Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive Star Modeling and Nucleosynthesis

After a brief introduction to stellar modeling, the main lines of massive star evolution are reviewed, with a focus on the nuclear reactions from which the star gets the needed energy to counterbalance its gravity. The different burning phases are described, as well as the structural impact they have on the star. Some general effects on stellar evolution of uncertainties in the reaction rates are presented, with more precise examples taken from the uncertainties of the 12C(α, γ)16O reaction and the sensitivity of the s-process on many rates. The changes in the evolution of massive stars brought by low or zero metallicity are reviewed. The impact of convection, rotation, mass loss, and binarity on massive star evolution is reviewed, with a focus on the effect they have on the global nucleosynthetic products of the stars.
Effectiveness of a Community-Based Muscle Strengthening Exercise Program to Increase Muscle Strength Among Pre-frail Older Persons in Malaysia: A Pilot Study

Deterioration in muscle mass and muscle strength is common among the frail older persons, cause functional dependence and decrease in the quality of life. Therefore, the identification of cost-effective interventions to prevent or ameliorate frailty is crucially needed. The aim of this study is to determine the effectiveness of a Community-based Muscle Strengthening Exercise (COME) program to increase muscle strength among pre-frail older persons. This study was a quasi-experimental study. A total of 32 older persons aged 60 years and older with pre-frail symptoms were recruited from the community center. The COME was developed based on the Growing Stronger program and the Otago Home Exercise Program. COME was designed to strengthen all of the major muscle groups in the upper and lower extremities. The exercise program was conducted for 12 weeks and divided into 3 parts; (1) to strengthen the body slowly and gently, using only body weight, (2) to introduce dumbbells and ankle weights to increase strength and (3) to add various new ways to boost strength even more. Functional tests were measured before and after the intervention. The results revealed non-significant p-value for pre- and post-intervention comparison for all study outcomes. Observing the values of mean difference, the study intervention was shown to have slightly improved the time up and go (Mean difference = −0.25), and sit-to-stand duration (Mean difference = −0.41) as well as the handgrip strength (Mean difference = 0.68) among the participants. On the assessment of Cohen ES, all three improvements exhibited small effect sizes. Sit-to-stand duration was shown to have most benefited from the intervention with highest ES among the outcome variables (ES = 0.20). COME intervention program among pre-frail older persons showed favorable trend toward improvement of upper and lower extremities muscle strength. This study should be further tested in randomized control trial to confirm its effectiveness.
Why Build a Robot With Artificial Consciousness? How to Begin? A Cross-Disciplinary Dialogue on the Design and Implementation of a Synthetic Model of Consciousness

Creativity is intrinsic to Humanities and STEM disciplines. In the activities of artists and engineers, for example, an attempt is made to bring something new into the world through counterfactual thinking. However, creativity in these disciplines is distinguished by differences in motivations and constraints. For example, engineers typically direct their creativity toward building solutions to practical problems, whereas the outcomes of artistic creativity, which are largely useless to practical purposes, aspire to enrich the world aesthetically and conceptually. In this essay, an artist (DHS) and a roboticist (GS) engage in a cross-disciplinary conceptual analysis of the creative problem of artificial consciousness in a robot, expressing the counterfactual thinking necessitated by the problem, as well as disciplinary differences in motivations, constraints, and applications. We especially deal with the question of why one would build an artificial consciousness and we consider how an illusionist theory of consciousness alters prominent ethical debates on synthetic consciousness. We discuss theories of consciousness and their applicability to synthetic consciousness. We discuss practical approaches to implementing artificial consciousness in a robot and conclude by considering the role of creativity in the project of developing an artificial consciousness.
A Framework for Optimizing Co-adaptation in Body-Machine Interfaces

The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive “tool” whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.
Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial-aware booster

: With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid-based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial-aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.
Novel framework for automatic localisation of gun carrying by moving person using various indoor and outdoor mimic and real-time views/Scenes

: Hand held gun detection has an important application in both the field of video forensic and surveillance, because, gun is operative by hand only while committing any crime with it. The significant application encompasses the vulnerable places, such as around airport, marketplace, shopping malls, etc. In view of non-availability of relevant public data set, this study provides a newly created mimicked video data set for detection of gun carried by a person and entitled as Tripura University Video Data set for Crime-Scene-Analysis (TUVD-CSA). Effects of illumination, occlusion, rotation, pan, tilt, scaling of gun are effectively demonstrated in it. Moreover, the authors proposed an Iterative Model Generation Framework (IMGF) for gun detection, which is immune to scaling and rotation. Instead of locating the best matched object (gun) in the whole reference image to a query model via exhaustive search, IMGF searches only where the moving person carrying gun appears, which drastically reduces the computational overhead associated with a general template matching scheme. This has been employed by the background subtraction algorithm. Experimental results demonstrate that the proposed IMGF performs efficiently in gun detection with lesser number of true-negatives compared with the state-of-the-art methods.
Photocatalytic degradation of organic compounds using carbon based composite catalysts

Three metal titanates, ZnTiO3 (Zn), FeTiO3 (Fe) and Bi4Ti3O12 (Bi), were employed to prepare supported cobalt catalysts for photocatalytic and photochemical oxidation of phenol with peroxymonosulfate (PMS) or peroxydisulfate (PDS) under visible light irradiations. It was found that the oxidation efficiencies varied greatly due to the different photochemical behaviours of various supports. Using PMS, both Co(5%)/Zn and Co(3%)/Bi achieved complete degradation of phenol in 120 min, as compared to 66.9% of phenol removal over Co(5%)/Fe in 150 min. However, those catalysts were found to be not efficient in activation of PDS, in which the best catalyst, Co(5%)/Bi, only provided 66.9% of phenol degradation in 150 min. The effects of the supports were from the varying photophysical properties and their interactions with loaded Co3O4. The stability of Co(5%)/Zn with PMS was further investigated and it was found that the activity remained at 85.4% phenol degradation in the fourth run. 3.
Edge-guided single facial depth map super-resolution using CNN

: In recent years, consumer depth cameras have been widely used in digital entertainment and human-machine interaction due to the advantages of real-time performance and low cost. Facial depth maps have shown great potential in 3D-face-related studies. However, disadvantages of low resolution and precision limit its further applications. In this work, the authors propose an edge-guided convolutional neural network for single facial depth map super-resolution. It consists of two parts: an edge prediction sub-network and a depth reconstruction sub-network. The edge prediction sub-network generates an edge guidance map to guide the depth reconstruction sub-network to recover sharp edges and fine structures. Effective data augmentation methods are proposed as well. The network is patch-based and able to cope with any size of the input depth maps. In addition, it is insensitive to the face pose since the synthetic training dataset they generated covers a wide range of face poses. The proposed method is validated with three datasets including a synthetic facial depth data set, a real Kinect V2 facial depth data set and Middlebury Stereo Data set. Experimental results show that it outperforms the state-of-the-art methods on all the three data sets.
Crowd-Sourcing Information Dissemination Based on Spatial Behavior and Social Networks

In the context of today’s network era, rich social networks and convenient network communication make different individuals and groups interact and transmit information in more diversified ways, which also bring new dissemination in information of crowd-sourcing tasks. (e paper analyzes mobile behavior characteristics of users from different perspectives, such as spatial activity behavior and location type preference, and constructs a user space mobile behavior model based on the physical world. At the same time, it analyzes the social influence of users in social networks and mode of information transmission. In the paper, real data sets are adopted, mathematical modeling and computer simulation are combined to build an information communication model around the social influence of users in social networks, and the rules of information communication in new environment of social networks are depicted in combination with users’ spatial movement.
A parametric approximation algorithm for spatial group keyword queries

With the application of big data, various queries arise for information retrieval. Spatial group keyword queries aim to find a set of spatial objects that cover the query keywords and minimize a goal function such as the total distance between the objects and the query point. This problem is widely found in database applications and is known to be NP-hard. Efficient algorithms for solving this problem can only provide approximate solutions, and most of these algorithms achieve a fixed approximation ratio (the upper bound of the ratio of an approximate goal value to the optimal goal value). Thus, to obtain a self-adjusting algorithm, we propose an approximation algorithm for achieving a parametric approximation ratio. The algorithm makes a trade-off between the approximation ratio and time consumption enabling the users to assign arbitrary query accuracy. Additionally, it runs in an on-the-fly manner, making it scalable to large-scale applications. The efficiency and scalability of the algorithm were further validated using benchmark datasets.
A Review of Security Concerns in Internet of Things

The Internet of Things (IoT) represents a technologically optimistic future where objects will be connected to the internet and make intelligent collaborations with other objects anywhere, anytime. Although it makes appreciable development, there are still uncertainties about security concepts of its usage that is usually considered as a major concern in the design of IoT architectures. This paper presents a general survey of all the security issues in IoT along with an analysis of IoT architectures. The study defines security requirements and challenges that are common in IoT implementations and discusses security threats and related solutions on each layer of IoT architecture to make this technology secure and more widespread accordingly.
Biomimetic drug nanocarriers to overcome biological barriers : Inspiration from pathogen invasion

Biologische barrieres zoals de huid, slijmvliezen, bloed-hersenbarriere en cel kernmembraan dienen de natuurlijke beschermende functie in het lichaam, beperken zij aflevering van geneesmiddelen in het lichaam. Echter, kon een aantal ziekteverwekkers fysiologische trajecten benutten om extracellulaire en intracellulaire barrieres voor hun infectie te overwinnen. Op basis van het moleculaire mechanisme van pathogene invasie zijn diverse biomimetische nanodeeltjes ontwikkeld voor medicijnafgifte om de barrieres te overwinnen, namelijk de intestinale barriere, de bloed-hersenbarriere en de endosomale barriere in vitro.
High-Performance Magnetic Activated Carbon from Solid Waste from Lignin Conversion Processes. 1. Their Use As Adsorbents for CO2

Lignin is naturally abundant and a renewable precursor with the potential to be used in the production of both chemicals and materials. As many lignin conversion processes suffer from a significant production of solid wastes in the form of hydrochars, this study focused on transforming hydrochars into magnetic activated carbons (MAC). The hydrochars were produced via hydrothermal treatment of lignins together with formic acid. The activation of the hydrochars was performed chemically with KOH with a focus on the optimization of the MACs as adsorbents for CO2. MACs are potentially relevant to carbon capture and storage (CCS) and gas purification processes. In general, the MACs had high specific surface areas (up to 2875 m2/g), high specific pore volumes, and CO2 adsorption capacities of up to 6.0 mmol/g (1 atm, 0 °C). The textual properties of the MACs depended on the temperature of the activation. MACs activated at a temperature of 700 °C had very high ultramicropore volumes, which are relevant for potent.
American Democracy in an Age of Rising Inequality

The Council of the American Political Science Association approved the appointment of a Task Force on Inequality and American Democracy in the fall of 2002. A fifteen-member task force was convened in January 2003 and collectively worked during the subsequent eighteen months to prepare extensive reviews of research on inequality and American democracy. (The research reviews are available on the APSA Web site—as are materials for undergraduate and graduate teaching—http://www.apsanet.org/inequality.) Based on three reviews, the task force prepared a short report, which forms the basis of the present text. It concludes that progress toward realizing American ideals of democracy may have stalled—and in some arenas reversed. The task force's work was extensively and rigorously debated among its members, scrutinized by three distinguished independent peers, and reviewed by the APSA Council. This report is ultimately the responsibility of its authors; no opinions, statements of fact, or conclusions should be attributed to the American Political Science Association or to the Russell Sage Foundation, which provided some support to the task force. The members of the task force are: Lawrence Jacobs (Chair, University of Minnesota), Ben Barber (University of Maryland), Larry Bartels (Princeton University), Michael Dawson (Harvard University), Morris Fiorina (Stanford University), Jacob Hacker (Yale University), Rodney Hero (Notre Dame University), Hugh Heclo (George Mason University), Claire Jean Kim (University of California, Irvine), Suzanne Mettler (Syracuse University), Benjamin Page (Northwestern University), Dianne Pinderhughes (University of Illinois, Champagne–Urbana), Kay Lehman Schlozman (Boston College), Theda Skocpol (Harvard University), and Sidney Verba (Harvard University).
A New Approach to Estimating Electoral Instability in Parties*

Recent scholarship has identified problems in the measurement of party system instability. To limit the conflation of different sources of instability in party systems (e.g., electoral shifts between stable parties and instability in parties, such as mergers, splinters or new parties), this article introduces a new indicator of electoral instability in parties, tests its robustness and construct validity and demonstrates its usefulness empirically. The indicators of party instability and the accompanying data of 27 European democracies, 1987–2011, will be valuable resources in comparative research on the interplay between elite and mass behavior, party and electoral systems, and democratic consolidation.
Nitrogen and carbon concentrations and isotopic compositions of the silica clathrate melanophlogite

Abstract The concentrations and isotopic compositions of N and C were obtained for five melanophlogite samples, ideal formula 46SiO2⋅6(CO2, N2)⋅2(CH4, N2), from various localities in Italy and California, U.S.A. The melanophlogite crystals enclathrate 132 to 1674 ppm N presumed to be speciated as molecular N2 and with δ15Nair ranging from −6.1 to +5.7‰. The higher δ15N values overlap those for organic/sedimentary N, the latter largely with values between 0 and+10‰. The samples also contain 1.2 to 2.9 wt% total C, with δ13CVPDB of −42.9 to −8.7‰, obtained from analyses of the bulk C in samples with probable varying proportions of CO2 and CH4 in the melanophlogite cages. Although the lower δ15N values for the melanophlogites (−6.1 and −2.8‰) are near upper mantle values (−5 ± 2‰), the full range in N2 δ15N can be explained by equilibration with NH4+ $\text{NH}^{+}_{4}$ in clay minerals bearing an organic-influenced N isotope signature, at temperatures of near 100 °C estimated for melanophlogite crystallization. The lower δ13C values (as low as −42.9‰) are suggestive of equilibration with carbonaceous matter (poorly recrystallized organic material) at high cage CH4CO2, perhaps representing lower oxygen fugacities. The growing number of reports of melanophlogite at terrestrial localities, and its occurrences in organic-rich settings, makes this clathrate mineral an intriguing candidate for preserving records of past surface or near-surface biogeochemical cycling on Earth and perhaps on Mars.
Next-generation genebanking: plant genetic resources management and utilization in the sequencing era

Advances in sequencing technologies have made it possible to analyse large amounts of germplasm against low production costs, which has opened the door to screen genebank collections more efficiently for DNA sequence variation. The present study explores how these developments may affect the operations of genebanks and, consequently, how genebank agendas may change. It is argued that the new developments are more likely to have an impact on the user-oriented activities than the housekeeping operations of genebanks. To better facilitate the user community, genebanks may have to strengthen their core business, in particular, by improving quality management procedures and by providing access to a wider diversity of a crop's gene pool. In addition, genebanks may have to provide novel services, such as the introduction of specific user-oriented collection types, including research populations and genetically purified lines, and the development of novel information services, including plant genetic resources portals that should guide users to the information and materials of specific interest. To improve their user-oriented services, genebanks need to increase their communication and collaboration with the user community and to develop strategic alliances with this sector.
PHYSICOCHEMICAL PROPERTIES AND ANTIMICROBIAL EFFECTS OF ROSELLE COROLLA, ONION PEELS AND PEANUT SKINS ANTHOCYANINS

Anthocyanins would make ideal natural food colourants with additional nutritional benefits however stability is a hindering factor. The stability, physicochemical properties and the biological activities of anthocyanins extracted from onion peels, peanut skins or roselle corolla were achieved. Crude anthocyanins were extracted using two different solvent systems (distilled water and acidified ethanol with HCl 1.5 N, 85:15, V/V). Roselle corolla pigment extracted by acidified ethanol was the highest in phenolic and anthocyanin contents compared with water extract content. The aqueous extract of roselle corolla, onion peels and peanut skin showed activities against Gram (Staphylococcus aureus and Bacillus subtilis) and Gram (Pseudomonas aeruginosa and Escherichia coli) bacteria. Aqueous extract of roselle corolla and peanut skin inhibited mycelial growth of Fusarium oxysporum. About 78% of aqueous extract pigments of onion peels were retained when heated at 75°C. Stability under different light sources showed general decline in pigment retention of the samples over the time period with all extracts. However, roselle corolla extracted by acidified ethanol showed more stability under different light treatments compared with distilled water extract. The extracted pigments were stable against oxidizing agents, whereas it reduced gradually when treated with cane sugar or salt.