Chengdu University of Information Technology

Abstract The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. In existing massive MIMO systems, however, conventional phased arrays are used for beamforming. This method results in excessive power consumption and high hardware costs. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we develop a new type of high-gain yet low-cost RIS that bears 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. This radical design forms the basis for the world’s first wireless communication prototype using RIS having 256 two-bit elements. The prototype consists of modular hardware and flexible software that encompass the following: the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in wireless communications. We show that, at 2.3 GHz, the proposed RIS can achieve a 21.7 dBi antenna gain. At the millimeter wave (mmWave) frequency, that is, 28.5 GHz, it attains a 19.1 dBi antenna gain. Furthermore, it has been shown that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

In experimental research a scientific conclusion is always drawn from the statistical testing of hypothesis, in which an acceptable cutoff of probability, such as 0.05 or 0.01, is used for decision-making. However, the probability of committing false statistical inferences would considerably increase when more than one hypothesis is simultaneously tested (namely the multiple comparisons), which therefore requires proper adjustment. Although the adjustment for multiple comparisons is proposed to be mandatory in some journals, it still remains difficult to select a proper method suitable for the various experimental properties and study purposes, especially for researchers without good background in statistics. In the present paper, we provide a brief review on mathematical framework, general concepts and common methods of adjustment for multiple comparisons, which is expected to facilitate the understanding and selection of adjustment methods.

Compared to the available protein sequences of different organisms, the number of revealed protein-protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11,474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.

Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

Depression is the leading cause of disability around the world, but little is known about its pathology. Currently, the diagnosis of depression is made based on clinical manifestations, with little objective evidence. Magnetic resonance imaging (MRI) has been used to investigate the pathological changes in brain anatomy associated with this disorder. MRI can identify structural alterations in depressive patients in vivo, which could make considerable contributions to clinical diagnosis and treatment. Numerous studies that focused on gray and white matter have found significant brain region alterations in major depressive disorder patients, such as in the frontal lobe, hippocampus, temporal lobe, thalamus, striatum, and amygdala. The results are inconsistent and controversial because of the different demographic and clinical characteristics. However, some regions overlapped; thus, we think that there may be a "hub" in MDD and that an impairment in these regions contributes to disease severity. Brain connections contain both structural connections and functional connections, which reflect disease from a different view and support that MDD may be caused by the interaction of multiple brain regions. According to previous reports, significant circuits include the frontal-subcortical circuit, the suicide circuit, and the reward circuit. As has been recognized, the pathophysiology of major depressive disorder is complex and changeable. The current review focuses on the significant alterations in the gray and white matter of patients with the depressive disorder to generate a better understanding of the circuits. Moreover, identifying the nuances of depressive disorder and finding a biomarker will make a significant contribution to the guidance of clinical diagnosis and treatment.

Developing a facile, cost-saving, and environment-friendly method for fabricating a multifunctional humidity sensor is of great significance to expand its practical applications. However, most humidity sensors involve a complex fabrication process, resulting in their high cost and narrow application fields. Herein, a multifunctional paper-based humidity sensor with many advantages is proposed. This humidity sensor is fabricated using conventional printing paper and flexible conductive adhesive tape by a facile pasting method, in which the paper is used as both the humidity-sensing material and the substrate of the sensor. Owing to the moderate hydrophilicity of the paper and the rational structure design of the paper-based humidity sensor, the sensor exhibits an excellent humidity-sensing response of more than 103 as well as good linearity (R2 = 0.9549) within the humidity range from 41.1 to 91.5% relative humidity. Furthermore, the paper-based humidity sensor has good flexibility and compatibility, endowing it with multifunctional applications for breath rate, baby diaper wetting, noncontact switch, skin humidity, and spatial localization monitoring. Although the resistance of the paper-based humidity sensor is relatively large, the humidity-sensing response signals of the sensor can be conveniently processed by the designed signal processing system. The readily available starting materials and facile fabrication technique provide useful strategies for the development of multifunctional humidity sensors.

In this paper, we intend to implement a class of fractional differential masks with high-precision. Thanks to two commonly used definitions of fractional differential for what are known as GrUmwald-Letnikov and Riemann-Liouville, we propose six fractional differential masks and present the structures and parameters of each mask respectively on the direction of negative x-coordinate, positive x-coordinate, negative y-coordinate, positive y-coordinate, left downward diagonal, left upward diagonal, right downward diagonal, and right upward diagonal. Moreover, by theoretical and experimental analyzing, we demonstrate the second is the best performance fractional differential mask of the proposed six ones. Finally, we discuss further the capability of multiscale fractional differential masks for texture enhancement. Experiments show that, for rich-grained digital image, the capability of nonlinearly enhancing complex texture details in smooth area by fractional differential-based approach appears obvious better than by traditional intergral-based algorithms.

Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping. In this process, the linear and non-linear relationships between PM2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM2.5.

Abstract Perovskite solar cells (PSCs) have developed rapidly over the past few years, and the power conversion efficiency of PSCs has exceeded 20%. Such high performance can be attributed to the unique properties of perovskite materials, such as high absorption over the visible range and long diffusion length. Due to the different diffusion lengths of holes and electrons, electron transporting materials (ETMs) used in PSCs play a critical role in PSCs performance. As an alternative to TiO 2 ETM, ZnO materials have similar physical properties to TiO 2 but with much higher electron mobility. In addition, there are many simple and facile methods to fabricate ZnO nanomaterials with low cost and energy consumption. This review focuses on recent developments in the use of ZnO ETM for PSCs. The fabrication methods of ZnO materials are briefly introduced. The influence of different ZnO ETMs on performance of PSCs is then reviewed. The limitations of ZnO ETM‐based PSCs and some solutions to these challenges are also discussed. The review provides a systematic and comprehensive understanding of the influence of different ZnO ETMs on PSCs performance and potentially motivates further development of PSCs by extending the knowledge of ZnO‐based PSCs to TiO 2 ‐based PSCs.

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Abstract. High concentrations of fine particles (PM2.5) are frequently observed during all seasons in Beijing, China, leading to severe air pollution and human health problems in this megacity. In this study, we conducted real-time measurements of non-refractory submicron aerosol (NR-PM1) species (sulfate, nitrate, ammonium, chloride, and organics) in Beijing using an Aerodyne Aerosol Chemical Speciation Monitor for 1 year, from July 2011 to June 2012. This is the first long-term, highly time-resolved (~ 15 min) measurement of fine particle composition in China. The seasonal average (±1σ) mass concentration of NR-PM1 ranged from 52 (±49) μg m−3 in the spring season to 62 (±49) μg m−3 in the summer season, with organics being the major fraction (40–51 %), followed by nitrate (17–25 %) and sulfate (12–17 %). Organics and chloride showed pronounced seasonal variations, with much higher concentrations in winter than in the other seasons, due to enhanced coal combustion emissions. Although the seasonal variations of secondary inorganic aerosol (SIA, i.e., sulfate + nitrate + ammonium) concentrations were not significant, higher contributions of SIA were observed in summer (57–61 %) than in winter (43–46 %), indicating that secondary aerosol production is a more important process than primary emissions in summer. Organics presented pronounced diurnal cycles that were similar among all seasons, whereas the diurnal variations of nitrate were mainly due to the competition between photochemical production and gas–particle partitioning. Our data also indicate that high concentrations of NR-PM1 (> 60 μg m−3) are usually associated with high ambient relative humidity (RH) (> 50 %) and that severe particulate pollution is characterized by different aerosol composition in different seasons. All NR-PM1 species showed evident concentration gradients as a function of wind direction, generally with higher values associated with wind from the south, southeast or east. This was consistent with their higher potential as source areas, as determined by potential source contribution function analysis. A common high potential source area, located to the southwest of Beijing along the Taihang Mountains, was observed during all seasons except winter, when smaller source areas were found. These results demonstrate a high potential impact of regional transport from surrounding regions on the formation of severe haze pollution in Beijing.

The significant growth of gas-fired power plants and emerging power-to-gas (PtG) technology has intensified the interdependency between electricity and natural gas systems. This paper proposes a robust co-optimization scheduling model to study the coordinated optimal operation of the two energy systems. The proposed model minimizes the total costs of the two systems, while considering power system key uncertainties and natural gas system dynamics. Because of the limitation on exchanging private data and the challenge in managing complex models, the proposed co-optimization model is tackled via alternating direction method of multipliers (ADMM) by iteratively solving a power system subproblem and a gas system subproblem. The power system subproblem is solved by column-and-constraint generation (C&CG) and outer approximation (OA), and the nonlinear gas system subproblem is solved by converting into a mixed-integer linear programming model. To overcome nonconvexity of the original problem with binary variables, a tailored ADMM with a relax-round-polish process is developed to obtain high-quality solutions. Numerical case studies illustrate the effectiveness of the proposed model for optimally coordinating electricity and natural gas systems with uncertainties.

Research on machine assisted text analysis follows the rapid development of digital media, and sentiment analysis is among the prevalent applications. Traditional sentiment analysis methods require complex feature engineering, and embedding representations have dominated leaderboards for a long time. However, the context-independent nature limits their representative power in rich context, hurting performance in Natural Language Processing (NLP) tasks. Bidirectional Encoder Representations from Transformers (BERT), among other pre-trained language models, beats existing best results in eleven NLP tasks (including sentence-level sentiment classification) by a large margin, which makes it the new baseline of text representation. As a more challenging task, fewer applications of BERT have been observed for sentiment classification at the aspect level. We implement three target-dependent variations of the BERT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</sub> model, with positioned output at the target terms and an optional sentence with the target built in. Experiments on three data collections show that our TD-BERT model achieves new state-of-the-art performance, in comparison to traditional feature engineering methods, embedding-based models and earlier applications of BERT. With the successful application of BERT in many NLP tasks, our experiments try to verify if its context-aware representation can achieve similar performance improvement in aspect-based sentiment analysis. Surprisingly, coupling it with complex neural networks that used to work well with embedding representations does not show much value, sometimes with performance below the vanilla BERT-FC implementation. On the other hand, incorporation of target information shows stable accuracy improvement, and the most effective way of utilizing that information is displayed through the experiment.

Due to the spread of COVID-19 worldwide, a large number of universities had to close their campuses. To maintain teaching and learning during this disruption to the traditional teaching, most universities have adopted online teaching model. The current study aimed at investigating the efficacy of various online teaching modes as well as comparing a proposed combined model of online and flipped learning to other online and traditional models. The Learning under COVID-19questionnaire was designed and administered to undergraduate engineering students at Chengdu University of Information Technology (CUIT). The questionnaire included five parts: demographic questions, frequencies of online courses, types of online courses, the communication and Q&A in online classes and the effect of online classes, as well as the effect of combined model learning. The results of the study showed that, students were dissatisfied with online learning in general, and they were especially dissatisfied with the communication and Q&A modes. In addition, the combined model of online teaching with the flipped learning improved students' learning, attention, and evaluation of courses.

Because of growing environmental concerns, the development of lead-free piezoelectric materials with enhanced properties has become of great interest. Here, we report a giant piezoelectric coefficient (d33) of 550 pC/N and a high Curie temperature (TC) of 237 °C in (1–x–y)K1–wNawNb1–zSbzO3–xBiFeO3–yBi0.5Na0.5ZrO3 (KNwNSz-xBF-yBNZ) ceramics by optimizing x, y, z, and w. Atomic-resolution polarization mapping by Z-contrast imaging reveals the intimate coexistence of rhombohedral (R) and tetragonal (T) phases inside nanodomains, that is, a structural origin for the R–T phase boundary in the present KNN system. Hence, the physical origin of high piezoelectric performance can be attributed to a nearly vanishing polarization anisotropy and thus low domain wall energy, facilitating easy polarization rotation between different states under an external field.

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) - the most widely used model in recommendation - as a demonstration, we show that optimizing it with BPR leads to a recommender model that is not robust. In particular, we find that the resultant model is highly vulnerable to adversarial perturbations on its model parameters, which implies the possibly large error in generalization. To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise ranking method BPR by performing adversarial training. It can be interpreted as playing a minimax game, where the minimization of the BPR objective function meanwhile defends an adversary, which adds adversarial perturbations on model parameters to maximize the BPR objective function. To illustrate how it works, we implement APR on MF by adding adversarial perturbations on the embedding vectors of users and items. Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR - by optimizing MF with APR, it outperforms BPR with a relative improvement of 11.2% on average and achieves state-of-the-art performance for item recommendation. Our implementation is available at: \urlhttps://github.com/hexiangnan/adversarial_personalized_ranking.

The voltage-fed Z-source inverter/quasi-Z-source inverter (qZSI) has been presented suitable for photovoltaic (PV) applications mainly because of its single-stage buck and boost capability and improved reliability. This paper further addresses detailed modeling and control issues of the qZSI used for distributed generation (DG), such as PV or fuel cell power conditioning. The dynamical characteristics of the qZSI network are first investigated by small-signal analysis. Based on the dynamic model, stand-alone operation and grid-connected operation with closed-loop control methods are carried out, which are the two necessary operation modes of DG in distributed power grids. Due to the mutual limitation between the modulation index and shoot-through duty ratio of qZSI, constant capacitor voltage control method is proposed in a two-stage control manner. Minimum switching stress on devices can be achieved by choosing a proper capacitor voltage reference. Experimental results are presented for validation of the theoretical analysis and controller design.

All-inorganic CsPbX3 (X = Cl, Br or I) perovskite nanocrystals have attracted extensive interest recently due to their exceptional optoelectronic properties. In an effort to improve the charge separation and transfer following efficient exciton generation in such nanocrystals, novel functional nanocomposites were synthesized by the in situ growth of CsPbBr3 perovskite nanocrystals on two-dimensional MXene nanosheets. Efficient excited state charge transfer occurs between CsPbBr3 NCs and MXene nanosheets, as indicated by significant photoluminescence (PL) quenching and much shorter PL decay lifetimes compared with pure CsPbBr3 NCs. The as-obtained CsPbBr3/MXene nanocomposites demonstrated increased photocurrent generation in response to visible light and X-ray illumination, attesting to the potential application of these heterostructure nanocomposites for photoelectric detection. The efficient charge transfer also renders the CsPbBr3/MXene nanocomposite an active photocatalyst for the reduction of CO2 to CO and CH4. This work provides a guide for exploration of perovskite materials in next-generation optoelectronics, such as photoelectric detectors or photocatalyst.