Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Glacial History of Sea-Level and Environmental Change in the Southern Baltic Sea

A new palaeoenvironmental record of the post-glacial history of the southern Baltic Sea (~14 ka to present) is presented. During this period, large water level and salinity changes occurred in the Baltic Basin due to opening and closing of connections to the North Atlantic. Previous attempts to establish a detailed chronology for these palaeoenvironmental changes have been conducted mainly in coastal settings, where organic material for 14C dating is abundant. Many of these records are, however, discontinuous due to large water level fluctuations. In the relatively deep water of the Arkona Basin (45 m deep) in the southern Baltic Sea the sediment record is expected to be more or less continuous, but lack of organic material for 14C dating has impeded previous studies. Here, palaeoenvironmental change in the Arkona Basin is reconstructed on the basis of geochemical, sedimentological, mineral magnetic and palaeontological investigations. Additionally, independent physically based chronological control is, for the first time, obtained using Optically Stimulated Luminescence (OSL) dating on fine quartz sand from a ~10.86 m long sediment core. Tests of luminescence characteristics confirmed the suitability of the material for OSL dating and the ages agree well with the available AMS 14C ages on shells; in contrast, bulk sediment 14C ages are generally ~1000 years too old. Stratigraphic marker horizons in this deep basin are now absolutely dated, allowing comparison and testing of existing models of post-glacial Baltic Sea regional development. Glacial varved clay was deposited during the Baltic Ice Lake stage and a sand layer representing the Baltic Ice Lake drainage to the North Atlantic is dated to ~11.6 ka. This event is followed by a period of low water level and enhanced influence from the Oder River. A period of very rapid sedimentation occurs between ~10.9 and ~10.4 ka and is attributed to the Ancylus Lake transgression. A first anomalous slightly brackish water inflow is recorded at ~9.8 ka, but there is no clear evidence for fully brackish conditions until ~6.5 ka. At that time, the lithologic change to clay gyttja represents a distinct shift in the circulation mode, with the onset of a high-productivity, brackish circulation system in the southern Baltic. Post-depositional diffusion of sulphur from the clay gyttja most likely explains the presence of greigite (Fe3S4) concretions in the underlying silty clay unit. With this new chronology an anomaly appears between the classical model of the Littorina transgressions with brackish conditions starting ~8.5 ka, supported by studies in coastal lagoons, and our first clear brackish/marine influence occurring as late as ~6.5 ka based on studies performed in the deeper basins. This implies that the circulation system of the present Baltic Sea, with fully brackish conditions and the Danish-German Straits as the dominant inflow areas, only started from ~6.5 ka onwards. (Less)
Use of Biomass Waste: Theoretical Model of Co-Firing Applied to Sines Thermal Power Plant

Environmental issues raised by the use of fossil fuels lead to the search for alternatives that promote the reduction of emissions of greenhouse gases. CO2 has been identified as being the most important and urgent to control. Co-firing is a technique that allows the simultaneous combustion of different types of fuels, for example coal and biomass, combining the advantages of both. This study characterizes the advantages of the system and the possibilities of using waste biomass as fuel in a thermal power plant. Hence, co-firing biomass waste from forestry operations with bituminous coal was theoretically simulated in this paper. The reductions in CO2 emissions into the atmosphere were calculated, showing that more than 1,000,000 tons/year of CO2 could be saved. Also, it was verified that although it poses environmental advantageous, co-firing is still not economically viable due to the high cost of the residual biomass, combined with its low-energy density and high transportation costs.
Original Article : Identifying Relative Importance of Quality Attributes of Dining Service for Older Adults Using Conjoint Analysis

Identifying quality attributes with the greatest value to the residents is important if the Continuing Care Retirement Community (CCRC) is to increase the frequency of residents` dining. However, it is not known which specific attribute is relatively more important than others to evaluate foodservice. The purpose of this study is to identify the relative importance of food and service quality attributes in the evaluation of foodservice in CCRC. This study surveyed the independent living residents of three CCRCs in the Midwestern states. Respondents placed great importance on taste and flavor of food which has the highest percentage of utility range (28.28%) and the second greatest on appearance of the server (23.46%). The important attributes were ultimately used to predict the most preferred choice of foodservice for residents. Knowledge of the importance of the attributes would enable the foodservice managers in CCRCs to manipulate those attributes to enhance the residents` perceptions of quality and identify strategies for continuous improvement. (J Community Nutrition 7(1): 58~63, 2005)
Primordial black holes from a tiny bump/dip in the inflaton potential

Scalar perturbations during inflation can be substantially amplified by tiny features in the inflaton potential. A bump-like feature behaves like a local speed-breaker and lowers the speed of the scalar field, thereby locally enhancing the scalar power spectrum. A bump-like feature emerges naturally if the base inflaton potential Vb(ϕ) contains a local correction term such as Vb(ϕ)[1+ε(ϕ)] at ϕ=ϕ0. The presence of such a localised correction term at ϕ0 leads to a large peak in the curvature power spectrum and to an enhanced probability of black hole formation. Remarkably this does not significantly affect the scalar spectral index nS and tensor to scalar ratio r on CMB scales. Consequently such models can produce higher mass primordial black holes (MPBH⩾ 1 M⊙) in contrast to models with `near inflection-point potentials' in which generating higher mass black holes severely affects nS and r. With a suitable choice of the base potential—such as the string theory based (KKLT) inflation or the α-attractor models—the amplification of primordial scalar power spectrum can be as large as 107 which leads to a significant contribution of primordial black holes (PBHs) to the dark matter density today, fPBH = Ω0, PBH/Ω0,DM ∼ O(1). Interestingly, our results remain valid if the bump is replaced by a dip. In this case the base inflaton potential Vb(ϕ) contains a negative local correction term such as Vb(ϕ)[1−ε(ϕ)] at ϕ=ϕ0 which leads to an enhanced probability of PBH formation. We conclude that primordial black holes in the mass range 10−17 M⊙ ⩽ MPBH ⩽ 100 M⊙ can easily form in single field inflation in the presence of small bump-like and dip-like features in the inflaton potential.
On the Measure of Intelligence

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
COI barcoding as a molecular assay for the identification of phytoseiid mites

Abstract Phytoseiid mites are the most studied family within the order Mesostigmata, especially because of their major interest for biological control programs. Phytoseiid mite species identification is essentially based on morphological characters. However, the development of faster and more accurate methods is under way. Molecular identification techniques, such as barcoding, are also used to improve mite diagnosis. The number of phytoseiid mite sequences used for diagnostic purpose has increased recently, but not all of them are reliable in the absence of voucher specimens or inaccurate taxonomy identification. In this study, we considered 14 common phytoseiid species collected from agricultural, urban, and wild ecosystems in different parts of China. We extracted genomic DNA from the mites and retained the mite specimens as vouchers to ensure the credibility of molecular data. Commonly used COI sequences were used as the most used barcoding marker. The results show that COI sequences are effective and capable of high-resolution molecular identification of the phytoseiid mite species herein considered.
Multilateration of Random Networks with Community Structure

The minimal number of nodes required to multilaterate a network endowed with geodesic distance (i.e., to uniquely identify all nodes based on shortest path distances to the selected nodes) is called its metric dimension. This quantity is related to a useful technique for embedding graphs in low-dimensional Euclidean spaces and representing the nodes of a graph numerically for downstream analyses such as vertex classification via machine learning. While metric dimension has been studied for many kinds of graphs, its behavior on the Stochastic Block Model (SBM) ensemble has not. The simple community structure of graphs in this ensemble make them interesting in a variety of contexts. Here we derive probabilistic bounds for the metric dimension of random graphs generated according to the SBM, and describe algorithms of varying complexity to find---with high probability---subsets of nodes for multilateration. Our methods are tested on SBM ensembles with parameters extracted from real-world networks. We show that our methods scale well with increasing network size as compared to the state-of-the-art Information Content Heuristic algorithm for metric dimension approximation.
The Impact of Gender's Variable on the Patterns and Motives of Elementary School Students in Using Social Networks: An Empirical Study on Selective Governmental Schools Jeddah, Saudi Arabia

This study discusses the contents of social and psychological studies about the Patterns and Motives of ElementarySchool Students in using Social Networks, This study is based on" uses and gratification approach" which isinstrumental in studying the impacts of communication. The study aims to search for the impact of the gendervariable on (the pattern, motives, positive and negative impacts) in relation to the children in the elementary schoolswho are using social networks. The study relies on the descriptive concept, the demographic survey form and thequestionnaire, The community of the study consisted of (254) students of both genders, the boys represent 48.8% and51.2% girls) from public elementary schools, whom were selected from two different schools (Boys only and Girlsonly). The results revealed there were differences between males and females on the pattern and motives using, as tosocial positive/negative impacts, the males are more impacted negatively and the females impacted positively byusing social networks.
Systematics in virial mass estimators for pressure-supported systems

Mass estimators are a key tool to infer the dark matter content in pressure-supported systems like dwarf spheroidal galaxies (dSphs). We construct an estimator for enclosed masses based on the virial theorem which is insensitive to anisotropy in the velocity dispersion and tailored to yield masses with minimum uncertainty introduced by our ignorance on (i) the shape of the inner halo profile, and (ii) how deeply the stellar component is embedded within the halo: $M(<1.8\,R_\mathrm{h}) \approx 3.5 \times 1.8\,R_\mathrm{h} \langle \sigma_\mathrm{los}^2 \rangle G^{-1}$, where by $R_\mathrm{h}$ we denote the projected half-light radius and by $\langle \sigma_\mathrm{los}^2 \rangle$ the luminosity-averaged squared line-of-sight velocity dispersion. Tests against controlled simulations show that this estimator provides unbiased enclosed masses with an accuracy of $\sim 10$ per cent. This confirms the robustness of similar previously proposed mass estimators. Application to published kinematic data of Milky Way dSphs reveals a tight correlation between enclosed mass and luminosity. Using $N$-body models we show that tidal stripping has little effect on this relation. Comparison against cuspy and cored dark matter haloes extracted from controlled re-simulations of the Aquarius A2 merger tree shows that the high mass densities of ultrafaint galaxies are not compatible with large dark matter cores, and that the (total) halo masses of the classical Milky Way dSphs span a remarkably narrow range ($8 \lesssim \mathrm{log_{10}}\,(M/\mathrm{M_\odot}) \lesssim 10$) at present, showing no clear trend with either galaxy size or luminosity.
On the use of liposomal delivery systems for oxaliplatin and in dual drug delivery in combination with chemo-sensitizing and chemotherapeutic agents

APA Citation Ibrahim, A. (2016). On the use of liposomal delivery systems for oxaliplatin and in dual drug delivery in combination with chemo-sensitizing and chemotherapeutic agents [Master’s thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/242 MLA Citation Ibrahim, Ayat Zein-elabedeen Mohamed. On the use of liposomal delivery systems for oxaliplatin and in dual drug delivery in combination with chemo-sensitizing and chemotherapeutic agents. 2016. American University in Cairo, Master's thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/242
Global Convergence of Gradient Descent for Deep Linear Residual Networks

We analyze the global convergence of gradient descent for deep linear residual networks by proposing a new initialization: zero-asymmetric (ZAS) initialization. It is motivated by avoiding stable manifolds of saddle points. We prove that under the ZAS initialization, for an arbitrary target matrix, gradient descent converges to an $\varepsilon$-optimal point in $O(L^3 \log(1/\varepsilon))$ iterations, which scales polynomially with the network depth $L$. Our result and the $\exp(\Omega(L))$ convergence time for the standard initialization (Xavier or near-identity) [Shamir, 2018] together demonstrate the importance of the residual structure and the initialization in the optimization for deep linear neural networks, especially when $L$ is large.
Low-temperature scattering with the R-matrix method: from nuclear scattering to atom-atom scattering and beyond

The R-matrix method is a fully-quantum time-independent scattering method, used to simulate nuclear, electron-atom, electron-ion, electron-molecule scattering and more. Here, a novel R-matrix method, RmatReact, is presented as applied to ultracold (sub-Kelvin) atom-atom scattering. This is in response to experimental methods which in recent decades have facilitated the routine use of ultracold atoms and small molecules in experiments. This project lays the groundwork for future codes to eventually simulate polyatom-polyatom reactive collisions. Results are presented in this paper for numerical comparisons to other established methods, and for resonances in the elastic scattering of ultracold argon atoms.
Privacy Protection in Distributed Fingerprint-based Authentication

To avoid identity theft or impersonation in biometric authentication schemes, fingerprint data is typically stored locally, e.g., in a trusted hardware module, in a single device that is used for user enrollment and authentication. Local storage, however, limits the ability to implement distributed applications, in which users can enroll their fingerprint once and use it to access multiple resources afterwards. In this paper, we present a distributed authentication system that stores fingerprint data in a server or cloud infrastructure in a privacy-preserving way. Multiple devices can be connected and perform user enrollment or verification. To secure the privacy and integrity of sensitive data, we employ a cryptographic construct called fuzzy vault. As a proof of concept, we build an authentication system for access control using resource-constrained devices (Raspberry Pis) connected to fingerprint scanners and the Microsoft Azure cloud environment. Furthermore, we evaluate the fingerprint matching algorithm against the well-known FVC2006 database and show that it can achieve comparable accuracy to widely-used matching techniques that are not designed for privacy, while remaining efficient with an authentication time of a few seconds.
Firms’ heterogeneity and performance in manufacturing during the great recession

This paper highlights how the heterogeneity of manufacturing firms impacted their performance and survival during the “Great Recession”. The findings indicate that firms that assumed a strategically proactive and innovative strategy in the pre-crisis period showed better economic performance during the crisis in terms of both sales and value added. The evidence also shows that the youngest firms and those that had a lower level of financial exposure were favored in terms of performance. Finally, the results also confirm the increased importance of different technological regimes. In contrast, survival estimates demonstrate the non-significance of pre-crisis strategic profiles: ceteris paribus, the results indicate that the most innovative, internationalized and dynamic firms did not register a greater likelihood of survival than other businesses. This result casts doubt on the efficiency and direction of the selection process.
Nine-Lump Kinetic Study of Catalytic Pyrolysis of Gas Oils Derived from Canadian Synthetic Crude Oil

Catalytic pyrolysis of gas oils derived from Canadian synthetic crude oil on a kind of zeolite catalyst was conducted in a confined fluidized bed reactor for the production of light olefins. The overall reactants and products were classified into nine species, and a nine-lump kinetic model was proposed to describe the reactions based on appropriate assumptions. This kinetic model had 24 rate constants and a catalyst deactivation constant. The kinetic constants at 620°C, 640°C, 660°C, and 680°C were estimated by means of nonlinear least-square regression method. Preexponential factors and apparent activation energies were then calculated according to the Arrhenius equation. The apparent activation energies of the three feed lumps were lower than those of the intermediate product lumps. The nine-lump kinetic model showed good calculation precision and the calculated yields were close to the experimental ones.
Modulation of heat transport in two-dimensional group-III chalcogenides

We systematically investigated the modulation of heat transport of experimentally accessible two-dimensional (2D) group-III chalcogenides by first-principles calculations. It was found that intrinsic thermal conductivity (κ) of chalcogenides MX (M = Ga, In; X = S, Se) were desirable for efficient heat dissipation. Meanwhile, we showed that the long-ranged harmonic and anharmonic interactions played an important role in heat transport of the chalcogenides. The difference of κ among the 2D group-III chalcogenides can be well described by the Slack model and can be mainly attributed to phonon group velocity. Based on that, we proposed three methods including strain engineering, size effect and making Janus structures to effectively modulate the κ of 2D group-III chalcogenides, with different underlying mechanisms. We found that tensile strain and rough boundary scattering could continuously decrease the κ while compressive strain could increase the κ of 2D group-III chalcogenides. On the other side, the change of κ by producing Janus structures is permanent and dependent on the structural details. These results provide guilds to modulate heat transport properties of 2D group-III chalcogenides for devices application.
Achieving the depairing limit along the 
c
 axis in 
Fe1+yTe1−xSex
 single crystals

We report the achieving of depairing current limit along $c$-axis in Fe$_{1+y}$Te$_{1-x}$Se$_x$ single crystals. A series of crystals with $T_{\rm{c}}$ ranging from 8.6 K to 13.7 K (different amount of excess Fe, $y$) were fabricated into $c$-axis bridges with a square-micrometer cross-section. The critical current density, $J_{\rm{c}}$, was directly estimated from the transport current-voltage measurements. The transport $J_{\rm{c}}$ reaches a very large value, which is about one order of magnitude larger than the depinning $J_{\rm{c}}$, but comparable to the calculated depairing $J_{\rm{c}}$ $\sim$ 2 $\times$ 10$^6$ A/cm$^2$ at 0 K, based on the Ginzburg-Landau (GL) theory. The temperature dependence of the depairing $J_{\rm{c}}$ follows the GL-theory ($\propto$ (1-$T/T_{\rm{c}}$)$^{3/2}$) down to $\sim$ 0.83 $T_{\rm{c}}$, then increases with a reduced slope at low temperatures, which can be qualitatively described by the Kupriyanov-Lukichev theory. Our study provides a new route to understand the behavior of depairing $J_{\rm{c}}$ in iron-based superconductors in a wide temperature range.
Study of Constrained Network Structures for WGANs on Numeric Data Generation

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.