Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectories of International Engagement with State and Local Actors: Evidence from South Sudan

ABSTRACT External actors have been engaged in what is now South Sudan from the colonial era through to the present day, providing humanitarian and development assistance and exerting political pressure during and since the second civil war that has helped to protect people, legitimize the Sudan People’s Liberation Movement/Army (SPLM/A), broker the peace agreements leading to independence, and undergird the new state of South Sudan. After the civil war and especially at independence, many international actors approached South Sudan as a tabula rasa, ready for peace and development; since then, engagement has shifted back to large-scale humanitarian efforts and crisis response. This paper investigates how international actors have engaged with the South Sudanese state and local actors in order to improve access to basic services and build state capacity to deliver those services and provide social protection and livelihood support, what the impacts of such engagement have been, and what aid actors can learn from this history. The paper draws on four years of fieldwork by the Secure Livelihoods Research Consortium (SLRC), with a focus on Jonglei State.
Impacts of Mentoring Program on At-Risk Students

Many middle school students are considered at-risk due to attendance, lower levels of academic achievement, and behavior referrals. Mentoring programs have been in effect for decades and continue to yield positive results. The purpose of the study is to determine if fourth and fifth grade at-risk students are impacted by mentoring. The researcher seeks to determine if mentoring increases students’ attendance, increases math and reading test scores, and decreases the amount of behavior referrals. Participants were compared to their own data using a pre-tests and posttests. Attendance comparisons were made between Spring 2016 and Spring 2017 semesters. Math and reading iReady pre-tests were taken in Fall 2015, Winter 2016, Spring 2016, and Fall 2016. The pre-test scores were compared to the post-test scores taken in Spring 2017. Discipline referrals were compared between Fall 2016 and Spring 2017 semesters. The results indicated that mentoring had a strong statistical significance on student attendance. Significant differences were not discovered in the iReady test scores or in the decrease of discipline referrals.
Atmospheric Chemistry and Physics Changes in Background Aerosol Composition in Finland during Polluted and Clean Periods Studied by Tem/edx Individual Particle Analysis

Aerosol samples were collected at a rural background site in southern Finland in May 2004 during pollution episode (PM 1 ∼16 µg m −3 , backward air mass trajectories from southeast), intermediate period (PM 1 ∼5 µg m −3 , backtrajectories from northeast) and clean period (PM 1 ∼2 µg m −3 , backtrajectories from north-west/north). The elemental composition, morphology and mixing state of individual aerosol particles in three size fractions were studied using transmission electron microscopy (TEM) coupled with energy dispersive X-ray (EDX) micro-analyses. The TEM/EDX results were complemented with the size-segregated bulk chemical measurements of selected ions and organic and elemental carbon. Many of the particles in PM 0.2−1 and PM 1−3.3 size fractions were strongly internally mixed with S, C and/or N. The major particle types in PM 0.2−1 samples were 1) soot and 2) (ammonium)sulphates and their mixtures with variable amounts of C, K, soot and/or other inclusions. Number proportions of those two particle groups in PM 0.2−1 samples were 0–12% and 83–97%, respectively. During the pollution episode, the proportion of Ca-rich particles was very high (26–48%) in the PM 1−3.3 and PM 3.3−11 samples, while the PM 0.2−1 and PM 1−3.3 samples contained elevated proportions of silicates (22–33%), metal oxides/hydroxides (1–9%) and tar balls (1–4%). These aerosols originated mainly from polluted areas of Eastern Europe, and some open biomass burning smoke was also brought by long-range transport. During the clean period, when air masses arrived from the Arctic Ocean, PM 1−3.3 samples contained mainly sea salt particles (67–89%) with a variable rate of Cl substitution (mainly by NO porous (sponge-like) Na-rich particles (35%) with abundant S, K and O. They might originate from the burning of wood pulp wastes of paper industry. The proportion of biological particles and C-rich fragments (probably also biological origin) were highest in the PM 3.3−11 samples (0–81% and 0–22%, respectively). The origin of different particle types and the effect of aging processes on particle composition and their hygroscopic and optical properties are discussed.
Study of bread staling by X-ray diffraction analysis

The crystallinity of bread stored at -18°, 4° and 25°C for up to 20 days was studied using X-Ray diffraction analysis. Relative crystallinity of bread was calculated from the total diffraction at specific angles. Firmness and crystallinity both increased during storage. When moisture migration from the crumb to the crust occurred during staling, the development of firmness was well described by a simple equation accounting for the changes in crystallinity, moisture and storage temperature.
DENSE GRANULAR FLOWS

Dense granular flows down inclines continue to defy understanding. However, the last three decades have witnessed progress in techniques and approaches that have moved the field closer to achieving ab initio predictions of practical relevance. Difficulties arise for three principal reasons. First, because granular flows dissipate mechanical energy on the particle scale, regions featuring a substantial net gain in agitation have limited extent, unless the flow is relatively dilute [1], and are generally established near boundaries where slip can produce fluctuation energy by the working of the mean shear. Any such excess in agitation quickly dissipates farther afield, condensing grains into a flow with correlated interactions among several particles [2, 3]. Recent calculations have predicted the corresponding correlation length with dense gas kinetic theory [4], established its role near boundaries [5, 6], or have acknowledged their presence by introducing a dissipation length scale [7]. Second, while steady, fully developed flows over bumpy boundaries originally elicited much attention [8, 9], progress has been made on situations that are relevant to natural or manufactured systems, such as dense flows over a flat base confined between side walls [10, 11], which are common in industrial and agricultural applications, and flows over an erodible base with [12, 13] or without natural levies [14], which arise in geophysical systems. In such flows, the underlying granular bed dissipates agitation, but it can feature minuscule grain jumps down to surprising depths [15, 16]. Theories of particle segregation in inclined flows have also advanced significantly [17]. Third, because microscopic interactions at grain contacts, such as friction and cohesion, ultimately determine the rate of particle dissipation, their understanding is a prerequisite for quantitative predictions. Although contact dynamics is progressing [18], and there is evidence that certain inclined flows can be independent of contact models [19], challenges remain in implementing realistic contact models in numerical simulations. An approach inspired by observations in several granular systems [20], and rooted in simply sheared flows [21], introduced an inertial number making the local shear rate dimensionless with normal stress, suggesting that granular flows would conform to a universal rheology relating effective friction and bulk density to the inertial number. Despite successes such as dense flows over bumpy boundaries [22], limits of this convenient approach arise, for example, with accelerating flows [23] or flows down flat walls, which feature a thin
Switching Fuzzy Filtering for Nonlinear Stochastic Delay Systems Using Piecewise Lyapunov-Krasovskii Function

In this paper we deal with switching fuzzy filtering design problems for a class of discrete-time nonlinear stochastic systems with time-varying distributed delays. To investigate the system stability, the considered nonlinear stochastic systems is presented by switching fuzzy stochastic models. To relax the stability conditions, an improved free-weighting matrix technique and the piecewise Lyapunov- Krasovskii function are employed. Some new delay-dependent sufficient conditions are established to ensure that the filtering error system is stochastically stable with a guaranteed H∞ performance index. By using a novel matrix decoupling technique, all sufficient conditions can be expressed in terms of linear matrix inequalities (LMIs) and therefore are numerically tractable, as shown in the example included in the paper.
Investigation of inertia-gravity waves in the upper troposphere/lower stratosphere over northern Germany observed with collocated VHF/UHF radars

Abstract. A case study to investigate the properties of inertia-gravity waves in the upper troposphere/lower stratosphere has been carried out over Northern Germany during the occurrence of an upper tropospheric jet in connection with a poleward Rossby wave breaking event from 17-19 December 1999. The investigations are based on the evaluation of continuous radar measurements with the OSWIN VHF radar at Kuhlungsborn (54.1 N, 11.8 E) and the 482 MHz UHF wind profiler at Lindenberg (52.2 N, 14.1 E). Both radars are separated by about 265 km. Based on wavelet transformations of both data sets, the dominant vertical wavelengths of about 2-4 km for fixed times as well as the dominant observed periods of about 11 h and weaker oscillations with periods of 6 h for the altitude range between 5 and 8 km are comparable. Gravity wave parameters have been estimated at both locations separately and by a complex cross-spectral analysis of the data of both radars. The results show the appearance of dominating inertia-gravity waves with characteristic horizontal wavelengths of 300 km moving in the opposite direction than the mean background wind and a secondary less pronounced wave with a horizontal wavelength in the order of about 200 km moving with the wind. Temporal and spatial differences of the observed waves are discussed.
The clinical significance of γ-catenin in acute myeloid leukemia

Dysregulation of γ-catenin may function as an oncogenic factor in various malignancies. We investigated γ-catenin expression in acute myeloid leukemia (AML) and explored its role in the pathogenesis of AML. γ-Catenin was significantly overexpressed in AML patients compared to healthy donors. The γ-catenin expression in AML patients with lower white blood cells (<30×109/L) was significantly higher than those with higher white blood cells (≥30×109/L). The expression levels of γ-catenin in AML patients with mutated CEBPα were significantly higher than those with unmutated CEBPα. AML patients with lower γ-catenin levels were more likely to achieve complete remission compared with patients who have higher γ-catenin levels. In K562 cells, γ-catenin knockdown suppressed cellular proliferation, while the cellular migration was greatly enhanced. Moreover, knocking down of γ-catenin enhanced the cytotoxicity of decitabine in K562 cells. Our investigation has indicated a potential role of γ-catenin in the pathogenesis of AML.
A novel model for system-level decision making with combined ASP and SMT solving

In this paper, we present a novel model enabling system-level decision making for time-triggered many-core architectures in automotive systems. The proposed application model includes shared data entities that need to be bound to memories during decision making. As a key enabler to our approach, we explicitly separate computation and shared memory communication over a network-on-chip (NoC). To deal with contention on a NoC, we model the necessary basis to implement a time-triggered schedule that guarantees freedom of interference. We compute fundamental design decisions, namely (a) spatial binding, (b) multi-hop routing, and (c) time-triggered scheduling, by a novel coupling of answer set programming (ASP) with satisfiability modulo theories (SMT) solvers. First results of an automotive case study demonstrate the applicability of our method for complex real-world applications.
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.
Transplantation of ovarian granulosa-like cells derived from human induced pluripotent stem cells for the treatment of murine premature ovarian failure

Premature ovarian failure (POF) is a common cause of female infertility, for which there are currently no ideal treatments or medications. Furthermore, apoptosis of ovarian granulosa cells (OGCs) is an important mechanism underlying the decline in ovarian reserve and function. In the present study, several cellular growth factors and hormones were used to induce the differentiation of human induced pluripotent stem cells (iPSCs) into ovarian granulosa-like cells (OGLCs) in vitro. Immunohistochemical staining demonstrated that OGLCs derived from iPSCs strongly expressed granulosa cell markers, including anti-Müllerian hormone, inhibin α, inhibin β and follicle-stimulating hormone receptor, but did not express stem cell markers, including octamer-binding transcription factor 4, SRY (sex determining region Y)-box 2, Nanog and stage-specific embryonic antigen-4 12 days post-induction. In addition, a mouse model of POF was generated by cyclophosphamide treatment. Subsequently, iPSC-derived OGLCs were transplanted into the POF mice (OGLCs-iPSCs-POF group) in vivo. Results indicated that, compared with the control group (POF mice treated with phosphate-buffered saline), the growth state of OGLCs was markedly improved, and mature follicles could be detected in the ovarian tissue of the OGLCs-iPSCs-POF group. Immunohistochemical staining demonstrated that iPSC-derived OGLCs transplanted into POF mice not only exhibited substantial growth in murine ovarian tissues, but also strongly expressed OGC markers. Furthermore, enzyme-linked immunosorbent assays indicated that the levels of the hormone estradiol in peripheral blood samples were significantly enhanced following transplantation of iPSC-derived OGLCs into POF mice. Furthermore, ovarian tissue weight was significantly higher in the OGLCs-iPSCs-POF group compared with in the control group, and the number of atretic follicles in OGLCs-iPSCs-POF mice was significantly reduced, as compared with in the control mice. These results suggest that OGLCs derived from human iPSCs may not only effectively enhance OGC growth and repair damaged ovarian tissue, but may also maintain the ovarian tissue niche, promoting follicular development and maturation in a mouse model of POF.
The crystal structures of severe acute respiratory syndrome virus main protease and its complex with an inhibitor

A newly identified severe acute respiratory syndrome coronavirus (SARS-CoV), is the etiological agent responsible for the outbreak of SARS. The SARS-CoV main protease, which is a 33.8-kDa protease (also called the 3C-like protease), plays a pivotal role in mediating viral replication and transcription functions through extensive proteolytic processing of two replicase polyproteins, pp1a (486 kDa) and pp1ab (790 kDa). Here, we report the crystal structures of the SARS-CoV main protease at different pH values and in complex with a specific inhibitor. The protease structure has a fold that can be described as an augmented serine-protease, but with a Cys-His at the active site. This series of crystal structures, which is the first, to our knowledge, of any protein from the SARS virus, reveal substantial pH-dependent conformational changes, and an unexpected mode of inhibitor binding, providing a structural basis for rational drug design.
Game Artificial Intelligence: Challenges for the Scientific Community

This paper discusses some of the most interesting challenges to which the games research community members may face in the area of the application of arti cial or computational intelligence techniques to the design and creation of video games. The paper focuses on three lines that certainly will in uence signi cantly the industry of game development in the near future, speci cally on the automatic generation of content, the a ective computing applied to video games and the generation of behaviors that manage the decisions of entities not controlled by the human player.
Intelligent optimization for an integrated inventory model with multi-product and multi-storehouse

Multi-product and multi-storehouse retailer companies are very common all over the world. For such companies, inventory management is a critical activity for determining their success. In this context, mathematical inventory models are useful to help finding optimal solutions for answering typical retailer company questions such as: when to order, how many products to order, where to store and what is the optimal retail price for a given product. In this paper, we propose a novel integrated supplier-buyer inventory model able to optimize operations of multi-product and multi-storehouse retailers. The optimal solution to the inventory model is found using swarm intelligence based on particle swarm optimization (PSO). Experimental results of the proposed model showed that the considered optimization is feasible and is not excessively computationally intensive. In addition to that, our approach allows the inclusion of strategic policies in that optimization model.
LIMITS ON INTERGALACTIC DUST DURING REIONIZATION

In this Letter, we constrain the dust-to-gas ratio in the intergalactic medium (IGM) at high redshifts. We employ models for dust in the local universe to constrain the dust-to-gas ratio during the epoch of reionization at redshifts z ∼ 6–10. The observed level of reddening of high redshift galaxies implies that the IGM was enriched to an intergalactic dust-to-gas ratio of less than 3% of the Milky Way value by a redshift of z = 10.
Gone with the Wind? Hurricane Risk, Fertility and Education

Despite a large literature on fertility and education there has been little research on how these joint decisions are affected by risks and shocks. This paper uses data on hurricanes in Guatemala combined with a household survey to analyse how households' decisions on fertility and investments in education respond to both risk and shocks. The data on hurricanes cover the period 1880 to 1997 and allow for the calculation of hurricane risk by municipality. An increase in risk leads to higher fertility for households with land, while households without land reduce fertility. For both types of households higher risk is associated with higher education but the effect is largest for households without land. Negative shocks lead to decreases in both fertility and education. There is a compensatory effect later in life for fertility, but not for education, indicating that births "lost" to shocks can be made up but lost schooling cannot. The most convincing explanation for these patterns is parents' need for insurance.
Functional zonation of the rat adrenal cortex: the development and maintenance

The adrenal cortex of mammals consists of three concentric zones, i.e., the zona glomerulosa (zG), the zona fasciculata (zF), and the zona reticularis (zR), which secrete mineralocorticoids, glucocorticoids, and adrenal androgens, respectively. In 1994, we identified immunohistochemically a new zone between zG and zF of the rat adrenal gland. The zone appeared to be devoid of any significant endocrine functions specific to adrenocortical zones, therefore, we designated the zone as “undifferentiated cell zone (zU)”. Further, BrdU (5-bromo-2′-deoxyuridine)-incorporating cells (cells in S-phase) were concentrated at the outer region and the inner region of zU, and these cells proliferated and migrated bidirectionally: toward zG centrifugally and toward zF centripetally. We proposed that cells in and around zU are stem/progenitor cells of the rat adrenal cortex, maintaining functional zonation of the adrenal cortex. The view is consistent with observations reported recently that Sonic hedgehog (Shh), an important factor in embryonic development and adult stem cell maintenance, exists in zU of the rat adrenal gland and the Shh-containing cells seem to migrate bidirectionally.
Driving word2vec: Distributed semantic vector representation for symbolized naturalistic driving data

This study describes driving word2vec (DW2V), a new method for forming semantic representations of naturalistic driving data (NDD). To use big NDD for developing driver assistance systems or other information services, it is important to compress large amounts of data into an abstract and compact representation without losing semantic information. For this purpose, this study uses a symbolization method using a double articulation analyzer (DAA) assuming that NDD and human speech signals share an analogous structure, called a double articulation structure. The DAA can encode driving behavior data into sequences of driving words. However, the amount of semantic information contained in these sequences has not been clarified. Very few attempts have been made to develop a method for obtaining an adequate semantic representation of driving words that explains the relationship between different driving words. DW2V uses word2vec, proposed by Mikolov et al., to make a system learn the distributed semantic vector representation of symbolized naturalistic driving data (SNDD). Through experiments, we show that DW2V can restore the semantic relationships between different driving scenes from only a set of sequences of driving words, i.e., SNDD. In addition to quantitative analysis, a qualitative analysis of DW2V and its potential applications are discussed.