Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Convolutional Capsule Network for Compound Fault Diagnosis of RV Reducers

In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults in the absence of compound fault training data. In an examination of the rotate vector (RV) reducer, a core component of industrial robots, this paper proposes a compound fault identification method that is based on an improved convolutional capsule network for compound fault diagnosis of RV reducers. First, one-dimensional convolutional neural networks are used as feature learners to deeply mine the feature information of a single fault from a one-dimensional time-domain signal. Then, a capsule network with a two-layer stack structure is designed and a dynamic routing algorithm is used to decouple and identify the single fault characteristics for compound faults to undertake the diagnosis of compound faults of RV reducers. The proposed method is verified on the RV reducer fault simulation experimental bench, the experimental results show that the method can not only diagnose a single fault, but it is also possible to diagnose the compound fault that is composed of two types of single faults through the learning of two types of single faults of the RV reducer when the training data of the compound faults of the RV reducer are missing. At the same time, the proposed method is used for compound fault diagnosis of bearings, and the experimental results confirm its applicability.
White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO2 Emission Design of Retaining Structures

This study proposes a new metaheuristic optimization algorithm, namely the white-tailed eagle algorithm (WEA), for global optimization and optimum design of retaining structures. Metaheuristic optimization methods are now broadly implemented to address problems in a variety of scientific domains. These algorithms are typically inspired by the natural behavior of an agent, which can be humans, animals, plants, or any physical agent. However, a specific metaheuristic algorithm (MA) may not be able to find the optimal solution for every situation. As a result, researchers will aim to propose and discover new methods in order to identify the best solutions to a variety of problems. The white-tailed eagle algorithm (WEA) is a simple but effective nature-inspired algorithm inspired by the social life and hunting activity of white-tailed eagles. The WEA’s hunting is divided into two phases. In the first phase (exploration), white-tailed eagles seek prey inside the searching region. The eagle goes inside the designated space according to the position of the best eagle to find the optimum hunting position (exploitation). The proposed approach is tested using 13 unimodal and multimodal benchmark test functions, and the results are compared to those obtained by some well-established optimization methods. In addition, the new algorithm automates the optimum design of retaining structures under seismic load, considering two objectives: economic cost and CO2 emissions. The results of the experiments and comparisons reveal that the WEA is a high-performance algorithm that can effectively explore the decision space and outperform almost all comparative algorithms in the majority of the problems.
Two Impurity Anderson problem: Perturbative Kondo-Doublet interaction

We reobtain the Kondo-Doublet interaction by means of the Brillouin-Wigner Perturbation theory. By applying the same method to the single impurity case we show that the Kondo-Doublet interaction is a direct consequence of the Kondo screening cloud. We fully confirm our previous results on this novel interaction. In particular, we found that for values of the system parameters typical of semiconductor Quantum Dots heterostructures, the ferromagnetic Kondo-Doublet interaction dominates over the RKKY one.
Is Semantics Physical

It is demonstrated that under the hypothesis of boundedness, the semantics appears as a property of spontaneous physical processes. It turns that both semantic structure and semantic meaning have their own physical agents each of which is represented trough generic for the state space property. The boundedness sets an exclusive two-fold representation of a semantic unit: as a specific sequence of letters and as a performance of a specific engine so that their interplay serves as grounds for building a multi-layer hierarchy of semantic structures. It is established that in this setting the semantics admits both non-extensivity, permutation sensitivity and Zipf`s law. The robustness of the hierarchical organization of semantic structures is maintained by new generic form of non-local feedback that appears as a result of the necessary for sustaining boundeness matter wave emitting.
Vemurafenib Inhibits Enterovirus A71 Genome Replication and Virus Assembly

Enterovirus A71 (EV-A71) infection is a major cause of hand, foot, and mouth disease (HFMD), which may be occasionally associated with severe neurological complications. There is currently a lack of treatment options for EV-A71 infection. The Raf-MEK-ERK signaling pathway, in addition to its critical importance in the regulation of cell growth, differentiation, and survival, has been shown to be essential for virus replication. In this study, we investigated the anti-EV-A71 activity of vemurafenib, a clinically approved B-Raf inhibitor used in the treatment of late-stage melanoma. Vemurafenib exhibits potent anti-EV-A71 effect in cytopathic effect inhibition and viral load reduction assays, with half maximal effective concentration (EC50) at nanomolar concentrations. Mechanistically, vemurafenib interrupts both EV-A71 genome replication and assembly. These findings expand the list of potential antiviral candidates of anti-EV-A71 therapeutics.
A Diachronic Study of Code-Switching Patterns in the Language of a Third Culture Filipino Kid in Korea

Code-switching has been of immense interest in bilingualism for decades, and most previous studies present different code-switching functions in the language of bilinguals. However, a diachronic exploration of code-switching patterns in young polyglots’ production is a road less ventured. The present study follows the three-year language development of a Filipino third culture kid (living in a culture other than their parents’) in Korea from when he was 5;5 to 8;5 years old. Discourse analyses and hours of ethnographic observation through audio/video recordings expose a substantial shift of code-switching patterns across the three stages of language development. Significant changes can be observed explicitly in code-switching as referential function, addressee specification, and cross-cultural solidarity. The current investigation proposes that there is a diachronic change in the patterns of code-switching when a child’s new language develops, and the results resonate with the argument that code-switching is used for increasingly sophisticated purposes to manifest multicompetence, behavior transformation, and identity change when a certain level of communicative fluency is reached. Finally, the study provides useful insights toward a cross-cultural understanding of the dynamic interplay of code-switching and multicultural kids’ language in a pluralistic community.
iNaturalist is useful at enhancing biodiversity studies as evident from southern African freshwater crabs (Decapoda: Brachyura: Potamonautidae)

The value of the citizen science platform iNaturalist was explored using photographic observations of southern African freshwater crabs (Potamonautidae Bott, 1970, among two genera Potamonautes sensu stricto MacLeay, 1838 and MartimonautesCumberlidge & Daniels, 2022) in combination with specimen data deposited in the South African Museum (Iziko Museums of Cape Town, South Africa). Species identification of photographic observations were assessed, and the identification of taxa corrected where necessary. From these records, the habitat type and distribution of the species were noted. The area of occupancy (AOO) and the extent of occurrence (EOO) were calculated where range extensions for species were observed. The results demonstrate that riverine/mountain stream freshwater crabs are over-represented in their frequency but species occurring in swamps/forests were under-represented. Using iNaturalist spatial data for South Africa we can identify several unsampled gaps in the country which included the Free State and North West, Gauteng and the Northern Cape, followed by Limpopo, Mpumalanga, and the Eastern Cape provinces. The Western Cape and KwaZulu-Natal had the highest number of freshwater crab records on iNaturalist. The remaining southern African countries, Angola, Botswana, Eswatini, Lesotho, Malawi, Mozambique, Namibia, and Zimbabwe, have few iNaturalist records. Range extensions were observed in four South African species based on iNaturalist records (P. clarusGouws, Stewart & Coke, 2000, P. flavusjoDaniels, Phiri, & Bayliss, 2014, P. isimangaliso Peer & Gouws, 2015, and P. mariepskoppieDaniels, Barnes, Marais & Gouws, 2021) with increases in AOO and EOO. We also observed a new undescribed freshwater crab on iNaturalist and corroborated this observation by sequencing these specimens for the cytochrome oxidase one subunit (COI) locus. Photographic quality is critical for taxonomic inference. Citizen science ­platforms such as iNaturalist can be invaluable for the discovery and documentation of biodiversity and provide new spatial data for species distributions that in turn can aid conservation tools.
Prevalence of mortality in mammals: A retrospective study from wildlife rescue center of Nepal

To assess threats to the population of native mammals and their survival, we performed a retrospective study utilizing a rescue and rehabilitation database from Nepal. Based on a total number of 1013 cases involving six taxonomic families of mammals admitted over five years, the outcome (mortality and survivability) differed significantly among six taxa (χ2[df = 5, N = 1013] = 65.08, p < .001). The mortality rate was highest in Felidae (74%), followed by Herpestidae (69%), Viverridae (62%), Bovidae (50%), Cervidae (50%), and Cercopithecidae (38%). The outcome also differed significantly among the causes of admission (χ2[df = 9, N = 1013] = 241.48, p < .001). The prevalence of mortality was highest in the cases of gunshot and poisoning, followed by collision with a vehicle, diseases/infection, entanglement/trapped, injured by dogs/cats, abandoned/orphaned, injured by humans, unknown causes, and domestic/captivity issues. Multiple logistic regression analysis showed the significant influence of taxonomic family, age group, and cause of admission on the likelihood of mortality of the rescued mammals. The odds ratio of mortality was higher for carnivore taxa, and juveniles were more likely to succumb to death than adults. Our study provides important information associated with the mortality of rescued mammals, thereby assisting in the development and formulation of strategies for threat mitigation and an efficient rehabilitation process.
Success does not imply knowledge: Preschoolers believe that accurate predictions reveal prior knowledge, but accurate observations do not

Much research has investigated how children track and reason about accuracy when deciding who to trust. The majority of this work assumes a static link between accuracy and knowledge; that is, children are expected to attribute greater knowledge to accurate agents. But while accuracy often reveals knowledge, the two are not deterministically related. Ignorant agents can be accurate (for example, one could take a lucky guess), and knowledgeable agents can be inaccurate (for example, one could accidentally err). Given this, how do children reason about the relation between knowledge and accuracy? Across three experiments, we show that fourand five-year-olds are sensitive to the distinction between knowledge and accuracy. Specifically, children judge that an agent who accurately predicts an outcome is knowledgeable, but an agent who merely observes and then accurately describes the same outcome is not. Our findings show that when children gauge agents’ knowledge, they do not rely on accuracy alone; they infer knowledge only when an agent is right in the right kind of way.
Chemical profile of Nepeta cataria L. var. citriodora (Becker) essential oil and in vitro evaluation of biological activities

Essential oil (EO) obtained by hydrodistillation in a Clevenger-type apparatus from aerial parts of Nepeta cataria L. var. citriodora (Becker), cultivated in Serbia was subjected to gas chromatography-mass spectroscopy (GC-MS) to determine the composition. Furthermore, N. cataria var. citriodora essential oil wastested to determine its antimicrobial, antioxidant, antihyperglycemic and anti-inflammatory activities in vitro.The antimicrobial activity was tested by broth microdilution method against 16 bacterial strains from American Type Culture Collection (ATCC). Four common tests for measuring in vitro antioxidant activity were used: 2, 2-diphenyl-1-picrylhydrazyl assay (DPPH), reducing power (RP), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and β-carotene bleaching assay (BCB). Antihyperglycemic activity was examined by using α-glucosidase inhibitory potential (AHgA), while anti-inflammatory activity (AIA) was determined by protein denaturation bioassay, using egg albumin. In total, 36 compounds were isolated and detected by GC-MS technique in N. cataria var. citriodora EO. The EO is mainly comprised of oxygenated monoterpenes (93.1%), and the main compounds were two monoterpenoid alcohols, nerol (38.5%) and geraniol (24.9%), followed by two aliphatic aldehyde, geranial (14.6%) and neral (11.0%). Antimicrobial activity of this EO shows growth inhibition of all tested bacteria strains, and exhibited good antioxidant, antihyperglycemic and anti-inflammatory activities. The EO obtained from N. cataria var. citriodora grown in Serbia shows valuable biological activity, indicating its potential for use as a supplement in everyday diet and as a natural preservative in food industry.
Optimization-Based Estimation and Model Predictive Control for High Performance, Low Cost Software-Defined Power Electronics

A combined optimization-based estimation (OBE)-model predictive control (MPC) technique is developed to improve the dynamic performance of a reconfigurable LC-based power module with lower cost and less noise/oscillation. The developed OBE-MPC technique and the corresponding power module are based on a software-defined power electronics concept that can be reconstructed as different topologies and applied to various load/source applications, e.g., dc/dc converters, dc/ac single/three-phase grid-connected inverters, and ac/ac motor traction inverter to improve the energy conversion performance. The software-defined power electronics are designed in a generalized way by manipulating a different number of OBE-MPC power modules with holistic high-level control functions for wide applications. Symmetrically mirrored to the MPC, the OBE is configured as a constrained finite time optimal estimation (CFTOE) problem to solve the quadratic cost function based on the past sampling information. With the designed OBE, the sensor count is reduced with less noise/oscillation. And the highly accurate OBE contributes to the correction of possible modeling parametric or sampling errors. The integration of OBE-MPC algorithms improves both the steady-state and dynamic performances with less noise/oscillation, more robust transient behavior, and higher control bandwidth. The explicit design of OBE-MPC algorithms makes it possible to implement the functions on a low-cost DSP. Also, the state-space modeling of OBE-MPC for the LC-based power module is immune to the output side unknown inductance, which further improves the parametric accuracy. The proposed methods have been validated experimentally.
SB-SAGE Based Joint MCFOs and Channel Estimation for DMIMO-OFDM Systems

Distributed multiple-input multiple-output (DMIMO) system with orthogonal frequency division multiplexing (OFDM) modulation is an emerging paradigm for high data rate and cell coverage extension. In order to make the paradigm shift from conventional network to intelligent DMIMO-OFDM systems, one must address the fundamental bottleneck in synchronization and channel estimation for coherent reception. The involvement of multiple cooperative nodes increases the number of corresponding channels and synchronization impairments to be estimated in comparison with collocated multiple input multiple output (MIMO) systems. As a consequence, the traditional pilot-based estimator requires a large pilot overhead to effectively estimate multiple impairments. This paper presents semi-blind space alternating generalized expectation maximization (SB-SAGE) algorithm for jointly estimating the multiple carrier frequency offsets (MCFOs) and frequency-selective channel gains in DMIMO-OFDM systems. SB-SAGE estimator uses soft information of partially received data symbols along with pilot symbols to obtain improved MCFOs and channel estimates with significantly reduced length of pilot overhead. It also increases spectral efficiency of the systems. The proposed estimator converges in almost two iterations and achieves significant improvement over pilot-based methods.
Logic versus approximation : Essays dedicated to Michael M. Richter on the occasion of his 65th birthday

A True Unprovable Formula of Fuzzy Predicate Logic. - The Inherent Indistinguishability in Fuzzy Systems. - On Models for Quantified Boolean Formulas. - Polynomial Algorithms for MPSP Using Parametric Linear Programming. - Discrete and Continuous Methods of Demography. - Computer Science between Symbolic Representation and Open Construction. - Towards a Theory of Information. - Retrieval by Structure from Chemical Data Bases. - Engineers Don't Search. - Randomized Search Heuristics as an Alternative to Exact Optimization. - Approximation of Utility Functions by Learning Similarity Measures. - Knowledge Sharing in Agile Software Teams. - Logic and Approximation in Knowledge Based Systems.
A Computational Model of the Acquisition of German Case

We present a computational model of the acquisition of German case that is evaluated against empirical data obtained from naturalistic speech. The model substitutes nouns into existing contexts, and proceeds through a number of stages that reflect increasing knowledge on the part of a child, both of the determiner-noun sequences that are legal in German, and of the determiner-noun sequences that are appropriate in specific sentential contexts (cases). The model provides a natural account of gender and case errors, the two most common error types produced by children, and shows the highest error rates in dative contexts and lowest error rates in nominative contexts, as is true of children learning German. However, the model ’s error rates in the early stages are considerably higher than those shown by children, suggesting that children possess a fairly sophisticated representation of how lexical contexts assign case from a relatively early age.
Skilled Bandits: Learning to Choose in a Reactive World

In uncertain environments we must balance our need to gather information with our desire to reap rewards by exploiting current knowledge. Achieving this balance is further complicated in reactive environments where actions produce long-lasting change to the system. In four experiments, we investigate how people learn to make effective decisions from experience in a dynamic multiarmed bandit task. In contrast to the typical exploitation-dependent diminishing rewards found in previous studies, options were framed as skills that developed greater rewards the more they were chosen. In Experiment 1, we provide a proof of concept, and in Experiments 2-4 we explore the boundaries of participants' sensitivity to reactive dynamics. Our results suggest that most individuals can learn effective strategies for coping with these reactive environments. A two-part comparison of several competing psychological models supports several conclusions: (a) a sizable minority of individuals learned that their environment was reactive, (b) evidence suggests several distinct groups of individuals employed unique decision strategies, and (c) testing models with the simulation method reveals qualitative misfits that motivate future theory development. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Computational evidence for hierarchically structured reinforcement learning in humans

Humans have the fascinating ability to achieve goals in a complex and constantly changing world, still surpassing modern machine-learning algorithms in terms of flexibility and learning speed. It is generally accepted that a crucial factor for this ability is the use of abstract, hierarchical representations, which employ structure in the environment to guide learning and decision making. Nevertheless, how we create and use these hierarchical representations is poorly understood. This study presents evidence that human behavior can be characterized as hierarchical reinforcement learning (RL). We designed an experiment to test specific predictions of hierarchical RL using a series of subtasks in the realm of context-based learning and observed several behavioral markers of hierarchical RL, such as asymmetric switch costs between changes in higher-level versus lower-level features, faster learning in higher-valued compared to lower-valued contexts, and preference for higher-valued compared to lower-valued contexts. We replicated these results across three independent samples. We simulated three models—a classic RL, a hierarchical RL, and a hierarchical Bayesian model—and compared their behavior to human results. While the flat RL model captured some aspects of participants’ sensitivity to outcome values, and the hierarchical Bayesian model captured some markers of transfer, only hierarchical RL accounted for all patterns observed in human behavior. This work shows that hierarchical RL, a biologically inspired and computationally simple algorithm, can capture human behavior in complex, hierarchical environments and opens the avenue for future research in this field.