic retention time (HRT) of five days in the presence of light under anaerobic conditions. The pH of the medium was maintained at 6 using a pH controller for the incubation period of 15 days. The maximum bioH2 concentrations of 421.1 μmol L-1 and 56.6 μmol L-1 were observed in the exponential and steady-state phases, respectively. The effluent had an unusually high amount of acetate of 16.6 g L-1, which remained high with an average of 11.9 g L-1 during the steady state phase. The amount of bioH2 produced was found to be inadequate but consistent when operating the SFBR with a constant OLR. Because of the limitations in CSTR handling, operating a SFBR by optimizing OLR and HRT might be more feasible in operation for bioH2 yield in upscaling. A logistic function model was also found to be the best fit for the experimental data for the prediction of bioH2 generation using co-culture in the SFBR.