Invariant Characteristics Research Articles

Impact of Alternating-Current Operation on All-Inorganic Quantum Dot Light-Emitting Diodes.

In colloidal quantum dot light-emitting diodes (QD-LEDs), replacing organic hole transport layers (HTLs) with their inorganic counterparts is expected to yield distinct advantages due to their inherent material robustness. However, despite the promising characteristics of all-inorganic QD-LEDs, some challenges persist in achieving stable operation; for example, the electron overflow toward the inorganic HTL and charge accumulation within working devices return a temporal inconsistency in device characteristics. To address these challenges, we propose an operational approach that employs an alternating-current (AC) in all-inorganic QD-LEDs. We carry out comprehensive studies on the optoelectrical characteristics of all-inorganic QD-LEDs under direct-current (DC) or AC operation and demonstrate that AC operation can facilitate efficient charge carrier recombination within the QD emissive layer, leading to improved device efficiency and temporally invariant optoelectronic characteristics. Leveraging the intrinsic material robustness of inorganic charge transport layers (CTLs), our current study suggests a promising pathway toward enhancing the performance and stability of QD-LEDs, particularly for futuristic display applications.

The Evolution of Symbolic Thought: At the Intersection of Schizophrenia Psychopathology, Ethnoarchaeology, and Neuroscience.

The human capacity for symbolic representation arises, evolutionarily and developmentally, from the exploitation of a widespread sensorimotor network, along a fundamental continuity between embodied and symbolic modes of experience. In this regard, the fine balancing between constrained sensorimotor connections (responsible for self-embodiment processing) and more untethered neural associations (responsible for abstract and symbolic processing) is context dependent and plastically neuromodulated, thus intersubjectively constructed within a specific socio-cultural milieu. Instead, in the schizophrenia spectrum this system falls off catastrophically, due to an unbalance toward too unconstrained sensorimotor connectivity, leading to a profound distortion of self/world relation with a symbolic activity detached from its embodied ground. For this very reason, however, schizophrenia psychopathology may contribute to unveil, in a distorted or magnified way, ubiquitous structural features of human symbolic activity, beneath the various, historically determined cultural systems. In this respect, a comparative approach, linking psychopathology and ethnoarchaeology, allows highlight the following invariant formal characteristics of symbolic processing: (1) Emergence of salient perceptive fragments, which stand out from the perceptual field. (2) Spreading of a multiplicity of new significances with suspension of common-sense meaning. (3) Dynamic and passive character through which meaning proliferation is experienced. This study emphasizes the importance of fine-grained psychopathology to elucidate, within a cross-disciplinary framework, the evolutionarily and developmental pathways that shape the basic structures of human symbolization.

Heterogeneous feature learning network for multimodal remote sensing image collaborative classification

ABSTRACT While multimodal remote sensing data processing has garnered growing interests in the geoscience field, how to effectively extract, represent, and fuse heterogeneous features remains challenging. In this article, a novel heterogeneous feature learning network (HFLN) architecture is investigated for multimodal remote sensing collaborative classification, which takes both advantages of long-range feature modelling capability of transformer as well as local feature extraction of convolutional neural network (CNN). First, the global spectral dependencies are extracted from hyperspectral images using the spectral transformer structure. Then, local spatial invariant characteristics are extracted from multimodal remote sensing images by convolutional operation. Next, heterogeneous spectral and spatial characteristics, having significant structural and semantic differences, are dynamically integrated through the feature coupling module in an interactive manner. Finally, the multi-stage network architecture is utilized for extracting hierarchical characteristics, where the numbers of feature maps in CNN and heads in transformer are gradually increased to represent more complex features. Four benchmark remote sensing datasets (i.e. hyperspectral images and multimodal remote sensing datasets) are utilized for experimental analysis, and extensive experimental results have certified the progressiveness of the investigated approach.

Macroeconomic Impacts of Oil Price Fluctuations: Panel Data Evidence from Oil-Importing Developing Countries

This study reconnoiters the impact of oil price fluctuations on the macroeconomic performance of 95 oil-importing countries for the period 1981-2023. Due to an unobserved heterogeneity among the countries, the fixed effect approach of panel data estimation is employed. This approach is advantageous because it controls for the country-specific time invariant characteristics. According to our empirical findings, oil price fluctuations substantially affect GDP and Inflation. However, the coefficient on unemployment remains statistically insignificant. Our findings suggest that governmental policies should focus on reducing oil dependency by promoting renewable energy sources for improved macroeconomic performance and sustainable development.

Performance monitoring and diagnosis of bridge structures based on time multiscale divide and conquer strategy

During the service life of bridge structures, they will be affected by various physical and environmental changes, which comprehensively manifest on different time scales. In other words, structural effects will have different variation patterns at different time scales, which will be reflected in the multi-scale characteristics of structural mechanical properties over time. However, most of the existing bridge performance indicators are defined on a time scale corresponding to a single factor. Therefore, it is necessary to analyze, process, and apply bridge monitoring signals differently at different time scales and components of different factors. This article proposes a signal decomposition and reconstruction method that extracts signals of different components from strain monitoring signals of bridge structures. On this basis, different feature extraction and indicator recognition are carried out for different extracted components. Among them, for component A related to bridge vibration, this paper proposes a strain mode identification method based on statistical stability. This method is based on the assumption that the strain mode under random noise has approximate time invariant characteristics, and noise is eliminated through statistical averaging. The component B of the vehicle induced effect is obtained through empirical mode decomposition (EMD) and bandpass filtering, and the lateral collaborative performance of the assembled beam bridge is characterized based on the correlation coefficient of component BA bridge hinge joint damage identification method based on temperature strain is proposed for component C of temperature effect. The effectiveness and feasibility of the above workflow were verified through real bridge data.

Genre of “Promise” as a Reflection of Intentional and Linguistic Organization in Virtual Pedagogical Discourse

This research is situated within the context of contemporary discourse studies. It examines the genre of “promise” functioning within virtual pedagogical discourse. The study explores the genre features of a speech act, considering their potential determination by the pragmatic properties of discourse. Drawing on existing models of speech genres in contemporary genre theory (models by Т. V. Shmeleva, N. B. Lebedeva), two types of genre features of promises are identified. The first type includes invariant characteristics of the genre “promise”: a) the communicative goal of the utterance, linked to a voluntary commitment by the subject to fulfill something; b) linguistic markers of the genre — syntactic constructions containing performative “I promise,” predicates “I swear” / “commit” / “give (<my word>)” with direct objects or explanatory clauses indicating the object of the promise. The second group of features comprises genre characteristics of the text, determined by its discursive nature: a) the dictum content of the genre (theme of “Education”), b) the status roles of participants in institutional-pedagogical discourse; c) properties of the speech act determined by the structural-functional features of the messenger (interactivity, media files). Ultimately, a promise emerges as a genre realization of shared and simultaneously adapted speechthinking intention and discursive means.

Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples.

Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.

CSAPSO-BPNN-Based Modeling of End Airbag Stiffness of Nursing Transfer Robot

The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human–robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then, the performance of the Back Propagation (BP) neural network is improved using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load–displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its three distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags.

A wideband and low-power mm-wave variable-gain phase shifter based on a source-switching scheme

This article presents a low-power and wideband variable-gain phase shifter (VGPS) designed and simulated in a CMOS 65 nm technology. It introduces a source-switching scheme for designing a variable gain amplifier (SSVGA) characterized by invariant linearity, impedance, and phase characteristics, showcasing improved gain and bandwidth compared to conventional VGAs. The current-reuse and staggered-tuning techniques are used to reduce power consumption and extend the bandwidth, respectively. The proposed VGPS enables gain tuning without the need for an additional gain tuning block, resulting in reduced chip area and sidelobe levels in phased array radar systems. The proposed VGPS, with a 5-bit resolution, achieves a 3-dB bandwidth of 18 GHz (53.5–71.5 GHz) and exhibits RMS gain error range from 0.23 to 0.5 dB and RMS phase error range from 0.54° to 2.65° with phase calibration based on simulation results. It achieves a peak average gain of 1.2 dB at 58.5 GHz while consuming a DC power consumption of only 5 mW with a 1.2 V power supply voltage. Moreover, it demonstrates a wide gain tuning of 20.6 dB, allowing for flexible gain adjustments.

Прогностический подход в процессе совершенствования личностного и профессионального уровня педагогов с учетом развития региональной образовательной системы

The article is devoted to the study of the issue of a prognostic approach in the process of improving the personal and professional level of teachers in view of the development of the regional educational system. One of the features of the socio-political development of the Russian education system today is the qualitative renewal of the federal structure of our state. Modernization and reform of the educational system in the constituent entities of the Russian Federation is justified by the increased responsibility of the regions for the quality of education and the need for integration into the educational space of the state. The regional education system of the various republics of the Russian Federation, having certain general invariant characteristics, always remains conditioned by geographical, economic, sociocultural, national-ethnic, and confessional specifics. The formation of regional labor markets is relevant, and the most important task of the process of improving the personal and professional level of teachers is to take into account the specifics of the regional educational system of vocational education, ensuring the social protection of teachers by introducing regional production specifics into the content and organizational forms of this type of activity.

Updating, evidence evaluation, and operator availability: A theoretical framework for understanding belief.

Decades of findings in psychology suggest that human belief is thoroughly irrational. At best, beliefs might be formed by heuristic processes that predictably lead to suboptimal outcomes. At worst, they are slaves to motivated reasoning, which allows people to come to whichever conclusions they prefer. In this article, we suggest that belief updating, narrowly construed, may be a rational process that is uniquely sensitive to evidence and cognitively impenetrable to desires or incentives. Before any updating can occur, however, a series of processes mediate between information in the world and subjectively compelling evidence. We distinguish between updating proper and processes of evidence search, acceptance, hypothesis specification, integration of relevant information, and reasoning. We review research highlighting the computational difficulty inherent to each of these problems and conclude that solutions must be heuristic and fallible. Beyond incidental failures, evidence evaluation processes-unlike updating-are penetrable to motivation and as such, may be biased by people's desires and goals. In light of this distinction, we propose a theoretical framework for integrating research on belief which divides the cognitive processes involved in belief into two distinct levels. At Level 1, updating is suggested to be approximately Bayesian and impenetrable to desires and goals. In contrast, Level 2 processes, which search for and evaluate evidence, are cognitively penetrable. In addition, we emphasize that Level 2 processes are necessarily heuristic and exhibit bounded rationality (Simon, 1956) given the difficulty of the problems they have to solve. Finally, we specify an additional set of relatively invariant characteristics, which influence how Level 2 processes are employed by making different methods of information processing available. Our framework offers a more nuanced understanding of belief, permits a granular localization of irrationality, and may help reconcile extant debates in the literature. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Scale Invariance in Gamma-Ray Flares of the Sun and 3C 454.3

Using the gamma-ray flare samples of the Sun and 3C 454.3 observed by the Fermi telescope, we investigate the statistical properties of sizes including fluence (energy), peak flux (luminosity), duration time, and waiting time in this work. We find that the cumulative distribution of the fluctuations of these sizes follow well the Tsallis q-Gaussian function. The obtained q values from q-Gaussian distribution remain stable around 2 without any significant change, implying that there is a scale invariance structure in gamma-ray flares of the Sun and 3C 454.3. This scale invariance characteristics of the Sun and 3C 454.3 indicated by q values are also comparable to those of earthquakes, soft gamma repeaters, fast radio burst (FRB 20121102), and X-ray flares of gamma-ray bursts. On top of that, we verify the relationship between q values and the power-law indices α from the size frequency distributions, which is expressed as q = (α + 2)/α. These statistical findings could be well explained within the physical framework of a self-organizing criticality system.

Determining the orientation of circular areas in video images

One of the ways to describe objects in images is to identify some of their characteristic points, or points of attention. The neighborhoods of attention points are described by descriptors (sets of features) in such way that they can be identified and compared. Based on these features, the search for identical points on other images is carried out. The feature selection process should take into account not only the position of the focus point, but also the size and possible shape of the local neighborhood. Different methods of finding identical characteristic points are known, which use rectangular or circular neighborhood to describe them. The rectangular windows used for neighborhood analysis do not allow selecting the same areas on the reference image and image which is rotated and analyzed. Round windows allow this. The article proposes and studies algorithms for determining the orientation of circular areas in video images, which makes it possible to calculate, in addition to invariant characteristics, characteristics that take into account the location of individual sections of such areas. These are the algorithms: • determination of the orientation of the circular area by moments of the first and second order; • calculation of the mutual orientation of the investigated circular area and the standard according to the moments of the first and second order. As a result of research and experiments, it was established that depending on the algorithm and the content of the circular areas, there are possible deviations in determining their orientation directions. Factors affecting this: accuracy of finding characteristic points; application of certain orientation determination algorithms; differences in the angle of video recording of the reference image and the image under investigation; the location of characteristic points, inside the object or on its border, especially when the background changes; size of neighborhood (analysis window) of characteristic points. It seems appropriate to continue the research orientation determination accuracy of circular areas by various algorithms. To reduce the influence of various side factors, artificial rather than real images should be studied

Regional manufacturing in the Czech Republic

ABSTRACT The New Economic Geography (NEG)’s agglomerative forces strongly determined the location of firms, in spite of policy intervention blatantly activist intended to reverberate the geography of production (during the communist regime). Recent models of geography and trade recognize a bijective interaction between NEG forces and comparative advantages, they fully explain productive specialization. Our interpretation of the Czech manufacturing is in accordance with the NEG and its recent integration with the comparative advantages stream. In the panel data float up some invariant characteristics enhancing the attractiveness of traditional industrialized territories. The estimates embody the population density and gravitational and agglomeration forces. The time-invariant characteristics are present in the most industrial regions, influencing the localization of manufacturing activity in these spots. In spite of the forceful influence of path dependence, the very manufacturing cores underwent a process of restructuration, adapting the old-fashioned specialization pattern toward a more coherent production tailored to local assets, endowments and technologies.

Identification of Finger Vein Images with Deep Neural Networks

To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.

Strategically positioning non-competitive individuals can rescue cooperation in scale-free networks

Cooperation rescue in populations has long been a challenging problem, and previous studies have suggested that strategically placing cooperators in important positions in structured populations at the initial moment can obtain the opportunity of cooperation expansion. How to effectively lay out the location and place personalized nodes occupies a very important position in solving social dilemmas. However, most previous studies have focused on how the distribution of initial cooperators or defectors in homogeneous groups affects evolutionary outcomes, and relatively few studies have examined the placement of individuals with invariant heterogeneous characteristics. To enrich this research, we extend the cooperation rescue model to investigate the importance of the positional layout of two types of individuals with heterogeneous social value orientation characteristics in BA scale-free networks. Our study specifically investigates the impact of the distribution of non-competitive individuals on cooperation. We introduce non-competitive individuals into a BA scale-free network with only competitive individuals using different ranking rules. Our findings demonstrate that the presence of non-competitive individuals can promote cooperation. Furthermore, we determine the minimum number of nodes required to sustain cooperation under different ranking rules. In summary, our study emphasizes the importance of non-competitive individuals being placed in positions of importance for cooperation to flourish.

Whom is economic aid meant for? The push vs. pull determinant factors of official development assistance

This study investigates the determinants of official development assistance (ODA). We use a push-pull factor framework, which represents a donor and recipient country's business cycle characteristics such as gross domestic product (GDP) and interest rate. Time-variant and invariant country-pair characteristics are also taken into consideration for correct estimation. Our empirical results are as follows: i) push factors rather than pull factors have a greater influence on the total amount of ODA; ii) the main determinants of grants (a major component of ODA) are generally very similar to the determinants of ODA. In loan determinants, however, the number of significant push factors is smaller compared to ODA and grants; iii) both push and pull factors of tax revenue and push factors of the population are closely associated with the size of the ODA; and iv) the push factor of the quality of governance plays different roles depending on the group of donor countries. Our main empirical results are robust to alternative measures and model specifications.

Russian Civilization: Civilizational Approach in Political Theory

Discussions about the applicability of the civilizational approach in political science correlate with methodological and discursive rethinking of linear philosophical and political interpretive projects. The search for new epistemological and ontological optics of modern political constructions is largely focused on the value-symbolic parameters underlying the civilizational approach. The civilizational paradigm is one of the conceptual schemes of political theory, which makes it possible to interpret political institutions, processes, and world politics through the lens of more stable and deeper factors - value systems and culture. Becoming the identifying basis of politics and the legitimizing ideological and semantic content of instrumental strategies, the civilizational approach appeals to the multiplicity and equality of the paths of historical and political formation. In political discourse, the relevant intentions are the plots of ensuring the completeness of sovereignty, historical memory and falsification of history, a common value-semantic space, national unity, and many others. The point of concentration of scientific and ideological-political search is the definition of the civilizational status of Russia. According to the authors, giving Russia a civilizational status allows us to offer adequate explanations of its socio-political ontology and phenomenology, stable and transient parameters, invariant and innovative characteristics. The emphasis is on the metaphysics of Russian statehood as determined by the specifics of civilization.

Socio-psychological specifications of volunteers taking into account the intergenerational approach

The article presents the results of a study aimed at studying the socio-psychological characteristics of young volunteers and volunteers over 55 years old, taking into account intergenerational characteristics. It is revealed that the socio-psychological characteristics of the volunteers of the two studied generations have predominantly invariant characteristics. These include the polymotivation of prosocial activity, expressed with high significance for volunteers of all values; the importance of active social contacts; altruistic orientation of the individual; compassion as the most pronounced effective component of prosocial behavior and a tendency to emotionally colored actions in providing assistance.

WetMapFormer: A unified deep CNN and vision transformer for complex wetland mapping

The Ramsar Convention of 1971 encourages wetland preservation, but it is unclear how climate change will affect wetland extent and related biodiversity. Due to the use of the self-attention mechanism, vision transformers (ViTs) gain better modeling of global contextual information and become a powerful alternative to Convolutional Neural Networks (CNNs). However, ViTs require enormous training datasets to activate their image classification power, and gathering training samples for remote sensing applications is typically costly. As such, in this study, we develop a deep learning algorithm called (WetMapFormer), which effectively utilizes both CNNs and vision transformer architectures for precise mapping of wetlands in three pilot sites around the Albert county, York county, and Grand Bay-Westfield located in New Brunswick, Canada. The WetMapFormer utilizes local window attention (LWA) rather than the conventional self-attention mechanism for improving the capability of feature generalization in a local area by substantially reducing the computational cost of vanilla ViTs. We extensively evaluated the robustness of the proposed WetMapFormer with Sentinel-1 and Sentinel-2 satellite data and compared it with the various CNNs and vision transformer models which include ViT, Swin Transformer, HybridSN, CoAtNet, a multimodel network, and ResNet, respectively. The proposed WetMapFormer achieves F-1 scores of 0.94, 0.94, 0.96, 0.97, 0.97, 0.97, and 1 for the recognition of aquatic bed, freshwater marsh, shrub wetland, bog, fen, forested wetland, and water, respectively. As compared to other vision transformers, the WetMapFormer limits receptive fields while adjusting translational invariance and equivariance characteristics. The codes will be made available publicly at https://github.com/aj1365/WetMapFormer.