Acceptable Prediction Research Articles

Intelligent system for solid waste classification using combination of image processing and machine learning models

ABSTRACT Solid waste is a major issue in all cities around the world. Classification and segregation of solid waste prior to reuse, recycle or recover is an important step towards sustainable waste management. Traditional manual sorting of solid waste is a labour-intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques is widely applied. This study aims to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet dataset, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

Physiologically Based Pharmacokinetic Modeling to Investigate the Disease-Drug-Drug Interactions between Voriconazole and Nirmatrelvir/Ritonavir in COVID-19 Patients with CYP2C19 Phenotypes.

Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis superinfection with cytokine storm is associated with increased mortality. This study aimed to establish a physiologically-based pharmacokinetic (PK) model to investigate the disease-drug-drug interactions between voriconazole and nirmatrelvir/ritonavir in patients with COVID-19 with elevated interleukin-6 (IL-6) levels carrying various CYP2C19 phenotypes. The model was constructed and validated using PK data on voriconazole, ritonavir, and IL-6, and was subsequently verified against clinical data from 78 patients with COVID-19. As a result, the model predicted voriconazole, ritonavir, and IL-6 PK parameters and drug-drug interaction-related fold changes in healthy subjects and patients with COVID-19 with acceptable prediction error, demonstrating its predictive capability. Simulations indicated ritonavir could increase voriconazole exposure to CYP2C19 intermediate and poor metabolizers rather than decrease it, in contrast to what is indicated in the drug package insert. However, the predicted ritonavir exposures were comparable across subjects. In patients with COVID-19, both ritonavir and IL-6 increased voriconazole trough concentrations, which may lead to CYP2C19 phenotype-dependent overexposure. In conclusion, COVID-19-induced IL-6 elevation and ritonavir increased voriconazole exposure, and the magnitude of interactions was influenced by CYP2C19 phenotype. Thus, caution is warranted when prescribing voriconazole concomitantly with Paxlovid in patients with COVID-19.

Modeling the repetitions-in-reserve-velocity relationship: a valid method for resistance training monitoring and prescription, and fatigue management.

Establishing a relationship between repetitions left in reserve and the mean absolute velocity (RIR-velocity relationship) during resistance training (RT) could allow for objective monitoring, prescription, and real-time adjustment of the training load and set-volume. Therefore, we examined the goodness of fit and prediction accuracy of general and individual RIR-velocity relationships in the free-weight back squat exercise. The effects of sex, training status and history, as well as personality traits, on the goodness of fit and the accuracy of these relationships were also investigated. Forty-six resistance-trained people (15 females and 31 males) performed a one-repetition maximum (1RM) test, and two repetitions to failure (RTF) tests 72 h apart. We found greater goodness of fit of individual RIR-velocity relationships compared to general RIR-velocity relationships. Individual, but not general RIR-velocity relationships established in the first testing session yielded acceptable prediction accuracy of RIR (mean error <2 repetitions) in the subsequent testing session, regardless of the load used. Similar results were obtained when both general and individual RIR-velocity relationships were averaged across the loads, suggesting that a single RIR-velocity relationship covering a range of loads can be used instead of traditional RT methods, potentially allowing for better fatigue management and more efficient adaptation.

Turbulence models performance to predict fluid mechanics and heat transfer characteristics of fluids flow in micro-scale channels

The predictability of the mathematical models on the details and the phenomenology for microscale flow conditions is an open topic in the literature. In this sense, this work evaluates the capacity of the following turbulence models: standard k − ε , RNG k − ε , k − ω standard, k − ω SST , Realizable k − ε , and Low-Re k − ε to predict the fluid mechanics and heat transfer characteristics of low Global Warming Potential (GWP) fluid flow in a 1.1 mm ID microchannel. These turbulence models are evaluated for Reynolds Numbers up to 104. The numerical results for velocity profile, friction factors, and Nusselt Numbers are validated with analytical and experimental data published in previous works for R134a, R1234yf, R1234ze(E), and R600a. Parametric behaviors of pressure drop and heat transfer coefficient are presented and analyzed. The results indicate that each of the models describes the qualitative behavior of flow and heat transfer processes. On the other hand, the quantitative results indicate that the Low-Re k − ε , k − ω , and k − ω SST models demonstrate an acceptable prediction of some variable’s behavior. Numerically, the Low-Re k − ε model presents an accurate prediction with the lowest mean absolute.

Learning beyond-pairwise interactions enables the bottom–up prediction of microbial community structure

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

A Pilot Study To Assess the Suitability of Riboflavin As a Surrogate Marker of Breast Cancer Resistance Protein in Healthy Participants.

We recently showed that riboflavin is a selected substrate of breast cancer resistance protein (BCRP) over P-glycoprotein (P-gp) and demonstrated its prediction performance in preclinical drug-drug interaction (DDI) studies. The aim of this study was to investigate the suitability of riboflavin to assess BCRP inhibition in humans. First, we assessed the substrate potential of riboflavin toward other major drug transporters using established transfected cell systems. Riboflavin is a substrate for organic anion transporter (OAT)1, OAT3, and multidrug and toxin extrusion protein (MATE)2-K, with uptake ratios ranging from 2.69 to 11.6, but riboflavin is not a substrate of organic anion-transporting polypeptide (OATP)1B1, OATP1B3, organic cation transporter (OCT)2, and MATE1. The effects of BMS-986371, a potent in vitro inhibitor of BCRP (IC 50 0.40 μM), on the pharmacokinetics of riboflavin, isobutyryl carnitine, and arginine were then examined in healthy male adults (N = 14 or 16) after oral administration of methotrexate (MTX) (7.5 mg) and enteric-coated (EC) sulfasalazine (SSZ) (1000 mg) alone or in combination with BMS-986371 (150 mg). Oral administration of BMS-986371 increased the area under the plasma concentration-time curves (AUCs) of rosuvastatin and immediate-release (IR) SSZ to 1.38- and 1.51-fold, respectively, and significantly increased AUC(0-4h), AUC(0-24h), and C max of riboflavin by 1.25-, 1.14-, and 1.11-fold (P-values of 0.003, 0.009, and 0.025, respectively) compared with the MTX/SSZ EC alone group. In contrast, BMS-986371 did not significantly influence the AUC(0-24h) and C max values of isobutyryl carnitine and arginine (0.96- to 1.07-fold, respectively; P > 0.05). Overall, these data indicate that plasma riboflavin is a promising biomarker of BCRP that may offer a possibility to assess drug candidate as a BCRP modulator in early drug development. SIGNIFICANCE STATEMENT: Endogenous compounds that serve as biomarkers for clinical inhibition of breast cancer resistance protein (BCRP) are not currently available. This study provides the initial evidence that riboflavin is a promising BCRP biomarker in humans. For the first time, the value of leveraging the substrate of BCRP with acceptable prediction performance in clinical studies is shown. Additional clinical investigations with known BCRP inhibitors are needed to fully validate and showcase the utility of this biomarker.

Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data

Predicting earthquakes through reasonable methods can significantly reduce the damage caused by secondary disasters such as tsunamis. Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamics data obtained from sheared granular fault experiments. Here, we adopt the combined finite-discrete element method (FDEM) to simulate a two-dimensional sheared granular fault system, from which abundant fault dynamics data (i.e., displacement and velocity) during stick-slip cycles are collected at 2203 “sensor” points densely placed along and inside the gouge. We use the simulated data to train LightGBM (Light Gradient Boosting Machine) models and predict the gouge-plate friction coefficient (an indicator of stick-slips and the friction state of the fault). To optimize the data, we build the importance ranking of input features and select those with top feature importance for prediction. We then use the optimized data and their statistics for training and finally reach a LightGBM model with an acceptable prediction accuracy (R2 = 0.94). The SHAP (SHapley Additive exPlanations) values of input features are also calculated to quantify their contributions to the prediction. We show that when sufficient fault dynamics data are available, LightGBM, together with the SHAP value approach, is capable of accurately predicting the friction state of laboratory faults and can also help pinpoint the most critical input features for laboratory earthquake prediction. This work may shed light on natural earthquake prediction and open new possibilities to explore useful earthquake precursors using artificial intelligence.

Modelling of tuberculosis dynamics incorporating indirect transmission of contaminated environment and infectivity of smear-negative individuals: A case study for Xinjiang, China

Xinjiang has been one of the high incidence areas of pulmonary tuberculosis (PTB) in China. Besides being infected by direct contacting with active PTB individuals (direct infection), the susceptible would be infected because of the exposure to the environment contaminated by Mycobacterium tuberculosis (indirect infection). Active PTB individuals include not only the smear-positive PTB (PTB+) but also the smear-negative PTB (PTB–) who are infectious due to their ability to release tiny Mycobacterium tuberculosis particles even in the absence of visible Mycobacterium tuberculosis in sputum. By taking account of direct/indirect infection and the difference between PTB+ and PTB– individuals in transmission capability, a periodic dynamical PTB transmission model is proposed. The model is fitted to the newly monthly PTB+ and PTB– cases in Xinjiang from 2008 to 2017 by Markov Chain Monte Carlo algorithm. Moreover, global sensitivity analysis is constructed to address the uncertainty of some key parameters by using Latin hypercube sampling and partial rank correlation coefficient methods. Basic reproduction number R0 for PTB transmission in Xinjiang is estimated to be 2.447 (95% CrI:(1.203, 3.844)), indicating that PTB has been prevalent in Xinjiang over the study period. Our results suggest that reducing the direct/indirect transmission rates, early screening, isolating and treating the latent, PTB+ and PTB– individuals, and enhancing the clearance of Mycobacterium tuberculosis in the environment could more effectively control PTB transmission in Xinjiang. The model fits the reported PTB data well and achieves acceptable prediction accuracy. We believe that our model can provide heuristic support for controlling PTB transmission in Xinjiang.

Power capture and power take-off load of a self-balanced dual-flap oscillating surge wave energy converter

Wave energy converters are an important part of future renewable energy infrastructure. Predicting their power matrix, capture width ratio, and power take-off loads at a targeted site is required for performance assessment before deployment. Because their testing is very expensive, numerical modeling and simulations play a significant role in those assessments. Linear potential flow theory has limited accuracy under large amplitude wave forcing. More accurate predictions can be obtained by using higher-fidelity models, which are computationally expensive. We present a framework for multi-fidelity numerical simulations to determine the hydrodynamic response, wave capture capability, and power take-off load of a full-scale dual-flap oscillating surge wave energy converter. This design exploits out-of-phase motion by setting the distance between the flaps to half the wavelength of the most occurring wave. The simulations are validated using a 1:10 model experiments in a wave tank. Based on these validations, it was determined that Euler simulations provide an acceptable prediction with 90% reduction in computational time with only 11% error. Utilizing Euler simulations at full-scale, the results demonstrate that the annual electrical energy output is 1.79 GWh under regular wave conditions. One significant improvement over single-flap designs is the capture width ratio which exceeds unity.

Model for estimating the maximum water content that would maintain the flammability of an aqueous−organic mixture

Adding water to liquid fuels can make them nonflammable; substantial amounts of water are required to do so for highly volatile fuels. A limited amount of water being required to eliminate the flammability of low-volatility fuels would enable adjustments to be made to fire and explosion (F&E) hazard assessments for such fuels and the requirements for preventing F&E hazards of various regulations. This study developed a novel generic model for determining two critical parameters used to assess F&E hazards and that can be leveraged to eliminate these hazards: (1) the maximum water composition in the liquid phase for which the aqueous solution remains flammable and (2) the maximum flash point (FP). The model was verified using experimental data on nine low-volatility aqueous−organic mixtures and published data on eight high-volatility aqueous−organic mixtures. The model made acceptable predictions for ignitable chemicals with an FP range of −19 to 145 °C. Both the simulations and measurements revealed that only a small amount of water is required to render low-volatility fuels nonflammable, whereas substantial amounts of water are required for highly volatile fuels. The proposed model and present results can be employed to reduce the cost of F&E protection.

Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study

Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. For the Chinese translation of the abstract see Supplementary Materials section.

NMGMDA: a computational model for predicting potential microbe–drug associations based on minimize matrix nuclear norm and graph attention network

The prediction of potential microbe–drug associations is of great value for drug research and development, especially, methods, based on deep learning, have been achieved significant improvement in bio-medicine. In this manuscript, we proposed a novel computational model named NMGMDA based on the nuclear norm minimization and graph attention network to infer latent microbe–drug associations. Firstly, we created a heterogeneous microbe–drug network in NMGMDA by fusing the drug and microbe similarities with the established drug–microbe associations. After this, by using GAT and NNM to calculate the predict scores. Lastly, we created a fivefold cross validation framework to assess the new model NMGMDA's progressiveness. According to the simulation results, NMGMDA outperforms some of the most advanced methods, with a reliable AUC of 0.9946 on both MDAD and aBioflm databases. Furthermore, case studies on Ciprofloxacin, Moxifoxacin, HIV-1 and Mycobacterium tuberculosis were carried out in order to assess the effectiveness of NMGMDA even more. The experimental results demonstrated that, following the removal of known correlations from the database, 16 and 14 medications as well as 19 and 17 microbes in the top 20 predictions were validated by pertinent literature. This demonstrates the potential of our new model, NMGMDA, to reach acceptable prediction performance.

Prediction of customer acceptance of artificial intelligence technology in Indian retail banks: a sentiment analysis approach

Prediction of customer acceptance of artificial intelligence technology in Indian retail banks: a sentiment analysis approach

Prediction of robo-advisory acceptance in banking services using tree-based algorithms.

The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by banks from various communication channels. In the research carried out in the work, an attempt was made to obtain an answer to the question whether the data collected by banks can also be used to determine the degree of consumer interest in this type of service. This is important because the identification of customers interested in the service will allow banks to direct a properly prepared message to a selected group of addressees, increasing the effectiveness of their promotional activities. The aim of the article is to construct and examine the effectiveness of predictive models of consumer acceptance of robo-advisory services provided by banks. Based on the authors' survey on the use of artificial intelligence technology in the banking sector in Poland, in this article we construct tree-based models to predict customers' attitudes towards using robo-advisory in banking services using, as predictors, their socio-demographic characteristics, behaviours and attitudes towards modern digital technologies, experience in using banking services, as well as trust towards banks. In our study, we use selected machine learning algorithms, including a decision tree and several tree-based ensemble models. We showed that constructed models allow to effectively predict consumer acceptance of robo-advisory services.

Evaluation of Concomitant Facial Fracture in Traumatic Brain Injury Patients-Simplification and External Validation of a Prediction Model.

Patients with traumatic brain injuries (TBIs) often experience concurrent facial bone fractures. In 2021, a prediction model with 10 variables was published and precisely predicted concomitant facial fractures in TBI patients. Herein, external validation and simplification of this model was performed. Traumatic brain injury patients treated at a major referral trauma center were retrospectively reviewed for 1 year. The original prediction model (published in 2021), which was developed from a rural level II trauma center, was applied for external validation. A new and simplified model from our level I trauma center was developed and backwardly validated by rural level II trauma center data. In total, 313 TBI patients were enrolled; 101 (32.3%) had concomitant facial fractures. When the previous prediction model was applied to the validation cohort, it achieved acceptable discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.713 and good precision, with a Brier score of 0.083. A new and simplified model with 6 variables (age, tooth rupture, epistaxis, facial lesion, eye injury, and intracranial hemorrhage) was created with excellent discrimination (AUC = 0.836) and good precision (Brier score of 0.055). The backward validation of this new model also showed excellent discrimination in the cohort used to develop the original model (AUC = 0.875). The original model provides an acceptable and reproducible prediction of concomitant facial fractures among TBI patients. A simplified model with fewer variables and the same accuracy could be applied in the emergency department and at higher- and lower-level trauma centers.

Diagnostic and Prognostic Values of S100B versus Neuron Specific Enolase for Traumatic Brain Injury; a Systematic Review and Meta-analysis.

Traumataic brain injury (TBI) represents a significant global health burden. This systematic review delves into the comparison of S100B and Neuron-Specific Enolase (NSE) regarding their diagnostic and prognostic accuracy in TBI within the adult population. Conducted on October 21, 2023, the search identified 24 studies encompassing 6454 adult patients. QUADAS-2 and QUAPAS tools were employed to assess the risk of bias. The analyses aimed to evaluate the diagnostic and prognostic performance of S100B and NSE based on sensitivity, specificity, and area under the curve (AUC). The outcomes were detecting intracranial injury, mortality, and unfavorable outcome. Pooled data analysis tended towards favoring S100B for diagnostic and prognostic purposes. S100B exhibited a diagnostic AUC of 0.74 (95% confidence interval (CI): 0.70-0.78), sensitivity of 80% (95% CI: 63%-90%), and specificity of 59% (95% CI: 45%-72%), outperforming NSE with an AUC of 0.66 (95% CI: 0.61-0.70), sensitivity of 74% (95% CI: 53%-88%), and specificity of 46% (95% CI: 24%-69%). Notably, both biomarkers demonstrated enhanced diagnostic value when blood samples were collected within 12 hours post-injury. The analyses also revealed the excellent diagnostic ability of S100B with a sensitivity of 99% (95% CI: 4%-100%) and a specificity of 76% (95% CI: 51%-91%) in mild TBI patients (AUC = 0.89 [0.86-0.91]). In predicting mortality, S100B showed a sensitivity of 90% (95% CI: 65%-98%) and specificity of 61% (95% CI: 39%-79%), slightly surpassing NSE's performance with a sensitivity of 88% (95% CI: 76%-95%) and specificity of 56% (95% CI: 47%-65%). For predicting unfavorable outcomes, S100B exhibited a sensitivity of 83% (95% CI: 74%-90%) and specificity of 51% (95% CI: 30%-72%), while NSE had a sensitivity of 80% (95% CI: 64%-90%) and specificity of 59% (95% CI: 46%-71%). Although neither biomarker has shown promising diagnostic performance in detecting abnormal computed tomography (CT) findings, they have displayed acceptable outcome prediction capabilities, particularly with regard to mortality.

AmiR-P3: An AI-based microRNA prediction pipeline in plants.

MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest. Here, we present AmiR-P3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge of plant miRNA properties. The pipeline starts with finding the potential homologs of the known plant miRNAs in the input sequence(s) and ensures that they do not overlap with protein-coding regions. Then, by computing the secondary structure of the presumed RNA sequence based on the minimum free energy, a deep learning classification model is employed to predict potential pre-miRNA structures. Finally, a set of criteria is used to select the most likely miRNAs from the set of predicted miRNAs. We show that our method yields acceptable predictions in a variety of plant species. AmiR-P3 does not (necessarily) require sequencing reads and/or assembled reference genomes, enabling it to identify conserved and novel putative miRNAs from any genomic or transcriptomic sequence. Therefore, AmiR-P3 is suitable for miRNA prediction even in less-studied plants, as it does not require any prior knowledge of the miRNA repertoire of the organism. AmiR-P3 is provided as a docker container, which is a portable and self-contained software package that can be readily installed and run on any platform and is freely available for non-commercial use from: https://hub.docker.com/r/micrornaproject/amir-p3.

Experimental investigation on utilization of substitute building materials in concrete using neural networks

The replacement of cement with sugarcane bagasse ash in concrete is considered due to its rich properties of projecting pozzolanic activity. The availability of aggregates is becoming scarce as a result of the non-renewable characteristic of fine and coarse aggregates. The construction waste end products like demolishing waste also cause the problem of improper disposal. Hence a majority of the construction industries have preferred the usage of construction and demolition (C&amp;D) waste as a replacement for coarse aggregate. Substitution of coarse aggregates by construction and demolition waste and fine aggregates by iron slag ash is considered. The Taguchi method is adopted for the determination of mix combinations. This paper focuses on determining the properties of concrete having pozzolanic properties by replacing the cement with sugarcane bagasse ash (SBA), coarse aggregate with demolished building waste (DBW), and Fine aggregate with iron slag ash (ISA). The experimental investigation proved that SBA, DBW and ISA have a potential sign to be used as an alternative sustainable building material. From the comparative analysis of experimental results with ANN, it is revealed that the concrete show an acceptable prediction of physical and strength properties.

Inorganic carbon as a key factor governing competition between methanogens and acetogens in high-rate anaerobic treatment of methanol wastewater

The anaerobic treatment of methanolic wastewater has become a crucial issue. Sodium bicarbonate (NaHCO3), as a typical representative of inorganic carbon substances, not only provides alkalinity in the anaerobic treatment of methanol but also governs the competition between methanogens and acetogens. However, a suitable dosage has rarely been mentioned. In this study, we demonstrate the crucial role of NaHCO3 in the anaerobic treatment of methanol using a long-term operated up-flow anaerobic sludge blanket (UASB) reactor and batch tests. The UASB results revealed that by adding 3 to 5 g/L of NaHCO3 to the influent at a high organic loading rate of 50 gCOD/L/d, a significant accumulation of acetic acid was detected, with a maximum value of up to 1400 mg/L. In the context of microbial community evolution, this study profiled substrate competition between the acetogens Acetobacterium and Methanomethylovorans, which directly utilize methanol, as well as the synergistic effects of Acetobacterium and acetoclastic Methanosaeta. Abundance analysis of genes encoding relevant pathway enzymes also revealed that the acetic acid production pathway was significantly enhanced when more NaHCO3 was added. According to the dose–response mechanism in the batch test based on kinetic analysis, an acceptable threshold prediction scheme was beneficial in reasonably adding NaHCO3 to improve acid production capacity and rapid methanol degradation, and ensuring that the generated acetate could be rapidly degraded without inhibition, in the high-rate anaerobic treatment of methanolic wastewater.

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.