Mutual Transfer Research Articles

Quality criteria and evaluation: a measuring instrument for the German language area

The University Network on Social Responsibility is a network of more than 40 universities in Germany and Austria established in 2009 that want to realise and expand their social responsibility by systematically promoting the civil society engagement of students, teachers, and other members of higher education, linking this with their educational mission and thus actively influencing society and contributing to the mutual transfer of knowledge. This can be done through the teaching-learning format of service learning (SL). In 2018, a workshop initiated a discussion between representatives of academia and civil society. The focus was the aspect of quality of SL. The group came up with ten different criteria which provide a frame of reference for the different manifestations of SL in the community. Based on these criteria, suitable procedures and instruments were developed to provide SL actors with orientation and suggestions for the implementation and evaluation of SL projects.

КОМУНИКАЦИОНЕН ОКТАГОН: АСПЕКТИ НА ИНТЕЛИГЕНТНОСТТА ПРЕЗ ПРИЗМАТА НА КОМУНИКАЦИОННАТА СРЕДА

The aim of the paper is to highlight certain aspects of the semantic profile of the concept of "intelligence" through a model called "communication octagon", focusing on the theory of dynamics of communication links, and to analyze the interaction between the intelligent system and the social environment. The focus of interest in the study is human intelligence, based on individual biological differences, which can be modified as a result of construction and transmission of knowledge, mutual emotional transfer, making cognitive requirements (directed inwards and outwards, towards the environment), and as a result of dynamic social interactions.

Methodology for Determining the Thermal and Thermal-Stress States of a Concrete Storage Container for Spent Nuclear Fuel for Assessment of Its Service Life

The work is devoted to the development of methodologies for determining the thermal and thermal-stress states of the main equipment in dry container storage facilities for spent nuclear fuel. Storage facilities of this type are most common for spent fuel of nuclear power reactors. The safety of storage equipment in terms of assessing its service life is not covered widely enough in the world scientific literature. In particular, there are no effective methods for calculating the thermal and thermal-stress states of the equipment that would take into account the influence of many external factors throughout the life of a storage facility. To assess the thermal state of the containers, forward conjugate heat transfer problems, accounting for the mutual heat transfer in both a solid body and in the fluid environment (air), are proposed to be solved. Based on the solution of the conjugate heat transfer problems, the boundary conditions are to be determined to further assess the thermal-stress state of storage containers using inverse heat transfer problems. The proposed approach to determining the thermal and thermal-stress states of a concrete spent fuel container will promote more effective methods for assessing the service life of dry spent fuel storage facilities, which is, in turn, necessary in the development of ageing management programs for storage equipment and long-term safe operation.

Nature of viruses and the radical change in viral taxonomy

A short report is devoted to the radical changes in the taxonomy of viruses. The metagenomic sequencing has revealed the presence of a vast variety of viruses in diverse environmental samples without any connections with banal parasitism, infectivity, or pathogenicity. The understanding of viruses has expanded beyond the original parasitic–pathogen model, and now virologists recognize the role of viruses in host regulation and the maintenance of natural ecosystems. Co-evolution of the viral and cellular genomes includes mutual horizontal gene transfer and joint development of new biological functions, such as the mechanism of phylogenesis and phylodynamics of coactants. The concepts of the origin of viruses and their relation to the Universal Tree of Life are formulated. In this regard, the International Committee on the Taxonomy of Viruses (ICTV) changed the previous Code of their classification hierarchy from five ranks to a fifteen-rank one, that emulates a Linnaean framework and accommodates the entire spectrum of genetic divergence in the virosphere. Changes in the rank hierarchy are based on the evolution of the recognition of virus taxa over time, from a traditional phenotype-based characterization process to a multistage process that includes comparative sequence analyses of conserved genes and proteins, including gene phylogeny, gene synteny and shared gene content. The ICTV, that oversees the official classification of viruses and nomenclature of taxa, accepts possible non-hierarchical classifications of viruses beyond taxonomic attribution. The above provisions are illustrated with schemes of chimeric polyphyletic origin of viruses and a new rank structure; the table gives examples of the modern classification of viruses that cause some socially significant infections.

Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.

Dynamika nadlokálneho a lokálneho v každodennosti života na hranici (na príklade regiónu Kysuce)

Border studies (currently also cross-border cooperation issues) is an interdisciplinary research specialization. The aim of the article is to present the spatial proximity influence of the state border on the everyday life reality of inhabitants of the Kysuce region in the 20th Century (overlapping with the present day) in both the local and supra-local context. With reference to the theory of the Irish sociologist Liam O'Dowd, it focuses on the Slovak state borders with Poland and the Czech Republic as a possible barrier, but also a bridge, a source of opportunities and a symbol of identity. It points out that in the villages bordering the Polish and Czech territories there has always been a relatively intensive mutual cultural transfer and contact of populations and therefore the borders cannot be perceived as an exclusively geopolitical phenomenon; their social and cultural dimension must be taken into account.

Co‐teaching based pseudo label refinery for cross‐domain object detection

Object detection is one of the main tasks in computer vision and has made great progress in recent years. However, the performance of target detectors is significantly dropped by the differences between existing datasets and application scenarios, leading to the so-called domain shift problem. To address such an issue, a novel co-teaching based pseudo label refinery framework for cross-domain object detection is developed, which cooperates with two models to select data from target domain for each other. This strategy can effectively purify the predicted pseudo labels and resist noisy labels. Specifically, the framework consists of two encoders (i.e. structure encoder and global encoder), two classifiers and one discriminator, in which structure encoder is used to extract structural features that are not disturbed by colour, and the global encoder is used to extract the complete discriminant features. The two encoders are each followed by a classifier. In training, the structure and global encoder with labelled source samples are first trained, so that it has the initial recognition ability. Then the samples assigned are used with pseudo labels by the classifier following the structure encoder to fine-tune the global encoder which pre-trained on the labelled source domain and obtain the refined labels for the target data. With the refined labels, the structure encoder is further optimised on the target domain. During this process, the proposal is to cross use the two classifiers to promote the mutual transfer of complementary capabilities of the two encoders. Moreover, a novel residual channel attention block (RCA) embedded with salient features is designed to pay more attention to the target regions. Extensive experiments demonstrate that the developed framework can generate clean labels for unlabelled target data and boost the performance of cross domain object detection. The code is available at http://www.msp-lab.cn:1436/msp/cbplr-master.

A novel mutual information-based feature selection approach forefficient transfer learning in aerial scene classification

ABSTRACT Several transfer learning methods based on deep Convolutional Neural Networks (CNNs) have been developed so far but obtaining satisfactory classification accuracy still remains a challenging task. However, effective classification performance mainly depends on the feature set of input data to be classified. During transfer learning, features are extracted from multiple layers or multiple models of pre-trained convolutional neural networks to form feature sets of input data, which may contain some redundant features thus affecting the classification results. To tackle this issue, a feature selection method must be applied to transfer features before being fed to the classifier. Hence, a feature selection method is proposed using the idea of mutual information theory to remove redundant features for effective transfer of the learning task. The proposed feature selection method is the combination of filter and embedded approaches that are cascaded one after another to form the final feature set for classification. The proposed method proves its effectiveness when compared to some existing methods on three aerial scene datasets.

Mutual mass transfer between the target and the substrate in the process of ion sputtering. Flat and cylindrical target

The ion sputtering process for the deposition of coatings from alternating layers of light and heavy metals are considered, taking into account the mutual mass transfer from the target to the substrate and from the substrate to the target as the result of resputtering the coating matter by reflected fast neutral gas atoms (former primary bombarding ions, backscattered from the target towards the substrate). The resputtering effect is more important in the case of deposition of a heavy metal of Ta type on layer from a light matter of Si type since it forms the intermediate layer of mixing composition “Ta + Si” between layers of pure metals.

Decentralized Federated Learning via Mutual Knowledge Transfer

In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server. Most of the existing DFL schemes are composed of two alternating steps, i.e., model updating and model averaging. However, averaging model parameters directly to fuse different models at the local clients suffers from client-drift especially when the training data are heterogeneous across different clients. This leads to slow convergence and degraded learning performance. As a possible solution, we propose the decentralized federated earning via mutual knowledge transfer (Def-KT) algorithm where local clients fuse models by transferring their learnt knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets reveal that the proposed Def-KT algorithm significantly outperforms the baseline DFL methods with model averaging, i.e., Combo and FullAvg, especially when the training data are not independent and identically distributed (non-IID) across different clients.

The influence of Tm3+ to enhance 2.7 μm mid-infrared emission of Er3+ in CaLaGa3O7 crystal

Er3+/Tm3+:CaLaGa3O7 (abbr. as Er3+/Tm3+:CLGO) crystal was grown successfully by Czochralski technology and detailed spectroscopic analyses were carried out. The effects of Tm3+ on the spectroscopic properties and mutual energy transfer mechanisms were investigated. It is found that the more suitable pumping scheme for laser applications at 2.7 μm laser is 980 nm excitation for Er3+/Tm3+ co-doped CLGO crystal. Under 980 nm excitation, the introductions of Tm3+ are helpful to achieve enhanced mid-infrared (MIR) emissions, as well as reduced near-infrared emissions. Furthermore, the introducing Tm3+ depopulated the lower laser lever through energy transfer and thus reduced the fluorescence lifetime of Er3+ : 4I13/2, without significantly affecting the fluorescence lifetime of the upper laser level Er3+ : 4I11/2, which reduces the self-saturation problem to a great degree. It concludes that this Er3+/Tm3+:CLGO crystal could be a potential MIR gain media for enhanced 2.7 μm laser under 980 nm pumping.

The volatility in financial time series based on granule complex network

The volatility is one of the essential characteristics of financial time series, which is vital for the knowledge acquisition from financial data. However, since the high noise and nonsteady features, the volatility identification of financial time series is still a challenging problem. In this paper, from a perspective of granule complex network, a novel approach is proposed to study this problem. First, numeric time series is structured into fuzzy information granules (FIGs), where the segments of time series in each granule would own similar volatility features. Second, by using the transfer relations among granules, granule complex network is to be constructed, which intuitively describes the transfer processes among the different volatility patterns. Third, a novel community detection algorithm is applied to divide the granule complex networks, where granules with frequent mutual transfers would belong to the same granule community. Finally, Markov chain model is carried out to analyze the higher level of transfer processes among different granule communities, which would further describe the larger-scale transitions of volatility in overall financial time series. An empirical study of the proposed system is applied in the Shanghai stock index market, where volatility patterns of financial data can be effectively acquired and the corresponding transfer processes can be analyzed by means of the granule communities.

Interaction of surface pattern and contour shape in the tilt after effects evoked by symmetry

Integration of multiple properties of an object is a fundamental function of the visual cortex in object recognition. For instance, surface patterns and contour shapes are thought to be crucial characteristics that jointly contribute to recognition. However, the mechanisms of integration and corresponding cortical representations have not been fully clarified. We investigated the integration of surfaces and shapes by examining the tilt after effects (TAEs) evoked by the symmetry of patterns and contours. As symmetry in both pattern and contour evokes TAEs, we can directly measure the interaction between the two. The measured TAEs exhibited mutual transfer between the symmetry of the pattern (SP) and that of the contour shape (SS), i.e., adaptation by SP (SS) evoked TAEs when tested by SS (SP), suggesting the existence of an integrated representation. Next, we examined the interaction between SP and SS when both were simultaneously presented in adaptation. Congruent adaptors wherein their symmetry axes aligned evoked compressive interaction, whereas incongruent adaptors wherein the axes of SP and SS tilted to the opposite directions evoked subtractive interaction. These results suggest the existence of a cortical representation that integrates the properties of the surface and shape with suppressive interactions, which can provide crucial insights into the formation of object representation as well as the integration of visual information in the cortex.

Electrical Tuning of Optical Properties of Quantum Dot–Graphene Hybrid Devices: Interplay of Charge and Energy Transfer

The combination of semiconductor quantum dots (QD) and single-layer graphene (SLG) can lead to the formation of optoelectronic devices with enhanced sensitivity and can have extensive applications in the field of the photodetector and photovoltaics. The optical properties of the resultant hybrid material are controlled by the interplay of energy transfer between QDs and charge transfer between the QDs and SLG. By studying the steady-state and time-resolved photoluminescence spectroscopy of hybrid QD–SLG devices, we observe a subtle interplay of short- and long-range energy transfer between cadmium selenide (CdSe) QDs in a compact monolayer solid film placed in close proximity to an SLG and the charge transfer from the QD solid to SLG. At larger separation, δ, between the compact monolayer QD and SLG, the emission properties are dominated by mutual energy transfer between the QDs. At relatively smaller separation the emission from QDs, which is strongly quenched, is dominated by charge transfer between QDs and SLG. In addition, we are also able to tune the relative strength of energy and charge transfer by electrostatic doping through the back gate voltage, which provides a novel pathway to tune emission properties of these devices for possible applications as photodetectors, in photovoltaics, and for sensing.

Precursory Slow Slip and Foreshocks on Rough Faults

AbstractForeshocks are not uncommon prior to large earthquakes, but their physical mechanism remains controversial. Two interpretations have been advanced: (1) foreshocks are driven by aseismic nucleation and (2) foreshocks are cascades, with each event triggered by earlier ones. Here, we study seismic cycles on faults with fractal roughness at wavelengths exceeding the nucleation length. We perform 2‐D quasi‐dynamic, elastic simulations of frictionally uniform rate‐state faults. Roughness leads to a range of slip behavior between system‐size ruptures, including widespread creep, localized slow slip, and microseismicity. These processes are explained by spatial variations in normal stress (σ) caused by roughness: regions with low σ tend to creep, while high σ regions remain locked until they break seismically. Foreshocks and mainshocks both initiate from the rupture of locked asperities, but mainshocks preferentially start on stronger asperities. The preseismic phase is characterized by feedback between creep and foreshocks: episodic seismic bursts break groups of nearby asperities, causing creep to accelerate, which in turns loads other asperities leading to further foreshocks. A simple analytical treatment of this mutual stress transfer, confirmed by simulations, predicts slip velocities and seismicity rates increase as 1/t, where t is the time to the mainshock. The model reproduces the observed migration of foreshocks toward the mainshock hypocenter, foreshock locations consistent with static stress changes, and the 1/t acceleration in stacked catalogs. Instead of interpreting foreshocks as either driven by coseismic stress changes or by creep, we propose that earthquake nucleation on rough faults is driven by the feedback between the two.

Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion

In intelligent fault diagnosis, transfer learning can reduce the requirement of sufficient labeled data and the same data distribution. However, for the diagnosis of a new machine, there are still some limitations, such as low accuracy or the demand for some labeled data with fault information in the new machine. In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF) to solve these problems. According to the characteristics of bearing faults, the proposed weighted domain fusion strategy can ensure the direction and balance in the transfer process. The proposed class separation degree can improve the accuracy of the target domain indirectly by extending the differences between the classes in the source domain. The effectiveness of the SAE-CSDF is verified via the mutual transfer of two public datasets and one laboratory dataset. The results show that the accuracy of the algorithm can reach 97% in the transfer between different machines, even if there is no labeled fault data in the new machine.

Analysis of the properties of metallorganic compounds containing the Fe3+ nanoprobes

Coordination compounds containing the Fe3+ ions like sodium salt Na[FeO6(C10H8N)3], kaolinite Al2[Si2O5]⋅(OH)4, metalloproteins (Ferritin, Maltofer, and Ferrum Lek) find a wide application. The analysis of the properties of these compounds has demonstrated a developed crystal structure. The diffractogram of Ferritin included an amorphous component. Within the temperature range of 4.2–300 К, all the compounds are characterized by transformation and mutual transfer of the lines of electron paramagnetic resonance spectrum of the Fe3+ ions (EPR). This fact allows estimation of a dynamical characteristic of the nearest neighbor environment of Fe3+.

Nd3+/Yb3+ co-doped mid-infrared luminescence fluorobromide glass with energy transfer and zero-thermal-quenching IR emission

Nd3+/Yb3+ co-doped fluorobromide glass samples were prepared by melt quenching. The mid-infrared (MIR) luminescence of the Nd3+/Yb3+ co-doped fluorobromide glass was investigated by Br-doping reduces the phonon state density of the matrix. The 3.9 μm MIR luminescence of the samples excited at 793 and 980 nm pump excitation was investigated in detail. There is an effective mutual energy transfer process between Nd3+ and Yb3+. It is proved under 793 nm excitation that the luminescence of Nd3+ at 3.9 μm is reduced by effective energy transfer from Nd3+:2H11/2 → Yb3+:2F5/2. At the same time, it is proved that the effective energy transfer from Yb3+:2F5/2 → Nd3+:2H11/2 under the excitation of 980 nm enhances the luminescence of Nd3+ at 3.9 μm. In addition, it is found that the samples still have good infrared (IR) luminescent properties when the temperature changes. The emission cross-sectional area and the absorption cross-sectional area are σem (3.87 × 10−20 cm2) and σabs (4.25 × 10−20 cm2). The fluorescence decay characteristics of the sample at 3.9 μm at the 2H11/2 level were investigated and the fluorescence lifetime was calculated. The gain performance of the sample was calculated and analyzed, which can reach 4.25 × 10−20 cm2. Those results prove that Nd3+/Yb3+ co-doped fluorobromide glass is the potential mid-infrared laser gain material.

Asymmetric bidirectional quantum state exchange between Alice and Bob through a third party

In this paper we present a quantum communication protocol which utilizes a multipartite multi-qubit entangled channel for mutual transfer of three-qubit generalized W-state and arbitrary two-qubit entangled state between two parties. The protocol is perfect with no cases of failure and is supervised by a third party. The work is in the direction of the recently initiated line of research on exploring the possibility of simultaneous quantum multitasking using multipartite entanglement.

Real‐Time Wireless Sensor Network‐Assisted Smart Tourism Environment Suitability Assessment for Tourism IoT

In this paper, we conduct an in‐depth study and analysis of the suitability of real‐time wireless sensor network‐assisted smart tourism environment for tourism IoT and evaluate its suitability. We use nodes in wireless sensor networks as relays and collaborative transmission using mutual information accumulation. Using riteless codes, nodes can accumulate information in advance to decode out the original text. Due to the mobility of mobile robots, the planned routing paths can fail quickly, so we propose a dynamic mutual information accumulation collaborative transmission algorithm. For both cases of information transmission from the cloud to the robot and from the robot to the cloud, we dynamically acquire routing paths using neighbour search and chance transmission, respectively. Experimental results show that our dynamic mutual information accumulation collaborative transfer algorithm has lower time complexity and latency. We use a cooperative transmission algorithm with mutual information accumulation, because wireless transmission has the nature of broadcasting, and all neighbour nodes can receive part of the information when the sender starts to transmit information. Riteless codes are used in single‐hop transmissions to improve the probability of successful transmissions. Since transmission failure requires waiting for the next neighbour to wake up, which consumes a lot of energy and time in a low duty cycle environment, using riteless codes to improve the transmission success rate of single‐hop transmissions can effectively improve the global transmission efficiency. Experimental results show that the opportunity routing algorithm using riteless codes has lower delay and energy consumption. The two major systems, surface, and underground are used to quantitatively measure 23 indicators in five subsystems, including geological environment, soil and water environment, sensitive geological bodies, groundwater resources, and mineral resources, to evaluate the two‐way grade of underground space development suitability, revealing the comprehensive bearing characteristics of the study area with an overall high bearing capacity of underground space resources and environment and an overall surplus of bearing status.