Explore the vast range of titles available.

Recently, interest in production capabilities as an integral part of economic development has revived. Fertility, an important component of population dynamics, remains a vibrant research topic in the empirical areas of economic development and public health. Examining the connection between productive capabilities and fertility outcomes is important. In this study, we propose the hypothesis that productive capabilities alter fertility choice. We rely on two indices of economic complexity (ECI+ and the original ECI) that try to measure productive capabilities indirectly by examining the mix of products exported by countries. Through a system GMM dynamic panel analysis conducted on a sample of low-and middle-income countries, we show that an improvement in the complexity of productive capabilities leads to a decrease in fertility rates. Our results survive a battery of robustness checks.

The development of an intelligent fault diagnosis system to identify automatically and accurately micro-faults affecting motors continues to be a challenge for industrial rotary machinery and needs to be addressed. In this paper, we put forward a novel approach based on ensemble empirical mode decomposition (EEMD) processing for incipient fault diagnosis of rotating machinery. Accurate selection and reconstruction processes are performed to reconstruct new vibration signals with less noise through the application of EEMD processing to original vibration signals. After the rebuilt of vibration signals, manually extracted features from the reconstructed vibration signals are fed then into a multi-class support vector machine and simultaneously to the mentioned technique, generated image representations of the same raw signals are taken afterward as an input to a deep convolutional neural network (CNN) for classification and fault diagnosis. The comparison between these developed methods demonstrates the effectiveness of the deep learning approach that identifies the differences between classes automatically and can successfully classify and locate the faulty bearing status with very high accuracy for the small size of training data.

In this paper, we propose a novel method for the classification of bearing faults using a convolutional neural network (CNN) and vibration spectrum imaging (VSI). The normalized amplitudes of the spectral content extracted from segmented temporal vibratory signals using a time-moving segmentation window are transformed into spectral images for training and testing of the CNN classifier. To show the efficiency of the proposed method, vibratory data for healthy and faulted bearings operating at different speeds are collected from an experimental test bench. The classification accuracy, variable load and speed testing, generalization, and robustness by adding noise to the collected data at different levels (SNR) are then evaluated. The obtained experimental classification results show excellent performance in terms of both accuracy and robustness.

This study investigated the potential of random forest (RF) algorithms for regionalizing the parameters of an hourly hydrological model. The relationships between model parameters and climate/landscape catchment descriptors were multidimensional and exhibited nonlinear features. In this case, machine-learning tools offered the option of efficiently handling such relationships using a large sample of data. The performance of the regionalized model using RF was assessed in comparison with local calibration and two benchmark regionalization approaches. Two catchment sets were considered: (1) A target pseudo-ungauged catchment set was composed of 120 urban ungauged catchments and (2) 2105 gauged American and French catchments were used for constructing the RF. By using pseudo-ungauged urban catchments, we aimed at assessing the potential of the RF to detect the specificities of the urban catchments. Results showed that RF-regionalized models allowed for slightly better streamflow simulations on ungauged sites compared with benchmark regionalization approaches. Yet, constructed RFs were weakly sensitive to the urbanization features of the catchments, which prevents their use in straightforward scenarios of the hydrological impacts of urbanization.

In this paper, numerous experimental tests were carried out to study the behavior of concrete containing glass fibers and confined with glass fiber-reinforced polymer (GFRP). Concrete specimens containing different fiber percentages ( 0.3 wt.%, 0.6 wt.%, 0.9 wt.% or 1.2 wt.%) and with different strengths of concrete (8.5 MPa, 16 MPa and 25 MPa) and different confinement levels (two, four and six layers of GFRP) were used as research parameters. The samples were tested to failure under pure axial compression. The results imply that the confinement effect with GFRP is relatively higher for concrete samples containing glass fiber (GFCC) with a percentage equal to 0.6 wt.%. The theoretical of stress ratios (fcc/fco) estimated by using existing ultimate strength models are found to be close to the experimental results for high strength of GFCC, but not close to the experimental results for low strength of GFCC.

In this paper, we establish a result for the existence of common fixed points for multi-valued mappings, satisfying some contractions for complex-valued b-metric spaces. Finally, we present an example to illustrate and support our results.

The future of groundwater is one of the key challenges for sustainable water management, hence the need to monitor its overall quality. The objective of this work is to assess the overall quality and determine the spatiotemporal evolution of the Angads aquifer in northeastern Morocco in 2014 and 2020, based on the parameters NH 4 + , NO 3 − , EC, Cl − , and FC, as well as the Geographic Information System (GIS). The results of the comparison of these five parameters between 2014 and 2020 show a general increase in NH 4 + and a decrease in NO 3 − and FC at most sampling points. These changes could be attributed to a shift in pollution sources or biological processes affecting water quality. On the other hand, the stability of EC and Cl − levels suggests a consistency in the inputs of salts or minerals. The quality percentages show a decrease in good, poor, and very poor quality, following an increase in average quality, from 10.52% (in 2014) to 5.26% (in 2020), 31.57% (in 2014) to 21.05% (in 2020), 31.57% (in 2014) to 26.31% (in 2020), and 26.31% (in 2014) to 47.36% (in 2020), respectively. Spatial and temporal mapping of the quality over these 2 years shows that the deterioration continues toward the east, southeast, and southwest. This is justified by very high measurements of the parameters NO 3 − , EC, and Cl − at sampling points 2, 3, 4, 5, 7, 8, and 15 for 2014 and 2020, reaching 156 mg/L, 10,570 µS/cm, and 3790 mg/L in 2014 and 134 mg/L, 10,355 µS/cm, and 3597 mg/L in 2020, respectively, due to effluents from pollution points such as the Oujda public landfill, the wastewater treatment plant, and the former Sidi Yahya landfill to the west. On the other hand, in the north, northeast, and northwest, there has been an improvement in quality due to the remoteness of these pollution points. In order to protect this vital resource, recommendations need to be put in place, in particular by treating leachates so as to ensure that the quality of the water is not discharged directly into the aquifer or used for other purposes, and to avoid discharging effluent from the wastewater treatment plant into the natural environment.

We report the synthesis of two novel halogenated nitro-arylhimachalene derivatives: 2-bromo-3,5,5,9-tetramethyl-1-nitro-6,7,8,9-tetrahydro-5H-benzo[7]annulene (bromo-nitro-arylhimachalene) and 2-chloro-3,5,5,9-tetramethyl-1,4-dinitro-6,7,8,9-tetrahydro-5H-benzo[7]annulene (chloro-dinitro-arylhimachalene). These compounds were derived from arylhimachalene, an important sesquiterpene component of Atlas cedar essential oil, via a two-step halogenation and nitration process. Characterization was performed using 1H and 13C NMR spectrometry, complemented by X-ray structural analysis. Quantum chemical calculations employing density functional theory (DFT) with the Becke3-Lee-Yang-parr (B3LYP) functional and a 6-31++G(d,p) basis set were conducted. The optimized geometries of the synthesized compounds were consistent with X-ray structure data. Frontier molecular orbitals and molecular electrostatic potential (MEP) profiles were identified and discussed. DFT reactivity indices provided insights into the compounds’ behaviors. Moreover, Hirshfeld surface and 2D fingerprint analyses revealed significant intermolecular interactions within the crystal structures, predominantly H–H and H–O contacts. Molecular docking studies demonstrate strong binding affinities of the synthesized compounds to the active site of protein 7B2W, suggesting potential therapeutic applications against various isolated smooth muscles and neurotransmitters.

The concept of external glass FRP composite confinement is a current process for strengthening concrete beams subjected to static loads. End anchorage glass FRP composites of 80 mm width and 90–130 mm length with different thicknesses (2.4 and 4.8 mm) have been fixed at the bottom of beams with bolts of various diameters (6 and 10 mm). For this purpose, the behavior of beams strengthened with bolt-end anchoring glass fiber polymer composites (BEGFPC) has been analyzed. It is concluded that the load capacity of the BEGFPC beams is improved by increasing the end-anchorage glass FRP composite thickness (about 98–188%). In addition, the BEGFPC system with bolts of 6 mm diameter has significantly improved the flexibility of beams. In contrast, the 10 mm bolts in diameter give a high ultimate load, whatever their quantity. Therefore, combining bolts with diameters of 6 and 10 mm would be the best solution for increasing the ultimate load and ductility of the retrofitted beams. Depending on the number and bolts' arrangement, there is also an enhancement in the crack patterns by changing from intermediate flexural failure to shear failure in beams.

Ferroelectric, phase-change, and magnetic materials are considered promising candidates for advanced memory devices. Under the development dilemma of traditional silicon-based memory devices, ferroelectric materials stand out due to their unique polarization properties and diverse manufacturing techniques. On the occasion of the 100th anniversary of the birth of ferroelectricity, scandium-doped aluminum nitride, which is a different wurtzite structure, was reported to be ferroelectric with a larger coercive, remanent polarization, curie temperature, and a more stable ferroelectric phase. The inherent advantages have attracted widespread attention, promising better performance when used as data storage materials and better meeting the needs of the development of the information age. In this paper, we start from the characteristics and development history of ferroelectric materials, mainly focusing on the characteristics, preparation, and applications in memory devices of ferroelectric wurtzite AlScN. It compares and analyzes the unique advantages of AlScN-based memory devices, aiming to lay a theoretical foundation for the development of advanced memory devices in the future.