In this paper, the development law of residual deformation of coal gangue subgrade filler is analyzed through large scale triaxial test, and the residual deformation model of coal gangue mainly ...sandstone and limestone is established. The purpose is to provide research basis for the applicability of coal gangue as subgrade filler. The results show that the deformation of coal gangue filler increases first and then tends to be constant under cyclic load of multiple vibration times. It is found that the Shenzhujiang residual deformation model cannot accurately predict the deformation law, and the corresponding modification is made to the residual deformation model of coal gangue filling body. Finally, according to the calculation of grey correlation degree, the influence degree of main factors of coal gangue filler on its residual deformation is sorted. Combined with the actual engineering situation represented by these main factors, it can be analyzed that the effect of packing particle density on residual deformation is greater than that of packing particle size composition.
Sonar images are inherently affected by speckle noise, which degrades image quality and hinders image exploitation. Despeckling is an important pre-processing task that aims to remove such noise so ...as to improve the accuracy of analysis tasks on sonar images. In this paper, we propose a novel transformer-based generative adversarial network named SID-TGAN for sonar image despeckling. In the SID-TGAN framework, transformer and convolutional blocks are used to extract global and local features, which are further integrated into the generator and discriminator networks for feature fusion and enhancement. By leveraging adversarial training, SID-TGAN learns more comprehensive representations of sonar images and shows outstanding performance in speckle denoising. Meanwhile, SID-TGAN introduces a new adversarial loss function that combines image content, local texture style, and global similarity to reduce image distortion and information loss during training. Finally, we compare SID-TGAN with state-of-the-art despeckling methods on one image dataset with synthetic optical noise and four real sonar image datasets. The results show that it achieves significantly better despeckling performance than existing methods on all five datasets.
Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set
of
elements, the problem asks for a subset
of k≪n ...elements with maximum diversity, as quantified by the dissimilarities among the elements in
. In this paper, we study diversity maximization with fairness constraints in streaming and sliding-window models. Specifically, we focus on the max-min diversity maximization problem, which selects a subset
that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set
is partitioned into
disjoint groups by a specific sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset
contains ki elements from each group i∈m. Although diversity maximization has been extensively studied, existing algorithms for fair max-min diversity maximization are inefficient for data streams. To address the problem, we first design efficient approximation algorithms for this problem in the (insert-only) streaming model, where data arrive one element at a time, and a solution should be computed based on the elements observed in one pass. Furthermore, we propose approximation algorithms for this problem in the sliding-window model, where only the latest
elements in the stream are considered for computation to capture the recency of the data. Experimental results on real-world and synthetic datasets show that our algorithms provide solutions of comparable quality to the state-of-the-art offline algorithms while running several orders of magnitude faster in the streaming and sliding-window settings.
Indium selenide (InSe) photodetection devices attract significant research interest. However, InSe is unstable and degrades rapidly in ambient conditions, thus it is still a challenge to fabricate ...stable optoelectronic devices. In this work, multilayer InSe FETs are fabricated, and their photoresponse properties are investigated. Both positive and negative photoconductivities are observed for the first time in the same InSe FET in a wide spectral range from 450 nm to 660 nm, which can be tuned through changing either the gate bias or the source-drain bias. A physical mechanism is proposed to explain the dual-photoresponse phenomenon in our devices. Based on the proposed physical mechanism, as a proof of concept, a facile and simple approach is used to eliminate the negative photoconductivity of the InSe FET. Our results will offer valuable strategies for stable multilayer InSe optoelectronic device design, and a practical scheme for improving the performance of other transition metal dichalcogenide devices as well.
Both positive and negative photoconductivities are observed in InSe FETs for the first time, and a physical mechanism is proposed.
Highlights
The structure–property relationship of PdSe
2
is discussed, i.e., layer number vs. tunable bandgap, pentagonal structure vs. anisotropy-based polarized light detection.
The synthesis ...approaches of PdSe
2
are thoroughly compared, including bottom-up methods such as chemical vapor transport for bulk crystals, chemical vapor deposition for thin films and single-crystal domains, selenization of Pd films. Besides, top-down strategies are discussed, covering the mechanical exfoliation of bulk crystals, plasma thinning, and vacuum annealing as well as phase transition.
The emerging devices of PdSe
2
and its van der Waals heterostructures have been delivered such as metal/semiconductor contact, Schottky junction transistors, field-effect transistors, photodetectors,
p
–
n
junction-based rectifiers, polarized light detector, and infrared image sensors.
Future opportunities of PdSe
2
-based van der Waals heterostructures are given including logic gate-based digital circuits, RF-integrated circuits, Internet of Things, and theoretical calculation as well as big data for materials science.
The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe
2
) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe
2
. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe
2
, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe
2
nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe
2
and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe
2
van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community.
The performance of superconducting quantum circuits for quantum computing has advanced tremendously in recent decades; however, a comprehensive understanding of relaxation mechanisms does not yet ...exist. In this work, we utilize a multimode approach to characterizing energy losses in superconducting quantum circuits, with the goals of predicting device performance and improving coherence through materials, process, and circuit design optimization. Using this approach, we measure significant reductions in surface and bulk dielectric losses by employing a tantalum-based materials platform and annealed sapphire substrates. With this knowledge we predict the relaxation times of aluminum- and tantalum-based transmon qubits, and find that they are consistent with experimental results. We additionally optimize device geometry to maximize coherence within a coaxial tunnel architecture, and realize on-chip quantum memories with single-photon Ramsey times of 2.0 - 2.7 ms, limited by their energy relaxation times of 1.0 - 1.4 ms. These results demonstrate an advancement towards a more modular and compact coaxial circuit architecture for bosonic qubits with reproducibly high coherence.
In recent years, people have been paying more and more attention to air quality because it directly affects people's health and daily life. Effective air quality prediction has become one of the hot ...research issues. However, this paper is suffering many challenges, such as the instability of data sources and the variation of pollutant concentration along time series. Aiming at this problem, we propose an improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h. In this paper, we resolve the issue of processing the high-dimensional large-scale data by employing the LightGBM model and innovatively take the forecasting data as one of the data sources for predicting the air quality. With exploring the forecasting data feature, we could improve the prediction accuracy with making full use of the available spatial data. Given the lack of data, we employ the sliding window mechanism to deeply mine the high-dimensional temporal features for increasing the training dimensions to millions. We compare the predicted data with the actual data collected at the 35 air quality monitoring stations in Beijing. The experimental results show that the proposed method is superior to other schemes and prove the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis.
Hand-foot-and-mouth disease (HFMD) is considered to be self-limited, however, severe HFMD is a deadly threat for children worldwide, therefore, it is essential to define the clinical and ...epidemiologic characteristics of children with severe HFMD and identify the risk factors of death.
Between 2013 and 2018, children who diagnosed with severe HFMD from Chongqing, China were enrolled in this population-based study. A total of 459 severe HFMD children cases were identified during the study period, including 415 survivors and 44 fatal cases. Demographic, geographical, epidemiological and clinical data of the cases were acquired and analyzed.
Risk factors of the death because of severe HFMD children included female, aged 1 ~ 3 years, enterovirus 71 infection, falling ill in winter, more than one children in home, being taken care of by grandparents, the caregivers' education not more than 9 years, having fever more than 3 days, consciousness disorders, general weakness, vomiting, general weakness, abnormal pupillary light reflex, repeated cough, tachypnea, moist rales, white frothy sputum, pink frothy sputum, and cyanosis on lips or the whole body, tachycardia, arrhythmia, cold limbs, pale complexion, weakened pulse. (all p < 0.05). Spatial-temporal analysis detected high-value clusters, the most likely cluster located at rural countries in the northern parts of Chongqing, from January, 2015 to July, 2017. (p < 0.01). Besides, some urban districts were also found high incidence of severe HFMD cases according to the incidence maps.
The detection of clinical risk factors and the temporal, spatial and socio-demographic distribution epidemiological characteristics of severe HFMD contribute to the timely diagnosis and intervention, the results of this study can be the reference of further clinical and public health practice.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The evolution of the contact scheme has driven the technology revolution of crystalline silicon (c‐Si) solar cells. The state‐of‐the‐art high‐efficiency c‐Si solar cells such as silicon ...heterojunction (SHJ) and tunnel oxide passivated contact (TOPCon) solar cells are featured with passivating contacts based on doped Si thin films, which induce parasitic optical absorption loss and require capital‐intensive deposition processes involving flammable and toxic gasses. A promising solution to tackle this problem is to employ dopant‐free passivating contact, involving the use of transparent and cost‐effective wide band gap materials. In this review, we first introduce the dopant‐free passivating contact, from carrier transport mechanisms, material classification to evaluation methods. Then we focus on the advances in different strategies to improve cell performance, including material property optimization, structural and interfacial engineering, as well as various post‐treatments. At the end, the challenge and perspective of dopant‐free passivating contact c‐Si solar cells are discussed.
This article provides an overview of the mechanism and materials of dopant‐free passivating contacts for crystalline silicon solar cells, and focuses on the recent advances in contact configuration and interface engineering for efficiency and stability enhancement.
Knowledge graph (KG) embedding is to embed the entities and relations of a KG into a low-dimensional continuous vector space while preserving the intrinsic semantic associations between entities and ...relations. One of the most important applications of knowledge graph embedding (KGE) is link prediction (LP), which aims to predict the missing fact triples in the KG. A promising approach to improving the performance of KGE for the task of LP is to increase the feature interactions between entities and relations so as to express richer semantics between them. Convolutional neural networks (CNNs) have thus become one of the most popular KGE models due to their strong expression and generalization abilities. To further enhance favorable features from increased feature interactions, we propose a lightweight CNN-based KGE model called IntSE in this paper. Specifically, IntSE not only increases the feature interactions between the components of entity and relationship embeddings with more efficient CNN components but also incorporates the channel attention mechanism that can adaptively recalibrate channel-wise feature responses by modeling the interdependencies between channels to enhance the useful features while suppressing the useless ones for improving its performance for LP. The experimental results on public datasets confirm that IntSE is superior to state-of-the-art CNN-based KGE models for link prediction in KGs.
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