Search

Photon management strategies are crucial to improve the efficiency of perovskite thin film (PTF) solar cell. In this work, a nano-cone (NC) based 2D photonic nanostructure is designed and simulated aiming at achieve superior light trapping performance by introducing strong light scattering and interferences within perovskite active layer. Compared to the planar PTF solar cell, the NC nanostructured device with 45 degrees half apex angle obtains highest short-circuit current density, which improved over 20% from 15.00 mA/cm2 to 18.09 mA/cm2. This work offers an alternative design towards effective light trapping performance using 2D photonic nanostructure for PTF solar cell and could potentially be adopted as the nano-structuring strategy for the future perovskite solar cell industry. [ABSTRACT FROM AUTHOR]

Solid waste management is a critical issue worldwide. Effectively utilizing these solid waste resources presents a viable solution. This study focuses on Iron ore tailings (IOTs), a solid waste generated during iron ore processing, which can be used as supplementary cementitious materials (SCMs) but have low reactivity, hindering their large-scale application in concrete production. To address this, ternary SCMs were prepared using ceramic powder (CP) and steel slag (SS) to enhance the performance of concrete incorporating IOTs. The study found that the synergistic effect of CP and SS significantly improved the compressive strength of concrete, with a notable increase of up to 21% compared to concrete with IOTs alone. Mercury intrusion porosimetry (MIP) and backscattering electron (BSE) analyses revealed that the ternary SCMs significantly optimized the characteristics of the interfacial transition zone (ITZ), which in turn enhanced the compressive properties of the concrete. This contributed to maintaining the structural integrity of the concrete, even amidst variations in the pore structure. Importantly, the incorporation of ternary SCMs led to a 23% reduction in carbon emissions, from 400.01 kg CO2/m3 to 307.48 kg CO2/m3, and elevated eco-strength efficiency from 0.1 to 0.14. The study highlights the role of multi-material synergy in developing composite SCMs systems, fostering the sustainable advancement of green building materials. [ABSTRACT FROM AUTHOR]

As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields. [ABSTRACT FROM AUTHOR]

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology. This study presents a deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine resolution satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types. [ABSTRACT FROM AUTHOR]

Ultra-High Performance Concrete (UHPC) was hindered by the use of non-renewable quartz sand, so there was a need to find a new sustainable material to replace quartz sand. This study investigates the possibility of using copper slag as an aggregate instead of quartz sand to make sustainable UHPC. To ensure the workability and good particle build-up of UHPC, the copper slag particle size was differentiated into three classes to replace quartz sand of the same particle size. UHPC mortars were prepared at 0, 25%, 50%, 75%, and 100% substitution rates and tested for flow, compressive, flexural, and pore structure. The test results showed that copper slag has a dense structure and good compression properties. The smooth surface and poor water absorption of copper slag can significantly improve the flowability of UHPC mortar. The inherent high density, high abrasiveness, high compressibility, and sharp edges of copper slag material can improve the strength of UHPC mortar. A good particle gradation allows the material to build up more compactly and reduces porosity. The results of the study show that it is feasible to use copper slag as a fine aggregate to replace quartz sand at a substitution level of 50%. [ABSTRACT FROM AUTHOR]

It is difficult to test the engineering properties of undisturbed cohesionless coarse-grained fillers in the laboratory, especially those with low water contents because most sampling methods disturb the natural structure of the filler. This study proposes a novel sampling method that uses menthol as a temporary cement to obtain samples that are more resistant to disturbance. Liquid menthol infiltrates the interparticle spaces and cements the particles together as it cools, forming a high-strength structure. The samples can then be cut with a cutting ring (for grain sizes ≤ 0.5 mm) or a cutting machine (for grain sizes > 0.5 mm) to prepare the final samples, from which the menthol is removed by heating. To demonstrate the feasibility of the method, a number of laboratory experiments were performed, including sampling tests, confined compressive strength tests, vibration tests, drop tests, and CT scans. The optimal particle temperature for introducing the liquid menthol is 70°C, while 90°C is optimal for removing the cemented menthol. For both cost and anti-disturbance reasons, 20°C is suggested as a suitable storage temperature for menthol-cemented samples. The interspace structure undergoes less change with increases in sample depth and with larger grain sizes. We recommend that the lower two-third of the depth of a menthol-cemented block is used as the final sample for laboratory testing. [ABSTRACT FROM AUTHOR]

The optimization of open pit mine production scheduling is not only a multistage decision-making problem but also involves space–time dynamic action among multiple factors, which makes it difficult to optimize production capacity, mining sequence, mining life, and other factors simultaneously in optimizing design. In addition, the production capacity is disorderly expanded, the calculation scale is large, and the optimization time is long. Therefore, this article designs a mobile capacity search domain method to improve computing efficiency without omitting the optimal production capacity. At the same time, taking the maximum net present value as the objective function, an enumeration method is used to optimize the possible paths in different capacity domains and calculate the infrastructure investment and facility idle cost required to meet the maximum production capacity on each possible path to control the disorderly expansion and violent fluctuation of production capacity. The research shows that the open pit mine production scheduling optimization algorithm proposed in this article can not only realize the simultaneous optimization of the three elements of production capacity, mining sequence, and mining life but also improve the computing efficiency by 200 times. Furthermore, the production capacity fluctuation is less than 1.4%. The mining life of the mine is extended by 13 years, and the overall economic benefit is increased by 18%. [ABSTRACT FROM AUTHOR]

Recently, the increasing of ultrafine-tailings increases the amount of ordinary Portland cement (OPC) in cemented paste backfill (CPB), which leads to the rise of CPB cost and carbon emission. As a result, it is necessary to develop alternative binders. The present work focuses on the preparation of a new binder, which is activated by a mixture of calcined quarry dust (CQD) and NaOH at a mass ratio of 1:1. The results indicated that CQD/NaOH was more effective than using NaOH or CQD alone in activating blast furnace slag (BFS) and also showed better performance than OPC. The compressive strength of the CPB samples using 10% CQD/NaOH was around 3.78 MPa after curing for 90 days, around 42% higher than the OPC-based CPB samples. The reaction products of CQD/NaOH-activated BFS consisted mainly of C-(A)-S–H, hydrotalcite like phases (Ht), and M-S–H. The generation of Ht phases lowered the Al incorporation into the structure of C-S–H, resulting in lower average Al/Si ratio and mean chain length. [ABSTRACT FROM AUTHOR]

Rainfall infiltration is the main factor of hydraulic erosion and deformation instability of tailings reservoir slope. Through the hydraulic erosion test of tailings reservoir slope based on rainfall, the parameters of slope internal moisture content, runoff sediment concentration, runoff, start time of surface erosion and gully erosion and slope velocity were tested. The slope protection effect of ecological base material of tailings reservoir slope determined in the previous study was evaluated. The results show that the vegetation allocation model combined with grass, shrub and flower is effective in hydraulic erosion protection of tailings reservoir slope. The critical slope range of ecological slope model of tailings reservoir is 37.5°–42.3° under three kinds of rain intensity. The research results can provide certain theoretical and technical support for the safe operation of tailings reservoir and the development of ecological slope protection theory of tailings reservoir. [ABSTRACT FROM AUTHOR]

In view of the problem of soil erosion control and ecological restoration of the tailings reservoir slope, the characteristics of runoff, sediment yield, and velocity of sheet flow of the tailings reservoir slope under four conditions (goosegrass, vetiver, clover, and bare slope) were studied by conducting a laboratory test of hydraulic erosion. Based on the direct shear test and triaxial shear test of the root–soil composites, the optimum range of root–soil composites was determined, and the stress–strain relationship, strength characteristics, and failure form of root–soil composites under different confining pressure were studied. The results show that as a whole, the characteristics of runoff, sediment yield, and velocity of sheet flow in the tailings slope under the condition of vetiver are more advantageous than other vegetation to the reinforcement of the slope. The shear strength of the root–soil composites increases with the increase of the root content, and the shear strength of the root–soil composites remains unchanged or decreased when it reaches a certain range; the interval of the root content 0.3–0.4% is the best root content of the root–soil composites. This study's results can provide an important theoretical basis and reference value for comprehensive control of a tailings reservoir slope and restoration of ecological stability. [ABSTRACT FROM AUTHOR]

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples. [ABSTRACT FROM AUTHOR]

The cyclic wetting-drying phenomenon is a complicated physical and chemical process. This kind of process will weaken the properties of rock to some extent. Some fundamental physical parameters of sandstone were first tested to study the weakening influence of wetting-drying cycles on the physical characteristics. Then, the water weakening effect on the mechanical and acoustic emission (AE) characteristics of rock on account of wetting-drying cycles was experimentally studied. Laboratory test results showed that when the number of cycles increased from 0 to 25, the density, P-wave velocity, uniaxial compressive strength (UCS), (elastic modulus (E)) and total AE counts of the rock specimens continuously decreased. However, the water absorption would increase as the number of cycles increased. The functional relationships between the total AE counts and the mechanical parameters (UCS and E) of sandstone after each cyclic number were established based on the test results. In addition, the chemical components and microstructure variations of rock undergoing wetting-drying cycles were examined. From the results, it can be concluded that microcrack growth and expansion inside the rocks undergoing wetting-drying cycles are the main reasons for the attenuation of the rock properties. Moreover, with increasing loading rate, the UCS, E and total AE counts of sandstone after each cyclic number would increase. [ABSTRACT FROM AUTHOR]

In order to address high dimensional problems, a new 'direction-aware' metric is introduced in this paper. This new distance is a combination of two components: (1) the traditional Euclidean distance and (2) an angular/directional divergence, derived from the cosine similarity. The newly introduced metric combines the advantages of the Euclidean metric and cosine similarity, and is defined over the Euclidean space domain. Thus, it is able to take the advantage from both spaces, while preserving the Euclidean space domain. The direction-aware distance has wide range of applicability and can be used as an alternative distance measure for various traditional clustering approaches to enhance their ability of handling high dimensional problems. A new evolving clustering algorithm using the proposed distance is also proposed in this paper. Numerical examples with benchmark datasets reveal that the direction-aware distance can effectively improve the clustering quality of the k-means algorithm for high dimensional problems and demonstrate the proposed evolving clustering algorithm to be an effective tool for high dimensional data streams processing. [ABSTRACT FROM AUTHOR]

Information hiding technology has always been a hot research field in the field of information security. How to hide information in images is also a major research field of image transformation. Today’s information hiding technology has the problems of complicated encryption technology and large amount of information. Based on the genetic algorithm, this paper studies the image information hiding technology based on genetic algorithm, performs image simulation on different types of mothers, and displays the results. For the images that need to be hidden, no matter how the size changes, the mother image can achieve good information hiding. The change of the parent image is not large. Compared with the Least Significant Bit (LSB) technique, this method has a larger peak signal-to-noise. [ABSTRACT FROM AUTHOR]

In order to effectively improve the flexural strength and fracture toughness of ceramics, two methods can be used to control the growth rate of grain during sintering of ceramic materials by adding a certain amount of (W,Ti)C material. Therefore, on the one hand, the paper simulates the composite ceramic materials based on cellular automata (CA) to optimize the ratio of two formulations. On the other hand, in order to optimize sintering process, CA modeling is also carried out for the sintering process of composite ceramics. Finally, in order to detect the temperature of the firing zone of the ceramic kiln by using the characteristics of the flame image of the firing zone, the K-means clustering method is used for the color segmentation of the flame image of the firing zone of the ceramic kiln. The experimental results show that the size of the grains is in accordance with the actual situation of the simulation and the microstructure evolution of the composites can be simulated well by using the CA theory to simulate the composites; with the increase of simulation time, the grain size distribution is basically unchanged, which accords with the normal distribution, and the simulation process of grain growth is very stable. Based on the K-mean clustering segmentation method, the segmentation of the flame image of ceramic kiln firing zone is realized. This method also provides a good technical means for feature extraction of flame image in ceramic kiln firing zone. [ABSTRACT FROM AUTHOR]