Artificial intelligence and machine learning models for predicting the metallurgical performance of complex sulfide ore flotation process

The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

Efficient maximum power tracking technique in grid connected PV-wind system

This paper presents an adaptive neuro fuzzy interference system (ANFIS) trained voltage response for a wind-diesel based isolated hybrid power system (IHPS). The system is studied by introducing 1% random variables as probabilistic pattern with 10% increase in reactive power load demand and wind input power simultaneously. STATCOM is used as dynamic reactive power compensator. The proportional integral gain constants in Static Compensators (STATCOM) are tuned with genetic algorithm (GA) technique, voltage response of GA tuned system is used for ANFIS training. The system performance is compared on the basis of system transient parameters.

In the traditional approach to a digital asset management system, the data processing mechanism is not transparent or visible to the data owners since the data is managed solely by the service provider. With the rapid development of blockchain technology, the above issues can be resolved by leveraging the tamper-resistance and decentralization characteristics of blockchain. However, post the implementation of the EU General Data Protection Rules (GDPR) in 2018, the protection of data owners has taken center stage. This has led to several principles of personal data deletion, such as Storage Limit and the Right to Be Forgotten to conflict with the blockchain. It is also observed that, out of the various smart contracts deployed to manage digital assets, often only specific smart contracts are invoked, while the rest of the deployed smart contracts are rarely invoked, leading to smart contract designs exhibiting similar patterns with very little creativity. This current scenario has motivated us to propose SPChain, a smarter and private GDPR-compliant digital asset management framework enabled by blockchain. In this approach, a decentralized InterPlanetary File System has been adopted to solve the problem of SPOF. In addition, the combination of digital assets with artificial intelligence models has been proposed so as to make digital assets accessible to a larger number of applications and to enable better creativity. In this design, artificial intelligence models have been run in independent, virtualized containers and invoked through smart contracts. The proposed SPChain can be applied to the field of digital art management to provide a complete implementation based on the Hyperledger Fabric. Using this proposed framework, model developers, digital art creators, collectors, service providers, as well as third parties can not only benefit from securely managing digital assets and combining them with AI models, but also from simultaneously complying with the rights stipulated in the GDPR. During the course of the experiments conducted, the latency, throughput, and resource consumption of different functions in the smart contracts have been measured. After adjusting the batch timeout of the block and the maximum number of transactions in a block, the throughputs were observed to be about 500 TPS, with 10 to 15 TPS for reading and writing operations, respectively. The latency ranges were found to range from 0 to 7 seconds, with 2.5 to 5 seconds for reading and writing operations, respectively.

In this paper, a fuzzy interference and an adaptive neuro-fuzzy interference system models have been presented in order to accelerate designing of the digital holographic setup without experiment. The setting parameters of experimental holographic setup, which affect the quality of images obtained from reconstructed holograms, are predicted digitally by proposed models before the recording process. Hence, we reduce the required time for designing of digital holographic setup with optimization process. The adaptive neuro-fuzzy interference system model for the optimization of the digital holographic setup is first attempt in the literature. The accuracy of the proposed models is examined by comparing the presented models and actual calculated experimental root-mean-square values. As a result, the accuracy of the adaptive neuro-fuzzy interference system shows the better performance than the fuzzy interference system. Moreover, the design of experimental setup can be occurred numerically in a short time by using adaptive neuro-fuzzy interference system models.

ABSTRACTObtaining high recovery in coarse particle size fractions has been always under investigation in recent years. In the present study, stage addition of chemical reagents is used to improve copper grade and recovery of coarse size fractions (i.e., >100 µm) at industrial rougher flotation cells. Due to the poor efficiency of primary grinding circuit, coarse particles were detected as predominate size fraction in rougher flotation feed. Sampling results from rougher tailings showed that 58% of precious copper minerals were distributed to size fraction of −105 + 297 µm. Stage addition of collectors including sodium isopropyl xanthate (Z11), dithiophosphate and mercaptobenzthiazole (Nascol-451), and xanthate-flomin (C-4132) and frothers as methyl isobutyl carbonyl (MIBC) and Aerofroth 65 (A65) were implemented to avoid losing of coarse valuable particles in rougher tailings. Three different patterns were given for stage additions as 75-25-0, 75-0-25, and 75-13-12. The results indicated that copper grade of tailings declined relatively 56% using optimized stage addition of reagents at the head of rougher banks (i.e., 75-13-12). In addition, copper recovery of rougher cells improved around 2%. Size by size analysis of copper recovery for the rougher cells revealed the significant effect of the stage addition of chemical reagents on recovery improvement of coarse particles.

The power quality of an electrical system is critical for industrial, commercial, and housing applications, and with the increasing use of sensitive loads, customers and utilities are beginning to pay more attention to it. A distribution static synchronous compensator (D-STATCOM) represents one of the best custom power devices (CPDs) for improving the power quality of a distribution system. The performance of this device relies upon the algorithm and strategy used for its control. Artificial intelligence was utilized to overcome these shortcomings, while a response optimizer tool was used for the tuning process. An adaptive controller design was also proposed, based on the integration of fuzzy logic with traditional proportional-integral (PI) controller. The fuzzy logic controller system was designed using the adaptive neuro fuzzy interference system (ANFIS) editor. In this work, a D-STATCOM controller was used to mitigate sag and swell problems, while the ANFIS together with the optimization method was used to improve the system response. This study was carried out using MATLAB/Simulink, and the results showed a superior and adaptive performance in mitigating voltage sag and swell problems at different loading conditions compared to the traditional PI.

This study evaluates the performance of AI models, ChatGPT-4o and Google Gemini, in answering rheumatology board-level questions, comparing their effectiveness, reliability, and applicability in clinical practice. A cross-sectional study was conducted using 420 rheumatology questions from the BoardVitals question bank, excluding 27 visual data questions. Both artificial intelligence models categorized the questions according to difficulty (easy, medium, hard) and answered them. In addition, the reliability of the answers was assessed by asking the questions a second time. The accuracy, reliability, and difficulty categorization of the AI models' response to the questions were analyzed. ChatGPT-4o answered 86.9% of the questions correctly, significantly outperforming Google Gemini's 60.2% accuracy (p < 0.001). When the questions were asked a second time, the success rate was 86.7% for ChatGPT-4o and 60.5% for Google Gemini. Both models mainly categorized questions as medium difficulty. ChatGPT-4o showed higher accuracy in various rheumatology subfields, notably in Basic and Clinical Science (p = 0.028), Osteoarthritis (p = 0.023), and Rheumatoid Arthritis (p < 0.001). ChatGPT-4o significantly outperformed Google Gemini in rheumatology board-level questions. This demonstrates the success of ChatGPT-4o in situations requiring complex and specialized knowledge related to rheumatological diseases. The performance of both AI models decreased as the question difficulty increased. This study demonstrates the potential of AI in clinical applications and suggests that its use as a tool to assist clinicians may improve healthcare efficiency in the future. Future studies using real clinical scenarios and real board questions are recommended. Key Points •ChatGPT-4o significantly outperformed Google Gemini in answering rheumatology board-level questions, achieving 86.9% accuracy compared to Google Gemini's 60.2%. •For both AI models, the correct answer rate decreased as the question difficulty increased. •The study demonstrates the potential for AI models to be used in clinical practice as a tool to assist clinicians and improve healthcare efficiency.

Effect of aging time, airflow rate, and nonionic surfactants on the surface tension of bulk nanobubbles water

This study aims to examine the virtues of the people of al-Shām through a human analytical lens and compare the findings with those generated by artificial intelligence models. The research adopts an inductive methodology for data collection and analysis, alongside an applied approach to compare human interpretations with the outputs of AI models. The results indicate that the Gemini model demonstrated superior performance in understanding and analyzing religious and cultural texts, offering interpretations that were more accurate and contextually appropriate than those provided by ChatGPT. These findings underscore the importance of advancing AI models capable of effectively engaging with religious and cultural content, while also highlighting the distinctions between human and machine-based analysis.

Introduction: Oral squamous cell carcinoma (OSCC) is the most prevalent oral malignant neoplasm. Cytopathology may represent an important tool in the screening of OSCC, and liquid-based oral brush cytology (LBOBC) has been widely studied because of its clearer cell sample results. These cytopathological analyses could be more efficient with the aid of artificial intelligence. The objective of this study was to analyze the effectiveness of two AI models (Papanicolaou and AgNOR Slide Image Examiners) in LBOBC analyses. Methods: Two human evaluators and the AI models performed cell maturation pattern analysis and mean nucleolar organizer region (NOR) per nucleus count in Papanicolaou and silver-stained nucleolar organizer region (AgNOR) oral cytopathological samples of 20 individuals, respectively. Inter-evaluator agreement was evaluated by kappa and intraclass correlation coefficient. Chi-square and Wilcoxon matched-pairs/Friedman tests analyzed differences between the conventional and LBOBC methods and among evaluators. Results: Kappa between the Papanicolaou AI model and each human researcher was substantial (k = 0.69) for the conventional method and moderate for the LBOBC (k = 0.55–0.53. There were statistical differences in the cellular type analysis between cytology methods and among evaluators (p < 0.001). The automated AgNOR model showed an excellent/highly good agreement with human evaluators for NOR count in both cytology methods, with and without bounding boxes. There was no statistical difference in the NOR count between methods (p > 0.05). In the conventional method, there were differences among evaluators (p < 0.05); in the LBOBC, there were not (p > 0.05). Conclusion: The AgNOR automated model is reliable when assessing NOR count in oral samples processed by different cytological methods, when compared to the human analysis. The Papanicolaou model still needs more training with LBOBC samples.

Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic geomechanical properties including: Poisson’s ratio, Young’s modulus, and Lame parameters. These parameters are mainly used in estimating the static properties of the formation rocks as well as the in situ stresses. The sonic logs are not always available, epically for old wellbores. Also, in several occasions when the sonic logs are available, missing sections found in the well logs might affect the analysis results. To the authors’ knowledge, there is no single straightforward correlation that can be used to accurately estimate both P- and S-wave travel times directly from the well log data. Most of the existing correlations use the P-wave velocity to measure the S-wave velocity. The main purpose of this study is to develop accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave). These wireline logs are slandered wireline log data that are commonly recorded in most of the wells. Three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance. Ultimately, using the weights and biases of optimized ANN model, a simple generalized empirical correlation is derived that can be used without the need of costly commercial software’s to run the AI models. The obtained results showed that ANN, ANFIS, and SVM can be used to estimate P-wave and S-wave travel times. ANN outperformed the ANFIS and SVM by yielding the lowest average absolute percentage error (AAPE) and the highest coefficient of determination $$\left( R^{2}\right) $$ for predicting P-wave and S-wave travel times. ANN model could predict the P-wave and S-wave travel times from wireline log data with high accuracy giving $$R^{2}$$ of 0.98 when compared to actual field data. In addition, the developed empirical correlations prediction completely matched the ANNs prediction. The AAPE of the predicted P and S-waves travel times was less than 5%. The developed correlations are very accurate and can help geomechanical engineers to determine the dynamic geomechanical properties (Poisson’s ratio and Young’s modulus) and propose any operation in case where sonic logs are missing.

CAMERON VU, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and DARIA PANOVA, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and JOSIAH KOWALSKI, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. W. LIAO (Faculty Advisor), Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. O. Guzide (Faculty Advisor), Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443. Smart Parking Space Detection with Generative Artificial Intelligence and Large Language Models. The increasing relevance of generative AI and large language models is reshaping various sectors of modern society. These advancements have spurred notable progress in fields such as healthcare, finance, and education. Yet, the application of AI extends beyond expert domains, offering simplified solutions to everyday tasks for the general populace. This project harnesses the power of generative artificial intelligence and large language models to develop a practical application: smart parking space detection. By leveraging these technologies, individuals can effortlessly ascertain the availability of parking spots in monitored lots via camera or photographic monitoring, facilitated by a straightforward algorithm. The overarching objective is twofold: to engineer a user-friendly system utilizing generative AI principles and to demonstrate the potential for such technologies to enhance the daily experiences of ordinary individuals.

In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI’s architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.

Influence of operating parameters on nanobubble-assisted flotation of graphite