Miracle-dz/UniFAR-FTUs huggingface dataset:
Force and Motion: Virtual Reality as a Study Instrument of Alternative Conceptions in Dynamics

Alternative conceptions in dynamics have been subject of research in physics teaching since the early 1980's. Currently, conceived as the fact that a normal student interpreting phenomena of nature according to their prior knowledge, usually titled alternative conceptions, which are not treated as "wrong", though not agree with the accepted scientific explanations. On the one hand, various studies have contributed to these developments, other research has focused on the development of teaching methodologies and tools for study and research to help students and teachers in raising awareness of the existence of these concepts and training of scientific knowledge. In this context, the Virtual Reality (VR) has gained ground and supporters of their use in physics teaching. Considered by many researchers as a powerful tool for studies of complex situations and capable of providing a direct user interaction with learning objects, VR offers many resources to the study of alternative conceptions in dynamics. Relying on this advantage, we developed the software Force & Motion, "which addresses alternative conceptions in dynamics from four simulations: Shot Gun; Salto Moto; Launch Aircraft; Rotational and platforms.
Adaptación al español de la escala de Condiciones de Efectividad en el Trabajo (CWEQ-II)

El objetivo de este estudio es adaptar al espanol el Cuestionario de Condiciones para la Efectividad en el Trabajo (Conditions of Work Effectiveness Questionnaire –CWEQII-) desarrollado por Laschinger, Finegan, y Shamian (2001). Se llevo a cabo un proceso de traduccion y retrotraduccion de los items que la componen y se exploraron sus propiedades psicometricas en una muestra de 346 empleados de una universidad publica espanol. Se investigo la fiabilidad de la escala y su estructura factorial (analisis factorial confirmatorio) y las relaciones entre el empowerment estructural y el empowerment general y el empowerment psicologico en el trabajo. Los resultados revelaron una estructura multidimensional de cuatro factores (acceso a las oportunidades, a la informacion, al apoyo y a los recursos) conforme a la escala original y relaciones positivas entre el empowerment estructural, general y psicologico en el trabajo. Se incluyen interpretaciones y sugerencias para futuros estudios.
A New United Analysis Method for Epicardial Mapping Signals

Epicardial potential mapping (EPM) system is a main tool for cardiac electrophysiological research and clinically precise diagnosis. One of EPM's technical difficulties is how to analyze EPM signals effectively to get pivotal information. Therefore a new united method based on dynamic iso-potential map (DIM), time-dependent correlation waveform analysis (CWA) and energy distribution were used to analyze EPM signals. The results indicated that the DIM could reflect depolarization waves' spread visually and dynamically. Time- dependent CWA and energy distribution could reflect the depolarization waves' spread directions and paths statistically. The concept of time-dependency made analysis focus on certain or several depolarization waves and the results were more significative. In conclusion, this united method can reflect depolarization waves' spread law roundly and accurately. It's fit for EPM system.
Dynamin and β-Arrestin Reveal Distinct Mechanisms for G Protein-coupled Receptor Internalization*

The process of agonist-promoted internalization (sequestration) of G protein-coupled receptors (GPCRs) is intimately linked to the regulation of GPCR responsiveness. Following agonist-mediated desensitization, sequestration of GPCR is presumably associated with the dephosphorylation and recycling of functional receptors. However, the exact mechanisms responsible for GPCR sequestration, even for the prototypic β2-adrenergic receptor (β2AR), have remained controversial. We demonstrate here that dynamin, a GTPase that regulates the formation and internalization of clathrin-coated vesicles, is essential for the agonist-promoted sequestration of the β2AR, suggesting that the β2AR internalizes via the clathrin-coated vesicle-mediated endocytic pathway. In contrast, internalization of the angiotensin II type 1A receptor (AT1AR), another typical GPCR, does not require dynamin. In addition, the AT1AR internalizes independent of the function of β-arrestin, a critical component for β2AR cellular trafficking, but additional AT1ARs are mobilized to the dynamin-dependent pathway upon overexpression of β-arrestin. These findings demonstrate that GPCRs can utilize distinct endocytic pathways, distinguishable by dynamin and β-arrestin, and that β-arrestins function as adaptor proteins specifically targeting GPCRs for dynamin-dependent endocytosis via clathrin-coated vesicles.
Denoising Method for Detecting Low-Probability-of-Intercept Radar Signal Using Convolutional Neural Network

Estimation of radar signal characteristics holds significant importance in the field of electronic warfare support. However, the emergence of low-probability-of-intercept (LPI) radar has witnessed widespread adoption, aiming to minimize the possibility of detection. LPI radar operates with a low signal-to-noise ratio and employs various modulation schemes, making it challenging to estimate signal characteristics. To address this problem and enhance the detection probability, this paper presents a convolutional neural network approach for noise reduction in LPI radar signals with various modulation schemes. We conduct simulations to validate the effectiveness of the proposed method.
Depressive symptoms in the very elderly—their prevalence and significance

A community sample of 1173 very elderly people (aged over 77) was interviewed by trained lay interviewers using a structured interview, including questions relating to emotional and physical health and social circumstances and the Mini‐Mental State Examination. Depressive symptoms such as loss of energy and feelings of tension and irritability were found frequently. High scores for depressive symptoms were associated with female sex, poor subjective physical health, loneliness and use of statutory services. Depressive symptom score was not found to be associated with age. Although only 6% of the sample described feeling depressed most of the time, 21% of respondents admitted that they sometimes felt that life was not worth living.
TrES-1: The Transiting Planet of a Bright K0 V Star

We report the detection of a transiting Jupiter-sized planet orbiting a relatively bright (V = 11.79) K0 V star. We detected the transit light-curve signature in the course of the TrES multisite transiting planet survey and confirmed the planetary nature of the companion via multicolor photometry and precise radial velocity measurements. We designate the planet TrES-1; its inferred mass is MJup, its radius is 1.08RJup, and its orbital period is 3.030065 ± 0.000008 days. This planet has an orbital period similar to that of HD 209458b but about twice as long as those of the OGLE transiting planets. Its mass is indistinguishable from that of HD 209458b, but its radius is significantly smaller and fits the theoretical models without the need for an additional source of heat deep in the atmosphere, as has been invoked by some investigators for HD 209458b.
Verification and diagnosis of SoC HDL-code

Xor-metrix for object relations in a vector logic space and a structural testing model are proposed. Assertion-based models and methods for the verification and diagnosis of HDL-code functional failures, which make possible to reduce considerably time-to-market of software and hardware, are developed. An architectural model of multimatrix reduced logical instruction set processor for embedded diagnosing is offered.
Examining the trait and functional motive antecedents of four gambling activities: Slot machines, skilled card games, sports betting, and promotional games.

Purpose – The purpose of this paper is to explore the functional motive and trait antecedents of four forms of gambling – slot machines, skilled card games, sports betting, and participating in marketing promotional games such as sweepstakes and contests.Design/methodology/approach – A hierarchical model of motivation and personality (3M model) was employed to investigate the trait and functional motive antecedents of four gambling activities. Five motives for gambling were identified – money, excitement, social interaction, escape from problems, and self‐esteem enhancement. In addition, the effects of demographic variables (i.e. age, gender, and education) on four gambling types were examined. A total of 1,158 responses were collected from an online consumer panel.Findings – The results suggest that the four forms of gambling have divergent motives, traits, and demographic profiles.Research limitations/implications – Future research should further explore other possible traits in predicting different gam.
A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy.
A big data based dynamic bandwidth allocation strategy with secrecy constraints

This paper investigates a dynamic bandwidth allocation strategy with secrecy constraints, where big data can be viewed as a resource instead of a burden from the traditional perspective. Unlike usual cases, we take into account big data and security issues along with bandwidth allocation. It is reasonable to assume that big data derived from mobile network, by a series of processing, can generate a binary set S consisting of pairs of users. According to S, a metric closeness can be redefined to describe whether the same confidential content can be shared between two users. On this basis, data driven clusters can be formed. Then two bandwidth allocation algorithms, aiming at increasing secrecy sum capacity and individual secrecy capacity by sharing content in clusters, are proposed. The fairness among users and computation complexity are considered in the first algorithm, while the objective of the second algorithm is to maximize the secrecy sum capacity. In order to validate our proposed schemes, a concise case is presented and numerical results show that a significant performance gain over both secrecy sum capacity and individual secrecy capacity is achieved.
Psychopathic Personality Inventory-Revised (PPI-R)

Das PPI-R beinhaltet 154 Aussagen, die auf einer 4-Punkte-Skala von «1 = falsch» bis «4 = richtig» beantwortet werden konnen. Daraus konnen neun Skalen gebildet werden. Die neunte Skala uberpruft inkonsistentes Antwortverhalten und soll Antwortmuster identifizieren, die auf dem Zufall, einem geringen Leseverstandnis oder absichtliche Fehlangaben basieren (z. B. Item 8: «Manchmal vergesse ich meinen Namen.» oder 37: «Ich habe niemals jemandem Schaden gewunscht.»). Zur Erfassung von Psychopathie konnen folgende Skalen gebildet werden: – Schuldexternalisierung (z. B. Item 16: «Wenn ich in meinem Leben weniger Pech gehabt hatte, ware ich erfolgreicher.»), – Rebellische Risikofreude (z. B. Item 13: «Wenn mein Leben langweilig wird, gehe ich ein Risiko ein.»), – Stressimmunitat (z. B. Item 32: «Ich lasse mich von Alltagsproblemen nicht nerven.»), – Sozialer Einfluss (z. B. Item 1: «Wenn ich wirklich will, kann ich die meisten Leute von fast allem uberzeugen.»), – Kaltherzigkeit (z. B. Item 53: «Ich fuhle mich oft wegen Kleinigkeiten schuldig.»), – Machiavellistischer Egoismus (z. B. Item 11: «Ich gebrauche viele Notlugen.»), – Sorglose Planlosigkeit (z. B. Item 29: «Ich treffe oft wiederholt dieselbe falsche Entscheidung.») und – Furchtlosigkeit (z. B. Item 69: «Hohen machen mich nervos.»).
Gender differences in the enactment of sociosexuality: An examination of implicit social motives, sexual fantasies, coercive sexual attitudes, and aggressive sexual behavior

An unrestricted sociosexual orientation (the endorsement of casual sex) has been found to correlate with undesirable behaviors and personality characteristics more so in men than in women. Using a community sample of men and women, we investigated the correlations between sociosexuality and behaviors, motives, attitudes, and fantasies related to sexual aggression. Participants (n = 168; ages 21–45) completed self‐report measures of sociosexual orientation, sexual conservatism, rape myth acceptance, adversarial sexual beliefs, attitudes toward women, sexual behaviors, and perpetration of sexual aggression. Participants also wrote five brief stories that were scored for power and affiliation‐intimacy motives and two sexual fantasies that were coded for the theme of dominance. For both men and women, an unrestricted sociosexual orientation was correlated with behavioral items indicating earlier life experiences with sex, a greater number of lifetime sex partners, and more frequent sexual activity. For men, an unrestricted sociosexual orientation was linked with higher levels of rape myth acceptance and adversarial sexual beliefs; more conservative attitudes toward women; higher levels of power motivation and lower levels of affiliation‐intimacy motivation; and past use of sexual aggression. For women, an unrestricted sociosexual orientation was associated with sexual fantasies of dominance and lower levels of sexual conservatism.
Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

Recent dysarthric speech recognition studies using mixed data from a collection of neurological diseases suggested articulatory data can help to improve the speech recognition performance. This project was specifically designed for the speakerindependent recognition of dysarthric speech due to amyotrophic lateral sclerosis (ALS) using articulatory data. In this paper, we investigated three across-speaker normalization approaches in acoustic, articulatory, and both spaces: Procrustes matching (a physiological approach in articulatory space), vocal tract length normalization (a data-driven approach in acoustic space), and feature space maximum likelihood linear regression (a model-based approach for both spaces), to address the issue of high degree of variation of articulation across different speakers. A preliminary ALS data set was collected and used to evaluate the approaches. Two recognizers, Gaussian mixture model (GMM) - hidden Markov model (HMM) and deep neural network (DNN) - HMM, were used. Experimental results showed adding articulatory data significantly reduced the phoneme error rates (PERs) using any or combined normalization approaches. DNN-HMM outperformed GMM-HMM in all configurations. The best performance (30.7% PER) was obtained by triphone DNN-HMM + acoustic and articulatory data + all three normalization approaches, a 15.3% absolute PER reduction from the baseline using triphone GMM-HMM + acoustic data. Index Terms: Dysarthric speech recognition, Procrustes matching, vocal track length normalization, fMLLR, hidden Markov models, deep neural network
Quality Prediction of Rocket Engine Combustion Chamber Shell Spinning Process Based on CGAN and Hybrid Neural Network

The combustion chamber shell is one of the main structures of rocket engines, and the quality prediction of its spinning process is crucial to improve the stability of the engine and the subsequent improvement process. In this article, we propose a quality prediction method based on data augmentation and a hybrid neural network model combining feature attention (FA) and temporal convolutional network and long short term memory network (TCN-LSTM). First, the feature multi-collinearity problem is solved by principal component analysis dimensionality reduction of the equipment time series data of the shell machining process. Then, conditional generative adversarial network is used for data augmentation to generate more samples using quality indicators as condition information to solve the problem of insufficient data collection in actual engineering. Finally, the key feature information is enhanced using the FA mechanism, while the dual quality metrics are predicted by TCN-LSTM, and the prediction results are corrected using the least squares method. In this paper, a real rocket engine combustion chamber shell machining dataset is used to validate the model effect, and the experimental results show that the method has higher prediction accuracy by comparing with different machine learning methods.
Analysis of Subspace Iteration for Eigenvalue Problems with Evolving Matrices

The subspace iteration algorithm, a block generalization of the classical power iteration, is known for its excellent robustness properties. Specifically, the algorithm is resilient to variations in the original matrix, and for this reason it has played an important role in applications ranging from density functional theory in electronic structure calculations to matrix completion problems in machine learning, and subspace tracking in signal processing applications. This note explores its convergence properties in the presence of perturbations. The specific question addressed is the following. If we apply the subspace iteration algorithm to a certain matrix and this matrix is perturbed at each step, under what conditions will the algorithm converge?
Link and Triadic Closure Delay: Temporal Metrics for Social Network Dynamics

Today, numerous models and metrics are available to capture and characterize static properties of online social networks. When it comes to understanding their dynamics and evolution, however, research offers little interms of metrics or models. Current metrics are limited to logical time clocks, and unable to capture interactions with external factors that rely on physical time clocks. In this paper, our goal is to take initial steps towards building a set of metrics for characterizing social network dynamics based on physical time. We focus our attention on two metrics that capture the "eagerness" of users in building social structure. More specifically, we propose metrics of link delay and triadic closure delay, two metrics that capture the time delay between when a link or triadic closure is possible,and when they actually instantiate in the trace. Considered over time or across traces, the value of these metrics can provide insight on the speed at which users act in building and extending their social neighborhoods. We apply these metrics to two real traces of social network dynamics from the Renren and Facebook networks. We show that these metrics are generally consistent across networks, but their differences reveal interesting properties of each system. We argue that they can be attributed to factors such as network maturity, environmental and social contexts, and services offered by network provider, all factors independent of the network topology and captured by our proposed metrics. Finally, we find that triadic closure delays capture the ease of neighbor discovery in social networks, and can be strongly influenced by friend recommendation systems.
Tikhonov training of the CMAC neural network

The architecture of the cerebellar model articulation controller (CMAC) presents a rigid compromise between learning and generalization. In the presence of a sparse training dataset, this limitation manifestly causes overfitting, a drawback that is not overcome by current training algorithms. This paper proposes a novel training framework founded on the Tikhonov regularization, which relates to the minimization of the power of the sigma-order derivative. This smoothness criterion yields to an internal cell-interaction mechanism that increases the generalization beyond the degree hardcoded in the CMAC architecture while preserving the potential CMAC learning capabilities. The resulting training mechanism, which proves to be simple and computationally efficient, is deduced from a rigorous theoretical study. The performance of the new training framework is validated against comparative benchmarks from the DELVE environment
A new measure for distance-field based shape matching

One popular method to match two shapes is to register their distance fields as images. We discuss the well-known robustness problems of this approach and identify the noncommutativity of distance transform and geometric transformations as a core issue. Building on this, we propose a simple modification of the method, deriving a new dissimilarity measure. As it involves multiple distance field computations, we also present an efficient GPU-based algorithm for this problem.
Reflectance Multispectral Imaging for Fungal Growth Level Estimation

Fungal contamination in food is a serious concern in food safety due to the harmful toxins it produces. Methods available in the current state of the art for fungal detection tend to be expensive and time consuming. To overcome this, Multispectral Imaging provides a practical and cost effective solution to estimate fungal growth levels. In this study, multispectral images are obtained via a simple, easy to use, in house built imaging system which facilitates onsite testing. It utilizes 13 spectral bands with peak wavelengths ranging from 365 nm to 940 nm. A meticulous procedure is followed when synthesizing fungal contaminated food samples. Fungal contaminated bread samples are imaged in several stages such as Day 0, Day 3, Day 5, and Day 6. A total of 1344 individual images are utilized for training and testing of the developed models with an 80% - 20% test-train split. Several preprocessing techniques are followed including dark current reduction. Principle Component Analysis (PCA) is employed as the dimensional reduction technique for the analysis. Classification models and a regression model as a functional map are developed for a better interpretation of the results. The classification model is validated with an accuracy of 95% for the KNN and SVM classifiers while the developed functional map modeled with a first order polynomial is proved to be accurate with a $R^{2}$ value of 0.989.
Nightmares of conservation and the bittersweet dreams of memory

Abstract Letters, books, photographs, long unworn clothing and scraps of ephemeral material, in their content and their condition, can move us in myriad ways. They can be a source of pain or joy or often both. In a museum we generally gaze at them behind glass. In our attics and living rooms we feel and smell and hear them. With our elders and our children we go through them as doors to our pasts and therefore to ourselves as human beings. This paper explores some of the ways in which students, seeking a career in the conservation of cultural heritage and therefore learning to make conservation decisions, may be motivated to think about the ways in which our senses give us access to the meanings of objects. Working with their own material, students of all ages and backgrounds are encouraged to share their ideas about the ways in which objects affect them as well as the ways in which people affect objects. They are stimulated to use their own experiences to guide their thoughts about the implications of possible conservation decisions that may affect a range of sensory characteristics which may be crucial to a deeper and richer understanding.