Input Combinations Research Articles

Journal homepage: http://ijai.iaescore.com Detecting fraudulent financial statement under imbalanced data using neural network

In this paper a novel approach for detecting fraudulent financial statements by employing a combination of neural networks and synthetic minority over sampling technique (SMOTE) is introduced. This approach is designed to tackle the problem of imbalanced datasets prevalent in fraudulent cases, which if left unaddressed will hinder the model to accurately identify fraud. Three neural network models, each representing different fraud predictors as the input layer: 28 inputs raw financial data; 14 inputs financial ratios data; and 42 inputs combination both raw financial and financial ratios data are developed. Experimental validation using established research datasets is conducted to assess the performance of the proposed method. Performance metrics, namely area under the curve (AUC), precision, and sensitivity, are used for evaluation, comparing the proposed model against existing benchmark models found in literature. Results indicate that the proposed model achieves an AUC score of 70.6% and a precision score of 2.89%, in comparable to the existing models, with a sensitivity score of 83% outperforming all counterparts. The high sensitivity rate of the proposed model underscores its practical utility for auditors and regulators, as it minimizes the risk of false negatives, thereby enhancing confidence in fraud detection.

Design and Development of PLC Based Chemical Mixing Plant

This paper introduces an efficient solution for integrating a Programmable Logic Controller (PLC) into chemical industry processes, emphasizing precise mixing of three solutions. Leveraging conventional PLC components and ladder logic, the project employs digital inputs through push buttons to control relay-based outputs. Accurate filling ratios are achieved using a combination of time-based sequencing and user input. Code transmission challenges are addressed with a data integrity buffer and CH 340 drivers. Timing control utilizes the PLC's onboard timer (TON), while open PLC software and Atmel microcontroller libraries ensure smooth operation. This concise project offers a streamlined approach for PLC integration, optimizing chemical industry applications

An investigation of the surface topography of wire tool and the modelling of wire electro-spark machined Ti-alloy using ANN

Abstract Titanium and its alloys are in demand in the field of biomedical and aerospace engineering because of its high strength-to-weight ratio and anti-corrosive properties. WESM emerged as a processing method as traditional methods could not process because of strain hardening and low material conductivity; however, wire tool consumption contributes to high costs while processing with WESM. The current Article investigates the effect of input factors on wire consumption and their significance level on wire consumption. 81 experimental runs were conducted while taking input factors Spark on, Spark off time, peak current and servo voltage at three levels, each using a full factorial approach. Experimental results were analysed using ANOVA and MLR (Multi-Linear regression). Moreover, ANN modelling is done to model the wire consumption using a 4-10-1 architecture that increased the coefficient of correlation (R2) up to 0.991. Lastly, after processing, the wire tool surface is evaluated using a scanning electron microscope to assess the effect of various input energy levels and find input combinations for minimum wire consumption.

Determinants of Technical Efficiency of Harumanis Mango Production in Perlis, Malaysia

The Harumanis mango (Mangifera indica L.) is renowned in Malaysia for its distinctive taste and strong market appeal. Despite the potential for exporting Harumanis mangoes to Japan, productivity in Perlis remains low. This study aims to analyse the determinants of technical efficiency in Harumanis production across two key regions in Perlis, Mata Ayer and Chuping, which are known for having the highest mango yields in Malaysia. Using Stochastic Frontier Analysis (SFA) and the Cobb-Douglas production function, a dataset of 150 observations was collected from 50 mango farms over three consecutive growing seasons. The analysis examined the impact of various production inputs—such as labour, agrichemicals, plot size—and environmental factors, including temperature and rainfal on mango yield. The findings revealed significant disparities in the efficiency and effectiveness of inputs between Mata Ayer and Chuping. Labour positively influenced mango yield in both regions, although the magnitude of its effect differed. Agrichemical use showed positive correlations in Mata Ayer but insignificant or negative associations in Chuping. Temperature emerged as a critical environmental factor, particularly in Chuping, where higher temperatures were linked to reduced output. The interaction between inputs underscores the importance of tailored management strategies that consider specific local conditions and input combinations. The technical efficiency analysis revealed greater inefficiencies in Mata Ayer compared to Chuping. This study enhances the understanding of mango production dynamics and provides insights for targeted interventions to improve yield and sustainability in Harumanis mango cultivation.

Unveiling Productivity Dynamics Among Regional Rural Banks in India

This study employs the non-parametric Malmquist productivity index to evaluate the total factor productivity (TFP) change among the regional rural banks (RRBs) in India from 2016 to 2023. The analysis of productivity change among the RRBs holds intrinsic significance due to its potential impact on overall performance and to the delivery of quality services among the rural population. To evaluate the productivity change under varying combinations of input and output selections, two distinct approaches were used: the intermediation approach and the production approach. The empirical results reveal substantial variation in average TEP estimates across the RRBs under consideration throughout the sample period. Furthermore, the observed productivity changes predominantly emanated from technological change (TC) rather than efficiency improvements. Additionally, we found that the smaller RRBs were performing better than their larger counterparts. These empirical findings bear noteworthy policy implications, offering valuable insights for policymakers and regulators to formulate effective strategies to ensure the competitiveness and sustainability of RRBs in India.

Natural and Organic Input-Based Integrated Nutrient-Management Practices Enhance the Productivity and Soil Quality Index of Rice–Mustard–Green Gram Cropping System

The effects of integrated nutrient-management (INM) practices on soil quality are essential for sustaining agro-ecosystem productivity. The soil quality index (SQI) serves as a tool to assess the physical, chemical, and biological potential of soils as influenced by various edaphic and agronomic practices. A multiyear (2018–2021) field experiment was performed at the University Organic Research Farm, Narendrapur, West Bengal, India, to investigate the influence of integrated and sole applications of different conventional fertilizers, organic (e.g., vermicompost), and natural farming inputs (e.g., Dhrava Jeevamrit and Ghana Jeevamrit) on SQIs and crop productivity of rice–mustard–green gram-based cropping systems. A total of 12 parameters were selected for the assessment of SQI, amongst which only four, namely pH, organic carbon %, total actinomycetes, and bulk density, were retained for the minimum data set based on principal component analysis (PCA). In this study, the maximum SQI value (0.901) of the experimental soil was recorded in the INM practice of 25% organic and 25% inorganic nutrient inputs, and the rest with natural farming inputs, which augments the SQI by 24% compared to the 100% inorganic nutrient treatment. Amongst the different soil parameters, the highest contribution was from the pH (35.18%), followed by organic carbon % (26.77%), total actinomycetes (10.95%), and bulk density (6.98%). The yields in different cropping systems varied year-wise across treatments. Notably, the highest yield in rainy rice was estimated in the 100% organic treatment, followed by INM practices in the subsequent years, and finally, the combination of organic and natural inputs in the final year. In the case of mustard, the combination of organic and natural inputs resulted in the highest productivity in the initial and last years of study, while the 100% organic treatment resulted in higher productivity in subsequent years. Green gram showed a dynamic shift in yield between the 100% organic and integrated treatments over the years. Further, a strong correlation was also established between the soil physico-chemical parameters and the SQI. Overall, this study concludes that the natural and organic input-based INM practice enhances the soil quality and crop productivity of the rice–mustard–green gram cropping system under the coastal saline zone.

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of desalination plants is a key task for designing and implementing these facilities. In this regard, artificial intelligence techniques have proven to be powerful assets in this field. These methods offer an expedited and effective means of estimating effective parameters, thus catalyzing their implementation in real-world scenarios. In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. The next phase of this research employed the SHAP interpretability method to illustrate the impact of individual variables on the model's output. Moreover, the predictive performance of the developed frameworks was evaluated using a set of five dependable statistical measures: RMSE, NS, MAE, MAPE and R 2. These indicators were utilized to provide a robust means of gauging the precision of the model's forecasts. A comparative analysis of the outcomes, as measured by the RMSE criteria, revealed that the SVR technique (RMSE = 0.125 L/(h·m2)) exhibited superior performance compared to NGBoost (RMSE = 0.163 L/(h·m2)), AdaBoost (RMSE = 0.219 L/(h·m2)), CatBoost (RMSE = 0.149 L/(h·m2)), GPR (RMSE = 0.156 L/(h·m2)), and ERT (RMSE = 0.167 L/(h·m2)) methodologies in predicting permeate flow rates. The modelling outcomes obtained during the evaluation stage demonstrated the efficacy of the SVR algorithm in enhancing the precision of permeate flow forecasts, utilizing relevant input variables.

Cost-Effectiveness of Temporary Financial Assistance for Veterans Experiencing Housing Instability

The US Department of Veterans Affairs (VA) partners with community organizations (grantees) across the US to provide temporary financial assistance (TFA) to vulnerable veterans through the Supportive Services for Veteran Families (SSVF) program. The goal of TFA for housing-related expenses is to prevent homelessness or to quickly house those who have become homeless. To assess the cost-effectiveness of the SSVF program with TFA vs without TFA as an intervention for veterans who are experiencing housing insecurity. This study used a Markov simulation model to compare cost and housing outcomes in a hypothetical cohort of veterans enrolled in the SSVF program. Enrollees who are homeless receive rapid rehousing services, while those who are at risk of becoming homeless receive homelessness prevention services. The SSVF program with TFA for veterans who are experiencing housing insecurity. The effectiveness measure was the incremental cost-effectiveness ratio (ICER) with quality-adjusted life-years (QALYs). The model was parameterized using a combination of inputs taken from published literature and internal VA data. The model had a 2-year time horizon and a 1-day cycle length. In addition, probabilistic sensitivity analyses were conducted using 10 000 Monte Carlo simulations. The base case analyses found that the SSVF program with TFA was more costly ($35 814 vs $32 562) and yielded more QALYs (1.541 vs 1.398) than the SSVF program without TFA. The resulting ICER was $22 676 per QALY, indicating that TFA is the preferred strategy at a willingness-to-pay threshold of $150 000 per QALY. This ICER was $19 114 per QALY for veterans in the rapid rehousing component of the SSVF program and $29 751 per QALY for those in the homelessness prevention component of the SSVF program. At a willingness-to-pay threshold of $150 000 per QALY, probabilistic sensitivity analyses showed that TFA was cost-effective in 8972 of the 10 000 Monte Carlo simulations (89.7%) for rapid rehousing and in 8796 of the 10 000 Monte Carlo simulations (88.0%) for homelessness prevention only. This economic evaluation suggests that TFA is a cost-effective approach (ie, yields improved health benefits at a reasonable cost) for addressing housing insecurity for veterans enrolling in the SSVF program. Future research could examine the cost effectiveness of large, nationwide housing interventions such as this one among subpopulations of veterans such as those with certain comorbidities including severe mental illness or substance use disorders, those with chronic diseases, or those experiencing long-term housing instability vs acute loss of housing.

Sediment Simulation Using Multi Layer Perceptron (MLP), Co-Active Neuro-Fuzzy Inference System (CANFIS) and Multiple Linear Regression Tecniques (MLR) for Hurdag Watershed

Sediment modeling plays a crucial role in sustainable water resources planning, development, and management. Techniques like the Multilayer Perceptron (MLP), Co-active Neuro-Fuzzy Inference System (CANFIS), and Multiple Linear Regression (MLR) have proven effective for sediment modeling and forecasting. This study aimed to develop and assess the applicability of MLP, CANFIS, and MLR models by training and testing them during the monsoon season (June to September) for the Hurdag watershed in the Damodar-Barakar basin, located in Hazaribagh district, Jharkhand, India. Daily rainfall, runoff (or streamflow), and sediment concentration data from 1997 to 2006 were used, with the data split into two sets: training set (1997–2004) and a testing set (2005–2006). The analysis was conducted using NeuroSolution 5.0 software and Microsoft Excel for performance evaluation indices. The best input combinations for sediment yield simulation were identified, and 10 optimal models were selected from 31 different input combinations. These input combinations were applied to the network for training using the back propagation algorithm for MLP and Gaussian and generalized bell membership functions for CANFIS models. Multiple networks were trained individually, with the most accurate predictions during testing being chosen as the best models. The models’ performance was evaluated using statistical indices such as root mean squared error (RMSE), coefficient of efficiency (CE), and correlation coefficient (r). The results showed that MLP and CANFIS models performed best in predicting sediment concentration for the Hurdag watershed, whereas MLR models showed poor performance for the given dataset. Specifically, sediment concentration for the current day could be modeled using current day rainfall and runoff data (MLP-7), while runoff could be simulated using the previous day's rainfall data (MLP-2).

Wingsuit Flying Search Optimization Algorithm Strategy for Combinatorial T-Way Test Suite Generation

Advancement in software development has resulted in complex software applications that encompass various functional and non-functional requirements. Such a complex system usually consists of many inputs either directly from users or from other connected systems or devices. Here, there is a potential for the system to go wrong due to certain combinations of inputs. Combinatorial Testing (or T-Way Testing) is effective in tackling the issue. Numerous studies have proposed strategies in generating T-Way test suite, and current trend indicates that researchers often incorporate metaheuristic algorithms in their proposed strategies. Many recent studies employ parameter optimization algorithms such as (Whale Optimization Algorithm, Particle Swarm Optimization, Gravitational Optimization Algorithm) in generating an optimized T-Way test suite. Often, researchers need to tune the parameters involved in the algorithm before the algorithm can be used for test suite generation. Since the system under test (SUT) can come in various numbers of input, it is impossible to find a single best value for every algorithm parameter. As a result, this paper proposed a T-Way Test suite generator utilizing Wingsuit Optimization Algorithm (a parameter free optimization algorithm) for combinatorial test suite generation. The algorithm learnt by itself as the optimization process progresses and hence eliminates the need for control parameters. Statistical analysis shows that WFS produces a smaller test suite compares to most T-Way strategies and in some cases, the difference between test suite size produce by WFS and other T-Way strategies are insignificant.

Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment

Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption conditions, and the maximum Pb adsorption capacity of selected adsorbents have not been adequately explored. A thorough understanding of these interactions is crucial for optimizing adsorption processes and enhancing the efficiency of agricultural wastes as sustainable adsorbents. To assess Pb adsorption by agricultural wastes and identify the key influencing factors, three artificial intelligence techniques, namely Extreme Learning Machine (ELM), Adaptive Nuro-Fuzzy Inference Systems (ANFIS), and Group Method of Data Handling (GMDH) have been employed in this study. Seven input variables, namely time, ratio, initial ion concentration, type of adsorbents from agricultural wastes, pH, temperature, and agitation speed, from 771 data points were used as inputs for model development, while the quantity of Pb adsorbed was chosen as target parameter. To identify the best input combinations with one to seven variables, 127 models were defined and analyzed using ELM integrated with the cross-validation technique. The results highlighted that the initial ion concentration is the most critical factor in enhancing heavy metal adsorption, and temperature is the least important factor. The top models, utilizing one to seven input variable(s), were then modeled with ANFIS and GMDH. Subsequently, all three models were compared. The GMDH model with four input variables (initial ion concentration, type of adsorbent, time, and agitation speed) demonstrated the highest performance in terms of accuracy and simplicity.

Application of a hybrid fuzzy-based algorithm to investigate the environmental impact of sewer overflow

PurposeThis study underscores the critical importance of well-functioning sewer systems in achieving smart and sustainable urban drainage within cities. It specifically targets the pressing issue of sewer overflows (SO), widely recognized for their detrimental impact on the environment and public health. The primary purpose of this research is to bridge significant research gaps by investigating the root causes of SO incidents and comprehending their broader ecological consequences.Design/methodology/approachTo fill research gaps, the study introduces the Multi-Phase Causal Inference Fuzzy-Based Framework (MCIF). MCIF integrates the fuzzy Delphi technique, fuzzy DEMATEL method, fuzzy TOPSIS technique and expert interviews. Drawing on expertise from developed countries, MCIF systematically identifies and prioritizes SO causes, explores causal interrelationships, prioritizes environmental impacts and compiles mitigation strategies.FindingsThe study's findings are multifaceted and substantially contribute to addressing SO challenges. Utilizing the MCIF, the research effectively identifies and prioritizes causal factors behind SO incidents, highlighting their relative significance. Additionally, it unravels intricate causal relationships among key factors such as blockages, flow velocity, infiltration and inflow, under-designed pipe diameter and pipe deformation, holes or collapse, providing a profound insight into the intricate web of influences leading to SO.Originality/valueThis study introduces originality by presenting the innovative MCIF tailored for SO mitigation. The combination of fuzzy techniques, expert input and holistic analysis enriches the existing knowledge. These findings pave the way for informed decision-making and proactive measures to achieve sustainable urban drainage systems.

Methods for projecting health workforce planning in Ireland: a collaborative approach

Abstract Background Health workforce planning (HWFP) is the process of analysing, forecasting, and planning workforce supply and demand, assessing gaps, and determining target workforce to ensure that health systems are prepared for future demand. HWFP elicits recommendations that are ideally utilised by the health system to support medical workforce over a defined time. Methods The objective of this project is to inform intake for medical and surgical specialties in Ireland to reach the necessary consultant workforce by 2038. A literature search was conducted to help inform the methodology for this project. The most common approach was a needs-based model, and additional reviews discussed the integration of medical engagement into the modelling approach. Results This project will employ a collaborative approach, combining mathematical modelling and a combination of quantitative and qualitative inputs, including the following phases: 1. The research phase will estimate baseline supply and demand data across medical and surgical specialties in Ireland using statistical models; 2. The presentation phase will include input from clinical leads to allow for more accurate assumptions in the baseline of each model; 3. Review of projections will include feedback from the clinical leads to help clarify and improve the initial model; 4. Presentation of the revised projection will include further collaboration with clinical leads to finalise the model. Conclusions The robust methodology for this HWFP project will ideally inform medical and surgical specialities in Ireland through 2038. Specific and extensive predictions of health workforce supply and demand will enhance strategic preparation, which is a key priority for the Health Service Executive in Ireland. Key messages • A collaborative approach to HWFP is essential for health service preparation and improvement. • The project will be finalised by the end of 2024, and the methodology for conducting similar HWFP projects can be adapted by other EU states.

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.

To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI. The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value = 50-1500 s/mm2). Three readers evaluated the vCE images with regard to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p < 0.05). Non-significant effects (p > 0.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training. Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research.

Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms.

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.

Optimizing Nitrogen Input Increased Yield and Efficiency in Maize-Soybean Strip Intercropping System

Optimizing nitrogen (N) fertilizer combination is a crucial measure to maximize yield and production efficiency in a maize-soybean strip intercropping system (MSSI). In this field experiment, six maize/soybean N input combinations (0 kg ha−1, F0; 255/30 kg ha−1, F1; 255/45 kg ha−1, F2; 255/60 kg ha−1, F3; 210/45 kg ha−1, F4; 300/45 kg ha−1, F5) were set in 2022 and 2023. The results indicated that optimizing N combination (maize/soybean, 255/45 kg ha−1) could synergistically increase yield and economic benefits. Path analysis results showed that the grain numbers in maize and soybean emerged as the most critical yield-affecting factors. Compared with F0, F5 showed the highest grain yield during the 2-year experiment, and the net return increased by 86.1% (F1), 133.3% (F2), 87.4% (F3), 104.7% (F4), and 128.3% (F5), respectively. Optimizing N input under F2 and F5 notably enhanced the leaf area index (LAI) of maize at the milk stage (R3) and soybean at the full pod stage (R4). Additionally, optimization of N distribution in maize stems at the tassel stage (VT) and soybean leaves at the initial flowering stage (R1) facilitated increased dry matter and N accumulation at the maturity stage, resulting in final land equivalent ratios (LER) of 1.44 and 1.55, respectively. Our results provide a more valuable field N combination for summer maize planting areas (sandy soil areas) in Huang-Huai-Hai and southern China, thus promoting the wider application of MSSI.

Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm

For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TNinf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Qw, pH, SS, TP, NH3-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH3-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TNinf) in full-scale wastewater treatment plants.

Students’ Interactions and Communication Skills Towards Their Maximum Learning

This paper explores the critical role of interaction and communication skills in educational Settings, especially in the context of higher education in China, and analyzes the impact of these skills on maximizing student learning outcomes. The paper emphasizes the importance of effective interaction and communication as core elements in the process of language learning, because language acquisition and ability development depend on the effective combination of input and output. The paper proposes that a comprehensive educational reform is necessary to address the identified communication barriers of students, aiming at promoting the improvement of students' interaction and communication skills. Such reforms are essential to achieve the goal of high-quality undergraduate education and to develop innovative and communicative skills.

Model-driven development for functional correctness of avionics systems: a verification framework for SysML specifications

AbstractCurrently, the most widespread software quality assurance methods in the avionics domain are semi-automated reviews and testing. However, their effort grows disproportionately to the size of the system under development. Also, these methods cannot achieve exhaustive coverage due to the complexity of today’s avionics systems and their potentially infinite set of combinations of possible inputs and system states. Furthermore, the later software issues are detected in the development process, the more expensive it is to fix them. To overcome these issues, a model-driven verification approach for modeling and analyzing avionics systems in early phases of the development is presented. To this end, semantics is given to SysML v2 models by a mapping to a theorem prover encoding. The development of a dedicated SysML v2 profile supporting event-driven data flow specifications, the encoding of corresponding structures in the theorem prover Isabelle, and a generator creating theorems from SysML v2 models are presented. The approach is evaluated by formally proving a representative liveness property of a hierarchical system model from the avionics domain. Since liveness properties can be negated only by infinite data sequences and thus cannot be covered exhaustively by testing, this case study demonstrates the added value for meeting typical safety requirements in the avionics domain. The results can be transferred from avionics to other domains, as well.

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75–90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.