Model Validation Results Research Articles

Deep learning-based gamma spectroscopic analysis considering multiple variables for in situ applications

Gamma spectroscopy in environments with fluctuating temperatures, magnetic fields, and radiation doses can lead to inaccurate material analysis and increased radiation exposure for workers. In this manuscript, we propose a deep learning model that reduces human intervention and remains robust against variables in uncontrolled environments, thereby enhancing the applicability of gamma spectroscopy in the field. The process of establishing a dataset to train, validate, and test the deep learning model involved simultaneous consideration of multiple variables, including gain shifts, detector energy resolutions, the number of mixed radioisotopes (RIs), mixed RI ratios, and statistical fluctuations of the spectrum. The model was trained using multi-label learning to perform RI identification and quantification simultaneously. Specifically, the RI quantification output of our model structure was designed to predict full-energy peak areas and mixed ratios for each RI. Highlighting the importance of a high-performance training dataset, the training and validation results of the gamma spectroscopy model were analyzed in depth. In the test results, organized by RI type and number of mixed RIs, the model demonstrated that following: even under complex conditions with multiple variables, all outcomes for RI identification and quantification were predicted with high accuracy and precision, exceeding 98%.

Analysis of the Effect of Quay Container Crane Available on Dwelling Time at Jakarta International Container Terminal

The efficiency of loading and unloading dwelling time at the port has been done a lot. The problem at PT JICT is that there has been a decrease in throughput for the last three years, this has an effect on operational performance. operational performance is a representation of the achievement of all targets, thus the company always aims to improve its operational performance. Another problem is that the terminal's operational equipment has begun to be obsolete, which hinders the loading and unloading operation time, the lack of operational equipment that supports the loading and unloading process. The operational equipment studied is the Quay Container Crane which is seen from the available condition. All of these problems will have an impact on dwelling time at PT JICT which can affect operational performance. This research is expected to provide recommendations to PT Jakarta International Container Terminal to prepare adequate operational equipment so that the resulting dwelling time is known. This research method uses a dynamic system model and validation test with behavior pattern test. The results of the dynamic system model validation obtained dwelling time between 2.79 - 4.56 days, mean error of 3% and standard deviation error of 11%. Maintenance of quay container cranes has an influence on unloading productivity which has an impact on dwelling time. Based on 60 data, the quay container crane available variable is seen based on quartile values consisting of quartile 1 (Q1), quartile 2 (Q2) and quartile 3 (Q3). In the quay container crane available variable, the dwelling time value in quartile one is 1.34 days, quartile two is 1.47 and quartile three is 1.51 and the dwelling time value in quartile one is 14 units, quartile two is 12 units and quartile three is 10 units.

Multiscale experimental characterization and physical-based constitutive modeling of silicone adhesive

Silicone adhesive has been widely used in assembly glass curtain walls which leads to its mechanical behaviors under heavy investigation. Due to its complexity, the microstructure of silicone adhesive stays unclear. A detailed description of microstructures of silicone adhesive is the basis to obtain an in-depth understanding of its mechanical behavior and the related mechanisms. In this work, series of multiscale experiments, uniaxial tension, uniaxial compression, simple shear and scanning electron microscopy (SEM) observation, were conducted to characterize mechanical behaviors and microstructure features of silicone adhesive. Stress-strain curves obtained through macroscale experiments were presented. Morphology and dispersion of filler clusters in silicone adhesive were analyzed using Gaussian mixed models. A modification of molecular chain spatial distribution was made to non-affine network model to consider anisotropic damage evolution of polymer matrix. A simplified term was further introduced to account for load-bearing capacity of filler clusters based on fractal theory where fractal dimensions of clusters work as a connection between microscale and macroscale. The modified model was further extended to describe Mullins effect combined with network alteration theory. The results of model validation and comparison demonstrated that due to the consideration of polymer chain anisotropic evolution and filler cluster microscopic features, the proposed model can predict the mechanical behavior of silicone adhesive under different loading conditions as well as the stress softening and permanent set of Mullins effect more accurately.

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Accurate machine learning (ML) predictions for the early stages of the building design are crucial to construct energy-efficient buildings utilizing limited resources. Several studies have employed ML methods for energy consumption (EC) prediction without considering the utmost crucial PCM-integrated building design parameters. In addition, reducing the dataset by considering only the most significant building design parameters before applying the ML-based method would be beneficial for reducing the computational power and memory usage of the system as well as utilizing less time in the modelling process. To this end, this research presents a novel framework to establish the most robust and reliable ML-based prediction model with less complexity, considering only the most influential PCM-integrated building design parameters. These parameters were identified for future scenarios of hot semi-arid (BSh) climate zones using multi-stage sensitivity analysis. Afterward, a reduced EC database based on the most significant building's early-design-stage parameters (EDSPs) was utilized to formulate several multi-expression programming (MEP) and support vector machines (SVM)-based forecasting models, considering the variations in their hyperparameter values. Formulated prediction models have shown less time utilization through the training and testing phases for the EC evaluations of selected PCM-integrated building compared to the physical-modelling process. Several statistical parameters were used to test and validate the performance of the formulated prediction models. The acquired model evaluation and validation results demonstrated that the MEP-based prediction model (MEP15) exhibited the highest level of reliability and accuracy, showing an R2 value of >95% for both the training and testing phases. The model's interpretability showed that, throughout the parametric analysis, the developed prediction model adhered to the system's physical boundary constraints. Also, the best-performing prediction model showed energy savings of up to 12% for building integrated with PCM having a melting temperature of 28 °C. Conclusively, this research demonstrated that the developed MEP-based prediction model could be employed to precisely forecast the EC for selected PCM-incorporated building in the whole BSh climate, considering important EDSPs while utilizing minimal resources.

External validation of a clinical mathematical model estimating post-operative urine output following cardiac surgery in children

BackgroundThis study aims to externally validate a clinical mathematical model designed to predict urine output (UOP) during the initial post-operative period in pediatric patients who underwent cardiac surgery with cardiopulmonary bypass (CPB).MethodsChildren aged 0–18 years admitted to the pediatric cardiac intensive care unit at Cleveland Clinic Children’s from April 2018 to April 2023, who underwent cardiac surgery with CPB were included. Patients were excluded if they had pre-operative kidney failure requiring kidney replacement therapy (KRT), re-operation or extracorporeal membrane oxygenation or KRT requirement within the first 32 post-operative hours or had indwelling urinary catheter for fewer than the initial 32 post-operative hours, or had vasoactive-inotrope score of 0, or those with missing data in the electronic health records.ResultsA total of 213 encounters were analyzed; median age (days): 172 (IQR 25–75th%: 51–1655), weight (kg): 6.1 (IQR 25–75th%: 3.8–15.5), median UOP ml/kg/hr in the first 32 post-operative hours: 2.59 (IQR 25–75th%: 1.93–3.26) and post-operative 30-day mortality: 1, (0.4%). The mathematical model achieved the following metrics in the entire dataset: mean absolute error (95th% Confidence Interval (CI)): 0.70 (0.67–0.73), median absolute error (95th% CI): 0.54 (0.52–0.56), mean squared error (95th% CI): 0.97 (0.89–1.05), root mean squared error (95th% CI): 0.99 (0.95–1.03) and R2 Score (95th% CI): 0.29 (0.24–0.34).ConclusionsThis study provides encouraging external validation results of a mathematical model predicting post-operative UOP in pediatric cardiac surgery patients. Further multicenter studies must explore its broader applicability.Graphical abstractA higher resolution version of the Graphical abstract is available as Supplementary information

Introducing the double validation metric for radar sensor models

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these sensors, as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called double validation metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation in comparison to the state of the art of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.

Optimization of geopolymer pervious concrete design using multi-phase discrete element modeling

This study aims to optimize mix designs of geopolymer pervious concrete using multi-phase discrete element modeling. Metakaolin based geopolymer concrete was prepared with different mix designs for laboratory testing. A multi-phase discrete element model (DEM) for pervious concrete was developed with randomly generated porous structure and realistic contact models between each component. Contact model parameters were calibrated using orthogonal analysis based on measurements of mechanical strengths and elastic modulus. Results of model validation indicate that the developed numerical model has good reliability to predict mechanical properties of pervious concrete with the influence of porosity. Both actual and simulated pervious concrete specimens present consistent crack propagation patterns and failure behavior. The optimization of mixture design is conducted by considering aggregate gradation and aggregate to paste ratio of geopolymer pervious concrete, which are based on experimental results and post-calibration simulations. The gradation with larger size aggregate is suitable for preparing geopolymer pervious concrete with higher permeability and tensile strength. More importantly, it is feasible to reduce paste content in geopolymer pervious concrete while maintaining similar or even higher mechanical strengths, which reduces the embedded carbon footprint. The study findings demonstrate the beneficial use of numerical modeling to determine the optimized mix design of composite material such as geopolymer pervious concrete.

Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study.

The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21-1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings.

Assessing water quality of the Saigon River under the impact of industrial wastewater incidents

The Saigon River, as its economic potential and clusters and industrial parks evolve, is threatened by a high potential for water pollution. The objective is to evaluate the water quality (BOD5 and COD) under the influence of industrial wastewater incidents in the Saigon River using MIKE 21FM model. The results of model calibration and validation have high reliability with NSE values larger than 0.65 for the flow simulation and percentage error values less than 25% for the water quality simulation. Subsequently, the study proceeds to develop hypothetical scenarios in case untreated wastewater from industrial parks is charged directly into the Saigon River with actual discharge volumes of 15,811 m3/day and 30,000 m3/day. The results indicated that when an incident occurs at a location 1.3 km upstream from the Hoa Phu pumping station with a discharge volume of 15,811 m3/day, that has the most consequential impact, in terms of time and concentration of water pollutants on the pumping station. Specifically, the water quality at the pumping station area experiences a remarkable decrease after one and three hours of incidents in the dry and rainy seasons, respectively. In general, the impacts of industrial wastewater incidents vary depending on their location. An incident beneath the Hoa Phu pumping station on the Saigon River is expected to have a faster impact on the raw water source during the dry season compared to the rainy season.

A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model.

Urban greening co-creation: Participatory spatial modelling to bridge data-driven and citizen-centred approaches

The unprecedented growth of metropolitan areas creates challenges for maintaining liveable and biodiverse cities. Urban areas face multiple demands on sparse space whilst various stakeholders similarly aim to promote greening efforts. In this context, authorities need to balance various policy objectives with demands from diverse urban stakeholders. Part of this challenge is the question how generalized data-driven green space planning approaches can be connected to local, contextualized understandings, practices and values related to green space.In this paper, we present a participative application of GIS that contributes to bridging the gap between data-driven and citizen-centred approaches in urban greening. Through an empirical study in Amsterdam, we show how this can link local priorities with larger-scale policy frameworks through deliberative and data-driven co-creation. In our case study, local stakeholders and researchers jointly identified criteria for greening, translated these into indicators and eventually identified potential locations for small-scale greening. Site visits by local experts helped to validate the model results and translate this into concrete plans for greening several locations.Our approach promoted a dialogue between stakeholders, linking spatial data with practical experiences and aligning local priorities with policy programmes. Combining various knowledges from involved stakeholders also contributed to the quality of analysis and validation of modelling results. By increasing transparency and inclusiveness of planning, it also contributed to acceptance of process outcomes and empowering local stakeholders. With increasing urgencies for environmental measures in many cities, we emphasize the potential of transdisciplinary GIS-approaches to navigate different interests and integrate various types of valuable knowledge. We suggest that similar approaches may be applied to other environmental challenges that have a strong spatial character.

Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling

AbstractMechanism models and model‐assisted process optimization play a vital role in biopharmaceutical industry. However, the parameter estimation remains a challenging task in modeling due to the presence of multiple local optima and ill‐conditioning. In this study, we firstly presented a comparative evaluation of particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) for parameter estimation and confirmed PSO was the most efficient approach. Then an improved PSO algorithm using second‐order oscillation and particle replacement strategies was developed to enhance fitting performance in fed‐batch cell culture modeling. The results of fitting and model validation demonstrated that the improved PSO could explore more reasonable parameters and leading to a significantly enhancement in predictive accuracy throughout the entire cell culture process under varying conditions. Moreover, 10 diverse fed‐batch experiments were conducted to validate the fitting abilities on different process condition and clones, the improved PSO method exhibited improvements in accuracy and universality of parameter estimation for modeling various cultivation processes, particularly those lacking any prior knowledge. This improved algorithm is implemented to make it available to both the scientific community and industry, offering customized solutions for specific projects.

Clinical prediction tool to identify children at risk of pulmonary embolism

IntroductionThe diagnosis of pediatric pulmonary embolism (PE) is often delayed due to non-specific symptoms, and clinical prediction tools designed for adults are unsuitable for children. This study aimed to create a PE predictive model and to evaluate the reported tools in the Thai pediatric population. Materials and methodsA multi-center retrospective study from 4 university hospitals included children ≤18 years of age undergoing computed tomography pulmonary angiogram from 2000 to 2020 with the suspicion of PE. Patients' clinical presentations and risk factors of venous thromboembolism (VTE) were compared between the PE-positive and PE-negative groups. Significant risk factors from univariate and multivariate logistic regression were included to create a clinical prediction tool. The performance of the model was demonstrated by sensitivity, specificity, area under the curve (AUC), Hosmer Lemeshow test, ratio of observed and expected outcomes and bootstrapping. ResultsOf the 104 patients included, 43 (41.3 %) were grouped as PE-positive and 61 (58.7 %) as PE-negative. Five parameters, including congenital heart disease/pulmonary surgery, known thrombophilia, previous VTE, nephrotic syndrome and chest pain showed significant differences between the two groups. Score ≥ 2 yielded a 74.4 % sensitivity and a 75.4 % specificity with an AUC of the model of 0.809. The model performance and validation results were within satisfactory ranges. ConclusionThe study created a clinical prediction tool indicating the likelihood of PE among Thai children. A score ≥2 was suggestive of PE.

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNNbased heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

Machine learning in female urinary incontinence: A scoping review.

The aim was to conduct a scoping review of the literature on the use of machine learning (ML) in female urinary incontinence (UI) over the last decade. A systematic search was performed among the Medline, Google Scholar, PubMed, and Web of Science databases using the following keywords: [Urinary incontinence] and [(Machine learning) or (Predict) or (Prediction model)]. Eligible studies were considered to have applied ML model to explore different management processes of female UI. Data analyzed included the field of application, type of ML, input variables, and results of model validation. A total of 798 papers were identified while 23 finally met the inclusion criteria. The vast majority of studies applied logistic regression to establish models (91.3%, 21/23). Most frequently ML was applied to predict postpartum UI (39.1%, 9/23), followed by de novo incontinence after pelvic floor surgery (34.8%, 8/23).There are also three papers using ML models to predict treatment outcomes and three papers using ML models to assist in diagnosis. Variables for modeling included demographic characteristics, clinical data, pelvic floor ultrasound, and urodynamic parameters. The area under receiver operating characteristic curve of these models fluctuated from 0.56 to 0.95, and only 11 studies reported sensitivity and specificity, with sensitivity ranging from 20% to 96.2% and specificity from 59.8% to 94.5%. Machine learning modeling demonstrated good predictive and diagnostic abilities in some aspects of female UI, showing its promising prospects in near future. However, the lack of standardization and transparency in the validation and evaluation of the models, and the insufficient external validation greatly diminished the applicability and reproducibility, thus a focus on filling this gap is strongly recommended for future research.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

RESULTS OF MODELLING OF SNOW WATER EQUIVALENT WITH USING MODSNOW MODEL IN ESIL RIVER MANAGEMENT BASIN

The article presents the results of modeling of snow water equivalent using the V03 module of the MODSNOW model, based on the empirical modeling method, for the territory of the Esil water management basin for 1980…2021 periods. The multi-year period was divided into two equal parts: 1980...1999 for model calibration, 2000...2021 for model validation. For this purpose, 20 meteorological stations located in the section of the water management basin, which have continuous input daily data of average daily air temperature, precipitation, snow cover height and ten-day data of snow water equivalent, were selected. The results of model calibration and validation for reproducibility were evaluated by Nash-Sutcliffe, RSR, and PBIAS criteria. Good reproducibility of modeled snow water equivalent at the meteorological stations Blagoveshenka, Balkashino, Sergeevka, Stepnogorsk, Yavlenka, Bulayevo was revealed; for these stations the results corresponded to the «good» reproducibility rating. It is concluded that the model for these stations can be used for short, medium- and long-term forecasting of snow water equivalent.

Nutrient sequestration and lipid production potential of Chlorella vulgaris under pharmaceutical wastewater treatment: experimental, optimization, and prediction modeling studies.

The efficient management and treatment of pharmaceutical industry wastewater (PIWW) have become a serious environmental issue due to its high toxicity. To overcome this problem, the present study deals with the phycoremediation of PIWW using Chlorella vulgaris microalga isolated from the Ganga River at Haridwar, India. For this, response surface methodology (RSM) and artificial neural network (ANN) tools were used to identify the best reduction of total phosphorus (TP) and total Kjeldahl's nitrogen (TKN) based pollutants along with the lipid production efficiency of C. vulgaris. Three different concentrations of pharmaceutical wastewater (0, 50, and 100%), operating temperatures (20, 25, and 30 °C), and light intensity (2000, 3000, and 4000 lx) were used to design the phycoremediation experiments having 6:18 h of dark/light period and reactor functional volume of 15L. Findings revealed that C. vulgaris was good enough to remove maximum TP (90.35%), TKN (83.55%) along 20.88% of lipid yield at 25.62 °C temperature, 60.73% PIWW concentration, and 4000 lx of light intensity, respectively. Based on the model performance and validation results, ANN showed more accuracy as compared to the RSM tool. Therefore, the findings of this study showed that C. vulgaris is capable of treating PIWW efficiently along with significant production of lipid content which can further be used in various applications including biofuel production.

Modelling carbon dioxide gas emission from Notopterus chitala fish ponds by stella software

The rapid development of the areas of ​​Notopterus chitala fish ponds in Hau Giang province in recent years has raised a question about greenhouse gas emissions, in the form of total carbon dioxide equivalents (CO2e). There are many parameters that affect greenhouse gas emissions in a fish pond, such as amount of feed, dissolved oxygen (DO), chemical oxygen demand (COD) in the water, pH, water temperature, windy velocity and sunlight reaching the pond surface. In this study, a System Thinking, Experimental Learning Laboratory with Animation, shortly called as Stella is applied as a visual programming language for system dynamics modelling in order to find the relationship between simulated CO2 and measured CO2 in Notopterus chitala fish pond. Three ponds were used for measuring average pH, temperature, feeds, DO, COD and phytoplankton inside the ponds while windy speed and light intensity data were collected from a Weather Station nearby. The results of model calibration and validation showed that the Stella 8.0 can be used as predictable tool for the change in time of CO2 emission during 240 days of fishing. Model can help fishing farmers to adjust the quantity of feeds and control the water quality in their Notopterus chitala fish ponds to reduce greenhouse gas emissions appropriately.