Miracle-dz/UniFAR-FTUs huggingface dataset:
PERILAKU CONE ALUMINUM TIPIS DENGAN SUDUT YANG BERBEDA DALAM MENYERAP ENERGI IMPAK

The use of thin-walled structures as energy absorbers has been widely known and applied in various fields. Thin-walled structures have the ability to absorb energy very well at various levels of impact speed. In this study, an analysis of the behavior of thin-walled structures with aluminum cone-shaped was carried out. This structure has a length of 200 mm thickness of 2 mm with one end whose diameter is left to remain 50 mm and the other end is made varied; 50 mm, 64 mm, 84 mm, 104 mm and 124 mm. A pounder made of cube-shaped steel measuring 15 cm x 15 cm x 15 cm weighing 26.49 kg pounding the specimen at a speed of 10 m / s so that the kinetic energy is received is 1324.7 Joules. Deformation with certain patterns in thin-walled structures and the results of the simulation obtained are total deformation, bending pattern, and energy per unit length. From the five specimens, it was found that the thin tube had a longer deformation than the cone shape. But the cone's ability to absorb more energy is indicated by the amount of energy per unit length.
Individual Protest Participation in the United States: Conventional and Unconventional Activism

type="main"> This study addresses differences in the predictors of participation in different forms of protest activity using nationally representative data. The two types of protest examined, referred to as conventional and unconventional forms of activism, are differentiated by differing levels of risk, demands, and political legitimacy. The analysis uses multinominal logistic regression and data from the World Values Survey to assess the effects of a wide range of independent variables on participation in protest. The results indicate that participation in conventional forms of protest, activities that are relatively undemanding, socially legitimate, and low risk, tend to follow patterns that are consistent with participation in institutional politics. That is, participants in this form of activism tend to be socially privileged and ideologically moderate. Participants in unconventional protest, those that are highly demanding, socially illegitimate, or carry substantial risks, tend to be more ideologically extreme, socially disadvantaged, and more alienated from the conventional political system. The findings in this study suggest that the lack of consistency in some existing research on protest participation may be attributable to differences in the predictors of participation associated with different forms of protest. This study indicates that protest participation is not a unitary construct and should not be treated as one in research.
Design of a genetic layered feedback controller in synthetic biological circuitry

Feedback control is the key to achieve robust performances for many engineered systems. However, its application in biological contexts is still largely unexplored. In this work, we designed, analyzed and simulated a layered controller functioning at both molecular and populational levels. First, we used a minimal model of three states to represent a system where state A activates state B; state R is a by-product of state B that acts as a negative feedback regulating both state A, B, and sequentially R. We call the feedback applied to state B a cis feedback and the one applied to state A a trans feedback. Through stability analysis via linearization at equilibrium and sensitivity analysis at transient state, we found that the cis feedback attenuates disturbances better but recovers slower; the trans feedback recovers faster but has more dramatic responses to fluctuations; the layered feedback demonstrates both advantageous traits of the two single layers. Then we designed two versions of synthetic genetic circuits to implement the layered controller in living cells. One version with an sRNA as regulator R, the other with a transcription factor protein as the regulator R. The analysis and dynamical simulation of the models confirmed the analytical results from the minimal model. At the same time, we found that the protein regulated feedback controls have faster recovery speed but the RNA version has a stronger disturbance attenuation effect.
Over-the-air Computation for IoT Networks: Computing Multiple Functions with Antenna Arrays

Over-the-air computation combines communication and computation efficiently by utilizing the superposition property of wireless channels, when Internet of Things (IoT) networks focus more on the computed functions than the individual messages. In this work, we study the computation of multiple linear functions of Gaussian sources over-the-air using antenna arrays at both the IoT devices and the IoT access point (AP). The key challenges in this study are the intra-node interference of multiple functions, the non-uniform fading between different IoT devices and the massive channel state information (CSI) required at the IoT AP. We propose a novel transmitter design at the IoT devices with zero-forcing beamforming to cancel the intra-node interference and uniform-forcing power control to compensate the non-uniform fading. In order to avoid massive CSI requirement, receive antenna selection is adopted at the IoT AP and a corresponding signaling procedure is proposed utilizing the “OR” property of the wireless channel. The performance of the proposed transceiver design is analyzed. The closed-form expression for the mean squared function error (MSFE) outage is derived. Due to the complexity of the expression, an asymptotic analysis of the MSFE outage is further provided to demonstrate the diversity order in terms of the transmit power constraint and the number of IoT devices. Simulation results are presented to show the performance of the proposed design.
Design, Modeling, and Testing of a Soft Pneumatic Glove With Segmented PneuNets Bending Actuators

This paper presents the design, modeling, and testing of a soft pneumatic glove with five segmented PneuNets bending actuators (SPBAs) made of elastomer, each actuator driving the corresponding finger to bend. The segmented structure of the actuator is designed according to the anatomy of human fingers, i.e., three flexible segments for finger joints and four inflexible segments for finger metacarpal and phalanges, which makes the actuator fit with finger better than those of homogenous structure. The number of inflatable chambers in the flexible segment can be designed according to range of motion (ROM) of the corresponding joint. To improve the air tightness of the actuator, an inner bladder is embedded into chambers. To predict the passive bending degree and grasping force of human fingers driven by the glove, a bottom–up model is constructed by using Yeoh's theory of hyperelasticity and Lagrange equations of serial robots. The model is verified by FEM simulations and experiments simultaneously. At last, some passive exercises are presented, which indicates its potential in hand rehabilitation.
Integrated management of Mi-1 virulent Meloidogyne incognita (Kofoid & White, 1919) Chitwood, 1949 (Tylenchida: Meloidogynidae) in greenhouse tomatoes

Domatesteki Mi-1 geni, kok-ur nematodlari kontrol icin guvenli ve ekonomik mucadele saglamaktadir. Bununla birlikte, Mi-1 geninin sagladigi dayaniklilik virulent kok-ur nematodlarina karsi etkili degildir. Bu calismada, solarizasyon uygulamasindan sonra serada yetistirilen domatesler icin Mi-1 virulent Meloidogyne incognita (Kofoid & White, 1919) Chitwood, 1949 (Tylenchida: Meloidogynidae) populasyonuna karsi Bacillus firmus Bredemann & Wermer, 1933 (Bacillales: Bacillaceae) + fluopyram kombinasyonlari ile Mi-1 geninin doz etkisinin kombinasyonunun etkinligi degerlendirilmistir. Calisma 2015-2016 yillari arasinda Antalya, Kepez bolgesindeki serada yurutulmustur. Solarizasyon uygulamasindan sonra toprakta iki ay boyunca M. incognita ikinci donem larva tespit edilmemistir. Sera kosullarinda Mi-1 virulent populasyona karsi, homozigot-heterezigot dayanikli domates bitkilerinin reaksiyonlarinda istatistiksel farklilik olmamistir. Ayrica, B. firmus + fluopyram kombinasyonu, hem bitki dikim doneminde uygulandiginda hem de toprakta J2 sayisi artmaya basladigi anda uygulandiginda kok-ur nematod populasyonunu kontrol altina almistir. Bu calisma, toprak solarizasyonunun diger mucadele yontemleriyle kombine edilebilir ve Mi-1 geninin doz etkisinin virulent kok-ur nematodlarina karsi onemli olmadigini gostermistir. Ayrica, B. firmus + fluopyram kombinasyonu, domates uretimlerinde kok-ur nematolarinin kontrolunde uygun bir mucadele araci olarak kullanilabilme potansiyeline sahiptir. Bu bulgular, Mi-1 virulent kok-ur nematod populasyonlarinin kontrolu icin entegre mucadele yontemlerinin gelismesine yardim edebilecektir.
Unraveling the complexity of the relations of metalinguistic skills to word reading with struggling adult readers: Shared, independent, and interactive effects

Abstract This study investigates the shared, independent, and interactive effects of metalinguistic skills (phonological awareness, morphological awareness, and orthographic knowledge) to word reading with a sample of struggling adult readers. Controlling for vocabulary knowledge, a second-order latent factor of metalinguistic awareness accounted for unique variance (62.5%) in adults’ word reading skills. Two-way latent interactions between the metalinguistic skills (phonological awareness × morphological awareness, morphological awareness × orthographic knowledge, and phonological awareness × orthographic knowledge) revealed unique interactive contributions (1%–5.2%) of these skills to word reading controlling for the metalinguistic skill main effects and vocabulary knowledge. In particular, high levels of morphological awareness are critical to word reading irrespective of high or low phonological awareness and orthographic knowledge. In addition, higher phonological awareness skills are critical to word reading irrespective of high or low orthographic knowledge. These results indicate the importance as well as the complexity of the nature of metalinguistic skills underlying word reading for struggling adult readers. The theoretical, empirical, and applied implications of these findings are discussed in the context of researchers and practitioners invested in improving outcomes in adult literacy programs.
Neural Mechanisms of Music and Language

Music and language are uniquely human forms of communication. What neural structures facilitate these abilities? This chapter conducts a review of music and language processing that follows these acoustic signals as they ascend the auditory pathway from the brainstem to auditory cortex and on to more specialized cortical regions. Acoustic, neural, and cognitive mechanisms are identified where processing demands from both domains might overlap, with an eye to examples of experience-dependent cortical plasticity, which are taken as strong evidence for common neural substrates. Following an introduction describing how understanding musical processing informs linguistic or auditory processing more generally, findings regarding the major components (and parallels) of music and language research are reviewed: pitch perception, syntax and harmonic structural processing, semantics, timbre and speaker identification, attending in auditory scenes, and rhythm. Overall, the strongest evidence that currently exists for neural overlap (and cross-domain, experience-dependent plasticity) is in the brainstem, followed by auditory cortex, with evidence and the potential for overlap becoming less apparent as the mechanisms involved in music and speech perception become more specialized and distinct at higher levels of processing.
Proceedings of the IEEE VisWeek Workshop on Visual Analytics in Healthcare: Open Health Data

Hospitals collect large amounts of data during their daily operation. Next to its immediate primary purpose, this data also contains implicit information that can be used to improve clinical and administrative processes. We present a case study of how strategic infrastructure planning can be supported by the analysis of enriched patient flow through a hospital. Data from various hospital information systems was collected, enriched with topographical and organizational data, and integrated into a coherent data store. Common analysis tools and methods do not support exploration and sense-making well for such large and complex problems. We therefore developed a highly interactive visual analytics application that offers various views onto the data. The analysts were able to validate their experiences, confirm hypotheses and generate new insights. As a result, several sub-systems of clinics were identified that will play a central role on the future hospital campus. In this case study we show that a different view on electronic health records can provide not only clinical insights but also help improve operational efficiency.
Enabling Decentralised Identifiers and Verifiable Credentials for Constrained IoT Devices using OAuth-based Delegation

—Decentralised identiﬁers (DIDs) and veriﬁable cre-dentials (VCs) are upcoming standards for self-sovereign privacy-preserving identiﬁers and authorisation, respectively. This focus on privacy can help improve many services and open up new business models, but using DIDs and VCs directly on constrained IoT devices can be problematic due to the management and resource overhead. This paper presents an OAuth-based method to delegate the processing and access policy management to the Authorisation Server thus allowing also systems with constrained IoT devices to beneﬁt from DIDs and VCs.
On Programming Languages for Probabilistic Modeling

The problem of probabilistic modeling and inference, at a high-level, can be viewed as constructing a (model, query, inference) tuple, where an inference algorithm implements a query on a model. Notably, the derivation of inference algorithms can be a difficult and error-prone task. Hence, researchers have explored how ideas from probabilistic programming can be applied. In the context of constructing these tuples, probabilistic programming can be seen as taking a language-based approach to probabilistic modeling and inference. For instance, by using (1) appropriate languages for expressing models and queries and (2) devising inference techniques that operate on encodings of models (and queries) as program expressions, the task of inference can be automated. Notably, these modeling languages will typically provide continuous distributions. Hence, there is no direct connection with traditional, discrete notions of computation. Nevertheless, there is an intuitive understanding of such languages given in terms of sampling. In this dissertation, I investigate aspects of probabilistic programming languages (PPLs) in two parts. In the first part, I show how (Type-2) computable distributions can be used to give both distributional and (algorithmic) sampling semantics to a high-level, PCF-like language extended with continuous distributions. One motivation for using computable distributions, as opposed to more generally measures, is so that we can think of a Turing-complete probabilistic modeling language as expressing computable distributions. In the second part, I describe a
Lexical Landscapes as large in silico data for examining advanced properties of fitness landscapes

In silico approaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line between in silico and empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts—adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greater Lexical Landscapes approach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.
Numerical analysis of advection-diffusion problems on 2D general-shaped domains by means of a RBF Collocation Meshless Method

A Collocation Meshless Method based on local Radial Basis Function (RBF) interpolation is employed to solve two-dimensional advection-diffusion problems with particular reference to the incompressible Navier-Stokes equations in their transient form, i.e., unsteady flows, using primitive variables (u, p). A projection scheme is employed to decouple the continuity and momentum equations; particular attention is given to the choice of the required solvers. This approach is applied to the simulation of unsteady flows for two typical test cases, i.e., the lid-driven cavity problem and the flow past a circular cylinder between parallel walls. Numerical results compare very favorably with literature ones, confirming that this approach can be effectively employed in the numerical simulation of unsteady flows on practical geometries where complex node distributions and large number of nodes are required.
Indigenous Knowledge As A Strategic Resource: An Ethical And Societal Challenge

Organizations, particularly large multinational firms, have a substantial impact on the economies and societies in which they operate. Unfortunately some far-reaching influences are not anticipated, and, the consequences of these organizational actions can create long-term, adverse outcomes. Increasingly, indigenous knowledge has been recognized as a valuable strategic resource offering rare, inimitable, non-substitutable and effectively commercialized insights for new agricultural, biological, pharmaceutical, and aesthetic products. While the business case for industrialized companies to acquire and use indigenous knowledge is compelling, and the economic results of these activities are highly profitable, the unanticipated costs can be serious and irreparable, particularly for the communities that provided the original knowledge resource. This paper describes the strategic attractiveness of indigenous knowledge and provides examples of unintended, but severe, adverse consequences for the surrounding business and social ecosystems that can result from the use of this knowledge by non-indigenous firms. We articulate the business logic for the use of indigenous knowledge from the perspective of the resource based view of the firm. We then discuss the ethical concept of ‘respect for persons’ and argue that explicit consideration of this principle in the strategic decision making process would illuminate the previously unrecognized outcomes. We use a case example to illustrate how considering both the resource-based strategic value of indigenous knowledge and the principle of ‘respect for persons’ when making choices about the use of these resources would yield more desirable and more sustainable outcomes for both the firm and the indigenous community.
Crowdsourcing-based Low-cost Indoor Positioning Using Smartphones

In recent years, WiFi-based indoor positioning technologies have been widely used in various scenarios. At present, most of the traditional WiFi-based schemes require pre-deployment, which is inefficiency and time-consuming. This paper proposes a crowdsourcing-based indoor positioning scheme, which uses GPS and PDR combined with signal propagation models to solve the position and propagation characteristics of WiFi access points, which effectively reduces the deployment costs compared to existing solutions. The experimental results show that the average positioning error of the scheme is 6 meters, which can meet the actual needs of indoor positioning.
Metrology in Life Sciences

Novel biological research technologies, such as Genomics and Proteomics, raised high hopes of creating a paradigm shiftbreakthroughs that will yield a new understanding of cellular processes and human disease, and pave the way to a bounty of new drugs and therapeutics. Unfortunately, first for Genomics and then for Proteomics, it became abundantly clear that the data produced, though of extreme interest, was insufficient for the anticipated breakthroughs [Miklos 01, 04]. So many puzzle pieces are still missing that the clear view of cellular machinery remains hidden from our eyes. As for the state of pharmaceutical technology, crisis is a word often used. In fact, despite huge increases in investments, the pace of drugs entering the market has not increased [http://www.fda. gov/cder/rdmt/default.htm].
Finding Truth in Fake News: Reverse Plagiarism and other Models of Classification

As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news” . Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as well as performing logistic regression and multinomial naïve Bayes classification. From the techniques and methodologies, we found that fake news can be classified in the simplest terms as fact-based or non-fact-based. Our model, built upon reverse plagiarism and natural language processing, produces positive results but is not as effective as logistic regression and multinomial naïve Bayes. These models classify fake news more correctly and efficiently than a human could and show that fake news is easily identifiable. The traditional classification models outperform the reverse plagiarism method, but improvements and refinements can be made.
Robust genetic interactions in cancer are enriched in protein-protein interaction pairs

Genetic interactions, where the mutation of one gene can be associated with altered sensitivity to the inhibition of another gene, can be exploited for the development of targeted therapies in cancer. Many large-scale genetic perturbation screens in tumour cell lines have been used to identify such genetic interactions, including both synthetic lethal and resistance relationships. Despite these efforts, relatively few of the candidate genetic interactions identified have been reproduced across multiple studies. Here, we develop a computational approach to identify genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. We identified 220 such robust genetic interactions and found that they are enriched for gene pairs whose protein products physically interact. This suggests that protein-protein interaction networks may be used not only to understand the mechanistic basis of genetic interaction effects, but also to prioritise robust candidates for further development.