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This paper is focused on the analysis of the morphological and thermal properties of the biomedical composites, polylactic acid (PLA) and polycaprolactone (PCL) matrix, reinforced with basalt fibers (BFs) and using halloysite nanotubes (HNT) as filler material. Four different composites, viz. PPHB 1, PPHB 2, PPHB 3 and PPHB 4, are obtained by varying the weight fractions of these materials using twin-screw extrusion followed by injection molding. The morphological characterization is performed on these composites using scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. SEM reveals homogenous and strong bonding between the matrix, reinforcement and filler. The BF are well embedded in the matrix with a random orientation. No formation of voids and cracks is observed. The functional groups present and the types of vibration experienced by the chemical bonds were observed in the FTIR spectra. The composites are subjected to thermal testing such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The PPHB 2, which contains 80% PLA, 10% BF, 7% PCL and 3% HNT, has the highest degree of crystallinity, as revealed by DSC, and exhibits the most optimum thermal degradation characteristics as indicated by TGA.

Water harvesting is a must and not an option for Jordan in the near future. The available water resources are limited and barely satisfy the current growing demands. In the basalt aquifer/NE of Jordan, there is a possibility to construct water harvesting ponds to assist the livestock owners in villages and towns in watering their livestock which will ease the pressure on the existing water resources. This research was based on the use of GIS techniques to select the optimum sites for water harvesting ponds within the basalt aquifer. Buffering, Union and arithmetic operations in GIS were all used in analysing the data based on different socio-economic and physical criteria. The outcomes of the GIS analysis resulted in selecting 72 suitable sites within the study area. These sites could be used as a guideline to the decision makers in Jordan when they decided to establish a water harvesting pond in the area. This will save time and money which could be directed to the construction of the actual water harvesting ponds.

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.