The field of bioceramics has emerged as a critical component in various medical and dental applications, with calcium phosphate (CaP) materials like tricalcium phosphate (TCP) gaining significant attention. CaP bioceramics are valued for their exceptional biocompatibility, osteoconductivity, and ability to promote new bone formation, making them invaluable in the optimization of dental implant integration and performance. This study explores a novel approach to developing versatile CaP-based ceramics that can find applications in the pharmaceutical, dental, and even ancient artifacts preservation domains, leveraging the power of Machine Learning ((ML)) Modeling techniques. Tricalcium phosphate, a widely studied CaP ceramic, was the focus of this investigation, as it can be fabricated with varying degrees of crystallinity and porosity to tailor its biodegradation and bone regeneration properties. Through the use of a feedforward artificial neural network (FFANN), the researchers were able to predict the changes in dental ceramics, biocompatibility, and tissue reactions across a wide range of non-toxicity and bone growth parameters. The FFANN Modeling approach provided valuable insights into the relationships between these key attributes, allowing for the optimization of CaP-based ceramics for specific clinical and preservation applications. The versatility of TCP extends beyond dental implants, with applications in periodontal regeneration, tooth root repair, and even direct pulp capping procedures. By manipulating the material's composition and microstructure, researchers and clinicians can tailor the performance of CaP bioceramics to meet the diverse needs of the healthcare and cultural heritage sectors. As the field of bioceramics continues to evolve, the integration of advanced (ML) Modeling techniques, such as the FFANN approach employed in this study, promises to unlock new possibilities for the development of innovative, tissue-friendly ceramics that can revolutionize dentistry, pharmaceutical formulations, and the preservation of precious ancient artifacts.

Soil Temperature (ST) is critical for environmental applications. While its measurement is often difficult, estimation from environmental parameters has shown promise. The purpose of this study was to model ST in cold season from soil properties and environmental parameters. This study was conducted as a pot experiment in Ardebil, Iran. Automatic thermal sensors were installed at 5 and 10 cm depths. Besides, soil properties and environmental parameters were determined based on field and laboratory works. Machine Learning methods including Multiple Linear Regression ((ML)R), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for Modeling ST. The air temperature was observed as the most effective factor in ST Modeling. The relationship between soil and air temperature was stronger at 5 cm depth compared to 10 cm. The R2 between soil and air temperature was higher in the absence of sunlight than in its presence. The prediction of ANFIS (R2= 0. 96 and MAPE= 10. 5) was closer to the observed ST values compared to the ANN (R2= 0. 91 and MAPE= 35) and (ML)R (R2= 0. 57 and MAPE= 41). The results revealed the advantage of ANFIS method for ST Modeling. This approach can be applied for soil depths and locations with data gap.

IntroductionFinding the potential of groundwater resources is one of the basic principles in water resources management. The aim of this research is to determine the potential of groundwater using support vector Machine Learning (SVM) models as well as metaheuristic algorithms (hybrid support vector Machine model and the bee metaheuristic optimization algorithm (SVM-BA) and hybrid model of the support vector Machine and particle swarm optimization algorithm (SVM-PSO).Materials and methodsThe factors of elevation, slope, aspect, topographic humidity index, distance from stream, drainage density, distance from fault, lithology, topographic position index, land roughness index, relative slope position and flow convergence index were selected in Bojnurd region. Information on the location of 359 springs was received from the regional water company. Random division algorithm was used to divide training points (70%) and validation points (30%). Based on the removal sensitivity analysis, the importance and contribution of the input variables in determining the groundwater potential were determined. The accuracy of the models was evaluated in two stages of training and validation based on the receiver operating characteristic (ROC) curve method.ResultsThe evaluation of the accuracy of the models based on the evaluation criteria of the area under curve (AUC) showed that the prediction accuracy of the hybrid model of the support vector Machine and the particle swarm optimization algorithm (SVM-PSO) is 0.945 more than other models (SVM: 0.918 and SVM-BA: 0.932). Based on the results of the superior model, the high potential class and the very high potential class accounted for 7.75% and 38.66% of the area respectively. Among the factors, relative slope position with 14.5%, distance from the fault with 13.4% and lithology with 12.3% were the most important in predicting groundwater potential.Discussion and ConclusionBased on the results of this research, the support vector Machine model has a high performance, and two optimization algorithms, the bee metaheuristic and particle swarm optimization algorithm, strengthen the predictive power of the model. Also Machine Learning models can identify the relationship between the environmental factors and the water supply of the springs and determine their role by using the available data. The relative slope position factor was identified as the most important variable and the distance from the fault factor was considered as the second most important variable in the present study. The results of the research showed that the faults in the region play an important role in aquifer recharge, storage and flow of groundwater. The lithological factor was also introduced by the model as the third important variable in identifying the state of groundwater potential. In this research, by presenting the groundwater potential map, it is possible to plan and verify land use planning for the Bojnurd watershed.

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical Modeling based on Machine Learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affect the performance of an algorithm, however, it is yet to be known why an algorithm is preferred over the other for a certain task. The work aims at highlighting the underlying principles of Machine Learning techniques and about their role in enhancing the prediction performance. The study adopts, 38 most relevant studies in the field of environmental science and engineering which have applied Machine Learning techniques during last 6 years. The review conducted explores several aspects of the studies such as: 1) the role of input predictors to improve the prediction accuracy; 2) geographically where these studies were conducted; 3) the major techniques applied for pollutant concentration estimation or forecasting; and 4) whether these techniques were based on Linear Regression, Neural Network, Support Vector Machine or Ensemble Learning algorithms. The results obtained suggest that, Machine Learning techniques are mainly conducted in continent Europe and America. Furthermore a factorial analysis named multicomponent analysis performed show that pollution estimation is generally performed by using ensemble Learning and linear regression based approaches, whereas, forecasting tasks tend to implement neural networks and support vector Machines based algorithms.

Recently, many neural network methods have been proposed for multilabel classification in the literature. One of these recent methods is the Multi-Layer Extreme Learning Machines ((ML)-ELMs) in which stack auto encoders are used for tuning their weights. However, (ML)-ELMs suffer from three primary drawbacks: First, input weights and biases are chosen rando(ML)y; second, the pseudoinverse solution for calculating output weights will increase the reconstruction error; third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper, Multi-Layer Kernel Extreme Learning Machine ((ML)-CK-ELM) that uses a linear combination of base kernels in each layer is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data are inherently characterized by multi-modal aspects due to a variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of (ML)-CK-ELM that implicitly assesses the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than other kernels. The empirical study indicates that (ML)-CK-ELM shows competitively better performance than other state-of-the-art methods, and experimental results of multilabel datasets verify the feasibility of (ML)-CK-ELM.

This study focuses on the characterization and investigation of effective agents in medicinal plants and nanoparticles, aiming to understand their potential applications. X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques were employed to analyze the structural and morphological properties of the samples. XRD provided valuable information on crystalline phases, crystal structure, and lattice parameters, while SEM revealed surface morphology, particle size distribution, and aggregation behavior. These techniques facilitated a comprehensive understanding of the physical and chemical properties, crucial for effective utilization. Machine Learning ((ML)) analysis was employed to uncover patterns and correlations within the data. (ML) algorithms were used to identify significant features, establish predictive models, and gain insights into the relationships between sample properties and effective agents. This enhanced understanding of the factors influencing efficacy, paving the way for targeted applications. The study encompassed two main research areas. Firstly, a (ML) was developed to estimate Z, P>|Z|, and the 95% confidence interval by manipulating coefficients (COEF) and robust standard errors (ROBUST STD.ERR) in wider intervals compared to the experimental samples. The study revealed a direct relationship between coefficients and robust standard errors, with increasing coefficients leading to higher robust standard errors and an expanded 95% confidence interval. Additionally, the study emphasized the significance of income from Chinese medicinal materials in the financing process for growers, as income variations impacted their willingness to finance technology adoption. By exploring the connection between technology adoption and financing, the research aimed to enhance understanding and logical linkage, contributing to more effective and sustainable agricultural development.