Predictive Value Of Model Research Articles

Immune checkpoints related-LncRNAs can identify different subtypes of lung cancer and predict immunotherapy and prognosis.

Non-small cell lung cancer is the most common subtype of lung cancer in the world. However, the survival rate of non-small cell lung cancer patients remains low currently. Immune checkpoint and long non-coding RNAs are emerging as critical roles in prognostic significance and the immunotherapeutic response of non-small cell lung cancer. It is critical to discern LncRNAs related with immune checkpoints in patients with Non-small cell lung cancer. In this study, immune checkpoint-linked LncRNAs were determined and achieved by the co-expression analysis. Immune checkpoint-linked LncRNAs with noteworthy prognostic value (P < 0.05) gained were next utilized to separate into two cluster by non-negative matrix factorization (NMF). Univariate and a least absolute shrinkage and selection operator were applied to construct an immune checkpoint-linked LncRNAs model. Kaplan-Meier analysis, Gene Set Enrichment Analysis, and the nomogram were utilized to investigate the LncRNAs model. Lastly, the capability immunotherapy and chemotherapy prediction value of this risk model were also estimated. The model consisting of ten immune checkpoint-related LncRNAs was acknowledged to be a self-determining predictor of prognosis. Through regrouping the NSCLC patients by this model, difference between them more efficiently on immunotherapeutic response, tumor microenvironment and chemotherapy response could be discovered. This risk model related to the immune checkpoint-based LncRNAs may have an excellent clinical prediction for prognosis and the immunotherapeutic response in patients with NSCLC. We performed an integrative analysis of LncRNAs linked with immune checkpoints and emphasized the significance of NSCLC subtypes classification, immune checkpoints related LncRNAs in estimating the tumor microenvironment score, immune cell infiltration of the tumor, immunotherapy, and chemotherapy response.

A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

Due to population growth and human activities, water shortages have become an increasingly serious concern in the North China Plain, which has become the world’s largest underground water funnel. Because the yield per unit area, planting area of crops, and effective precipitation in the region are uncertain, it is not easy to plan the amount of irrigation water for crops. In order to improve the applicability of the uncertainty programming model, a hybrid LSTM-CPP-FPP-IPP model (long short-term memory, chance-constrained programming, fuzzy possibility programming, interval parameter programming) was developed to plan the irrigation water allocation of irrigation system under uncertainty. The LSTM (long short-term memory) model was used to predict crop yield per unit area, and CPP-FPP-IPP programming (chance-constrained programming, fuzzy possibility programming, interval parameter programming) was used to plan the crop area and the effective precipitation under uncertainty. The hybrid model was used for the crop production profit of winter wheat and summer corn in five cities in the North China Plain. The average absolute error between the model prediction value and the actual value of the yield per unit area of winter wheat and summer maize in four cities in 2020 was controlled within the range of 14.02 to 696.66 kg/hectare. It shows that the model can more accurately predict the yield per unit area of crops. The planning model for the benefit of irrigation water allocation generated three scenarios of rainfall level and four planting intentions, and compared the planned scenarios with the actual production benefits of the two crops in 2020. In a dry year, the possibility of planting areas for winter wheat and summer corn is optimized. Compared with the traditional deterministic planning method, the model takes into account the uncertain parameters, which helps decision makers seek better solutions under uncertain conditions.

Modelling of autogenerative high-pressure anaerobic digestion in a batch reactor for the production of pressurised biogas

BackgroundPressurised anaerobic digestion allows the production of biogas with a high content of methane and, at the same time, avoid the energy costs for the biogas upgrading and injection into the distribution grid. The technology carries potential, but the research faces practical constraints by a.o. the capital investment needed in high-pressure reactors and sensors and associated sampling limitations. In this work, the kinetic model of an autogenerative high-pressure anaerobic digestion of acetate, as the representative compound of the aceticlastic methanogenesis route, in batch configuration, is proposed to predict the dynamic performance of pressurised digesters and support future experimental work. The modelling of autogenerative high-pressure anaerobic digestion in batch configuration, which is not extensively studied and simulated in the present literature, was developed, calibrated, and validated by using experimental results available from the literature.ResultsUnder high-pressure conditions, the assessment of the Monod maximum specific uptake rate, the half-saturation constant and the first-order decay rate was carried out, and the values of 5.9 kg COD kg COD−1 d−1, 0.05 kg COD m−3 and 0.02 d−1 were determined, respectively. By using the predicted values, excellent fittings of the final pressure, the CH4 molar fraction and the specific methanogenic yield calculation were obtained. Likewise, the variation in the gas–liquid mass transfer coefficient by several orders of magnitude showed negligible effects on the model predictive values in terms of methane molar fraction of the produced biogas, while the final pressure seemed to be slightly influenced.ConclusionsThe proposed model allowed to estimate the Monod maximum specific uptake rate for acetate, the half-saturation rate for acetate and the first-order decay rate constant, which were comparable with literature values reported for well-studied methanogens under anaerobic digestion at atmospheric pressure. The methane molar fraction and the final pressure predicted by the model showed different responses towards the variation of the gas–liquid mass transfer coefficient since the former seemed not to be affected by the variation of the gas–liquid mass transfer coefficient; in contrast, the final pressure seemed to be slightly influenced. The proposed approach may also allow to potentially identify the methanogens species able to be predominant at high pressure.

Comparison of Response Surface Methodology and Artificial Neural Network approach in predicting the performance and properties of palm oil clinker fine modified asphalt mixtures

Recently with the increase in traffic loading, the traditional materials used for road construction deteriorate at a faster rate due to repetitive traffic loading which greatly necessitates bitumen modification to improve its quality. Amid an ever-increasing waste generation and disposal crisis, researchers came up with multiple ideas, however, the implementation was halted due to different practitioners' policies. Palm oil clinker (POC) waste is a prevalent waste dumped around the oil palm mill that pollutes the environment. To harness sustainability, this study utilizes varying dosages of POC fine (POCF) at 2%, 4%, 6%, and 8% to produce the POCF modified bitumen (POCF-MB). Also, the conventional and microstructure properties were evaluated. The objective of this study is to utilize response surface methodology (RSM) and artificial neural networks (ANN) to optimize and predict the stiffness modulus and rutting characteristic of asphalt mixtures prepared with POCF modified bitumen (POCF-MB). The conventional test results revealed that the incorporation of POCF improves the plain bitumen properties with enhanced stiffness and temperature susceptibility. Microstructural analysis highlighted that a new functional group Si-OH was formed because of the crystalline structure of Si-O that indicates bitumen properties enhancement with POCF inclusion. Two input and output variables were considered which are POCF dosage, test temperature, and stiffness modulus and rutting depth respectively. Results showed that all mixtures containing POCF-MB show better performance than the control mixture. Though, 6% POCF dosage shows improved performance compared to other mixtures increasing stiffness by 33.33% and 57.42% respectively at 25 °C and 40 °C, while rutting at 45 °C shows increased resistance by 25.91%. For both approaches, there was a high degree of agreement between the model-predicted values and actual. For the model statistical performance index, the RSM indicates that R2 for stiffness and rutting response were (99.700 and 99.668), RMSE (266.091 and 0.597), and MRE (68.793 and 3.841) respectively. The ANN R2 for stiffness and rutting response were (99.972 and 99.880), RMSE (61.605 and 0.280), and MRE (12.093 and 2.044) respectively. The ANN use 70% data for training, 15% data for testing, and 15% data for validation processes. The ANN model outperforms the RSM model for the prediction of POCF-MB asphalt mixtures' stiffness modulus and rutting properties.

A Regression Model to Predict Linezolid Induced Thrombocytopenia in Neonatal Sepsis Patients: A Ten-Year Retrospective Cohort Study.

Background: Linezolid-induced thrombocytopenia (LIT) is the main factor limiting the clinical application of linezolid (LZD). The incidence and risk factors of LIT in neonatal patients were possibly different from other populations based on pathophysiological characteristics. The purpose of this study was to establish a regression model for predicting LIT in neonatal sepsis patients. Methods: We retrospectively included 518 patients and divided them into the LIT group and the non-LIT group. A logistic regression analysis was used to analyze the factors related to LIT, and a regression model was established. A receiver operating characteristic (ROC) curve was drawn to evaluate the model’s predictive value. We prospectively collected 39 patients’ data to validate the model and evaluate the effect of LZD pharmacokinetics on LIT. Results: Among the 518 patients, 103 patients (19.9%) developed LIT. The Kaplan–Meier plot revealed that the overall median time from the initiation of LZD treatment to the onset of LIT in preterm infants was much shorter when compared with term infants [10 (6, 12) vs. 13 (9.75, 16.5), p = 0.004]. Multiple logistic regression analysis indicated that the independent risk factors of LIT were lower weight at medication, younger gestational ages, late-onset sepsis, necrotizing enterocolitis, mechanical ventilation, longer durations of LZD treatment, and lower baseline of platelet level. We established the above seven-variable prediction regression model and calculated the predictive probability. The ROC curve showed that the predicted probability of combined body weight, gestational age, duration of LZD treatment, and baseline of platelet had better sensitivity (84.4%), specificity (74.2%), and maximum AUC (AUC = 0.873). LIT occurred in 9 out of 39 patients (23.1%), and the accuracies of positive and negative predictions of LIT were 88.9 and 76.7%, respectively. Compared with the non-LIT patients, the LIT patients had higher trough concentration [11.49 (6.86, 15.13) vs. 5.51 (2.80, 11.61) mg/L; p = 0.028] but lower apparent volume of distribution (Vd) [0.778 (0.687, 1.421) vs. 1.322 (1.099, 1.610) L; p = 0.010]. Conclusion: The incidence of LIT was high in neonatal sepsis patients, especially in preterm infants. LIT occurred earlier in preterm infants than in term infants. The regression model of seven variables had a high predictive value for predicting LIT. LIT was correlated with higher trough concentration and lower Vd.

Ecosystem services assessment and sensitivity analysis based on ANN model and spatial data: A case study in Miaodao Archipelago

Ecosystem services (ESs) assessment is an important basis for the island protection and utilization plan development. The application of artificial neural network (ANN) to ESs assessment is a new attempt. Herein, an ANN model for the assessment of 5 ESs including carbon sequestration (CS), habitat quality (HQ), nutrient retention (NR), sediment retention (SR), and water yield (WY) of Miaodao Archipelago is established by taking the advantages of InVEST model and the powerful spatial analysis function of the geographic information system (GIS), as well as the self-learning and self-adaptive characteristics, and prediction function of ANN. The independent sample test shows that the correlation coefficient between the model prediction value and InVEST model simulation value is 0.88 (P < 0.001), and the average absolute error of the simulation is 10.33%. The 5 ESs of the Miaodao Archipelago in 2019 was then simulated using the trained model. The comparison with the corresponding data in 2010 suggests that CS, SR and HQ are in decline, while WY is in upward. The impacts of land use on ESs are then analyzed with the ecosystem service change index (ESCI) in different land use scenarios. An ANN model based quantitative method for evaluating the sensitivities of ecosystem service (ES) to typical environmental factors is proposed. The modeling results suggest that the ANN based model can accurately quantify the importance of different environmental factors to the island ESs, and give the comprehensive impact of multiple factors on the ESs. Therefore, the ANN model combined spatial data analysis provides an important means for ESs assessment and sensitivity analysis.

Parametric analysis and new performance correlation of the surface conventional rigid pavement temperature

A statistical model for predicting pavement surface temperature has been developed for the first time in this study. The main objective of this work is to analyse the impact of the input factors (solar radiation, ambient temperature, wind speed, and albedo) most affecting the conventional rigid pavement surface temperature. A multi-physics numerical model combining radiative, conductive, and convective heat transfers with a response surface methodology (RSM) model was created as an innovative and integrated approach. The numerical model of two-dimensional heat transport based on volume finite techniques is performed using FORTRAN software. Moreover, the analysis of variance (ANOVA) is used to establish the polynomial model or new correlation (quadratic regression) and analyse each input factor's statistical importance. Heat transfer model findings were in good agreement with the experimental data. For pavement surface temperature, the results show that the adjusted Radj2and predicted determination coefficients R2 achieved are 0.9912 and 0.9604, respectively. A good agreement is obtained between the statistical model's prediction values and the heat transfer model's numerical data. Based on the four analysed input factors, a simple polynomial statistical model is developed to forecast and reduce the conventional rigid surface pavement temperature. A wind speed of 1.215 m/s, an air temperature of 293.634 K, solar radiation of 222.305 W/m2 and albedo of 0.782 were found to be the optimal operating conditions for obtaining the lowest possible temperature on the pavement's surface.

Effect of Stress–Strength Ratio and Fiber Length on Creep Property of Polypropylene Fiber-Reinforced Alkali-Activated Slag Concrete

Alkali-activated slag (AAS) is an environmentally friendly green cementitious material that can replace ordinary Portland cement (OPC) and has attracted extensive research by scholars all over the world. However, research regarding its creep performance has been lacking, which in turn affects its further application. The creep of alkali-activated slag concrete is large, and fiber addition has been shown to improve this problem. Polypropylene (PP) fiber has good alkali resistance and is economical. This paper studies the effect of the stress–strength ratio and fiber length on the creep property of PP fiber-reinforced alkali-activated slag (FRAAS) concrete. At the stress–strength ratio of 0.15, PP fiber addition is able to greatly reduce the creep of concrete. When the stress–strength ratio increases, the shorter fiber loses the anchoring force and the holes caused by the longer fiber crack. This in turn leads to the deterioration of the inhibition effect on concrete creep. The CEB-FIP 2010 model is highly accurate, but the final value prediction is small. The early prediction value of the GL2000 model is rather large and conservative. The creep coefficient of the prediction model and the measured secant modulus of PP FRAAS concrete with different fiber lengths under different stress–strength ratios may solve the issue of creep prediction.

Rapid Prototypable Biomimetic Peristalsis Bioreactor Capable of Concurrent Shear and Multi-Axial Strain

Peristalsis is a nuanced mechanical stimulus comprised of multi-axial strain (radial and axial strain) and shear stress. Forces associated with peristalsis regulate diverse biological functions including digestion, reproductive function, and urine dynamics. Given the central role peristalsis plays in physiology and pathophysiology, we were motivated to design a bioreactor capable of holistically mimicking peristalsis. We engineered a novel rotating screw-drive based design combined with a peristaltic pump, in order to deliver multi-axial strain and concurrent shear stress to a biocompatible polydimethylsiloxane (PDMS) membrane “wall.” Radial indentation and rotation of the screw drive against the wall demonstrated multi-axial strain evaluated via finite element modeling. Experimental measurements of strain using piezoelectric strain resistors were in close alignment with model-predicted values (15.9 ± 4.2% vs. 15.2% predicted). Modeling of shear stress on the “wall” indicated a uniform velocity profile and a moderate shear stress of 0.4 Pa. Human mesenchymal stem cells (hMSCs) seeded on the PDMS “wall” and stimulated with peristalsis demonstrated dramatic changes in actin filament alignment, proliferation, and nuclear morphology compared to static controls, perfusion, or strain, indicating that hMSCs sensed and responded to peristalsis uniquely. Lastly, significant differences were observed in gene expression patterns of calponin, caldesmon, smooth muscle actin, and transgelin, corroborating the propensity of hMSCs toward myogenic differentiation in response to peristalsis. Collectively, our data suggest that the peristalsis bioreactor is capable of generating concurrent multi-axial strain and shear stress on a “wall.” hMSCs experience peristalsis differently than perfusion or strain, resulting in changes in proliferation, actin fiber organization, smooth muscle actin expression, and genetic markers of differentiation. The peristalsis bioreactor device has broad utility in the study of development and disease in several organ systems.

A simulation-based analysis of the effects of variable prosthesis stiffness on interface dynamics between the prosthetic socket and residual limb.

Introduction: Loading of a residual limb within a prosthetic socket can cause tissue damage such as ulceration. Computational simulations may be useful tools for estimating tissue loading within the socket, and thus provide insights into how prosthesis designs affect residual limb-socket interface dynamics. The purpose of this study was to model and simulate residual limb-socket interface dynamics and evaluate the effects of varied prosthesis stiffness on interface dynamics during gait.Methods: A spatial contact model of a residual limb-socket interface was developed and integrated into a gait model with a below-knee amputation. Gait trials were simulated for four subjects walking with low, medium, and high prosthesis stiffness settings. The effects of prosthesis stiffness on interface kinematics, normal pressure, and shear stresses were evaluated.Results: Model-predicted values were similar to those reported previously in sensor-based experiments; increased stiffness resulted in greater average normal pressure and shear stress (p < 0.05).Conclusions: These methods may be useful to aid experimental studies by providing insights into the effects of varied prosthesis design parameters or gait conditions on residual limb-socket interface dynamics. The current results suggest that these effects may be subject-specific.

Plasma parameters and the reduction potential at a plasma-liquid interface.

Nonthermal plasmas in contact with liquids have been shown to generate a variety of reactive species capable of initiating reduction-oxidation (redox) reactions at the electrochemically active plasma-liquid interface. In conventional electrochemical cells, selective redox chemistry is achieved by controlling the reduction potential at the solid electrode-electrolyte interface by applying a bias via an external circuit. In the case of plasma-liquid systems, an analogous means of tuning the reduction potential near the interface has not clearly been identified. When treated as a floating surface, the liquid is expected to adopt a net negative charge to balance the flux of hot electrons and relatively cold positive ions. The reduction potential near the plasma-liquid interface is hypothesized to be proportional to the floating potential, which can be approximated using an analytical model provided the plasma parameters are known. Herein, we present a framework for correlating the electron density and electron temperature of a noble gas plasma jet to the reduction potential near the plasma-liquid interface. The plasma parameters were acquired for an argon atmospheric plasma jet in contact with an aqueous solution by means of laser Thomson scattering. The reduction potential was determined using identical reference electrodes to measure the potential difference between the plasma-liquid interface and bulk solution. Interestingly, the measured reduction potentials near the plasma-liquid interface were found to be in good agreement with the model-predicted values determined using the plasma parameters obtained from the Thomson scattering experiments.

The Epidemic of Sexually Transmitted Diseases Under the Influence of COVID-19 in China.

Background: Prevention and control of HIV/AIDS and other sexually transmitted diseases (STDs) are major public health priorities in China, but are influenced by the COVID-19 epidemic. In this study, we aimed to quantitatively explore the impact of the COVID-19 epidemic and its control measures on five major STD epidemics in China.Methods: A monthly number of newly reported cases of HIV/AIDS, hepatitis B and C, gonorrhea, and syphilis from January 2010 to December 2020 were extracted to establish autoregressive integrated moving average (ARIMA) models. Each month's absolute percentage error (APE) between the actual value and model-predicted value of each STD in 2020 was calculated to evaluate the influence of the COVID-19 epidemic on the STDs. Pearson correlation analysis was conducted to explore the confirmed COVID-19 case numbers and the COVID-19 control measures' correlations with the case numbers and the APEs of five STDs in 2020.Results: The actual number of five STDs in China was more than 50% lower than the predicted number in the early days of the COVID-19 epidemic, especially in February. Among them, the actual number of cases of hepatitis C, gonorrhea, and syphilis in February 2020 was more than 100% lower than the predicted number (APE was −102.3, −109.0, and −100.4%, respectively). After the sharply declines of STDs' reported cases in early 2020, the case numbers recovered quickly after March. The epidemic of STDs was negatively associated with the COVID-19 epidemic and its control measures, especially for restrictions on gathering size, close public transport, and stay-at-home requirements (p < 0.05).Conclusion: COVID-19 had a significant but temporary influence on the STD epidemic in China. The effective control of COVID-19 is vital for STD prevention. STD services need to be improved to prevent STDs from becoming a secluded corner in the shadow of COVID-19.

Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

In this work, a machine learning technique based on a regression tree model was used to model the surfactant enhanced drying of poly(styrene)-p-xylene coatings. The predictions of the developed model based on regression trees are in excellent agreement with the experimental data. A total of 16,258 samples were obtained through experimentation. These samples were separated into two parts: 12,960 samples were used for the training of the regression tree, and the remaining 3298 samples were used to test the tree’s prediction accuracy. MATLAB software was used to grow the regression tree. The mean squared error between the model-predicted values and actual outputs was calculated to be 8.8415 × 10−6. This model has good generalizing ability; predicts weight loss for given values of time, thickness, and triphenyl phosphate; and has a maximum error of 1%. It is robust and for this system, can be used for any composition and thickness for this system, which will drastically reduce the need for further experimentations to explain diffusion and drying.

Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma.

BackgroundPancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.MethodsAutophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P<0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.ResultsRisk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p<0.001), Treg cells (r=-0.31, p<0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p<0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.ConclusionOur results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

Application of Cationic Surfactant Modified Mengkuang Leaves (Pandanus atrocapus) for the Removal of Reactive Orange 16 from Batik Wastewater: A Column Study

The feasibility of Mengkuang leaves (Pandanus atrocarpus) as a non-conventional low-cost adsorbent for the removal of an anionic dye, Reactive Orange 16 (RO16), was investigated. Among the dyes that have been commonly used in the Batik industry was reactive dye. In this study, Mengkuang leaves were chemically modified with cetyltrimethylammonium bromide (CTAB), a cationic surfactant, to improve their adsorption performance toward anionic dyes. The adsorbent’s morphological characteristics were analyzed using a scanning electron microscope (SEM). The surface of modified Mengkuang leaves seems to be irregular and uneven, with more porous structures than raw Mengkuang leaves. Adsorption of RO16 dye in fixed bed column using modified Mengkuang leaves adsorbent indicated the breakthrough time increased at higher bed height and lower flow rate. The breakthrough times for bed height of 0.5, 2, and 4 cm were at 16, 68, and 165 min, respectively. Meanwhile, breakthrough time for the flow rate of 2,5 and 7 mL.min-1 were at 327, 104, and 43 min, respectively. However, the study utilizing raw Mengkuang leaves showed no significant removal of RO16. Thus, it can be concluded that the cationic surfactant modification of Mengkuang leaves is advantageous for anionic dye removal. This anionic dye removal is significantly influenced by column parameters such as bed height and flow rate as the plotted breakthrough curves obtained from experimental data were similar to the typical breakthrough curve. When applied to the Yoon-Nelson model, the adsorption data provided the best fit with the R2 value above 0.95. The time taken for the breakthrough is very similar to model prediction values. Experiments with real batik dye wastewater showed the immense potential of modified Mengkuang leaves where total removal of real Batik wastewater was instantaneous.

Do fishes enjoy the view? A MaxEnt assessment of fish habitat suitability within scenic rivers

Scenic rivers programs are a potential tool to conserve freshwater habitat but few studies have attempted to characterize fish habitat within scenic rivers. Using the Virginia Scenic Rivers Program as a case study, we built species distribution models for a representative set of Virginia freshwater fishes and tested whether model-predicted habitat suitability values for scenic rivers are consistently higher than the range-wide average values. We began by selecting 33 fish species that were broadly representative of the complete functional trait space comprised by Virginia's freshwater ichthyofauna. This subset included 11 state-listed imperiled species. Next, we built maximum entropy species distribution models for each of the 33 fishes and used the models to predict habitat suitability throughout each species' range. Habitat suitability within state-listed scenic rivers was then summarized and compared with the complete, state-wide distribution of habitat suitability for each species. For 21 of 33 species, model-predicted habitat suitability was significantly higher in currently listed Virginia scenic rivers than in the state-wide distribution; of these 21 species, 5 were imperiled taxa. Furthermore, habitat suitability within scenic rivers is predicted to exceed the range-wide average by a significant margin for 25 species, including eight imperiled species, if all pending scenic listing petitions are approved. We conclude that scenic rivers provide important conservation benefits to Virginia's freshwater fishes. We also note that our flexible, model-based process can be applied in other rivers and used to inform other types of freshwater conservation programs that seek to identify and protect the highest quality habitats.

Ensemble ecological niche modeling of West Nile virus probability in Florida.

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models—boosted regression tree, random forest, and maximum entropy—developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422–0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988–1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800–0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes

This study was aimed to discuss the predictive value of infectious disease dynamics model (IDD model) and dynamic Bayesian network (DBN) for scenario deduction of public health emergencies (PHEs). Based on the evolution law of PHEs and the meta-scenario representation of basic knowledge, this study established a DBN scenario deduction model for scenario deduction and evolution path analysis of PHEs. At the same time, based on the average field dynamics model of the SIR network, the dimensionality reduction process was performed to calculate the epidemic scale and epidemic time based on the IDD model, so as to determine the calculation methods of threshold value and epidemic time under emergency measures (quarantine). The Corona Virus Disease (COVID) epidemic was undertaken as an example to analyze the results of DBN scenario deduction, and the infectious disease dynamics model was used to analyze the number of reproductive numbers, peak arrival time, epidemic time, and latency time of the COVID epidemic. It was found that after the M1 measure was used to process the S1 state, the state probability and the probability of being true (T) were the highest, which were 91.05 and 90.21, respectively. In the sixth stage of the development of the epidemic, the epidemic had developed to level 5, the number of infected people was about 26, and the estimated loss was about 220 million yuan. The comprehensive cumulative foreground (CF) values of O1 ∼ O3 schemes were −1.34, −1.21, and −0.77, respectively, and the final CF values were −1.35, 0.01, and -0.08, respectively. The final CF value of O2 was significantly higher than the other two options. The household infection probability was the highest, which was 0.37 and 0.35 in Wuhan and China, respectively. Under the measures of home quarantine, the numbers of confirmed cases of COVID in China and Wuhan were 1.503 (95% confidential interval (CI) = 1.328 ∼ 1.518) and 1.729 (95% CI = 1.107 ∼ 1.264), respectively, showing good fits with the real data. On the 21st day after the quarantine measures were taken, the number of COVID across the country had an obvious peak, with the confirmed cases of 24495, and the model prediction value was 24085 (95% CI = 23988 ∼ 25056). The incubation period 1/q was shortened from 8 days to 3 days, and the number of confirmed cases showed an upward trend. The peak period of confirmed cases was advanced, shortening the overall epidemic time. It showed that the prediction results of scenario deduction based on DBN were basically consistent with the actual development scenario and development status of the epidemic. It could provide corresponding decisions for the prevention and control of COVID based on the relevant parameters of the infectious disease dynamic model, which verified the rationality and feasibility of the scenario deduction method proposed in this study.

Variations in human body water isotope composition across the United States

Accurate human provenancing using stable isotopes depends directly on solid understandings of the geographic and individual factors affecting isotope variability and incorporation into human tissues. Transfer of isotopic, and therefore spatial, information between environmental water and biological tissues is mediated by the isotopic composition of body water. Thus, there is a need to study body water isotope ratios at a population level and over a large isotopic and geographic range. We evaluated oxygen (δ18Obw) and hydrogen (δ2Hbw) isotope values of body water from 72 volunteers in 10 different cities across the US, and over a 5–10-day period. We analyzed covariates (e.g., water intake, physical activity, biometrics, gender) that might explain individual stable isotope ratio variations and tested a predictive model that incorporates the δ-values of drinking water, food, and O2 as well as individual variables to predict the δ-values of body water. The individual variability in body water isotope values overtime (mean 0.3‰ for δ18Obw and 2.3‰ for δ2Hbw) was lower than the intra-city variability (mean 0.9‰ for δ18Obw and 6.9‰ for δ2Hbw). Body water isotope values differed among cities (ANOVA: δ18ObwF = 97.2, p < 0.001; δ2HbwF = 176.2, p < 0.001). However, significant overlap among some cities with different drinking water was discovered. We detected significant covariation of measured drinking water and human body water isotope values (both isotope systems R2 ≥ 0.89, p < 0.001) and small but significant effects of the average daily exercise and amount of fluid intake. The differences between measured and model-predicted body water values (mean 0.12 ± 1.2‰ for Δδ18O and −1.2 ± 8.2‰ for Δδ2H) were statistically indistinguishable from zero (Δδ18O t = −0.751, p = 0.45; Δδ2H t = 1.133, p = 0.26). Here we show that community level variation exists in the δ18Obw and δ2Hbw values and the primary drivers are the regional differences in drinking water isotopes. Consistency of the body water isotope composition over the study period suggests that tissues would incorporate a stable isotope signal over time. The amount of drinking water and physical activity influence body water values, while the variation in the isotopic values of food may contribute to regional level variability, but that still remains to be assessed further. The human body water model provides accurate estimates for measured values, capturing and reproducing the main features of the body water isotope variation across space.

Fault detection of pressure sensor of blast furnace fan based on Chaos Sparrow Search Algorithm-Support vector machine regression

The sensor diagnosis system of the blast furnace axial fan is very important to the safety of the blast furnace fan control system. In view of the fact that the sensor fault detection of the blast furnace fan is not considered in the existing research, this paper proposes to use SVR to establish a fault detection model for the blast furnace fan outlet pressure sensor, and then use the improved chaotic sparrow algorithm to optimize the selection of the SVR penalty parameters and kernel function parameters Find the optimal parameters. By comparing the model prediction value and the residual error of the diagnostic sensor output value, the sensor fault diagnosis is realized. When the fault is judged, the model prediction value is used instead of the fault sensor output value to be used by the blast furnace fan control system to realize the fault-tolerant control of the blast furnace fan control system. The simulation results show that this method realizes the fault detection of the pressure sensor of the blast furnace fan and improves the safety of the blast furnace production.