Miracle-dz/UniFAR-FTUs huggingface dataset:
COMbiNEd EFFECTS OF GRAViTY, bENdiNG MOMENT, bEARiNG

This computational work investigates planetary gear load sharing of three-mount suspension wind turbine gearboxes. A three-dimensional multibody dynamic model is established, addressing gravity, bending moments, fluctuating mesh stiffness, nonlinear tooth contact, and bearing clearance. A flexible main shaft, planetary carrier, housing, and gear shafts are modeled using reduced degrees-of-freedom through modal condensation. This drivetrain model is validated against the experimental data of the Gearbox Reliability Collaborative for gearbox internal loads. Planet load sharing is a combined effect of gravity, bending moment, bearing clearance, and input torque. Influences of each of these parameters and their combined effects on the resulting planet load sharing are investigated. Bending moments and gravity induce fundamental excitations in the rotating carrier frame, which can increase gearbox internal loads and disturb load sharing. Clearance in carrier bearings reduces the bearing stiffness, and thus the bending moment from the rotor can be transmitted into gear meshes. With bearing clearance, the bending moment can cause tooth micropitting and can induce planet bearing fatigue, By: Yi Guo, Jonathan Keller, and William LaCava Jonathan Keller is a senior engineer and Yi Guo is a postdoctoral researcher with the National Wind Technology Center, a research facility of the National Renewable Energy Laboratory. William LaCava is a Ph.d. student at the UMass Amherst. For more information, visit www.nrel. gov/nwtc. Researchers from the National Renewable Energy Laboratory share a paper they presented at the American Gear Manufacturers Association Fall Technical Meeting in October 2012.
Morphological, phaneroptic, and dominant gene characterization of the naked neck creole hen nudicollis in Sucre, Colombia

Objective. Estimate the morphometric, phaneroptic, and four dominant gene diversity in backyard native birds with naked neck (Gallus domesticus L. Subspecie nudicollis) in the Sabanas subregion, department of Sucre Colombia. Materials and methods. Overall, 650 adult birds (480 hens and 170 roosters) from 10 different locations, 14 morphometric variables, eight phaneroptics, and four dominant genes were measured. The data were subjected to descriptive analysis. Animals were compared between localities by means of the analysis of variance and Duncan test. Sexual dimorphism was evaluated from body weight and tarsal size, morphostructural harmony was evaluated from the correlations between the measurements, and the frequencies of the Frizzled feather, silky feather, pilopody, and polydactyly genes and their influence were estimated on four morphometric variables. Phaneroptic variables were evaluated using frequencies. Results. The analyzed descriptors showed a superiority in roosters than over hens as well as between localities (p<0.001). Bodyweight and tarsal length were sexual differentiators (p<0.001). Low bodily harmony was evidenced. The estimated allele frequencies were low except for the Polydactyly gene, because the birds with this gene were significantly larger (p<0.001). The phaneroptic characteristics describe a bird with normal brown plumage, white tarsi, a red lobe color, and a red-orange eye, with a simple crest and small-medium size in hens and medium-large in roosters. Conclusions. A heterogeneous population with great phaneroptic variability was found. There are indications of introgression of specialized breeds. The frequencies of the dominant genes were low, with higher performance for the characteristics BW, BL, TP, LL and WL in birds with polydactyly genotype.
Simulation-Based Learning of Complex Skills: Predicting Performance With Theoretically Derived Process Features

Simulation-based learning is often used to facilitate complex problem-solving skills, such as collaborative diagnostic reasoning (CDR). Simulations can be especially effective if additional instructional support is provided. However, adapting instructional support to the learners’ needs remains a challenge when performance is only assessed as the outcome after using the simulation. Researchers are, therefore, increasingly interested in whether process data analyses can predict outcomes of simulated learning tasks and whether such analyses allow early identification of the need for support. This study developed a random forest classification model based on theoretically derived process indicators to predict success in a simulated learning environment. The context of the simulated learning environment was medicine. Internists interacted with a simulated radiologist to identify possible causes of an illness. Participants’ CDR was conceptualized via log-data, coded on a broad, domain-general level for better generalizability. Results showed a satisfactory prediction rate for CDR performance, indicated by diagnostic accuracy. The model predicted accurate and inaccurate diagnoses and was therefore suitable for making statements about the performance by only using process data of CDR. The findings
A Semantic Level Matching Method for Unstructured Documents

: This paper proposes a text semantic matching model, designs multiple features for text matching from word level, phrase level, sentence level and semantic level, and uses ranking learning method to fuse features. Experiments are carried out to verify the performance of the proposed method in text matching and real scene human-computer dialogue tasks. Experiments show that the multi granularity text semantic matching model can achieve high accuracy in text matching task, and has good domain migration ability.
Application of high-resolution ultrasonic spectroscopy for detection of the plasmin activity toward Î2-casein

High-resolution ultrasonic spectroscopy (HR-US) was applied for precise detection of plasmin activity towards β-casein in buffer at pH 7.8 and 37 C. The evolution of ultrasonic velocity and ultrasonic attenuation measured at 15.5 MHz is related to the concentration of peptide bonds hydrolyzed and loss of β-casein aggregates, respectively. The ultrasonic assay presents sensitive and direct activity-based quantification of plasmin levels in milk. The variation in plasmin concentration between HR-US and ELISA method owed to the differing detection principles. The real-time ultrasonic profiles of hydrolysis were utilized to describe the kinetic aspect of plasmin activity. The non-linear activity curve was fitted with classic and inverse Michaelis–Menten type models. Within 1–8.6 mg⋅mL 1 β-casein, the Vmax and KM obtained were (6.30 ± 2.21) × 10 5 mol.kg 1⋅min− 1 and 10.33 ± 3.50 mg⋅mL 1, respectively. The maximum peptide bond cleaved was 5–6 (2.7% degree of hydrolysis) achieved at 1 mg⋅mL 1 β-casein.
Supernatural Attitude and Mental Health Practices among the Tribal with Special Reference to Jharkhand

Hansda et al: Supernatural Attitude and Mental Health Practices among Tribes Indian Journal of Psychiatric Social Work, 2021;12(2): 20-25 91 real benefactors and can be approached easily. Hindu literature has suggested that mental disorders are a reflection of abstract supernatural beings, due to witchcraft. However, Ayurveda principles suggest that ill health is an outcome of an imbalance of dosha. This imbalance is caused by inappropriate diet, disregard to the gods, teachers or others; excessive emotional imbalances unable to enjoy life may cause inappropriate behaviour. As per the symptoms, treatments were given from of herbs and ointments. The supernatural force is derived from the Latin word "supernatural" where supra means "above" and naturalis means "nature", that means beyond nature. This paper emphasises on the tribals attitude towards mental illness and their causes, and the practices used by them to cure any physical or psychological problems. It also describes supernatural phenomena prevailing among them. MATERIALS AND METHOD Participants and procedures: The purpose of this study was to understand the attitude and practice of mental health among tribes of the Jharkhand state of India. The participants for the study were selected from two districts (Ranchi and Pakur) of the Jharkhand. The villages were determined by using the stratified random method, selected villages were situated in a remote area of both the district. Sixty participants were selected for the study. The study participants were informed regarding the aims of the study and informed consent was taken from participants. Tools of Data Collection: The questionnaire consists of three sections. Section 1 contains items regarding the socio-demographic background of the participants such as gender, education, occupation, marital status, religion, socioeconomic status, and type of family, physical and psychiatric history. Section 2 consists of questions related to the practices of healing for physical and especially mental illness and section 3 was the Supernatural Attitude Questionnaire (SAQ), which is a twenty-seven item questionnaire. [3] The questionnaire inquired into the attitudes and beliefs in various types of magico-religious and supernatural influences and their role in causing mental illness. Procedure: Ethical clearance for the study was obtained from the institute ethics committee RINPAS, Kanke, Ranchi. The village leader (Mukhiya) were explained the aim of the study and the list of the population obtained, further a total of 60 samples were drawn keeping a 10% proportion of the total population of the village. After obtaining informed consent from the participant’s questionnaire was administered on an individual basis in the village itself. Analysis of the data: Collected data tabulated and descriptive statistics obtained using statistical package for social sciences 16.0 version.
Emotional Intelligence and Self-efficacy as Predictor Factors of Student Resilience in Online Learning during Pandemic Era

BACKGROUND: The COVID-19 pandemic has an impact on the educational aspect. The education was conducted through learning, but not all students are accustomed to online learning so there is a process of self-adjustment that makes the learning does not run smoothly. Although there are many difficulties in doing the online learning process, students must be ready to obey the government’s policy. The students are required to be able to adjust and be able to survive with this condition. Therefore, it takes resilience in students, so that they can rise and survive in the pandemic era. AIM: This study aims to analyze emotional intelligence and self-efficacy as predictor factors of resilience among university students in doing online learning in the pandemic era. METHODOLOGY: This type of research was correlation analysis. The population of this study was a student of UNUSA, in which sampling used random sampling with a total of 503 students. The dependent variables in this study were emotional intelligence and self-efficacy. The dependent variable was the resilience of students in online learning. The instruments used in this study were the emotional intelligence questionnaire, general self-efficacy scale, and brief resilience scale. The Pearson correlation method was used for statistical analysis. RESULTS: The correlation showed the presence of relationship between emotional intelligence (r = 0.895) and self-efficacy (r = 0.546) with resilience in students with a significance level of 0.000 (p < 0.05). CONCLUSION: There was a relationship between emotional intelligence and self-efficacy with resilience among university students in doing online learning in the pandemic era.
Images, Words, and Questions: Variables That Influence Beliefs about Vision in Children and Adults

In three studies we used animated computer graphics to examine beliefs among children and adults that vision involved input to the eyes (the intromission theory) or emissions from the eye (the extramission theory). Results supported previous findings which showed a decrease in extramission and an increase in intromission responses across age. The findings also indicated that there were more extramission interpretations when subjects were tested with graphic images, and more intromission interpretations when the questioning was purely verbal. However, the magnitude of the effect was highly dependent upon question format. The differences between graphic and verbal question presentations (A) are consistent with our theory on the origins of extramission beliefs, (B) suggest that beliefs can vary as a function of form of symbolization, and (C) are contrary to long-standing beliefs of educators and psychologists that emphasize the importance of concrete, pictorial representation. q 1996 Academic
Cyber-Psychopathy Trait as Predictor of Cybersex Addiction among University Students

Ahmad Bilal , Arif Nadeem , Muhammad Saleem 3 1 Assistant Professor, Department of Applied Psychology, The Islamia University of Bahawalpur, Pakistan. Email: ahmadbilal4@gmail.com 2 Lecturer, Department of Applied Psychology, Government College University Faisalabad, Pakistan. Email: arif.nadeem. 79@gmail.com 3 Associate Professor, Department of Applied Psychology, The Islamia University of Bahawalpur, Pakistan. Email: chsaleem_1@hotmail.com
THE ROLE OF HOFSTEDE DIMENSIONS ON THE READINESS OF IOT IMPLEMENTATION CASE STUDY: SAUDI UNIVERSITIES

The importance of the Internet of Things (IoT) in various institutions and organizations is crucial in the quality of services, provision, and facilitation in providing those with related services. Therefore, before deciding to implement these regulations in the institutions, it is essential to study the institution’s situation from various aspects to avoid loss and failure. This research study is based on the role of Hofstede Dimensions on the readiness of IoT adoption in Saudi universities. This study reviews the literature about the concept of Hofstede dimensions on the readiness of the IoT on a global scale. In-depth study and practical implications are assessed by hypothetical testing based upon predetermined variable and their possible impact of adopting IoT. A questionnaire was conducted and distributed in 6 universities in located the kingdom of Saudi Arabia. The questionnaire received 390 responses. SPSS 24.0 was used to examine the possible impact and degree of impact on the adoption of IoT in these Universities. The results demonstrated that there was a positive relationship between Hofstedefactors and the readiness of Saudi universities in applying the IoT.
ARE DIFFERENT STANDARDS WARRANTED TO EVALUATE PSI?1

Throughout the debate on psi, skeptics have almost universally insisted on different standards for evaluating the evidence, claiming that psi represents a radical departure from our current scientific understanding. Thus, there is considerable ambiguity about what standard of evaluation psi must meet. Little attention has been paid to the possible harm to the integrity of scientific investigation from this resulting inconsistency in testing standards. Some have proposed using a Bayesian framework as an improvement on this dilemma in order to more explicitly model beliefs, assumptions, and background scientific knowledge, especially when evaluating a controversial hypothesis. Recently, Kuhn’s notion of paradigms, which constrains scientific research within boundaries believed to be most productive, has been incorporated into a Bayesian framework. Within this framework, I explore a likely paradigm or meta-theory used by skeptics that typically constrains research and makes it difficult for psi evidence to be accepted. It appears that such a paradigm would in many respects have difficulty accounting for consciousness, which is fundamental to an understanding of psi. I discuss why psi data are likely to play a key role in making progress in solving the problem of consciousness. Thus, applying different standards of evaluation to psi data is likely counterproductive.
SELF-ATTENTION MECHANISM FOR SOUND EVENT LOCALIZATION AND DETECTION Technical Report

This technical report describes the system submitted to DCASE 2021 Task 3: Sound Event Localization and Detection (SELD) with Directional Interference. The goal of Task 3 is to classify poly-phonic events with temporal activity into a given class and detect their direction in the presence of hidden sound events. Our system uses a Transformer that utilizes the self-attention mechanism that is now successfully used in many ﬁelds. We propose an architecture called Many-to-Many Audio Spectrogram Transformer (M2M-AST) that uses a pure Transformer to reduce the dependency of CNNs and easily change the output resolution. Using the architecture for Sound Event Detection (SED) and Direction of Arrival Estimation (DOAE), which are small sub-problems that consist of SELD, we show that our system outperforms the baseline system.
Generalized source multipole moments of dynamical horizons in binary black hole mergers

In this work, we uncover new features in the evolution of the deformations of the dynamical horizon geometry in a binary black hole merger scenario using numerical relativity. First, in the inspiral phase, owing to the deformations, the dynamical horizons of the two black holes are found to steadily acquire multipole moments that vanish when the horizons are isolated. Out of these, the dominant moment is found to be the quadrupole moment. Second, we show that they encode detailed information about the dynamics of the binary black hole system. The dominant quadrupole multipole moment is particularly shown to be strongly correlated with the gravitational field of the system at future null infinity. Therefore, the gravitational waves carried away from the system contain information about the geometrical structure of the black holes in the strong-field regime. Third, we also find that, in the post-merger phase, the multipolar structure of the outer common dynamical horizon of the system is strongly correlated with that of the individual horizons just before the merger. The outer common horizon then settles down to equilibrium as suggested by the decay of the multipole moments gained by the system through the inspiral phase.
Can depth-adaptive BERT perform better on binary classification tasks

In light of the success of transferring language models into NLP tasks and the magnitude of those language models, we ask whether the full language model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size by layer truncation and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily both in fine-tuning and testing with little even no accuracy loss.
Graph Neural Network-based Android Malware Classification with Jumping Knowledge

This paper presents a new Android malware de-tection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.
A Replication Study on Measuring the Growth of Open Source

Context: Over the last decades, open-source software has pervaded the software industry and has become one of the key pillars in software engineering. The incomparable growth of open source reflected that pervasion: Prior work described open source as a whole to be growing linearly, polynomially, or even exponentially. Objective: In this study, we explore the long-term growth of open source and corroborating previous findings by replicating previous studies on measuring the growth of open source projects. Method: We replicate four existing measurements on the growth of open source on a sample of 172,833 open-source projects using Open Hub as the measurement system: We analyzed lines of code, commits, new projects, and the number of open-source contributors over the last 30 years in the known open-source universe. Results: We found growth of open source to be exhausted: After an initial exponential growth, all measurements show a monotonic downwards trend since its peak in 2013. None of the existing growth models could stand the test of time. Conclusion: Our results raise more questions on the growth of open source and the representativeness of Open Hub as a proxy for describing open source. We discuss multiple interpretations for our observations and encourage further research using alternative data sets.
Agafonov's Proof of Agafonov's Theorem: A Modern Account and New Insights

We give a modern account of Agafonov’s original proof of his eponymous theorem. The original proof was only reported in Russian [10, 11] in a journal not widely available, and the work most commonly cited in western literature is instead the English translation [1] of a summary version containing no proofs [2], and the main proof relied heavily on material well-known in Russian mathematical circles of the day, which perhaps obscures the main thrust of argumentation for modern readers. Our present account recasts Aganofov’s arguments using more basic building blocks than in the original proof, and contains some further embellishments to Agafonov’s original arguments, made in the interest of clarity. We posit that the modern account provides new insight to the underlying phenomena of the theorem. We also provides some historical context to Agafonov’s work, including a short description of some of the ideas that led to Agafonov’s own proof, especially emphasizing the important work of Postnikova.
Efficient PAC Learning from the Crowd with Pairwise Comparisons

We study crowdsourced PAC learning of threshold functions, where the labels are gathered from a pool of annotators some of whom may behave adversarially. This is yet a challenging problem and until recently has computationally and query efficient PAC learning algorithm been established by Awasthi et al. (2017). In this paper, we show that by leveraging the more easily acquired pairwise comparison queries, it is possible to exponentially reduce the label complexity while retaining the overall query complexity and runtime. Our main algorithmic contributions are a comparison-equipped labeling scheme that can faithfully recover the true labels of a small set of instances, and a label-efficient filtering process that in conjunction with the small labeled set can reliably infer the true labels of a large instance set.